Exercicios-Cap03.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Data Science Academy</font>\n",
    "# <font color='blue'>Big Data Real-Time Analytics com Python e Spark</font>\n",
    "\n",
    "# <font color='blue'>Capítulo 3</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercícios \n",
    "\n",
    "Esta lista de exercícios é a continuação do Capítulo 03 do curso Big Data Real-Time Analytics com Python e Spark da DSA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/dataframe_saved_v2.csv', parse_dates = ['Data'], usecols = list(range(0,6)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Data          datetime64[ns]\n",
       "Distancia            float64\n",
       "Tempo                 object\n",
       "Segundos             float64\n",
       "Minutos              float64\n",
       "Min_Por_Km           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Data</th>\n",
       "      <th>Distancia</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Segundos</th>\n",
       "      <th>Minutos</th>\n",
       "      <th>Min_Por_Km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-04</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-06</td>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-07</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Data  Distancia Tempo  Segundos  Minutos  Min_Por_Km\n",
       "0 2015-08-02       1.70   NaN       NaN      NaN         NaN\n",
       "1 2015-08-03       1.40   NaN       NaN      NaN         NaN\n",
       "2 2015-08-04       2.10   NaN       NaN      NaN         NaN\n",
       "3 2015-08-06       2.38   NaN       NaN      NaN         NaN\n",
       "4 2015-08-07       2.30   NaN       NaN      NaN         NaN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "260.72"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 1 - Qual o valor máximo da coluna Minutos?\n",
    "valor_maximo_min = round(df['Minutos'].max(), 2)\n",
    "valor_maximo_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.01"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 2 - Qual o valor mínimo de distância acima de 2.0?\n",
    "valor_minimo_dist = df.Distancia[df.Distancia > 2].min()\n",
    "valor_minimo_dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercício 3 - Crie um plot com a frequência acumulada da coluna Distancia.\n",
    "df['Distancia'].hist(bins = 30, cumulative = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 4 - Qual o dia da semana no índice de posição zero?\n",
    "df['Data'][0].weekday()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# O método weekday retorna um valor para o dia semana, no qual começa em 0, segunda-feira, e vai até domingo (dia 6).\n",
    "# Portanto, 6 é domingo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    0\n",
       "2    1\n",
       "3    3\n",
       "4    4\n",
       "Name: Data, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 5 - Qual o dia da semana nos índices nas 5 primeiras posições?\n",
    "def weekday(x):\n",
    "    return x.weekday()\n",
    "\n",
    "df1 = df['Data'][0:5]\n",
    "df1.map(weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Data</th>\n",
       "      <th>Distancia</th>\n",
       "      <th>Tempo</th>\n",
       "      <th>Segundos</th>\n",
       "      <th>Minutos</th>\n",
       "      <th>Min_Por_Km</th>\n",
       "      <th>Dia_da_Semana</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-08-02</td>\n",
       "      <td>1.70</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-08-03</td>\n",
       "      <td>1.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-08-04</td>\n",
       "      <td>2.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-08-06</td>\n",
       "      <td>2.38</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-08-07</td>\n",
       "      <td>2.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Friday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Data  Distancia Tempo  Segundos  Minutos  Min_Por_Km Dia_da_Semana\n",
       "0 2015-08-02       1.70   NaN       NaN      NaN         NaN        Sunday\n",
       "1 2015-08-03       1.40   NaN       NaN      NaN         NaN        Monday\n",
       "2 2015-08-04       2.10   NaN       NaN      NaN         NaN       Tuesday\n",
       "3 2015-08-06       2.38   NaN       NaN      NaN         NaN      Thursday\n",
       "4 2015-08-07       2.30   NaN       NaN      NaN         NaN        Friday"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 6 - Extraia todos os dias da semana (em formato texto) e insira em uma nova coluna no dataframe df.\n",
    "Dias = df['Data'].map(weekday)\n",
    "\n",
    "def dayNameFromWeekday(weekday):\n",
    "    days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
    "    return days[weekday]\n",
    "\n",
    "df['Dia_da_Semana'] = Dias.map(dayNameFromWeekday)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dia_da_Semana'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exercício 7 - Crie um gráfico de barras com o total da distância percorrida em cada dia da semana.\n",
    "df[['Distancia', 'Dia_da_Semana']].groupby('Dia_da_Semana').sum().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Distancia</th>\n",
       "      <th>Segundos</th>\n",
       "      <th>Minutos</th>\n",
       "      <th>Min_Por_Km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dia_da_Semana</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>50.52</td>\n",
