Python_Module_Kampus_Merdeka_MSIB_6___Ammar.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#**Welcome to Python Module!!** 👋~ムアマル ヤシール"
      ],
      "metadata": {
        "id": "kbc0pTUuOGoI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Day 1 (月曜日) -  18/03/2024"
      ],
      "metadata": {
        "id": "pO9UwL7G6YY4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Introduction to Data Scientist\n",
        "---\n",
        "\n",
        "`1. What is Data Scientist?` <br>\n",
        "`2. Roles and responsibilities of a Data Scientist` <br>\n",
        "`3. The difference between of Data Analyst, Data Scientist, or Business Analyst` <br>\n",
        "`4. Tools and technology used` <br>\n",
        "`5. Introduction to Python`\n",
        "\n"
      ],
      "metadata": {
        "id": "YBEGFy0b47dD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "7dkyHEgA_aM9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###1. What is Data Scientist?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UeOvFrrPEOO_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**A data scientist** is a professional who utilizes scientific methods, algorithms, processes, and systems to extract insights and knowledge from structured and unstructured data. They possess a blend of analytical, statistical, programming, and domain-specific skills to analyze complex datasets and derive valuable insights that can inform business decisions, improve products or services, or solve various challenges.\n",
        "\n"
      ],
      "metadata": {
        "id": "kZm45zxz9WUu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###2. Roles and responsibilities?\n",
        "\n"
      ],
      "metadata": {
        "id": "qgoRESm4FzVX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Scientists** play the lead role in data science applications in organizations. They're commonly tasked with finding information that enables more effective marketing campaigns, improved customer service, stronger supply chain management and better business decisions and strategies overall. To do so, they analyze sets of quantitative and qualitative data, depending on the needs of specific applications."
      ],
      "metadata": {
        "id": "NhsWODcTEKxX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# Key Responsibilities\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "mOKWODldHXGJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Key responsibilities of a data scientist may include:\n",
        "\n",
        "1. ***Data Collection and Cleaning***: Gathering data from various sources and ensuring its quality and reliability by preprocessing and cleaning it.\n",
        "2. ***Data Analysis***: Applying statistical and machine learning techniques to analyze data, identify patterns, trends, and correlations, and gain actionable insights.\n",
        "3. ***Data Visualization***: Presenting findings and insights effectively through data visualization techniques such as charts, graphs, and dashboards to make complex information more understandable for non-technical stakeholders.\n",
        "4. ***Machine Learning and Predictive Modeling***: Building predictive models and machine learning algorithms to make predictions, classifications, or recommendations based on historical data.\n",
        "5. ***Interpretation and Communication***: Interpreting analytical results and communicating findings, recommendations, and implications to stakeholders in a clear and understandable manner.\n",
        "\n"
      ],
      "metadata": {
        "id": "6eeFtB2dHgFq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# The quotes\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "V1dIyLQBKT2d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "  print(\"Python is like love - it's easy to start, but hard to end. byアマル ~ くん\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yI0xcpbTG6TQ",
        "outputId": "3c863d54-3dd1-42e3-d3cf-871b2d0634fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python is like love - it's easy to start, but hard to end. byアマル ~ くん\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*A data scientist is that unique blend of skills that can both unlock the insights of data and tell a fantastic story via the data.* - (DJ Patil, Mathematician and computer scientist)"
      ],
      "metadata": {
        "id": "SWa0HacKJvbm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3. The difference between of Data Analyst, Data Scientist, or Business Analyst\n"
      ],
      "metadata": {
        "id": "wZWAPRBMMEjs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Data Analyst (Translators)**, translate statistical information into a language everyday people can understand and use as a basis to make practical decisions\n",
        "\n",
        "> **Data Scientist (Problem Solvers)**, as the name suggests, are truly scientists or problem solvers because they use a high paced scientific method to diagnose a problem, determine the\n",
        "method and tools needed to solve it, explore what works, and try again\n",
        "\n",
        "> **Business Analyst (Communicators)**, are generally a little less technologically savvy but offer a deep knowledge of the processes involved in running a successful business, they are good at connecting this insight to real world strategies that enable businesses to become more successful.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "OmcclDGCMHe8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###4. Tools and technology used\n",
        "\n"
      ],
      "metadata": {
        "id": "yL2pY2WvQA1Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here are some tools that Data Analysts can use:\n",
        "\n",
        "\n",
        "1.   Analytical tools: Python, R, SPSS\n",
        "2.   Database tools: MySQL, PostgreSQL, Big Query\n",
        "3.   Data Visualization tools: Tableau, Power BI, Google Data Studio\n",
        "4.   Data Integration tools: Pentaho, Talend\n",
        "\n"
      ],
      "metadata": {
        "id": "XR4hEfCUQ6Hh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Introduction to Python\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "mBjCOd0g6c4g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###What is Python?"
      ],
      "metadata": {
        "id": "Tqym9anTW_vb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Python** is the greatest computer programming language that enables you to fully harness the power of a computer, in a language that’s *clean* and *easy to understand.*\n",
        "- **Python** is a programming language conceived in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to the AB language."
      ],
      "metadata": {
        "id": "itU9A9k3cFkn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Why? (何で?)"
      ],
      "metadata": {
        "id": "y3Hmd0Rlu35W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concern:\n",
        "\n",
        "- Python is a programming language that is intended for general-purpose programming and belongs to the category of high-level programming language (easy to read and write by humans);\n",
        "- Python has a nice and simple design concept python;\n",
        "- Python code is also much less then code written in other programming languages such as C, C++, C# and Java. Python can increase productivity and save time for programmers;\n",
        "- Program written in python can run on almost ani operating system (Unix, Windows, Mac, etc);\n",
        "- Python has a lot of library support;\n",
        "- Finally, Python is free and open source, even though it is used for commercial purposes."
      ],
      "metadata": {
        "id": "Ozi7kgDedT1U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###What can we do with Python?"
      ],
      "metadata": {
        "id": "vd0nJmSgckbI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. ***Big Data***, python plays a substantial role in big data. It’s extensively used to analyze huge chunks of the available big data and extract specific information. There are countless libraries and free modules that enable fast, secure, and accurate processing of data. Example: CERN.\n",
        "2. ***Artificial Intelligence***, have you ever heard AI about recommendation playlists based on what you’ve previously listened to? for this example, Spotify, uses Python in their based code. Python is at the forefront of the way Internet will work in future.\n",
        "3. ***Web Development***, with python, a web developer has the ability to create dynamic and highly secure web apps.\n",
        "3. ***Gaming***, the primary use of Python in gaming is in the form of scripting, where Python can add customization to the core game engine."
      ],
      "metadata": {
        "id": "ES9L9NUJc4Mz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Python in Numbers\n",
        "\n",
        "`source: https://www.jetbrains.com/lp/devecosystem-2023/python/`"
      ],
      "metadata": {
        "id": "IhAwmJQFdO2w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The survey results indicate a consistent growth in Python's adoption across various domains, with data analysis (47%), machine learning (42%), and web development (40%) emerging as the top use cases. This versatility underscores Python's resilience and adaptability, which make it a go-to choice for developers working across different industries.\n",
        "\n",
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      ],
      "metadata": {
        "id": "U9krPWXBdtra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NumPy, pandas, and Matplotlib remain the most popular frameworks for data science tasks. PyTorch improved its position by 7 percentage points since last year, probably as a result of the growing popularity of deep learning.\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "VkEMmPA-nPwn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Quotes (引用 )"
      ],
      "metadata": {
        "id": "F3_JzWL1pBLt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Python is fast enough for our site and allows us to produce maintainable features in record times, with a minimum of developers.* <br>\n",
        "**Cuong Do, Software Architect, YouTube.com** <br>\n",
        "<br>\n",
        "*Python plays a key role in our production pipeline. Without it a project the size of Star Wars: Episode II would have been very difficult to pull off. From crowd rendering to batch processing to compositing, Python binds all things together.* <br>\n",
        "**Tommy Burnette, Senior Technical Director, Industrial Light & Magic** <br>\n",
        "<br>\n",
        "*Python has been an important part of Google since the beginning, and remains so as the system grows and evolves. Today dozens of Google engineers use Python, and we're looking for more people with skills in this language.* <br>\n",
        "**Peter Norvig, Director of Search Quality, Google, Inc**\n"
      ],
      "metadata": {
        "id": "Q02EZ_33pE6C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###The Zen of Python"
      ],
      "metadata": {
        "id": "FkZ2mlfOqZvV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import this"
      ],
      "metadata": {
        "id": "G-wh2I-1AOF4",
        "outputId": "a9af4a0f-8834-4978-b72c-4157afb2b31b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Zen of Python, by Tim Peters\n",
            "\n",
            "Beautiful is better than ugly.\n",
            "Explicit is better than implicit.\n",
            "Simple is better than complex.\n",
            "Complex is better than complicated.\n",
            "Flat is better than nested.\n",
            "Sparse is better than dense.\n",
            "Readability counts.\n",
            "Special cases aren't special enough to break the rules.\n",
            "Although practicality beats purity.\n",
            "Errors should never pass silently.\n",
            "Unless explicitly silenced.\n",
            "In the face of ambiguity, refuse the temptation to guess.\n",
            "There should be one-- and preferably only one --obvious way to do it.\n",
            "Although that way may not be obvious at first unless you're Dutch.\n",
            "Now is better than never.\n",
            "Although never is often better than *right* now.\n",
            "If the implementation is hard to explain, it's a bad idea.\n",
            "If the implementation is easy to explain, it may be a good idea.\n",
            "Namespaces are one honking great idea -- let's do more of those!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Python Syntax\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "ckPYd5vtquId"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Python Identifier / Variable"
      ],
      "metadata": {
        "id": "1t-nqVWRq-l5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "> A Python identifier is the name of a variable, function, class, module, or other. Python object, and a valid identifier conforms to the following rules:\n",
        "1. It starts with a letter A to Z, a to z, or an underscore (_)\n",
        "2. It includes zero or more letters, underscores, and digits (0 to 9)\n",
        "3. Spaces are not allowed in variable names, so we use underscores instead of spaces. For example, use student_name instead of \"student name\".\n",
        "4. Variable names cannot use existing python keywords such as if, while, for, etc. <br>\n",
        "***Notes: Python identifiers cannot contain characters such as @, $, and %.*** <br>\n",
        "\n",
        "> Python is a case-sensitive language, so Abc and abc different identifiers in Python. In addition, Python has the following naming convention:\n",
        "* Class names start with an uppercase letter and all other identifiers with a lowercase letter\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wBSS_UKxrEbj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Lines & Indentation"
      ],
      "metadata": {
        "id": "06VhzBHXsq6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Indentation is indented syntax writing. indentation greatly affects the execution result. The program code that is on the same left side is read as a block, to create a sub-block it is enough to leave a space or tab to the right. <br>\n",
        "<br>\n",
        "```\n",
        "angka1 = 1\n",
        "angka2 = 2\n",
        "if angka1*angka2 == 2;\n",
        "  print(\"benar\") #indentation\n",
        "else\n",
        "  print(\"salah\") #indentation\n",
        "```\n",
        "<br>\n",
        "Why is indentation necessary? Because some commands like IF..ELSE require indentation to separate which part belongs to IF, and which part belongs to ELSE. that's why Python requires indentation\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "O2Oiq-9DszB7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Mulitilines\n",
        "\n",
        "**Multiline statements in Python can terminate with a new line or the backslash (“\\”) character**"
      ],
      "metadata": {
        "id": "xWwz6nYOuokj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Obviously you can place x1, x2, and x3 on the same line, so there is no reason to use three separate lines; however, this functionality is available in case you need to add a set of variables that do not fit on a single line.<br>\n",
        "\n",
        "You can specify multiple statements in one line by using a semicolon (“;”) to separate each statement, as shown here:<br>\n",
        "`a=10; b=5; print(a); print(a+b)` <br>\n",
        "The output of the preceding code snippet is here:<br>\n",
        "`10`<br>\n",
        "`15`<br>\n",
        "The use of semicolons and the continuation character are discouraged in Python.\n"
      ],
      "metadata": {
        "id": "ir5zZRZautQ4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Python Package & Library (PIP)\n",
        "`PIP is a package manager in python, pip is installed automatically if we use Python 2 >= 2.7.9 or Python 3 >= 3.4`\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "kFFxNR9H4hxo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In python, we have to call (import) the library so that we can use the library in our code.<br>\n",
        "<br>\n",
        "For example, if we want to use the function to open / write a csv file we must first import the csv package as follows:<br>\n",
        "\n",
        "`import csv`\n",
        "\n",
        "<br>If this module is not installed in our python environment, you can download it first with the PIP command:<br>\n",
        "`pip install python-csv`\n",
        "\n",
        "Run the command in your computer's command-line or in the cell.\n"
      ],
      "metadata": {
        "id": "Ut1qLkqq4lJv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Types Python\n",
        "---\n",
        "\n",
        "    > Text Type:\tstr\n",
        "    > Numeric Types:\tint, float, complex\n",
        "    > Sequence Types:\tlist, tuple, set, range\n",
        "    > Mapping Type:\tdict\n",
        "    > Set Types:\tset, frozenset\n",
        "    > Boolean Type:\tbool\n",
        "    > Binary Types:\tbytes, bytearray, memoryview\n",
        "    > None Type:\tNoneType"
      ],
      "metadata": {
        "id": "wakwuivE-o0w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Variable\n",
        "\n",
        "- Variables are containers for storing data values.\n",
        "- Python has no command for declaring a variable.\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "WNAJOcDT_nXO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Day 2 (水曜日) -  20/03/2024"
      ],
      "metadata": {
        "id": "HoBgHyzgAQcU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Range in Python\n",
        "---"
      ],
      "metadata": {
        "id": "YhxvpkSDAV8B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`The range() function returns a sequence of numbers, starting from 0 by default, and increments by 1 (by default), and stops before a specified number.`"
      ],
      "metadata": {
        "id": "clupvukIAjfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = range(6) #0<=x<6\n",
        "print(x)\n",
        "for n in x:\n",
        "  print(n)"
      ],
      "metadata": {
        "id": "FgAJh4m_AntL",
        "outputId": "708d8397-c38b-42da-86c0-0d4650f47ea3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "range(0, 6)\n",
            "0\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***range(n) := 0 <= x < n*** <br>\n",
        "for example: range(5) means 0 <= x < 5"
      ],
      "metadata": {
        "id": "VAwCeEbcAsLe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Syntax function range\n",
        "---\n",
        "`range(start, stop, step)` -> syntax Range, default step 1 if not define"
      ],
      "metadata": {
        "id": "FXfTCKvzAwZz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = range(0, 100, 10) #urutan bilangan antara 0 dan 100 dengan selisih 10\n",
        "for n in x:\n",
        "  print(n)"
      ],
      "metadata": {
        "id": "AXKqgZrUBLY_",
        "outputId": "b8ba4308-c606-45a9-f55e-e1b8bfef6a87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "10\n",
            "20\n",
            "30\n",
            "40\n",
            "50\n",
            "60\n",
            "70\n",
            "80\n",
            "90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numeric Types\n",
        "---"
      ],
      "metadata": {
        "id": "1jHedaKUBm9B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- ***INT*** or integer, is a whole number, positive or negative, without decimals, of unlimited length.\n",
        "\n",
        "- ***FLOAT*** or \"floating point number\" is a number, positive or negative, containing one or more decimals.\n",
        "\n",
        "- ***COMPLEX*** numbers are written with a \"j\" as the imaginary part"
      ],
      "metadata": {
        "id": "-KWsCiHXBu2K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = 100 #a adalah integer\n",
        "b = 13.89756 #b adalah bilangan desimal / float\n",
        "c = 5j #c adalah bilangan kompleks\n",
        "\n",
        "print(a);print(type(a))\n",
        "print(b);print(type(b))\n",
        "print(c);print(type(c))"
      ],
      "metadata": {
        "id": "RZvb121RBzuh",
        "outputId": "12152202-536e-4e25-8699-4f5e13d85614",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n",
            "<class 'int'>\n",
            "13.89756\n",
            "<class 'float'>\n",
            "5j\n",
            "<class 'complex'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 10 #integer\n",
        "y = 15.2 #float\n",
        "\n",
        "#convert / casting dari integer ke float\n",
        "p = float(x)\n",
        "\n",
        "#convert / casting dari float ke integer\n",
        "q = int(y)\n",
        "\n",
        "#convert / casting dari integer ke kompleks\n",
        "r = complex(x)\n",
        "\n",
        "print(p);print(q);print(r)\n",
        "print(type(p));print(type(q));print(type(r))"
      ],
      "metadata": {
        "id": "E_F2IhGeB3P4",
        "outputId": "e9ec4031-7ce8-4f43-dfb5-c6a60536474d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.0\n",
            "15\n",
            "(10+0j)\n",
            "<class 'float'>\n",
            "<class 'int'>\n",
            "<class 'complex'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Python Arithmatics Operator\n",
        "---"
      ],
      "metadata": {
        "id": "-W9xfFvaB_2s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Arithmetic operators are used with numeric values to perform common mathematical operations`"
      ],
      "metadata": {
        "id": "dhywfsQ9CDNX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Operator|\tName|\tExample|\n",
        "|:-|:-|-:|\n",
        "|+|\tAddition|\tx + y|\n",
        "|-|\tSubtraction|\tx - y|\n",
        "|*|\tMultiplication|\tx * y|\n",
        "|/|\tDivision|\tx / y|\n",
        "|%|\tModulus|\tx % y|\n",
        "|**|\tExponentiation|\tx ** y|\n",
        "|//|\tFloor division|\tx // y|"
      ],
      "metadata": {
        "id": "tnqNOn7xCKqG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = 6 ; y = 20\n",
        "\n",
        "print(\"Addition:\",x+y) #penjumlahan\n",
        "print(\"Substraction:\",x-y) #pengurangan\n",
        "print(\"Multiplication:\",x*y) #perkalian\n",
        "print(\"Division:\",x/y) #pembagian\n",
        "print(\"Modulus:\",x%y) #sisa bagi / modulo\n",
        "print(\"Exponentiation:\",x**y) #akar pangkat dua\n",
        "print(\"Floor Division:\",x//y) #pembagian dengan pembulatan"
      ],
      "metadata": {
        "id": "wPOnWfxOCLg_",
        "outputId": "22bd6801-ea36-4a67-a4bb-f9f6c7e45a87",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Addition: 26\n",
            "Substraction: -14\n",
            "Multiplication: 120\n",
            "Division: 0.3\n",
            "Modulus: 6\n",
            "Exponentiation: 3656158440062976\n",
            "Floor Division: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Python Assignment Operators\n",
        "---"
      ],
      "metadata": {
        "id": "pJfuMRXLCX94"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Assignment operators are used to assign values to variables`"
      ],
      "metadata": {
        "id": "QI8PYq1rCZ9M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "|Operator|\tExample|\tSame As|\n",
        "|:-|:-|-:|\n",
        "|=|\tx = 5|\tx = 5|\n",
        "|+=|\tx += 3|\tx = x + 3|\n",
        "|-=|\tx -= 3|\tx = x - 3|\n",
