P38: Estudando a base de dados flags.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a7387fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#P38: Estudando a base de dados flags\n",
    "# Gráficos de barras com as frequências das cores\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "97292a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "#1: Importa a base de dados\n",
    "flags = pd.read_csv('/Users/TullioPires/flags.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7287839b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#2: Gera a tabela de frequências\n",
    "df_cores = pd.DataFrame()\n",
    "\n",
    "for c in flags.columns:\n",
    "    if c in ['red', 'green', 'blue', 'gold', 'white', 'black', 'orange']:\n",
    "        df_cores[c] = flags[c].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "06ee590d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'red'}>,\n",
       "       <AxesSubplot:title={'center':'green'}>,\n",
       "       <AxesSubplot:title={'center':'blue'}>,\n",
       "       <AxesSubplot:title={'center':'gold'}>,\n",
       "       <AxesSubplot:title={'center':'white'}>,\n",
       "       <AxesSubplot:title={'center':'black'}>,\n",
       "       <AxesSubplot:title={'center':'orange'}>], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1800 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3: Gera os gráficos de barras\n",
    "lst_cores = ['red', 'green', 'blue', 'gold', 'white', 'black', 'orange']\n",
    "\n",
    "df_cores.plot(kind='barh',\n",
    "             subplots=True,\n",
    "             figsize = (8,25),\n",
    "             color = lst_cores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac2d606",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}