Pandas2.py-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Níveis de Índice\n",
    "outside = ['G1','G1','G1','G2','G2','G2']\n",
    "inside = [1,2,3,1,2,3]\n",
    "hier_index = list(zip(outside,inside))\n",
    "hier_index = pd.MultiIndex.from_tuples(hier_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['G1', 'G1', 'G1', 'G2', 'G2', 'G2']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 1, 2, 3]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,2), index=hier_index, columns=['A', 'B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex([('G1', 1),\n",
       "            ('G1', 2),\n",
       "            ('G1', 3),\n",
       "            ('G2', 1),\n",
       "            ('G2', 2),\n",
       "            ('G2', 3)],\n",
       "           )"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hier_index"
   ]
  },
  {
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   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "      <th rowspan=\"3\" valign=\"top\">G1</th>\n",
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       "      <td>-0.491298</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">G2</th>\n",
       "      <th>1</th>\n",
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      "text/plain": [
       "             A         B\n",
       "G1 1 -0.545687  0.037090\n",
       "   2  0.435995 -0.398380\n",
       "   3  0.951561 -0.491298\n",
       "G2 1 -0.068940 -0.775637\n",
       "   2 -1.082656 -0.990120\n",
       "   3 -0.632859  1.136987"
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     "execution_count": 20,
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   "source": [
    "df"
   ]
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  {
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   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Numero</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      "text/plain": [
       "               A         B\n",
       "Numero                    \n",
       "1      -0.545687  0.037090\n",
       "2       0.435995 -0.398380\n",
       "3       0.951561 -0.491298"
      ]
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     "execution_count": 36,
     "metadata": {},
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   ],
   "source": [
    "df.loc['G1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.545687\n",
       "B    0.037090\n",
       "Name: 1, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['G1'].loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.names = ['Grupo','Numero']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
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       "                     A         B\n",
       "Grupo Numero                    \n",
       "G1    1      -0.545687  0.037090\n",
       "      2       0.435995 -0.398380\n",
       "      3       0.951561 -0.491298\n",
       "G2    1      -0.068940 -0.775637\n",
       "      2      -1.082656 -0.990120\n",
       "      3      -0.632859  1.136987"
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   "source": [
    "df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               A         B\n",
       "Numero                    \n",
       "1      -0.545687  0.037090\n",
       "2       0.435995 -0.398380\n",
       "3       0.951561 -0.491298"
      ]
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     "execution_count": 37,
     "metadata": {},
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   ],
   "source": [
    "df.xs('G1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Grupo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>G1</th>\n",
       "      <td>-0.545687</td>\n",
       "      <td>0.037090</td>\n",
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      "text/plain": [
       "              A         B\n",
       "Grupo                    \n",
       "G1    -0.545687  0.037090\n",
       "G2    -0.068940 -0.775637"
      ]
     },
     "execution_count": 40,
     "metadata": {},
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   ],
   "source": [
    "df.xs(1, level = 'Numero')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'A': [1, 2, np.nan], 'B':[5, np.nan, np.nan],'C':[1, 2, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': [1, 2, nan], 'B': [5, nan, nan], 'C': [1, 2, 3]}"
      ]
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     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
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       "     A    B  C\n",
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       "1  2.0  NaN  2\n",
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   "metadata": {},
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       "     A    B  C\n",
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    "df1.dropna()"
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   "execution_count": 48,
   "metadata": {},
   "outputs": [
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       "     A    B  C\n",
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       "1  2.0  NaN  2"
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     "execution_count": 48,
     "metadata": {},
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   "source": [
    "df1.dropna(thresh = 2)"
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   "cell_type": "code",
   "execution_count": 51,
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ausente</td>\n",
       "      <td>Ausente</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A        B  C\n",
       "0      1.0      5.0  1\n",
       "1      2.0  Ausente  2\n",
       "2  Ausente  Ausente  3"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value='Ausente')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2</td>\n",
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       "      <th>2</th>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  1.5  2\n",
       "2  1.5  1.5  3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value=df1['A'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>2.0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "     A    B  C\n",
       "0  1.0  5.0  1\n",
       "1  2.0  5.0  2\n",
       "2  2.0  5.0  3"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GroupBy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cria um DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'Empresa':['GOOG','GOOG', 'MSFT','MSFT','FB','FB'],\n",
    "        'Nome':['Sam', 'Charlie','Amy', 'Vanessa', 'Carl','Sarah'],\n",
    "        'Venda':[200,120,340,124,243,350]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOG</td>\n",
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       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>Charlie</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Amy</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FB</td>\n",
       "      <td>Carl</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FB</td>\n",
       "      <td>Sarah</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Empresa     Nome  Venda\n",
       "0    GOOG      Sam    200\n",
       "1    GOOG  Charlie    120\n",
       "2    MSFT      Amy    340\n",
       "3    MSFT  Vanessa    124\n",
       "4      FB     Carl    243\n",
       "5      FB    Sarah    350"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = df2.groupby('Empresa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x03F00730>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Venda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empresa</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>593</td>\n",
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       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>320</td>\n",
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       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Venda\n",
       "Empresa       \n",
       "FB         593\n",
       "GOOG       320\n",
       "MSFT       464"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Venda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empresa</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>296.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>160.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>232.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Venda\n",
       "Empresa       \n",
       "FB       296.5\n",
       "GOOG     160.0\n",
       "MSFT     232.0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">Venda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empresa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>2.0</td>\n",
       "      <td>296.5</td>\n",
       "      <td>75.660426</td>\n",
       "      <td>243.0</td>\n",
       "      <td>269.75</td>\n",
       "      <td>296.5</td>\n",
       "      <td>323.25</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>56.568542</td>\n",
       "      <td>120.0</td>\n",
       "      <td>140.00</td>\n",
       "      <td>160.0</td>\n",
       "      <td>180.00</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>152.735065</td>\n",
       "      <td>124.0</td>\n",
       "      <td>178.00</td>\n",
       "      <td>232.0</td>\n",
       "      <td>286.00</td>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Venda                                                        \n",
       "        count   mean         std    min     25%    50%     75%    max\n",
       "Empresa                                                              \n",
       "FB        2.0  296.5   75.660426  243.0  269.75  296.5  323.25  350.0\n",
       "GOOG      2.0  160.0   56.568542  120.0  140.00  160.0  180.00  200.0\n",
       "MSFT      2.0  232.0  152.735065  124.0  178.00  232.0  286.00  340.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nome</th>\n",
       "      <th>Venda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Empresa</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>FB</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOG</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Nome  Venda\n",
       "Empresa             \n",
       "FB          2      2\n",
       "GOOG        2      2\n",
       "MSFT        2      2"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "group = df2.groupby('Nome')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Venda</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amy</th>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carl</th>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charlie</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sam</th>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sarah</th>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vanessa</th>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Venda\n",
       "Nome          \n",
       "Amy        340\n",
       "Carl       243\n",
       "Charlie    120\n",
       "Sam        200\n",
       "Sarah      350\n",
       "Vanessa    124"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Venda    350\n",
       "Name: Sarah, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum().loc['Sarah']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Venda    243\n",
       "Name: Carl, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.sum().loc['Carl']"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importante!!!"
   ]
  }
 ],
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