        "|*=|\tx *= 3|\tx = x * 3|\n",
        "|/=|\tx /= 3|\tx = x / 3|\n",
        "|%=|\tx %= 3|\tx = x % 3|\n",
        "|//=|\tx //= 3|\tx = x // 3|\n",
        "|**=|\tx **= 3|\tx = x ** 3|\n",
        "|&=|\tx &= 3|\tx = x & 3|\n",
        "|^=|\tx ^= 3|\tx = x ^ 3|\n",
        "|>>=|\tx >>= 3|\tx = x >> 3|\n",
        "|<<=|\tx <<= 3|\tx = x << 3|"
      ],
      "metadata": {
        "id": "NQxGJ2HkCdE6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = 10     #menambahkan x dengan 5 sehingga nilai x menjadi 10 + 5\n",
        "x += 5\n",
        "print(x)"
      ],
      "metadata": {
        "id": "T00bhVeOCjzq",
        "outputId": "8eeae5c3-4e9a-4119-8ed3-811386daafa1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## String\n",
        "---"
      ],
      "metadata": {
        "id": "T11nNuB7Co5S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Strings in python are surrounded by either single quotation ('text') marks, or double quotation (\"text\") marks.`"
      ],
      "metadata": {
        "id": "ZC5zvEOuCu_I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#string python menggunakan kutip dua\n",
        "a = \"Muammar Yasir\"\n",
        "\n",
        "#string menggunakan kutip satu\n",
        "b = 'Ammar Aja'\n",
        "\n",
        "print(type(a));print(type(b))"
      ],
      "metadata": {
        "id": "G18q_Oa9Cwrq",
        "outputId": "62890c03-72f7-431d-9bff-67ac8e379509",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n",
            "<class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat paragraf\n",
        "paragraf = '''\n",
        "一歩ずつでも前に進むことが大切です。諦めずに努力し続ければ、\n",
        "必ず夢は叶います。(Ippoずつでもmae ni susumu koto ga taisetsu desu.\n",
        "Akiramenai de doryoku shi tsuzukereba, kanarazu yume wa kanaimasu)''' #bisa menggunakan triple single quote atau double qute\n",
        "\n",
        "print(paragraf)"
      ],
      "metadata": {
        "id": "bbH92fBxC1dV",
        "outputId": "bdb4869b-9a95-4887-955e-aad446e2d968",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "一歩ずつでも前に進むことが大切です。諦めずに努力し続ければ、\n",
            "必ず夢は叶います。(Ippoずつでもmae ni susumu koto ga taisetsu desu. \n",
            "Akiramenai de doryoku shi tsuzukereba, kanarazu yume wa kanaimasu)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "paragraf2 =\"Melangkah maju meskipun hanya selangkah demi selangkah,\\\n",
        "adalah hal yang penting,\\\n",
        "jangan menyerah dan teruslah berusaha\\\n",
        "maka mimpimu pasti akan terwujud.\"\n",
        "\n",
        "print(paragraf2)"
      ],
      "metadata": {
        "id": "ruC8yEofC5pZ",
        "outputId": "442c02cd-a59f-4ea9-aecb-1575470d60ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Melangkah maju meskipun hanya selangkah demi selangkah,adalah hal yang penting,jangan menyerah dan teruslah berusahamaka mimpimu pasti akan terwujud.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#beberapa case yang ada di string\n",
        "nama = \"Ammar\"\n",
        "\n",
        "print(type(nama)) #untuk mengetahui tipe data\n",
        "print(len(nama)) #untuk menghitung jumlah karakter / panjang kata\n",
        "print(nama) #untuk melihat value dari variabel nama\n",
        "print(nama[3]) #untuk membaca karakterk ke empat berdasarkan value dari variabel nama\n",
        "print(nama[1:3]) #titik dua disini mendefiniskan sebagai range\n",
        "print(nama[-3:-1]) #membaca karakter dari belakang\n",
        "print(nama+\" \"+ \"siapa?\") #menjumlakan kata dengan spasi\n",
        "print(nama*3) #perkalian nama"
      ],
      "metadata": {
        "id": "xJQaBbtFC-tu",
        "outputId": "4c15f75e-9d51-4de2-f500-bc56d98ed5c2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n",
            "5\n",
            "Ammar\n",
            "a\n",
            "mm\n",
            "ma\n",
            "Ammar siapa?\n",
            "AmmarAmmarAmmar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = \"Latihan Python\"\n",
        "nilai = \"75\"\n",
        "spasi = \" \"\n",
        "nama = 'Ammar'\n",
        "\n",
        "print(data)  #print value dari variabel data\n",
        "print(len(data)) #menghitung panjang kata\n",
        "print(type(nilai)) #cek tipe data dari suatu variabel\n",
        "\n",
        "print(data.upper()) #merubah string jadi huruf kapital\n",
        "print(data.capitalize()) #merubah huruf pertama (besar kecil) di setiap kalimat\n",
        "print(data.title()) #merubah string jadi format judul (huruf kapital setiap kata)\n",
        "print(data.isspace()) #ngecek apakah variabel data adalah spasi\n",
        "print(data.isnumeric()) #ngecek apakah variabel data adalah numerik\n",
        "print(data.swapcase()) #swapcase\n",
        "print(nilai*3)\n",
        "\n",
        "print(\"Python\" in data) #cek apabila kata \"Python\" apakah ada di variabel data\n",
        "print(\"SQL\" not in data) #cek kata \"SQL\" apakah tidak ada di variabel data\n",
        "print(\"SQL\" in data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aatkYlUzaQuC",
        "outputId": "687544c7-258c-42ea-e7a5-1bd4bbd2e4ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Latihan Python\n",
            "14\n",
            "<class 'str'>\n",
            "LATIHAN PYTHON\n",
            "Latihan python\n",
            "Latihan Python\n",
            "False\n",
            "False\n",
            "lATIHAN pYTHON\n",
            "757575\n",
            "True\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variabel = \"Training Python\"\n",
        "\n",
        "print(variabel.find(\"n\")) #menemukan kata n ada di indeks berapa\n",
        "print(variabel.find(\"n\", 7)) #sama case diatas tapi dihitung setelah indeks ke 7"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AmAeR8rna07Z",
        "outputId": "e8be5996-5051-4e1d-f1a0-f68ee8c9a667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4\n",
            "14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "number = \"123\"\n",
        "number1 = \"ab\"\n",
        "\n",
        "print(number.zfill(10)) #membuat karakter dengan length 10\n",
        "print(number1.zfill(5)) #membuat karakter dengan length 5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUw40mIkbVAn",
        "outputId": "5aaaf775-d307-477d-e0dc-50e4a716cfae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0000000123\n",
            "000ab\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "title = \"latihan\"\n",
        "title1 = \"training\"\n",
        "\n",
        "print(title.center(10,\"-\")) #membuat karakter dengan length 10 dan menambahkan -\n",
        "print(title1.center(10,\"-\"))\n",
        "\n",
        "print(title.replace('a','x')) #mengganti karakter\n",
        "print(title.zfill(8).replace(\"0\",\"p\"))\n",
        "print(title.rjust(10, \"1\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdB6iIEMbXo_",
        "outputId": "52fa2ae6-da7b-408a-9106-b608fd80bd7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-latihan--\n",
            "-training-\n",
            "lxtihxn\n",
            "platihan\n",
            "111latihan\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 3 (木曜日) - 21/03/2024"
      ],
      "metadata": {
        "id": "mq6kX9vwdRW2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## String\n",
        "---"
      ],
      "metadata": {
        "id": "JpuZZA0Zk9Ft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "\n",
        "kode = 'aiueo'\n",
        "kode2 = '12345'\n",
        "kalimat = 'Latihan Python'\n",
        "transform = kalimat.maketrans(kode,kode2) #fungsi yang ada di package string\n",
        "\n",
        "print(kalimat)\n",
        "print(transform) #aturan ascii\n",
        "print(kalimat.translate(transform)) #translate kalimat menggunakan aturan\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2qg6PGmLlGod",
        "outputId": "4b497667-2b8e-4b87-cb6a-95282f4c9cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Latihan Python\n",
            "{97: 49, 105: 50, 117: 51, 101: 52, 111: 53}\n",
            "L1t2h1n Pyth5n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "usia = 22, '年齢は22歳です'\n",
        "text =\"わたし の なまえ わ アマール です, usia {}\"\n",
        "\n",
        "print(text.format(usia))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNpxD6JhnXBZ",
        "outputId": "2227ec74-ee4c-430c-99c4-95b635ce74d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "わたし の なまえ わ アマール です, usia (22, '年齢は22歳です')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qty = 6\n",
        "produk_no = 567\n",
        "harga = 100000\n",
        "order = \"Saya mau membeli {} buah produk {} dengan harga {}\"\n",
        "\n",
        "print(order.format(qty, produk_no, harga)) #harus sesuai urutan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B2gVTvzJoHPe",
        "outputId": "05bc1b53-304f-412c-c5ce-659029832d5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saya mau membeli 6 buah produk 567 dengan harga 100000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qty = 6\n",
        "produk_no = 567\n",
        "harga = 100000\n",
        "order = \"Saya mau membeli {1} buah produk {2} dengan harga {0}\"\n",
        "print(order.format(harga, qty, produk_no)) #isi bracket dengan index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fjc7dLh6o1ZY",
        "outputId": "8988a4f0-90be-4d5d-ff10-523cafee9df0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saya mau membeli 6 buah produk 567 dengan harga 100000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### String Method"
      ],
      "metadata": {
        "id": "_yl7oIcrpybS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Python has a set of built-in methods that you can use on strings.` <br>\n",
        "**Note:** All string methods return new values. They do not change the original string.\n",
        "\n",
        "    - capitalize():\tConverts the first character to upper case\n",
        "    - casefold():\tConverts string into lower case\n",
        "    - center():\tReturns a centered string\n",
        "    - count():\tReturns the number of times a specified value occurs in a string\n",
        "    - encode():\tReturns an encoded version of the string\n",
        "    - endswith():\tReturns true if the string ends with the specified value\n",
        "    - expandtabs():\tSets the tab size of the string\n",
        "    - find():\tSearches the string for a specified value and returns the position of where it was found\n",
        "    - format():\tFormats specified values in a string\n",
        "    - format_map():\tFormats specified values in a string\n",
        "    - index():\tSearches the string for a specified value and returns the position of where it was found\n",
        "    - isalnum():\tReturns True if all characters in the string are alphanumeric\n",
        "    - isalpha():\tReturns True if all characters in the string are in the alphabet\n",
        "    - isdecimal():\tReturns True if all characters in the string are decimals\n",
        "    - isdigit():\tReturns True if all characters in the string are digits\n",
        "    - isidentifier():\tReturns True if the string is an identifier\n",
        "    - islower():\tReturns True if all characters in the string are lower case\n",
        "    - isnumeric():\tReturns True if all characters in the string are numeric\n",
        "    - isprintable():\tReturns True if all characters in the string are printable\n",
        "    - isspace():\tReturns True if all characters in the string are whitespaces\n",
        "    - istitle():\tReturns True if the string follows the rules of a title\n",
        "    - isupper():\tReturns True if all characters in the string are upper case\n",
        "    - join():\tJoins the elements of an iterable to the end of the string\n",
        "    - ljust():\tReturns a left justified version of the string\n",
        "    - lower():\tConverts a string into lower case\n",
        "    - lstrip():\tReturns a left trim version of the string\n",
        "    - maketrans():\tReturns a translation table to be used in translations\n",
        "    - partition():\tReturns a tuple where the string is parted into three parts\n",
        "    - replace():\tReturns a string where a specified value is replaced with a specified value\n",
        "    - rfind():\tSearches the string for a specified value and returns the last position of where it was found\n",
        "    - rindex():\tSearches the string for a specified value and returns the last position of where it was found\n",
        "    - rjust():\tReturns a right justified version of the string\n",
        "    - rpartition():\tReturns a tuple where the string is parted into three parts\n",
        "    - rsplit():\tSplits the string at the specified separator, and returns a list\n",
        "    - rstrip():\tReturns a right trim version of the string\n",
        "    - split():\tSplits the string at the specified separator, and returns a list\n",
        "    - splitlines():\tSplits the string at line breaks and returns a list\n",
        "    - startswith():\tReturns true if the string starts with the specified value\n",
        "    - strip():\tReturns a trimmed version of the string\n",
        "    - swapcase():\tSwaps cases, lower case becomes upper case and vice versa\n",
        "    - title():\tConverts the first character of each word to upper case\n",
        "    - translate():\tReturns a translated string\n",
        "    - upper():\tConverts a string into upper case\n",
        "    - zfill():\tFills the string with a specified number of 0 values at the beginning"
      ],
      "metadata": {
        "id": "uz0Qq0Skp09A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Boolean\n",
        "---"
      ],
      "metadata": {
        "id": "mPawz364rexS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Booleans represent one of two values: ***True*** or ***False***."
      ],
      "metadata": {
        "id": "jXr57oZMrhwE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Python Comparison Operators\n",
        "`Comparison operators are used to compare two values:`\n",
        "\n",
        "|Operator |Name| Example|\n",
        "|:-|:-|-:|\n",
        "|==|Equal|\tx == y|\n",
        "|!=|Not equal|\tx != y|\n",
        "|>| Greater than|\tx > y|\n",
        "|<|Less than|\tx < y|\n",
        "|>=\t|Greater than or equal to|\tx >= y|\n",
        "|<=\t|Less than or equal to|\tx <= y|"
      ],
      "metadata": {
        "id": "aLdCwElqrpWi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(10 < 9) #False\n",
        "print(10 == 9) #False\n",
        "print (10 > 9) #True"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n1ianWhlr4_x",
        "outputId": "d6d8896e-5a4a-472c-dbed-7680755057fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Simple example using if else\n",
        "\n",
        "a = 10\n",
        "b = 7\n",
        "\n",
        "if b > a:\n",
        "  print(\"b greater than a\")\n",
        "else:\n",
        "  print(\"b less than a\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYkax_7tsoTr",
        "outputId": "00ac9319-73e0-46dd-a7e4-2143a16c1a7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b less than a\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Another case with boolean output TRUE\n",
        "\n",
        "print(bool('Ammar'))\n",
        "print(bool(15))\n",
        "print(bool(['(マンゴー', 'リンゴ', 'オレンジ']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMiGrRWAtVPb",
        "outputId": "dd914971-d8a6-4793-c885-ee1ac8074e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "True\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Another case with boolean output FALSE\n",
        "\n",
        "print(bool(False))\n",
        "print(bool(None))\n",
        "print(bool(0))\n",
        "print(bool(''))\n",
        "print(bool(()))\n",
        "print(bool([]))\n",
        "print(bool({}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Ex0dys5t5AQ",
        "outputId": "ab4b3b97-3974-4405-b942-b9205897ecc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## None Type\n",
        "---"
      ],
      "metadata": {
        "id": "MWhTiqCyu2dP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The None keyword is used to define a null value, or no value at all.\n",
        "- None is not the same as 0, False, or an empty string. None is a data type of its own (NoneType) and only None can be None.`"
      ],
      "metadata": {
        "id": "cou4mHGuu-FS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = None #NoneType\n",
        "b = 0 #integer\n",
        "c = False #bool\n",
        "d = '' #string\n",
        "print(type(a));\n",
        "print(type(b));\n",
        "print(type(c));\n",
        "print(type(d))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqI-NJKTvDZk",
        "outputId": "43f0ecc7-a23d-493b-9271-c48d4ea8140e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'NoneType'>\n",
            "<class 'int'>\n",
            "<class 'bool'>\n",
            "<class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = None\n",
        "\n",
        "if x is True:\n",
        "  print(\"Do you think None is True\")\n",
        "\n",
        "elif x is False:\n",
        "  print(\"Do you think None is False\")\n",
        "\n",
        "else:\n",
        "  print(\"None is not True or False, None is just None\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pvcvi_MdvvOR",
        "outputId": "fd4ed442-489f-40c1-97c7-1988fe4b3754"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None is not True or False, None is just None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Python Collections (Arrays)\n",
        "---"
      ],
      "metadata": {
        "id": "RuLnV5cLw2JO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are four collection data types in the Python programming language:\n",
        "\n",
        "- **List** is a collection which is ordered and changeable. Allows duplicate members.\n",
        "- **Tuple** is a collection which is ordered and unchangeable. Allows duplicate members.\n",
        "- **Set** is a collection which is unordered, unchangeable#, and unindexed. No duplicate members.\n",
        "- **Dictionary** is a collection which is ordered## and changeable. No duplicate members.\n",
        "\n",
        "*# Set items are unchangeable, but you can remove and/or add items whenever you like.* <br>\n",
        "*## As of Python version 3.7, dictionaries are ordered. In Python 3.6 and earlier, dictionaries are unordered*"
      ],
      "metadata": {
        "id": "WIFJ-yhOw-rN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## List\n",
        "---"
      ],
      "metadata": {
        "id": "nlEIVRdRxhwr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Lists are used to store multiple items in a single variable.`\n",
        "\n",
        "`Lists are one of 4 built-in data types in Python used to store collections of data, the other 3 are Tuple, Set, and Dictionary, all with different qualities and usage.`"
      ],
      "metadata": {
        "id": "lm3eSXu3xnWO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = [75, 'Nilai', 80.6, 50] #kurung siku digunakan untuk tipe data list, bisa berisi beberapa tipe data\n",
        "\n",
        "print(data)\n",
        "print(type(data))\n",
        "print(data[0]) #membaca data pertama dari list yang di input dalam data (index 0)\n",
        "print(len(data)) #menghitung jumlah value yang ada di list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9o1DLir9xrtj",
        "outputId": "0212bbc9-2fe4-479f-8437-5b21e3706efc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[75, 'Nilai', 80.6, 50]\n",
            "<class 'list'>\n",
            "75\n",
            "4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### List Items"
      ],
      "metadata": {
        "id": "1vlhQTBDyt44"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "nilai = [95, 70, 75, 65, 11]\n",
        "print(nilai[0]) #output berupa nilai pertama yang ada di list (index 0)\n",
        "print(nilai[0:2]) #output berupa nilai pertama dan kedua (index 0 dan 1)\n",
        "print(nilai[0:]) #output berupa nilai pertama - nilai akhir (index 0 - akhir)\n",
        "print(nilai[:2]) #output nilai pertama sampai batas nilai kesatu (index 0 - 2)\n",
        "print(nilai*3) #output nilai yang diulang sebanyak 3x\n",
        "\n",
        "nilai[4] = 120 #fungsi replace\n",
        "print(nilai)\n",
        "\n",
        "nilaibaru = nilai+[90,25,105] #menambahkan value dalam list lebih dari 1\n",
        "print(nilaibaru)\n",
        "\n",
        "nilai.append(100) #menambahkan sebuah value dalam list\n",
        "print(nilai)\n",
        "\n",
        "print('Nilai baru: ', nilaibaru)\n",
        "nilaibaru.sort() #mengurutkan nilai dalam list dalam kecil ke besar (ascending)\n",
        "print('Nilai baru setelah di sort: ', nilaibaru)\n",
        "nilaibaru.reverse() #mengurutkan nilai dalam list dalam besar ke kecil\n",
        "print('Nilai baru dari besar ke kecil: ', nilaibaru)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPUcO8Upyzy_",
        "outputId": "658bb6f9-990d-4262-918d-b58d2fef9974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "95\n",
            "[95, 70]\n",
            "[95, 70, 75, 65, 11]\n",
            "[95, 70]\n",
            "[95, 70, 75, 65, 11, 95, 70, 75, 65, 11, 95, 70, 75, 65, 11]\n",
            "[95, 70, 75, 65, 120]\n",
            "[95, 70, 75, 65, 120, 90, 25, 105]\n",
            "[95, 70, 75, 65, 120, 100]\n",
            "Nilai baru:  [95, 70, 75, 65, 120, 90, 25, 105]\n",
            "Nilai baru setelah di sort:  [25, 65, 70, 75, 90, 95, 105, 120]\n",
            "Nilai baru dari besar ke kecil:  [120, 105, 95, 90, 75, 70, 65, 25]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isi = ['Sample', 'Training', 'Latihan']\n",
        "isi.sort() #mengurutkan sesuai abjad\n",
        "print(isi)\n",
        "\n",
        "nilai = [50, 60, 70]\n",
        "nilai.sort(reverse=True) #mengurutkan dari besar ke kecil\n",
        "print(nilai)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOinGcne3Uiz",
        "outputId": "9a7b8b80-079d-43f3-87af-d99633524c1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Latihan', 'Sample', 'Training']\n",
            "[70, 60, 50]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kata = ['abc','ABD',\"aBe\",\"aBd\"]\n",
        "kata.sort()\n",
        "print(kata)\n",
        "\n",
        "#fungsi key berguna untuk mengurutkan karakter bds sifat yang digunakan\n",
        "#parameter [abc, abd, abe, abd]\n",
        "kata.sort(key=str.lower)\n",
        "print(kata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "obNq2mJw4MLR",
        "outputId": "84839ea2-2439-45fa-93c4-afc2634b6788"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ABD', 'aBd', 'aBe', 'abc']\n",
            "['abc', 'ABD', 'aBd', 'aBe']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "angka = [90, 20, 70, 89, 34, 57]\n",
        "angka.append(78) #menambahkan sebuah angka di paling belakang\n",
        "print(angka)\n",
        "\n",
        "angka.insert(2,75) #menyisipkan angka sesuai yang diinginkan\n",
        "print(angka)\n",
        "\n",
        "angka[5] = 20\n",
        "print(angka)\n",
        "print('Angka 20 ada sebanyak: ', angka.count(20))\n",
        "\n",
        "angka.remove(20) #menghapus nilai tertentu (1 aja)\n",
        "print(angka)\n",
        "angka.remove(20) #menghapus yang satunya lagi\n",
        "print(angka)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dRkjyKSl5qYC",
