{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Pandas\n",
    "\n",
    "**[Pandas](https://pandas.pydata.org/)** is an open-source, BSD-licensed Python library. Pandas is a handy and useful data-structure tool for analyzing large and complex data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial we will practice: \n",
    "\n",
    "* DataFrame, \n",
    "* Data Selection, \n",
    "* Group-By, \n",
    "* Series, \n",
    "* Sorting,  \n",
    "* Searching,  \n",
    "* statistics. \n",
    "\n",
    "Let us practice Data analysis using Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, we are using **[automobile_data.csv](/content/tools/pandas/automobile_data.csv)** for data analysis. This Dataset has different characteristics of an auto such as body-style, wheel-base, engine-type, price, mileage, horsepower, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What included in this Pandas exercise?**\n",
    "\n",
    "It contains 10 questions. The solution is provided for each question.\n",
    "Each question includes a specific Pandas topic you need to learn.\n",
    "When you complete each question, you get more familiar with data analysis using pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1: From the given dataset print the first and last five rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:32:35.366922Z",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>company</th>\n",
       "      <th>body-style</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>average-mileage</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>convertible</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>convertible</td>\n",
       "      <td>88.6</td>\n",
       "      <td>168.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>four</td>\n",
       "      <td>111</td>\n",
       "      <td>21</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>94.5</td>\n",
       "      <td>171.2</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>six</td>\n",
       "      <td>154</td>\n",
       "      <td>19</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>audi</td>\n",
       "      <td>sedan</td>\n",
       "      <td>99.8</td>\n",
       "      <td>176.6</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>audi</td>\n",
       "      <td>sedan</td>\n",
       "      <td>99.4</td>\n",
       "      <td>176.6</td>\n",
       "      <td>ohc</td>\n",
       "      <td>five</td>\n",
       "      <td>115</td>\n",
       "      <td>18</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      company   body-style  wheel-base  length engine-type  \\\n",
       "0      0  alfa-romero  convertible        88.6   168.8        dohc   \n",
       "1      1  alfa-romero  convertible        88.6   168.8        dohc   \n",
       "2      2  alfa-romero    hatchback        94.5   171.2        ohcv   \n",
       "3      3         audi        sedan        99.8   176.6         ohc   \n",
       "4      4         audi        sedan        99.4   176.6         ohc   \n",
       "\n",
       "  num-of-cylinders  horsepower  average-mileage    price  \n",
       "0             four         111               21  13495.0  \n",
       "1             four         111               21  16500.0  \n",
       "2              six         154               19  16500.0  \n",
       "3             four         102               24  13950.0  \n",
       "4             five         115               18  17450.0  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#df = pd.read_csv(\"C:\\\\Users\\\\Milaan\\\\01_Learn_Python4Data\\\\10_Python_Pandas_Module\\\\automobile_data.csv\")\n",
    "df = pd.read_csv(\"automobile_data.csv\")  # just give the name of file only if the file is in the same folder.\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2021-06-17T11:32:37.510453Z"
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    "scrolled": true
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>company</th>\n",
       "      <th>body-style</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>average-mileage</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>81</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>sedan</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>85</td>\n",
       "      <td>27</td>\n",
       "      <td>7975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>82</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>sedan</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>52</td>\n",
       "      <td>37</td>\n",
       "      <td>7995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>86</td>\n",
       "      <td>volkswagen</td>\n",
       "      <td>sedan</td>\n",
       "      <td>97.3</td>\n",
       "      <td>171.7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>100</td>\n",
       "      <td>26</td>\n",
       "      <td>9995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>87</td>\n",
       "      <td>volvo</td>\n",
       "      <td>sedan</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>114</td>\n",
       "      <td>23</td>\n",
       "      <td>12940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>88</td>\n",
       "      <td>volvo</td>\n",
       "      <td>wagon</td>\n",
       "      <td>104.3</td>\n",
       "      <td>188.8</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>114</td>\n",
       "      <td>23</td>\n",
       "      <td>13415.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index     company body-style  wheel-base  length engine-type  \\\n",
       "56     81  volkswagen      sedan        97.3   171.7         ohc   \n",
       "57     82  volkswagen      sedan        97.3   171.7         ohc   \n",
       "58     86  volkswagen      sedan        97.3   171.7         ohc   \n",
       "59     87       volvo      sedan       104.3   188.8         ohc   \n",
       "60     88       volvo      wagon       104.3   188.8         ohc   \n",
       "\n",
       "   num-of-cylinders  horsepower  average-mileage    price  \n",
       "56             four          85               27   7975.0  \n",
       "57             four          52               37   7995.0  \n",
       "58             four         100               26   9995.0  \n",
       "59             four         114               23  12940.0  \n",
       "60             four         114               23  13415.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"automobile_data.csv\")\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2: Clean the dataset and update the CSV file\n",
    "\n",
    "**Hint:** Replace all column values which contain **`?`**, **`n.a`**, or **`NaN`**."
