dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6075927d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pydataset import data\n",
    "import matplotlib as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e154c6f3",
   "metadata": {},
   "source": [
    "All the datasets loaded from the pydataset library will be pandas dataframes.\n",
    "\n",
    "1. Copy the code from the lesson to create a dataframe full of student grades.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "986ecf4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "np.random.seed(123)\n",
    "\n",
    "students = ['Sally', 'Jane', 'Suzie', 'Billy', 'Ada', 'John', 'Thomas',\n",
    "            'Marie', 'Albert', 'Richard', 'Isaac', 'Alan']\n",
    "\n",
    "# randomly generate scores for each student for each subject\n",
    "# note that all the values need to have the same length here\n",
    "math_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "english_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "reading_grades = np.random.randint(low=60, high=100, size=len(students))\n",
    "\n",
    "df = pd.DataFrame({'name': students,\n",
    "                   'math': math_grades,\n",
    "                   'english': english_grades,\n",
    "                   'reading': reading_grades})\n",
    "\n",
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "69785303",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading\n",
       "0     Sally    62       85       80\n",
       "1      Jane    88       79       67\n",
       "2     Suzie    94       74       95\n",
       "3     Billy    98       96       88\n",
       "4       Ada    77       92       98\n",
       "5      John    79       76       93\n",
       "6    Thomas    82       64       81\n",
       "7     Marie    93       63       90\n",
       "8    Albert    92       62       87\n",
       "9   Richard    69       80       94\n",
       "10    Isaac    92       99       93\n",
       "11     Alan    92       62       72"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ef0b17",
   "metadata": {},
   "source": [
    "\n",
    "    - a. Create a column named passing_english that indicates whether each student has a passing grade in english.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "dd74e6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['passing_english'] = df.english >= 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d46f4da9",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>reading</th>\n",
       "      <th>passing_english</th>\n",
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       "      <td>Sally</td>\n",
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       "      <td>True</td>\n",
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       "      <td>Suzie</td>\n",
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       "      <td>Billy</td>\n",
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       "      <td>88</td>\n",
       "      <td>True</td>\n",
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       "      <td>Ada</td>\n",
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       "      <td>98</td>\n",
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       "      <th>5</th>\n",
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       "      <td>79</td>\n",
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       "      <td>True</td>\n",
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       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
       "      <td>82</td>\n",
       "      <td>64</td>\n",
       "      <td>81</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>90</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Albert</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading  passing_english\n",
       "0     Sally    62       85       80             True\n",
       "1      Jane    88       79       67             True\n",
       "2     Suzie    94       74       95             True\n",
       "3     Billy    98       96       88             True\n",
       "4       Ada    77       92       98             True\n",
       "5      John    79       76       93             True\n",
       "6    Thomas    82       64       81            False\n",
       "7     Marie    93       63       90            False\n",
       "8    Albert    92       62       87            False\n",
       "9   Richard    69       80       94             True\n",
       "10    Isaac    92       99       93             True\n",
       "11     Alan    92       62       72            False"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a9770c9",
   "metadata": {},
   "source": [
    "    - b. Sort the english grades by the passing_english column. How are duplicates handled?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "5c1b3114",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Marie</td>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>True</td>\n",
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       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Billy</td>\n",
       "      <td>98</td>\n",
       "      <td>96</td>\n",
       "      <td>88</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
       "      <td>77</td>\n",
       "      <td>92</td>\n",
       "      <td>98</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
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       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading  passing_english\n",
       "6    Thomas    82       64       81            False\n",
       "7     Marie    93       63       90            False\n",
       "8    Albert    92       62       87            False\n",
       "11     Alan    92       62       72            False\n",
       "0     Sally    62       85       80             True\n",
       "1      Jane    88       79       67             True\n",
       "2     Suzie    94       74       95             True\n",
       "3     Billy    98       96       88             True\n",
       "4       Ada    77       92       98             True\n",
       "5      John    79       76       93             True\n",
       "9   Richard    69       80       94             True\n",
       "10    Isaac    92       99       93             True"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = 'passing_english')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a68eb2",
   "metadata": {},
   "source": [
    "    - c. Sort the english grades first by passing_english and then by student name. All the students that are failing english should be first, and within the students that are failing english they should be ordered alphabetically. The same should be true for the students passing english. (Hint: you can pass a list to the .sort_values method)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "e7a94b92",
   "metadata": {},
   "outputs": [
    {
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       "      <th>6</th>\n",
       "      <td>Thomas</td>\n",
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       "      <th>4</th>\n",
       "      <td>Ada</td>\n",
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       "      <td>Billy</td>\n",
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       "      <td>True</td>\n",
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       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
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       "      <th>1</th>\n",
       "      <td>Jane</td>\n",
       "      <td>88</td>\n",
       "      <td>79</td>\n",
       "      <td>67</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>John</td>\n",
       "      <td>79</td>\n",
       "      <td>76</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sally</td>\n",
       "      <td>62</td>\n",
       "      <td>85</td>\n",
       "      <td>80</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suzie</td>\n",
       "      <td>94</td>\n",
       "      <td>74</td>\n",
       "      <td>95</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading  passing_english\n",
       "11     Alan    92       62       72            False\n",
       "8    Albert    92       62       87            False\n",
       "7     Marie    93       63       90            False\n",
       "6    Thomas    82       64       81            False\n",