        "outputId": "588cfeed-a792-4b4f-8759-af10e9f32abf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[90, 20, 70, 89, 34, 57, 78]\n",
            "[90, 20, 75, 70, 89, 34, 57, 78]\n",
            "[90, 20, 75, 70, 89, 20, 57, 78]\n",
            "Angka 20 ada sebanyak:  2\n",
            "[90, 75, 70, 89, 20, 57, 78]\n",
            "[90, 75, 70, 89, 57, 78]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = [\"Sample\",[90,22,34,75],75.75]\n",
        "print(data[0])\n",
        "print(data[1])\n",
        "print(data[1][2]) #memunculkan data pada list dalam list (multi dimentional array)\n",
        "print(data[1] [3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u8Ojmejs9SdU",
        "outputId": "b69efad0-a843-49a2-d469-ac29aef4e508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample\n",
            "[90, 22, 34, 75]\n",
            "34\n",
            "75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "judul = \"Latihan untuk Training Python\"\n",
        "print(judul.split(\" \")) #Melakukan split karakter terhadap variabel lalu membuat tipe data string menjadi bentuk list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x1VHijSY-LQY",
        "outputId": "85b8df94-0b82-419b-e12b-03a0f2fb34c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Latihan', 'untuk', 'Training', 'Python']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### List Methods"
      ],
      "metadata": {
        "id": "4zP_5g-TA_LH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Python has a set of built-in methods that you can use on lists.`\n",
        "\n"
      ],
      "metadata": {
        "id": "kNJB9NHfBE36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Method Description\n",
        "\n",
        "  - append() Adds an element at the end of the list\n",
        "  - clear() Removes all the elements from the list\n",
        "  - copy() Returns a copy of the list\n",
        "  - count() Returns the number of elements with the specified value\n",
        "  - extend() Add the elements of a list (or any iterable), to the end of the current list\n",
        "  - index() Returns the index of the first element with the specified value\n",
        "  - insert() Adds an element at the specified position\n",
        "  - pop() Removes the element at the specified position\n",
        "  - remove() Removes the item with the specified value\n",
        "  - reverse() Reverses the order of the list\n",
        "  - sort() Sorts the list"
      ],
      "metadata": {
        "id": "mrvMXSkc1znG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 4 (月曜日) - 25/03/2024"
      ],
      "metadata": {
        "id": "PWU8616NKREl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tuple\n",
        "---"
      ],
      "metadata": {
        "id": "UfDHPuNlKs0i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Tuples are used to store multiple items in a single variable.`\n",
        "\n",
        "`Tuple is one of 4 built-in data types in Python used to store collections of data, the other 3 are List, Set, and Dictionary, all with different qualities and usage.`\n",
        "\n",
        "`A tuple is a collection which is ordered and unchangeable.`\n",
        "\n",
        "`Tuples are written with round brackets. \"( )\"`"
      ],
      "metadata": {
        "id": "_KgiGSqQNT0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = (4, 60, 25, 39, 18) #tuple menggunakan tanda kurung biasa\n",
        "print(data)\n",
        "print(len(data))\n",
        "print(type(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVYDUxKyNVur",
        "outputId": "72a54103-22e4-4f5f-cdae-8a2f847dc35c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4, 60, 25, 39, 18)\n",
            "5\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test tuple apakah dapat diubah / tidak\n",
        "print(data)\n",
        "data[2] = 20"
      ],
      "metadata": {
        "id": "zWwqG49qOwWa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thistuple = (\"apple\") #ini bukan tuple\n",
        "print(type(thistuple))\n",
        "\n",
        "thistuple = (\"mango\",) #membuat tuple dengan 1 variable\n",
        "print(type(thistuple))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTaOqMf0POwO",
        "outputId": "c8535e1e-dab5-4ffa-b0e8-31e1200349ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = (1,2,3,4,5)\n",
        "print(data+(6,7,8)) #menambahkan elemen di tuple\n",
        "\n",
        "month = (\"Jan\", \"Feb\", \"Mar\") #case yang sama tapi di tipe data string\n",
        "print(month+(\"Apr\", \"May\", \"Jun\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZmQvUhtGQM6I",
        "outputId": "7e69a56d-1ff7-4b80-b9dd-312fcf014d28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 2, 3, 4, 5, 6, 7, 8)\n",
            "('Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isidata = (\"Latihan\",\"python\",\"hari\",\"keempat\")\n",
        "print(isidata)\n",
        "print(type(isidata))\n",
        "\n",
        "separator=\" \" #menambahkan spasi di variable isi data\n",
        "hasil = separator.join(isidata) #menggabungkan elemen tuple sekaligus mengubah tipe data\n",
        "print(hasil)\n",
        "print(type(hasil))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8fNlLojrQ7Vi",
        "outputId": "e55f347b-c07c-43ea-9e86-13756522102c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Latihan', 'python', 'hari', 'keempat')\n",
            "<class 'tuple'>\n",
            "Latihan python hari keempat\n",
            "<class 'str'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#tuple dapat dimasukkan berbagai variabel tipe data\n",
        "data = (\"abc\", 30, True, 34.5, \"60\")\n",
        "print(data)\n",
        "print(type(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKb1xYidT8uX",
        "outputId": "80050a47-f316-4573-f1d8-44dc79e40432"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('abc', 30, True, 34.5, '60')\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mengubah tipe data variable jadi tuple\n",
        "thistuple = tuple((\"Apple\", \"Mango\", \"Chery\"))\n",
        "print(thistuple)\n",
        "print(type(thistuple))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-xny1ukyUaov",
        "outputId": "83054db7-efed-4c54-bdfe-b8c042e93d67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Apple', 'Mango', 'Chery')\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mengubah elemen apple menjadi spill 1 per 1 huruf\n",
        "thistuple = tuple((\"apple\"))\n",
        "print(thistuple)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tl3_AbJDU2Ea",
        "outputId": "a1ffbdc0-77b9-4247-807f-0d8771ddf717"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('a', 'p', 'p', 'l', 'e')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thistuple = (\"apple\", \"banana\", \"cherry\", \"orange\", \"kiwi\", \"melon\", \"mango\")\n",
        "print(thistuple[-1])\n",
        "print(thistuple[2:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvmRB41OWT5r",
        "outputId": "e100a2cb-6ced-4cd6-c02d-39a92d5834ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mango\n",
            "('cherry', 'orange', 'kiwi')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#update tuple\n",
        "x = (\"apple\", \"banana\", \"mango\")\n",
        "y = list(x) #convert tuple menjadi list\n",
        "\n",
        "y[1] = \"kiwi\"\n",
        "x = tuple(y) #update data di list\n",
        "\n",
        "print(x)\n",
        "print(type(x)) #convert lagi menjadi tuple"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sotpG_eRXLlG",
        "outputId": "c82e71aa-a780-4f9c-9b35-fa24e408312a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('apple', 'kiwi', 'mango')\n",
            "<class 'tuple'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = (\"apple\", \"banana\", \"mango\")\n",
        "x.count('apple')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1B3-ghogYimz",
        "outputId": "932e3163-490a-47aa-afd1-7eb64433e8f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Set\n",
        "---"
      ],
      "metadata": {
        "id": "azRF9OINYt69"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Sets are used to store multiple items in a single variable.`\n",
        "\n",
        "`Set is one of 4 built-in data types in Python used to store collections of data, the other 3 are List, Tuple, and Dictionary, all with different qualities and usage.`\n",
        "\n",
        "`A set is a collection which is unordered, unchangeable*, and unindexed.`\n",
        "\n",
        "`Set are written with curly brackets. \"{ }\"`\n",
        "\n",
        "*Note: Set items are unchangeable, but you can remove items and add new items.*"
      ],
      "metadata": {
        "id": "Ua5FzCMOYwhK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {1, 3, 4} #tipe data menggunakan tanda kurung kurawal\n",
        "print(thisset)\n",
        "print(type(thisset))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v0O2QYkhZmvG",
        "outputId": "861c197f-fb4e-4bfa-d45d-a960207ec61e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 3, 4}\n",
            "<class 'set'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {1, 1, 2, 2, 3, 4, 5} #duplicate isnt allowed in set\n",
        "print(thisset)\n",
        "print(len(thisset)) #cek jumlah variabel data yang muncul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1C2ZVKQZ4ZC",
        "outputId": "431e9c47-fa52-46d3-f1c4-936610621008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{1, 2, 3, 4, 5}\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#set bisa terdiri dari berbagai macam data\n",
        "set1 = {'abc', 60, True, 60.5, \"60\"}\n",
        "print(type(set1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6hBSISRaeNd",
        "outputId": "7da13696-893e-471d-c4a7-ce2ccbddd98f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'set'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = set((\"apple\", \"banana\")) #mengubah tipe data tuple jadi set\n",
        "print(data)\n",
        "print(type(data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kI17EgWaa04Z",
        "outputId": "e18d6c2d-65ad-46d4-d276-05be18d7738d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'apple', 'banana'}\n",
            "<class 'set'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cek index di set\n",
        "data = {'abc', 60, True, 60.5, \"60\"}\n",
        "print(data)\n",
        "print(data[1]) #index not allowed in set"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnYZKpmGcNxC",
        "outputId": "7dcbda7c-23ff-4e98-d29d-48c2ef4f91e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{True, 'abc', 60.5, 60, '60'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- You cannot access items in a set by referring to an index or a key.\n",
        "- But you can loop through the set items using a for loop, or ask if a specified value is present in a set, by using the in keyword."
      ],
      "metadata": {
        "id": "_OueDaqdc6Yc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"banana\", \"mango\"}\n",
        "\n",
        "for x in thisset:\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yV7yg_cdKJx",
        "outputId": "7ba54254-57f8-4d19-b744-21461011dc68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apple\n",
            "mango\n",
            "banana\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "this = {\"apple\", \"Banana\", \"Mango\"}\n",
        "\n",
        "for x in thisset:\n",
        "  print(x,end=\" \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3bjc1d7SemCI",
        "outputId": "faf7ea19-f461-464d-d363-4e8dda5b2473"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apple mango banana "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "this = {\"apple\", \"Banana\", \"Mango\"}\n",
        "print(\"Banana\" in this)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYGml7Dcf3Il",
        "outputId": "f1c8d43f-1774-4295-96d1-24d61fc5e5b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "this = {\"apple\", \"Banana\", \"Mango\"}\n",
        "this.add(\"Orange\") #menambahkan elemen di set\n",
        "print(this)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qktslpwsgQxA",
        "outputId": "8f10a01c-54d0-4725-bbc3-2484a2d52f4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'apple', 'Banana', 'Orange', 'Mango'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"Banana\", \"Mango\"}\n",
        "buahlain = {\"orange\", \"Avocade\"}\n",
        "lagi = {\"pear\", \"melon\"}\n",
        "thisset.update(buahlain, lagi) #menggabungkan 3 elemen\n",
        "print(thisset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uBqRhZJkg42x",
        "outputId": "20917534-d372-4877-886e-a3306a734403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'apple', 'Mango', 'orange', 'melon', 'Banana', 'pear', 'Avocade'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"Banana\", \"Mango\"}\n",
        "buah_list = [\"orange\", \"Avocade\"] #untuk menggabungkan set dan tuple\n",
        "thisset.update(buah_list)\n",
        "print(thisset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxN57GiyiyU_",
        "outputId": "0e6c8611-60b4-4cd0-c73a-5ee469ff7d81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'apple', 'Mango', 'orange', 'Banana', 'Avocade'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
        "thisset.remove(\"banana\") #apus banana di elemen set\n",
        "print(thisset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oC7PcwoijRnN",
        "outputId": "b17b8356-7e8e-45a2-b7b9-1cc5ed55caf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'apple', 'cherry'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
        "thisset.discard(\"banaaa\") #apus banana di elemen set\n",
        "print(thisset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mY5kW2yxj9jv",
        "outputId": "5ebf6cae-3a74-494b-f17a-f3e21d854ce6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'banana', 'apple', 'cherry'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"banana\", \"apple\", \"cherry\"}\n",
        "x = thisset.pop() #mendefinisikan ke variabel nilai yang di-pop (yang terhapus)\n",
        "print(x) #nampilin nilai yg terhapus\n",
        "print(type(x))\n",
        "print(thisset) #nampilin set setelah ada nilai yg dihapus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cmPSbuSkOOb",
        "outputId": "53f010f3-569b-45d7-dadc-1c14ee7398e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "banana\n",
            "<class 'str'>\n",
            "{'apple', 'cherry'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
        "thisset.clear() #metode untuk menghapus semua item di set\n",
        "print(thisset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHOUjyijlO85",
        "outputId": "48c224cd-2ec1-4565-9ec5-59659316d364"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "set()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "thisset = {\"apple\", \"banana\", \"cherry\"}\n",
        "del thisset #menghapus keseluruhan set\n",
        "#print(thisset) -> gabisa ditampilin karena setnya dihapus"
      ],
      "metadata": {
        "id": "52TL7F6Rllsn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set Methods"
      ],
      "metadata": {
        "id": "FZbymPMbmZpr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Method\tDescription\n",
        "\n",
        "    - add()\tAdds an element to the set\n",
        "    - clear()\tRemoves all the elements from the set\n",
        "    - copy()\tReturns a copy of the set\n",
        "    - difference()\tReturns a set containing the difference between two or more sets\n",
        "    - difference_update()\tRemoves the items in this set that are also included in another, specified set\n",
        "    - discard()\tRemove the specified item\n",
        "    - intersection()\tReturns a set, that is the intersection of two other sets\n",
        "    - intersection_update()\tRemoves the items in this set that are not present in other, specified set(s)\n",
        "    - isdisjoint()\tReturns whether two sets have a intersection or not\n",
        "    - issubset()\tReturns whether another set contains this set or not\n",
        "    - issuperset()\tReturns whether this set contains another set or not\n",
        "    - pop()\tRemoves an element from the set\n",
        "    - remove()\tRemoves the specified element\n",
        "    - symmetric_difference()\tReturns a set with the symmetric differences of two sets\n",
        "    - symmetric_difference_update()\tinserts the symmetric differences from this set and another\n",
        "    - union()\tReturn a set containing the union of sets\n",
        "    - update()\tUpdate the set with the union of this set and others"
      ],
      "metadata": {
        "id": "roTZe7B3mcy8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 5 (水曜日) - 27/03/2024"
      ],
      "metadata": {
        "id": "bFIoSL63cmLV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dictionary\n",
        "---"
      ],
      "metadata": {
        "id": "_GVOeHXqc7w6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Dictionaries are used to store data values in key:value pairs.`\n",
        "\n",
        "`A dictionary is a collection which is ordered*, changeable and do not allow duplicates.`"
      ],
      "metadata": {
        "id": "TssI1BXFghpk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "isi = {'nilai':75, 'deskripsi':'Testing','hasil':50} #key value\n",
        "print(isi)\n",
        "print(type(isi)) #melihat tipe dalam dictionary\n",
        "print(isi.keys()) #melihat key dalam dictionary\n",
        "print(isi.values()) #melihat value dalam dictionary\n",
        "print(isi.items()) #melihat elemen dalam dictionary\n",
        "print(len(isi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hrwcIQ7MgjNB",
        "outputId": "66cfc99f-3d9c-42ae-d049-a9034917e8ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nilai': 75, 'deskripsi': 'Testing', 'hasil': 50}\n",
            "<class 'dict'>\n",
            "dict_keys(['nilai', 'deskripsi', 'hasil'])\n",
            "dict_values([75, 'Testing', 50])\n",
            "dict_items([('nilai', 75), ('deskripsi', 'Testing'), ('hasil', 50)])\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#melihat value berdasarkan key yang dipilih\n",
        "print(isi['deskripsi'])\n",
        "x = isi.get('deskripsi')\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zt1K15kfh68f",
        "outputId": "c8d987b8-d857-483f-96b7-891e4d652335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing\n",
            "Testing\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dictionary tidak boleh ada key yang sama\n",
        "isi = {'nilai':75, 'deskripsi':'Testing','hasil':50, 'hasil':100}\n",
        "print(isi)\n",
        "print(type(isi))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9A_KZHXiaiI",
        "outputId": "d1afe41a-275c-4756-b350-4d044bbe304d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nilai': 75, 'deskripsi': 'Testing', 'hasil': 100}\n",
            "<class 'dict'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isi[500] = 'sample' #menambahkan value baru & key tidak harus tipe string\n",
        "print(isi)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u7WP1ksci1OH",
        "outputId": "fe8862cc-b555-4ad6-93e1-7e2cf71a848d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nilai': 75, 'deskripsi': 'Testing', 'hasil': 100, 500: 'sample'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car = {\n",
        "    \"brand\": \"Ford\",\n",
        "    \"model\": \"Mustang\",\n",
        "    \"year\": 1964\n",
        "}\n",
        "\n",
        "x = car.keys()\n",
        "print(x) #sebelum ditambahkan value baru\n",
        "\n",
        "car['color'] = 'Red Maroon'\n",
        "print(x) #setelah ditambahkan value baru\n",
        "\n",
        "print(car)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHBSrWi9jKdk",
        "outputId": "43169561-c1c5-4cdf-a77d-4b96b7fbbc94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['brand', 'model', 'year'])\n",
            "dict_keys(['brand', 'model', 'year', 'color'])\n",
            "{'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'color': 'Red Maroon'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "biodata = {} #membuat dictionary lebih dinamis menggunakan empty dictionary\n",
        "biodata['nama'] = 'Muammar Yasir'\n",
        "biodata['email'] = 'muamaryssr2@gmail.com'\n",
        "biodata['usia'] = 22\n",
        "print(biodata)\n",
        "\n",
        "biodata['usia'] = 20 #update value dari key usia\n",
        "print(biodata)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4480oR0_kpsC",
        "outputId": "8d526465-1bb4-415a-be0e-8ebd2b2c78e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nama': 'Muammar Yasir', 'email': 'muamaryssr2@gmail.com', 'usia': 22}\n",
            "{'nama': 'Muammar Yasir', 'email': 'muamaryssr2@gmail.com', 'usia': 20}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = set('tri')\n",
        "print(x)\n",
        "\n",
        "y = set([2,3,1,1])\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R94miIRRmeZ3",
        "outputId": "be86f459-952d-4e72-9d2c-4fc3bf82b25a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'i', 'r', 't'}\n",
            "{1, 2, 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "z = dict(zip(x,y))\n",
        "print(z)\n",
        "print(type(z))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZJdww7gm_GU",
        "outputId": "2b801319-9bcb-4ba3-d8cc-7c3658dfdba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'i': 1, 'r': 2, 't': 3}\n",
            "<class 'dict'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = ['a', 'b']\n",
        "b = [1,2]\n",
        "c = dict(zip(a,b))\n",
        "print(a); print(b); print(c)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XU-x_dMhnxqf",
        "outputId": "b0f6c8fe-cc5f-42c7-9495-d5537f66d1f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['a', 'b']\n",
            "[1, 2]\n",
            "{'a': 1, 'b': 2}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dictionary Method"
      ],
      "metadata": {
        "id": "FCtrQpbfniJ4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Method\tDescription\n",
        "\n",
        "    - clear()\tRemoves all the elements from the dictionary\n",
        "    - copy()\tReturns a copy of the dictionary\n",
        "    - fromkeys()\tReturns a dictionary with the specified keys and value\n",
        "    - get()\tReturns the value of the specified key\n",
        "    - items()\tReturns a list containing a tuple for each key value pair\n",
        "    - keys()\tReturns a list containing the dictionary's keys\n",
        "    - pop()\tRemoves the element with the specified key\n",
        "    - popitem()\tRemoves the last inserted key-value pair\n",
        "    - setdefault()\tReturns the value of the specified key. If the key does not exist: insert the key, with the specified value\n",
        "    - update()\tUpdates the dictionary with the specified key-value pairs\n",
        "    - values()\tReturns a list of all the values in the dictionary"
      ],
      "metadata": {
        "id": "OOdsBD5untcx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Block Statement"
      ],
      "metadata": {
        "id": "1lQLa-b6pecs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A block statement, or compound statement, lets you group any number of data definitions, declarations, and statements into one statement."
