Pandas_Introduction.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Pandas Introduction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tawfiqam/MI564/blob/main/Pandas_Introduction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWCNCbq_xvaM"
      },
      "source": [
        "# What is Pandas?\n",
        "\n",
        "The pandas package is the most important tool at the disposal of Data Scientists and Analysts working in Python today. The powerful machine learning and glamorous visualization tools may get all the attention, but pandas is the backbone of most data projects [1]. \n",
        "\n",
        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)\n",
        "\n",
        "Pandas is derived from the term \"panel data\" which is an econometrics term for data sets that shows different readings over periods of time [2]. The rest of this guide is built on [1]. \n",
        "\n",
        "**References** <br>\n",
        "[1] https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/ <br> \n",
        "[2] McKinney, W. (2011). pandas: A foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9), 1–9.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lnr3rMU-0K_c"
      },
      "source": [
        "Things that can be done with a Pandas dataframe:\n",
        "\n",
        "1.   Average, median, max, min or each column in the dataframe​\n",
        "2.   Correlations between columns​\n",
        "3.   Lookup different types of information about the data source\n",
        "4.   What does the distribution of data look like?\n",
        "5.   Clean data by removing or managing missing data \n",
        "6.   Store cleaned dataset as CSV, DB, JSON or other readable files\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_BOktl0Q4hXL"
      },
      "source": [
        "# Series and Dataframes\n",
        "\n",
        "The two main components of pandas are Series and DataFrame. \n",
        "\n",
        "Basically, a Series is a column in a table. A DataFrame is a multi-dimensional table made up of a collection of series. \n",
        "\n",
        "Both DataFrames and Series are similar in that you can do similar operations in each such as filling missing values with zeros/summing values/calculating the means. \n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v0En19Rf7rym"
      },
      "source": [
        "#Starting a DataFrame from scratch\n",
        "\n",
        "One way to do so is starting with a dictionary. \n",
        "\n",
        "Remember the example from the earlier week where we saw differet fruits? In this dictionary, we show the different friuts:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjTO3ftqx1d0"
      },
      "source": [
        "fruits = {'apples':[3,2,0,1],\n",
        "          'oranges':[0,3,4,5]\n",
        "          }"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwfNzMXf8kX1"
      },
      "source": [
        "OK...so, we have this dictionary...to understand what it means, if you want to see the data assocated with apples, you say..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29KhndFg8dgb",
        "outputId": "c54a2df3-cb79-4f2f-8a90-ba5d0180f4ad"
      },
      "source": [
        "fruits['apples']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[3, 2, 0, 1]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i1FtuRq-80Xn"
      },
      "source": [
        "Alright...now, let's turn this into a Pandas DataFrame..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sALwXqER8x5S"
      },
      "source": [
        "#import the Pandas python package\n",
        "import pandas as pd\n",
        "#now create the fruits dataframe\n",
        "fruits_df = pd.DataFrame(fruits)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9u_l0Aj9LiL"
      },
      "source": [
        "Now, let's take a look at this new dataframe..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "R8jpFUmK88aE",
        "outputId": "b0f21b3a-9761-45d7-d5e5-32c14385c7c1"
      },
      "source": [
        "fruits_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   apples  oranges\n",
              "0       3        0\n",
              "1       2        3\n",
              "2       0        4\n",
              "3       1        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsd-Dnmk9Sxx"
      },
      "source": [
        "Notice how now the data is arranged in a table\n",
        "\n",
        "Now, let's say that we want to know who has what fruit...This basically means that we want to name the index instead of the default numbering..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y59EPNuG9QJ7"
      },
      "source": [
        "divided_fruits_df = pd.DataFrame(data=fruits, index=['Tawfiq','John','Sawant','Lily'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "i7Jjt7VdEjk5",
        "outputId": "2ff1ee98-d96d-4ecc-ecb0-33a28481170f"
      },
      "source": [
        "divided_fruits_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Tawfiq</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>John</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sawant</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lily</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        apples  oranges\n",
              "Tawfiq       3        0\n",
              "John         2        3\n",
              "Sawant       0        4\n",
              "Lily         1        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpVKr5JhEm4b"
      },
      "source": [
        "Now you can locate a customer's order using the index or name...so, if you wanted to know what fruits Tawfiq has..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfmEZpY2ElZt",
        "outputId": "5527eb27-dc89-47a1-a35c-a2fc866b6f7e"
      },
      "source": [
        "divided_fruits_df.loc['Tawfiq']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "apples     3\n",
              "oranges    0\n",
              "Name: Tawfiq, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e68BWJobEy5u"
      },
      "source": [
        "Apparently...Tawfiq only has apples!!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qtkx5vHReYr3"
      },
      "source": [
        "#Reading and writing to files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cptYEPEYExPG",
        "outputId": "5b0b4318-d6c4-4618-e882-62b3af0abb38"
      },
      "source": [
        "#First, you have to write this code to mount Google drive. \n",
        "#Then, you will have to upload the file you want to your drive so you can read it. \n",
        "#You can also write to this drive when you are saveing a file\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "twHVjQkHew7a"
      },
      "source": [
        "Writing out the divided fruit dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7JmgweJTxGX"
      },
      "source": [
        "divided_fruits_df.to_csv('DividedFruits.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hPCRCt8xfALH"
      },
      "source": [
        "Now, let's test if we can read it again to make sure that we saved the file in the correct fashion"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjKMTncAe6J5"
      },
      "source": [
        "test_df = pd.read_csv('DividedFruits.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "fzoCEvNDfKHF",
        "outputId": "b1cdedab-c250-4f66-c5b1-ebee3d1f5494"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Unnamed: 0</th>\n",
              "      <th>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Tawfiq</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>John</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sawant</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Lily</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Unnamed: 0  apples  oranges\n",
              "0     Tawfiq       3        0\n",
              "1       John       2        3\n",
              "2     Sawant       0        4\n",
              "3       Lily       1        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWkk7pAAfMlD"
      },
      "source": [
        "Woppps! Kinda looks the same. But the index is called Unnamed:0. \n",
        "This problem is common only when reading from csv\n",
        "\n",
        "You can resolve this by adding index_col=0, which removes the Unnamed: 0 column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lRs5tcjNfK89"
      },
      "source": [
        "test_df = pd.read_csv('DividedFruits.csv', index_col=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "aJcL8H5l87wM",
        "outputId": "78257f0c-f121-4ba3-80e4-4c5b0415b82b"
      },
      "source": [
        "test_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Tawfiq</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>John</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sawant</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lily</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        apples  oranges\n",
              "Tawfiq       3        0\n",
              "John         2        3\n",
              "Sawant       0        4\n",
              "Lily         1        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g7uOrMZ9Bqb"
      },
      "source": [
        "Cool. How about another method that is used to save data often, JSON. What is JSON you may ask? \n",
        "\n",
        "JSON is a text-based data format following JavaScript object syntax. Do not worry. You do not need to learn a new language! JSON can and is often used independently from JavaScript.\n",
        "\n",
        "JSON is stored as a string. This is important when you want to store/share large datasets. \n",
        "\n",
        "OK. So, let's try to save the dataframe as json\n",
        "\n",
        "\n",
        "Source: https://developer.mozilla.org/en-US/docs/Learn/JavaScript/Objects/JSON"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cd9t-eGH_8t1"
      },
      "source": [
        "test_df.to_json('DividedFruits.json')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DrAtzPT6ANIG"
      },
      "source": [
        "Now, let's take at look at how a json file looks like...\n",
        "\n",
        "`{\"apples\":{\"Tawfiq\":3,\"John\":2,\"Sawant\":0,\"Lily\":1},\"oranges\":{\"Tawfiq\":0,\"John\":3,\"Sawant\":4,\"Lily\":5}}`\n",
        "\n",
        "So, it basically looks like a dictionary.\n",
        "\n",
        "OK. Let's try to read it again\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I2DC3TEd88h_"
      },
      "source": [
        "test_df = pd.read_json('DividedFruits.json')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Uk6M73KcAi7V",
        "outputId": "55fd66ac-a3ad-431a-dce8-e410fe57a404"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>apples</th>\n",
              "      <th>oranges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Tawfiq</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>John</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sawant</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lily</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        apples  oranges\n",
              "Tawfiq       3        0\n",
              "John         2        3\n",
              "Sawant       0        4\n",
              "Lily         1        5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RufGzU8IAlvq"
      },
      "source": [
        "# Reading a big dataset - Movies DB\n",
        "\n",
        "When you have a big dataset, one thing you'd want to do is to explore it to see what the data \"looks like\". First, we'll read the csv file and set the index as the Movie Title. \n",
        "\n",
        "The IMDB-Move-Data.csv file is [here](https://drive.google.com/file/d/1th-zhc-b_c6DxXFS4W6_G01DofyHYLj1/view?usp=sharing). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bhT2dlYAkP8"
      },
      "source": [
        "movies_df = pd.read_csv(\"IMDB-Movie-Data.csv\", index_col=\"Title\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rr39hUo_CFqE",
        "outputId": "57c60235-a049-4fc3-a663-1b6eb97407e8"
      },
      "source": [
        "#let's see the number of rows in this DataFrame\n",
        "len(movies_df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "IsSADfW9CLTH",
        "outputId": "e58d27c7-a1f2-480a-ef91-ed17d1997344"
      },
      "source": [
        "#let's take a look the top five records...\n",
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Rank</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Description</th>\n",
              "      <th>Director</th>\n",
              "      <th>Actors</th>\n",
              "      <th>Year</th>\n",
              "      <th>Runtime (Minutes)</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Votes</th>\n",
              "      <th>Revenue (Millions)</th>\n",
              "      <th>Metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>1</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A group of intergalactic criminals are forced ...</td>\n",
              "      <td>James Gunn</td>\n",
              "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>121</td>\n",
              "      <td>8.1</td>\n",
              "      <td>757074</td>\n",
              "      <td>333.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>3</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
              "      <td>M. Night Shyamalan</td>\n",
              "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
              "      <td>2016</td>\n",
              "      <td>117</td>\n",
              "      <td>7.3</td>\n",
              "      <td>157606</td>\n",
              "      <td>138.12</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
              "      <td>Christophe Lourdelet</td>\n",
              "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
              "      <td>2016</td>\n",
              "      <td>108</td>\n",
              "      <td>7.2</td>\n",
              "      <td>60545</td>\n",