       "4       Ada    77       92       98             True\n",
       "3     Billy    98       96       88             True\n",
       "10    Isaac    92       99       93             True\n",
       "1      Jane    88       79       67             True\n",
       "5      John    79       76       93             True\n",
       "9   Richard    69       80       94             True\n",
       "0     Sally    62       85       80             True\n",
       "2     Suzie    94       74       95             True"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='name').sort_values(by='passing_english')\n",
    "#or:\n",
    "#df = df.sort_values(by=['passing_english', 'name'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdfb3bb5",
   "metadata": {},
   "source": [
    "    - d. Sort the english grades first by passing_english, and then by the actual english grade, similar to how we did in the last step.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "5928ae63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
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       "      <td>True</td>\n",
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       "      <th>0</th>\n",
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       "      <td>Billy</td>\n",
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       "      <td>88</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading  passing_english\n",
       "8    Albert    92       62       87            False\n",
       "11     Alan    92       62       72            False\n",
       "7     Marie    93       63       90            False\n",
       "6    Thomas    82       64       81            False\n",
       "2     Suzie    94       74       95             True\n",
       "5      John    79       76       93             True\n",
       "1      Jane    88       79       67             True\n",
       "9   Richard    69       80       94             True\n",
       "0     Sally    62       85       80             True\n",
       "4       Ada    77       92       98             True\n",
       "3     Billy    98       96       88             True\n",
       "10    Isaac    92       99       93             True"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='english').sort_values(by='passing_english')\n",
    "#df.sort_values(by=[]'passing_english', 'english'], ascending=[False,False])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb662c86",
   "metadata": {},
   "source": [
    "    - e. Calculate each students overall grade and add it as a column on the dataframe. The overall grade is the average of the math, english, and reading grades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "6f3a338a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>84.833333</td>\n",
       "      <td>77.666667</td>\n",
       "      <td>86.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.134168</td>\n",
       "      <td>13.371158</td>\n",
       "      <td>9.643651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>67.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>78.500000</td>\n",
       "      <td>63.750000</td>\n",
       "      <td>80.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <td>77.500000</td>\n",
       "      <td>89.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>92.250000</td>\n",
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       "      <td>93.250000</td>\n",
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       "    <tr>\n",
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      ],
      "text/plain": [
       "            math    english    reading\n",
       "count  12.000000  12.000000  12.000000\n",
       "mean   84.833333  77.666667  86.500000\n",
       "std    11.134168  13.371158   9.643651\n",
       "min    62.000000  62.000000  67.000000\n",
       "25%    78.500000  63.750000  80.750000\n",
       "50%    90.000000  77.500000  89.000000\n",
       "75%    92.250000  86.750000  93.250000\n",
       "max    98.000000  99.000000  98.000000"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "cd462434",
   "metadata": {},
   "outputs": [
    {
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       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
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      ],
      "text/plain": [
       "       name  math  english  reading  passing_english  average_grade\n",
       "0     Sally    62       85       80             True      75.666667\n",
       "1      Jane    88       79       67             True      78.000000\n",
       "2     Suzie    94       74       95             True      87.666667\n",
       "3     Billy    98       96       88             True      94.000000\n",
       "4       Ada    77       92       98             True      89.000000\n",
       "5      John    79       76       93             True      82.666667\n",
       "6    Thomas    82       64       81            False      75.666667\n",
       "7     Marie    93       63       90            False      82.000000\n",
       "8    Albert    92       62       87            False      80.333333\n",
       "9   Richard    69       80       94             True      81.000000\n",
       "10    Isaac    92       99       93             True      94.666667\n",
       "11     Alan    92       62       72            False      75.333333"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['average_grade'] = (df.math + df.english + df.reading)/3\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "815b018b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>62</td>\n",
       "      <td>87</td>\n",
       "      <td>False</td>\n",
       "      <td>80.333333</td>\n",
       "      <td>80.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Richard</td>\n",
       "      <td>69</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>True</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Isaac</td>\n",
       "      <td>92</td>\n",
       "      <td>99</td>\n",
       "      <td>93</td>\n",
       "      <td>True</td>\n",
       "      <td>94.666667</td>\n",
       "      <td>94.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Alan</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>72</td>\n",
       "      <td>False</td>\n",
       "      <td>75.333333</td>\n",
       "      <td>75.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       name  math  english  reading  passing_english  average_grade    average\n",
       "0     Sally    62       85       80             True      75.666667  75.666667\n",
       "1      Jane    88       79       67             True      78.000000  78.000000\n",
       "2     Suzie    94       74       95             True      87.666667  87.666667\n",
       "3     Billy    98       96       88             True      94.000000  94.000000\n",
       "4       Ada    77       92       98             True      89.000000  89.000000\n",
       "5      John    79       76       93             True      82.666667  82.666667\n",
       "6    Thomas    82       64       81            False      75.666667  75.666667\n",
       "7     Marie    93       63       90            False      82.000000  82.000000\n",
       "8    Albert    92       62       87            False      80.333333  80.333333\n",
       "9   Richard    69       80       94             True      81.000000  81.000000\n",
       "10    Isaac    92       99       93             True      94.666667  94.666667\n",
       "11     Alan    92       62       72            False      75.333333  75.333333"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['average'] = df[['math','reading','english']].mean(axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c00a4c0f",
   "metadata": {},
   "source": [
    "## 2. Load the mpg dataset. Read the documentation for the dataset and use it for the following questions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "2d935250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mpg\n",
      "\n",
      "PyDataset Documentation (adopted from R Documentation. The displayed examples are in R)\n",
      "\n",
      "## Fuel economy data from 1999 and 2008 for 38 popular models of car\n",
      "\n",
      "### Description\n",
      "\n",
      "This dataset contains a subset of the fuel economy data that the EPA makes\n",