      ],
      "metadata": {
        "id": "fl3BsX-Mpm-j"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### If Else Statement"
      ],
      "metadata": {
        "id": "7wIOY1uKp0nP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The if-else statement is used to execute both the true part and the false part of a given condition."
      ],
      "metadata": {
        "id": "YFqBWoxPp6jb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a = 115\n",
        "\n",
        "if a > 100:\n",
        "  print('a is big number') #output jika a memenuhi kondisi pertama\n",
        "else:\n",
        "  print('a is small number') #output jika a tidak memenuhi kondisi pertama"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QxRqXCxTqsM0",
        "outputId": "3af56065-5a3d-4f35-a6dd-2a82b585c03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a is big number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#if else untuk 2 kondisi\n",
        "\n",
        "m = 100\n",
        "\n",
        "if m < 100: #kondisi satu\n",
        "  print('small number')\n",
        "\n",
        "elif m > 100: #kondisi dua\n",
        "  print('medium number')\n",
        "\n",
        "else: #kondisi lain\n",
        "  print('big number')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9HmobUEWrN0Q",
        "outputId": "65ea4caa-0f1e-4fe2-8d1f-a3233866fa74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "big number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#if else dengan 2 kondisi, dan variable yang didefinisikan memenuhi 2 kondisi tsb\n",
        "n = 125\n",
        "\n",
        "if n < 100: #kondisi satu\n",
        "  print('small number')\n",
        "\n",
        "elif n < 150: #kondisi dua\n",
        "  print('medium number')\n",
        "\n",
        "else: #kondisi lain\n",
        "  print('big number')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCc_CKs3sXuk",
        "outputId": "eca5f7d4-63eb-480e-fa6d-a5e4c98cf3dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "big number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#if else untuk string\n",
        "\n",
        "isi = 'mobil'\n",
        "\n",
        "if 'm' in isi: #ngecek apakah ada huruf a di key mobil\n",
        "  print('terdapat huruf a')\n",
        "else:\n",
        "  print('tidak terdapat huruf a')"
      ],
      "metadata": {
        "id": "EGFfCLj9t5NN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#if else untuk membandingkan 2 variable\n",
        "\n",
        "bilangan1 = 20\n",
        "bilangan2 = 20\n",
        "\n",
        "if bilangan1 == bilangan2:\n",
        "  print('bilangan1 sama dengan bilangan2')\n",
        "else:\n",
        "  print('bilangan1 tidak sama dengan bilangan2')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ikAAx_FXu95b",
        "outputId": "3ad2bdc8-7fd0-47f7-c09f-27780ce7e435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bilangan1 sama dengan bilangan2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = 0\n",
        "y = 0\n",
        "\n",
        "if x < 0 and y < 0:\n",
        "  print('x dan y bilangan negatif')\n",
        "\n",
        "elif x < 0 and y > 0:\n",
        "  print('x bilangan negatif dan y bilangan positif')\n",
        "\n",
        "elif x > 0 and y < 0:\n",
        "  print('x bilangan positif dan y bilangan negatif')\n",
        "\n",
        "elif x > 0 and y > 0:\n",
        "  print('x dan y bilangan positif')\n",
        "\n",
        "elif x == 0 and y == 0:\n",
        "  print('kedua bilangan sama sama 0')\n",
        "\n",
        "else:\n",
        "  print('salah satunya ada yang 0')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z53X3a1hv89L",
        "outputId": "a3d47e55-9907-4fca-d08b-624382e6112c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "kedua bilangan sama sama 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### For Statement"
      ],
      "metadata": {
        "id": "gfBA6n6kyFC5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A control flow statement that is used to repeatedly execute a group of statements as long as the condition is satisfied"
      ],
      "metadata": {
        "id": "oaK0uIRQyoHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for x in range(1, 6): #membaca range melalui proses looping\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTGpQj7ayqRg",
        "outputId": "b2c32615-20fa-4091-f905-614feef98ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for nilai in range(2 ,11, 2): #tentukan batas awal dan akhir, ditengah nya batas antara keduanya\n",
        "  print(nilai)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dvHTcrvbzfxu",
        "outputId": "36dfba07-4fe5-49d7-8033-44591d837441"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n",
            "4\n",
            "6\n",
            "8\n",
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for nilai in range(10, 1, -2): #tentukan batas awal dan range di akhir, ditengah nya batas antar keduanya\n",
        "  print(nilai)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fzUja2i0BEc",
        "outputId": "9e713c21-d1af-40ea-eb8d-9b0656791c67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "8\n",
            "6\n",
            "4\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for untuk string\n",
        "isi = 'training'\n",
        "\n",
        "for x in isi:\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ft9YWFmF1yRA",
        "outputId": "bc4d0a03-c8a0-4e09-ea63-dee51c39ff13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "t\n",
            "r\n",
            "a\n",
            "i\n",
            "n\n",
            "i\n",
            "n\n",
            "g\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for untuk list\n",
        "\n",
        "data = ['testing', 'sample', 'coba']\n",
        "for x in data:\n",
        "  print(x)\n",
        "\n",
        "#mencetak index dan nilai secara bersamaan\n",
        "for idx, x in enumerate(data):\n",
        "  print(idx,x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WySWv1Kn2SYx",
        "outputId": "9a22f6e4-051c-4e5c-cfe8-4c82582db200"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "testing\n",
            "sample\n",
            "coba\n",
            "0 testing\n",
            "1 sample\n",
            "2 coba\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fruit = ['apple', 'banana', 'manggo']\n",
        "color = ['red', 'yellow', 'green']\n",
        "\n",
        "for fruit, color in zip(fruit, color):\n",
        "  print(f\"the color of {fruit} is {color}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PS0WOdPV3DFH",
        "outputId": "d3b9c7d7-8701-46b4-b6df-641d42a58539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "the color of apple is red\n",
            "the color of banana is yellow\n",
            "the color of manggo is green\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for pada dictionary, ngebaca key\n",
        "\n",
        "member = {\"nama\":\"someone else\",\"nilai\":75,\"kredit\":15000} #untuk membaca key dict\n",
        "print(member)\n",
        "\n",
        "print(\" \") #bertindak sebagai enter\n",
        "\n",
        "for k in member:\n",
        "    print(k)\n",
        "\n",
        "print(\"\\n\")\n",
        "\n",
        "for k in member:\n",
        "    print(member[k])\n",
        "\n",
        "print(\" \")\n",
        "\n",
        "for k in member:\n",
        "  print(k, \":\", member[k])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYnYHoYf4I3A",
        "outputId": "01ddd79d-0770-4c69-bbc4-2406bbadc852"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nama': 'someone else', 'nilai': 75, 'kredit': 15000}\n",
            " \n",
            "nama\n",
            "nilai\n",
            "kredit\n",
            "\n",
            "\n",
            "someone else\n",
            "75\n",
            "15000\n",
            " \n",
            "nama : someone else\n",
            "nilai : 75\n",
            "kredit : 15000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#for pada dictionary, ngebaca key dan value\n",
        "\n",
        "member = {\"nama\":\"someone\",\"nilai\":75,\"kredit\":15000} #untuk membaca anggota list\n",
        "print(member)\n",
        "print(member.items())\n",
        "\n",
        "print(\" \")\n",
        "\n",
        "for (k,v) in member.items(): #menggunakan hasil dari member.items\n",
        "    print(k,\"=\",v)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrTBQgPz6J5f",
        "outputId": "2519e312-6f07-4966-b6ed-1875ac92875e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'nama': 'someone', 'nilai': 75, 'kredit': 15000}\n",
            "dict_items([('nama', 'someone'), ('nilai', 75), ('kredit', 15000)])\n",
            " \n",
            "nama = someone\n",
            "nilai = 75\n",
            "kredit = 15000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 6 (木曜日) - 28/03/2024"
      ],
      "metadata": {
        "id": "FYDHsC3ijJNm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Block Statement\n",
        "---"
      ],
      "metadata": {
        "id": "Z4UIXnVYjYTi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Membuat Barisan Bilangan Ganjil atau Genap')\n",
        "print('\\n Masukkan Nilai Awal dan Nilai Akhir (nilai_awal < nilai_akhir)')\n",
        "\n",
        "nilai_awal = int(input('Nilai Awal: '))\n",
        "nilai_akhir = int(input('Nilai Akhir: '))\n",
        "\n",
        "print('''\\n Mau Barisan Bilangan Apa?\n",
        " 1. Ganjil\n",
        " 2. Genap''')\n",
        "\n",
        "pilihan = int(input('1 atau 2?'))\n",
        "\n",
        "print(' ')\n",
        "\n",
        "if pilihan not in [1,2]:\n",
        "  print('Pilihan Salah!')\n",
        "else:\n",
        "\n",
        " for x in range(nilai_awal, nilai_akhir+1):\n",
        "  if pilihan == 1 and x % 2 == 1:\n",
        "    print(x,end=' ')\n",
        "  elif pilihan == 2 and x % 2 == 0:\n",
        "    print(x,end=' ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XTkz-EvHocZi",
        "outputId": "042ff3cf-c6f2-4c0a-c03a-f950c53f3107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Membuat Barisan Bilangan Ganjil atau Genap\n",
            "\n",
            "Masukkan Nilai Awal dan Nilai Akhir (nilai_awal < nilai_akhir)\n",
            "Nilai Awal: 1\n",
            "Nilai Akhir: 20\n",
            "\n",
            "Mau Barisan Bilangan Apa?\n",
            " 1. Ganjil\n",
            " 2. Genap\n",
            "1 atau 2?2\n",
            " \n",
            "2 4 6 8 10 12 14 16 18 20 "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### While Statement"
      ],
      "metadata": {
        "id": "ZYGrTgfUuTwV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A while statement in python sets aside a block of code that is to be executed repeatedly until a condition is falsified."
      ],
      "metadata": {
        "id": "9B1DDZxvubQ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#while True:\n",
        "# print('Welcome to Celerates Training')"
      ],
      "metadata": {
        "id": "8exLHQ-UvTwX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "angka = 0\n",
        "\n",
        "while angka < 5:\n",
        "  print('Welcome to Celerates Training')\n",
        "  angka += 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wMjL3WdJun7x",
        "outputId": "2687b951-5620-4b69-d63f-efa387149b4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to Celerates Training\n",
            "Welcome to Celerates Training\n",
            "Welcome to Celerates Training\n",
            "Welcome to Celerates Training\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "angka = 1\n",
        "\n",
        "while angka < 10:\n",
        "  print('Bilangan ke: ', angka)\n",
        "  angka += 2 #syarat supaya berhenti\n",
        "else:\n",
        "  print('Loop diakhiri dengan angka bilangan: ', angka)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nE3A_Qs7vK_2",
        "outputId": "512613f0-bc6c-409e-f61a-eab1c50c77f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bilangan ke:  1\n",
            "Bilangan ke:  3\n",
            "Bilangan ke:  5\n",
            "Bilangan ke:  7\n",
            "Bilangan ke:  9\n",
            "Loop diakhiri dengan angka bilangan:  11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "jawaban = random.randint(1, 5)\n",
        "tebakan = int(input('Pilih bilangan antara 1 - 5: '))\n",
        "\n",
        "while tebakan != jawaban:\n",
        "  tebakan = int(input('Salah Kasian Deh Lo! Coba lagi'))\n",
        "\n",
        "print('Yeay! Tebakanmu Bener Jago amat')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_d8S4mh7zet6",
        "outputId": "4fe2198c-f834-4df9-ccc0-ceefcd07267a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pilih bilangan antara 1 - 5: 2\n",
            "Salah Kasian Deh Lo! Coba lagi5\n",
            "Salah Kasian Deh Lo! Coba lagi3\n",
            "Yeay! Tebakanmu Bener Jago amat\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import random as rd\n",
        "answer = rd.randint(1, 100)\n",
        "print(\"Tebak angka antara 1-100\")\n",
        "while True:\n",
        "    guess = int(input(\"Tebakan: \"))\n",
        "    if guess == answer:\n",
        "        print(\"Tebakan benar\", guess)\n",
        "        break\n",
        "    elif guess < answer:\n",
        "        print(guess, \"Tebakan terlalu kecil\")\n",
        "    else:\n",
        "        print(guess, \"Tebakan terlalu besar\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2o_g9gmkztgT",
        "outputId": "5059be7a-7db4-492b-fc28-dff8b3e39fcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tebak angka antara 1-100\n",
            "Tebakan: 50\n",
            "50 Tebakan terlalu kecil\n",
            "Tebakan: 72\n",
            "72 Tebakan terlalu besar\n",
            "Tebakan: 52\n",
            "52 Tebakan terlalu kecil\n",
            "Tebakan: 65\n",
            "65 Tebakan terlalu besar\n",
            "Tebakan: 60\n",
            "60 Tebakan terlalu kecil\n",
            "Tebakan: 61\n",
            "61 Tebakan terlalu kecil\n",
            "Tebakan: 62\n",
            "62 Tebakan terlalu kecil\n",
            "Tebakan: 63\n",
            "Tebakan benar 63\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exception Statement"
      ],
      "metadata": {
        "id": "4QWReUl917In"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An event, which occurs during the execution of a program that disrupts the normal flow of the program's instructions"
      ],
      "metadata": {
        "id": "ViXEDbO82C2O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#block exception handling\n",
        "#run error namun masih bisa dijalankan\n",
        "#block except bisa lebih dari satu\n",
        "#block finally bisa ditambahkan tanpa syarat untuk menjalankan runtime\n",
        "#kalau success try langsung ke finally\n",
        "#jika error try ke except baru ke finally\n",
        "\n",
        "a = 4\n",
        "b = 'test'\n",
        "# c = a + b\n",
        "# print(c)\n",
        "\n",
        "try:\n",
        "  c = a + b\n",
        "  print(c)\n",
        "except Exception as error:\n",
        "  print('Error: ', error)\n",
        "finally:\n",
        "  print('Done!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2wkML4jd2hAZ",
        "outputId": "fdf2137b-4331-4b73-f2d2-d103a37aa91a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error:  unsupported operand type(s) for +: 'int' and 'str'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isi = [1, 2, 3, 4, 5]\n",
        "#print(isi[6])\n",
        "\n",
        "try:\n",
        "  print(isi[6])\n",
        "except IndexError as a:\n",
        "  print('Error: ', a)\n",
        "except Exception as e:\n",
        "  print('Error: ', e)\n",
        "\n",
        "finally:\n",
        "  print('Done!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLtUtvCu6oS_",
        "outputId": "812bbb07-acc3-4701-9347-1edb1a5522dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error:  list index out of range\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "isi = [1, 2, 3, 4, 5]\n",
        "#print(isi['Test'])\n",
        "\n",
        "try:\n",
        "  print(isi['Test'])\n",
        "\n",
        "except IndexError as a:\n",
        "  print('No Data Available')\n",
        "except TypeError as e:\n",
        "  print('Index Must be Numeric')\n",
        "except Exception as b:\n",
        "  print('error: ', b)\n",
        "\n",
        "finally:\n",
        "  print('Done!')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KViud6w72RW",
        "outputId": "b56fbef7-6dac-4595-847f-ef8809b223b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index Must be Numeric\n",
            "Done!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "while True:\n",
        "  try:\n",
        "    x = float(input('Masukkan Bilangan Pertama: '))\n",
        "    y = float(input('Masukkan Bilangan Kedua: '))\n",
        "\n",
        "    result = x/y\n",
        "    print('Hasil Pembagian adalah ', result)\n",
        "    break\n",
        "  except ValueError:\n",
        "      print('Anda harus input bilangan')\n",
        "  except ZeroDivisionError:\n",
        "      print('Bilangan kedua tidak boleh nol')\n",
        "  except Exception as a:\n",
        "      print('Error: ', a)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_1EQY3480YV",
        "outputId": "1ab2b54f-4c5b-4e21-c0ac-8a0fad89c442"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Masukkan Bilangan Pertama: 0\n",
            "Masukkan Bilangan Kedua: 3\n",
            "Hasil Pembagian adalah  0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function"
      ],
      "metadata": {
        "id": "r-FiGd3T_2At"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A function is a block of code which only runs when it is called"
      ],
      "metadata": {
        "id": "gZz4s7p-_3gZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pesan():\n",
        "  print(\"Jangan Lupa Sholat 5 Waktu\")\n",
        "\n",
        "pesan()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jyRJuvnpNeX9",
        "outputId": "feffbc4d-59d7-47ae-d422-d19f97552fcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jangan Lupa Sholat 5 Waktu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def perkalian(x,y):\n",
        "  print(x*y)"
      ],
      "metadata": {
        "id": "MCboBF7iBpxg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "perkalian(2,7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZiEksQeB2JV",
        "outputId": "8149878b-4951-47ac-b341-aed7c3a729ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def message(pesan):\n",
        "  print(pesan)"
      ],
      "metadata": {
        "id": "27vrnjQxCN8l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "message('Selamat Datang')\n",
        "message(pesan='Selamat Datang')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqE4NxSRCbsw",
        "outputId": "63102fcc-8cb1-4da5-f372-fa90e9bdb565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selamat Datang\n",
            "Selamat Datang\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 7 (月曜日) - 01/04/2024"
      ],
      "metadata": {
        "id": "FGDIg3UtM_i9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function\n",
        "---"
      ],
      "metadata": {
        "id": "zooJQuAUNLb9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def pembagian(x = 1, y = 1):\n",
        "  return x / y\n",
        "\n",
        "print('Pembagian 20 dengan 3 adalah ', pembagian(20,3))\n",
        "pembagian()\n",
        "pembagian(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9hSoa40PbTC",
        "outputId": "c5a80d40-d935-4e6a-97ba-671f0c64f566"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pembagian 20 dengan 3 adalah  6.666666666666667\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25.0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def nilai(x, y, z):\n",
        "  return (x + y) / z\n",
        "\n",
        "print(nilai(2, 3, 5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVVdqIxrQBqb",
        "outputId": "a86697e6-d78d-4b4d-c786-40f4e8290c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def jumlah(x, y, z):\n",
        "  return x + y + z\n",
        "\n",
        "print(jumlah('20', '35', '27')) #kalau type string jadi gabung semua"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSQ-xrmuQa3Y",
        "outputId": "f1cbee67-9617-49f7-a9ec-075c371afc18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "203527\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_number (x, y):\n",
        "  result = x + y\n",
        "  return result\n",
        "\n",
        "num1 = int(input(\"masukkan nilai x: \"))\n",
        "num2 = int(input(\"masukkan nilai y: \"))\n",
        "\n",
        "sum = add_number(num1, num2)\n",
        "print(\"jumlah dari x dan y adalah \", sum)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ChuMwtOQ7IX",
        "outputId": "2854f14c-82c6-4bb7-d47e-0bf56cab0968"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "masukkan nilai x: 50\n",
            "masukkan nilai y: 50\n",
            "jumlah dari x dan y adalah  100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "\n",
        "def hitung_keliling(jari_jari):\n",
        "  keliling = 2*math.pi*jari_jari\n",
        "  return keliling\n",
        "\n",
        "def hitung_luas(jari_jari):\n",
        "  luas = math.pi*jari_jari*jari_jari\n",
        "  return luas\n",
        "\n",
        "r = float(input('Masukkan panjang jari-jari lingkaran: '))\n",
        "\n",
        "keliling = hitung_keliling(r)\n",
        "L = hitung_luas(r)\n",
        "\n",
        "print('Keliling lingkaran adalah ', keliling)\n",
        "print('Luas lingkaran adalah ', L)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JiYCdEiMRsHn",
        "outputId": "a6287ed7-2956-451e-8b6c-11800014c00b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Masukkan panjang jari-jari lingkaran: 200\n",
            "Keliling lingkaran adalah  1256.6370614359173\n",
            "Luas lingkaran adalah  125663.70614359173\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import math library\n",
        "import math\n",
        "\n",
        "jari_jari = float(input('Masukkan panjang jari-jari lingkaran: '))\n",
        "tinggi = float(input('Masukkan panjang tinggi lingkaran: '))\n",
        "\n",
        "luas_selimut = 2 * math.pi * jari_jari * tinggi\n",
        "luas_alas = math.pi * jari_jari ** 2\n",
        "luas_tutup = math.pi * jari_jari ** 2\n",
        "luas_permukaan = luas_selimut + 2 * luas_alas\n",
        "volume = math.pi * jari_jari * jari_jari * tinggi\n",
        "\n",
        "print(\"Luas selimut tabung:\", luas_selimut)\n",
        "print(\"Luas alas tabung:\", luas_alas)\n",
        "print(\"Luas tutup tabung:\", luas_tutup)\n",
        "print(\"Luas permukaan tabung:\", luas_permukaan)\n",
        "print(\"Luas volume tabung:\", volume)\n"
      ],
      "metadata": {
        "id": "XB3DKZqYTeYm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def jumlah(x, y):\n",
        "  if isinstance(x, int) & isinstance(y,int):\n",
        "    return x + y\n",
        "  elif isinstance(x, str) & isinstance(y, str):\n",
        "    if x.isdigit() & y.isdigit():\n",
        "      return int(x) + int(y)\n",
        "    else:\n",
        "      return 'Sorry, Number Only'\n",
        "\n",
        "print(jumlah(3, 4))\n",
        "print(jumlah(\"2\", \"3\"))\n",
        "print(jumlah('3a', '3a'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u1sk3FXSWrce",
        "outputId": "a1203be8-572f-410c-a2a8-43dea94ff37e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "5\n",
            "Sorry, Number Only\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def jumlah(x, y):\n",
        "  if isinstance(x, int) and isinstance(y, int):\n",
        "    return x + y\n",
        "  elif isinstance(x, str) and isinstance(y, int):\n",
        "    if x.isdigit():\n",
        "      return int(x) + y\n",
        "    else:\n",
        "      return 'Sorry, Number Only'\n",
        "  elif isinstance(x, int) and isinstance(y, str):\n",
        "    if y.isdigit():\n",
        "      return x + int(y)\n",
        "    else:\n",
        "      return 'Sorry, Number Only'\n",
        "  elif isinstance(x, str) and isinstance(y, str):\n",
        "    if x.isdigit() & y.isdigit():\n",
        "      return int(x) + int(y)\n",
        "    else:\n",
        "      return 'Sorry, Number Only'\n",
        "\n",
        "print(jumlah(3, 4))\n",
        "print(jumlah(\"2\", \"3\"))\n",
        "print(jumlah('3a', '3a'))\n",
        "print(jumlah(3, '4'))\n",
        "print(jumlah('3', 4))\n",
        "print(jumlah(3, '4a'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HPci8_YYC3u",
        "outputId": "4415b70f-e31a-4420-c1cd-fef29d668f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7\n",
            "5\n",
            "Sorry, Number Only\n",
            "7\n",
            "7\n",
            "Sorry, Number Only\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat function untuk tipe data yang memiliki banyak nilai\n",
        "\n",
        "def totaldata(nilai):\n",
        "  total = 0\n",