   ]
  },
  {
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   "execution_count": 3,
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    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    index      company   body-style  wheel-base  length engine-type  \\\n",
      "0       0  alfa-romero  convertible        88.6   168.8        dohc   \n",
      "1       1  alfa-romero  convertible        88.6   168.8        dohc   \n",
      "2       2  alfa-romero    hatchback        94.5   171.2        ohcv   \n",
      "3       3         audi        sedan        99.8   176.6         ohc   \n",
      "4       4         audi        sedan        99.4   176.6         ohc   \n",
      "..    ...          ...          ...         ...     ...         ...   \n",
      "56     81   volkswagen        sedan        97.3   171.7         ohc   \n",
      "57     82   volkswagen        sedan        97.3   171.7         ohc   \n",
      "58     86   volkswagen        sedan        97.3   171.7         ohc   \n",
      "59     87        volvo        sedan       104.3   188.8         ohc   \n",
      "60     88        volvo        wagon       104.3   188.8         ohc   \n",
      "\n",
      "   num-of-cylinders  horsepower  average-mileage    price  \n",
      "0              four         111               21  13495.0  \n",
      "1              four         111               21  16500.0  \n",
      "2               six         154               19  16500.0  \n",
      "3              four         102               24  13950.0  \n",
      "4              five         115               18  17450.0  \n",
      "..              ...         ...              ...      ...  \n",
      "56             four          85               27   7975.0  \n",
      "57             four          52               37   7995.0  \n",
      "58             four         100               26   9995.0  \n",
      "59             four         114               23  12940.0  \n",
      "60             four         114               23  13415.0  \n",
      "\n",
      "[61 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "#df = pd.read_csv(\"C:\\\\Users\\\\Milaan\\\\01_Learn_Python4Data\\\\10_Python_Pandas_Module\\\\automobile_data.csv\", na_values={\n",
    "df = pd.read_csv(\"automobile_data.csv\", na_values={\n",
    "'price':[\"?\",\"n.a\"],\n",
    "'stroke':[\"?\",\"n.a\"],\n",
    "'horsepower':[\"?\",\"n.a\"],\n",
    "'peak-rpm':[\"?\",\"n.a\"],\n",
    "'average-mileage':[\"?\",\"n.a\"]})\n",
    "print (df)\n",
    "\n",
    "\n",
    "#df.to_csv(\"C:\\\\Users\\\\Milaan\\\\01_Learn_Python4Data\\\\10_Python_Pandas_Module\\\\automobile_data1.csv\")\n",
    "df.to_csv(\"automobile_data1.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3: Find the most expensive car company name\n",
    "\n",
    "Print most expensive car’s company name and price."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:32:42.119287Z",
     "start_time": "2021-06-17T11:32:42.077297Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>company</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          company    price\n",
       "35  mercedes-benz  45400.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"automobile_data1.csv\")\n",
    "df = df [['company','price']][df.price==df['price'].max()]\n",
    "df\n",
    "\n",
    "#or\n",
    "\n",
    "#df=df.groupby([\"company\"]).sum().sort_values(\"price\", ascending=False)\n",
    "#df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4: Print All Toyota Cars details"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:32:43.901490Z",
     "start_time": "2021-06-17T11:32:43.813114Z"
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       "      <td>6338.0</td>\n",
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       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>51</td>\n",
       "      <td>69</td>\n",
       "      <td>toyota</td>\n",
       "      <td>wagon</td>\n",
       "      <td>95.7</td>\n",
       "      <td>169.7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>62</td>\n",
       "      <td>31</td>\n",
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       "      <td>95.7</td>\n",
       "      <td>169.7</td>\n",
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       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>71</td>\n",
       "      <td>toyota</td>\n",
       "      <td>wagon</td>\n",
       "      <td>95.7</td>\n",
       "      <td>169.7</td>\n",
       "      <td>ohc</td>\n",
       "      <td>four</td>\n",
       "      <td>62</td>\n",
       "      <td>27</td>\n",
       "      <td>8778.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>79</td>\n",
       "      <td>toyota</td>\n",
       "      <td>wagon</td>\n",
       "      <td>104.5</td>\n",
       "      <td>187.8</td>\n",
       "      <td>dohc</td>\n",
       "      <td>six</td>\n",
       "      <td>156</td>\n",
       "      <td>19</td>\n",
       "      <td>15750.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  index company body-style  wheel-base  length engine-type  \\\n",
       "48          48     66  toyota  hatchback        95.7   158.7         ohc   \n",