      "available on http://fueleconomy.gov. It contains only models which had a new\n",
      "release every year between 1999 and 2008 - this was used as a proxy for the\n",
      "popularity of the car.\n",
      "\n",
      "### Usage\n",
      "\n",
      "    data(mpg)\n",
      "\n",
      "### Format\n",
      "\n",
      "A data frame with 234 rows and 11 variables\n",
      "\n",
      "### Details\n",
      "\n",
      "  * manufacturer. \n",
      "\n",
      "  * model. \n",
      "\n",
      "  * displ. engine displacement, in litres \n",
      "\n",
      "  * year. \n",
      "\n",
      "  * cyl. number of cylinders \n",
      "\n",
      "  * trans. type of transmission \n",
      "\n",
      "  * drv. f = front-wheel drive, r = rear wheel drive, 4 = 4wd \n",
      "\n",
      "  * cty. city miles per gallon \n",
      "\n",
      "  * hwy. highway miles per gallon \n",
      "\n",
      "  * fl. \n",
      "\n",
      "  * class. \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "mpg = data('mpg')\n",
    "data('mpg', show_doc = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "9098afd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "      <th>fl</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(av)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</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>230</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    manufacturer   model  displ  year  cyl       trans drv  cty  hwy fl  \\\n",
       "1           audi      a4    1.8  1999    4    auto(l5)   f   18   29  p   \n",
       "2           audi      a4    1.8  1999    4  manual(m5)   f   21   29  p   \n",
       "3           audi      a4    2.0  2008    4  manual(m6)   f   20   31  p   \n",
       "4           audi      a4    2.0  2008    4    auto(av)   f   21   30  p   \n",
       "5           audi      a4    2.8  1999    6    auto(l5)   f   16   26  p   \n",
       "..           ...     ...    ...   ...  ...         ...  ..  ...  ... ..   \n",
       "230   volkswagen  passat    2.0  2008    4    auto(s6)   f   19   28  p   \n",
       "231   volkswagen  passat    2.0  2008    4  manual(m6)   f   21   29  p   \n",
       "232   volkswagen  passat    2.8  1999    6    auto(l5)   f   16   26  p   \n",
       "233   volkswagen  passat    2.8  1999    6  manual(m5)   f   18   26  p   \n",
       "234   volkswagen  passat    3.6  2008    6    auto(s6)   f   17   26  p   \n",
       "\n",
       "       class  \n",
       "1    compact  \n",
       "2    compact  \n",
       "3    compact  \n",
       "4    compact  \n",
       "5    compact  \n",
       "..       ...  \n",
       "230  midsize  \n",
       "231  midsize  \n",
       "232  midsize  \n",
       "233  midsize  \n",
       "234  midsize  \n",
       "\n",
       "[234 rows x 11 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3f88e6f",
   "metadata": {},
   "source": [
    "\n",
    "- a. How many rows and columns are there?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ee4cbd24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(234, 11)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c48fc91b",
   "metadata": {},
   "source": [
    "- b. What are the data types of each column?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "f41dc777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "manufacturer     object\n",
       "model            object\n",
       "displ           float64\n",
       "year              int64\n",
       "cyl               int64\n",
       "trans            object\n",
       "drv              object\n",
       "cty               int64\n",
       "hwy               int64\n",
       "fl               object\n",
       "class            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef4c537",
   "metadata": {},
   "source": [
    "- c. Summarize the dataframe with .info and .describe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "456760b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 234 entries, 1 to 234\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   manufacturer  234 non-null    object \n",
      " 1   model         234 non-null    object \n",
      " 2   displ         234 non-null    float64\n",
      " 3   year          234 non-null    int64  \n",
      " 4   cyl           234 non-null    int64  \n",
      " 5   trans         234 non-null    object \n",
      " 6   drv           234 non-null    object \n",
      " 7   cty           234 non-null    int64  \n",
      " 8   hwy           234 non-null    int64  \n",
      " 9   fl            234 non-null    object \n",
      " 10  class         234 non-null    object \n",
      "dtypes: float64(1), int64(4), object(6)\n",
      "memory usage: 21.9+ KB\n"
     ]
    }
   ],
   "source": [
    "mpg.info()\n",
    "#good practice would be to check dtypes are correct. is maybe 1 individual value screwing it up for all the rest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "5bab61ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>cty</th>\n",
       "      <th>hwy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "      <td>234.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.471795</td>\n",
       "      <td>2003.500000</td>\n",
       "      <td>5.888889</td>\n",
       "      <td>16.858974</td>\n",
       "      <td>23.440171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.291959</td>\n",
       "      <td>4.509646</td>\n",
       "      <td>1.611534</td>\n",
       "      <td>4.255946</td>\n",
       "      <td>5.954643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.300000</td>\n",
       "      <td>2003.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.600000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>44.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            displ         year         cyl         cty         hwy\n",
       "count  234.000000   234.000000  234.000000  234.000000  234.000000\n",
       "mean     3.471795  2003.500000    5.888889   16.858974   23.440171\n",
       "std      1.291959     4.509646    1.611534    4.255946    5.954643\n",
       "min      1.600000  1999.000000    4.000000    9.000000   12.000000\n",
       "25%      2.400000  1999.000000    4.000000   14.000000   18.000000\n",
       "50%      3.300000  2003.500000    6.000000   17.000000   24.000000\n",
       "75%      4.600000  2008.000000    8.000000   19.000000   27.000000\n",
       "max      7.000000  2008.000000    8.000000   35.000000   44.000000"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40267d83",
   "metadata": {},
   "source": [
    "- d. Rename the cty column to city.\n",
    "- e. Rename the hwy column to highway.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "3846e6d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>city</th>\n",
       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
       "      <th>car_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(av)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</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>230</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    manufacturer   model  displ  year  cyl       trans drv  city  highway fl  \\\n",
       "1           audi      a4    1.8  1999    4    auto(l5)   f    18       29  p   \n",
       "2           audi      a4    1.8  1999    4  manual(m5)   f    21       29  p   \n",
       "3           audi      a4    2.0  2008    4  manual(m6)   f    20       31  p   \n",
       "4           audi      a4    2.0  2008    4    auto(av)   f    21       30  p   \n",
       "5           audi      a4    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "..           ...     ...    ...   ...  ...         ...  ..   ...      ... ..   \n",
       "230   volkswagen  passat    2.0  2008    4    auto(s6)   f    19       28  p   \n",
       "231   volkswagen  passat    2.0  2008    4  manual(m6)   f    21       29  p   \n",
       "232   volkswagen  passat    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "233   volkswagen  passat    2.8  1999    6  manual(m5)   f    18       26  p   \n",
       "234   volkswagen  passat    3.6  2008    6    auto(s6)   f    17       26  p   \n",
       "\n",
       "    car_class  \n",
       "1     compact  \n",
       "2     compact  \n",
       "3     compact  \n",
       "4     compact  \n",
       "5     compact  \n",