        "  if isinstance(nilai, list) | isinstance(nilai, tuple):\n",
        "    for value in nilai:\n",
        "      total += value\n",
        "  return total\n",
        "\n",
        "print(totaldata([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))\n",
        "\n",
        "print(totaldata((1, 2, 3, 4, 5)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W5padfaYZcI2",
        "outputId": "e24f609b-0cf2-41c5-f9e2-6945cf7efff7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55\n",
            "15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lambda\n",
        "---"
      ],
      "metadata": {
        "id": "BoiyoEINbcd8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lambda is a keyword in Python for defining the anonymous function"
      ],
      "metadata": {
        "id": "gjYEpdMobgQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "perkalian = lambda x,y : x*y; #seperti variable tapi isinya function\n",
        "\n",
        "print(perkalian(4, 5))\n",
        "print(perkalian(5, 'a'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l1TFq7yebs8-",
        "outputId": "b04925d3-b322-4b61-a64b-097e4325daea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "aaaaa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class\n",
        "---"
      ],
      "metadata": {
        "id": "Gw4hbT8Wcx2u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A class is a code template for creating objects. Objects have member variables and have behaviour associated with them. In python a class is created by the keyword class"
      ],
      "metadata": {
        "id": "30R0wYPvc2Ex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class biodata:\n",
        "  basicsalary = 5000000\n",
        "\n",
        "  def filldata(self, nama, telp):\n",
        "    self.fullname = nama\n",
        "    self.phone = telp\n",
        "\n",
        "\n",
        "  def printdata(self):\n",
        "    print('Full Name: ', self.fullname)\n",
        "    print('Phone Number: ', self.phone)"
      ],
      "metadata": {
        "id": "Tv151c-ec8KN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = biodata()\n",
        "\n",
        "data.filldata(\"Ammar\", '085811545669')\n",
        "data.printdata()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gv4IXLEd-xa",
        "outputId": "01e8ade7-e5ca-44b6-b054-79e5e7a0b3a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Full Name:  Ammar\n",
            "Phone Number:  085811545669\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.email = 'muamaryssr2@gmail.com'\n",
        "data.socmed = '@muamarasta'\n",
        "\n",
        "hasattr(data,'Gender')\n",
        "hasattr(data,'Email')\n",
        "hasattr(data, 'Socmed')\n",
        "setattr(data,'Gender','Male')"
      ],
      "metadata": {
        "id": "VgGiBQqvfzoD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hasattr(data, 'Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AREuxduJg6Pi",
        "outputId": "4afa23fc-a1f5-4877-db84-d24fd634002f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hasattr(data, 'email')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISgLrPCUiFWq",
        "outputId": "46320cec-a4e5-4c99-e5d2-42026bd495b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hasattr(data, 'socmed')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ntdr2OS8P5rd",
        "outputId": "6263ef5e-cdaa-4cf5-99ca-bc960731f826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Nama: ', data.fullname)\n",
        "print('Gender: ', data.Gender)\n",
        "print('No_telp: ', data.phone)\n",
        "print('Gaji: ', data.basicsalary)\n",
        "print('Email: ', data.email)\n",
        "print('Medsos: ', data.socmed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONuoYAOiiMas",
        "outputId": "afd067b1-4fbb-49a8-8ac1-a70bc16d55d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nama:  Ammar\n",
            "Gender:  Male\n",
            "No_telp:  085811545669\n",
            "Gaji:  5000000\n",
            "Email:  muamaryssr2@gmail.com\n",
            "Medsos:  @muamarasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class person:\n",
        "  def info(self, nama):\n",
        "    self.fullname = nama\n",
        "    if hasattr(self, 'title'):\n",
        "      title = getattr(self, 'title')\n",
        "      self.pesan = 'Welcome, ' + title + ' ' + nama\n",
        "    else:\n",
        "      self.pesan = 'Welcome, ' + nama"
      ],
      "metadata": {
        "id": "UVELhyXgia6s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat objek dengan title menggunakan kelas person\n",
        "p = person()\n",
        "p.title = 'Mr.'\n",
        "p.info('Ammar')\n",
        "p.pesan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "kFmfyiBcjLTN",
        "outputId": "15b45331-2a79-46f3-a765-25dc4e2f13b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Welcome, Mr. Ammar'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat objek tanpa title menggunakan kelas person\n",
        "q = person()\n",
        "q.info('Gema')\n",
        "q.pesan"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "benHdQ8AkBzj",
        "outputId": "1c27fb47-f595-455f-8f88-ee0727475630"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Welcome, Gema'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 8 (水曜日) - 03/04/2024"
      ],
      "metadata": {
        "id": "6YGq-JRgNisR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numpy \"Math Function\"\n",
        "---"
      ],
      "metadata": {
        "id": "b-Z5F_5ANp3G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.\n",
        "\n",
        "(Check Numpy Documentation for more: https://numpy.org/doc/stable/user/)"
      ],
      "metadata": {
        "id": "VzH1cQtviDNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "5uR7_vDfi2xH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "print(np.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCqDsLpodtio",
        "outputId": "bc86c76a-7f60-4523-a4fd-63c39b3b1f9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.25.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a_list = [1.0, 2.0, 3.0, 4.0, 5.0]\n",
        "print(a_list)\n",
        "\n",
        "a_list.append(6.0) #menambahkan nilai\n",
        "print(a_list)\n",
        "\n",
        "a_list.remove(6.0) #menghapus nilai\n",
        "print(a_list)\n",
        "\n",
        "a_list[1] = 7.0 #replace\n",
        "print(a_list)\n",
        "\n",
        "print(type(a_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hf3gALIejDpL",
        "outputId": "b4cf9ac0-5938-4ec0-edfc-cdc2f95289eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.0, 2.0, 3.0, 4.0, 5.0]\n",
            "[1.0, 2.0, 3.0, 4.0, 5.0, 6.0]\n",
            "[1.0, 2.0, 3.0, 4.0, 5.0]\n",
            "[1.0, 7.0, 3.0, 4.0, 5.0]\n",
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## vs Numpy"
      ],
      "metadata": {
        "id": "ZD_dco4Tj13Y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Numpy mirip seperti list, tapi numpy immutable (append, remove, insert)"
      ],
      "metadata": {
        "id": "bmC-CpvTkIOs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1 Dimensi Array"
      ],
      "metadata": {
        "id": "r54E2IFomqkV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1D Array\n",
        "\n",
        "a_numpy = np.array(a_list) #mengubah list jadi array 1 Dimensi\n",
        "print(a_numpy)\n",
        "\n",
        "#a_numpy.append(5)\n",
        "#a_numpy.remove(4.0)\n",
        "a_numpy[1] = 2.0\n",
        "print(a_numpy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAJN07JFkVQa",
        "outputId": "cf44f605-73a4-4556-a201-8c513363ba6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 7. 3. 4. 5.]\n",
            "[1. 2. 3. 4. 5.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cek nilai di suatu indeks\n",
        "a_numpy[2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u9YTCOALl6Mi",
        "outputId": "0a7fb2ee-2c18-4283-d5d5-6ab09412292d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cek dimensi\n",
        "a_numpy.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T46xHWlwmGau",
        "outputId": "adec8dad-15c5-42a3-d4a1-eb6e780e7708"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5,)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Multi Dimensi Array (Matriks = 2D)"
      ],
      "metadata": {
        "id": "OZpDzbhzmvE8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- struktur data disebut sebagai ndarray (number of dimension dr array-nya)\n",
        "- array bisa dibentuk menjadi dimensi yang lebih dari 2 tergantung pendefinisiannya\n",
        "- sebuah array yang memiliki dimensi 2 disebut sbg matriks"
      ],
      "metadata": {
        "id": "4HhgbAy6nEto"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "b_list = [1, 2, 3, 4, 5]\n",
        "print(b_list)\n",
        "print(len(b_list))\n",
        "\n",
        "c_list = [[1, 2, 3, 4, 5]]\n",
        "print(c_list)\n",
        "print(len(c_list))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kuPhXNspmYOf",
        "outputId": "dd9f9388-778b-4701-d610-5dd13ca9ebf2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2, 3, 4, 5]\n",
            "5\n",
            "[[1, 2, 3, 4, 5]]\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check shape while matrix made with 1-row list\n",
        "c_numpy = np.array(c_list)\n",
        "print(c_numpy)\n",
        "print(type(c_numpy))\n",
        "print(c_numpy.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_4NKzhb5nyQW",
        "outputId": "cfa9f2bb-24c0-49ea-9d40-b4f58dec1e89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3 4 5]]\n",
            "<class 'numpy.ndarray'>\n",
            "(1, 5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]\n",
        "\n",
        "print(a)\n",
        "print(a[1][1])\n",
        "print(type(a))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZy5jsEcocF_",
        "outputId": "31f37144-30d3-4233-ab0d-d2292c06f580"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]\n",
            "5\n",
            "<class 'list'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriks_a = np.array(a)\n",
        "print(matriks_a)\n",
        "\n",
        "#mengambil 1 array berdasarkan indeks\n",
        "print('ambil 1 array: ', matriks_a[2]) #mengambil 1 array ber\n",
        "\n",
        "#ambil spesifik nilai\n",
        "print('ambil suatu nilai di sebuah array: ', matriks_a[1,1])\n",
        "print('ambil suatu nilai di sebuah array: ', matriks_a[2,1])\n",
        "\n",
        "#cek type matriks\n",
        "print(type(matriks_a))\n",
        "\n",
        "#check shape / ukuran matriks\n",
        "print(matriks_a.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Le2JGNzio1Lk",
        "outputId": "82d1cb26-0b54-41e5-e911-24ce7acfe5f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  2  3]\n",
            " [ 4  5  6]\n",
            " [ 7  8  9]\n",
            " [10 11 12]]\n",
            "ambil 1 array:  [7 8 9]\n",
            "ambil suatu nilai di sebuah array:  5\n",
            "ambil suatu nilai di sebuah array:  8\n",
            "<class 'numpy.ndarray'>\n",
            "(4, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('dimensi: ', matriks_a.ndim)\n",
        "print('ordo: ', matriks_a.shape)\n",
        "print('item: ', matriks_a.size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQIOPIo1qyO8",
        "outputId": "db4c1fd9-056b-48a8-8849-8645aa0b19b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dimensi:  2\n",
            "ordo:  (4, 3)\n",
            "item:  12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat matrix 0\n",
        "matriks_nol = np.zeros((4,2))\n",
        "print(matriks_nol)\n",
        "\n",
        "#membuat matriks 1\n",
        "matriks_satu = np.ones((4,4))\n",
        "print(matriks_satu)\n",
        "\n",
        "#matriks identitas\n",
        "matriks_identitas = np.eye((2))\n",
        "print(matriks_identitas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJ2HQ1jYr8OS",
        "outputId": "556246d9-f7f7-4140-fb01-0b7305ce1cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0. 0.]\n",
            " [0. 0.]\n",
            " [0. 0.]\n",
            " [0. 0.]]\n",
            "[[1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]\n",
            " [1. 1. 1. 1.]]\n",
            "[[1. 0.]\n",
            " [0. 1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Indexing Numpy"
      ],
      "metadata": {
        "id": "8oS3jPonuEJJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([[1, 2, 3 ,4], [5, 6, 7, 8], [9, 10, 11, 12]])\n",
        "print(x)\n",
        "print('\\n')\n",
        "\n",
        "#indexing 1 elemen\n",
        "print(x[0,1])\n",
        "\n",
        "#indexing 1 baris\n",
        "print(x[1])\n",
        "\n",
        "#indexing range\n",
        "print(x[1:3])\n",
        "\n",
        "#indexing matriks\n",
        "print(x[1:3, 0:2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mz3mj_hUuK7h",
        "outputId": "34993f24-40ba-42c2-cbef-554f0c466cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n",
            "\n",
            "\n",
            "2\n",
            "[5 6 7 8]\n",
            "[[ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n",
            "[[ 5  6]\n",
            " [ 9 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#output dijadikan matriks\n",
        "\n",
        "y = np.array(x[1:3, 0:2])\n",
        "print(y)\n",
        "print(type(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7wjPEGNw081",
        "outputId": "c77939c6-f5c0-4354-b8c0-45e2ffae6800"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 5  6]\n",
            " [ 9 10]]\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x[1, 0], y[0, 0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PuxK4knxr-Q",
        "outputId": "55e84f1c-078a-41b0-9dc0-8d9ca9833b7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[0 ,0] = 55 #replace index matriks\n",
        "\n",
        "print(x)\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bdf0P9yCx7GK",
        "outputId": "2316e38c-eca5-4413-a325-12857d6353f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  2  3  4]\n",
            " [ 5  6  7  8]\n",
            " [ 9 10 11 12]]\n",
            "[[55  6]\n",
            " [ 9 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row_1 = x[1, :] #titik dua merepresentasikan ambil seluruh nilai di baris\n",
        "\n",
        "print(row_1)\n",
        "print(row_1.ndim) #n.dim merubah jadi 1 dimensi\n",
        "print(row_1.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CM2hZBmuzFdn",
        "outputId": "4a65ec60-3623-41c9-b364-a15361c1ea74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[5 6 7 8]\n",
            "1\n",
            "(4,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "row_2 = x[1:2, :]\n",
        "\n",
        "print(row_2)\n",
        "print(row_2.ndim) #n.dim merubah jadi 1 dimensi\n",
        "print(row_2.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_tIUco8zqOH",
        "outputId": "45720c22-955c-4bfb-ebd9-70e0807b9ffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[5 6 7 8]]\n",
            "2\n",
            "(1, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "h = np.array([[1, 2], [3, 4], [5, 6]]) #np.array mengubah jadi matrix\n",
        "print(h)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvccRC-E0E1N",
        "outputId": "f277ad8b-ce54-47ea-a9ce-62b09b06e846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2]\n",
            " [3 4]\n",
            " [5 6]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(h[0,0]); print(h[1,1]); print(h[2,0])\n",
        "\n",
        "print(np.array([h[0,0],h[1,1],h[2,0]]))\n",
        "\n",
        "print(h[[0,1,2], [0,1,0]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a-E_mTun0ax3",
        "outputId": "ab3f41a5-d0c4-422e-9433-9117825d4ceb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "4\n",
            "5\n",
            "[1 4 5]\n",
            "[1 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Indexing Boolean"
      ],
      "metadata": {
        "id": "gmwAKTX1cbdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boolean_idx = (h > 2)\n",
        "print(boolean_idx)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YFMKpqF2CEo",
        "outputId": "1fa6de55-6449-415a-d5c6-38b3ba1db386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[False False]\n",
            " [ True  True]\n",
            " [ True  True]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "m = h[ h > 2]\n",
        "\n",
        "print(m)\n",
        "print(m.ndim)\n",
        "print(type(m))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t7gDJ0ZC2cKn",
        "outputId": "c8c45ff4-1a38-41f3-db9b-f17651ab7572"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 4 5 6]\n",
            "1\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Numpy Math"
      ],
      "metadata": {
        "id": "3Km9Yaia3Csl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = np.array([1, 2])\n",
        "\n",
        "print(s)\n",
        "print(type(s)) #check tipe data dari variabel s\n",
        "print(s.dtype) #check tipe dari elemen yang ada di variabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-nuVPjD-3OXB",
        "outputId": "a83b0020-4f8a-4fbf-c640-653ae5b2a0bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2]\n",
            "<class 'numpy.ndarray'>\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t = np.array([1.0, 2])\n",
        "\n",
        "print(t)\n",
        "print(type(t)) #check tipe data dari variabel s\n",
        "print(t.dtype) #check tipe dari elemen yang ada di variabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wR2IdLwN33D-",
        "outputId": "f885956e-4a22-4a9b-9b80-237db314c53a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1. 2.]\n",
            "<class 'numpy.ndarray'>\n",
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "u = np.array([1.0, 2, 3.6], dtype = np.int64)\n",
        "\n",
        "print(u)\n",
        "print(type(u)) #check tipe data dari variabel s\n",
        "print(u.dtype) #check tipe dari elemen yang ada di variabel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h6C1MBiS4Tyw",
        "outputId": "3706a852-16cd-4669-af18-12d4a604b9ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3]\n",
            "<class 'numpy.ndarray'>\n",
            "int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x1 = np.array([[1, 2], [3, 4]], dtype = np.float64)\n",
        "y1 = np.array([[5, 5], [7, 6]], dtype = np.float64)\n",
        "\n",
        "print(x1)\n",
        "print(y1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGQeipKa4ras",
        "outputId": "f8a95f37-d45c-4774-d292-15e670ee5b41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1. 2.]\n",
            " [3. 4.]]\n",
            "[[5. 5.]\n",
            " [7. 6.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#penjumlahan\n",
        "\n",
        "print(x1 + y1)\n",
        "print(np.add(x1, y1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWo4JXb75Fih",
        "outputId": "97484c41-d13e-487e-9c4d-493c290b5089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 6.  7.]\n",
            " [10. 10.]]\n",
            "[[ 6.  7.]\n",
            " [10. 10.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pengurangan\n",
        "\n",
        "print(x1 - y1)\n",
        "print(np.subtract(x1, y1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bR-4wtdp5c0j",
        "outputId": "d77f56db-e663-4581-b6ab-69988bf0db87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-4. -3.]\n",
            " [-4. -2.]]\n",
            "[[-4. -3.]\n",
            " [-4. -2.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#perkalian\n",
        "\n",
        "print(x1); print(x2)\n",
        "print(x1 * y1)"
      ],
      "metadata": {
        "id": "4sxZAKgd5uYl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.dot(y1, y1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xukMPoz06HyB",
        "outputId": "d37807ca-e09a-42e9-929f-90e33c3eb4bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[60. 55.]\n",
            " [77. 71.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# akar\n",
        "print(np.sqrt(x1, y1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSoi6b2X61Gb",
        "outputId": "bdfd1567-e050-4a1d-9de4-1d4693dae072"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1.         1.41421356]\n",
            " [1.73205081 2.        ]]\n",
            "[[ 1.  4.]\n",
            " [ 9. 16.]]\n",
            "[[ 7. 10.]\n",
            " [15. 22.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#kuadrat\n",
        "print(np.array(x1 ** 2))\n",
        "print(np.dot(x1, x1))\n",
        "print(np.power(x1, x1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1g0M78u7oiB",
        "outputId": "4e0543c2-f597-453b-c010-876158ca06a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.  4.]\n",
            " [ 9. 16.]]\n",
            "[[ 7. 10.]\n",
            " [15. 22.]]\n",
            "[[  1.   4.]\n",
            " [ 27. 256.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# operasi matematika matriks (versi sendiri)\n",
        "import numpy as np\n",
        "\n",
        "# Mendefinisikan dua matriks\n",
        "A = np.array([[1, 2], [3, 4]])\n",
        "B = np.array([[5, 6], [7, 8]])\n",
        "\n",
        "# Penjumlahan matriks\n",
        "C = A + B\n",
        "print(\"Penjumlahan Matriks:\")\n",
        "print(C); print('\\n'); print(np.add(A, B))\n",
        "\n",
        "# Pengurangan matriks\n",
        "D = A - B\n",
        "print(\"Pengurangan Matriks:\")\n",
        "print(D); print('\\n'); print(np.subtract(A, B))"
      ],
      "metadata": {
        "id": "2fftmUyDmhzU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a52e803-917c-4056-e105-07f20298a42d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Penjumlahan Matriks:\n",
            "[[ 6  8]\n",
            " [10 12]]\n",
            "\n",
            "\n",
            "[[ 6  8]\n",
            " [10 12]]\n",
            "Pengurangan Matriks:\n",
            "[[-4 -4]\n",
            " [-4 -4]]\n",
            "\n",
            "\n",
            "[[-4 -4]\n",
            " [-4 -4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perkalian matriks\n",
        "E = np.dot(A, B)\n",
        "print(\"Perkalian Matriks:\")\n",
        "print(E)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZALC2fVneCL",
        "outputId": "a2e2d771-7055-44ab-b9f6-1379b3a0af1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Perkalian Matriks:\n",
            "[[19 22]\n",
            " [43 50]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transpose matriks\n",
        "F = A.T\n",
        "print(\"Transpose Matriks:\")\n",
        "print(F)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SYpuByRppAe",
        "outputId": "21872649-015d-4b87-afae-b35344b186fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transpose Matriks:\n",
            "[[1 3]\n",
            " [2 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Determinan matriks\n",
        "det_A = np.linalg.det(A)\n",
        "print(\"Determinan Matriks:\")\n",
        "print(int(det_A))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KCagLIWXps8E",
        "outputId": "a98d5290-1be3-476d-aa34-a220d7eab284"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Determinan Matriks:\n",
            "-2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Invers matriks\n",
        "inv_A = np.linalg.inv(A)\n",
        "print(\"Invers Matriks:\")\n",
        "print(inv_A)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qw_LVsUnpw59",
        "outputId": "f7e94706-6cde-44e0-9d36-5f08584d0b5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Invers Matriks:\n",
            "[[-2.   1. ]\n",
            " [ 1.5 -0.5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Day 9 (木曜日) - 04/04/2024"
      ],
      "metadata": {
        "id": "XwfK7TGZaWp4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pandas \"Data Analyze\"\n",
        "---"
      ],
      "metadata": {
        "id": "OSo-16a-8Gw0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- **Create Data** - We begin by creating our own data set for analysis. This prevents the end user reading this tutorial from having to download any files to replicate the results below. We will export this data set to a text file so that you can get some experience pulling data from a text file.\n",
        "\n",
        "- **Get Data** - We will learn how to read in the text file. The data consist of baby names and the number of baby names born in the year 1880.\n",
        "\n",
        "- **Prepare Data** - Here we will simply take a look at the data and make sure it is clean. By clean I mean we will take a look inside the contents of the text file and look for any anomalities. These can include missing data, inconsistencies in the data, or any other data that seems out of place. If any are found we will then have to make decisions on what to do with these records.\n",
        "**Analyze Data** - We will simply find the most popular name in a specific year.  \n",
        "**Present Data** - Through tabular data and a graph, clearly show the end user what is the most popular name in a specific year."
