Module 1_Introduction to Data Analysis in python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1- Module 1 Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "In this section, you will learn how to approach data acquisition in various ways, and obtain necessary insights from a dataset. By the end of this lab, you will successfully load the data into Jupyter Notebook, and gain some fundamental insights via Pandas Library.\n",
    "As pandas is a great tool and very efficient for implementation rather than python and we can create queries like sql quickly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "First we need to read the data, our dataset is in csv format but there is no problrm we can also use the methods  for json or xlsx formats too."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-1 Read Data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use \"pandas.read_csv()\" function to read the csv file. In the bracket, we put the file path along\n",
    "with a quotation mark, so that pandas will read the file into a data frame from that address. The\n",
    "file path can be either an URL or your local file address. Because the data does not include headers,\n",
    "we can add an argument \" headers = None\" inside the \"read_csv()\" method, so that pandas will\n",
    "not automatically set the first row as a header. You can also assign the dataset to any variable you\n",
    "create."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file_name = 'imports-85_csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(file_name, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reading data from and store it into  a data frame which we call it df here we can show the top 5 rows using df.head(). Also it is possible to send i parameter as input to df.head(n) to get first n rows.\n",
    "There is also possible to get buttom rows using df.tail(n)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
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   "outputs": [
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       "      <th></th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
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       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>4 rows × 26 columns</p>\n",
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      "text/plain": [
       "   0    1            2    3    4     5            6    7      8     9   ...    \\\n",
       "0   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...     \n",
       "1   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.6  ...     \n",
       "2   1    ?  alfa-romero  gas  std   two    hatchback  rwd  front  94.5  ...     \n",
       "3   2  164         audi  gas  std  four        sedan  fwd  front  99.8  ...     \n",
       "\n",
       "    16    17    18    19    20   21    22  23  24     25  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  13495  \n",
       "1  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "2  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "3  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "\n",
       "[4 rows x 26 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "df.head(4)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-2 Adding Headers:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "when we look at dataset it is not very clear to understand without any lable as columns so we are going to create a list of column as  a python list and name it column_list and pass it to data frame. Then we use dataframe.columns = column_list to replace the headers by the list we created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['symboling',\n",
       " 'normalized-losses',\n",
       " 'make',\n",
       " 'fuel-type',\n",
       " 'aspiration',\n",
       " 'num-of-doors',\n",
       " 'body-style',\n",
       " 'drive-wheels',\n",
       " 'engine-location',\n",
       " 'wheel-base',\n",
       " 'length',\n",
       " 'width',\n",
       " 'height',\n",
       " 'curb-weight',\n",
       " 'engine-type',\n",
       " 'num-of-cylinders',\n",
       " 'engine-size',\n",
       " 'fuel-system',\n",
       " 'bore',\n",
       " 'stroke',\n",
       " 'compression-ratio',\n",
       " 'horsepower',\n",
       " 'peak-rpm',\n",
       " 'city-mpg',\n",
       " 'highway-mpg',\n",
       " 'price']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_list= [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]\n",
    "column_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = column_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "head() method give use first 5 elements of dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
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       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
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       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
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       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
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       "      <td>13950</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 26 columns</p>\n",
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      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...    engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...            130   \n",
       "1  convertible          rwd           front        88.6  ...            130   \n",
       "2    hatchback          rwd           front        94.5  ...            152   \n",
       "3        sedan          fwd           front        99.8  ...            109   \n",
       "4        sedan          4wd           front        99.4  ...            136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
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