act_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling Project - WeRateDogs\n",
    "## Insights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import das libs\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lendo o arquivo csv e criando um data frame \n",
    "df = pd.read_csv('twitter_archive_master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reset para o padrao do matplotlib\n",
    "plt.rcdefaults()\n",
    "# dados para plotar no grafico\n",
    "df_count = df.loc[df.retweeted_status_id.isnull() & \n",
    "                  df.in_reply_to_status_id.isnull()].dog_stage.value_counts()\n",
    "# itens do eixo x\n",
    "objects = df_count.keys().tolist()\n",
    "# qtd de pontos do grafico\n",
    "y_pos = np.arange(len(objects))\n",
    "# valores do eixo y\n",
    "performance = df_count.tolist()\n",
    " \n",
    "# criando um grafico de barra\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "# colocar os valores do eixo X Y\n",
    "plt.xticks(y_pos, objects)\n",
    "# defino o label do eixo y\n",
    "plt.ylabel('Qtd')\n",
    "# titulo do grafico\n",
    "plt.title('Quantidade de Cachorro x Estagio')\n",
    "# rotação dos label do eixo x\n",
    "plt.xticks(rotation=90)\n",
    "# mostro o grafico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de Comparação Quantidade de Cachorro x Estágio\n",
    "- Como pode ser visto no grafico de comparação acima, a maior parte dos cachorros estão classificados no estágio `pupper`, esta classificação representa mais que o dobro das classificações do segundo maior estágio `doggo`, contando com mais de 200 animais nesse estágio.\n",
    "- Um ponto a se destacar é que existem cachorros com mais de uma classifição, por exemplo, `doggopupper`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dados que serao plotados no grafico\n",
    "df_count = df.loc[df.retweeted_status_id.isnull() & \n",
    "                  df.in_reply_to_status_id.isnull() &\n",
    "                  df.name.notnull()]\n",
    "# buscando somente o 10 maiores valores (top 10)\n",
    "df_count = df_count.name.value_counts().nlargest(10)\n",
    "\n",
    "# criando os valores do eixo X\n",
    "objects = df_count.keys().tolist()\n",
    "# qtd de itens a ser plotado no grafico\n",
    "y_pos = np.arange(len(objects))\n",
    "# valores de cada ponto no grafico\n",
    "performance = df_count.tolist()\n",
    "\n",
    "# crio grafico de barra\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "# crio o eixo X, com os label e valores do eixo Y\n",
    "plt.xticks(y_pos, objects)\n",
    "# label do eixo Y\n",
    "plt.ylabel('Qtd')\n",
    "# titulo do grafico\n",
    "plt.title('Top 10 Nomes mais Comuns')\n",
    "# rotação do label no eixo X\n",
    "plt.xticks(rotation=90)\n",
    "# mostro o grafico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico top 10 Nomes mais comuns\n",
    "- Como pode ser visto a maioria dos tweets não foi possível identificar o nome dos cachorros, o que já era esperado, pois existiam muitos tweets onde a foto tinha mais de 1 cachorro, essa quantidade enorme de tweets sem a classificação do nome leva a distorção da escala do grafico, pois, existem quase 600 tweets sem a classificação do nome, enquanto os demais nomes no Top 10, nao chega nem perto de 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# consulto os valores que serão exibidos no grafico\n",
    "df_count = df.loc[df.retweeted_status_id.isnull() & \n",
    "                  df.in_reply_to_status_id.isnull() &\n",
    "                  df.name.notnull()]\n",
    "\n",
    "# busco os 10 maiores valores onde a coluna name seja diferentes de NONE, a, the\n",
    "df_count = df_count.query(\"name != 'None' & name != 'a' & name != 'the'\").name.value_counts().nlargest(10)\n",
    "\n",
    "# labels do eixo X\n",
    "objects = df_count.keys().tolist()\n",
    "# qtd de itens a ser plotado\n",
    "y_pos = np.arange(len(objects))\n",
    "# valores de cada ponto\n",
    "performance = df_count.tolist()\n",
    "\n",
    "# crio o grafico\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "# ploto os valores do eixo Y e os labels do eixo X\n",
    "plt.xticks(y_pos, objects)\n",
    "# label do euxo Y\n",
    "plt.ylabel('Qtd')\n",
    "# titulo do grafico\n",
    "plt.title('Top 10 Nomes mais Comuns*')\n",
    "# rotação do eixo x\n",
    "plt.xticks(rotation=90)\n",
    "# mostro o grafico\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico top 10 Nomes mais comuns\n",
    "#### Retirando Nomes sem sentido\n",
    "- Quando se retira do data set os tweets que possuem classificação de nomes sem sentido, tais como: `None` `a` `the` , é possível visualizar novos nomes entrando no Top 10. Nomes como `Daisy` `Winston` não apareciam anteriormente no grafico de Top 10, pois estavam sendo ocultados devido a classificação dos nomes sem sentidos."
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}