      ],
      "metadata": {
        "id": "wPKBPgQU8JLK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "-qKW5Pwi8Rpz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "print(pd.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTDd-8ECdmBS",
        "outputId": "8dde8d1f-dcb4-41c0-f42a-7adc2269ffaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.0.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Create Data"
      ],
      "metadata": {
        "id": "BzZxTX0owtYf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#membuat data berupa banyaknya penggunaan sebuah nama di indonesia\n",
        "\n",
        "nama = ['Putra', 'Gema', 'Nusa', 'Bangsa', 'Negara', 'Indonesia']\n",
        "jumlah_nama = [998, 756, 531, 870, 349, 211]"
      ],
      "metadata": {
        "id": "NQ1cROX9dzkX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "nphlsBOlw7jO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec6f6ee6-e075-4928-f732-eb7d19d8fdba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = list(zip(nama, jumlah_nama))\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnUQh-BqtC1x",
        "outputId": "6e870be7-ab17-4442-ff00-0f65fb461335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('Putra', 998), ('Gema', 756), ('Nusa', 531), ('Bangsa', 870), ('Negara', 349), ('Indonesia', 211)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.DataFrame(data=dataset, columns=['nama','jumlah_nama'])\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Nhh3XxpotfQe",
        "outputId": "a72a0f3c-bfe6-440f-9c81-35087f85854a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        nama  jumlah_nama\n",
              "0      Putra          998\n",
              "1       Gema          756\n",
              "2       Nusa          531\n",
              "3     Bangsa          870\n",
              "4     Negara          349\n",
              "5  Indonesia          211"
            ],
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              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"nama\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Putra\",\n          \"Gema\",\n          \"Indonesia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jumlah_nama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 307,\n        \"min\": 211,\n        \"max\": 998,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          998,\n          756,\n          211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.to_csv('sample_data.csv', index = False, header = False)"
      ],
      "metadata": {
        "id": "2oD-SkNDuv6k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('sample_data.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "xfrQ_MBGvAfJ",
        "outputId": "c0530403-1b5d-4910-c959-43332469c5dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_66eb9499-90f5-4b6c-bc13-7a7d2c5a6637\", \"sample_data.csv\", 64)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Get Data"
      ],
      "metadata": {
        "id": "4RIR2u5S82fG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "`Link if you want to read more about getting data in google colaboratory`: https://towardsdatascience.com/3-ways-to-load-csv-files-into-colab-7c14fcbdcb92"
      ],
      "metadata": {
        "id": "lwqCkTal9CHb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WY0GC_LwqeV",
        "outputId": "5bf04435-afad-4d80-f152-a0019400b7b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mengubah directory\n",
        "%cd /content/drive/MyDrive/Celerates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UGMcBm7Jz2bO",
        "outputId": "1385d59a-66e1-4df4-8fa5-59214363ffa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Celerates\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama = pd.read_csv(r'sample_data.csv')"
      ],
      "metadata": {
        "id": "aFHtr3ph0ePe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "K5YpMo1t0vw_",
        "outputId": "70302dc3-155c-4e40-d6ad-820930394651"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Putra  998\n",
              "0       Gema  756\n",
              "1       Nusa  531\n",
              "2     Bangsa  870\n",
              "3     Negara  349\n",
              "4  Indonesia  211"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "count_nama",
              "summary": "{\n  \"name\": \"count_nama\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Putra\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Nusa\",\n          \"Indonesia\",\n          \"Bangsa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"998\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 273,\n        \"min\": 211,\n        \"max\": 870,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          531,\n          211,\n          870\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama = pd.read_csv(r'sample_data.csv', header = None)\n",
        "count_nama"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           0    1\n",
              "0      Putra  998\n",
              "1       Gema  756\n",
              "2       Nusa  531\n",
              "3     Bangsa  870\n",
              "4     Negara  349\n",
              "5  Indonesia  211"
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a99fc4a4-954e-475b-a13b-0dedf217d7a0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5218970b-ecb3-4854-adb0-59296a5e7eae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5218970b-ecb3-4854-adb0-59296a5e7eae')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5218970b-ecb3-4854-adb0-59296a5e7eae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "count_nama",
              "summary": "{\n  \"name\": \"count_nama\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Putra\",\n          \"Gema\",\n          \"Indonesia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 307,\n        \"min\": 211,\n        \"max\": 998,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          998,\n          756,\n          211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama = pd.read_csv(r'sample_data.csv', header = None, names = ['nama', 'jumlah_nama'])\n",
        "count_nama"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "_QbNDze_5Ync",
        "outputId": "30dda15c-0f0d-47e9-8707-edce222d7955"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        nama  jumlah_nama\n",
              "0      Putra          998\n",
              "1       Gema          756\n",
              "2       Nusa          531\n",
              "3     Bangsa          870\n",
              "4     Negara          349\n",
              "5  Indonesia          211"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eb5f152d-ccd9-46b6-a67e-0cace87b86ea\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>nama</th>\n",
              "      <th>jumlah_nama</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Putra</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Gema</td>\n",
              "      <td>756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Nusa</td>\n",
              "      <td>531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bangsa</td>\n",
              "      <td>870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Negara</td>\n",
              "      <td>349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Indonesia</td>\n",
              "      <td>211</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb5f152d-ccd9-46b6-a67e-0cace87b86ea')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-eb5f152d-ccd9-46b6-a67e-0cace87b86ea button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-eb5f152d-ccd9-46b6-a67e-0cace87b86ea');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b6e0fd0d-6e3f-41ca-8e2b-cd257b794458\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b6e0fd0d-6e3f-41ca-8e2b-cd257b794458')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b6e0fd0d-6e3f-41ca-8e2b-cd257b794458 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "count_nama",
              "summary": "{\n  \"name\": \"count_nama\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"nama\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Putra\",\n          \"Gema\",\n          \"Indonesia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jumlah_nama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 307,\n        \"min\": 211,\n        \"max\": 998,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          998,\n          756,\n          211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Prepare Data"
      ],
      "metadata": {
        "id": "X6Y74btd8Bet"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check summary\n",
        "count_nama.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gIxRU5nV8GJZ",
        "outputId": "8e45fff7-5841-4aba-e73a-87c16f1f75f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6 entries, 0 to 5\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   nama         6 non-null      object\n",
            " 1   jumlah_nama  6 non-null      int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 224.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check data type for every columns\n",
        "count_nama.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b7UjClO8f7G",
        "outputId": "c44c6729-e333-4d44-f1a7-88c4a891f21e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nama           object\n",
              "jumlah_nama     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check data type for spesific columns\n",
        "count_nama.jumlah_nama.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "COI7Nvw79ibM",
        "outputId": "c424f325-65ab-4a72-d7d0-d414089a2a02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check ordo\n",
        "count_nama.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jX5s40wp-zem",
        "outputId": "d5b7d131-7e54-487e-ec34-8b696a6c42e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check statistic of summary type numeric column\n",
        "count_nama.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "NloTcz1Z_K0z",
        "outputId": "32778380-c274-4ae2-c5ae-3e7c4c70db30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       jumlah_nama\n",
              "count     6.000000\n",
              "mean    619.166667\n",
              "std     307.414649\n",
              "min     211.000000\n",
              "25%     394.500000\n",
              "50%     643.500000\n",
              "75%     841.500000\n",
              "max     998.000000"
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"count_nama\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"jumlah_nama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 332.7676171087573,\n        \"min\": 6.0,\n        \"max\": 998.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          619.1666666666666,\n          643.5,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analyze Data"
      ],
      "metadata": {
        "id": "X10aRMCxAEED"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#melihat 3 nama dengan jumlah terbanyak\n",
        "urutan = count_nama.sort_values(['jumlah_nama'], ascending = False)\n",
        "urutan.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "iZE7SR84AG91",
        "outputId": "7672246d-2b56-40f0-97fc-2fbbe1469ef3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     nama  jumlah_nama\n",
              "0   Putra          998\n",
              "3  Bangsa          870\n",
              "1    Gema          756"
            ],
            "text/html": [
              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-46a5c044-fa02-4adc-b2fd-457c03f31a59 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "urutan",
              "summary": "{\n  \"name\": \"urutan\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"nama\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Putra\",\n          \"Bangsa\",\n          \"Indonesia\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"jumlah_nama\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 307,\n        \"min\": 211,\n        \"max\": 998,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          998,\n          870,\n          211\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama['jumlah_nama'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YhO8H34cBKqC",
        "outputId": "0bf27e89-9016-492c-9420-7e4d196b3763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "998"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama['jumlah_nama'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzP7ar6kBY_H",
        "outputId": "82f840e0-a574-41b3-de79-7593a5673ce5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "307.41464940153173"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama['jumlah_nama'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ub9uTLZnBciy",
        "outputId": "6d7a2b73-1354-4cfb-beb9-469caf8ed09d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "619.1666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Present Data"
      ],
      "metadata": {
        "id": "SP1oA-bJBptH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count_nama['jumlah_nama'].plot.bar()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "id": "k3hxCMudBrBq",
        "outputId": "677b16b7-e5f2-4305-c8a1-ce538e5c3664"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# General syntax to import a library but no functions:\n",
        "##import (library) as (give the library a nickname/alias)\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd #this is how I usually import pandas\n",
        "import sys #only needed to determine Python version number\n",
        "import matplotlib #only needed to determine Matplotlib version number\n",
        "# Create graph\n",
        "count_nama['jumlah_nama'].plot.bar()\n",
        "\n",
        "# Maximum value in the data set\n",
        "MaxValue = urutan['jumlah_nama'].max()\n",
        "\n",
        "# Name associated with the maximum value\n",
        "MaxName = urutan['nama'][urutan['jumlah_nama'] == urutan['jumlah_nama'].max()].values\n",
        "\n",
        "# Text to display on graph\n",
        "Text = str(MaxValue) + \" - \" + MaxName\n",
        "\n",
        "# Add text to graph\n",
        "plt.annotate(Text, xy=(1, MaxValue), xytext=(8, 0),\n",
        "                   xycoords=('axes fraction', 'data'), textcoords='offset points')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "h8Mz_OSVCLVq",
        "outputId": "9dcdc2a4-4491-40b6-c6f6-f963beda5195"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(8, 0, \"['998 - Putra']\")"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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u0FNPPaXExER/jw0AAAAD/H5ls3fv3iopKdGePXskSR9++KH+9re/6fbbb5ck7du3Tx6PR+np6fZjoqOjlZaWptLSUklSaWmpYmJi7NCUpPT0dAUFBamsrOysz1tXVyev1+tzAwAAQGD5/crm5MmT5fV61alTJwUHB6uxsVGPP/64srKyJEkej0eSFB8f7/O4+Ph4e5/H41FcXJzvoCEhio2Ntdd8W35+vqZPn+7vlwMAAICL4PcrmytWrNDSpUu1bNkybd26VcXFxXrqqadUXFzs76fykZubq9raWvtWWVlp9PkAAADw/fx+ZfORRx7R5MmTlZmZKUnq1q2bPvvsM+Xn52vkyJFyuVySpKqqKiUkJNiPq6qqUo8ePSRJLpdL1dXVPsc9deqUDh8+bD/+28LCwhQWFubvlwMAAICL4Pcrm8ePH1dQkO9hg4OD1dTUJElKSUmRy+VSSUmJvd/r9aqsrExut1uS5Ha7VVNTo4qKCnvNunXr1NTUpLS0NH+PDAAAAEP8fmVzyJAhevzxx9WuXTv927/9m95//30VFhbqP/7jPyRJDodDOTk5mjlzpjp06KCUlBTl5eUpMTFRd955pySpc+fOuu222zR69GgVFRWpoaFB2dnZyszM5JPoAAAAzYjfY3PevHnKy8vTAw88oOrqaiUmJuo3v/mNpk6daq+ZNGmSjh07pjFjxqimpkZ9+/bV6tWrFR4ebq9ZunSpsrOzNWDAAAUFBWnYsGGaO3euv8cFAACAQQ7rm3/a5wri9XoVHR2t2tpaRUVF+e24yZPf9tuxTNk/a1CgRwCaJf77BgLP1M9vBA5/Gx0AAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADG+P3PVQLnozn8pRaJv9YCAMDF4somAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjDESm59//rnuuecetWnTRk6nU926ddN7771n77csS1OnTlVCQoKcTqfS09O1d+9en2McPnxYWVlZioqKUkxMjEaNGqWjR4+aGBcAAACG+D02jxw5oj59+ig0NFTvvPOOdu7cqaefflqtW7e21xQUFGju3LkqKipSWVmZWrVqpYyMDJ08edJek5WVpR07dmjt2rVauXKlNm7cqDFjxvh7XAAAABgU4u8Dzp49W0lJSVq8eLG9LSUlxf63ZVmaM2eOpkyZoqFDh0qSXn75ZcXHx+uNN95QZmamdu3apdWrV2vLli1KTU2VJM2bN0933HGHnnrqKSUmJvp7bAAAABjg9yubb775plJTU/Xv//7viouL04033qgXXnjB3r9v3z55PB6lp6fb26Kjo5WWlqbS0lJJUmlpqWJiYuzQlKT09HQFBQWprKzM3yMDAADAEL/H5qeffqqFCxeqQ4cO+stf/qJx48bpP//zP1VcXCxJ8ng8kqT4+Hifx8XHx9v7PB6P4uLifPaHhIQoNjbWXvNtdXV18nq9PjcAAAAElt9/jd7U1KTU1FQ98cQTkqQbb7xR27dvV1FRkUaOHOnvp7Pl5+dr+vTpxo4PAACAC+f3K5sJCQnq0qWLz7bOnTvrwIEDkiSXyyVJqqqq8llTVVVl73O5XKqurvbZf+rUKR0+fNhe8225ubmqra21b5WVlX55PQAAAPjh/B6bffr00e7du3227dmzR+3bt5f09YeFXC6XSkpK7P1er1dlZWVyu92SJLfbrZqaGlVUVNhr1q1bp6amJqWlpZ31ecPCwhQVFeVzAwAAQGD5/dfoEyZMUO/evfXEE0/oV7/6lcrLy/X888/r+eeflyQ5HA7l5ORo5syZ6tChg1JSUpSXl6fExETdeeedkr6+Enrbbbdp9OjRKioqUkNDg7Kzs5WZmckn0QEAAJoRv8fmTTfdpNdff125ubmaMWOGUlJSNGfOHGVlZdlrJk2apGPHjmnMmDGqqalR3759tXr1aoWHh9trli5dquzsbA0YMEBBQUEaNmyY5s6d6+9xAQAAYJDfY1OSBg8erMGDB59zv8Ph0IwZMzRjxoxzromNjdWyZctMjAcAAIBLhL+NDgAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABgTEugBAFyc5MlvB3qE77V/1qBAjwAACBCubAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAY47E5a9YsORwO5eTk2NtOnjyp8ePHq02bNoqIiNCwYcNUVVXl87gDBw5o0KBBatmypeLi4vTII4/o1KlTpscFAACAHxmNzS1btui///u/9ZOf/MRn+4QJE/TWW2/p1Vdf1YYNG3Tw4EH98pe/tPc3NjZq0KBBqq+v16ZNm1RcXKwlS5Zo6tSpJscFAACAnxmLzaNHjyorK0svvPCCWrdubW+vra3VSy+9pMLCQvXv3189e/bU4sWLtWnTJm3evFmStGbNGu3cuVP/8z//ox49euj222/XY489pvnz56u+vt7UyAAAAPAzY7E5fvx4DRo0SOnp6T7bKyoq1NDQ4LO9U6dOateunUpLSyVJpaWl6tatm+Lj4+01GRkZ8nq92rFjx1mfr66uTl6v1+cGAACAwAoxcdDly5dr69at2rJlyxn7PB6PWrRooZiYGJ/t8fHx8ng89ppvhubp/af3nU1+fr6mT5/uh+kBAADgL36/sllZWakHH3xQS5cuVXh4uL8Pf065ubmqra21b5WVlZfsuQEAAHB2fo/NiooKVVdX66c//alCQkIUEhKiDRs2aO7cuQoJCVF8fLzq6+tVU1Pj87iqqiq5XC5JksvlOuPT6afvn17zbWFhYYqKivK5AQAAILD8HpsDBgzQtm3b9MEHH9i31NRUZWVl2f8ODQ1VSUmJ/Zjdu3frwIEDcrvdkiS3261t27apurraXrN27VpFRUWpS5cu/h4ZAAAAhvj9PZuRkZHq2rWrz7ZWrVqpTZs29vZRo0Zp4sSJio2NVVRUlH7729/K7XarV69ekqSBAweqS5cuGjFihAoKCuTxeDRlyhSNHz9eYWFh/h4ZAAAAhhj5gND3eeaZZxQUFKRhw4aprq5OGRkZWrBggb0/ODhYK1eu1Lhx4+R2u9WqVSuNHDlSM2bMCMS4AAAA+IEuSWyuX7/e5354eLjmz5+v+fPnn/Mx7du316pVqwxPBgAAAJP42+gAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjLkkfxsdAPDjkTz57UCPcF72zxoU6BGAHwWubAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABgT4u8D5ufn67XXXtPHH38sp9Op3r17a/bs2erYsaO95uTJk3rooYe0fPly1dXVKSMjQwsWLFB8fLy95sCBAxo3bpzeffddRUREaOTIkcrPz1dIiN9HBgDgspQ8+e1Aj/C99s8aFOgRcJnz+5XNDRs2aPz48dq8ebPWrl2rhoYGDRw4UMeOHbPXTJgwQW+99ZZeffVVbdiwQQcPHtQvf/lLe39jY6MGDRqk+vp6bdq0ScXFxVqyZImmTp3q73EBAABgkN8vE65evdrn/pIlSxQXF6eKigrdfPPNqq2t1UsvvaRly5apf//+kqTFixerc+fO2rx5s3r16qU1a9Zo586d+utf/6r4+Hj16NFDjz32mB599FH9/ve/V4sWLfw9NgAAAAww/p7N2tpaSVJsbKwkqaKiQg0NDUpPT7fXdOrUSe3atVNpaakkqbS0VN26dfP5tXpGRoa8Xq927Nhx1uepq6uT1+v1uQEAACCwjMZmU1OTcnJy1KdPH3Xt2lWS5PF41KJFC8XExPisjY+Pl8fjsdd8MzRP7z+972zy8/MVHR1t35KSkvz8agAAAHChjMbm+PHjtX37di1fvtzk00iScnNzVVtba98qKyuNPycAAAC+m7GPdmdnZ2vlypXauHGjrrnmGnu7y+VSfX29ampqfK5uVlVVyeVy2WvKy8t9jldVVWXvO5uwsDCFhYX5+VUAAADgYvj9yqZlWcrOztbrr7+udevWKSUlxWd/z549FRoaqpKSEnvb7t27deDAAbndbkmS2+3Wtm3bVF1dba9Zu3atoqKi1KVLF3+PDAAAAEP8fmVz/PjxWrZsmf785z8rMjLSfo9ldHS0nE6noqOjNWrUKE2cOFGxsbGKiorSb3/7W7ndbvXq1UuSNHDgQHXp0kUjRoxQQUGBPB6PpkyZovHjx3P1EgAAoBnxe2wuXLhQktSvXz+f7YsXL9Z9990nSXrmmWcUFBSkYcOG+Xyp+2nBwcFauXKlxo0bJ7fbrVatWmnkyJGaMWOGv8cFAACAQX6PTcuyvndNeHi45s+fr/nz559zTfv27bVq1Sp/jgYAAIBLjL+NDgAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMAYYhMAAADGEJsAAAAwhtgEAACAMcQmAAAAjCE2AQAAYAyxCQAAAGOITQAAABhDbAIAAMCYyzo258+fr+TkZIWHhystLU3l5eWBHgkAAAAX4LKNzf/93//VxIkTNW3aNG3dulXdu3dXRkaGqqurAz0aAAAAztNlG5uFhYUaPXq07r//fnXp0kVFRUVq2bKlFi1aFOjRAAAAcJ5CAj3A2dTX16uiokK5ubn2tqCgIKWnp6u0tPSsj6mrq1NdXZ19v7a2VpLk9Xr9OltT3XG/Hs8Ef79mE5rDeZQ4l/7SHM6jxLn0l+ZwHiXOpb/4+zyePp5lWX49LgLnsozNQ4cOqbGxUfHx8T7b4+Pj9fHHH5/1Mfn5+Zo+ffoZ25OSkozMeDmLnhPoCa4cnEv/4Dz6D+fSfziX/mHqPH711VeKjo42c3BcUpdlbP4Qubm5mjhxon2/qalJhw8fVps2beRwOAI42bl5vV4lJSWpsrJSUVFRgR6nWeNc+g/n0j84j/7DufSf5nAuLcvSV199pcTExECPAj+5LGOzbdu2Cg4OVlVVlc/2qqoquVyusz4mLCxMYWFhPttiYmJMjehXUVFRl+1/9M0N59J/OJf+wXn0H86l/1zu55IrmleWy/IDQi1atFDPnj1VUlJib2tqalJJSYncbncAJwMAAMCFuCyvbErSxIkTNXLkSKWmpupnP/uZ5syZo2PHjun+++8P9GgAAAA4T5dtbA4fPlxffvmlpk6dKo/Hox49emj16tVnfGioOQsLC9O0adPO+PU/Lhzn0n84l/7BefQfzqX/cC4RCA6L7xYAAACAIZflezYBAABwZSA2AQAAYAyxCQAAAGOITQAAABhDbAIALnt8lhVovi7brz66Eh06dEiLFi1SaWmpPB6PJMnlcql379667777dNVVVwV4QgC4PIWFhenDDz9U586dAz0KgAvEVx9dIlu2bFFGRoZatmyp9PR0+/tCq6qqVFJSouPHj+svf/mLUlNTAzxp81dZWalp06Zp0aJFgR6lWThx4oQqKioUGxurLl26+Ow7efKkVqxYoXvvvTdA0zUfu3bt0ubNm+V2u9WpUyd9/PHHevbZZ1VXV6d77rlH/fv3D/SIzcLEiRPPuv3ZZ5/VPffcozZt2kiSCgsLL+VYV4Rjx45pxYoV+uSTT5SQkKC7777bPp+AScTmJdKrVy91795dRUVFcjgcPvssy9LYsWP10UcfqbS0NEATXjk+/PBD/fSnP1VjY2OgR7ns7dmzRwMHDtSBAwfkcDjUt29fLV++XAkJCZK+/j9DiYmJnMvvsXr1ag0dOlQRERE6fvy4Xn/9dd17773q3r27mpqatGHDBq1Zs4bgPA9BQUHq3r27YmJifLZv2LBBqampatWqlRwOh9atWxeYAZuRLl266G9/+5tiY2NVWVmpm2++WUeOHNENN9ygf/zjHwoJCdHmzZuVkpIS6FFxhSM2LxGn06n3339fnTp1Ouv+jz/+WDfeeKNOnDhxiSdrft58883v3P/pp5/qoYceIpDOw1133aWGhgYtWbJENTU1ysnJ0c6dO7V+/Xq1a9eO2DxPvXv3Vv/+/TVz5kwtX75cDzzwgMaNG6fHH39ckpSbm6uKigqtWbMmwJNe/mbNmqXnn39eL774ok+ch4aG6sMPPzzj6jvOLSgoSB6PR3Fxcbrnnnu0b98+rVq1StHR0Tp69KjuuusuXXXVVVq2bFmgR8WVzsIlkZycbBUXF59zf3FxsdW+fftLN1Az5nA4rKCgIMvhcJzzFhQUFOgxm4W4uDjro48+su83NTVZY8eOtdq1a2f94x//sDweD+fyPERFRVl79+61LMuyGhsbrZCQEGvr1q32/m3btlnx8fGBGq/ZKS8vt2644QbroYcesurr6y3LsqyQkBBrx44dAZ6seXE4HFZVVZVlWZZ17bXXWmvWrPHZ//e//91KSkoKxGj4keHT6JfIww8/rDFjxujBBx/Um2++qbKyMpWVlenNN9/Ugw8+qLFjx2rSpEmBHrNZSEhI0Guvvaampqaz3rZu3RroEZuNEydOKCTk/39O0OFwaOHChRoyZIhuueUW7dmzJ4DTNS+n3x4TFBSk8PBwRUdH2/siIyNVW1sbqNGanZtuukkVFRX68ssvlZqaqu3bt5/x9iOcn9Pn7eTJk/bbY067+uqr9eWXXwZiLPzI8Gn0S2T8+PFq27atnnnmGS1YsMD+tWRwcLB69uypJUuW6Fe/+lWAp2weevbsqYqKCg0dOvSs+x0OB1+Tcp46deqk995774xP+D733HOSpF/84heBGKvZSU5O1t69e3XddddJkkpLS9WuXTt7/4EDB874QY/vFhERoeLiYi1fvlzp6em8leMHGjBggEJCQuT1erV792517drV3vfZZ5/xASFcEsTmJTR8+HANHz5cDQ0NOnTokCSpbdu2Cg0NDfBkzcsjjzyiY8eOnXP/9ddfr3ffffcSTtR83XXXXXrllVc0YsSIM/Y999xzampqUlFRUQAma17GjRvnE0Pf/IEuSe+88w4fDvqBMjMz1bdvX1VUVKh9+/aBHqdZmTZtms/9iIgIn/tvvfWWfv7zn1/KkfAjxQeEAAAAYAzv2QQAAIAxxCYAAACMITYBAABgDLEJAAAAY4hNAAAAGENsAgAAwBhiEwAAAMYQmwAAADDm/wHENht7LjjWzAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Additional Material ( 補足資料)"
      ],
      "metadata": {
        "id": "11dn4bPsxMak"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Objective 1 - A/B Testing"
      ],
      "metadata": {
        "id": "AI7E3xnwxQm5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A/B testing allows individuals, teams and companies to make careful changes to their user experiences while collecting data on the impact it makes. This allows them to construct hypotheses and to learn what elements and optimizations of their experiences impact user behavior the most."
      ],
      "metadata": {
        "id": "ef6xR2Gs1N36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Background\n",
        "---\n",
        "\n",
        "An online news portal aims to expand its business by acquiring new subscribers. Every visitor to the website takes certain actions based on their interest. **The company plans to analyze these interests and wants to determine whether a new feature will be effective or not**.\n",
        "\n",
        "100 users are divided equally into two groups. The old landing page is served to the first group (control group) and the new landing page is served to the second group (treatment group).\n",
        "\n",
        "Various data about the customers in both groups are collected in 'abtest.csv'."
      ],
      "metadata": {
        "id": "XdSyLkUp1UDI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import library yang dibutuhkan\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set(color_codes=True)\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import scipy.stats as stats"
      ],
      "metadata": {
        "id": "87TqgcVu1eo2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Dictionary\n",
        "---\n",
        "\n",
        "* user_id - This represents the user ID of the person visiting the website.\n",
        "* group - This represents whether the user belongs to the first group (control) or the second group (treatment).\n",
        "* landing_page - This represents whether the landing page is new or old.\n",
        "* time_spent_on_the_page - This represents the time (in minutes) spent by the user on the landing page.\n",
        "* converted - This represents whether the user gets converted to a subscriber of the news portal or not.\n",
        "* language_preferred - This represents the language chosen by the user to view the landing page."
      ],
      "metadata": {
        "id": "xhdfxZqn1mZT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download data [here](https://drive.google.com/drive/folders/18rTPNqENdzlkIvF9D4CmpcfOH6IZ8LIA?usp=sharing)! #get data"
      ],
      "metadata": {
        "id": "vnlMroX41tXX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#mengubah directory\n",
        "%cd /content/drive/MyDrive/Celerates"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tSqOSebX1vgz",
        "outputId": "843fc7f3-94ad-4462-e414-40fc37f38c92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Celerates\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#mengread data abtest.csv yang sudah didownload\n",
        "data = pd.read_csv(\"/content/drive/MyDrive/Celerates/abtest.csv\")\n",
        "data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "3Yv-ZnmZ3FZC",
        "outputId": "11518530-cf1a-4a19-9915-5c182fdbd875"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   user_id      group landing_page  time_spent_on_the_page converted  \\\n",
              "0   546592    control          old                    3.48        no   \n",
              "1   546468  treatment          new                    7.13       yes   \n",
              "2   546462  treatment          new                    4.40        no   \n",
              "3   546567    control          old                    3.02        no   \n",
              "4   546459  treatment          new                    4.75       yes   \n",
              "5   546558    control          old                    5.28       yes   \n",
              "6   546448  treatment          new                    5.25       yes   \n",
              "7   546581    control          old                    6.53       yes   \n",
              "8   546461  treatment          new                   10.71       yes   \n",
              "9   546548    control          old                    2.08        no   \n",
              "\n",
              "  language_preferred  \n",
              "0            Spanish  \n",
              "1            English  \n",
              "2            Spanish  \n",
              "3             French  \n",
              "4            Spanish  \n",
              "5            English  \n",
              "6             French  \n",
              "7            Spanish  \n",
              "8             French  \n",
              "9            English  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12e06d7e-d566-4b60-9ed4-4c882ad601d0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>user_id</th>\n",
              "      <th>group</th>\n",
              "      <th>landing_page</th>\n",
              "      <th>time_spent_on_the_page</th>\n",
              "      <th>converted</th>\n",
              "      <th>language_preferred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>546592</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>3.48</td>\n",
              "      <td>no</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>546468</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>7.13</td>\n",
              "      <td>yes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>546462</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>4.40</td>\n",
              "      <td>no</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>546567</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>3.02</td>\n",
              "      <td>no</td>\n",
              "      <td>French</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>546459</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>4.75</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>546558</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>5.28</td>\n",
              "      <td>yes</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>546448</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>5.25</td>\n",
              "      <td>yes</td>\n",
              "      <td>French</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>546581</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>6.53</td>\n",
              "      <td>yes</td>\n",
              "      <td>Spanish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>546461</td>\n",
              "      <td>treatment</td>\n",
              "      <td>new</td>\n",
              "      <td>10.71</td>\n",
              "      <td>yes</td>\n",
              "      <td>French</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>546548</td>\n",
              "      <td>control</td>\n",
              "      <td>old</td>\n",
              "      <td>2.08</td>\n",
              "      <td>no</td>\n",
              "      <td>English</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"user_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 546443,\n        \"max\": 546592,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          546580,\n          546576,\n          546568\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"treatment\",\n          \"control\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"landing_page\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"new\",\n          \"old\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time_spent_on_the_page\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.378166078933492,\n        \"min\": 0.19,\n        \"max\": 10.71,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          8.46,\n          3.65\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"converted\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language_preferred\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Spanish\",\n          \"English\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis\n",
        "---\n",
        "\n",
        "Explore the dataset and extract insights using Exploratory Data Analysis :\n",
        "1. How many variables and records of the dataset?\n",
        "2. Is there a variable that has a blank record? if so, which variables and how many?\n",
        "3. Group the variable into the type of data\n",
        "4. Total & average time spent on the old landing page\n",
        "5. Total & average time spent on the new landing page\n",
        "6. What is the distribution of time spent on the landing page for each landing_page. which on is better?\n",
        "7. % change in conversion for new landing page vs. old landing based on user language_preferred. what do you find?\n",
        "\n",
        "**Please answer the task using python code and the result using text sel**"
      ],
      "metadata": {
        "id": "0tl8LVmw3mkk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 1 一\n",
        "\n",
        "\n",
        "```\n",
        "How many variables and records of the dataset?\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "OstBJAL_3ydI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_variables = len(data.columns) #menemukan jumlah variable pada column sebuah data\n",
        "num_records = len(data) #mencari jumlah banyaknya record\n",
        "\n",
        "print(\"Number of variables:\", num_variables)\n",
        "print(\"Number of records:\", num_records)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c7PFBYno3wG8",
        "outputId": "7d997efc-a54c-4e92-a3e3-524de92cb92c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of variables: 6\n",
            "Number of records: 100\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 2 二\n",
        "\n",
        "\n",
        "```\n",
        "Is there a variable that has a blank record? if so, which variables and how many?\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "Q2r_1GFm4HrZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.isnull().sum()) #mencari berapa jumlah data yang bernilai null"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nbiNBAcq4I6R",
        "outputId": "acc3d5a0-04c6-446f-ea6d-3907f1693d83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "user_id                   0\n",
            "group                     0\n",
            "landing_page              0\n",
            "time_spent_on_the_page    0\n",
            "converted                 0\n",
            "language_preferred        0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 3 三\n",
        "\n",
        "\n",
        "```\n",
        "Group the variable into the type of data\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "7y423FmZ4RwE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#melihat tipe data\n",
        "data.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kI8CDA4K4WZb",
        "outputId": "1e3f3013-4c2f-4ac3-f125-c18ba8d583de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "user_id                     int64\n",
              "group                      object\n",
              "landing_page               object\n",
              "time_spent_on_the_page    float64\n",
              "converted                  object\n",
              "language_preferred         object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 4 四\n",
        "\n",
        "\n",
        "```\n",
        "Total & average time spent on the old landing page\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "4vY2Vxmt4mXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Memfilter data menjadi data dari halaman landind page lama\n",
        "data_old = data[ data[\"landing_page\"] == \"old\" ]\n",
        "\n",
        "# Menghitung total waktu yang dihabiskan pada halaman landind page lama\n",
        "Total_old = str(round(data_old[\"time_spent_on_the_page\"].sum(), 2))\n",
        "\n",
        "# Menghitung rata-rata waktu yang dihabiskan pada halaman landind page lama\n",
        "Mean_old = str(round(data_old[\"time_spent_on_the_page\"].mean(), 2))\n",
        "\n",
        "# Menampilkan hasil total dan rata-rata waktu yang dihabiskan pada halaman landind page lama\n",
        "print(\"Total & average time spent on the old landing page across 50 users (control group): \" + Total_old + \" mins and \" + Mean_old + \" mins.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TaP6bRyZ4pPO",
        "outputId": "db46b573-21ef-454a-ee61-41ade3d33e10"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total & average time spent on the old landing page across 50 users (control group): 226.62 mins and 4.53 mins.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 5 五\n",
        "\n",
        "\n",
        "```\n",
        "Total & average time spent on the new landing page\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "U10tKo4i71Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Memfilter data menjadi data dari halaman landing page baru\n",
        "data_new = data[ data[\"landing_page\"] == \"new\"]\n",
        "\n",
        "# Menghitung total waktu yang dihabiskan pada halaman landing page baru\n",
        "Total_new = str(round(data_new[\"time_spent_on_the_page\"].sum(), 2))\n",
        "\n",
        "# Menghitung rata-rata waktu yang dihabiskan pada halaman landing page baru\n",
        "Mean_new = str(round(data_new[\"time_spent_on_the_page\"].mean(), 2))\n",
        "\n",
        "# Menampilkan hasil total dan rata-rata waktu yang dihabiskan pada halaman landing page baru\n",
        "print(\"Total & average time spent on the new landing page across 50 users (treatment group): \" + Total_new + \" mins and \" + Mean_new + \" mins.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLneGM7_73fc",
        "outputId": "2a96205a-c60e-49a6-cc63-dc5d0976f9c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total & average time spent on the new landing page across 50 users (treatment group): 311.16 mins and 6.22 mins.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 6 六\n",
        "\n",
        "\n",
        "```\n",
        "What is the distribution of time spent on the landing page for each landing_page. which on is better?\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "AOlQVyrf8Ue8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat plot dengan 3 subplots berukuran 20x5\n",
        "fig, ax = plt.subplots(1,3, figsize=(20,5))\n",
        "\n",
        "# Membuat histogram dengan kde untuk time_spent_on_the_page dari seluruh data\n",
        "sns.histplot(data[\"time_spent_on_the_page\"], ax=ax[0], kde = True)\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[0].set_ylim((0,25))\n",
        "\n",
        "# Menambahkan garis vertikal merah pada rata-rata time_spent_on_the_page\n",
        "ax[0].axvline(data[\"time_spent_on_the_page\"].mean(), c = \"r\")\n",
        "\n",