              "      <td>270.32</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>5</td>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>A secret government agency recruits some of th...</td>\n",
              "      <td>David Ayer</td>\n",
              "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
              "      <td>2016</td>\n",
              "      <td>123</td>\n",
              "      <td>6.2</td>\n",
              "      <td>393727</td>\n",
              "      <td>325.02</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Rank  ... Metascore\n",
              "Title                          ...          \n",
              "Guardians of the Galaxy     1  ...      76.0\n",
              "Prometheus                  2  ...      65.0\n",
              "Split                       3  ...      62.0\n",
              "Sing                        4  ...      59.0\n",
              "Suicide Squad               5  ...      40.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "NCSxojk-CU83",
        "outputId": "4661bdcb-5cdf-4415-e764-916d996969a7"
      },
      "source": [
        "#Let's take a look at the last five records in the DataFrame\n",
        "movies_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Rank</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Description</th>\n",
              "      <th>Director</th>\n",
              "      <th>Actors</th>\n",
              "      <th>Year</th>\n",
              "      <th>Runtime (Minutes)</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Votes</th>\n",
              "      <th>Revenue (Millions)</th>\n",
              "      <th>Metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Secret in Their Eyes</th>\n",
              "      <td>996</td>\n",
              "      <td>Crime,Drama,Mystery</td>\n",
              "      <td>A tight-knit team of rising investigators, alo...</td>\n",
              "      <td>Billy Ray</td>\n",
              "      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n",
              "      <td>2015</td>\n",
              "      <td>111</td>\n",
              "      <td>6.2</td>\n",
              "      <td>27585</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hostel: Part II</th>\n",
              "      <td>997</td>\n",
              "      <td>Horror</td>\n",
              "      <td>Three American college students studying abroa...</td>\n",
              "      <td>Eli Roth</td>\n",
              "      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n",
              "      <td>2007</td>\n",
              "      <td>94</td>\n",
              "      <td>5.5</td>\n",
              "      <td>73152</td>\n",
              "      <td>17.54</td>\n",
              "      <td>46.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Step Up 2: The Streets</th>\n",
              "      <td>998</td>\n",
              "      <td>Drama,Music,Romance</td>\n",
              "      <td>Romantic sparks occur between two dance studen...</td>\n",
              "      <td>Jon M. Chu</td>\n",
              "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
              "      <td>2008</td>\n",
              "      <td>98</td>\n",
              "      <td>6.2</td>\n",
              "      <td>70699</td>\n",
              "      <td>58.01</td>\n",
              "      <td>50.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Search Party</th>\n",
              "      <td>999</td>\n",
              "      <td>Adventure,Comedy</td>\n",
              "      <td>A pair of friends embark on a mission to reuni...</td>\n",
              "      <td>Scot Armstrong</td>\n",
              "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
              "      <td>2014</td>\n",
              "      <td>93</td>\n",
              "      <td>5.6</td>\n",
              "      <td>4881</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nine Lives</th>\n",
              "      <td>1000</td>\n",
              "      <td>Comedy,Family,Fantasy</td>\n",
              "      <td>A stuffy businessman finds himself trapped ins...</td>\n",
              "      <td>Barry Sonnenfeld</td>\n",
              "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
              "      <td>2016</td>\n",
              "      <td>87</td>\n",
              "      <td>5.3</td>\n",
              "      <td>12435</td>\n",
              "      <td>19.64</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Rank  ... Metascore\n",
              "Title                         ...          \n",
              "Secret in Their Eyes     996  ...      45.0\n",
              "Hostel: Part II          997  ...      46.0\n",
              "Step Up 2: The Streets   998  ...      50.0\n",
              "Search Party             999  ...      22.0\n",
              "Nine Lives              1000  ...      11.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jk5-yIDNGtry"
      },
      "source": [
        "## Exploring the DataFrame\n",
        "OK. Let's try to do a little more to describe our DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDYYMz_3ChCb",
        "outputId": "62eebfae-f362-4a81-f56c-c68fd09efd3c"
      },
      "source": [
        "#getting some general info about your dataset\n",
        "movies_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1000 entries, Guardians of the Galaxy to Nine Lives\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Rank                1000 non-null   int64  \n",
            " 1   Genre               1000 non-null   object \n",
            " 2   Description         1000 non-null   object \n",
            " 3   Director            1000 non-null   object \n",
            " 4   Actors              1000 non-null   object \n",
            " 5   Year                1000 non-null   int64  \n",
            " 6   Runtime (Minutes)   1000 non-null   int64  \n",
            " 7   Rating              1000 non-null   float64\n",
            " 8   Votes               1000 non-null   int64  \n",
            " 9   Revenue (Millions)  872 non-null    float64\n",
            " 10  Metascore           936 non-null    float64\n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 93.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzS1zzX4G5rq"
      },
      "source": [
        "What the above tells us is that in this dataframe, there are 11 columns. The 11 columns are listed in the table (starting with 0). \n",
        "\n",
        "The **number of non-null values** are also shown. Notice that Revenue and Metascore are both missing for a subset of the data. So, there are some movies for which these data are not available. We will talk later about how pandas give you some tools to \"impute\" these values. Imputation is the process of replacing missing data with substituted values.\n",
        "\n",
        "The **Dtype** is the data type for each of the columns. So, this column says that Rank is an integer, so are the Year, Runtime, and Votes columns. The rating and Revenues though are floats (meaning that your the number can include fractions).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-3eHw0LEIjTS"
      },
      "source": [
        "Next, you might want to see what the data looks like"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7gfq7aE2GoWo",
        "outputId": "6b19218b-cdaa-4594-8733-7bab9e39becb"
      },
      "source": [
        "movies_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jJtnwLgDSwuk"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jfWF6bNbJx2z"
      },
      "source": [
        "What does this tell you? Well, in this DataFrame there are 1000 rows and 11 columns\n",
        "\n",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgJnT0w2Szaw"
      },
      "source": [
        "## Cleaning the DataFrame\n",
        "\n",
        "Your DataFrame might not be perfect. Most of the time, they are not. Some of the issues you might have are:\n",
        "\n",
        "\n",
        "1.   Duplicates: there are duplicate recrods (rows) that contain the same data..just repeated.\n",
        "2.   Columns: column names or their data types might need to be corrected. \n",
        "3.   Incomplete data: some of your data points are incomplete. As you might remember, we found some missing data points in our DataFrame. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pO0XXoLzUg5P"
      },
      "source": [
        "###Handling duplicates (cleaning rows)\n",
        "First, let's test how to deal with duplicats. In this case, we will create our own duplication by appending the same table twice to the dataset. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jt-cd_AS4JI"
      },
      "source": [
        "#this command basically adds the same DataFrame to itself...basically doubling each row\n",
        "duplicate_movies_df = movies_df.append(movies_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZdvphRtVAoR",
        "outputId": "04e3db27-610d-4483-b688-a31400b331b5"
      },
      "source": [
        "#let's see how this one looks like\n",
        "duplicate_movies_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxqsWG-CVQ9C",
        "outputId": "db4805ef-39cc-4650-fa1d-12a35732ce2a"
      },
      "source": [
        "#But does this change anything when it comes to columns in this dataset?\n",
        "duplicate_movies_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 2000 entries, Guardians of the Galaxy to Nine Lives\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Rank                2000 non-null   int64  \n",
            " 1   Genre               2000 non-null   object \n",
            " 2   Description         2000 non-null   object \n",
            " 3   Director            2000 non-null   object \n",
            " 4   Actors              2000 non-null   object \n",
            " 5   Year                2000 non-null   int64  \n",
            " 6   Runtime (Minutes)   2000 non-null   int64  \n",
            " 7   Rating              2000 non-null   float64\n",
            " 8   Votes               2000 non-null   int64  \n",
            " 9   Revenue (Millions)  1744 non-null   float64\n",
            " 10  Metascore           1872 non-null   float64\n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 187.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V1WsS0B3Vc4c"
      },
      "source": [
        "As you can see, the shape of the DataFrame has changed. There are 1,000 new rows (because we just added them!) but there are not changes in the nature of the data under any of the columns. We did not do anything to change the columns so that makes sense. \n",
        "\n",
        "OK. Now that we determined that there duplicates in our DataFrame, how do we clean it by getting rif of them. Well, Pandas has your back, there is a drop_duplicates function that does this for you. Let's try it here, shall we"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjxGR-_SVPBq"
      },
      "source": [
        "droped_duplicates_df = duplicate_movies_df.drop_duplicates()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_d59nkRWAen",
        "outputId": "f0ed69ab-b1ae-4b0d-e3f3-f227539b5673"
      },
      "source": [
        "#let's take a look at the shape of the dataframe after dropping duplicates\n",
        "droped_duplicates_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "omibMGK0XDsD"
      },
      "source": [
        "The drop duplicates function is even better. You can make some adjustments as to what records get to be dropped. In this case, it was pretty clear, but what if we want to manage the following scenario:\n",
        "You collected Tweets about #covid19 throughout the month of the August. You collected both the original tweet and any retweets. As you are doing your analysis, you decide that you only need to keep the first retweet. \n",
        "\n",
        "In this case, you add keep argument in the drop_duplicates funtion. The default for this argument is \"first.\" This means that the first record is kept and the second disposed of. You could change that to \"last,\" in which case the last record is kept and the first disposed of. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxOGmmW2WF5-"
      },
      "source": [
        "drop_duplicates_df = duplicate_movies_df.drop_duplicates(keep='first')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TLqPhxRjYSLf",
        "outputId": "13edc776-b844-4b88-98f2-6dcb647fa8f2"
      },
      "source": [
        "#checking on the shape again\n",
        "drop_duplicates_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fvdr0DK8Ym8U"
      },
      "source": [
        "OK. But what happens you might ask if we remove first or last from the keep argument. What if we said that keep = False? Care to venture a guess? "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xH35RLQ9YljK"
      },
      "source": [
        "drop_duplicates_df = duplicate_movies_df.drop_duplicates(keep=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9S6jCedY6pS",
        "outputId": "c27b9383-4c06-4911-bed8-919a67d48e5d"
      },
      "source": [
        "#let's check now what the shape looks like\n",
        "drop_duplicates_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OU1MBHMHZApE"
      },
      "source": [
        "This is an empty DataFrame!! There are no records because there is a duplicate for each and we specified that no duplicate records are to be kept. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDMqIuhRZPDF"
      },
      "source": [
        "Finally, there is one more function that might make this easier for you.\n",
        "Instead of having to copy the DataFrame every time, you can just make the changes in place "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RjxjG2BJY_fp"
      },
      "source": [
        "duplicate_movies_df.drop_duplicates(inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLUKjETnZfXP",
        "outputId": "b1f53312-8d67-4919-8879-2091f763567f"
      },
      "source": [
        "#checking the duplicate_movies_df dataframe. Now it shold also be a (1000,11) shape\n",
        "duplicate_movies_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VPU5ESIZ4QX"