       "..        ...  \n",
       "230   midsize  \n",
       "231   midsize  \n",
       "232   midsize  \n",
       "233   midsize  \n",
       "234   midsize  \n",
       "\n",
       "[234 rows x 11 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.rename(columns={'cty':'city', 'hwy': 'highway', 'class': 'car_class'}, inplace = True)\n",
    "mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f022c5",
   "metadata": {},
   "source": [
    "- f. Do any cars have better city mileage than highway mileage?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "45b16fdf",
   "metadata": {},
   "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",
       "    .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>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>city</th>\n",
       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
       "      <th>car_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [manufacturer, model, displ, year, cyl, trans, drv, city, highway, fl, car_class]\n",
       "Index: []"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.city > mpg.highway]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "418f7a7e",
   "metadata": {},
   "source": [
    "### g. Create a column named mileage_difference this column should contain the difference between highway and city mileage for each car.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "eb72b265",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>city</th>\n",
       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
       "      <th>car_class</th>\n",
       "      <th>mileage_difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>20</td>\n",
       "      <td>31</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(av)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>10</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>19</td>\n",
       "      <td>28</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    manufacturer   model  displ  year  cyl       trans drv  city  highway fl  \\\n",
       "1           audi      a4    1.8  1999    4    auto(l5)   f    18       29  p   \n",
       "2           audi      a4    1.8  1999    4  manual(m5)   f    21       29  p   \n",
       "3           audi      a4    2.0  2008    4  manual(m6)   f    20       31  p   \n",
       "4           audi      a4    2.0  2008    4    auto(av)   f    21       30  p   \n",
       "5           audi      a4    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "..           ...     ...    ...   ...  ...         ...  ..   ...      ... ..   \n",
       "230   volkswagen  passat    2.0  2008    4    auto(s6)   f    19       28  p   \n",
       "231   volkswagen  passat    2.0  2008    4  manual(m6)   f    21       29  p   \n",
       "232   volkswagen  passat    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "233   volkswagen  passat    2.8  1999    6  manual(m5)   f    18       26  p   \n",
       "234   volkswagen  passat    3.6  2008    6    auto(s6)   f    17       26  p   \n",
       "\n",
       "    car_class  mileage_difference  \n",
       "1     compact                  11  \n",
       "2     compact                   8  \n",
       "3     compact                  11  \n",
       "4     compact                   9  \n",
       "5     compact                  10  \n",
       "..        ...                 ...  \n",
       "230   midsize                   9  \n",
       "231   midsize                   8  \n",
       "232   midsize                  10  \n",
       "233   midsize                   8  \n",
       "234   midsize                   9  \n",
       "\n",
       "[234 rows x 12 columns]"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg['mileage_difference'] = mpg.highway - mpg.city\n",
    "mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d2b96b",
   "metadata": {},
   "source": [
    "### h. Which car (or cars) has the highest mileage difference?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c80b7431",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mpg.sort_values('mileage_difference', ascending=False)\n",
    "#the above code works\n",
    "mpg.mileage_difference.max()\n",
    "# df['mileage_difference' == mpg.mileage_difference.max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "a1cbf5ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(mpg.mileage_difference.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1c041765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "manufacturer                audi\n",
       "model                 a4 quattro\n",
       "displ                        2.8\n",
       "year                        1999\n",
       "cyl                            6\n",
       "trans                 manual(m5)\n",
       "drv                            4\n",
       "city                          17\n",
       "highway                       25\n",
       "fl                             p\n",
       "car_class                compact\n",
       "mileage_difference             8\n",
       "Name: 13, dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.iloc[mpg.mileage_difference.max()] #there is one other i have to get : /"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "f35eee90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
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       "      <th>cyl</th>\n",
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       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
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       "      <th>mileage_difference</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>106</th>\n",
       "      <td>honda</td>\n",
       "      <td>civic</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>25</td>\n",
       "      <td>36</td>\n",
       "      <td>r</td>\n",
       "      <td>subcompact</td>\n",
       "      <td>11</td>\n",
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       "      <th>213</th>\n",
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       "      <td>33</td>\n",
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       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
       "      <td>a4</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l5)</td>\n",
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       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>new beetle</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l4)</td>\n",
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       "      <td>29</td>\n",
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       "      <td>d</td>\n",
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       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>honda</td>\n",
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       "      <td>1.8</td>\n",
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       "      <td>4</td>\n",
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       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "      <td>c</td>\n",
       "      <td>subcompact</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    manufacturer       model  displ  year  cyl       trans drv  city  highway  \\\n",
       "106        honda       civic    1.8  2008    4    auto(l5)   f    25       36   \n",
       "213   volkswagen       jetta    1.9  1999    4  manual(m5)   f    33       44   \n",
       "1           audi          a4    1.8  1999    4    auto(l5)   f    18       29   \n",
       "223   volkswagen  new beetle    1.9  1999    4    auto(l4)   f    29       41   \n",
       "107        honda       civic    1.8  2008    4    auto(l5)   f    24       36   \n",
       "\n",
       "    fl   car_class  mileage_difference  \n",
       "106  r  subcompact                  11  \n",
       "213  d     compact                  11  \n",
       "1    p     compact                  11  \n",
       "223  d  subcompact                  12  \n",
       "107  c  subcompact                  12  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg.sort_values(by='mileage_difference').tail(5) #ok, now we're talking. also, see adam's solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "623b6bdc",
   "metadata": {},
   "source": [
    "### i. Which compact class car has the lowest highway mileage? The best?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "24dcc0e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