        "# Memberikan judul pada subplot pertama\n",
        "ax[0].set_title(\"Time spent on either the old or new landing page\")\n",
        "\n",
        "# Membuat histogram dengan kde untuk time_spent_on_the_page dari data lama\n",
        "sns.histplot(data_old[\"time_spent_on_the_page\"], ax=ax[1], kde = True)\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[1].set_ylim((0,25))\n",
        "\n",
        "# Menambahkan garis vertikal merah pada rata-rata time_spent_on_the_page dari data lama\n",
        "ax[1].axvline(data_old[\"time_spent_on_the_page\"].mean(), c = \"r\")\n",
        "\n",
        "# Memberikan judul pada subplot kedua\n",
        "ax[1].set_title(\"Time spent on the old landing page\")\n",
        "\n",
        "# Membuat histogram dengan kde untuk time_spent_on_the_page dari data baru\n",
        "sns.histplot(data_new[\"time_spent_on_the_page\"], ax=ax[2], kde = True)\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[2].set_ylim((0,25))\n",
        "\n",
        "# Menambahkan garis vertikal merah pada rata-rata time_spent_on_the_page dari data baru\n",
        "ax[2].axvline(data_new[\"time_spent_on_the_page\"].mean(), c = \"r\")\n",
        "\n",
        "# Memberikan judul pada subplot ketiga\n",
        "ax[2].set_title(\"Time spent on the new landing page\")\n",
        "\n",
        "# Menampilkan plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "F7iiv2Tc8Yn7",
        "outputId": "a95615f7-c6cd-45c9-8dad-22f060dc37d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Nomor 7 七\n",
        "\n",
        "\n",
        "```\n",
        "% change in conversion for new landing page vs. old landing based on user language_preferred. what do you find?\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "lNch3h3E-ehP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat plot countplot dengan x-axis adalah \"converted\" dan data yang digunakan adalah data\n",
        "sns.countplot(x=\"converted\", data=data, hue=\"landing_page\")\n",
        "\n",
        "# Menampilkan plot\n",
        "plt.show()\n",
        "\n",
        "# Menghitung persentase converted dari data lama\n",
        "print(\"% converted - old landing page: \" + str((data_old[data[\"converted\"]==\"yes\"].count()[\"group\"]/50)*100) + \"%\")\n",
        "\n",
        "# Menghitung persentase converted dari data baru\n",
        "print(\"% converted - new landing page: \" + str((data_new[data[\"converted\"]==\"yes\"].count()[\"group\"]/50)*100) + \"%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "UZntmQXi-kIh",
        "outputId": "ac642efd-a53a-4362-ff5b-50afdff4b5d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% converted - old landing page: 42.0%\n",
            "% converted - new landing page: 66.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Membuat plot dengan 3 subplots berukuran 20x7\n",
        "fig, ax = plt.subplots(1,3, figsize=(20,7))\n",
        "\n",
        "# Membuat plot countplot dengan x-axis adalah \"language_preferred\", data yang digunakan adalah data dan diurutkan berdasarkan \"language_preferred\"\n",
        "sns.countplot(x=\"language_preferred\", data=data.sort_values(\"language_preferred\", ascending = True), hue=\"converted\", ax=ax[0])\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[0].set_ylim((0,25))\n",
        "\n",
        "# Memberikan judul pada subplot pertama\n",
        "ax[0].set_title(\"Conversion rate\")\n",
        "\n",
        "# Membuat plot countplot dengan x-axis adalah \"language_preferred\", data yang digunakan adalah data lama dan diurutkan berdasarkan \"language_preferred\"\n",
        "sns.countplot(x=\"language_preferred\", data=data_old.sort_values(\"language_preferred\", ascending = True), hue=\"converted\", ax=ax[1])\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[1].set_ylim((0,25))\n",
        "\n",
        "# Memberikan judul pada subplot kedua\n",
        "ax[1].set_title(\"Conversion rate - old landing page\")\n",
        "\n",
        "# Membuat plot countplot dengan x-axis adalah \"language_preferred\", data yang digunakan adalah data baru dan diurutkan berdasarkan \"language_preferred\"\n",
        "sns.countplot(x=\"language_preferred\", data=data_new.sort_values(\"language_preferred\", ascending = True), hue=\"converted\", ax=ax[2])\n",
        "\n",
        "# Mengatur batas y-axis dari 0 hingga 25\n",
        "ax[2].set_ylim((0,25))\n",
        "\n",
        "# Memberikan judul pada subplot ketiga\n",
        "ax[2].set_title(\"Conversion rate - new landing page\")\n",
        "\n",
        "# Menampilkan plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "YeoqlyL3_Dm8",
        "outputId": "bc22be43-7806-4636-d590-2222f40a59e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 3 Axes>"
            ],
            "image/png": 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48SIOHTok10wm06DfPREREcAswiwyPGYRIiIaS8wizCLDYRahyYBPtBCNscDAQDg5OSEjIwOJiYmwsrJCSUmJxR+1kVi1ahXy8/Oxbt06JCUlwc3NDaWlpfJjoQNP9GNjY1FWVobU1FRERUWhvr4epaWlFo+nLlq0COXl5dDpdFi0aBEaGhrwzTffQK1W4+rVq3K78PBw3Hfffdi6dSvq6+sxa9YsfP/992hvb7eY++2338bq1asRHR0NrVYLb29vtLa24uTJk/j777+xf//+Ee1bkiSkpKQgMTERtra2KCgoAACkpaUN29fHxwevvvoqPvroIzQ2NiI8PBwODg5oaGjA4cOHodVqkZKS8n/7Ozo6YtOmTXjttdcQExODZcuWwdnZGU1NTfjpp5/wwAMP4K233hrRfgZat24djh07Bq1Wi9jYWKjVarS3t6Ompga//PILjh8/DgDQarXYvXs30tPTUVNTAzc3N5SUlFg8dktERGTGLMIsciOYRYiIaKwwizCL3AhmEbrd8aCFaIxNnz4dn376KbZu3Ypt27Zh2rRpWLFiBR5++OEh/4ANxcHBAV9++SXeeecdfPXVV1CpVHjyyScRGBiItLQ0xXs4w8LCkJGRgV27duG9995DQECAvJ6BYmJi0NraisLCQhw5cgRqtRoffvghDh06JP8xAyC/n/Tdd9+FXq+HtbU1IiIioNPpEBcXp5hbrVajuLgYOTk50Ov1aGtrg7OzM+bOnQudTjfifYeEhOD+++9Hbm4umpqaoFarsWXLFvj7+99Q/xdeeAF+fn7Iy8tDbm4ugP4f2QsNDcXixYuH7R8dHQ13d3d89tln+OKLL9Dd3Q0PDw8EBwcjJiZmxPsZyNXVFUVFRcjNzUVFRQUKCgpw9913Q61WY/369XI7e3t75OXlYfPmzcjPz8fUqVMRHR2NhQsXIjU19abWQEREkxOzCLPIjWAWISKiscIswixyI5hF6HZnJW7m+JiIbil5eXnYsmULqqqq4OHhMa5zHz58GDqdDnv27EFQUNC/Pv7s2bMRHx9/U3dHEBER0dhiFiEiIqKJxCxCRBOFv9FCdJvq7OxUXHd1daGwsBB+fn5jHiaun7u3txdff/01HB0dce+9947p3ERERHRrYBYhIiKiicQsQkS3Er46jOg29dJLL8HT0xP+/v4wGo3Yv38/zp49i8zMzDGfe/Pmzejs7ERgYCC6u7tRXl6OEydOYO3atSN+J2ZLS8uQn0+dOhV33XXXzSyXiIiIxgCzCBEREU0kZhEiupXwoIXoNqXRaLB3716Ulpait7cXarUaH3/8MZYtWzbmcz/00EPYtWsXfvzxR3R1dcHX1xcbN25EQkLCiMfSaDRDfr5y5Uq8//77o10qERERjRFmESIiIppIzCJEdCvhb7QQ0YQ6evTokJ+7u7tDrVaP02qIiIjoTsMsQkRERBOJWYRocuBBCxERERERERERERER0ShZT/QCiIiIiIiIiIiIiIiIblc8aCEiIiIiIiIiIiIiIholHrQQERERERERERERERGNEg9aiIiIiIiIiIiIiIiIRokHLURERERERERERERERKPEgxYiIiIiIiIiIiIiIqJR4kELERERERERERERERHRKPGghYiIiIiIiIiIiIiIaJT+Cw4+k8FgwUJxAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Menampilkan jumlah pengguna yang memilih bahasa Inggris, Prancis, dan Spanyol dalam data\n",
        "print(\"Number of users that prefer English, French and Spanish in the dataset are \" + str(data.groupby(\"language_preferred\").count()[\"user_id\"][\"English\"]) + \", \" + str(data.groupby(\"language_preferred\").count()[\"user_id\"][\"French\"]) + \", and \" + str(data.groupby(\"language_preferred\").count()[\"user_id\"][\"Spanish\"]) + \".\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bduo75av_Qs7",
        "outputId": "6aa45a21-28b2-4408-a577-afe808d8cd88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of users that prefer English, French and Spanish in the dataset are 32, 34, and 34.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Memfilter data menjadi data yang telah converted pada halaman landing page baru\n",
        "data_new_converted = data_new[data[\"converted\"]==\"yes\"]\n",
        "\n",
        "# Memfilter data menjadi data yang telah converted pada halaman landing page lama\n",
        "data_old_converted = data_old[data[\"converted\"]==\"yes\"]\n",
        "\n",
        "# Menampilkan perubahan persentase converted pada halaman landing page baru dibandingkan dengan halaman landing page lama\n",
        "print(\"% change in converted - new landing page vs old landing page:\")\n",
        "print(round(100*(data_new_converted.groupby(\"language_preferred\").count()[\"user_id\"] - data_old_converted.groupby(\"language_preferred\").count()[\"user_id\"])/data_new_converted.groupby(\"language_preferred\").count()[\"user_id\"], 2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44NXSsl6_drC",
        "outputId": "13d1756d-d0df-4ae2-aed5-4c845457eb8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "% change in converted - new landing page vs old landing page:\n",
            "language_preferred\n",
            "English   -10.00\n",
            "French     75.00\n",
            "Spanish    36.36\n",
            "Name: user_id, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* There are approximately equal number of English, French and Spanish preferring users (32, 34 and 34) in the dataset.\n",
        "* % change in conversion for new landing page vs. old landing page is -10% for english. This means users that prefer English, converted marginally more when on the older landing page.\n",
        "* % change in conversion for new landing page vs. old is 75% for French and 36.36% for Spanish. Users that prefer these languages, hence, converted more when on the new landing page. Conversion on the new landing page is very strong among users prefering French."
      ],
      "metadata": {
        "id": "c_TOqcn-_kzM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Objective 2"
      ],
      "metadata": {
        "id": "JBJO7l32_wrR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### No 1 - Do the users spend more time on the new landing page than the old landing page?\n",
        "---"
      ],
      "metadata": {
        "id": "C1ZXrkcpA5-9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* H0 : Null Hypothesis - The mean time spent on the new landing page is the same as the mean time spent on the old landing page.\n",
        "* H1 : Alternate Hypothesis - The mean time spent on the new landing page is higher than the mean time spent on the old landing page.\n",
        "Let μ1 and μ2 be the mean times spent on the new and old landing pages.\n",
        "\n",
        "H0 : μ1 = μ2\n",
        "H1 : μ1 > μ2\n",
        "\n",
        "level of significance = 5%"
      ],
      "metadata": {
        "id": "9q037QdPBCXx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung rata-rata waktu yang dihabiskan pada halaman landing page baru dan halaman landing page lama\n",
        "μ1, μ2 = data_new[\"time_spent_on_the_page\"].mean() , data_old[\"time_spent_on_the_page\"].mean()\n",
        "\n",
        "# Menghitung standar deviasi waktu yang dihabiskan pada halaman landing page baru dan halaman landing page lama\n",
        "std1, std2 = data_new[\"time_spent_on_the_page\"].std() , data_old[\"time_spent_on_the_page\"].std()\n",
        "\n",
        "# Menampilkan hasil rata-rata dan standar deviasi waktu yang dihabiskan pada halaman landing page baru dan halaman landing page lama\n",
        "print(\"μ1, μ2, std1, std2:\", str(round(μ1,2))+\",\", str(round(μ2,2))+\",\", str(round(std1,2))+\",\" , str(round(std2,2))+\".\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Nc64aWMBJNI",
        "outputId": "b7dd5b66-75b2-46ce-feef-99946ef94186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "μ1, μ2, std1, std2: 6.22, 4.53, 1.82, 2.58.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Continuous data - Yes, the time spent on landing pages is measured on a continuous scale.\n",
        "* Normally distributed populations - Looking at the histogram probability density curves, as well as considering larger sample sizes of 50 (>i.e., 30), normal distribution assumption holds.\n",
        "* Independent populations - As we are taking random samples from population for two different groups, the two samples are assumed to be independent.\n",
        "* Unequal population standard deviations - As the sample standard deviations are different, the population standard deviations may be assumed to be different.\n"
      ],
      "metadata": {
        "id": "UoAbEtK2BXRx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two Independent Sample T-test for Equality of Means - Unequal Std Dev"
      ],
      "metadata": {
        "id": "TbHPeh9tBbZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import library yang dibutuhkan\n",
        "from scipy.stats import ttest_ind\n",
        "\n",
        "#perform p-test\n",
        "test_stat, p_value = ttest_ind(data_new[\"time_spent_on_the_page\"], data_old[\"time_spent_on_the_page\"], \\\n",
        "                     equal_var = False, alternative = 'greater')\n",
        "\n",
        "#print p-value\n",
        "print('The p-value is ', p_value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYNZuubHBrGj",
        "outputId": "f38e82dd-952c-4f9c-a666-b6553f8271b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is  0.0001392381225166549\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insight:\n",
        "* As the p-value (~0.00014) is less than the level of significance(0.05), we can reject the null hypothesis. Hence, we do have enough evidence to support the claim that mean time spent on the new landing page is higher than mean time spent on the old landing page."
      ],
      "metadata": {
        "id": "ExDEyDehCSrH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###No 2 -  Is the conversion rate (the proportion of users who visit the landing page and get converted) for the new page greater than the conversion rate for the old page?\n",
        "---"
      ],
      "metadata": {
        "id": "4mQuyFoOCWRk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* H0 : Null Hypothesis - The conversion rate (the proportion of users who visit the landing page and get converted) for the new page is the same as the conversion rate for the old page.\n",
        "* H1 : Alternate Hypothesis - The conversion rate for the new page is greater than the conversion rate for the old page.\n",
        "\n",
        "Let p1 and p2 be the conversion rates for the new and old landing pages.\n",
        "\n",
        "* H0 : p1 = p2\n",
        "* H1 : p1 > p2\n",
        "\n",
        "\n",
        "level of significance = 5%\n",
        "\n",
        "The formulated hypotheses are concerned with proportions. A test of proportions can be used to analyse the hypotheses and draw a conclusion. We shall use a Proportions Z test for this problem."
      ],
      "metadata": {
        "id": "Pa-sj_MDCYYz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# menghitung jumlah pengguna yang dikonversi untuk halaman arahan baru\n",
        "convertedn_new = float(data_new[data[\"converted\"]==\"yes\"].count()[\"group\"])\n",
        "\n",
        "# menghitung tingkat konversi untuk halaman arahan baru\n",
        "p1 = float(round(convertedn_new/50, 2))\n",
        "\n",
        "# menghitung jumlah pengguna yang dikonversi untuk halaman arahan lama\n",
        "convertedn_old = float(data_old[data[\"converted\"]==\"yes\"].count()[\"group\"])\n",
        "\n",
        "# menghitung tingkat konversi untuk halaman arahan lama\n",
        "p2 = float(round(convertedn_old/50, 2))\n",
        "\n",
        "# menampilkan tingkat konversi\n",
        "print(f'The conversion rates for new landing page and old landing page are {p1} and {p2} respectively.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RaWFYiPtCedR",
        "outputId": "6a352d3f-6005-4934-f8fc-ae7a8230c416"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The conversion rates for new landing page and old landing page are 0.66 and 0.42 respectively.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The proportions in the sample are larger for the new landing page. Let's conduct the test to see if this is significant"
      ],
      "metadata": {
        "id": "Qe3IgCILDSiT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Two Proportion Z-test"
      ],
      "metadata": {
        "id": "sbuLoaKdDTCp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengimpor fungsi proportions_ztest dari library statsmodels untuk melakukan tes proporsi\n",
        "from statsmodels.stats.proportion import proportions_ztest\n",
        "\n",
        "# Melakukan tes proporsi untuk membandingkan proporsi konversi antara kelompok baru dan lama\n",
        "stat, pval = proportions_ztest([convertedn_new, convertedn_old], [50, 50], alternative='larger')\n",
        "\n",
        "# Mencetak nilai p-value dari tes proporsi\n",
        "print('The p-value is '+ str(pval))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gJg__P2DVQU",
        "outputId": "32bf5db7-3ea2-438f-ecd2-3dfec209ba60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The p-value is 0.008026308204056278\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insight:\n",
        "* As the p-value (~0.008) is less than the level of significance (0.05), we can reject the null hypothesis. Hence, we do have enough evidence to support the claim that conversion rate for the new landing page is higher than conversion rate for the old landing page."
      ],
      "metadata": {
        "id": "ZYSr3d6fDk41"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summary (結論)"
      ],
      "metadata": {
        "id": "F4fRAI92DoZ0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis**\n",
        "* Total (across all users) and average time spent on the new landing page (311.16 & 6.22 mins) is higher than on the old landing page (226.62 & 4.53 mins).\n",
        "* % conversion rate is higher for new landing page (66%) when compared to old landing page (42%).\n",
        "* There are apporximately equal number of English, French and Spanish preferring users (32, 34 and 34) in the dataset. % change in conversion for new landing page vs. old landing page is -10% for English, 75% for French and 36.36% for Spanish. Conversion when on the new landing page is very strong among users prefering French and marginally poor for users prefering English.\n",
        "\n",
        "**Statistical Analysis**\n",
        "\n",
        "* We statistically found enough evidence to support the claim that mean time spent on the new landing page is higher than mean time spent on the old landing page using a two independent sample T-test for equality of means - unequal std dev.\n",
        "* We statistically found enough evidence to support the claim that conversion rate for the new landing page is higher than conversion rate for the old landing page using a two proportion Z-test."
      ],
      "metadata": {
        "id": "zKRRWPZjEMjQ"
      }
    }
  ]
}