      },
      "source": [
        "###Cleaning Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mEeOb6cZ5u5",
        "outputId": "ceb568bd-a7a7-45e1-c652-34e18c25bd8f"
      },
      "source": [
        "#first, if you want to read all the columns in your dataset, you use:\n",
        "movies_df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Rank', 'Genre', 'Description', 'Director', 'Actors', 'Year',\n",
              "       'Runtime (Minutes)', 'Rating', 'Votes', 'Revenue (Millions)',\n",
              "       'Metascore'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QuEyc4NPavb0"
      },
      "source": [
        "Suppose that we want to change the column Runtime (Minutes) to just be Runtime. This is probably easier to show when you're working with your dataset and presenting it to others. \n",
        "\n",
        "In this case, you will use the dename function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BkBLqZz7aCU-"
      },
      "source": [
        "movies_df.rename(columns={'Runtime (Minutes)':'Runtime','Revenue (Millions)':'Revenue_millions'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "BOhwRkzqbWeW",
        "outputId": "c3f5d295-2fd0-4d54-815b-7135a3b5284b"
      },
      "source": [
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rank</th>\n",
              "      <th>Genre</th>\n",
              "      <th>Description</th>\n",
              "      <th>Director</th>\n",
              "      <th>Actors</th>\n",
              "      <th>Year</th>\n",
              "      <th>Runtime</th>\n",
              "      <th>Rating</th>\n",
              "      <th>Votes</th>\n",
              "      <th>Revenue_millions</th>\n",
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              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>1</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A group of intergalactic criminals are forced ...</td>\n",
              "      <td>James Gunn</td>\n",
              "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>121</td>\n",
              "      <td>8.1</td>\n",
              "      <td>757074</td>\n",
              "      <td>333.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>3</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
              "      <td>M. Night Shyamalan</td>\n",
              "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
              "      <td>2016</td>\n",
              "      <td>117</td>\n",
              "      <td>7.3</td>\n",
              "      <td>157606</td>\n",
              "      <td>138.12</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
              "      <td>Christophe Lourdelet</td>\n",
              "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
              "      <td>2016</td>\n",
              "      <td>108</td>\n",
              "      <td>7.2</td>\n",
              "      <td>60545</td>\n",
              "      <td>270.32</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>5</td>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>A secret government agency recruits some of th...</td>\n",
              "      <td>David Ayer</td>\n",
              "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
              "      <td>2016</td>\n",
              "      <td>123</td>\n",
              "      <td>6.2</td>\n",
              "      <td>393727</td>\n",
              "      <td>325.02</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         Rank  ... Metascore\n",
              "Title                          ...          \n",
              "Guardians of the Galaxy     1  ...      76.0\n",
              "Prometheus                  2  ...      65.0\n",
              "Split                       3  ...      62.0\n",
              "Sing                        4  ...      59.0\n",
              "Suicide Squad               5  ...      40.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_rBK5vwYbdSs"
      },
      "source": [
        "movies_df.columns = [col.lower() for col in movies_df]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "reyX1ZBco3QF",
        "outputId": "655bd637-44f8-4086-8939-9fa1a1f9eff9"
      },
      "source": [
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>1</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A group of intergalactic criminals are forced ...</td>\n",
              "      <td>James Gunn</td>\n",
              "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>121</td>\n",
              "      <td>8.1</td>\n",
              "      <td>757074</td>\n",
              "      <td>333.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>65.0</td>\n",
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              "      <td>3</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
              "      <td>M. Night Shyamalan</td>\n",
              "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
              "      <td>2016</td>\n",
              "      <td>117</td>\n",
              "      <td>7.3</td>\n",
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              "      <td>138.12</td>\n",
              "      <td>62.0</td>\n",
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              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
              "      <td>Christophe Lourdelet</td>\n",
              "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
              "      <td>2016</td>\n",
              "      <td>108</td>\n",
              "      <td>7.2</td>\n",
              "      <td>60545</td>\n",
              "      <td>270.32</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>5</td>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>A secret government agency recruits some of th...</td>\n",
              "      <td>David Ayer</td>\n",
              "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
              "      <td>2016</td>\n",
              "      <td>123</td>\n",
              "      <td>6.2</td>\n",
              "      <td>393727</td>\n",
              "      <td>325.02</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         rank  ... metascore\n",
              "Title                          ...          \n",
              "Guardians of the Galaxy     1  ...      76.0\n",
              "Prometheus                  2  ...      65.0\n",
              "Split                       3  ...      62.0\n",
              "Sing                        4  ...      59.0\n",
              "Suicide Squad               5  ...      40.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lHtZDjC2qRn_"
      },
      "source": [
        "##Handling incomplete data\n",
        "\n",
        "There are two options in dealing with nulls:\n",
        "\n",
        "1.   Get rid of rows or columns with nulls\n",
        "2.   Replace nulls with non-null values, a technique known as imputation\n",
        "\n",
        "Recall that when we first used the describe() function, we found that there are missing values in the revenue and metascore columns. Let's see other Pandas functions that deal with this issue.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "DCqBY6_zo4oa",
        "outputId": "e2c7e339-72ff-41f3-d966-8b4c7f206271"
      },
      "source": [
        "movies_df.isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "        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>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
              "      <th>actors</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Secret in Their Eyes</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hostel: Part II</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Step Up 2: The Streets</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Search Party</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Nine Lives</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                          rank  genre  ...  revenue_millions  metascore\n",
              "Title                                  ...                             \n",
              "Guardians of the Galaxy  False  False  ...             False      False\n",
              "Prometheus               False  False  ...             False      False\n",
              "Split                    False  False  ...             False      False\n",
              "Sing                     False  False  ...             False      False\n",
              "Suicide Squad            False  False  ...             False      False\n",
              "...                        ...    ...  ...               ...        ...\n",
              "Secret in Their Eyes     False  False  ...              True      False\n",
              "Hostel: Part II          False  False  ...             False      False\n",
              "Step Up 2: The Streets   False  False  ...             False      False\n",
              "Search Party             False  False  ...              True      False\n",
              "Nine Lives               False  False  ...             False      False\n",
              "\n",
              "[1000 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gPvSNxNr6kA"
      },
      "source": [
        "But this is not very helpful! Let's try to get the sum of all null values in a column..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-pmTrhcr3IO",
        "outputId": "36a93972-6c95-47b6-a672-32388145d838"
      },
      "source": [
        "movies_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                  0\n",
              "genre                 0\n",
              "description           0\n",
              "director              0\n",
              "actors                0\n",
              "year                  0\n",
              "runtime               0\n",
              "rating                0\n",
              "votes                 0\n",
              "revenue_millions    128\n",
              "metascore            64\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZsMSA13tsIfX"
      },
      "source": [
        "As you can see, revenue (128) and metascore (64) are the two columns with null values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pthzNp82tOL_"
      },
      "source": [
        "####Removing null values in DataFrame rows\n",
        "\n",
        "Now, we want to create a new DataFrame where any row with a null value is removed\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CQIkDQH8tUDy"
      },
      "source": [
        "movies_no_null_rows_df = movies_df.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR0hIcOntmER",
        "outputId": "d500e1b2-a6c1-4f6d-f2ad-67da11fcd51a"
      },
      "source": [
        "#let's take a look at the new DataFrame's shape\n",
        "movies_no_null_rows_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(838, 11)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zP2w66uwt3Xe"
      },
      "source": [
        "So, 162 rows have been removed, but the columns have not changed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lB2tPnyLtsD-",
        "outputId": "98d410c3-3c10-49b2-e668-ae4be7f564d1"
      },
      "source": [
        "movies_no_null_rows_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 838 entries, Guardians of the Galaxy to Nine Lives\n",
            "Data columns (total 11 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   rank              838 non-null    int64  \n",
            " 1   genre             838 non-null    object \n",
            " 2   description       838 non-null    object \n",
            " 3   director          838 non-null    object \n",
            " 4   actors            838 non-null    object \n",
            " 5   year              838 non-null    int64  \n",
            " 6   runtime           838 non-null    int64  \n",
            " 7   rating            838 non-null    float64\n",
            " 8   votes             838 non-null    int64  \n",
            " 9   revenue_millions  838 non-null    float64\n",
            " 10  metascore         838 non-null    float64\n",
            "dtypes: float64(3), int64(4), object(4)\n",
            "memory usage: 78.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AF-AKRvFuNR-"
      },
      "source": [
        "####Removing null values in DataFrame columns\n",
        "\n",
        "Now, we want to create a new DataFrame where any **column** with a null value is removed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HcQ31dekt91G"
      },
      "source": [
        "movies_no_null_columns_df = movies_df.dropna(axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYA8C1cAuclz",
        "outputId": "bd1c527a-9492-4c5d-bcc6-327905336c82"
      },
      "source": [
        "#let's take a look at the columns again\n",
        "#note that metascore and revenue are removed\n",
        "movies_no_null_columns_df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1000 entries, Guardians of the Galaxy to Nine Lives\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   rank         1000 non-null   int64  \n",
            " 1   genre        1000 non-null   object \n",
            " 2   description  1000 non-null   object \n",
            " 3   director     1000 non-null   object \n",
            " 4   actors       1000 non-null   object \n",
            " 5   year         1000 non-null   int64  \n",
            " 6   runtime      1000 non-null   int64  \n",
            " 7   rating       1000 non-null   float64\n",
            " 8   votes        1000 non-null   int64  \n",
            "dtypes: float64(1), int64(4), object(4)\n",
            "memory usage: 78.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6DLNDVwpupLS"
      },
      "source": [
        "In order to understand why we used axis=1, remember how the DataFrame shape looks like. It is a tuple with the 0 index showing the rows and the 1 index showing the columns, thus columns (axis=1). \n",
        "\n",
        "If you see the shape of the DataFrame after removing the columns, it should look like **(1000,9)**\n",
        "\n",
