manipulacao_dados_pandas_multidimensionais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estruturas de Dados em Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "pandas.Series()          - 1 Dimensão  - 1D\n",
    "\n",
    "pandas.DataFrame()  - 2 Dimensões - 2D\n",
    "\n",
    "pandas.Panel()           - 3 Dimensões - 3D\n",
    "\n",
    "numpy.ndarray()     - x Dimensões \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.randint(1, 10, (5, 3, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "painel = pd.Panel(\n",
    "    data, \n",
    "    items = ['item {}'.format(i) for i in range(1, 6)], \n",
    "    major_axis = [2015, 2016, 2017], \n",
    "    minor_axis = ['US', 'UK'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.panel.Panel'>\n",
      "Dimensions: 5 (items) x 3 (major_axis) x 2 (minor_axis)\n",
      "Items axis: item 1 to item 5\n",
      "Major_axis axis: 2015 to 2017\n",
      "Minor_axis axis: US to UK\n"
     ]
    }
   ],
   "source": [
    "print(painel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.reshape(5, 6).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    data = data,\n",
    "    index = pd.MultiIndex.from_product([[2015, 2016, 2017], ['US', 'UK']]),\n",
    "    columns = ['item {}'.format(i) for i in range(1, 6)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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></th>\n",
       "      <th>item 1</th>\n",
       "      <th>item 2</th>\n",
       "      <th>item 3</th>\n",
       "      <th>item 4</th>\n",
       "      <th>item 5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>US</th>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016</th>\n",
       "      <th>US</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2017</th>\n",
       "      <th>US</th>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         item 1  item 2  item 3  item 4  item 5\n",
       "2015 US       1       9       7       4       6\n",
       "     UK       2       1       2       8       9\n",
       "2016 US       1       1       6       6       6\n",
       "     UK       8       5       7       3       3\n",
       "2017 US       2       8       2       5       5\n",
       "     UK       2       3       7       9       4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Criando objetos multidimensionais com Panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "USData = pd.DataFrame(np.array([[249.62 , 8900],\n",
    "                              [282.16,12680],\n",
    "                              [309.35,14940]]),\n",
    "                      columns = ['População(M)','PIB($B)'],\n",
    "                      index = [1995,2005,2015])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>População(M)</th>\n",
       "      <th>PIB($B)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>249.62</td>\n",
       "      <td>8900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>282.16</td>\n",
       "      <td>12680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>309.35</td>\n",
       "      <td>14940.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      População(M)  PIB($B)\n",
       "1995        249.62   8900.0\n",
       "2005        282.16  12680.0\n",
       "2015        309.35  14940.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "USData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ChinaData = pd.DataFrame(np.array([[1133.68, 390.28],\n",
    "                                 [1266.83,1198.48],\n",
    "                                 [1339.72, 9923.47]]),\n",
    "                       columns=['População(M)','PIB($B)'],\n",
    "                       index=[1995,2005,2015])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>População(M)</th>\n",
       "      <th>PIB($B)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>1133.68</td>\n",
       "      <td>390.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1266.83</td>\n",
       "      <td>1198.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1339.72</td>\n",
       "      <td>9923.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      População(M)  PIB($B)\n",
       "1995       1133.68   390.28\n",
       "2005       1266.83  1198.48\n",
       "2015       1339.72  9923.47"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChinaData"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_ChinaData = {'US' : USData,           #União de DataFrames, objeto multidimensional \n",
    "                'China': ChinaData}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'US':       População(M)  PIB($B)\n",
       " 1995        249.62   8900.0\n",
       " 2005        282.16  12680.0\n",
       " 2015        309.35  14940.0, 'China':       População(M)  PIB($B)\n",
