bigdata_aula3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     9\n",
       "2     8\n",
       "3     2\n",
       "4     5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie1 = pd.Series([10,9,8,2,5])\n",
    "serie1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9,  8,  2,  5], dtype=int64)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie1.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     0.484806\n",
       "2     0.461708\n",
       "3     0.754274\n",
       "4     0.071966\n",
       "5     0.694480\n",
       "6     0.979848\n",
       "7     0.459704\n",
       "8     0.256632\n",
       "9     0.701789\n",
       "10    0.110152\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie2 = pd.Series(np.random.rand(10), index=np.arange(1,11))\n",
    "serie2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    4\n",
       "b    7\n",
       "c    6\n",
       "d    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie2 = pd.Series([4,7,6,3], index=['a','b','c','d'])\n",
    "serie2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.5    True\n",
       "b         7\n",
       "c         6\n",
       "d         3\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie2 = pd.Series([True, 7, 6, 3], index=[4.5, 'b','c', 'd'])\n",
    "serie2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercicio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "capitais = {\n",
    "    'Pernambuco' : 'Recife',\n",
    "    'Paraiba' : 'João Pessoa',\n",
    "    'Ceará' : 'Fortaleza',\n",
    "    'Bahia' : 'Salvador',\n",
    "    'Alagoas' : 'Maceio'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pernambuco         Recife\n",
       "Paraiba       João Pessoa\n",
       "Ceará           Fortaleza\n",
       "Bahia            Salvador\n",
       "Alagoas            Maceio\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie3 = pd.Series(capitais)\n",
    "serie3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dictEmpresas = {\n",
    "    'Apple' : 947.062,\n",
    "    'Google' : 819.573,\n",
    "    'Amazon' : 705.646,\n",
    "    'Microsoft' : 611.460,\n",
    "    'Tencent' : 214.023,\n",
    "    'McDonalds' : 196.526,\n",
    "    'Visa' : 191.032,\n",
    "    'Facebook' : 186.421,\n",
    "    'Alibaba' : 169.966,\n",
    "    'Louis vuitton' : 124.273\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apple            947.062\n",
       "Google           819.573\n",
       "Amazon           705.646\n",
       "Microsoft        611.460\n",
       "Tencent          214.023\n",
       "McDonalds        196.526\n",
       "Visa             191.032\n",
       "Facebook         186.421\n",
       "Alibaba          169.966\n",
       "Louis vuitton    124.273\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie4 = pd.Series(dictEmpresas)\n",
    "serie4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "empresas = ['Apple', 'Google', 'Amazon', 'microsoft', 'Tecent', 'McDonalds', 'Visa', 'Facebook', 'Alibaba', 'Louis vuitton']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "bilhoes = [947.062,\n",
    "819.573,\n",
    "705.646,\n",
    "611.460,\n",
    "214.023,\n",
    "196.526,\n",
    "191.032,\n",
    "186.421,\n",
    "169.966,\n",
    "124.273 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apple            947.062\n",
       "Google           819.573\n",
       "Amazon           705.646\n",
       "microsoft        611.460\n",
       "Tecent           214.023\n",
       "McDonalds        196.526\n",
       "Visa             191.032\n",
       "Facebook         186.421\n",
       "Alibaba          169.966\n",
       "Louis vuitton    124.273\n",
       "dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serie5 = pd.Series(bilhoes, empresas)\n",
    "serie5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "serie5.plot()"
   ]
  }
 ],
 "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": "3.10.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}