        "Let's check...."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HUZjBuDxugxf",
        "outputId": "fd7ecb2e-ec94-425a-f009-893fc5d95031"
      },
      "source": [
        "movies_no_null_columns_df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0OozJ2dwjPY"
      },
      "source": [
        "####Impuation\n",
        "\n",
        "What if we did not want to remove all these rows/columns from the DataFrame when there are missing data points. Instead, we can impute that null values with other values - a zero, the mean or the median of values in that column.\n",
        "\n",
        "Let's impute the missing values in the revenue_millions column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7T8F65T9vJBo"
      },
      "source": [
        "#first, let's get the mean for the revenue_millions column\n",
        "#there are two ways to call a column, either use the [brackets] or .\n",
        "#so, this would be revenue_mean = movies_df.revenue_millions.mean()\n",
        "revenue_mean = movies_df['revenue_millions'].mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L172hAEMxYn8",
        "outputId": "037e0789-1583-479d-df6f-b9bd48d8edea"
      },
      "source": [
        "revenue_mean"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "82.95637614678897"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZtdMu_zxZ8R"
      },
      "source": [
        "#now that we have the mean value, let's impute it in the DataFrame\n",
        "#just as with duplicates, we can use the inplace argument as well\n",
        "movies_df.revenue_millions.fillna(revenue_mean,inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2aLJuKf2yNUq",
        "outputId": "796f5efc-c71b-4e02-8b8e-a1839da383cb"
      },
      "source": [
        "#Let's check on the null values\n",
        "movies_df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                 0\n",
              "genre                0\n",
              "description          0\n",
              "director             0\n",
              "actors               0\n",
              "year                 0\n",
              "runtime              0\n",
              "rating               0\n",
              "votes                0\n",
              "revenue_millions     0\n",
              "metascore           64\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83TpOw43yUHV"
      },
      "source": [
        "As you can see from the above, now there are only null values in the revenue_millions column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFWHDLyFzOjF"
      },
      "source": [
        "#Exploring the variables in the DataFrame\n",
        "\n",
        "1.   Use the describe function to get a summary of the data in the DataFrame\n",
        "2.   Slicing, selecting, and extracting from a DataFrame\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZebN_Ihq2cRh"
      },
      "source": [
        "##General DataFrame descriptions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mB3Ucm3CySFR",
        "outputId": "77e58aff-a52d-4eb0-96f9-7e23e95189a0"
      },
      "source": [
        "movies_df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>rank</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>936.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>500.500000</td>\n",
              "      <td>2012.783000</td>\n",
              "      <td>113.172000</td>\n",
              "      <td>6.723200</td>\n",
              "      <td>1.698083e+05</td>\n",
              "      <td>82.956376</td>\n",
              "      <td>58.985043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>288.819436</td>\n",
              "      <td>3.205962</td>\n",
              "      <td>18.810908</td>\n",
              "      <td>0.945429</td>\n",
              "      <td>1.887626e+05</td>\n",
              "      <td>96.412043</td>\n",
              "      <td>17.194757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>2006.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>1.900000</td>\n",
              "      <td>6.100000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>250.750000</td>\n",
              "      <td>2010.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>3.630900e+04</td>\n",
              "      <td>17.442500</td>\n",
              "      <td>47.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>500.500000</td>\n",
              "      <td>2014.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>1.107990e+05</td>\n",
              "      <td>60.375000</td>\n",
              "      <td>59.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>750.250000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>7.400000</td>\n",
              "      <td>2.399098e+05</td>\n",
              "      <td>99.177500</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1000.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>191.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.791916e+06</td>\n",
              "      <td>936.630000</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              rank         year  ...  revenue_millions   metascore\n",
              "count  1000.000000  1000.000000  ...       1000.000000  936.000000\n",
              "mean    500.500000  2012.783000  ...         82.956376   58.985043\n",
              "std     288.819436     3.205962  ...         96.412043   17.194757\n",
              "min       1.000000  2006.000000  ...          0.000000   11.000000\n",
              "25%     250.750000  2010.000000  ...         17.442500   47.000000\n",
              "50%     500.500000  2014.000000  ...         60.375000   59.500000\n",
              "75%     750.250000  2016.000000  ...         99.177500   72.000000\n",
              "max    1000.000000  2016.000000  ...        936.630000  100.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3W4cnhh0zxyU"
      },
      "source": [
        "As you can see, the count (1000 since we have 1000 rows), mean, standard deviation, minimum, maximum, and the quartiles"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICQgxgphz-5R"
      },
      "source": [
        "You can also desribe columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h2PryJDpzuW-",
        "outputId": "cd1fd5cf-34be-4cad-ffec-d526ca20ae48"
      },
      "source": [
        "movies_df['genre'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                        1000\n",
              "unique                        207\n",
              "top       Action,Adventure,Sci-Fi\n",
              "freq                           50\n",
              "Name: genre, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X8P0EyR70PWq"
      },
      "source": [
        "This means that in the 1,000 rows the genre column has 207 unique values, the top value is Action/Adventure/Sci-Fi, which shows up 50 times (freq). \n",
        "\n",
        "Let's drill into this a little more. What is the next top genre? What is the number of times it was repeated?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-tfFuHlg0GRx",
        "outputId": "5bb72709-46f9-4e08-8acc-f0cdda3f380c"
      },
      "source": [
        "movies_df['genre'].value_counts().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Action,Adventure,Sci-Fi    50\n",
              "Drama                      48\n",
              "Comedy,Drama,Romance       35\n",
              "Comedy                     32\n",
              "Drama,Romance              31\n",
              "Name: genre, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AE7trm50tMX"
      },
      "source": [
        "The next genre is Drama at a count of 48"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E3Nv9Vef06KQ"
      },
      "source": [
        "How about a we take a quick look at correlations to figure out if there are some relationships between columns. Correlatios represent the degree to which a pair of variables are linearly related. Positive numbers indicate a positive correlation — one goes up the other goes up — and negative numbers represent an inverse correlation — one goes up the other goes down. 1.0 indicates a perfect correlation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "hu9vax120rZD",
        "outputId": "501c8c9d-ce50-4629-d0b4-1ba48607d28a"
      },
      "source": [
        "movies_df.corr()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>rank</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>rank</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.261605</td>\n",
              "      <td>-0.221739</td>\n",
              "      <td>-0.219555</td>\n",
              "      <td>-0.283876</td>\n",
              "      <td>-0.252996</td>\n",
              "      <td>-0.191869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>-0.261605</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.164900</td>\n",
              "      <td>-0.211219</td>\n",
              "      <td>-0.411904</td>\n",
              "      <td>-0.117562</td>\n",
              "      <td>-0.079305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>runtime</th>\n",
              "      <td>-0.221739</td>\n",
              "      <td>-0.164900</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.392214</td>\n",
              "      <td>0.407062</td>\n",
              "      <td>0.247834</td>\n",
              "      <td>0.211978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rating</th>\n",
              "      <td>-0.219555</td>\n",
              "      <td>-0.211219</td>\n",
              "      <td>0.392214</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.511537</td>\n",
              "      <td>0.189527</td>\n",
              "      <td>0.631897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>votes</th>\n",
              "      <td>-0.283876</td>\n",
              "      <td>-0.411904</td>\n",
              "      <td>0.407062</td>\n",
              "      <td>0.511537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.607941</td>\n",
              "      <td>0.325684</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>revenue_millions</th>\n",
              "      <td>-0.252996</td>\n",
              "      <td>-0.117562</td>\n",
              "      <td>0.247834</td>\n",
              "      <td>0.189527</td>\n",
              "      <td>0.607941</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.133328</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>metascore</th>\n",
              "      <td>-0.191869</td>\n",
              "      <td>-0.079305</td>\n",
              "      <td>0.211978</td>\n",
              "      <td>0.631897</td>\n",
              "      <td>0.325684</td>\n",
              "      <td>0.133328</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      rank      year  ...  revenue_millions  metascore\n",
              "rank              1.000000 -0.261605  ...         -0.252996  -0.191869\n",
              "year             -0.261605  1.000000  ...         -0.117562  -0.079305\n",
              "runtime          -0.221739 -0.164900  ...          0.247834   0.211978\n",
              "rating           -0.219555 -0.211219  ...          0.189527   0.631897\n",
              "votes            -0.283876 -0.411904  ...          0.607941   0.325684\n",
              "revenue_millions -0.252996 -0.117562  ...          1.000000   0.133328\n",
              "metascore        -0.191869 -0.079305  ...          0.133328   1.000000\n",
              "\n",
              "[7 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D-uBGvs91eRz"
      },
      "source": [
        "So now, looking at the correlation between metascore and rating at (0.63), meaning that the higher the rating the higher the metascore. Similarly, at a correlation of 0.607, it seems that the more the votes, the more the revenue. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dI7VR_VT2hSy"
      },
      "source": [
        "##Slicing, selecting, and extracting\n",
        "\n",
        "Up until now we've focused on some basic summaries of our data. We've learned about simple column extraction using single brackets, and we imputed null values in a column using fillna(). Below are the other methods of slicing, selecting, and extracting you'll need to use constantly.\n",
        "\n",
        "It's important to note that, although many methods are the same, DataFrames and Series have different attributes, so you'll need be sure to know which type you are working with or else you will receive attribute errors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mLlW2_V_2vZO"
      },
      "source": [
        "###By column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03rPNuR21Zck"
      },
      "source": [
        "#let's go back to the genre column\n",
        "genre_col = movies_df['genre']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hLZVMPia22Rf",
        "outputId": "f917f81c-4ab5-4d62-f4eb-5faff5bd56a8"
      },
      "source": [
        "#let's check the type\n",
        "type(genre_col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZTKh9LJ28tp"
      },
      "source": [
        "So, a Pandas DataFrame column is a Series as we introduced early on in this notebook. But, you can still select this column and make changes to it as though it were a DataFrame. To do so, all you have to do in nest the brackets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l-DoCYsM26W8"
      },
      "source": [
        "genre_col = movies_df[['genre']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_dtPgFb3SP4",
        "outputId": "951a749a-9c61-4205-e937-30784323a45d"
      },
      "source": [
        "type(genre_col)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amJ2QpOP3ddQ"
      },
      "source": [
        "OK. So, let's create a portion of the DataFrame that shows the genre and ratings"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "tplUfuST3Tcw",
        "outputId": "5e08b799-f078-49a4-bdc5-a8499e1c371c"
      },
      "source": [
        "subset = movies_df[['genre','rating','revenue_millions']]\n",