       " 1995       1133.68   390.28\n",
       " 2005       1266.83  1198.48\n",
       " 2015       1339.72  9923.47}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_ChinaData #terceiro objeto multidimensional "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.core.panel.Panel'>\n",
       "Dimensions: 2 (items) x 3 (major_axis) x 2 (minor_axis)\n",
       "Items axis: US to China\n",
       "Major_axis axis: 1995 to 2015\n",
       "Minor_axis axis: População(M) to PIB($B)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Panel(US_ChinaData) #converter o objeto em painel 3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.panel.Panel"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(pd.Panel(US_ChinaData)) #objeto do tipo panel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convertendo DataFrame para Panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "mIdx = pd.MultiIndex(levels = [['US', 'China'], [1995,2005, 2015]], #Criar um conjunto de dados com a func MultiIndex\n",
    "                     labels = [[1,1,1,0,0,0], [0,1,2,0,1,2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiIndex(levels=[['US', 'China'], [1995, 2005, 2015]],\n",
       "           codes=[[1, 1, 1, 0, 0, 0], [0, 1, 2, 0, 1, 2]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mIdx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "ChinaUSDF = pd.DataFrame({'População(M)' : [1133.68, 1266.83, 1339.72, 249.62, 282.16,309.35], \n",
    "                          'PIB($B)': [390.28, 1198.48, 6988.47, 8900,12680,14940]}, \n",
    "                         index = mIdx)                             #Converter China US em Data frame, aplicando o índice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>População(M)</th>\n",
       "      <th>PIB($B)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">China</th>\n",
       "      <th>1995</th>\n",
       "      <td>1133.68</td>\n",
       "      <td>390.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1266.83</td>\n",
       "      <td>1198.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>1339.72</td>\n",
       "      <td>6988.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">US</th>\n",
       "      <th>1995</th>\n",
       "      <td>249.62</td>\n",
       "      <td>8900.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>282.16</td>\n",
       "      <td>12680.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>309.35</td>\n",
       "      <td>14940.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            População(M)   PIB($B)\n",
       "China 1995       1133.68    390.28\n",
       "      2005       1266.83   1198.48\n",
       "      2015       1339.72   6988.47\n",
       "US    1995        249.62   8900.00\n",
       "      2005        282.16  12680.00\n",
       "      2015        309.35  14940.00"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChinaUSDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xarray\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/21/5f/dfacc151ae39439aad024e94644d0c6c4db5d82f258bcba4cf0441f51dca/xarray-0.12.1-py2.py3-none-any.whl (522kB)\n",
      "\u001b[K    100% |████████████████████████████████| 532kB 6.0MB/s ta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: pandas>=0.19.2 in /Users/dmpm/anaconda3/lib/python3.7/site-packages (from xarray) (0.24.2)\n",
      "Requirement already satisfied: numpy>=1.12 in /Users/dmpm/anaconda3/lib/python3.7/site-packages (from xarray) (1.16.2)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in /Users/dmpm/anaconda3/lib/python3.7/site-packages (from pandas>=0.19.2->xarray) (2.8.0)\n",
      "Requirement already satisfied: pytz>=2011k in /Users/dmpm/anaconda3/lib/python3.7/site-packages (from pandas>=0.19.2->xarray) (2018.9)\n",
      "Requirement already satisfied: six>=1.5 in /Users/dmpm/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.5.0->pandas>=0.19.2->xarray) (1.12.0)\n",
      "Installing collected packages: xarray\n",
      "Successfully installed xarray-0.12.1\n"
     ]
    }
   ],
   "source": [
    "# ChinaUSDF.to_panel()\n",
    "!pip install xarray #todo comando \"!\", será executado diretamente no sistema operacional "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (level_0: 2, level_1: 3)\n",
       "Coordinates:\n",
       "  * level_0       (level_0) object 'US' 'China'\n",
       "  * level_1       (level_1) int64 1995 2005 2015\n",
       "Data variables:\n",
       "    População(M)  (level_0, level_1) float64 249.6 282.2 ... 1.267e+03 1.34e+03\n",
       "    PIB($B)       (level_0, level_1) float64 8.9e+03 1.268e+04 ... 6.988e+03"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ChinaUSDF.to_xarray()"
   ]
  }
 ],
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