ExercisesPandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Criação de Objeto\n",
    "**Confira todos os comandos em: https://pandas.pydata.org/docs/user_guide/10min.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Criando um **Series** passando uma lista de valores, permitindo que os pandas criem um índice inteiro padrão:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1,2,3,4,np.nan,6,7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    4.0\n",
       "4    NaN\n",
       "5    6.0\n",
       "6    7.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Criando um **DataFrame** passando uma matriz **NumPy**, com um índice datetime e colunas rotuladas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = pd.date_range(\"20210204\",periods = 6 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-02-04', '2021-02-05', '2021-02-06', '2021-02-07',\n",
       "               '2021-02-08', '2021-02-09'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(6,4), index = dates, columns = list(\"ABCD\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Criando um DataFrame, passando um **dicionário de objetos** que podem ser convertidos em séries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(\n",
    "       {\n",
    "            \"A\": 1.0,\n",
    "            \"B\": pd.Timestamp(\"20210205\"),\n",
    "            \"C\": pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n",
    "            \"D\": np.array([3] * 4, dtype=\"int32\"),\n",
    "            \"E\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "            \"F\": \"foo\",\n",
    "   } )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>foo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2021-02-05</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>train</td>\n",
       "      <td>foo</td>\n",
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      "text/plain": [
       "     A          B    C  D      E    F\n",
       "0  1.0 2021-02-05  1.0  3   test  foo\n",
       "1  1.0 2021-02-05  1.0  3  train  foo\n",
       "2  1.0 2021-02-05  1.0  3   test  foo\n",
       "3  1.0 2021-02-05  1.0  3  train  foo"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* As colunas do resultante DataFrame têm diferentes tipos de d ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A           float64\n",
       "B    datetime64[ns]\n",
       "C           float32\n",
       "D             int32\n",
       "E          category\n",
       "F            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Se você estiver usando IPython, o preenchimento de tabulação para nomes de coluna (bem como atributos públicos) é habilitado automaticamente. Aqui está um subconjunto dos atributos que serão concluídos:*\n",
    "\n",
    "df2.<TAB> (TAB)\n",
    "    \n",
    "df2.A                  df2.bool\n",
    "df2.abs                df2.boxplot\n",
    "df2.add                df2.C\n",
    "df2.add_prefix         df2.clip\n",
    "df2.add_suffix         df2.columns\n",
    "df2.align              df2.copy\n",
    "df2.all                df2.count\n",
    "df2.any                df2.combine\n",
    "df2.append             df2.D\n",
    "df2.apply              df2.describe\n",
    "df2.applymap           df2.diff\n",
    "df2.B                  df2.duplicated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizando Dados:\n",
    " *Veja como visualizar as linhas superior e inferior do quadro:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Exibir o índice, colunas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-02-04', '2021-02-05', '2021-02-06', '2021-02-07',\n",
       "               '2021-02-08', '2021-02-09'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A', 'B', 'C', 'D'], dtype='object')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.to_numpy of                    A         B         C         D\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.to_numpy of      A          B    C  D      E    F\n",
       "0  1.0 2021-02-05  1.0  3   test  foo\n",
       "1  1.0 2021-02-05  1.0  3  train  foo\n",
       "2  1.0 2021-02-05  1.0  3   test  foo\n",
       "3  1.0 2021-02-05  1.0  3  train  foo>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0, Timestamp('2021-02-05 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [1.0, Timestamp('2021-02-05 00:00:00'), 1.0, 3, 'train', 'foo'],\n",
       "       [1.0, Timestamp('2021-02-05 00:00:00'), 1.0, 3, 'test', 'foo'],\n",
       "       [1.0, Timestamp('2021-02-05 00:00:00'), 1.0, 3, 'train', 'foo']],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**describe( )** mostra um resumo estatístico dos seus dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.252643</td>\n",
       "      <td>0.028430</td>\n",
       "      <td>-0.244736</td>\n",
       "      <td>-0.146656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.816691</td>\n",
       "      <td>0.751292</td>\n",
       "      <td>1.064152</td>\n",
       "      <td>1.696197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>-2.699501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.892034</td>\n",
       "      <td>-0.519259</td>\n",
       "      <td>-0.603846</td>\n",
       "      <td>-1.055228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-0.168461</td>\n",
       "      <td>0.174266</td>\n",
       "      <td>-0.506993</td>\n",
       "      <td>-0.064703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.066674</td>\n",
       "      <td>0.305302</td>\n",
       "      <td>-0.161582</td>\n",
       "      <td>1.236922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              A         B         C         D\n",
       "count  6.000000  6.000000  6.000000  6.000000\n",
       "mean  -0.252643  0.028430 -0.244736 -0.146656\n",
       "std    0.816691  0.751292  1.064152  1.696197\n",
       "min   -1.194867 -0.915646 -1.481533 -2.699501\n",
       "25%   -0.892034 -0.519259 -0.603846 -1.055228\n",
       "50%   -0.168461  0.174266 -0.506993 -0.064703\n",
       "75%    0.066674  0.305302 -0.161582  1.236922\n",
       "max    1.004106  1.139150  1.709678  1.663575"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Transpondo seus dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2021-02-04</th>\n",
       "      <th>2021-02-05</th>\n",
       "      <th>2021-02-06</th>\n",
       "      <th>2021-02-07</th>\n",
       "      <th>2021-02-08</th>\n",
       "      <th>2021-02-09</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.004041</td>\n",
       "      <td>0.090246</td>\n",
       "      <td>1.004106</td>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-1.078418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.712400</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>0.310946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>-0.421721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>-0.828644</td>\n",
       "      <td>1.663575</td>\n",
       "      <td>0.699239</td>\n",
       "      <td>-1.130756</td>\n",
       "      <td>1.416150</td>\n",
       "      <td>-2.699501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   2021-02-04  2021-02-05  2021-02-06  2021-02-07  2021-02-08  2021-02-09\n",
       "A   -0.332882   -0.004041    0.090246    1.004106   -1.194867   -1.078418\n",
       "B   -0.712400    1.139150    0.288370    0.060163   -0.915646    0.310946\n",
       "C   -0.074868   -0.607706   -0.592266    1.709678   -1.481533   -0.421721\n",
       "D   -0.828644    1.663575    0.699239   -1.130756    1.416150   -2.699501"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Classificando por eixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>D</th>\n",
       "      <th>C</th>\n",
       "      <th>B</th>\n",
       "      <th>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.828644</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.332882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>1.663575</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.004041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.699239</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>0.090246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>-1.130756</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.004106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>1.416150</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.194867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-2.699501</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-1.078418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   D         C         B         A\n",
       "2021-02-04 -0.828644 -0.074868 -0.712400 -0.332882\n",
       "2021-02-05  1.663575 -0.607706  1.139150 -0.004041\n",
       "2021-02-06  0.699239 -0.592266  0.288370  0.090246\n",
       "2021-02-07 -1.130756  1.709678  0.060163  1.004106\n",
       "2021-02-08  1.416150 -1.481533 -0.915646 -1.194867\n",
       "2021-02-09 -2.699501 -0.421721  0.310946 -1.078418"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(axis = 1, ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Classificando por valores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = \"B\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtendo :\n",
    "* Selecionar uma única coluna, que produz um Series, equivalente a df.A:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-02-04   -0.332882\n",
       "2021-02-05   -0.004041\n",
       "2021-02-06    0.090246\n",
       "2021-02-07    1.004106\n",
       "2021-02-08   -1.194867\n",
       "2021-02-09   -1.078418\n",
       "Freq: D, Name: A, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Selecionando via [ ], que corta as linhas.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.71240</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.13915</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B         C         D\n",
       "2021-02-04 -0.332882 -0.71240 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.13915 -0.607706  1.663575"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seleção por Rótulos\n",