        "subset.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>genre</th>\n",
              "      <th>rating</th>\n",
              "      <th>revenue_millions</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>8.1</td>\n",
              "      <td>333.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>7.0</td>\n",
              "      <td>126.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>7.3</td>\n",
              "      <td>138.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>7.2</td>\n",
              "      <td>270.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>6.2</td>\n",
              "      <td>325.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                            genre  rating  revenue_millions\n",
              "Title                                                                      \n",
              "Guardians of the Galaxy   Action,Adventure,Sci-Fi     8.1            333.13\n",
              "Prometheus               Adventure,Mystery,Sci-Fi     7.0            126.46\n",
              "Split                             Horror,Thriller     7.3            138.12\n",
              "Sing                      Animation,Comedy,Family     7.2            270.32\n",
              "Suicide Squad            Action,Adventure,Fantasy     6.2            325.02"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PGwCj4PX-Yrz"
      },
      "source": [
        "You could also want to see the unique values in a particular column. For example, maybe we want to know who the unique authors are..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zf-gfQPf9es-",
        "outputId": "380823c9-6d76-49ed-d73b-48f8b24fe49b"
      },
      "source": [
        "movies_df.director.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['James Gunn', 'Ridley Scott', 'M. Night Shyamalan',\n",
              "       'Christophe Lourdelet', 'David Ayer', 'Yimou Zhang',\n",
              "       'Damien Chazelle', 'Sean Foley', 'James Gray', 'Morten Tyldum',\n",
              "       'David Yates', 'Theodore Melfi', 'Gareth Edwards', 'Ron Clements',\n",
              "       'Nacho Vigalondo', 'Chris Renaud', 'Mel Gibson', 'Paul Greengrass',\n",
              "       'Garth Davis', 'Denis Villeneuve', 'Stephen Gaghan',\n",
              "       'Kenneth Lonergan', 'Ben Young', 'Walt Dohrn', 'Roland Emmerich',\n",
              "       'Dominique Abel', 'S.S. Rajamouli', 'Phillip Guzman', 'Jon Lucas',\n",
              "       'Justin Kurzel', 'John Hamburg', 'Tom Ford', 'Bryan Singer',\n",
              "       'Tim Miller', 'Paul W.S. Anderson', 'Anthony Russo',\n",
              "       'Christopher Nolan', 'Scott Derrickson', 'Antoine Fuqua',\n",
              "       'Patrick Read Johnson', 'Greg Tiernan', 'Barry Jenkins',\n",
              "       'Shawn Burkett', 'John Lee Hancock', 'Ricardo de Montreuil',\n",
              "       'Rob Marshall', 'John Madden', 'Scott Hicks', 'Justin Lin',\n",
              "       'Sean Penn', 'J.J. Abrams', 'Anna Foerster', 'Garry Marshall',\n",
              "       'Chad Stahelski', 'Martin Scorsese', 'Fede Alvarez',\n",
              "       'Thea Sharrock', 'Lone Scherfig', 'Clint Eastwood', 'Zack Snyder',\n",
              "       'André Øvredal', 'Tate Taylor', 'Sam Taylor-Johnson',\n",
              "       'Matthew Vaughn', 'Peter Berg', 'George Miller', 'Robin Swicord',\n",
              "       'Terry George', 'Robert Zemeckis', 'J.A. Bayona', 'David Frankel',\n",
              "       'Byron Howard', 'Gore Verbinski', 'Joss Whedon',\n",
              "       'Quentin Tarantino', 'Paul Feig', 'Matt Ross', 'David Fincher',\n",
              "       'James Wan', 'Colin Trevorrow', 'Ben Affleck', 'James Cameron',\n",
              "       \"Gavin O'Connor\", 'Duncan Jones', 'Todd Phillips', 'Shane Black',\n",
              "       'Makoto Shinkai', 'Jeremy Gillespie', 'Olivier Assayas',\n",
              "       'Brian Helgeland', 'Kenneth Branagh', 'Oriol Paulo', 'Guy Ritchie',\n",
              "       'David Mackenzie', 'Taylor Hackford', 'Katie Holmes',\n",
              "       'Alex Garland', 'Greg McLean', 'Steve McQueen',\n",
              "       'Ana Lily Amirpour', 'Josh Gordon', 'Nicolas Winding Refn',\n",
              "       'Nitesh Tiwari', 'Dan Trachtenberg', 'Andrew Stanton',\n",
              "       'Tim Burton', 'Neil Burger', 'Jake Szymanski', 'Todor Chapkanov',\n",
              "       'Jon Favreau', 'Michael Bay', 'Henry Joost', 'Phyllida Lloyd',\n",
              "       'Alejandro González Iñárritu', 'Denzel Washington',\n",
              "       'Jaume Collet-Serra', 'Derek Cianfrance', 'Ethan Coen',\n",
              "       'Baz Luhrmann', 'Martin Koolhoven', 'Thor Freudenthal',\n",
              "       'Adam McKay', 'Lenny Abrahamson', 'Chan-wook Park',\n",
              "       'Kelly Fremon Craig', 'Greg Mottola', 'Ron Howard',\n",
              "       'Steven Spielberg', 'Gary Ross', 'Elizabeth Wood', 'David Lynch',\n",
              "       'Colin Strause', 'Guillermo del Toro', 'Glenn Ficarra',\n",
              "       'Edgar Wright', 'Fabio Guaglione', 'Ben Wheatley', 'Edward Zwick',\n",
              "       'Martin Campbell', 'Catherine Hardwicke', 'Jon M. Chu',\n",
              "       'Simon Curtis', 'Sam Mendes', 'Dan Gilroy', 'Travis Knight',\n",
              "       'Jackson Stewart', 'Spike Jonze', 'Chris Buck', 'Brad Bird',\n",
              "       'Matt Reeves', 'Ben Stiller', 'Peyton Reed', 'Sharon Maguire',\n",
              "       'Robert Eggers', 'Mateo Gil', 'Asghar Farhadi', 'Gaspar Noé',\n",
              "       'Ang Lee', 'Judd Apatow', 'April Mullen', 'Tom McCarthy',\n",
              "       'Luke Scott', 'Joe Johnston', 'Akan Satayev',\n",
              "       'Rawson Marshall Thurber', 'Doug Liman', 'Oliver Stone',\n",
              "       'Robert Schwentke', 'Julia Ducournau', 'Jim Jarmusch',\n",
              "       'Colm McCarthy', 'Brad Peyton', 'Harmony Korine', 'Spike Lee',\n",
              "       'Alan Taylor', 'Alex Proyas', 'Chris Wedge', 'Liam Gavin',\n",
              "       'Ilya Naishuller', 'John Lasseter', 'David Robert Mitchell',\n",
              "       'Andrea Arnold', 'Yorgos Lanthimos', 'Nimród Antal',\n",
              "       'Robert Stromberg', 'Steven Shainberg', 'Andy Goddard',\n",
              "       'Christopher McQuarrie', 'Cedric Nicolas-Troyan',\n",
              "       'Stephen Chbosky', 'Pablo Larraín', 'D.J. Caruso', 'Wes Anderson',\n",
              "       'Andrew Niccol', 'Pete Docter', 'Ash Brannon', 'Chris Columbus',\n",
              "       'Tom Hooper', 'Alfonso Cuarón', 'Mike Mills', 'Olivier Nakache',\n",
              "       'Eleanor Coppola', 'F. Gary Gray', 'Marc Webb', 'Rupert Wyatt',\n",
              "       'David F. Sandberg', 'Joseph Cedar', 'Darren Aronofsky',\n",
              "       'Justin Simien', 'Lars von Trier', 'Dave Green', 'Eli Roth',\n",
              "       'Dan Mazer', 'Tom Tykwer', 'Gavin Hood', 'Jeffrey G. Hunt',\n",
              "       'Peter Jackson', 'Phil Lord', 'John Carney', 'Eric Summer',\n",
              "       'Joseph Kosinski', 'Richard Linklater', 'Louis Leterrier',\n",
              "       'Brett Ratner', 'Lana Wachowski', 'Jared Hess', 'Jalil Lespert',\n",
              "       'Sang-ho Yeon', 'Jason Moore', 'Nicholas Stoller', 'David Leveaux',\n",
              "       'Ross Katz', 'Mike Thurmeier', 'Brad Furman',\n",
              "       'Paul Thomas Anderson', 'Drew Goddard', 'Fred Wolf',\n",
              "       'Jim Field Smith', 'James Bobin', 'Gary Michael Schultz',\n",
              "       'Christian Ditter', 'Abdellatif Kechiche', 'Jennifer Kent',\n",
              "       'Bong Joon Ho', 'J Blakeson', 'Dan Berk', 'Anne Fletcher',\n",
              "       'So Yong Kim', 'Breck Eisner', 'Paul McGuigan',\n",
              "       'Roger Spottiswoode', 'Jeremy Saulnier', 'Michael Mann',\n",
              "       'Jean-Marc Vallée', 'Mike Flanagan', 'David O. Russell',\n",
              "       'Alexi Pappas', 'Josh Boone', 'Frank Coraci', 'Rian Johnson',\n",
              "       'David Lowery', 'Sam Raimi', 'Karyn Kusama', 'Woody Allen',\n",
              "       'William Oldroyd', 'Jocelyn Moorhouse', 'Sergei Bodrov',\n",
              "       'Hugo Gélin', 'James Marsh', 'Evan Goldberg', 'Richard Curtis',\n",
              "       'Jon Watts', 'Ruben Fleischer', 'Danny Boyle', 'Bill Condon',\n",
              "       'Alex Ranarivelo', 'Timur Bekmambetov', 'J.D. Dillard', 'Wes Ball',\n",
              "       'Ariel Vromen', 'Stephen Frears', 'Eran Creevy', 'Scott Cooper',\n",
              "       'Ryan Coogler', 'Dan Kwan', 'Patrick Hughes', 'Jemaine Clement',\n",
              "       'Baltasar Kormákur', 'James Mangold', 'Peter Atencio',\n",
              "       'Pierre Morel', 'Gillies MacKinnon', 'Dennis Dugan',\n",
              "       'Lee Toland Krieger', 'Peter Billingsley', 'Terry Jones',\n",
              "       'Steven Soderbergh', 'Kyle Balda', 'Rolfe Kanefsky',\n",
              "       'Pierre Coffin', 'Michael Hoffman', 'Kathryn Bigelow',\n",
              "       'Nathan Greno', 'Francis Lawrence', 'John Francis Daley',\n",
              "       'Elizabeth Banks', 'Dan Scanlon', 'Paul Verhoeven',\n",
              "       'Dennis Gansel', 'Kevin Smith', 'Mark Williams', 'Joe Wright',\n",
              "       'David Ross', 'Burr Steers', 'Noam Murro', 'Babak Najafi',\n",
              "       'Frank Miller', 'Srdjan Spasojevic', 'James DeMonaco',\n",
              "       'Rajkumar Hirani', 'Marc Forster', 'Corey Asraf', 'Jon S. Baird',\n",
              "       'George Tillman Jr.', 'Jeff Wadlow', 'Jeff Grace', 'John Crowley',\n",
              "       'Henry Selick', 'Michael J. Gallagher', 'Christophe Gans',\n",
              "       'Pete Travis', 'Taika Waititi', 'Don Hall', 'Kimberly Peirce',\n",
              "       'Christian Alvart', 'Thomas Vinterberg', 'Etan Cohen',\n",
              "       'William Brent Bell', 'Mick Jackson', 'Michael Spierig',\n",
              "       'Rob Letterman', 'Phillip Noyce', 'Neill Blomkamp',\n",
              "       'Sofia Coppola', 'Dexter Fletcher',\n",
              "       'Florian Henckel von Donnersmarck', 'Carles Torrens',\n",
              "       'Amber Tamblyn', 'John Hillcoat', 'Paolo Genovese', 'Clay Kaytis',\n",
              "       'Adam Shankman', 'Lasse Hallström', 'Jim Mickle', 'Albert Hughes',\n",
              "       'Ericson Core', 'Jonathan Glazer', 'Barry Sonnenfeld',\n",
              "       'Todd Haynes', 'Daniel Ragussis', 'Paolo Sorrentino',\n",
              "       'Jaco Van Dormael', 'Mark Herman', 'S. Craig Zahler',\n",
              "       'Gregory Jacobs', 'Will Gluck', 'Biyi Bandele', 'Sean Ellis',\n",
              "       'Andrey Kravchuk', 'David Schwimmer', 'George Nolfi',\n",
              "       'Tony Gilroy', 'Simon Verhoeven', 'Tony Scott', 'Luc Besson',\n",
              "       'Terence Davies', 'Scott Waugh', 'Steven Brill',\n",
              "       'Alexandros Avranas', 'Ewan McGregor', 'Roman Polanski',\n",
              "       'Nic Mathieu', 'Richard LaGravenese', 'Mora Stephens',\n",
              "       'Jeff Nichols', 'Mike Birbiglia', 'Philippe Falardeau',\n",
              "       'Ken Loach', 'Josh Trank', 'Stephen Fingleton',\n",
              "       'Florian Gallenberger', 'Rob Cohen', 'Joel Edgerton', 'Gary Shore',\n",
              "       'Mike Judge', 'Sylvester Stallone', 'Rich Moore', 'Rupert Sanders',\n",
              "       'Jason Reitman', 'Jodie Foster', 'Nick Cassavetes', 'Kevin Lima',\n",
              "       'Nancy Meyers', 'Jonathan Dayton', 'Ben Younger',\n",
              "       'Craig Gillespie', 'Andrew Jarecki', 'John Stockwell',\n",
              "       'Jake Kasdan', 'Kirk Jones', 'Bennett Miller',\n",
              "       'Mark Steven Johnson', 'Angelina Jolie', 'Tarsem Singh',\n",
              "       'Mark Andrews', 'Niels Arden Oplev', 'Anne Fontaine',\n",
              "       'Michaël R. Roskam', 'Andy Fickman', 'Sean Anders', 'Tom McGrath',\n",
              "       'Jean-François Richet', 'Ken Kwapis', 'Alessandro Carloni',\n",
              "       'Zackary Adler', 'John Butler', 'Debra Granik', 'Seth Gordon',\n",
              "       'Xavier Dolan', 'Maren Ade', 'Andrew Dominik',\n",
              "       'Joseph Gordon-Levitt', 'James Watkins', 'Joey Curtis',\n",
              "       'Paco Cabezas', 'Rick Famuyiwa', 'Måns Mårlind', 'Marcus Nispel',\n",
              "       'Olivier Megaton', 'Len Wiseman', 'Will Slocombe', 'Courtney Hunt',\n",
              "       'Shawn Levy', 'Michael Goi', 'Levan Gabriadze', 'Jon Hurwitz',\n",
              "       'Gabriele Muccino', 'Damián Szifron', 'François Ozon',\n",
              "       'John Wells', 'Jonathan Holbrook', 'Julia Leigh', 'Mark Waters',\n",
              "       'Afonso Poyart', 'Akiva Schaffer', 'Hope Dickson Leach',\n",
              "       'Jonathan Liebesman', 'David Cronenberg', 'Simon Stone',\n",
              "       'David Gordon Green', 'Jon Kasdan', 'Mia Hansen-Løve',\n",
              "       'Alejandro Amenábar', 'Seth MacFarlane', 'Atom Egoyan',\n",
              "       'Daniel Espinosa', 'John Michael McDonagh', 'Frank Darabont',\n",
              "       'Mikael Håfström', 'Ari Sandel', 'Jee-woon Kim', 'Lynne Ramsay',\n",
              "       'Whit Stillman', 'Harald Zwart', 'Lee Unkrich', 'Tomas Alfredson',\n",
              "       'Gauri Shinde', 'Michael Grandage', 'Jamie Linden', 'Mike Cahill',\n",
              "       'Caleb Vetter', 'Michael Haneke', 'Marcus Dunstan',\n",
              "       'John Krasinski', 'David Dobkin', 'Michael Dudok de Wit',\n",
              "       'Jorge R. Gutiérrez', 'Bobby Farrelly', 'Doug Ellin',\n",
              "       'Bryan Bertino', 'Adam Wingard', 'Joe Carnahan', 'Simon Aboud',\n",
              "       'Bruce Beresford', 'Oz Perkins', 'Terrence Malick', 'Ben Falcone',\n",
              "       'Jonathan Jakubowicz', 'Juan José Campanella', 'Max Joseph',\n",