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       "  <tbody>\n",
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       "      <th>220</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>jetta</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l4)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>r</td>\n",
       "      <td>compact</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    manufacturer  model  displ  year  cyl     trans drv  city  highway fl  \\\n",
       "220   volkswagen  jetta    2.8  1999    6  auto(l4)   f    16       23  r   \n",
       "\n",
       "    car_class  mileage_difference  \n",
       "220   compact                   7  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.car_class == 'compact'].sort_values(by='highway').head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "9d481b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>trans</th>\n",
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       "      <th>fl</th>\n",
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       "  <tbody>\n",
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       "      <th>213</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>jetta</td>\n",
       "      <td>1.9</td>\n",
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       "      <td>4</td>\n",
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       "    </tr>\n",
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      "text/plain": [
       "    manufacturer  model  displ  year  cyl       trans drv  city  highway fl  \\\n",
       "213   volkswagen  jetta    1.9  1999    4  manual(m5)   f    33       44  d   \n",
       "\n",
       "    car_class  mileage_difference  \n",
       "213   compact                  11  "
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.car_class == 'compact'].sort_values(by='highway').tail(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af3639d4",
   "metadata": {},
   "source": [
    "### j. Create a column named average_mileage that is the mean of the city and highway mileage.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "27bc318c",
   "metadata": {},
   "outputs": [],
   "source": [
    "mpg['average_mileage'] = ((mpg.highway + mpg.city) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "81eb7eaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>audi</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
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       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>compact</td>\n",
       "      <td>10</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(s6)</td>\n",
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       "      <td>19</td>\n",
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       "    <tr>\n",
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       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>f</td>\n",
       "      <td>21</td>\n",
       "      <td>29</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>8</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>f</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>10</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>2.8</td>\n",
       "      <td>1999</td>\n",
       "      <td>6</td>\n",
       "      <td>manual(m5)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>8</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>volkswagen</td>\n",
       "      <td>passat</td>\n",
       "      <td>3.6</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>auto(s6)</td>\n",
       "      <td>f</td>\n",
       "      <td>17</td>\n",
       "      <td>26</td>\n",
       "      <td>p</td>\n",
       "      <td>midsize</td>\n",
       "      <td>9</td>\n",
       "      <td>21.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>234 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    manufacturer   model  displ  year  cyl       trans drv  city  highway fl  \\\n",
       "1           audi      a4    1.8  1999    4    auto(l5)   f    18       29  p   \n",
       "2           audi      a4    1.8  1999    4  manual(m5)   f    21       29  p   \n",
       "3           audi      a4    2.0  2008    4  manual(m6)   f    20       31  p   \n",
       "4           audi      a4    2.0  2008    4    auto(av)   f    21       30  p   \n",
       "5           audi      a4    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "..           ...     ...    ...   ...  ...         ...  ..   ...      ... ..   \n",
       "230   volkswagen  passat    2.0  2008    4    auto(s6)   f    19       28  p   \n",
       "231   volkswagen  passat    2.0  2008    4  manual(m6)   f    21       29  p   \n",
       "232   volkswagen  passat    2.8  1999    6    auto(l5)   f    16       26  p   \n",
       "233   volkswagen  passat    2.8  1999    6  manual(m5)   f    18       26  p   \n",
       "234   volkswagen  passat    3.6  2008    6    auto(s6)   f    17       26  p   \n",
       "\n",
       "    car_class  mileage_difference  average_mileage  \n",
       "1     compact                  11             23.5  \n",
       "2     compact                   8             25.0  \n",
       "3     compact                  11             25.5  \n",
       "4     compact                   9             25.5  \n",
       "5     compact                  10             21.0  \n",
       "..        ...                 ...              ...  \n",
       "230   midsize                   9             23.5  \n",
       "231   midsize                   8             25.0  \n",
       "232   midsize                  10             21.0  \n",
       "233   midsize                   8             22.0  \n",
       "234   midsize                   9             21.5  \n",
       "\n",
       "[234 rows x 13 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "634af1c5",
   "metadata": {},
   "source": [
    "### k. Which dodge car has the best average mileage? The worst?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "2d7571f8",
   "metadata": {},
   "outputs": [
    {
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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>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>city</th>\n",
       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
       "      <th>car_class</th>\n",
       "      <th>mileage_difference</th>\n",
       "      <th>average_mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>dodge</td>\n",
       "      <td>caravan 2wd</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1999</td>\n",
       "      <td>4</td>\n",
       "      <td>auto(l3)</td>\n",
       "      <td>f</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>r</td>\n",
       "      <td>minivan</td>\n",
       "      <td>6</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   manufacturer        model  displ  year  cyl     trans drv  city  highway  \\\n",
       "38        dodge  caravan 2wd    2.4  1999    4  auto(l3)   f    18       24   \n",
       "\n",
       "   fl car_class  mileage_difference  average_mileage  \n",
       "38  r   minivan                   6             21.0  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.manufacturer == 'dodge'].sort_values(by='average_mileage').tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "41213c8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>manufacturer</th>\n",
       "      <th>model</th>\n",
       "      <th>displ</th>\n",
       "      <th>year</th>\n",
       "      <th>cyl</th>\n",
       "      <th>trans</th>\n",
       "      <th>drv</th>\n",
       "      <th>city</th>\n",
       "      <th>highway</th>\n",
       "      <th>fl</th>\n",
       "      <th>car_class</th>\n",
       "      <th>mileage_difference</th>\n",
       "      <th>average_mileage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>dodge</td>\n",
       "      <td>ram 1500 pickup 4wd</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>manual(m6)</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>e</td>\n",
       "      <td>pickup</td>\n",
       "      <td>3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>dodge</td>\n",
       "      <td>ram 1500 pickup 4wd</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>e</td>\n",
       "      <td>pickup</td>\n",