    "Para obter uma seção transversal usando um rótulo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.332882\n",
       "B   -0.712400\n",
       "C   -0.074868\n",
       "D   -0.828644\n",
       "Name: 2021-02-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecionando em um multi-eixo por rótulo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2021-02-04 -0.332882 -0.712400\n",
       "2021-02-05 -0.004041  1.139150\n",
       "2021-02-06  0.090246  0.288370\n",
       "2021-02-07  1.004106  0.060163\n",
       "2021-02-08 -1.194867 -0.915646\n",
       "2021-02-09 -1.078418  0.310946"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,[\"A\",\"B\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostrando a divisão de rótulos, ambos os pontos de extremidade estão incluídos :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2021-02-05 -0.004041  1.139150\n",
       "2021-02-06  0.090246  0.288370\n",
       "2021-02-07  1.004106  0.060163"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20210205\":\"20210207\",[\"A\",\"B\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Redução nas dimensões do objeto devolvido:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.332882\n",
       "B   -0.712400\n",
       "Name: 2021-02-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[\"20210204\",[\"A\",\"B\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obter um valor escalar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.33288214760511237"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0],\"A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obter acesso rápido a um escalar (equivalente ao método anterior):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.33288214760511237"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.at[dates[0], \"A\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seleção por posição "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecione por meio da posição dos inteiros passados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    1.004106\n",
       "B    0.060163\n",
       "C    1.709678\n",
       "D   -1.130756\n",
       "Name: 2021-02-07 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por fatias inteiras, agindo de forma semelhante a numpy / Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C\n",
       "2021-02-07  1.004106  0.060163  1.709678\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5 , 0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por listas de localizações de posições inteiras, semelhantes ao estilo NumPy / Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>A</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         D\n",
       "2021-02-05 -0.004041  1.663575\n",
       "2021-02-06  0.090246  0.699239\n",
       "2021-02-08 -1.194867  1.416150"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4],[0,3]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para fatiar linhas explicitamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.13915</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.28837</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B         C         D\n",
       "2021-02-05 -0.004041  1.13915 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.28837 -0.592266  0.699239"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora, para fatiar colunas explicitamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2021-02-04 -0.712400 -0.074868\n",
       "2021-02-05  1.139150 -0.607706\n",
       "2021-02-06  0.288370 -0.592266\n",
       "2021-02-07  0.060163  1.709678\n",
       "2021-02-08 -0.915646 -1.481533\n",
       "2021-02-09  0.310946 -0.421721"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[: ,1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obter um valor explicitamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.139150368011421"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obter acesso rápido a um escalar (equivalente ao método anterior):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.139150368011421"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iat[1, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexação booleana "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usando os valores de uma única coluna para selecionar dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
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       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
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      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756"
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     "execution_count": 38,
     "metadata": {},
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   ],
   "source": [
    "df[df[\"A\"]>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seleção de valores de um DataFrame onde uma condição booleana é atendida."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>1.663575</td>\n",
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       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-04       NaN       NaN       NaN       NaN\n",
       "2021-02-05       NaN  1.139150       NaN  1.663575\n",
       "2021-02-06  0.090246  0.288370       NaN  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678       NaN\n",
       "2021-02-08       NaN       NaN       NaN  1.416150\n",
       "2021-02-09       NaN  0.310946       NaN       NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usando o isin()método de filtragem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2[\"E\"] = [\"one\",\"one\",\"two\",\"three\",\"four\",\"three\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644    one\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575    one\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239    two\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756  three\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150   four\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501  three"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644    one\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575    one\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239    two\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756  three\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150   four\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501  three"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239   two\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150  four"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2[\"E\"].isin([\"two\",\"four\"])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ambiente"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definir uma nova coluna alinha automaticamente os dados pelos índices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range(\"20210305\", periods=6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-03-05    1\n",
       "2021-03-06    2\n",
       "2021-03-07    3\n",
       "2021-03-08    4\n",
       "2021-03-09    5\n",
       "2021-03-10    6\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>-0.332882</td>\n",
       "      <td>-0.712400</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>-0.828644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>1.663575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>0.699239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>-1.130756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>1.416150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>-2.699501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2021-02-04 -0.332882 -0.712400 -0.074868 -0.828644\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  1.663575\n",
       "2021-02-06  0.090246  0.288370 -0.592266  0.699239\n",
       "2021-02-07  1.004106  0.060163  1.709678 -1.130756\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  1.416150\n",
       "2021-02-09 -1.078418  0.310946 -0.421721 -2.699501"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo valores por rótulo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.at[dates[0], \"A\"] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo valores por posição:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.iat[0,1]=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configurando atribuindo com uma matriz NumPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,\"D\"]= np.array([5]*len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O resultado das operações de configuração anteriores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-1.194867</td>\n",
       "      <td>-0.915646</td>\n",
       "      <td>-1.481533</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.078418</td>\n",
       "      <td>0.310946</td>\n",
       "      <td>-0.421721</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D\n",
       "2021-02-04  0.000000  0.000000 -0.074868  5\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  5\n",
       "2021-02-06  0.090246  0.288370 -0.592266  5\n",
       "2021-02-07  1.004106  0.060163  1.709678  5\n",
       "2021-02-08 -1.194867 -0.915646 -1.481533  5\n",
       "2021-02-09 -1.078418  0.310946 -0.421721  5"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dados ausentes \n",
    "O pandas usa principalmente o valor np.nan para representar os dados ausentes. Por padrão, ele não é incluído nos cálculos. Consulte a seção Dados ausentes .\n",
    "\n",
    "A reindexação permite que você altere / adicione / exclua o índice em um eixo especificado. Isso retorna uma cópia dos dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.reindex(index = dates[0:4], columns = list(df.columns) + [\"E\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[dates[0] : dates[1], \"E\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    E\n",