              "       'Chris Stokes', 'Warren Beatty', 'Gregory Hoblit',\n",
              "       'Martin McDonagh', 'McG', 'Filip Renc', 'Ed Gass-Donnelly',\n",
              "       'Jamal Hill', 'Kirsten Sheridan', 'Jake Schreier', 'Walter Hill',\n",
              "       'Dean DeBlois', 'Rob Zombie', 'Tyler Shields', 'Hisonni Johnson',\n",
              "       'Matt Brown', 'Bruce A. Evans', 'Adam Leon', 'Chris Evans',\n",
              "       'Jim Sheridan', 'Stewart Hendler', 'Genndy Tartakovsky',\n",
              "       'Claude Barras', 'Asger Leth', 'Ivan Reitman', 'Werner Herzog',\n",
              "       'Scott Frank', 'Mike Newell', 'James Ponsoldt', 'Amma Asante',\n",
              "       'Mario Van Peebles', 'François Simard', 'Andrés Muschietti',\n",
              "       'John Moore', 'John R. Leonetti', 'Saul Dibb', 'Terry Gilliam',\n",
              "       'Stephen Sommers', 'Antonio Campos', 'Dito Montiel',\n",
              "       'Phil Claydon', 'Farren Blackburn', 'Matthias Hoene',\n",
              "       'Robert Rodriguez', 'Jason Friedberg', 'David Bowers',\n",
              "       'Cary Joji Fukunaga', 'Andy Tennant', 'Larry Charles',\n",
              "       'Erik Van Looy', 'Cristian Mungiu', 'Gee Malik Linton',\n",
              "       'Aisling Walsh', 'Luca Guadagnino', 'Tim Johnson',\n",
              "       'Giuseppe Tornatore', 'Nathan Morlando', 'Robert Luketic',\n",
              "       'Mark Osborne', 'Malcolm D. Lee', 'Alexandre Aja', 'Allen Coulter',\n",
              "       'Naoko Yamada', 'Miguel Arteta', 'Steven Knight', 'James Schamus',\n",
              "       'Kyle Patrick Alvarez', 'Hannes Holm', 'James Wong', 'Dan Bradley',\n",
              "       'Greg Berlanti', 'Juan Carlos Fresnadillo', 'Jonathan Levine',\n",
              "       'Hideaki Anno', 'Wally Pfister', 'Carlos Saldanha',\n",
              "       'Drake Doremus', 'Femi Oyeniran', 'Michael Patrick King',\n",
              "       'Peter Landesman', 'Gary Winick', 'Leslye Headland',\n",
              "       'David R. Ellis', 'Michael Dowse', 'José Padilha', 'James Franco',\n",
              "       'Michael Showalter', 'Matteo Garrone', 'Gabor Csupo',\n",
              "       'James Ward Byrkit', 'Hong-jin Na', 'Simon West', 'Gregory Wilson',\n",
              "       'Chris Weitz', 'Neil Marshall', 'Christopher Landon',\n",
              "       'John Erick Dowdle', 'Lee Daniels', 'Gus Van Sant', 'Chris McCoy',\n",
              "       'Craig Brewer', 'R.J. Cutler', 'Ido Fluk', 'Christopher Smith',\n",
              "       'Anna Biller', 'Tom Six', 'Richie Smyth', 'Ryûhei Kitamura',\n",
              "       'David Slade', 'Greg Kwedar', 'Mark Mylod', 'David Twohy',\n",
              "       'Justin Kelly', 'Justin Tipping', 'Alfonso Gomez-Rejon',\n",
              "       'Alexander Payne', 'Jordan Vogt-Roberts', 'Tom Gormican',\n",
              "       'Scott Stewart', 'Patricia Rozema', 'Justin Chadwick',\n",
              "       'Steven R. Monroe', 'James Lapine', 'Jérôme Salle',\n",
              "       'Micheal Bafaro', 'John Luessenhop', 'Wes Craven', 'Mira Nair',\n",
              "       'Gilles Paquet-Brenner', 'Lisa Addario', 'Pedro Almodóvar',\n",
              "       'Patricia Riggen', 'Julie Taymor', 'Luke Greenfield', 'Boaz Yakin',\n",
              "       'Steven Quale', 'Shana Feste', 'Pascal Laugier', 'Ava DuVernay',\n",
              "       'Patrick Tatopoulos', 'Aamir Khan', 'Nima Nourizadeh', 'Billy Ray',\n",
              "       'Scot Armstrong'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mW_LXrzS-ifl",
        "outputId": "e2659bab-8413-4d73-d37d-969d116ebb46"
      },
      "source": [
        "#and getting the number of unique authors\n",
        "#So, for the 1,000 movies, there are only 644 unique directors\n",
        "len(movies_df.director.unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "644"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUKfHKU27MQV"
      },
      "source": [
        "##By Row\n",
        "\n",
        "For rows, we have two options:\n",
        "\n",
        "    .loc - locates by name\n",
        "    .iloc- locates by numerical index"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6BiEiex7GFL",
        "outputId": "94bbefc8-96f4-4784-c5e3-1b4f6e85984a"
      },
      "source": [
        "prometheus = movies_df.loc['Prometheus']\n",
        "prometheus"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "rank                                                                2\n",
              "genre                                        Adventure,Mystery,Sci-Fi\n",
              "description         Following clues to the origin of mankind, a te...\n",
              "director                                                 Ridley Scott\n",
              "actors              Noomi Rapace, Logan Marshall-Green, Michael Fa...\n",
              "year                                                             2012\n",
              "runtime                                                           124\n",
              "rating                                                              7\n",
              "votes                                                          485820\n",
              "revenue_millions                                               126.46\n",
              "metascore                                                          65\n",
              "Name: Prometheus, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1I1zdYVP7q3k"
      },
      "source": [
        "iloc works on the numerical index "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "-hOa5mgx7pvv",
        "outputId": "007513de-8d09-4a61-eb90-97898b2a7203"
      },
      "source": [
        "movie_subset = movies_df.iloc[1:4]\n",
        "movie_subset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
              "      <th>actors</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>3</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
              "      <td>M. Night Shyamalan</td>\n",
              "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
              "      <td>2016</td>\n",
              "      <td>117</td>\n",
              "      <td>7.3</td>\n",
              "      <td>157606</td>\n",
              "      <td>138.12</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
              "      <td>Christophe Lourdelet</td>\n",
              "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
              "      <td>2016</td>\n",
              "      <td>108</td>\n",
              "      <td>7.2</td>\n",
              "      <td>60545</td>\n",
              "      <td>270.32</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            rank                     genre  ... revenue_millions metascore\n",
              "Title                                       ...                           \n",
              "Prometheus     2  Adventure,Mystery,Sci-Fi  ...           126.46      65.0\n",
              "Split          3           Horror,Thriller  ...           138.12      62.0\n",
              "Sing           4   Animation,Comedy,Family  ...           270.32      59.0\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VVAZO5GU78sL"
      },
      "source": [
        "###Conditional selections\n",
        "\n",
        "We’ve gone over how to select columns and rows, but what if we want to make a conditional selection?\n",
        "\n",
        "For example, what if we want to filter our movies DataFrame to show only films directed by Ridley Scott or films with a rating greater than or equal to 8.0?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DWRcbFKL728i"
      },
      "source": [
        "RidleyScott = movies_df[movies_df['director']=='Ridley Scott']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "sk9kHgSQ8VEn",
        "outputId": "03ea658e-a8ec-46ea-a15d-154572cbc75f"
      },
      "source": [
        "RidleyScott.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
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              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Martian</th>\n",
              "      <td>103</td>\n",
              "      <td>Adventure,Drama,Sci-Fi</td>\n",
              "      <td>An astronaut becomes stranded on Mars after hi...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Matt Damon, Jessica Chastain, Kristen Wiig, Ka...</td>\n",
              "      <td>2015</td>\n",
              "      <td>144</td>\n",
              "      <td>8.0</td>\n",
              "      <td>556097</td>\n",
              "      <td>228.43</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Robin Hood</th>\n",
              "      <td>388</td>\n",
              "      <td>Action,Adventure,Drama</td>\n",
              "      <td>In 12th century England, Robin and his band of...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Russell Crowe, Cate Blanchett, Matthew Macfady...</td>\n",
              "      <td>2010</td>\n",
              "      <td>140</td>\n",
              "      <td>6.7</td>\n",
              "      <td>221117</td>\n",
              "      <td>105.22</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>American Gangster</th>\n",
              "      <td>471</td>\n",
              "      <td>Biography,Crime,Drama</td>\n",
              "      <td>In 1970s America, a detective works to bring d...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Denzel Washington, Russell Crowe, Chiwetel Eji...</td>\n",
              "      <td>2007</td>\n",
              "      <td>157</td>\n",
              "      <td>7.8</td>\n",
              "      <td>337835</td>\n",
              "      <td>130.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Exodus: Gods and Kings</th>\n",
              "      <td>517</td>\n",
              "      <td>Action,Adventure,Drama</td>\n",
              "      <td>The defiant leader Moses rises up against the ...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Christian Bale, Joel Edgerton, Ben Kingsley, S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>150</td>\n",
              "      <td>6.0</td>\n",
              "      <td>137299</td>\n",
              "      <td>65.01</td>\n",
              "      <td>52.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        rank  ... metascore\n",
              "Title                         ...          \n",
              "Prometheus                 2  ...      65.0\n",
              "The Martian              103  ...      80.0\n",
              "Robin Hood               388  ...      53.0\n",
              "American Gangster        471  ...      76.0\n",
              "Exodus: Gods and Kings   517  ...      52.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlVFJv6r8WFA"
      },
      "source": [
        "HighlyRated = movies_df[movies_df['rating']>=8]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "OqhFbVrg8pvP",
        "outputId": "bca54a08-0a1a-4681-bd31-b3c6683f7647"
      },
      "source": [
        "HighlyRated.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
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              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>1</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A group of intergalactic criminals are forced ...</td>\n",
              "      <td>James Gunn</td>\n",
              "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>121</td>\n",
              "      <td>8.1</td>\n",
              "      <td>757074</td>\n",
              "      <td>333.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>La La Land</th>\n",
              "      <td>7</td>\n",
              "      <td>Comedy,Drama,Music</td>\n",
              "      <td>A jazz pianist falls for an aspiring actress i...</td>\n",
              "      <td>Damien Chazelle</td>\n",
              "      <td>Ryan Gosling, Emma Stone, Rosemarie DeWitt, J....</td>\n",
              "      <td>2016</td>\n",
              "      <td>128</td>\n",
              "      <td>8.3</td>\n",
              "      <td>258682</td>\n",
              "      <td>151.06</td>\n",
              "      <td>93.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hacksaw Ridge</th>\n",
              "      <td>17</td>\n",
              "      <td>Biography,Drama,History</td>\n",
              "      <td>WWII American Army Medic Desmond T. Doss, who ...</td>\n",
              "      <td>Mel Gibson</td>\n",
              "      <td>Andrew Garfield, Sam Worthington, Luke Bracey,...</td>\n",
              "      <td>2016</td>\n",
              "      <td>139</td>\n",
              "      <td>8.2</td>\n",
              "      <td>211760</td>\n",
              "      <td>67.12</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lion</th>\n",
              "      <td>19</td>\n",
              "      <td>Biography,Drama</td>\n",
              "      <td>A five-year-old Indian boy gets lost on the st...</td>\n",
              "      <td>Garth Davis</td>\n",
              "      <td>Dev Patel, Nicole Kidman, Rooney Mara, Sunny P...</td>\n",
              "      <td>2016</td>\n",
              "      <td>118</td>\n",
              "      <td>8.1</td>\n",
              "      <td>102061</td>\n",
              "      <td>51.69</td>\n",
              "      <td>69.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Arrival</th>\n",
              "      <td>20</td>\n",
              "      <td>Drama,Mystery,Sci-Fi</td>\n",
              "      <td>When twelve mysterious spacecraft appear aroun...</td>\n",
              "      <td>Denis Villeneuve</td>\n",
              "      <td>Amy Adams, Jeremy Renner, Forest Whitaker,Mich...</td>\n",
              "      <td>2016</td>\n",
              "      <td>116</td>\n",
              "      <td>8.0</td>\n",
              "      <td>340798</td>\n",
              "      <td>100.50</td>\n",
              "      <td>81.0</td>\n",
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              "</div>"