       "      <td>3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>dodge</td>\n",
       "      <td>durango 4wd</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>e</td>\n",
       "      <td>suv</td>\n",
       "      <td>3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>dodge</td>\n",
       "      <td>dakota pickup 4wd</td>\n",
       "      <td>4.7</td>\n",
       "      <td>2008</td>\n",
       "      <td>8</td>\n",
       "      <td>auto(l5)</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>e</td>\n",
       "      <td>pickup</td>\n",
       "      <td>3</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>dodge</td>\n",
       "      <td>ram 1500 pickup 4wd</td>\n",
       "      <td>5.9</td>\n",
       "      <td>1999</td>\n",
       "      <td>8</td>\n",
       "      <td>auto(l4)</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>r</td>\n",
       "      <td>pickup</td>\n",
       "      <td>4</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   manufacturer                model  displ  year  cyl       trans drv  city  \\\n",
       "70        dodge  ram 1500 pickup 4wd    4.7  2008    8  manual(m6)   4     9   \n",
       "66        dodge  ram 1500 pickup 4wd    4.7  2008    8    auto(l5)   4     9   \n",
       "60        dodge          durango 4wd    4.7  2008    8    auto(l5)   4     9   \n",
       "55        dodge    dakota pickup 4wd    4.7  2008    8    auto(l5)   4     9   \n",
       "74        dodge  ram 1500 pickup 4wd    5.9  1999    8    auto(l4)   4    11   \n",
       "\n",
       "    highway fl car_class  mileage_difference  average_mileage  \n",
       "70       12  e    pickup                   3             10.5  \n",
       "66       12  e    pickup                   3             10.5  \n",
       "60       12  e       suv                   3             10.5  \n",
       "55       12  e    pickup                   3             10.5  \n",
       "74       15  r    pickup                   4             13.0  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg[mpg.manufacturer == 'dodge'].sort_values(by='average_mileage').head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57d1a29b",
   "metadata": {},
   "source": [
    "## Load the Mammals dataset. Read the documentation for it, and use the data to answer these questions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "b29322ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>weight</th>\n",
       "      <th>speed</th>\n",
       "      <th>hoppers</th>\n",
       "      <th>specials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6000.000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4000.000</td>\n",
       "      <td>26.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3000.000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1400.000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>400.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>350.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>300.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>260.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>250.000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3800.000</td>\n",
       "      <td>25.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1000.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>900.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>900.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>800.000</td>\n",
       "      <td>29.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>750.000</td>\n",
       "      <td>57.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>500.000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>450.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>300.000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>300.000</td>\n",
       "      <td>90.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>250.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>250.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>170.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>150.000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>130.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>120.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>120.000</td>\n",
       "      <td>61.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>110.000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>100.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>85.000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>80.000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>72.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>70.000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>65.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>62.000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>50.000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>50.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>50.000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>50.000</td>\n",
       "      <td>47.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>37.000</td>\n",
       "      <td>105.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>35.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>34.000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>30.000</td>\n",
       "      <td>97.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>30.000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>30.000</td>\n",
       "      <td>45.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>20.000</td>\n",
       "      <td>81.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>400.000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>300.000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>230.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>150.000</td>\n",
       "      <td>59.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>135.000</td>\n",
       "      <td>48.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>65.000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>60.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>55.000</td>\n",
       "      <td>110.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>45.000</td>\n",
       "      <td>50.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>40.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>25.000</td>\n",
       "      <td>67.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>20.000</td>\n",
       "      <td>70.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>16.000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>12.000</td>\n",
       "      <td>24.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>11.000</td>\n",
       "      <td>30.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>10.000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>7.000</td>\n",
       "      <td>60.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>6.000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>5.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5.000</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>127.000</td>\n",
       "      <td>32.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>70.000</td>\n",
       "      <td>40.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>13.000</td>\n",
       "      <td>37.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>9.000</td>\n",
       "      <td>3.2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>4.000</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.600</td>\n",
       "      <td>36.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>0.600</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>0.550</td>\n",
       "      <td>27.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0.500</td>\n",
       "      <td>18.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0.400</td>\n",
       "      <td>20.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0.300</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>0.250</td>\n",
       "      <td>9.7</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>0.220</td>\n",
       "      <td>15.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0.110</td>\n",
       "      <td>9.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>0.100</td>\n",
       "      <td>17.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>0.056</td>\n",