       "2021-02-04  0.000000  0.000000 -0.074868  5  1.0\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  5  1.0\n",
       "2021-02-06  0.090246  0.288370 -0.592266  5  NaN\n",
       "2021-02-07  1.004106  0.060163  1.709678  5  NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para eliminar quaisquer linhas que contenham dados ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.13915</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A        B         C  D    E\n",
       "2021-02-04  0.000000  0.00000 -0.074868  5  1.0\n",
       "2021-02-05 -0.004041  1.13915 -0.607706  5  1.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.dropna(how = \"any\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preenchendo dados ausentes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.607706</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.090246</td>\n",
       "      <td>0.288370</td>\n",
       "      <td>-0.592266</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.004106</td>\n",
       "      <td>0.060163</td>\n",
       "      <td>1.709678</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C  D    E\n",
       "2021-02-04  0.000000  0.000000 -0.074868  5  1.0\n",
       "2021-02-05 -0.004041  1.139150 -0.607706  5  1.0\n",
       "2021-02-06  0.090246  0.288370 -0.592266  5  5.0\n",
       "2021-02-07  1.004106  0.060163  1.709678  5  5.0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(value = 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obter a máscara booleana onde estão os valores nan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                A      B      C      D      E\n",
       "2021-02-04  False  False  False  False  False\n",
       "2021-02-05  False  False  False  False  False\n",
       "2021-02-06  False  False  False  False   True\n",
       "2021-02-07  False  False  False  False   True"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Operações"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estatísticas \n",
    "As operações em geral excluem dados perdidos.\n",
    "\n",
    "Executando uma estatística descritiva:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -0.197162\n",
       "B    0.147164\n",
       "C   -0.244736\n",
       "D    5.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mesma operação no outro eixo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-02-04    1.231283\n",
       "2021-02-05    1.381851\n",
       "2021-02-06    1.196587\n",
       "2021-02-07    1.943487\n",
       "2021-02-08    0.351988\n",
       "2021-02-09    0.952702\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Operar com objetos que possuem dimensionalidade diferente e precisam de alinhamento. Além disso, o pandas transmite automaticamente ao longo da dimensão especificada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8], index = dates).shift(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-02-04    NaN\n",
       "2021-02-05    NaN\n",
       "2021-02-06    1.0\n",
       "2021-02-07    3.0\n",
       "2021-02-08    5.0\n",
       "2021-02-09    NaN\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>-0.909754</td>\n",
       "      <td>-0.711630</td>\n",
       "      <td>-1.592266</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>-1.995894</td>\n",
       "      <td>-2.939837</td>\n",
       "      <td>-1.290322</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-6.194867</td>\n",
       "      <td>-5.915646</td>\n",
       "      <td>-6.481533</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\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": [
       "                   A         B         C    D\n",
       "2021-02-04       NaN       NaN       NaN  NaN\n",
       "2021-02-05       NaN       NaN       NaN  NaN\n",
       "2021-02-06 -0.909754 -0.711630 -1.592266  4.0\n",
       "2021-02-07 -1.995894 -2.939837 -1.290322  2.0\n",
       "2021-02-08 -6.194867 -5.915646 -6.481533  0.0\n",
       "2021-02-09       NaN       NaN       NaN  NaN"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sub(s, axis = \"index\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aplicar \n",
    "Aplicação de funções aos dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-04</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.074868</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-05</th>\n",
       "      <td>-0.004041</td>\n",
       "      <td>1.139150</td>\n",
       "      <td>-0.682574</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-06</th>\n",
       "      <td>0.086205</td>\n",
       "      <td>1.427520</td>\n",
       "      <td>-1.274840</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-07</th>\n",
       "      <td>1.090311</td>\n",
       "      <td>1.487683</td>\n",
       "      <td>0.434837</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-08</th>\n",
       "      <td>-0.104556</td>\n",
       "      <td>0.572036</td>\n",
       "      <td>-1.046696</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>-1.182974</td>\n",
       "      <td>0.882983</td>\n",
       "      <td>-1.468417</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C   D\n",
       "2021-02-04  0.000000  0.000000 -0.074868   5\n",
       "2021-02-05 -0.004041  1.139150 -0.682574  10\n",
       "2021-02-06  0.086205  1.427520 -1.274840  15\n",
       "2021-02-07  1.090311  1.487683  0.434837  20\n",
       "2021-02-08 -0.104556  0.572036 -1.046696  25\n",
       "2021-02-09 -1.182974  0.882983 -1.468417  30"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2.198973\n",
       "B    2.054797\n",
       "C    3.191211\n",
       "D    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograma "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size = 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    4\n",
       "2    3\n",
       "3    2\n",
       "4    0\n",
       "5    3\n",
       "6    2\n",
       "7    2\n",
       "8    4\n",
       "9    5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    3\n",
       "3    2\n",
       "4    2\n",
       "0    1\n",
       "1    1\n",
       "5    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Métodos de String \n",
    "A série é equipada com um conjunto de métodos de processamento de string no str atributo que facilita a operação em cada elemento da matriz, como no trecho de código abaixo. Observe que a correspondência de padrões str geralmente usa expressões regulares por padrão (e em alguns casos sempre as usa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.Series([\"A\", \"B\", \"C\",\"AbBa\", \"qUEen\", np.nan, \"FlórIDA\", \"DOG\", \"cat\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          a\n",
       "1          b\n",
       "2          c\n",
       "3       abba\n",
       "4      queen\n",
       "5        NaN\n",
       "6    flórida\n",
       "7        dog\n",
       "8        cat\n",
       "dtype: object"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          A\n",
       "1          B\n",
       "2          C\n",
       "3       ABBA\n",
       "4      QUEEN\n",
       "5        NaN\n",
       "6    FLÓRIDA\n",
       "7        DOG\n",
       "8        CAT\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concat\n",
    "\n",
    "O pandas fornece vários recursos para combinar facilmente objetos Series e DataFrame com vários tipos de lógica de conjunto para os índices e funcionalidade de álgebra relacional no caso de operações do tipo join / merge.\n",
    "\n",
    "\n",
    "Concatenar objetos pandas junto com concat():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(10, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>-0.349988</td>\n",
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       "      <th>1</th>\n",
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       "      <td>1.081430</td>\n",
       "      <td>-1.028794</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.004293</td>\n",
       "      <td>0.169301</td>\n",
       "      <td>-0.318856</td>\n",
       "      <td>-2.650129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.713330</td>\n",
       "      <td>-0.657893</td>\n",
       "      <td>-0.439774</td>\n",
       "      <td>1.048037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.841458</td>\n",
       "      <td>-0.746180</td>\n",
       "      <td>-0.107858</td>\n",
       "      <td>-2.669961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.210471</td>\n",
       "      <td>-0.208765</td>\n",
       "      <td>0.503332</td>\n",
       "      <td>-0.604467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.085115</td>\n",
       "      <td>-0.603295</td>\n",
       "      <td>0.067055</td>\n",
       "      <td>-1.141710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.941407</td>\n",
       "      <td>-2.000358</td>\n",
       "      <td>0.402194</td>\n",
       "      <td>-1.608255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.870275</td>\n",
       "      <td>0.162395</td>\n",
       "      <td>2.846023</td>\n",
       "      <td>-0.249975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.093305</td>\n",
       "      <td>-2.113630</td>\n",
       "      <td>1.083365</td>\n",
       "      <td>-0.293461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.191844  1.232882 -0.349988  0.594959\n",
       "1 -0.670406  0.385959  1.081430 -1.028794\n",
       "2 -1.004293  0.169301 -0.318856 -2.650129\n",
       "3 -0.713330 -0.657893 -0.439774  1.048037\n",
       "4 -2.841458 -0.746180 -0.107858 -2.669961\n",
       "5  0.210471 -0.208765  0.503332 -0.604467\n",
       "6  0.085115 -0.603295  0.067055 -1.141710\n",
       "7 -1.941407 -2.000358  0.402194 -1.608255\n",
       "8  0.870275  0.162395  2.846023 -0.249975\n",
       "9  1.093305 -2.113630  1.083365 -0.293461"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Quebre em pedaços\n",
    "pieces = [df[:3], df[3:7], df[7:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>1.081430</td>\n",
