Parte 3 - Data Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Science"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pandas\n",
    "\n",
    "Pandas é fundamental para Análise de Dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Tamanho(m)</th>\n",
       "      <th>Peso(Kg)</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Poder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Charmander</th>\n",
       "      <td>0.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pidgey</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Voador</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulbasaur</th>\n",
       "      <td>0.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Planta</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squirtle</th>\n",
       "      <td>0.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caterpie</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Inseto</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pikachu</th>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Elétrico</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charizard</th>\n",
       "      <td>1.7</td>\n",
       "      <td>90.5</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lapras</th>\n",
       "      <td>2.5</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ponyta</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsea</th>\n",
       "      <td>0.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tamanho(m)  Peso(Kg)      Tipo  Poder\n",
       "Nome                                             \n",
       "Charmander         0.6       8.5      Fogo     12\n",
       "Pidgey             0.3       1.8    Voador      5\n",
       "Bulbasaur          0.7       6.9    Planta     10\n",
       "Squirtle           0.5       9.0      Água     11\n",
       "Caterpie           0.3       2.9    Inseto      5\n",
       "Pikachu            0.4       6.0  Elétrico     15\n",
       "Charizard          1.7      90.5      Fogo     50\n",
       "Lapras             2.5     220.0      Água     60\n",
       "Ponyta             1.0      30.0      Fogo     45\n",
       "Horsea             0.4       8.0      Água      5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pokemons = pd.read_excel('TabelaPokemon.xlsx', index_col=0)  \n",
    "\n",
    "pokemons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Tamanho(m)</th>\n",
       "      <th>Peso(Kg)</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Poder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nome</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pidgey</th>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Voador</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Caterpie</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Inseto</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pikachu</th>\n",
       "      <td>0.4</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Elétrico</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bulbasaur</th>\n",
       "      <td>0.7</td>\n",
       "      <td>6.9</td>\n",
       "      <td>Planta</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Horsea</th>\n",
       "      <td>0.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charmander</th>\n",
       "      <td>0.6</td>\n",
       "      <td>8.5</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Squirtle</th>\n",
       "      <td>0.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ponyta</th>\n",
       "      <td>1.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Charizard</th>\n",
       "      <td>1.7</td>\n",
       "      <td>90.5</td>\n",
       "      <td>Fogo</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lapras</th>\n",
       "      <td>2.5</td>\n",
       "      <td>220.0</td>\n",
       "      <td>Água</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Tamanho(m)  Peso(Kg)      Tipo  Poder\n",
       "Nome                                             \n",
       "Pidgey             0.3       1.8    Voador      5\n",
       "Caterpie           0.3       2.9    Inseto      5\n",
       "Pikachu            0.4       6.0  Elétrico     15\n",
       "Bulbasaur          0.7       6.9    Planta     10\n",
       "Horsea             0.4       8.0      Água      5\n",
       "Charmander         0.6       8.5      Fogo     12\n",
       "Squirtle           0.5       9.0      Água     11\n",
       "Ponyta             1.0      30.0      Fogo     45\n",
       "Charizard          1.7      90.5      Fogo     50\n",
       "Lapras             2.5     220.0      Água     60"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons.sort_values('Peso(Kg)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nome\n",
       "Charmander     21.1\n",
       "Pidgey          7.1\n",
       "Bulbasaur      17.6\n",
       "Squirtle       20.5\n",
       "Caterpie        8.2\n",
       "Pikachu        21.4\n",
       "Charizard     142.2\n",
       "Lapras        282.5\n",
       "Ponyta         76.0\n",
       "Horsea         13.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Soma cada item\n",
    "pokemons.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tamanho(m)                                                  8.4\n",
       "Peso(Kg)                                                  383.6\n",
       "Tipo          FogoVoadorPlantaÁguaInsetoElétricoFogoÁguaFogo...\n",
       "Poder                                                       218\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Soma cada coluna\n",
    "pokemons.sum(axis=0)\n",
    "# ou apenas pokemons.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tamanho(m)     0.875\n",
       "Peso(Kg)      43.200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tira a média dos items\n",
    "pokemons.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tamanho(m)</th>\n",
       "      <th>Peso(Kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.875000</td>\n",
       "      <td>43.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.797765</td>\n",
       "      <td>77.375559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.375000</td>\n",
       "      <td>5.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.550000</td>\n",
       "      <td>7.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.950000</td>\n",
       "      <td>29.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>220.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Tamanho(m)    Peso(Kg)\n",
       "count    8.000000    8.000000\n",
       "mean     0.875000   43.200000\n",
       "std      0.797765   77.375559\n",
       "min      0.300000    1.800000\n",
       "25%      0.375000    5.225000\n",
       "50%      0.550000    7.700000\n",
       "75%      0.950000   29.375000\n",
       "max      2.500000  220.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pokemons.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000211763CA430>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021176AFC8B0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000021176B2BD00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000021176B651F0>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokemons.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21176c829a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pokemons.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x211780b7fa0>"
      ]
     },
     "execution_count": 26,
     "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": [
    "pokemons.groupby(['Tipo']).Poder.count().sort_values()[-5:].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy\n",
    "\n",
    "NumPy é uma biblioteca para a linguagem de programação Python, adicionando suporte para matrizes e arrays multidimensionais grandes, junto com uma grande coleção de funções matemáticas de alto nível para operar nesses arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "english_parts_of_speech = (\"noun\", \"pronoun\", \"verb\", \"adverb\", \"adjective\", \"conjunction\", \"preposition\", \"interjection\")\n",
    "english_parts_of_speech"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "baseline_high = 14.48\n",
    "highs = np.array([baseline_high - 0.880, baseline_high - 0.093, baseline_high + 0.105, baseline_high + 0.684])\n",
    "highs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(9/5) * highs + 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.diff(highs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sum(highs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#np.prod            Multiplicar todos os elementos juntos\n",
    "#np.sum             Adicionar todos os elementos juntos\n",
    "#np.any             Testar se pelo menos um dos elementos é um valor verdadeiro (números diferentes de zero são verdadeiros)\n",
    "#np.count_nonzero   Contar o número de elementos diferentes de zero"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}