       "      <td>21.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0.050</td>\n",
       "      <td>11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0.045</td>\n",
       "      <td>16.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0.035</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0.035</td>\n",
       "      <td>14.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0.030</td>\n",
       "      <td>6.8</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>0.030</td>\n",
       "      <td>9.1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0.025</td>\n",
       "      <td>11.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.025</td>\n",
       "      <td>8.6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0.018</td>\n",
       "      <td>8.9</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.016</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0.100</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0.100</td>\n",
       "      <td>2.4</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.016</td>\n",
       "      <td>3.6</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>4.600</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>4.400</td>\n",
       "      <td>72.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>4.000</td>\n",
       "      <td>72.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>3.500</td>\n",
       "      <td>56.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.900</td>\n",
       "      <td>56.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.500</td>\n",
       "      <td>50.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1.500</td>\n",
       "      <td>40.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>50.000</td>\n",
       "      <td>65.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>5.000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>0.024</td>\n",
       "      <td>13.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>4.000</td>\n",
       "      <td>1.6</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       weight  speed  hoppers  specials\n",
       "1    6000.000   35.0    False     False\n",
       "2    4000.000   26.0    False     False\n",
       "3    3000.000   25.0    False     False\n",
       "4    1400.000   45.0    False     False\n",
       "5     400.000   70.0    False     False\n",
       "6     350.000   70.0    False     False\n",
       "7     300.000   64.0    False     False\n",
       "8     260.000   70.0    False     False\n",
       "9     250.000   40.0    False     False\n",
       "10   3800.000   25.0    False      True\n",
       "11   1000.000   60.0    False     False\n",
       "12    900.000   70.0    False     False\n",
       "13    900.000   56.0    False     False\n",
       "14    800.000   29.0    False     False\n",
       "15    750.000   57.0    False     False\n",
       "16    500.000   32.0    False     False\n",
       "17    450.000   56.0    False     False\n",
       "18    300.000   72.0    False     False\n",
       "19    300.000   90.0    False     False\n",
       "20    250.000   80.0    False     False\n",
       "21    250.000   56.0    False     False\n",
       "22    170.000   80.0    False     False\n",
       "23    150.000   48.0    False     False\n",
       "24    130.000   70.0    False     False\n",
       "25    120.000   80.0    False     False\n",
       "26    120.000   61.0    False     False\n",
       "27    110.000   33.0    False     False\n",
       "28    100.000   64.0    False     False\n",
       "29     85.000   55.0    False     False\n",
       "30     80.000   65.0    False     False\n",
       "31     72.000   56.0    False     False\n",
       "32     70.000   45.0    False     False\n",
       "33     65.000   60.0    False     False\n",
       "34     62.000   81.0    False     False\n",
       "35     50.000  100.0    False     False\n",
       "36     50.000   60.0    False     False\n",
       "37     50.000   40.0    False     False\n",
       "38     50.000   47.0    False     False\n",
       "39     37.000  105.0    False     False\n",
       "40     35.000   80.0    False     False\n",
       "41     34.000   97.0    False     False\n",
       "42     30.000   97.0    False     False\n",
       "43     30.000   80.0    False     False\n",
       "44     30.000   45.0    False     False\n",
       "45     20.000   81.0    False     False\n",
       "46    400.000   40.0    False     False\n",
       "47    300.000   48.0    False     False\n",
       "48    230.000   56.0    False     False\n",
       "49    150.000   59.0    False     False\n",
       "50    135.000   48.0    False     False\n",
       "51     65.000   65.0    False     False\n",
       "52     60.000   60.0    False     False\n",
       "53     55.000  110.0    False     False\n",
       "54     45.000   50.0    False     False\n",
       "55     40.000   64.0    False     False\n",
       "56     25.000   67.0    False     False\n",
       "57     20.000   70.0    False     False\n",
       "58     16.000   65.0    False     False\n",
       "59     12.000   24.0    False      True\n",
       "60     11.000   30.0    False      True\n",
       "61     10.000   56.0    False     False\n",
       "62      7.000   60.0    False     False\n",
       "63      6.000   72.0    False     False\n",
       "64      5.000   64.0    False     False\n",
       "65      5.000   27.0    False      True\n",
       "66      3.000   16.0    False      True\n",
       "67    127.000   32.0    False     False\n",
       "68     70.000   40.0    False      True\n",
       "69     13.000   37.0    False      True\n",
       "70      9.000    3.2    False      True\n",
       "71      4.000   16.0    False     False\n",
       "72      0.600   36.0    False     False\n",
       "73      0.600   20.0    False     False\n",
       "74      0.550   27.0    False     False\n",
       "75      0.500   18.0    False     False\n",
       "76      0.400   20.0    False     False\n",
       "77      0.300   13.0    False     False\n",
       "78      0.250    9.7    False     False\n",
       "79      0.220   15.0    False     False\n",
       "80      0.110    9.0    False     False\n",
       "81      0.100   17.0    False     False\n",
       "82      0.056   21.0     True     False\n",
       "83      0.050   11.0    False     False\n",
       "84      0.045   16.0    False     False\n",
       "85      0.035   32.0     True     False\n",
       "86      0.035   14.0     True     False\n",
       "87      0.030    6.8    False     False\n",
       "88      0.030    9.1    False     False\n",
       "89      0.025   11.0    False     False\n",
       "90      0.025    8.6    False     False\n",
       "91      0.018    8.9    False     False\n",
       "92      0.016   13.0    False     False\n",
       "93      0.100    4.0    False     False\n",
       "94      0.100    2.4    False     False\n",
       "95      0.016    3.6    False     False\n",
       "96      4.600   64.0     True     False\n",
       "97      4.400   72.0     True     False\n",
       "98      4.000   72.0     True     False\n",
       "99      3.500   56.0     True     False\n",
       "100     2.000   64.0     True     False\n",
       "101     1.900   56.0     True     False\n",
       "102     1.500   50.0     True     False\n",
       "103     1.500   40.0     True     False\n",
       "104    50.000   65.0    False     False\n",
       "105     5.000    7.4    False      True\n",
       "106     0.024   13.0    False     False\n",
       "107     4.000    1.6    False      True"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals = data('Mammals')\n",
    "Mammals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25deae70",
   "metadata": {},
   "source": [
    "\n",
    "### 1. How many rows and columns are there?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "e321f72c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(107, 4)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e80dac5",
   "metadata": {},
   "source": [
    "### 2. What are the data types?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7c12bc03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weight      float64\n",
       "speed       float64\n",
       "hoppers        bool\n",