            ],
            "text/plain": [
              "                         rank  ... metascore\n",
              "Title                          ...          \n",
              "Guardians of the Galaxy     1  ...      76.0\n",
              "La La Land                  7  ...      93.0\n",
              "Hacksaw Ridge              17  ...      71.0\n",
              "Lion                       19  ...      69.0\n",
              "Arrival                    20  ...      81.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0nVIpf19CBE"
      },
      "source": [
        "But you could also create more complex conditions. For example, what if we wanted to find the movies that are directed by Ridley Scott or Christopher Nolan.\n",
        "\n",
        "In this case, you will use the sign for the OR function (\"|\") between the two parts of the condition"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "H0P0bs3d8q8M",
        "outputId": "e7eab4b1-8a0a-4e3a-999e-2cc1cd3304cf"
      },
      "source": [
        "movies_df[(movies_df['director'] == 'Christopher Nolan') | (movies_df['director'] == 'Ridley Scott')].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Title</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
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              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Interstellar</th>\n",
              "      <td>37</td>\n",
              "      <td>Adventure,Drama,Sci-Fi</td>\n",
              "      <td>A team of explorers travel through a wormhole ...</td>\n",
              "      <td>Christopher Nolan</td>\n",
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              "      <td>2014</td>\n",
              "      <td>169</td>\n",
              "      <td>8.6</td>\n",
              "      <td>1047747</td>\n",
              "      <td>187.99</td>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Dark Knight</th>\n",
              "      <td>55</td>\n",
              "      <td>Action,Crime,Drama</td>\n",
              "      <td>When the menace known as the Joker wreaks havo...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale, Heath Ledger, Aaron Eckhart,Mi...</td>\n",
              "      <td>2008</td>\n",
              "      <td>152</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1791916</td>\n",
              "      <td>533.32</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Prestige</th>\n",
              "      <td>65</td>\n",
              "      <td>Drama,Mystery,Sci-Fi</td>\n",
              "      <td>Two stage magicians engage in competitive one-...</td>\n",
              "      <td>Christopher Nolan</td>\n",
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              "      <td>2006</td>\n",
              "      <td>130</td>\n",
              "      <td>8.5</td>\n",
              "      <td>913152</td>\n",
              "      <td>53.08</td>\n",
              "      <td>66.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Inception</th>\n",
              "      <td>81</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A thief, who steals corporate secrets through ...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Leonardo DiCaprio, Joseph Gordon-Levitt, Ellen...</td>\n",
              "      <td>2010</td>\n",
              "      <td>148</td>\n",
              "      <td>8.8</td>\n",
              "      <td>1583625</td>\n",
              "      <td>292.57</td>\n",
              "      <td>74.0</td>\n",
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            ],
            "text/plain": [
              "                 rank                     genre  ... revenue_millions metascore\n",
              "Title                                            ...                           \n",
              "Prometheus          2  Adventure,Mystery,Sci-Fi  ...           126.46      65.0\n",
              "Interstellar       37    Adventure,Drama,Sci-Fi  ...           187.99      74.0\n",
              "The Dark Knight    55        Action,Crime,Drama  ...           533.32      82.0\n",
              "The Prestige       65      Drama,Mystery,Sci-Fi  ...            53.08      66.0\n",
              "Inception          81   Action,Adventure,Sci-Fi  ...           292.57      74.0\n",
              "\n",
              "[5 rows x 11 columns]"
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bGNCv0q9qWi"
      },
      "source": [
        "But what if I wanted more than just the two? You can see what that the command can get really long and difficult to code. \n",
        "\n",
        "What if I wanted to add a third director, Yimou Zhang. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "s4pj6hf7921m",
        "outputId": "5dc3bf2a-89a4-43b9-ebdd-6e0229929dac"
      },
      "source": [
        "movies_df[movies_df['director'].isin(['Christopher Nolan', 'Ridley Scott','Yimou Zhang'])].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>7.0</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Great Wall</th>\n",
              "      <td>6</td>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>European mercenaries searching for black powde...</td>\n",
              "      <td>Yimou Zhang</td>\n",
              "      <td>Matt Damon, Tian Jing, Willem Dafoe, Andy Lau</td>\n",
              "      <td>2016</td>\n",
              "      <td>103</td>\n",
              "      <td>6.1</td>\n",
              "      <td>56036</td>\n",
              "      <td>45.13</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Interstellar</th>\n",
              "      <td>37</td>\n",
              "      <td>Adventure,Drama,Sci-Fi</td>\n",
              "      <td>A team of explorers travel through a wormhole ...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Matthew McConaughey, Anne Hathaway, Jessica Ch...</td>\n",
              "      <td>2014</td>\n",
              "      <td>169</td>\n",
              "      <td>8.6</td>\n",
              "      <td>1047747</td>\n",
              "      <td>187.99</td>\n",
              "      <td>74.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Dark Knight</th>\n",
              "      <td>55</td>\n",
              "      <td>Action,Crime,Drama</td>\n",
              "      <td>When the menace known as the Joker wreaks havo...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale, Heath Ledger, Aaron Eckhart,Mi...</td>\n",
              "      <td>2008</td>\n",
              "      <td>152</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1791916</td>\n",
              "      <td>533.32</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Prestige</th>\n",
              "      <td>65</td>\n",
              "      <td>Drama,Mystery,Sci-Fi</td>\n",
              "      <td>Two stage magicians engage in competitive one-...</td>\n",
              "      <td>Christopher Nolan</td>\n",
              "      <td>Christian Bale, Hugh Jackman, Scarlett Johanss...</td>\n",
              "      <td>2006</td>\n",
              "      <td>130</td>\n",
              "      <td>8.5</td>\n",
              "      <td>913152</td>\n",
              "      <td>53.08</td>\n",
              "      <td>66.0</td>\n",
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 rank                     genre  ... revenue_millions metascore\n",
              "Title                                            ...                           \n",
              "Prometheus          2  Adventure,Mystery,Sci-Fi  ...           126.46      65.0\n",
              "The Great Wall      6  Action,Adventure,Fantasy  ...            45.13      42.0\n",
              "Interstellar       37    Adventure,Drama,Sci-Fi  ...           187.99      74.0\n",
              "The Dark Knight    55        Action,Crime,Drama  ...           533.32      82.0\n",
              "The Prestige       65      Drama,Mystery,Sci-Fi  ...            53.08      66.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zj-06NvQ_Cq0"
      },
      "source": [
        "Let's say we want all movies that were released between 2005 and 2010, have a rating above 8.0, but made below the 25th percentile in revenue.\n",
        "\n",
        "Here's how we could do all of that:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0NkGF7BF9md5",
        "outputId": "338db181-0972-4e43-f671-17ed90697cca"
      },
      "source": [
        "movies_df[\n",
        "    ((movies_df['year'] >= 2005) & (movies_df['year'] <= 2010))\n",
        "    & (movies_df['rating'] > 8.0)\n",
        "    & (movies_df['revenue_millions'] < movies_df['revenue_millions'].quantile(0.25))\n",
        "].head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
              "      <th>actors</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3 Idiots</th>\n",
              "      <td>431</td>\n",
              "      <td>Comedy,Drama</td>\n",
              "      <td>Two friends are searching for their long lost ...</td>\n",
              "      <td>Rajkumar Hirani</td>\n",
              "      <td>Aamir Khan, Madhavan, Mona Singh, Sharman Joshi</td>\n",
              "      <td>2009</td>\n",
              "      <td>170</td>\n",
              "      <td>8.4</td>\n",
              "      <td>238789</td>\n",
              "      <td>6.52</td>\n",
              "      <td>67.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>The Lives of Others</th>\n",
              "      <td>477</td>\n",
              "      <td>Drama,Thriller</td>\n",
              "      <td>In 1984 East Berlin, an agent of the secret po...</td>\n",
              "      <td>Florian Henckel von Donnersmarck</td>\n",
              "      <td>Ulrich Mühe, Martina Gedeck,Sebastian Koch, Ul...</td>\n",
              "      <td>2006</td>\n",
              "      <td>137</td>\n",
              "      <td>8.5</td>\n",
              "      <td>278103</td>\n",
              "      <td>11.28</td>\n",
              "      <td>89.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Incendies</th>\n",
              "      <td>714</td>\n",
              "      <td>Drama,Mystery,War</td>\n",
              "      <td>Twins journey to the Middle East to discover t...</td>\n",
              "      <td>Denis Villeneuve</td>\n",
              "      <td>Lubna Azabal, Mélissa Désormeaux-Poulin, Maxim...</td>\n",
              "      <td>2010</td>\n",
              "      <td>131</td>\n",
              "      <td>8.2</td>\n",
              "      <td>92863</td>\n",
              "      <td>6.86</td>\n",
              "      <td>80.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taare Zameen Par</th>\n",
              "      <td>992</td>\n",
              "      <td>Drama,Family,Music</td>\n",
              "      <td>An eight-year-old boy is thought to be a lazy ...</td>\n",
              "      <td>Aamir Khan</td>\n",
              "      <td>Darsheel Safary, Aamir Khan, Tanay Chheda, Sac...</td>\n",
              "      <td>2007</td>\n",
              "      <td>165</td>\n",
              "      <td>8.5</td>\n",
              "      <td>102697</td>\n",
              "      <td>1.20</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     rank               genre  ... revenue_millions metascore\n",
              "Title                                          ...                           \n",
              "3 Idiots              431        Comedy,Drama  ...             6.52      67.0\n",
              "The Lives of Others   477      Drama,Thriller  ...            11.28      89.0\n",
              "Incendies             714   Drama,Mystery,War  ...             6.86      80.0\n",
              "Taare Zameen Par      992  Drama,Family,Music  ...             1.20      42.0\n",
              "\n",
              "[4 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ixr8_8GBcgf"
      },
      "source": [
        "#Apply functions to DataFrames\n",
        "\n",
        "If we want to apply a function to all the data in a DataFrame, we can apply() a function we wrote separately to the DataFrame. \n",
        "\n",
        "For example, we could use a function to convert movies with an 8.0 or greater to a string value of \"good\" and the rest to \"bad\" and use this transformed values to create a new column.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9V7-YfG-BgmC"
      },
      "source": [
        "#setting up the function to change the ratings\n",
        "def rating_function(x):\n",
        "  if x>=8.0:\n",
        "    return \"good\"\n",
        "  else:\n",
        "    return \"bad\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9tJuI_5JCEdI"
      },
      "source": [
        "Now, let's apply the function to the ratings column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vWZpEXkwCBaO"
      },
      "source": [
        "movies_df['rating'] = movies_df['rating'].apply(rating_function)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "dcTrq_MPCSLa",
        "outputId": "4b392d00-f162-4cc1-e6c8-616f87e87747"
      },
      "source": [
        "movies_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>rank</th>\n",
              "      <th>genre</th>\n",
              "      <th>description</th>\n",
              "      <th>director</th>\n",
              "      <th>actors</th>\n",
              "      <th>year</th>\n",
              "      <th>runtime</th>\n",
              "      <th>rating</th>\n",
              "      <th>votes</th>\n",
              "      <th>revenue_millions</th>\n",
              "      <th>metascore</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Guardians of the Galaxy</th>\n",
              "      <td>1</td>\n",
              "      <td>Action,Adventure,Sci-Fi</td>\n",
              "      <td>A group of intergalactic criminals are forced ...</td>\n",
              "      <td>James Gunn</td>\n",
              "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
              "      <td>2014</td>\n",
              "      <td>121</td>\n",
              "      <td>good</td>\n",
              "      <td>757074</td>\n",