       "49          49     67  toyota  hatchback        95.7   158.7         ohc   \n",
       "50          50     68  toyota  hatchback        95.7   158.7         ohc   \n",
       "51          51     69  toyota      wagon        95.7   169.7         ohc   \n",
       "52          52     70  toyota      wagon        95.7   169.7         ohc   \n",
       "53          53     71  toyota      wagon        95.7   169.7         ohc   \n",
       "54          54     79  toyota      wagon       104.5   187.8        dohc   \n",
       "\n",
       "   num-of-cylinders  horsepower  average-mileage    price  \n",
       "48             four          62               35   5348.0  \n",
       "49             four          62               31   6338.0  \n",
       "50             four          62               31   6488.0  \n",
       "51             four          62               31   6918.0  \n",
       "52             four          62               27   7898.0  \n",
       "53             four          62               27   8778.0  \n",
       "54              six         156               19  15750.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"automobile_data1.csv\")\n",
    "car_Manufacturers = df.groupby('company')\n",
    "toyotaDf = car_Manufacturers.get_group('toyota')\n",
    "toyotaDf\n",
    "\n",
    "#or\n",
    "\n",
    "#df=pd.DataFrame(pd.read_csv(r'C:\\\\Users\\\\Milaan\\\\01_Learn_Python4Data\\\\10_Python_Pandas_Module\\\\automobile_data1.csv'))\n",
    "#df[(df.company == 'toyota')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5: Count total cars per company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:32:55.486312Z",
     "start_time": "2021-06-17T11:32:55.462878Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toyota           7\n",
       "bmw              6\n",
       "nissan           5\n",
       "mazda            5\n",
       "volkswagen       4\n",
       "audi             4\n",
       "mitsubishi       4\n",
       "mercedes-benz    4\n",
       "isuzu            3\n",
       "alfa-romero      3\n",
       "honda            3\n",
       "jaguar           3\n",
       "chevrolet        3\n",
       "porsche          3\n",
       "volvo            2\n",
       "dodge            2\n",
       "Name: company, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"automobile_data1.csv\")\n",
    "df['company'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 6: Find each company’s Higesht price car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:32:58.961855Z",
     "start_time": "2021-06-17T11:32:58.854438Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-c8f749f7a9ca>:5: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  priceDf = car_Manufacturers['company','price'].max()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\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>company</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alfa-romero</th>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>audi</td>\n",
       "      <td>18920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>bmw</td>\n",
       "      <td>41315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet</th>\n",
       "      <td>chevrolet</td>\n",
       "      <td>6575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>dodge</td>\n",
       "      <td>6377.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda</th>\n",
       "      <td>honda</td>\n",
       "      <td>12945.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>isuzu</td>\n",
       "      <td>6785.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>jaguar</td>\n",
       "      <td>36000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>mazda</td>\n",
       "      <td>18344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>mitsubishi</td>\n",
       "      <td>8189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>nissan</td>\n",
       "      <td>13499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>porsche</td>\n",
       "      <td>37028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota</th>\n",
       "      <td>toyota</td>\n",
       "      <td>15750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>9995.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>volvo</td>\n",
       "      <td>13415.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     company    price\n",
       "company                              \n",
       "alfa-romero      alfa-romero  16500.0\n",
       "audi                    audi  18920.0\n",
       "bmw                      bmw  41315.0\n",
       "chevrolet          chevrolet   6575.0\n",
       "dodge                  dodge   6377.0\n",
       "honda                  honda  12945.0\n",
       "isuzu                  isuzu   6785.0\n",
       "jaguar                jaguar  36000.0\n",
       "mazda                  mazda  18344.0\n",
       "mercedes-benz  mercedes-benz  45400.0\n",
       "mitsubishi        mitsubishi   8189.0\n",
       "nissan                nissan  13499.0\n",
       "porsche              porsche  37028.0\n",
       "toyota                toyota  15750.0\n",
       "volkswagen        volkswagen   9995.0\n",