       "specials       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1975bef4",
   "metadata": {},
   "source": [
    "### 3. Summarize the dataframe with .info and .describe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "127acd30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 107 entries, 1 to 107\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   weight    107 non-null    float64\n",
      " 1   speed     107 non-null    float64\n",
      " 2   hoppers   107 non-null    bool   \n",
      " 3   specials  107 non-null    bool   \n",
      "dtypes: bool(2), float64(2)\n",
      "memory usage: 2.7 KB\n"
     ]
    }
   ],
   "source": [
    "Mammals.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "83d59073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>107.000000</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>278.688178</td>\n",
       "      <td>46.208411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>839.608269</td>\n",
       "      <td>26.716778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.016000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.700000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>142.500000</td>\n",
       "      <td>65.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6000.000000</td>\n",
       "      <td>110.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            weight       speed\n",
       "count   107.000000  107.000000\n",
       "mean    278.688178   46.208411\n",
       "std     839.608269   26.716778\n",
       "min       0.016000    1.600000\n",
       "25%       1.700000   22.500000\n",
       "50%      34.000000   48.000000\n",
       "75%     142.500000   65.000000\n",
       "max    6000.000000  110.000000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab361a9",
   "metadata": {},
   "source": [
    "### 4. What is the the weight of the fastest animal?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "a83f2749",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>weight</th>\n",
       "      <th>speed</th>\n",
       "      <th>hoppers</th>\n",
       "      <th>specials</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>55.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "    weight  speed  hoppers  specials\n",
       "53    55.0  110.0    False     False"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "speed_mask = Mammals.index == Mammals.speed.idxmax()\n",
    "Mammals[speed_mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "7be90673",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53    55.0\n",
       "Name: weight, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals[speed_mask].weight\n",
    "#good enough, although i feel like there's a way to return this in the pretty format"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6615aa",
   "metadata": {},
   "source": [
    "### 5. What is the overal percentage of specials?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d7d3437a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09345794392523364"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Mammals.loc[Mammals.specials == True]) / len(Mammals.index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c5a32e0",
   "metadata": {},
   "source": [
    "### 6. How many animals are hoppers that are above the median speed? What percentage is this?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "dcefd1ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46.20841121495327"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_speed = Mammals.speed.mean()\n",
    "median_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "88fb1e8f",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>4.000</td>\n",
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       "      <td>True</td>\n",
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       "      <th>99</th>\n",
       "      <td>3.500</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2.000</td>\n",
       "      <td>64.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1.900</td>\n",
       "      <td>56.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.500</td>\n",
       "      <td>50.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1.500</td>\n",
       "      <td>40.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weight  speed  hoppers  specials\n",
       "82    0.056   21.0     True     False\n",
       "85    0.035   32.0     True     False\n",
       "86    0.035   14.0     True     False\n",
       "96    4.600   64.0     True     False\n",
       "97    4.400   72.0     True     False\n",
       "98    4.000   72.0     True     False\n",
       "99    3.500   56.0     True     False\n",
       "100   2.000   64.0     True     False\n",
       "101   1.900   56.0     True     False\n",
       "102   1.500   50.0     True     False\n",
       "103   1.500   40.0     True     False"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hoppers_mask = Mammals[Mammals.hoppers == True]\n",
    "hoppers_mask\n",
    "#this is all the indices that are hoppers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5066e78c",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "      <th>8</th>\n",
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       "      <td>1000.0</td>\n",
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      "text/plain": [
       "    weight  speed  hoppers  specials\n",
       "5    400.0   70.0    False     False\n",
       "6    350.0   70.0    False     False\n",
       "7    300.0   64.0    False     False\n",
       "8    260.0   70.0    False     False\n",
       "11  1000.0   60.0    False     False"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals_above_median_speed = Mammals.loc[Mammals['speed'] > median_speed]\n",
    "Mammals_above_median_speed.head()\n",
    "#and here we have all mammals above median speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "3091eb92",
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
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       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1.5</td>\n",
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      "text/plain": [
       "     weight  speed  hoppers  specials\n",
       "96      4.6   64.0     True     False\n",
       "97      4.4   72.0     True     False\n",
       "98      4.0   72.0     True     False\n",
       "99      3.5   56.0     True     False\n",
       "100     2.0   64.0     True     False\n",
       "101     1.9   56.0     True     False\n",
       "102     1.5   50.0     True     False"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mammals.loc[(Mammals.speed > median_speed)]\n",
    "Mammals.loc[(Mammals.hoppers == True) & (Mammals.speed > median_speed)]\n",
    "\n",
    "\n",
    "# got it. : ) didn't even use all those masks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "dc67459b",
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     weight  speed  hoppers  specials\n",
       "96      4.6   64.0     True     False\n",
       "97      4.4   72.0     True     False\n",
       "98      4.0   72.0     True     False\n",
       "99      3.5   56.0     True     False\n",
       "100     2.0   64.0     True     False\n",
       "101     1.9   56.0     True     False\n",
       "102     1.5   50.0     True     False"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Mammals[(Mammals.hoppers == True) & (Mammals.speed > Mammals.speed.median())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "65882bac",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = len(Mammals[(Mammals.hoppers == True) & (Mammals.speed > Mammals.speed.median())]) / len(Mammals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "eb98c4e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.54% of the dataset are hoppers above the median speed\n"
     ]
    }
   ],
   "source": [
    "print(f'{round(a*100, 2)}% of the dataset are hoppers above the median speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92f52be0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0deea691",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "372a0bf5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8192fde1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1d5093fa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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