       "      <td>-1.028794</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>-1.004293</td>\n",
       "      <td>0.169301</td>\n",
       "      <td>-0.318856</td>\n",
       "      <td>-2.650129</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.713330</td>\n",
       "      <td>-0.657893</td>\n",
       "      <td>-0.439774</td>\n",
       "      <td>1.048037</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.841458</td>\n",
       "      <td>-0.746180</td>\n",
       "      <td>-0.107858</td>\n",
       "      <td>-2.669961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.210471</td>\n",
       "      <td>-0.208765</td>\n",
       "      <td>0.503332</td>\n",
       "      <td>-0.604467</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.085115</td>\n",
       "      <td>-0.603295</td>\n",
       "      <td>0.067055</td>\n",
       "      <td>-1.141710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-1.941407</td>\n",
       "      <td>-2.000358</td>\n",
       "      <td>0.402194</td>\n",
       "      <td>-1.608255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.870275</td>\n",
       "      <td>0.162395</td>\n",
       "      <td>2.846023</td>\n",
       "      <td>-0.249975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.093305</td>\n",
       "      <td>-2.113630</td>\n",
       "      <td>1.083365</td>\n",
       "      <td>-0.293461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.191844  1.232882 -0.349988  0.594959\n",
       "1 -0.670406  0.385959  1.081430 -1.028794\n",
       "2 -1.004293  0.169301 -0.318856 -2.650129\n",
       "3 -0.713330 -0.657893 -0.439774  1.048037\n",
       "4 -2.841458 -0.746180 -0.107858 -2.669961\n",
       "5  0.210471 -0.208765  0.503332 -0.604467\n",
       "6  0.085115 -0.603295  0.067055 -1.141710\n",
       "7 -1.941407 -2.000358  0.402194 -1.608255\n",
       "8  0.870275  0.162395  2.846023 -0.249975\n",
       "9  1.093305 -2.113630  1.083365 -0.293461"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(pieces)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Junte-se \n",
    "O estilo SQL se funde. Veja a seção de junção de estilo de banco de dados ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"lval\":[1, 2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "right = pd.DataFrame({\"key\": [\"foo\", \"foo\"], \"rval\": [4, 5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  foo     2"
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     "execution_count": 77,
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    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>rval</th>\n",
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       "      <th>0</th>\n",
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      "text/plain": [
       "   key  rval\n",
       "0  foo     4\n",
       "1  foo     5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key</th>\n",
       "      <th>lval</th>\n",
       "      <th>rval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval  rval\n",
       "0  foo     1     4\n",
       "1  foo     1     5\n",
       "2  foo     2     4\n",
       "3  foo     2     5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on = \"key\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outro exemplo que pode ser dado é:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({\"key\": [\"foo\", \"bar\"], \"lval\": [1, 2]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "right = pd.DataFrame({\"key\": [\"foo\",\"bar\"], \"lval\": [4, 5]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     1\n",
       "1  bar     2"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>key</th>\n",
       "      <th>lval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval\n",
       "0  foo     4\n",
       "1  bar     5"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>key</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   key  lval_x  lval_y\n",
       "0  foo       1       4\n",
       "1  bar       2       5"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on = \"key\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agrupamento \n",
    "Por \"agrupar por\", estamos nos referindo a um processo que envolve uma ou mais das seguintes etapas:\n",
    "\n",
    "* Dividindo os dados em grupos com base em alguns critérios\n",
    "\n",
    "* Aplicar uma função a cada grupo de forma independente\n",
    "\n",
    "* Combinando os resultados em uma estrutura de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    " df = pd.DataFrame(\n",
    "        {\n",
    "            \"A\": [\"foo\", \"bar\", \"foo\", \"bar\", \"foo\", \"bar\",\"foo\", \"foo\"],\n",
    "            \"B\": [\"five\", \"three\", \"two\", \"three\", \"one\", \"two\", \"seven\", \"six\"],\n",
    "            \"C\": np.random.randn(8),\n",
    "            \"D\": np.random.randn(8),\n",
    "        }\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>five</td>\n",
       "      <td>0.418599</td>\n",
       "      <td>-1.484218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>-0.394443</td>\n",
       "      <td>1.389734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>-1.768691</td>\n",
       "      <td>-0.091942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>0.942414</td>\n",
       "      <td>0.246598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0.515590</td>\n",
       "      <td>1.343427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>-0.479316</td>\n",
       "      <td>-0.773621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>seven</td>\n",
       "      <td>0.143192</td>\n",
       "      <td>-0.335724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>six</td>\n",
       "      <td>-1.777456</td>\n",
       "      <td>-1.250439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B         C         D\n",
       "0  foo   five  0.418599 -1.484218\n",
       "1  bar  three -0.394443  1.389734\n",
       "2  foo    two -1.768691 -0.091942\n",
       "3  bar  three  0.942414  0.246598\n",
       "4  foo    one  0.515590  1.343427\n",
       "5  bar    two -0.479316 -0.773621\n",
       "6  foo  seven  0.143192 -0.335724\n",
       "7  foo    six -1.777456 -1.250439"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agrupando e aplicando a sum() função aos grupos resultantes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>0.068655</td>\n",
       "      <td>0.862711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>-2.468766</td>\n",
       "      <td>-1.818896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            C         D\n",
       "A                      \n",
       "bar  0.068655  0.862711\n",
       "foo -2.468766 -1.818896"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"A\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O agrupamento por várias colunas forma um índice hierárquico e, novamente, podemos aplicar a sum() função."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>three</th>\n",
       "      <td>0.547971</td>\n",
       "      <td>1.636332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-0.479316</td>\n",
       "      <td>-0.773621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">foo</th>\n",
       "      <th>five</th>\n",
       "      <td>0.418599</td>\n",
       "      <td>-1.484218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>0.515590</td>\n",
       "      <td>1.343427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seven</th>\n",
       "      <td>0.143192</td>\n",
       "      <td>-0.335724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>six</th>\n",
       "      <td>-1.777456</td>\n",
       "      <td>-1.250439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>-1.768691</td>\n",
       "      <td>-0.091942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  C         D\n",
       "A   B                        \n",
       "bar three  0.547971  1.636332\n",
       "    two   -0.479316 -0.773621\n",
       "foo five   0.418599 -1.484218\n",
       "    one    0.515590  1.343427\n",
       "    seven  0.143192 -0.335724\n",
       "    six   -1.777456 -1.250439\n",
       "    two   -1.768691 -0.091942"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"A\", \"B\"]).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Remodelando"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pilha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuples = list(\n",
    "            zip(\n",
    "                *[\n",
    "                    [\"bar\", \"bar\", \"baz\", \"foo\", \"foo\", \"qux\", \"qux\", \"qux\"],\n",
    "                    [\"one\", \"two\", \"one\", \"two\", \"one\", \"two\", \"one\", \"two\"],\n",
    "                    \n",
    "                ]\n",
    "       )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples(tuples, names = [\"first\", \"second\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(8, 2), index = index, columns = [\"A\", \"B\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.555826</td>\n",
       "      <td>-1.154736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.322950</td>\n",
       "      <td>-0.699903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.708167</td>\n",
       "      <td>-0.140524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th>two</th>\n",
       "      <td>-0.355368</td>\n",
       "      <td>-0.452900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.555826 -1.154736\n",
       "      two     1.322950 -0.699903\n",
       "baz   one    -0.708167 -0.140524\n",
       "foo   two    -0.355368 -0.452900"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O stack() método “compacta” um nível nas colunas do DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked = df2.stack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first  second   \n",
       "bar    one     A   -0.555826\n",
       "               B   -1.154736\n",
       "       two     A    1.322950\n",