       "volvo                  volvo  13415.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"automobile_data1.csv\")\n",
    "car_Manufacturers = df.groupby('company')\n",
    "priceDf = car_Manufacturers['company','price'].max()\n",
    "priceDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 7: Find the average mileage of each car making company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:33:09.006171Z",
     "start_time": "2021-06-17T11:33:08.975899Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-c02acf54079b>:5: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  mileageDf = car_Manufacturers['company','average-mileage'].mean()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>average-mileage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alfa-romero</th>\n",
       "      <td>20.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audi</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmw</th>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chevrolet</th>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dodge</th>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>honda</th>\n",
       "      <td>26.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>isuzu</th>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jaguar</th>\n",
       "      <td>14.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mazda</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mercedes-benz</th>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mitsubishi</th>\n",
       "      <td>29.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nissan</th>\n",
       "      <td>31.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>porsche</th>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toyota</th>\n",
       "      <td>28.714286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volkswagen</th>\n",
       "      <td>31.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volvo</th>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               average-mileage\n",
       "company                       \n",
       "alfa-romero          20.333333\n",
       "audi                 20.000000\n",
       "bmw                  19.000000\n",
       "chevrolet            41.000000\n",
       "dodge                31.000000\n",
       "honda                26.333333\n",
       "isuzu                33.333333\n",
       "jaguar               14.333333\n",
       "mazda                28.000000\n",
       "mercedes-benz        18.000000\n",
       "mitsubishi           29.500000\n",
       "nissan               31.400000\n",
       "porsche              17.000000\n",
       "toyota               28.714286\n",
       "volkswagen           31.750000\n",
       "volvo                23.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"automobile_data1.csv\")\n",
    "car_Manufacturers = df.groupby('company')\n",
    "mileageDf = car_Manufacturers['company','average-mileage'].mean()\n",
    "mileageDf\n",
    "\n",
    "\n",
    "#or\n",
    "\n",
    "#company_grp=df.groupby('company')\n",
    "#company_grp.agg({\n",
    "#    'average-mileage':np.mean\n",
    "#})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 8: Sort all cars by Price column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:33:16.908908Z",
     "start_time": "2021-06-17T11:33:16.822974Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>index</th>\n",
       "      <th>company</th>\n",
       "      <th>body-style</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>length</th>\n",
       "      <th>engine-type</th>\n",
       "      <th>num-of-cylinders</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>average-mileage</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>hardtop</td>\n",
       "      <td>112.0</td>\n",
       "      <td>199.2</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>184</td>\n",
       "      <td>14</td>\n",
       "      <td>45400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>bmw</td>\n",
       "      <td>sedan</td>\n",
       "      <td>103.5</td>\n",
       "      <td>193.8</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>182</td>\n",
       "      <td>16</td>\n",
       "      <td>41315.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>46</td>\n",
       "      <td>mercedes-benz</td>\n",
       "      <td>sedan</td>\n",
       "      <td>120.9</td>\n",
       "      <td>208.1</td>\n",
       "      <td>ohcv</td>\n",
       "      <td>eight</td>\n",
       "      <td>184</td>\n",
       "      <td>14</td>\n",
       "      <td>40960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>46</td>\n",
       "      <td>62</td>\n",
       "      <td>porsche</td>\n",
       "      <td>convertible</td>\n",
       "      <td>89.5</td>\n",
       "      <td>168.9</td>\n",
       "      <td>ohcf</td>\n",
       "      <td>six</td>\n",
       "      <td>207</td>\n",
       "      <td>17</td>\n",
       "      <td>37028.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>bmw</td>\n",
       "      <td>sedan</td>\n",
       "      <td>110.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>ohc</td>\n",
       "      <td>six</td>\n",