              "      <td>333.13</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Prometheus</th>\n",
              "      <td>2</td>\n",
              "      <td>Adventure,Mystery,Sci-Fi</td>\n",
              "      <td>Following clues to the origin of mankind, a te...</td>\n",
              "      <td>Ridley Scott</td>\n",
              "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
              "      <td>2012</td>\n",
              "      <td>124</td>\n",
              "      <td>bad</td>\n",
              "      <td>485820</td>\n",
              "      <td>126.46</td>\n",
              "      <td>65.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Split</th>\n",
              "      <td>3</td>\n",
              "      <td>Horror,Thriller</td>\n",
              "      <td>Three girls are kidnapped by a man with a diag...</td>\n",
              "      <td>M. Night Shyamalan</td>\n",
              "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
              "      <td>2016</td>\n",
              "      <td>117</td>\n",
              "      <td>bad</td>\n",
              "      <td>157606</td>\n",
              "      <td>138.12</td>\n",
              "      <td>62.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sing</th>\n",
              "      <td>4</td>\n",
              "      <td>Animation,Comedy,Family</td>\n",
              "      <td>In a city of humanoid animals, a hustling thea...</td>\n",
              "      <td>Christophe Lourdelet</td>\n",
              "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
              "      <td>2016</td>\n",
              "      <td>108</td>\n",
              "      <td>bad</td>\n",
              "      <td>60545</td>\n",
              "      <td>270.32</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Suicide Squad</th>\n",
              "      <td>5</td>\n",
              "      <td>Action,Adventure,Fantasy</td>\n",
              "      <td>A secret government agency recruits some of th...</td>\n",
              "      <td>David Ayer</td>\n",
              "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
              "      <td>2016</td>\n",
              "      <td>123</td>\n",
              "      <td>bad</td>\n",
              "      <td>393727</td>\n",
              "      <td>325.02</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         rank  ... metascore\n",
              "Title                          ...          \n",
              "Guardians of the Galaxy     1  ...      76.0\n",
              "Prometheus                  2  ...      65.0\n",
              "Split                       3  ...      62.0\n",
              "Sing                        4  ...      59.0\n",
              "Suicide Squad               5  ...      40.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8E4uFgTtCWmB"
      },
      "source": [
        "So, I'll know to watch Guardians of the Galaxy but not Prometheus!! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jUguhwkiDO8y"
      },
      "source": [
        "#Basics of visualizations: Plotting\n",
        "\n",
        "In order to plot the data in DataFrames, we first have to include the requisite python libraries.\n",
        "\n",
        "For categorical variables utilize Bar Charts and Boxplots. Categorical variables are those variables that can take a limited set of possible values. For example, integer ratings (see the Dtype for ratings in the DataFrame movies_df) are categorical varibles.\n",
        "\n",
        "For continuous variables utilize Histograms, Scatterplots, Line graphs, and Boxplots. Continous variables are usually represeted as floats (see the Dtype for revenue in the DataFrame movies_df). "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIhd30VZDSSX"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 20, 'figure.figsize': (10, 8)}) # set font and plot size to be larger"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_tN-6IrjMjOH"
      },
      "source": [
        "Let's plot the relationship between ratings and revenue. All we need to do is call .plot() on movies_df with some info about how to construct the plot:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "5_yVeKZzDieG",
        "outputId": "dbccbb8b-ed80-4ea9-e922-3ea3d2f8a0ec"
      },
      "source": [
        "movies_df.plot(kind='scatter', x='rating', y='revenue_millions', title='Revenue (millions) vs Rating');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hVscqHjOMz2t"
      },
      "source": [
        "Remember that we changed the rating a number to Good/Bad...so, I will read the IMDB database oncre more...I will also change the column names and change the column names to lower case once more"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U2HV0vNZM8_3"
      },
      "source": [
        "movies_df = pd.read_csv(\"IMDB-Movie-Data.csv\", index_col=\"Title\")\n",
        "movies_df.rename(columns={'Runtime (Minutes)':'Runtime','Revenue (Millions)':'Revenue_millions'}, inplace=True)\n",
        "movies_df.columns = [col.lower() for col in movies_df]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "k7IUXQmoMmmq",
        "outputId": "cd7d9f1e-bb11-4efb-925c-1aa6b1f7fd1d"
      },
      "source": [
        "movies_df.plot(kind='scatter', x='rating', y='revenue_millions', title='Revenue (millions) vs Rating');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Cra2TKFNgI2"
      },
      "source": [
        "So, as expected, revenue is positively related to the rating. Remember that we also found a similar realationship betweem reneue and ratings when we tried correlations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SDup6VwENysW"
      },
      "source": [
        "What's with the semicolon? It's not a syntax error, just a way to hide the \n",
        "\n",
        "`<matplotlib.axes._subplots.AxesSubplot at 0x26613b5cc18> `\n",
        "\n",
        "output when plotting in Jupyter notebooks. So, let's try again without the ;"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "cWgwj10NNC2k",
        "outputId": "a5a6de4e-2cb5-4f6e-8eb4-d280fceed0ac"
      },
      "source": [
        "movies_df.plot(kind='scatter', x='rating', y='revenue_millions', title='Revenue (millions) vs Rating')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2be03122b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-0exPw1_OAvG"
      },
      "source": [
        "Now, let's do a histogram of the movie ratings..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "nZ99JK0cN8t0",
        "outputId": "db195ace-cee1-4ea0-8f62-5410207b52d5"
      },
      "source": [
        "movies_df['rating'].plot(kind='hist', title='Rating');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zM2_-mJOMC8"
      },
      "source": [
        "Let's try describe again..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XEfBLWrsOGpb",
        "outputId": "c7f7bab7-8af0-407e-cdf7-cc16d60c7b78"
      },
      "source": [
        "movies_df['rating'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1000.000000\n",
              "mean        6.723200\n",
              "std         0.945429\n",
              "min         1.900000\n",
              "25%         6.200000\n",
              "50%         6.800000\n",
              "75%         7.400000\n",
              "max         9.000000\n",
              "Name: rating, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oy0jxGcPOWzn"
      },
      "source": [
        "And a Boxplot can visualize the data in describe(). \n",
        "\n",
        "![box-plot-explained.gif](data:image/gif;base64,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)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "Y6z7_jl_OjyR",
        "outputId": "98680339-7453-480a-c2b5-a81a7b0b14a9"
      },
      "source": [
        "movies_df['rating'].plot(kind=\"box\");"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YZ6AEFLAoVVUOHDiQ00477ci+Bx54IKtXr3a3c2AsXMIDlpyVK1dmbm7uEfvm5uaycuXKCU0ELCeehQcsShdeeGG2bNmSJNm8eXPm5uayZcuWbN68ecKTAcuBS3jAonXuuefmlltuSWstVZVzzjknN99886THApYIl/CAJWfXrl254447cuutt+bBBx/MrbfemjvuuCO7du2a9GjAMnDcAVVVL66qNvz58XEOBfB4tm/fnp07d2bjxo1ZsWJFNm7cmJ07d2b79u2THg1YBo4roKrqq5P8WpLPjnccgNHcfvvt2bBhwyP2bdiwIbfffvuEJgKWk+6AqqpK8vokn0oy9ziHAyyImZmZ7Nmz5xH79uzZk5mZmQlNBCwnx/MtvJ9K8r1JnjPcApx027Ztywtf+MKsXr06H//4x7N27docOHAgV1999aRHA5aBrjNQVTWT5IokV7fW3rUwIwH0ceNM4GQbOaCq6tQkb0ry8SRbF2wigBFs3749N9xwQ+688848/PDDufPOO3PDDTdYRA6cFD2X8H4+yTOTbGitfW7UP6qqi5JclCRr167tmw7gGCwiByZppDNQVfVtGZx1urK19p6eD2itXddaW99aW79mzZrjmRHgi1hEDkzS4wbU8NLdG5N8OMnPLfhEACPYtm1bNm3alN27d+fQoUPZvXt3Nm3alG3btk16NGAZeNxHuVTVk5N8esT3u7q19opjvehRLsA4nX322dm3b9+R388666x88IMfnOBEwFJyoo9y+XySncf4ef/wmD3D37su7wEcr3PPPTf79u3LxRdfnHvvvTcXX3xx9u3bl3PPPXfSowHLwOMuIh8uGJ/3US1V9aoMFpa/obX2uvGOBnBst9xySy6++OL8+q//epIc2c7Nub8vsPA8TBhYlFprufzyyx+x7/LLL3dPKOCkEFDAolRVueyyyx6x77LLLsvgaVMAC+uEAqq19qrWWrl8B5xs55xzTq699tq87GUvy3333ZeXvexlufbaa3POOedMejRgGXjcb+GNk2/hAeN07rnn5pZbbklrLVWVc845JzfffPOkxwKWiMf6Ft7xPEwYYCqIJWBSrIECAOgkoAAAOgkoAIBOAgpYtGZnZ7Nq1apUVVatWpXZ2dlJjwQsEwIKWJRmZ2czNzeXHTt25MCBA9mxY0fm5uZEFHBSuI0BsCitWrUqO3bsyCWXXHJk31VXXZWtW7fm4MGDE5wMWCoe6zYGAgpYlKoqBw4cyGmnnXZk3wMPPJDVq1d7nAswFo8VUC7hAYvSypUrv+jBwXNzc1m5cuWEJgKWEzfSBBalCy+8MFu2bEmSbN68OXNzc9myZUs2b9484cmA5UBAAYvSNddckyTZunVrLr300qxcuTKbN28+sh9gIVkDBQAwD2ugAADGSEABAHQSUAAAnQQUsGh5lAswKQIKWJQ8ygWYJN/CAxYlj3IBFppHuQBLjke5AAvNbQyAJcejXIBJcidyYFHyKBdgkgQUsCh5lAswSdZAAQDMwxooAIAxElAAAJ0EFABAJwEFANBJQAEAdBJQAACdBBQAQCcBBQDQSUABAHQSUMCitWvXrqxbty6nnHJK1q1bl127dk16JGCZ8Cw8YFHatWtXtm3blp07d2bDhg3Zs2dPNm3alCR50YteNOHpgKXOs/CARWndunW55pprsnHjxiP7du/endnZ2dx2220TnAxYKh7rWXgCCliUTjnllBw8eDArVqw4su/QoUNZtWpVHnrooQlOBiwVHiYMLDkzMzPZs2fPI/bt2bMnMzMzE5oIWE4EFLAobdu2LZs2bcru3btz6NCh7N69O5s2bcq2bdsmPRqwDFhEDixKhxeKz87O5vbbb8/MzEy2b99uATlwUlgDBQAwj7Gsgaqq11TVrVX1d1X1uaq6p6reX1W/UFWnj29cAIDp1rMG6r8kWZ3kliRXJ/ntJF9I8qokH6yqrx77dAAAU6hnDdSXt9YOPnpnVW1PsjXJZUleNq7BAACm1chnoOaLp6H/Ndx+44mPAwAw/cZxG4PnDbcfHMN7AQBMve7bGFTVTyf5siRPSrI+yYYM4umK8Y4GADCdjuc+UD+d5GlH/f72JC9tre2f7+CquijJRUmydu3a4/g4AIDp0n0Jr7X29NZaJXl6kh9K8nVJ3l9V33qM469rra1vra1fs2bNiU0LADAFjnsNVGvtk621tyT5gSSnJ3nj2KYCAJhiJ7yIvLX2sSQfSvItVXXGiY8EADDdxvUw4a8cbh8a0/sBAEytkQKqqr6pqp40z/4vGd5I8yuSvLu19ulxDwgAMG1G/RbeDya5vKr2JLkzyacy+Cbev85gEfldSS5ckAkBAKbMqAH1ziTfkME9n56Z5MlJDiT5cJI3JfnV1to9CzIhAMCUGSmgWmu3JXn5As8CALAojGsROQDAsiGgAAA6CSgAgE4CCgCgk4ACFq3Z2dmsWrUqVZVVq1ZldnZ20iMBy4SAAhal2dnZzM3NZceOHTlw4EB27NiRubk5EQWcFNVaO2kftn79+rZ3796T9nnA0rVq1ars2LEjl1xyyZF9V111VbZu3ZqDBw9OcDJgqaiq97XW1s/7moACFqOqyoEDB3Laaacd2ffAAw9k9erVOZn/XgOWrscKKJfwgEVp5cqVmZube8S+ubm5rFy5ckITAcvJqI9yAZgqF154YbZs2ZIk2bx5c+bm5rJly5Zs3rx5wpMBy4GAAhala665JkmydevWXHrppVm5cmU2b958ZD/AQrIGCgBgHtZAAQCMkYACAOgkoAAAOgkoAIBOAgoAoJOAAgDoJKAAADoJKACATgIKAKCTgAIA6CSgAAA6CSgAgE4CCgCgk4ACAOgkoAAAOgkoAIBOAgoAoJOAAgDoJKAAADoJKACATgIKAKCTgAIA6CSgAAA6CSgAgE4CCgCgk4ACAOgkoAAAOgkoAIBOIwVUVZ1eVT9eVW+pqr+tqs9V1X1VtaeqNlWVEAMAlo1TRzzuBUmuTfKPSXYn+XiSpyX5oSSvS/LcqnpBa60tyJQAAFNk1ID6cJLzk7y1tfbw4Z1VtTXJe5P8cAYx9btjnxAAYMqMdOmttfbHrbU/ODqehvvvSjI3/PU5Y54NAGAqjWPt0qHh9gtjeC8AgKl3QgFVVacm+U/DX99+4uMAAEy/Ez0DdUWSdUne1lq7eb4DquqiqtpbVXv3799/gh8HADB5xx1QVfVTSS5N8n+TvORYx7XWrmutrW+trV+zZs3xfhwAwNQ4roCqqpcnuTrJh5JsbK3dM9apAACmWHdAVdUrklyT5LYM4umusU8FMIJdu3Zl3bp1OeWUU7Ju3brs2rVr0iMBy8So94FKklTVlgzWPX0gyTmttbsXZCqAx7Fr165s27YtO3fuzIYNG7Jnz55s2rQpSfKiF71owtMBS93IZ6Cq6ucyiKf3Jfk+8QRM0vbt27Nz585s3LgxK1asyMaNG7Nz585s37590qMBy0CN8vSVqvrRJNcneSiDy3f3zXPYR1tr1z/W+6xfv77t3bu3f0qARznllFNy8ODBrFix4si+Q4cOZdWqVXnooYcmOBmwVFTV+1pr6+d7bdRLeF873J6S5BXHOOZPM4gsgAU3MzOTPXv2ZOPGjUf27dmzJzMzMxOcClguRn2Uy6taa/U4P89Z4FkBjti2bVs2bdqU3bt359ChQ9m9e3c2bdqUbdu2TXo0YBnoWkQOMC0OLxSfnZ3N7bffnpmZmWzfvt0CcuCkGGkN1LhYAwUALBaPtQZqHA8TBgBYVgQUAEAnAQUA0ElAAQB0ElAAAJ0EFABAJwEFANBJQAEAdBJQAACdBBQAQCcBBQDQSUABAHQSUAAAnQQUAEAnAQUA0Klaayfvw6r2J/nYSftAYLk4I8ndkx4CWHK+prW2Zr4XTmpAASyEqtrbWls/6TmA5cMlPACATgIKAKCTgAKWgusmPQCwvFgDBQDQyRkoAIBOAgpY9KrqVVXVquo5k54FWB4EFDD1quqlw0B66aRnAUgEFLA0/FqSmSTvnfQgwPJw6qQHADhRrbW7407kwEnkDBQwdlV15vCS2/VV9U1VdUNV/VNVPVxVz6mqZ1XV1VX1V1V1T1UdrKo7qurKqnrKo97rT5K8fvjr64fve/jnzOEx866BGu77k6o6o6quq6p/rKrPV9VfV9WPHWP2lcP3+8jw2Dur6peG+9twHmCZcwYKWEhfn+Qvknw4yW8neUKS+5NclOT5Sf40yTsz+I+5ZyW5JMlzq+rbWmufGb7H9UnuTXJBkhuTfOCo9793hBmenOTPkzyY5M1JViZ5QZLfrKqHW2tvOHxgVVWS301yXpI7Mrg0uCLJS5N8S9f/cmBJE1DAQtqQ5PLW2tajd1bV5Ul+srX20KP2b0ryuiQvS/KaJGmtXT/omlyQ5Pdba9d3zvCMJDuT/MThz6uq1yb5YJItSd5w1LEvziCe/izJ97fWHhwe//NJ/nfn5wJLmEt4wEL6ZJJXP3pna+1jj46nod/M4AzVuWOc4YEklxz9ea21D2VwVmqmqr7sqGN/dLj9r4fjaXj8vUl+cYwzAYucgAIW0l+11j7/6J1VtaKqXl5Ve4ZroB6qqpbk4SRfnuSrxjjDHa21++fZ/3fD7dFrrp45nOHd8xy/Z4wzAYucS3jAQrrrGPtvyGAN1EcyWNd0V5LDofWKDNYpjcux1kl9Ybg95ah9T0pyT2vtC/Mc/8kxzgQscgIKWEhf9LDNqlqfQTy9M8lzj46VqvqSJD978sb7IvcneWpVnTpPRD1tEgMB08klPOBk+4bh9qZ5IuXZGXxT79EOr186ZZ7Xxun9Gfx78TvneW3DAn82sIgIKOBk++hw+5yjd1bVVyT5H8f4m08Nt2sXZqQj3jjc/lJVfenhnVX1pCQ/t8CfDSwiLuEBJ9v/yeAbcD9UVe/OYHH205I8N8nfJPmHef7mPRl8m+4VVXV6/v/aqmtaa/eNcbY3JvmRJP8myW1VdVMG94H64eHc35zBInNgmXMGCjiphrcTOD/JtUm+MslPZXB57HUZ3L7g0Dx/8+kMIuZDGdzU8heHP0959LEnOFvLYH3WL2YQTrMZ3H/qDUlePjxsvm/0ActMDf59AcBjqapzkrwjyRWttcsmPQ8wWc5AARylqr5ynn2nJ7li+OtbTu5EwDSyBgrgka6qqmdkcDPN/Un+eQbrs56a5Ddaa++d5HDAdBBQAI/0exksan9eBg8iPpjkrzN4nt7OCc4FTBFroAAAOlkDBQDQSUABAHQSUAAAnQQUAEAnAQUA0ElAAQB0+n+r3rgvFxS3GQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0g8rFcPQgqL"
      },
      "source": [
        "By **combining categorical and continuous data**, we can create a Boxplot of revenue that is grouped by the Rating Category we created above. So, we  can **compare the revenue of good and bad movies**. First, let's do the the apply rating_function againg to label\n",
        " good and bad movies. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1w_MyjZOoJN"
      },
      "source": [
        "movies_df['rating'] = movies_df['rating'].apply(rating_function)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "IT9bV-TlQd18",
        "outputId": "af322192-5257-412d-aa50-17470f6f43ed"
      },
      "source": [
        "movies_df.boxplot(column='revenue_millions', by='rating');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  return array(a, dtype, copy=False, order=order)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}