       "               B   -0.699903\n",
       "baz    one     A   -0.708167\n",
       "               B   -0.140524\n",
       "foo    two     A   -0.355368\n",
       "               B   -0.452900\n",
       "dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Com um DataFrame ou Série \"empilhado\" (tendo um MultiIndexcomo o index), a operação inversa de stack() é unstack(), que por padrão descompacta o último nível :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>A</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th>second</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.555826</td>\n",
       "      <td>-1.154736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>1.322950</td>\n",
       "      <td>-0.699903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>baz</th>\n",
       "      <th>one</th>\n",
       "      <td>-0.708167</td>\n",
       "      <td>-0.140524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <th>two</th>\n",
       "      <td>-0.355368</td>\n",
       "      <td>-0.452900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     A         B\n",
       "first second                    \n",
       "bar   one    -0.555826 -1.154736\n",
       "      two     1.322950 -0.699903\n",
       "baz   one    -0.708167 -0.140524\n",
       "foo   two    -0.355368 -0.452900"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>second</th>\n",
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       "      <th>two</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bar</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.555826</td>\n",
       "      <td>1.322950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.154736</td>\n",
       "      <td>-0.699903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">baz</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.708167</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.140524</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">foo</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.355368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.452900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "second        one       two\n",
       "first                      \n",
       "bar   A -0.555826  1.322950\n",
       "      B -1.154736 -0.699903\n",
       "baz   A -0.708167       NaN\n",
       "      B -0.140524       NaN\n",
       "foo   A       NaN -0.355368\n",
       "      B       NaN -0.452900"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>first</th>\n",
       "      <th>bar</th>\n",
       "      <th>baz</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.555826</td>\n",
       "      <td>-0.708167</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-1.154736</td>\n",
       "      <td>-0.140524</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>1.322950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.355368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.699903</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.452900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "first          bar       baz       foo\n",
       "second                                \n",
       "one    A -0.555826 -0.708167       NaN\n",
       "       B -1.154736 -0.140524       NaN\n",
       "two    A  1.322950       NaN -0.355368\n",
       "       B -0.699903       NaN -0.452900"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stacked.unstack(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tabelas dinâmicas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "{\n",
    "    \"A\": [\"one\", \"one\", \"two\", \"three\"] * 3,\n",
    "    \"B\": [\"A\", \"B\", \"C\"] * 4,\n",
    "    \"C\": [\"foo\",\"foo\", \"foo\", \"bar\", \"bar\", \"bar\"] * 2,\n",
    "    \"D\": np.random.randn(12),\n",
    "    \"E\": np.random.randn(12),\n",
    "}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>1.436046</td>\n",
       "      <td>-0.613245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-1.397590</td>\n",
       "      <td>1.442849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>0.804350</td>\n",
       "      <td>1.025386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>three</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.727030</td>\n",
       "      <td>0.584228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>one</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.831690</td>\n",
       "      <td>-1.091384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.440320</td>\n",
       "      <td>-1.020493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>A</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.788830</td>\n",
       "      <td>0.448044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>three</td>\n",
       "      <td>B</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.775321</td>\n",
       "      <td>0.255537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>one</td>\n",
       "      <td>C</td>\n",
       "      <td>foo</td>\n",
       "      <td>-0.318350</td>\n",
       "      <td>0.615163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>one</td>\n",
       "      <td>A</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.084055</td>\n",
       "      <td>1.223395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>two</td>\n",
       "      <td>B</td>\n",
       "      <td>bar</td>\n",
       "      <td>-0.790084</td>\n",
       "      <td>-0.962951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>three</td>\n",
       "      <td>C</td>\n",
       "      <td>bar</td>\n",
       "      <td>0.302172</td>\n",
       "      <td>0.645642</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A  B    C         D         E\n",
       "0     one  A  foo  1.436046 -0.613245\n",
       "1     one  B  foo -1.397590  1.442849\n",
       "2     two  C  foo  0.804350  1.025386\n",
       "3   three  A  bar  0.727030  0.584228\n",
       "4     one  B  bar  1.831690 -1.091384\n",
       "5     one  C  bar  1.440320 -1.020493\n",
       "6     two  A  foo -0.788830  0.448044\n",
       "7   three  B  foo -0.775321  0.255537\n",
       "8     one  C  foo -0.318350  0.615163\n",
       "9     one  A  bar -0.084055  1.223395\n",
       "10    two  B  bar -0.790084 -0.962951\n",
       "11  three  C  bar  0.302172  0.645642"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos produzir tabelas dinâmicas a partir desses dados com muita facilidade:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>bar</th>\n",
       "      <th>foo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">one</th>\n",
       "      <th>A</th>\n",
       "      <td>-0.084055</td>\n",
       "      <td>1.436046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1.831690</td>\n",
       "      <td>-1.397590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1.440320</td>\n",
       "      <td>-0.318350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">three</th>\n",
       "      <th>A</th>\n",
       "      <td>0.727030</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.775321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>0.302172</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">two</th>\n",
       "      <th>A</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.788830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>-0.790084</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.804350</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "C             bar       foo\n",
       "A     B                    \n",
       "one   A -0.084055  1.436046\n",
       "      B  1.831690 -1.397590\n",
       "      C  1.440320 -0.318350\n",
       "three A  0.727030       NaN\n",
       "      B       NaN -0.775321\n",
       "      C  0.302172       NaN\n",
       "two   A       NaN -0.788830\n",
       "      B -0.790084       NaN\n",
       "      C       NaN  0.804350"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values = \"D\", index = [\"A\", \"B\"], columns = [\"C\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Série Temporal\n",
    "\n",
    "O pandas tem uma funcionalidade simples, poderosa e eficiente para realizar operações de reamostragem durante a conversão de frequência (por exemplo, converter dados secundários em dados de 5 minutos). Isso é extremamente comum, mas não se limita a aplicativos financeiros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods = 100, freq = \"S\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randint(0,500, len(rng)), index = rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01    23694\n",
       "Freq: 5T, dtype: int32"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.resample(\"5Min\").sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Representação de fuso horário:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"3/6/2012 00:00\", periods = 5, freq = \"D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06    0.591256\n",
       "2012-03-07    0.413739\n",
       "2012-03-08    0.317990\n",
       "2012-03-09   -0.639753\n",
       "2012-03-10    0.042330\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts_utc = ts.tz_localize(\"UTC\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-06 00:00:00+00:00    0.591256\n",
       "2012-03-07 00:00:00+00:00    0.413739\n",
       "2012-03-08 00:00:00+00:00    0.317990\n",
       "2012-03-09 00:00:00+00:00   -0.639753\n",
       "2012-03-10 00:00:00+00:00    0.042330\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convertendo para outro fuso horário:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-03-05 19:00:00-05:00    0.591256\n",
       "2012-03-06 19:00:00-05:00    0.413739\n",
       "2012-03-07 19:00:00-05:00    0.317990\n",
       "2012-03-08 19:00:00-05:00   -0.639753\n",