       "      <td>25359.0</td>\n",
       "      <td>422.650000</td>\n",
       "      <td>40.197717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>26.65</td>\n",
       "      <td>9486.0</td>\n",
       "      <td>158.100000</td>\n",
       "      <td>59.710723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>41.79</td>\n",
       "      <td>11668.0</td>\n",
       "      <td>194.466667</td>\n",
       "      <td>61.981259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>45.45</td>\n",
       "      <td>16815.0</td>\n",
       "      <td>280.250000</td>\n",
       "      <td>49.109769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>26.52</td>\n",
       "      <td>11769.0</td>\n",
       "      <td>196.150000</td>\n",
       "      <td>54.805434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>36.50</td>\n",
       "      <td>13346.0</td>\n",
       "      <td>222.433333</td>\n",
       "      <td>74.632908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>26.71</td>\n",
       "      <td>10450.0</td>\n",
       "      <td>174.166667</td>\n",
       "      <td>61.277645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Distancia  Segundos     Minutos  Min_Por_Km\n",
       "Dia_da_Semana                                             \n",
       "Friday             50.52   25359.0  422.650000   40.197717\n",
       "Monday             26.65    9486.0  158.100000   59.710723\n",
       "Saturday           41.79   11668.0  194.466667   61.981259\n",
       "Sunday             45.45   16815.0  280.250000   49.109769\n",
       "Thursday           26.52   11769.0  196.150000   54.805434\n",
       "Tuesday            36.50   13346.0  222.433333   74.632908\n",
       "Wednesday          26.71   10450.0  174.166667   61.277645"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Dia_da_Semana').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Exercício 8 - Delete a coluna Tempo do dataframe df.\n",
    "del df['Tempo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Data</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dia_da_Semana</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Data\n",
       "Dia_da_Semana      \n",
       "Friday            8\n",
       "Monday           11\n",
       "Saturday         15\n",
       "Sunday           14\n",
       "Thursday          8\n",
       "Tuesday          14\n",
       "Wednesday        11"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 9 - Qual o total de corridas de taxi por dia da semana?\n",
    "df2 = df[['Dia_da_Semana', 'Data']]\n",
    "df2.groupby('Dia_da_Semana').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Distancia</th>\n",
       "      <th>Segundos</th>\n",
       "      <th>Minutos</th>\n",
       "      <th>Min_Por_Km</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dia_da_Semana</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Friday</th>\n",
       "      <td>6.315000</td>\n",
       "      <td>5071.800000</td>\n",
       "      <td>84.530000</td>\n",
       "      <td>8.039543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Monday</th>\n",
       "      <td>2.422727</td>\n",
       "      <td>1185.750000</td>\n",
       "      <td>19.762500</td>\n",
       "      <td>7.463840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saturday</th>\n",
       "      <td>2.786000</td>\n",
       "      <td>1458.500000</td>\n",
       "      <td>24.308333</td>\n",
       "      <td>7.747657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunday</th>\n",
       "      <td>3.246429</td>\n",
       "      <td>2802.500000</td>\n",
       "      <td>46.708333</td>\n",
       "      <td>8.184961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thursday</th>\n",
       "      <td>3.315000</td>\n",
       "      <td>1681.285714</td>\n",
       "      <td>28.021429</td>\n",
       "      <td>7.829348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tuesday</th>\n",
       "      <td>2.607143</td>\n",
       "      <td>1334.600000</td>\n",
       "      <td>22.243333</td>\n",
       "      <td>7.463291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wednesday</th>\n",
       "      <td>2.428182</td>\n",
       "      <td>1306.250000</td>\n",
       "      <td>21.770833</td>\n",
       "      <td>7.659706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Distancia     Segundos    Minutos  Min_Por_Km\n",
       "Dia_da_Semana                                               \n",
       "Friday          6.315000  5071.800000  84.530000    8.039543\n",
       "Monday          2.422727  1185.750000  19.762500    7.463840\n",
       "Saturday        2.786000  1458.500000  24.308333    7.747657\n",
       "Sunday          3.246429  2802.500000  46.708333    8.184961\n",
       "Thursday        3.315000  1681.285714  28.021429    7.829348\n",
       "Tuesday         2.607143  1334.600000  22.243333    7.463291\n",
       "Wednesday       2.428182  1306.250000  21.770833    7.659706"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exercício 10 - Qual a média para cada uma das colunas por dia da semana?\n",
    "df.groupby('Dia_da_Semana').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FIM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Obrigado - Data Science Academy - <a href=\"http://facebook.com/dsacademybr\">facebook.com/dsacademybr</a>"
   ]
  }
 ],
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