       "      <td>182</td>\n",
       "      <td>15</td>\n",
       "      <td>36880.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  index        company   body-style  wheel-base  length  \\\n",
       "35          35     47  mercedes-benz      hardtop       112.0   199.2   \n",
       "11          11     14            bmw        sedan       103.5   193.8   \n",
       "34          34     46  mercedes-benz        sedan       120.9   208.1   \n",
       "46          46     62        porsche  convertible        89.5   168.9   \n",
       "12          12     15            bmw        sedan       110.0   197.0   \n",
       "\n",
       "   engine-type num-of-cylinders  horsepower  average-mileage    price  \n",
       "35        ohcv            eight         184               14  45400.0  \n",
       "11         ohc              six         182               16  41315.0  \n",
       "34        ohcv            eight         184               14  40960.0  \n",
       "46        ohcf              six         207               17  37028.0  \n",
       "12         ohc              six         182               15  36880.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "carsDf = pd.read_csv(\"automobile_data1.csv\")\n",
    "carsDf = carsDf.sort_values(by=['price', 'horsepower'], ascending=False)\n",
    "carsDf.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 9: Concatenate two data frames using the following conditions\n",
    "\n",
    "**Hint:** Create two data frames using the following two **[dictionary](http://localhost:8888/notebooks/01_Learn_Python4Data/02_Python_Datatypes/005_Python_Dictionary.ipynb)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:33:18.682321Z",
     "start_time": "2021-06-17T11:33:18.647168Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Germany</th>\n",
       "      <th>0</th>\n",
       "      <td>Ford</td>\n",
       "      <td>23845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mercedes</td>\n",
       "      <td>171995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMV</td>\n",
       "      <td>135925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>71400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">Japan</th>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>29995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>23600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nissan</td>\n",
       "      <td>61500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mitsubishi</td>\n",
       "      <td>58900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Company   Price\n",
       "Germany 0         Ford   23845\n",
       "        1     Mercedes  171995\n",
       "        2          BMV  135925\n",
       "        3         Audi   71400\n",
       "Japan   0       Toyota   29995\n",
       "        1        Honda   23600\n",
       "        2       Nissan   61500\n",
       "        3  Mitsubishi    58900"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "GermanCars = {'Company': ['Ford', 'Mercedes', 'BMV', 'Audi'], 'Price': [23845, 171995, 135925 , 71400]}\n",
    "carsDf1 = pd.DataFrame.from_dict(GermanCars)\n",
    "\n",
    "japaneseCars = {'Company': ['Toyota', 'Honda', 'Nissan', 'Mitsubishi '], 'Price': [29995, 23600, 61500 , 58900]}\n",
    "carsDf2 = pd.DataFrame.from_dict(japaneseCars)\n",
    "\n",
    "carsDf = pd.concat([carsDf1, carsDf2], keys=[\"Germany\", \"Japan\"])\n",
    "carsDf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 10: Merge two data frames using the following condition\n",
    "\n",
    "**Hint:** Create two data frames using the following two Dicts, Merge two data frames, and append the second data frame as a new column to the first data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-06-17T11:33:26.107039Z",
     "start_time": "2021-06-17T11:33:26.044054Z"
    }
   },
   "outputs": [
    {
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>Price</th>\n",
       "      <th>horsepower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toyota</td>\n",
       "      <td>23845</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Honda</td>\n",
       "      <td>17995</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMV</td>\n",
       "      <td>135925</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Audi</td>\n",
       "      <td>71400</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   Price  horsepower\n",
       "0  Toyota   23845         141\n",
       "1   Honda   17995          80\n",
       "2     BMV  135925         182\n",
       "3    Audi   71400         160"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "Car_Price = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'Price': [23845, 17995, 135925 , 71400]}\n",
    "carPriceDf = pd.DataFrame.from_dict(Car_Price)\n",
    "\n",
    "car_Horsepower = {'Company': ['Toyota', 'Honda', 'BMV', 'Audi'], 'horsepower': [141, 80, 182 , 160]}\n",
    "carsHorsepowerDf = pd.DataFrame.from_dict(car_Horsepower)\n",
    "\n",
    "carsDf = pd.merge(carPriceDf, carsHorsepowerDf, on=\"Company\")\n",
    "carsDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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