       "2012-03-09 19:00:00-05:00    0.042330\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_utc.tz_convert(\"US/Eastern\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conversão entre representações de intervalo de tempo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = pd.date_range(\"1/1/2012\", periods = 12, freq = \"M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(rng)), index = rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-31   -1.101626\n",
       "2012-02-29   -0.993880\n",
       "2012-03-31    0.332957\n",
       "2012-04-30    0.451847\n",
       "2012-05-31    0.046633\n",
       "2012-06-30    0.947654\n",
       "2012-07-31    0.367616\n",
       "2012-08-31    0.665672\n",
       "2012-09-30    2.139122\n",
       "2012-10-31   -0.720708\n",
       "2012-11-30    0.146720\n",
       "2012-12-31   -0.883292\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps = ts.to_period()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01   -1.101626\n",
       "2012-02   -0.993880\n",
       "2012-03    0.332957\n",
       "2012-04    0.451847\n",
       "2012-05    0.046633\n",
       "2012-06    0.947654\n",
       "2012-07    0.367616\n",
       "2012-08    0.665672\n",
       "2012-09    2.139122\n",
       "2012-10   -0.720708\n",
       "2012-11    0.146720\n",
       "2012-12   -0.883292\n",
       "Freq: M, dtype: float64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2012-01-01   -1.101626\n",
       "2012-02-01   -0.993880\n",
       "2012-03-01    0.332957\n",
       "2012-04-01    0.451847\n",
       "2012-05-01    0.046633\n",
       "2012-06-01    0.947654\n",
       "2012-07-01    0.367616\n",
       "2012-08-01    0.665672\n",
       "2012-09-01    2.139122\n",
       "2012-10-01   -0.720708\n",
       "2012-11-01    0.146720\n",
       "2012-12-01   -0.883292\n",
       "Freq: MS, dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps.to_timestamp()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A conversão entre período e carimbo de data / hora permite que algumas funções aritméticas convenientes sejam usadas. No exemplo a seguir, convertemos uma frequência trimestral com o ano terminando em novembro para 9h do final do mês seguinte ao final do trimestre:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "prng = pd.period_range(\"1990Q1\", \"2000Q4\", freq = \"Q-NOV\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(len(prng)), prng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.index = (prng.asfreq(\"M\", \"e\") + 1).asfreq(\"H\", \"s\") + 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990-03-01 09:00    1.012252\n",
       "1990-06-01 09:00    0.502668\n",
       "1990-09-01 09:00    1.479945\n",
       "1990-12-01 09:00    0.289843\n",
       "1991-03-01 09:00    0.357985\n",
       "Freq: H, dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Categóricos \n",
    "\n",
    "Pandas podem incluir dados categóricos em um DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame( \n",
    "    {\"id\": [1, 2, 3, 4, 5, 6], \"raw_grade\": [\"a\", \"b\", \"b\", \"a\", \"a\", \"e\"]}\n",
    "    \n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade\n",
       "0   1         a\n",
       "1   2         b\n",
       "2   3         b\n",
       "3   4         a\n",
       "4   5         a\n",
       "5   6         e"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converta as notas brutas em um tipo de dados categórico."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"raw_grade\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    b\n",
       "3    a\n",
       "4    a\n",
       "5    e\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): ['a', 'b', 'e']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Renomeie as categorias com nomes mais significativos (a atribuição a Series.cat.categories() está em vigor!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"].cat.categories = [\"very good\", \"good\", \"very bad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (3, object): ['very good', 'good', 'very bad']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"grade\"] = df[\"grade\"].cat.set_categories(\n",
    "    [\"very bad\", \"bad\", \"medium\", \"good\", \"very good\" ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    very good\n",
       "1         good\n",
       "2         good\n",
       "3    very good\n",
       "4    very good\n",
       "5     very bad\n",
       "Name: grade, dtype: category\n",
       "Categories (5, object): ['very bad', 'bad', 'medium', 'good', 'very good']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"grade\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A classificação é por ordem nas categorias, não por ordem lexical."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "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>id</th>\n",
       "      <th>raw_grade</th>\n",
       "      <th>grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>e</td>\n",
       "      <td>very bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>a</td>\n",
       "      <td>very good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id raw_grade      grade\n",
       "5   6         e   very bad\n",
       "1   2         b       good\n",
       "2   3         b       good\n",
       "0   1         a  very good\n",
       "3   4         a  very good\n",
       "4   5         a  very good"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by = \"grade\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O agrupamento por uma coluna categórica também mostra categorias vazias."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "grade\n",
       "very bad     1\n",
       "bad          0\n",
       "medium       0\n",
       "good         2\n",
       "very good    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"grade\").size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotando\n",
    "\n",
    "Usamos a convenção padrão para fazer referência à API matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index = pd.date_range(\"1/1/2000\", periods = 1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = ts.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x80dcbf0>"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gj6yworHKike/PENeVmU3Yd0DC3HDKaNhM+mxSWHFLd/eglufWQfOudj8JP9TEsryDZMUFy4lw8utso8fAPa39+Ofm4R8NM45+v0h2M3JTz5LoajRbRxLhb2tQkSU1aTPeG+AgS6iL649jAv/ZyU+2dOe0WMSRLbQlOBX201Yefc5cWPeLUY9rjypATuO9co/6lv+ug7vbWtBtzsAtz9xD9hcoqzdf8nMkXHXqXWY5eqeAHDR/6zEt5/fAM45fMEwQmGeUgJZRPBL06UjJea9v60FY+9bltE7HU+CeRGXN4C3Nh8DADR3uTN2PILIJpoS/MGoK7Og3x+CPxRWWXLtfT6xQ5S2BN+RwEp3mPVwB0IIhzme+/SgLCq+YFhuWZjK3YrU+7ZUXTpHe9TusW5P5iZYlS4bZf2jxY+vwiqy7IkCo6AEXxL0x97ZibH3RXK52ly+hB2tco1B4bN3WOKPx242gHNgxc5W/PD1L+Tlvd6AHL1jT0Hwpc8lkTVa7ERPgGeyz+8tf10nPz7QEbHkD3VGHku1j97YeAQ/fH1Lxo5NEJmmoATfLgr6U6v2q5Yf7/XCGwjH7Vmba5TuKHuCC5Ak5koxAYRyDH2yhU8+/GTxB8OqCft06go9smw7bn1GOC/R8wGJIsT+JVZBvfOFjXju00PUY5jQLAUl+LYEIij1vtWCS0dJYgs//jhd3qDsh0/lbkWauyhVl04gFFYlqvX7QrjsiU/w1T9/lvK+lny8D+9ta8E1f1wd04chUc8BpeUPUI9hQrsUlOBLFvPIcnWp4mfXHAKgPcFPlC2byPJ3eQOREhEpuXSEdaMnbdfs6yiJZKxAiGNcjV1+7vIGsPFwNz7c2Tbgdj3ugMovH1b0Yvh0f2fMnUI8wa9xmGK6jVGZC0KrFJTgS4KujGH/6mljFK/n34evpEHRUF1JIjHv9QRlH35qk7bC5/LTf26DT2yAfqzHg2uXrMHdr2xOZcgFSSAURrXDhJV3nw0A2JZkbPw5//0hTnroA/l5dJ2m6ES2bndE8CfWCbkPl82qhz8Ylu8yAWDhrz9K7Q0QRI4oKMGXhFLZFGtGfbn8uC5Bk5JcI03cJhLtRFm0Lm9AFp1U4vCV2cgbDwl5ClKryA+2x09UKyYCoTCMeh1qnUI5iuc/O5TUdtJkq3QXJM2B1FdYAUAOnX3qxjmosBnR1ufFt577HO98cQyBEMfFM0fCbNTBHwrjcKfarVOqWc+EtikowVd2lLpu7ihs/PF58o8cAOorrfkYVgz/+v5ZeOk/T034eqIL084Wl2yRJ3L7xEM5UbyzxQUg0kSlmCdyfcEQDnW44Q8Kgi+Vk27vE967jgkXUYl/bDgSt7Wk1JxGiu6RvkdSJI7DYoDVqMezaw5h6ZZjuO3Zz+ENhGAx6GDS6xEKc/RGuX+m/+RdmrwlNEdBCX6NM+ITv/n0MaiwmVQF00aUa8PCH1Vtw9yxVQlfr4oq8jZW9D+v3htp8JKKD1/J8R6vKkxxgD4rBc+P/vEFFjy2Ar3eoJz/oGwLGebAjJ++BwA42u3Bd17ciDte2BCzn263H3taXTjnvwVXTEOU4DstBhyLivX3BIREP+m47a7YC0myriWCyBUFJfhKH31TnRMAMKpK8JOPqbZlvPlJtohu4HLrgnFwWgyqSUJlAlcqHO/1qrJ4c10uOpes3B1JfDLqhc/0g+8tAABMEr8fEpL//ZM9HdjT2hfz2pMf7ZOfS3MvkpumzGLEw5dPV23j8YdgMerl47b3+WDUM9WFfvHjq7BUzMYlCC1Q8GpQbjPine/MxzM3z8v3UFJi/88vwg2njAIg+KDLLEbZtfDMzXOHvN82l0/uugWoE8GKDWXehVG8sFXYTDjw6GL5swWEePp2hSvnzY1HVPu559XN+OJIj/xcalgvu3TMBlw2q161jS8YhsNskKujHu32oNxqxMIpw1TrfbSrdcjvjyAyjbbCWpLg3e8siJnQnJygSJmWYYzJfvo+XxBORcz+CQ3liTZLyPymGqzc3Q6XNyj774HUon0KDWXtJGVIJSAIv0S3O4AtCkEPRK3b3ueX/f5AJH/isMKHb9TrUGYxqHz1DrNBvhNbsbMNF80YLvdWlhiosxlB5JqCs/AnDXcmDHcsNOT4eV9INSE9FJH+2y3zcNGM4ejzBWUf/vlT64q63ILSwo+OkVfmQLy/vQWPvRspndzrCcgTqs6oz/p7503E+VPrAACtLh90LHL3ML1efSF2Wgwq19vEOifOnyZs+8535mNmYwX60sj6JYhMU3CCX0ycPFbolTujoVy28O0m/ZD97k6zES5vQHbpjKywwhcID7JV4aKcs4mOklFOekuRT7efLTSpcXmDchbtf509AV87fYy87h3nNqn2q7wZMEfNqzgtRvliAACVNhOmjCjDgUcXY/LwMjjNBgrPJDQFCX4eOW18DT67/1xcMG24LPjxOmQli8NiQJ83iC63H2aDDhU2I/yhMIKh4hR9v+J9Rde5idcM564LJmFGfTl6vQE5XNVq1CEUTi58Mnoi3aBjqjmS6PwKh9lAFj6hKUjw88wwcYJQasxeFqdBe7I4LQb0+0PYdrQXlTaT7PLwBotT8JVVMaPDYMfW2PG7606M2UaKhpJcXTaTIWGNnGjMBvXc0bAys8q9aNCpf05mow47W1wZrd5JEOlAgq8RyqyChe9MUHAtGcbVCun+q/a043ivV76IJKryWOhIVvp/njkO35g/Lub1i+M0oLGZ9PD4Q/CIGc0Wkx53nT8Ji2eMwLoHFg54vJNGCy64l287FZ/cew5OaKjA9PpyvPrN09BQacWcMZWq9SW30rqDXTH7Ioh8QIKvESRxNuiHHkZ5ycyRqjILw8uFJKSB+ucWMn2+IK6b24j7LpwSk9sQjTQ5azbo4Q2G5Ggaq1GPxiobnrh+tippS0Lp37/x1NF4+875OHlMlVx+ARAuBKvuOScmg/qGeaMBQL64EES+Kd6YvQJDsuwZ0oubt5sM8Ab8+MWXZ8gCdLynOAXf7Ru86c3TN83BLX9dhzsXNgEQ3Cy+QFjh0omfrPfCrafgeI9X1aaSMYYpI5IPAZb2LRXEI4h8Q4KvESQLP92+vFJdnen15bLgt8VJ+y90wmGp2fvAX+Fzp9Rh3yMXQSo3ZDHq4QuG5FLSibKzTxlXnfYY5cY0RRwaSxQW5NLRCKGw4GJIt6a/JGxlFqM8AVxsPnx/MIxx9wstLvVxGt5Ho9Mx+UJoMejhDYTlCpnZ7KEQaUxDLh1CG5DgawSLGAEyVtHIYyhI/QGq7CaYDDpYjXr0eotL8FfvixSZO9jRn9K2FqMO3kBIEZaZPcGX3E2PLNuBvW19g6xNENmHXDoa4YJpw/HLK0+IqdmSKv911njcumCcnBBUZk0+7LBQUFrMl52Y2udlMeoRDHO5/EQ6eQ+DoVdMJO9ucWG8GEVFEPmCBF8j6HQMV89pTHs/jDG5giMguHZ6PcXjUtjT6sID//hCfr5gYm1K20vZsg8t3Q4gfoJWNijmEhdE4UAunSKn3GpEt8c/+IoFws1/WScXOhtfm7r7K3qSVp/laqL/fdVMAKCMW0ITkOAXObVOMzYc6o5ptF2oKJuyv/OdBSlvPybNOZJUWXzCCACAi7JtCQ1Agl/kcC7Ubv/ms+vzPZSMoAzDNA6hyNyCphr58UUzhmdkTANhNuhg0DGy8AlNQD78IkcvdYLaXhyNONKNqmGMYf0DCxHiHMOc2W+JyRiDI6qbGUHkC7Lwi5yfXjwNgHb6/aZLOAONwasd5pyIvYTdZCjqZvJE4UCCX+TUOs34zwXj0Nnvl5t+FDKSD3/CsMIJcZRi/wki35DglwBVdhN8wXBRWJm+YBgXzxyJpXecke+hJI3NZJBLORBEPklL8BljVzHGtjLGwoyxOVGv3ccY28MY28kYuyC9YRLpUCm2+1P2ui1UvIEQKqzGmNr0WsZq1FMcPqEJ0rXwvwBwBYCPlQsZY1MBXAtgGoBFAP7AGCucX2iRUS0K/u/+tTvPI0kfXzAc02pQ61jFGvwEkW/S+uVwzrdzznfGeelSAC9wzn2c8/0A9gCYm86xiKEjWfgvrWvO80jSg3MObyCUsMKlViELn9AK2TKV6gEcVjxvFpfFwBi7lTG2jjG2rq2tLUvDKW1yVT4g2wTDHGEe20xc69hM+qKYPyEKn0F/OYyxDxhjX8T5u3SgzeIsixsiwjlfwjmfwzmfU1ubWl0UIjkaKiPdmXa1uPI4kvSQIl0KzsInl07BUwwRbkASgs85X8g5nx7n740BNmsGoKwE1gDgaLqDJYaG2aDHWZOEi+lX/+8ztBZoy0Of2IzdbCwsC7/WaUaX2y9fsLqKYPK8lNh4uBtj71uGjYe7AQA7j7vQ4y7MCrTZ+uW8CeBaxpiZMTYWQBOAz7J0LCIJpEzPoz1ezH1keZ5HMzRkC7+AInQAoGmYE2EObD/Wi8/2d+LEB9/H8u0t+R4WkSSbmwWhf2rlPvR6A7jgtx/jzhc35HlUQyPdsMzLGWPNAE4FsJQx9i4AcM63AngJwDYA7wD4Fuec7mnzyDfPHK96vnJ34c2XSH5wSxa7VGWDpjohSezyP/wbDy3dBgBYf7Arn0MiUkAq59Hl9mPNXqH5zu6Wwmxok26Uzuuc8wbOuZlzXsc5v0Dx2sOc8/Gc80mc87fTHyqRDgun1qG+IuLL/8rThXXD5fGHcMNTnwIAah3mPI8mNcZURyp0bm7uAVB48xCljHRn2e8LYY/YuWx4gZYqKSxnKJEW9YrJ20LjvW3H0So2Yx9WVliCb4oTVVQMSXClgnRn6fGHcKjDDQAFW26cBL+E+M01s+BQlBcupPouzV0e+fEwZ2EJfjzaxIsXoX2kHIp+f1Dua1CoF2wS/BKivsKKZXfMl58f7ymcaB0psuj0CdVwWgo/r6DVVTiffakjhdR6A2G4RcEv1LwKEvwSY1S1DX//+jwAwNEezyBra4f2Pj/G1drx7C3z8j2UIRHdbIUs/MJBsvC9gZAs9IV0d6yEBL8EGSFO3haShf/F0R7UOMxgLLs9aLPF766bjbvOnyg/T0XwQ2EOl7cw476LAdmHrxB8tz+EJ1bsyeewhgQJfgkiNUM50lUYFv7Tq/bjYIcb43LcjzaT6HUMw8oikR39/lDSVuKDb23DjJ++B3+wMCcKCx0pOz0U5uhVXHgfezdeGTFtQ4JfgliMetQ6zTjc5c73UJLib6sPAAB+esm0vI4jXaJrGnUnma356udC0btesvJzDuccO465IN1YdvQV5mStBAl+iTKqyobDndq38Pe19eFAhxu3nDG24GPXK6IEP9lID6n2f4+HBD/X+ENh+ENhjBDvzvp8QRh0EbdisjV2Nhzq0oQLiAS/RKlxmAoitOzG/yusBLGBOKGhAgAwebgTgJC5ORifH+pCe5/g709V8H3BEM56bAU+3FkcDezzQb9PcLvVKEKBpXLjANDrCeJbf/8cH2wbuFTG5X/4Nx57dyfC4fwWYSPBL1HKrcaCsBj7xDC4QiuJHA+rSY/tP1uEX101EwCSmoi96snV8uNUz1dzlwcHOtz4f//cltpACZl+8ftXo8jurlYI/ke727B08zE8+dHepPYnxfHni8L/FRFDolAEf87oSgDA7edMyPNIMoPVpEeZmEfQ5xt80jaksAhT9R9L1qSuMAObNIFkcNQpsrurFILfkeLdV76rbJLglyjlViM8gZDmIz+63QGcNr4aNpNh8JULBLtZ8Mn3p2jt7TzeO+Drnf1+vL4h0tXML6b/60nxh4x0jk5srJSXKftLSBfhwRw1UnmNbk9+3agk+CWKFDGS7y9gNH/8aC9OfvgD2TrtdPtVPtNiwC6Wt+gbRPC/ONKjer6/feCoqh+8vAnffXETbnjqU4TCXM4Q1RVo7oIWkM7R+GGRkGBpLgYAOvrj51Mc6nBj/P3LsEO8SEsuyXzPm5HglyhSTPiW5p5B1swtP397B9pcPoy7fxl+8c4OdPb7UWUrLsE3G3TQ69iAFv7m5m586XerVMt8wYFdQN2iW2HVnnbsbnXJSUIk+ENHmrS1mw248qQGAIBRz3D1HOGxlLwYHa3z7tbjCIU57n5lMw51uFFhM6rWzxdz5f/RAAAgAElEQVQk+CVKY6UNAHDLX9fleSSJ+d8P96LbHVD5TIsBxhjsg/S5jZeJ6xvE/VZpi4R9dvUH5P2TS2foSBdlu8kAo16QS3+I464LJkHHgBU7hb4SoajoG534mW9u7sGCx1bIIdD5zn0hwS9RRlfb8j2EGIIJSs4WSz9RJU7LwJPmypLKBh2DxagbVPCVF8ZjPR54AoJYSeLj8gbwxsYj6Qy75JBcOg6zAaeMqwIATBzmwDCnBcOckczpg51uVevQRJfYfGe3k+CXKHazAYtnjNCU9dySoL5MsfnwAWBsjR372vvjvtba65Vj7wHghVtPwRkTagecYH9p3WG8uzUSC97Z749Y+KL6/PD1L3DnCxtj5gaIxLj9ooVvNuDSWfX45N5zMG9cNQB15jTnQr0nAFixsxU/eyt+KGwH+fCJfDGq2gaXN6AZC7pdIfgXzxwpP/7KKaPzMZysMmGYA3taXHE/+7mPLMd3X9wkP7cY9TAbdfAP4MO/+5XNqjuGfl8IfWIfY8kVcbTbI76W31jwQqLPF4JJr5PvuJRd48qswuS75DGTSmX86B9fJNwfTdoSeaPMYkQgxOENaCM0UypDazHqcPcFkzCqyobLZo2EQV98X9OmOgf6/SEcTWISz2LUw6wf3KUjYTXq0ecLyLV3JB++5NoJaeQCXwj0+4JyGG00koUvdZL73kub8N7W43KzngcWT1Gtb9LrSPCJ/CFZKFpJwJLCCJ//xilorLLh47vPxm+vPTHPo8oOE2qFxuZ7Wgdvhm0x6kQLP77gR1fdtJsN6POF4BIt/H/v7cDqvR2yJVqotdxzTbfbj7+tORi3RSUAOYFuRFnE6r/3tS0AhDmYr88fh/lNNQCA+U01+Mqpo9Hm8uX1DosEv4Sxi8lMkp8y30g+52JKskrEiHJBJJQTfYmwGvUwDWDht/aq5z4cZj16PH48s/qgvOym//sMa/Z1AoB8ISAG5hfv7AAAtPTGn1uaLWaBK+diJAv+/e8uAADcumAcrj25EU/dNAfnTB6GYJhj7YHObA57QEjwSxip+qRHIxafNA5rgVfFTIYapzAR3dY3eCMUwYevR48ngG88ExtGezzqomE3G7Bsy3HVMr8iAir6AkHEp6t/4DtfKWqnvc+H5d8/U17eWGXF6GohUWt+Uy0e/fIJMBv0qBNzX/J5R02CX8JYTYKwauUW3yPeaUjjKmaku5inVu5XLY83iWsx6uVMzffjVGVUCv4Di6eozme8i+drGyg0czB+/6/deGfr8QHXqa+IhDaPF110ABKWHZfmAvLZD5cEv4SxicLq8Wtj0lb6IZSC4Et09vtVIq+0xJ0WA1bcdRb0OqZaHo3kFqqvsOIrp46W67r86EtTse1nF+CWM8aq1u/zaWPORsv86r1dg64jfU9vPn3sIGsK2GQXKgk+kQes5NLJK/dfNBmA0O5QQumnr7KbMFZs63jDvMShqcd7vLAYdVh1z9kwG/Ry0s+oKhsYY5g6okxe9/QJ1egs8K5NuWDumKqk1jvw6GL8+OKpSa0rGVhumrQl8oHWfPgubxBWo75kSgFIUR7Kuvg+RYis8sLXWGXDf8wbparFLtHi8mF4mSWmwbvkQqgVm3eMr7XjtPE1KfXTLVUC4bDso88URr0OJr1OdYHPNST4JYxVduloI2qjvc8ni1Mp4BQF/2CHGxsOdQFQF0gLRLlxnBYDXN4gnlq5Dy+tPSwvP9btUTVIl3CIVTml5h3BMJcvGJ39fhzudOPuVzZpvkR2rlixoxUvrj0EQAgRli7Id2SwF4PNrM9rVFzxx78RCZFdOnm0OJS0uXyocRRfGYVESHkQ1y5ZAwDY+8hFKpfO3jZ16YUqmwn+UBgPLd0OADijqQbDyyzYedyFS08ciWikXriVdkG46soscFgipZl/+PoWrD3QhavnNGJOki6MYuZrf1krP+73B2E3G3Dg0cVJb28TC+IpXWgx6xj1cgXOfEAWfgnjtBhgMujkzMB8smZfB/69t6OkLHzJgpTocvtV5SWiuXD6CNXzn7+9A4e73HD5gpg+slxe3lglRI9IkT0jyq341VUz8cR/zJZr8T/50V6sPSDcVRR6c/hMoOw1e8+rW+Dxh1IOHnj/e2fi8etOxMu3nZpwnTKrUc6Azgdk4ZcwRr0OM+rLsam5O29j6HEH8NDSbXh5vdCpKVoEi5kyq/q9Hu/x4tvPbwAA3L1oEmY1VqheH1VtQ12ZWU4EWrGjVc7knF4fEfzfXjMLH+5sw5iaSNMOqZa75OZ57fNIaOZAEUClwDOrD2BzVF+Ifl8I9hQFv77Cqqq1E498txYlwS9xKm0mNOepRndHnw8nPfSBalkp9eoos6h/fn/59wG0ihb+N88cHzMJG02fL4i7X9kMo56hqS4SB15hM+GyE+vjbmOPk8Xs00gtpXzx4ze2xizzBEJZMT4qbEYcGKRzWTYhl06JYzbo8mbhvf1FbGJLKdX1ckYJyt62SF2dRGIfjvP5NA1zyv76wZAsfCWDddIqVeriTISnS4XVlNe2oiT4JY7JkLgoV7YxxAm/PHV8dR5Gkh+ii3Il0/4u3gVx5CBuBCXKyo8zGwQ3ULJVOIuJ2/62HmPuXQqPPyTHxwNC+0KJuvIsCL7diJZeH3717s6M7zsZSPBLHKkoVz7KtrZH1ZGpdZpxxeyGnI9DKxxLSvBjFT9R+d542BUWvhSuWYqCL5VN+MmbX6gmrQOhyOc7YZgjZrt0kXz8v1+xB6v3dmR8/4NBgl/imI06tLl8mP3g+znvhNTe54dTIUA/u2RaTo+vBe5ZNBm/vnpm0uuH49XaSdKdAwguPOnGqloMgS2lJKwVO1vx5qaj8vOX1jWrjB1pEvy1/zpt0AnYodBQGdnndX9ak/H9DwZN2pY4JkVzkX/taFVFe2QLzjk+3d8JlzcIp8WAC2cMR43DjAtnjBh84yLjm2eNByBMHEr9U5+9ZV7C9eNNcXhT8MEzxuR5gOFiieZSsvC/9mch1r7CZpQ7VCm58qQG/OaaWfLdT6Y5aVQVTh5TibUHujCxLvN3EINBFn6Jo/QjB+PNCGaBj3e349ola/Dq581wWAz45ZUzcfeiyTk5tlaRxH5+Uw3OEK3MeNx/0RSYDDo8cvkMLJwyDMDQE+emDHcCAHwlZOFLxJu8BgQDKFtiDwDlNiNevu00nDquWtUTN1eQ4Jc4SsHP1Q+/VxGHHB2pUuoMVkfo6jmN2PXQhfiPeaNwvVhQbai1kKaIGaEPLd0uX3BKhZ441j0Q6f+bbexmA7Ye7UXbAIl22SCtd8cYe4wxtoMxtpkx9jpjrELx2n2MsT2MsZ2MsQvSHyqRDZSCnytfrrKWSCJLq9T4zTWCHz8VwZEyQeP59ZNhVFWknvuB9v4B1iw+XIoL3OPXnYg5Yvcqgz43iSAOs1CG4Yxf/Csnx5NI93L2PoDpnPMTAOwCcB8AMMamArgWwDQAiwD8gTFG+dsaROnDz3Yz871tfRhz71Is394qL3NYSPCVmBP0T43HnNGVuH7eKDxy+YwhHUunY/jD9bMBCC6lNzcdxdOr9g+yVfFxycyRstAbdLmx8G2ioeMLhrE/hxfbtN4d5/w9zrl0qVwDQIqpuxTAC5xzH+d8P4A9AOamcywiOyit+lQm/4bC2v1CL8/3FF2b4pX7LUXOmzoc502tw70XJj+XYdDr8PDlM+R2esnywfcW4K1vnwEgEjXi8gZxx/Mb8OBb21LaV6GR6I5SurMK5SjzL6CYKH/hs0M5OSaQWR/+zQDeFh/XAziseK1ZXBYDY+xWxtg6xti6tra2DA6HSAblPG22XTrxilE1ZSHWuRBxmA34041z0FBpG3zlNJkwzClHY0lzKPF65RYjoTCH02zAPYsmq+ZLpMehcG4ilpQJhh05zIEZ9H6aMfYBgOFxXvoh5/wNcZ0fAggCeE7aLM76cS+dnPMlAJYAwJw5c0oosV4bfGP+OJzQUI5fv78r6+F5Lm/sxOBs0XdK5Ifoej7FzPEeLzyBEG5dMA7fPGs8rjm5Uc4yl1w5wVBuJOiK2Q2YWOfEl363Cq+sb8ad5zbJVU6zyaBnm3O+cKDXGWM3AfgSgHN5JA2wGUCjYrUGAEejtyXyj9Wkx1mThuEPK/Zm3cJ/4B9fyI9HVdlw/0WTMW1k9uP+icREV+wsZpZtOQYgUqCvSuFOHFdrB7bnNmpMmfOy9kBnTgQ/3SidRQDuAXAJ51xZAu5NANcyxsyMsbEAmgB8ls6xiOxiNurgyWHVxBMayrFoeuklWmmNeFFB0Z22igUpmunrZ4yLee2u8ydhyVdOynktp99eMwtAbJmRbJGuD//3AJwA3meMbWSMPQkAnPOtAF4CsA3AOwC+xTkvveyOAmJEuQUHO/rj1mrJBlU0WasZLpmp7pbl1kgHtEwj5RrE++6ZDDqcPy2e5zq7XDprJMwGHdpz1Fg+3SidCZzzRs75LPHvNsVrD3POx3POJ3HO3x5oP0T+OWl0JbrdAezLYoiYMuSw0kaCrxUev+5E1XOttLzMNP2+IKxG/aDJbbmEMYYahzlnCViUaUsAEAQfANYf7MrofoOhMF5edxihMFe5CkrJd1wIrLnvXHxnYRMA5LXJdroEQ2Ec6Y7fsrPPF1JVC9UKY2vs2NPaN/iKGYAEnwAAjK0RwiOPZLi/7V9XH8QPXtmMF9ceVoWA9pdYKr/WGV5uwQxxErE3TjRVofDX1Qdx+qP/wvZjvTGv9fmCcKRQSjpXTB1Zhp3HXTlxp5LgEwCEOGS7SZ/xmipSF6c+n1C75JzJQsGvkygcU3NUir7trjz0RsgUksESL5mp3xfUpIVfYTPCHwrnpGqp9t49kTccFgP6MmzdSUWqpDjn08ZX43fXnajJH16pI82r5KMZTqaQSnVsidPboU+jgm8TG7B4/CFVM5ZsQBY+IeMwGzJu4QfFzMVt4i22Ua/T5I+OAKpEwe9yF67ge8T5h0CcBKp+X1CTxfqkDHR3DooXkuATMg6LUVVFMBNIlv0r65sB5K78LJE6TtE6Vpav1jLbjvbG5Az0ixFGW470YHNzt/o1jVr4VpMwJk8OJsvp10fIOM0G9Hkz+2MPRtUmMeao/CyROjodg9mg02wHrG63H73i9/NwpxsXPb4SP1+2Q7WOMqT0kt9/opoI7fOFNDlpG3HpZP9zJ8EnZGwmfcaTbqJLLpOFr20sRr1me9zO+tn7OPmhDwBAbk+4Zl+kEfihDndMdM5RRWP4fl8QdpMWLXzRpUMWPpFLzEY9dhx34V87WgZfOUmixYMEX9tYjLqs90UYCkfF2HpfMIynVu6TyxgrXToLHluBHcddqu2O9wjbvfZ5MzwBbcbhS4Kfi4ql9OsjZKRM2Jv/krkvnjcYxoKJtfJzculoG7NBn/W+CEPh/N98LD9+aOl27G8Xwn0Hq/tzvEfIYP3eS5sAADXO7PWrHSrS7y4X+Q8k+IRMKt2WksUXCMFi0MnREcYsHIPIHBajDj4NWvjR0WO7WgTBP9Dhxtj7lsq+/Whaer2q55ecMDLuevlkqthbeNrIsqwfi359hIzZkPkJLW8gBKtJD6l8STYuKkTmsBi1aeFHs1PhuuEc+GxfZ9z1pB4MI8otuPKkBpTbtFfSgzGGi2YMz8lkOf36CBmzMfNfB08gBItBL/tOlTXACe1hMWhv0tYfRwijQy4Pd7lj1gGEnIJFv/0Yx3q8qNBw/San2QhXhiPk4qG9GQwibyhLeYTCPCNVBb2BMCxGHZ65eS52HHehLIcNJojUMRt1cTuT5ZN4mb/R5YQ7xOfzxlYhFOZYJxYBPNDRL0/klmtZ8C2GnHzuZOETMsqwsOYEFlOqeANCunhTnRMXz9Se/5RQo8WwTKXl+8OLpsAgGiIjyi3y8n3iJO5VcxpRq5iYXbm7XX6sRXeORIXNCLc/lPXPngSfkOn3Rb5sZz72Ydr745zDFwzDnOX6IETmKLca0aOxTFspN+TRK2bg6/PHwiTOA11zciPWPyB0YJXKek8e7sTDl8/Aw5dPx6zGCoQUJVq1bOFL7Q0zZWglggSfkIkuWfz0qv246+VNQ96fNAllJcEvGIaXWdDq8qmEMt9Igj+q2gbGWOR5lQ3VDjOGOc1o6RXCLxurbKiym3D9vNGoiLLotSz4DZWC4B/qJMEncsToGnUT5Qff2oZX1jcP+ccv3Z5asjAZTGSHujIzQmGOjhz1WE0GT0AwRGxRWbJSfofNFDEolMXRGiqtqvVz2aA8VaS2izf/ZR3e2Hgka8ehXyIh8/3zJuHvX58Xs/zwEK0Ojyz4ZOEXCtUOwf/dqaGKmZJFLwl7rdMMi1GHGnGsBzoi309loEFjpdqAMWiotWE0SqPozhc2Zu04JPiEjMmgw2kTavDA4imq5S5vEBPuX4a/fLI/pf1JKfpk4RcO0rnSUnkFSfAl1+CHd52Fz3903qDbDRcndcssBtx1/kSc0KDdkOBcuT3pl0jEIJXJlTje60UwzPHoOzsSbKGmxxPAfa9tkbsPWbKQ0EVkB+lcfbyrLc8jiSAVSJNqztjNBpV755mb58bdTnKTjK2x4/ZzmsCYli189W8knKU5FBJ8IoZoX+nBjn4AQIXVlNT2T360F89/dgg3PP0pAHLpFBJSRNWv39+FLc2xXaPywWufCz7tRJUuZ42qiLtc6uClpbuVRERnoLe4vAnWTA8SfCIGU9SXT4ociI56SER0T1QS/MJBKTweDcTjt4q1cGaPqpAt/GgSJfNJPn8tvI/BiL77ONSRnWgdyrQlYjBFlTA+LtYUT/SDiyYYdTtKPvzCQXlxzkV99sFYLbpzbl0wfsD1Xrz1lJjM8MYqG06fUI07z52YtfFli0OdbswbVw0AaHV5sWZfJy7JQOIi/RKJGKIt/G4xEUefpA80GFWyliz8wkF5cf7qn9eiNUuuhWSRIlai55WimTeuGnPGVKmWGfU6PPf1UzB3bFWCrbSLMjLu+y9twh3PbxhytJwSEnwihmjB/2y/UIlQl6TgewNhVQgcCX7hEF0xdY9YhjjfDCb4xYRJr1MlYB0Rm79sONydaJOkIcEnYoh26UgkG+TgC4ZQr0h6IZdO4RB9rvKZb6vsR+vQYKeqbDFhmAMHFYIvNXmJnhsbCvRLJGJI1IbQFwwnVWfFFwyj1hEpYEWlFQqH6Lux6HIbuUTZ9MRRQhb+xDqHyn0j3VlHN4EZCiT4RAzRLh2JjYe7MfP/vada5g2E8JdP9qvKL0gVMiXIpVM4GPU63HluE06fIEwY9udx4rarP2JcOM3aLYuQKRZOGQYAGFvjQHufHz6xEU1YvNMhwSeygjI07w/Xzx5w3ceX78ZP/7kNS7cck5f5gmGVa4C6XBUW3z1vIn5z9SwA6gqqucQbCGG/mP9x64JxSUeIFTJPXD8bK+8+G5V24eIm1ceX8gj6MlAvv3Tuk4ikUVr4sxpjk1p8wZA8uSdNKAUUXYm8gZBq8k/LGY5EfGyiz3zr0d68HH/yj96RH98wb3RexpBrzAY9Gqtscl5BryeAGodZLkKYCfcamV5EDEofvrLJhESHotuQVOfEoI+Iui8YJqu+wLGJbrjnPzukmjzNNXodw8iK2O9gMSNFJEkWvlRm3EWCT2QDpYXPGMN1c0epXm9XlM6Vv4yK280edwBmox5/u2UufnDBpCyPlsgGOp3QWBtQV6PMNSMrLDAkCCIoVqQyzr3eAMJhLvf0zYRLp7Q+SSIpJOt8wjAHAODnV8zAZbMiWX5KwZfi7SXB33S4Gy5fEGaDDvObavGtsyfkathEhrlqTiMAoDvHpZKV9eCHOUvLugeAMqtg4fd6grJBBWRmAp0En4jBqNfh79+Yh1dvO01eprypb3fFCsD/LN8FANjbJiTqfHl2Q1bHSGQfqXJmrmvR/ODlzfLjUgzplYrEeQLqHrc0aUtkjdPG16ieK924bQoLX/pCSn7/Vpfw2thae5ZHSGQbKTLGl+NqkyHFl60U5/sll6ovGFLd7ZAPn8gZIxQTZ72K5CtJ8KXfaGuvD3aTvqQyI4sVybrOtYWvzOkoxQgvyaXqC4Tx039uAwCMLLegzeXDvrb0Sl2Q4BNJ8d2FE/Hrq2eiym5S+RI9ovXnDYTw7tbj+L9P9mO86PsnChspl+Lnb2+PeS0c5li1u33ACJ7WXi8W/vqjlERKSjaSKD25j9QzUvrv/SHhc/7dv/akte+0BJ8x9iBjbDNjbCNj7D3G2EhxOWOMPc4Y2yO+PnD2DqF5LEY9rpjdALtZLyfjePwh+ETrLxjm+M+/rQcAnDS6Mm/jJDKHZOEf7vRgT6tL9dqf/30ANzz9KZZvb024/bvbWrCntQ9PrNib1PFCYY6r/7hGtUzDbWizhuTS6ewX3KP3XjgZ9ywSot2iG7OnSroW/mOc8xM457MAvAXgx+LyCwE0iX+3AvjfNI9DaAS7yYB+XxB/+WQ/pvz4Hexr749Z57ypdXkYGZFpLIrs1s8Pqis1bhIrNz7/2aGY7Q53ujHvkQ/QLs7nJFti+d972+X9Spw4qvSMB72OwahnONotfG41DjOuPKkBeh2TyywMlbQEn3OuTMOzIxLMcSmAZ7jAGgAVjLER6RyL0AY2kx5ufwi/fHdnwnWiJ3yJwkQZIbOpWS3ELq8wj7N8RytW7FRb+a+sb0ZLrw9LPt4HIPmy2tuPReSkrsyMZXfMx+0lGtZrNujR7RGi4WwmPRhjsJv0aUfqpD2zxhh7GMCNAHoAnC0urgdwWLFas7jsGKJgjN0K4S4Ao0aNin6Z0Bh2swF9vqCcYUsUL8qM6y1HejDm3qUAgJ0PLVKJ+Nf+vBYHHl0sP193UOifIE32xtP7rUd7sO5AF66fN0pOrGrv88Ni1GHl3efAZNCh3Fr8BdMSYTbo5Mq00lyK02JEX5q1jQa18BljHzDGvojzdykAcM5/yDlvBPAcgNulzeLsKu69COd8Ced8Dud8Tm1t7VDfB5Ej7CYD3HkqqEXknvUPLMS1Jzdix7GID3/ZlmMJXQucc3yyp0O1LF4NmMWPr8JP3tyKa5ZEfPYubxAOsxG1TnNJiz0gCH6vR/jcpHwIh9mAPt/g5ckHYlALn3O+MMl9/R3AUgA/gWDRNypeawBwNOXREZrDZtaj16v+0s1srMCmw904dVw17l5EpRSKiWqHGfUVVvgVbStX7mrHip1tqvVCYQ69jqHLHStIrgHcEOsPdsmP+31BOMyll2gVD7NRjy4xw9ksWvgOiyGmRHIozNHSm3wbyrRcOoyxJs75bvHpJQB2iI/fBHA7Y+wFAPMA9HDOY9w5ROFhNxlUxdMA4JtnjsPwcmvcyppE4RPdz+C1DUIyUH2FVa6W6vYH4bQY4/Zd3XHchS//77/x6jeFzO3XNzTHrPPOF8ewcnebqlNaKWM26NAtXjzNCgtfWebixbWH8It3dqIzhU5Y6UbpPCq6dzYDOB/AneLyZQD2AdgD4E8A/ivN4xAawWbWq6w9AKiym0nsi5gzJ8V3tZ7QUI6HL58OAGjuEoS/K0HdnfUHu8A5RzjM8d0XN6leC4bCuO3Zz9HlDshlBUodfZye0A5zxML3BkK459UtKYk9kKaFzzn/coLlHMC30tk3oU3i/SBHVdnyMBIiV0ysc+LAo4vR6vJi7sPL5eWeQEj2L1/5v//G1p8tgmeAyXxvIKwqvCehDO0tpWblA7GvLfKZSJm3kuBvOtyNv64+MKT90qdLpIQ9TsmEujJznDWJYqPKZlI9H+Y0y7WT+kWh7x9A8F3eAA53xbp8/mf5bvkxleQQsJr0cpSTbOFbDGjp9eHSJz4Z8n6ptAKREnZFMs4PLpiEv90ytyTrnZQiyrr0T904Bz++eBoWzxDSa2bUlwMAPGLZjYcumx6zfa83qOpTK7F0c2R6L55BUYooO8hJYZmZuBiS4BMpYVN86WY2VGB+E4XSlhL3XjgZ157ciIVT6+AwGzCq2obp9WWodQp3eSt3twMArphdj2+fMwFP3zRH3tblDcjlAj774bkYXR3rCnSQSwcAcM+FkwEA42rtsIlu1Ey4u+jTJVJCsuSI0uS2M8fHLLMZDXCLlv1721oACLHj3z9fCNEdVWXDoU43er1BdIiTjJU2E06fUIODHerSDE6y8AEAN5wyGjecou7lm4m7H7LwiZQYW2PHo1fMAABUO0yDrE2UAlaTPmayVqeIMvnz104GAHT1+9HtDsBpMcCo18VtkE4uncSQS4fIC9fOHYXP7j8XU0aU5XsohAawm/Xo94cQDsfPvq2xC+6e9j4fejwBOYv2+rmjYNLrsOnH5+OCaULBPSrZkZhMuLtI8IkhMays9HqNEvGxGg1CqWxxovGeRZNVr5dZDTDoGDr6/ehVCP7VJzdi18MXotxmxO1nNwEApo4kIyIR0e6uu86fCCC1kskk+ARBpEWZ1YAeT0BujGM1qmWFMYZqhwmdfX70eAIos8TWyZnRUI4dDy7C2ZOG5WTMhUi0hf+VU8bgwKOLMTWFO20SfIIg0qLGYUafL4g5D30AINILV0m13YyOfrVLJ5roEg6EGuXn9tzX56HcJjxfcuOcRJvEQDMkBEGkRbVdPXkfT7irHSa09/nR600s+MTAjCi34nvnTcSIcgtOnzC0nhMk+ARBpEW1Q51pHU/waxxmHOjoFyx8Gwn+ULnj3Ka0tieXDkEQaXHa+GosnBJpaxlP8KvsJhzv8cIbCKOMkqvyBgk+QRBpYTcb8JQio9YaR/AdZgMCISFsk1w6+YMEnyCIjGIxxsqKMmmIJmfzBwk+QRAZJa6Fr3DjNNU5czkcQgEJPkEQGSWeBS+VTBhRbqFmOXmEBJ8giIwST/ClJh7jau25Hg6hgASfIIiMEi/xSiq70DSM3Dn5hOKjCILIKBZDrB25aNpwfGdhE74xf1weRkRIkOATBJFRlJ2xJEwGHb6zcGIeRkMoIZcOQWxCNBgAAAV2SURBVBBEiUCCTxBERrjypIZ8D4EYBMZ5/KYF+WDOnDl83bp1+R4GQRBEQcEYW885H7RsJln4BEEQJQIJPkEQRIlAgk8QBFEikOATBEGUCCT4BEEQJQIJPkEQRIlAgk8QBFEikOATBEGUCJpKvGKMuQDsTHL1cgA9GVgn1XXztV4+j52N91IDoD0Px6bzl9t9Jnuek91nMX02mTz2JM754KVIOeea+QOwLoV1l2RinVTXzdd6hTDGFN9LUuda6++lmM5flo6dl990gXw2GTt2sp9zIbt0/pmhdVJdN1/r5fPY2XgvyaL191JM5y9b+8zksYvps8nGsQdEay6ddTyJehBE4UPnujSg85wbkv2ctWbhL8n3AIicQee6NKDznBuS+pw1ZeETBEEQ2UNrFj5BEASRJUjwcwxjrG+Q1z9kjJHPs8Ch81waFNp5zovgD/YhEcUDnevSgM5zYUAWfh5gjJ3FGHtL8fz3jLGv5nFIRBag81waFNJ5zpvgM8YcjLHljLHPGWNbGGOXisvHMMa2M8b+xBjbyhh7jzFmzdc4ifShc10a0HnWPvm08L0ALueczwZwNoD/Zowx8bUmAE9wzqcB6Abw5TyNkcgMdK5LAzrPGseQx2MzAI8wxhYACAOoB1Anvrafc75RfLwewJjcDy+rBKG+2FryNZAcUarnms4znWdNkU8L/3oAtQBO4pzPAtCCyAflU6wXQn4vTNngIICpjDEzY6wcwLn5HlCWKdVzTeeZzrOmyOeHXg6glXMeYIydDWB0HseSExhjBgA+zvlhxthLADYD2A1gQ35HlnVK6lzTeabznN+RJSbngi99SACeA/BPxtg6ABsB7Mj1WPLANAB7AYBzfjeAu6NX4JyfleMxZY0SPtd0nuk8Q1x+Vo7HNCA5L63AGJsJ4E+c87k5PXCeYYzdBuAOAN/hnL+X7/HkglI813SeS4NCPc85FfxC/ZCI1KFzXRrQeS4sqHgaQRBEiUCZtgRBECVCVgWfMdbIGFshZtltZYzdKS6vYoy9zxjbLf6vFJczxtjjjLE9jLHNjLHZin3dJK6/mzF2UzbHTaROhs/1O4yxbmW6OqENMnWeGWOzGGOrxX1sZoxdk8/3VTIk21NxKH8ARgCYLT52AtgFYCqAXwK4V1x+L4BfiI8vAvA2hASOUwB8Ki6vArBP/F8pPq7M5tjpLz/nWnztXAAXA3gr3++L/rJzngFMBNAkPh4J4BiAiny/v2L/y6qFzzk/xjn/XHzsArAdQvbdpQD+Kq72VwCXiY8vBfAMF1gDoIIxNgLABQDe55x3cs67ALwPYFE2x06kRgbPNTjnywG4cjl+IjkydZ4557s457vF/RwF0AohaYvIIjnz4TPGxgA4EcCnAOo458cA4QsEYJi4Wj2Aw4rNmsVliZYTGiTNc00UCJk6z4yxuQBMEGPaieyRE8FnjDkAvAohdKt3oFXjLOMDLCc0RgbONVEAZOo8i3d1fwPwNc55OLOjJKLJuuAzxowQvhjPcc5fExe3SLfv4v9WcXkzgEbF5g0Ajg6wnNAQGTrXhMbJ1HlmjJUBWArgAdHdQ2SZbEfpMABPA9jOOf+14qU3AUiRNjcBeEOx/EZxZv8UAD3i7eG7AM5njFWKs//ni8sIjZDBc01omEydZ8aYCcDrEPz7L+do+EQ2Z4QBnAHh9m0zhNoaGyHM2lcDWA6h0NByAFXi+gzAExB8eVsAzFHs62YAe8S/r+V7tpv+snquVwJoA+CBYCFekO/3R3+ZPc8AbgAQUOxjI4BZ+X5/xf5HmbYEQRAlAmXaEgRBlAgk+ARBECUCCT5BEESJQIJPEARRIpDgEwRBlAgk+ARBECUCCT5BEESJ8P8BxcQF3drueHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Em um DataFrame, o plot() método é conveniente para plotar todas as colunas com rótulos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "              np.random.randn(1000, 4), index = ts.index, columns = [\"A\", \"B\", \"C\", \"D\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83c9d10>"
      ]
     },
     "execution_count": 138,
     "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": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x84c7d90>"
      ]
     },
     "execution_count": 139,
     "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": [
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obtendo dados de entrada / saída\n",
    "\n",
    "## CSV\n",
    "\n",
    "Gravando em um arquivo csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"foo.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lendo de um arquivo csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "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>Unnamed: 0</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-01-01</td>\n",
       "      <td>-0.010059</td>\n",
       "      <td>-0.664340</td>\n",
       "      <td>0.405537</td>\n",
       "      <td>-1.083025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-02</td>\n",
       "      <td>-0.314223</td>\n",
       "      <td>-1.421444</td>\n",
       "      <td>1.043053</td>\n",
       "      <td>-1.231948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>0.564825</td>\n",
       "      <td>-1.705600</td>\n",
       "      <td>0.467362</td>\n",
       "      <td>-0.430200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>0.525854</td>\n",
       "      <td>-0.506995</td>\n",
       "      <td>1.490750</td>\n",
       "      <td>-1.649859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>-1.028027</td>\n",
       "      <td>0.610657</td>\n",
       "      <td>2.011226</td>\n",
       "      <td>-0.953999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>2002-09-22</td>\n",
       "      <td>-60.971672</td>\n",
       "      <td>-22.897422</td>\n",
       "      <td>-6.578861</td>\n",
       "      <td>-18.969342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>2002-09-23</td>\n",
       "      <td>-60.242804</td>\n",
       "      <td>-23.340334</td>\n",
       "      <td>-5.868193</td>\n",
       "      <td>-18.707998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2002-09-24</td>\n",
       "      <td>-61.539446</td>\n",
       "      <td>-23.577778</td>\n",
       "      <td>-4.809893</td>\n",
       "      <td>-19.357749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>2002-09-25</td>\n",
       "      <td>-60.427582</td>\n",
       "      <td>-23.885636</td>\n",
       "      <td>-5.349180</td>\n",
       "      <td>-18.939354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2002-09-26</td>\n",
       "      <td>-61.840201</td>\n",
       "      <td>-24.718048</td>\n",
       "      <td>-5.195190</td>\n",
       "      <td>-18.446823</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0          A          B         C          D\n",
       "0    2000-01-01  -0.010059  -0.664340  0.405537  -1.083025\n",
       "1    2000-01-02  -0.314223  -1.421444  1.043053  -1.231948\n",
       "2    2000-01-03   0.564825  -1.705600  0.467362  -0.430200\n",
       "3    2000-01-04   0.525854  -0.506995  1.490750  -1.649859\n",
       "4    2000-01-05  -1.028027   0.610657  2.011226  -0.953999\n",
       "..          ...        ...        ...       ...        ...\n",
       "995  2002-09-22 -60.971672 -22.897422 -6.578861 -18.969342\n",
       "996  2002-09-23 -60.242804 -23.340334 -5.868193 -18.707998\n",
       "997  2002-09-24 -61.539446 -23.577778 -4.809893 -19.357749\n",
       "998  2002-09-25 -60.427582 -23.885636 -5.349180 -18.939354\n",
       "999  2002-09-26 -61.840201 -24.718048 -5.195190 -18.446823\n",
       "\n",
       "[1000 rows x 5 columns]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"foo.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Excel\n",
    "\n",
    "Gravando em um arquivo Excel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"foo.xlsx\", sheet_name = \"Sheet1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pegadinhas \n",
    "\n",
    "Se você estiver tentando realizar uma operação, poderá ver uma exceção como:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-144-5c782b38cd2f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"I was true\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1442\u001b[0m         raise ValueError(\n\u001b[1;32m-> 1443\u001b[1;33m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1444\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1445\u001b[0m         )\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "if pd.Series([False, True, False]):\n",
    "    print(\"I was true\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}