artigos/bd_for_beginners.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Base dos dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base dos Dados is a Brazilian project of consolidation of datasets in a common repository with easy to follow codes. \n",
    "\n",
    "Download *clean, integrated and updated* datasets in an easy way through SQL, Python, R or CLI (Stata in development). With Base dos Dados you have freedom to:\n",
    "- download whole tables\n",
    "- download tables partially\n",
    "- cross datasets and download final product\n",
    "\n",
    "This Jupyter Notebook aims to help Python coders in main funcionalities of the platform. If you just heard about Base Dos Dados this is the place to come and start your journey. \n",
    "\n",
    "This manual is divided into three sections:\n",
    "- Installation and starting commands\n",
    "- Using Base dos Dados with Pandas package\n",
    "- Examples\n",
    "\n",
    "It is recommended to have know about the package Pandas before using *basedosdados* since data will be stored as dataframes. \n",
    "If you have any feedback about this material please send me an email and I will gladly help: tomasdanobrega@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Installation and starting commands"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Base dos Dados are located in pip and to install it you have to run the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install basedosdados==1.3.0a3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using an exclamation mark before the command will pass the command to the shell (not to the Python interpreter). If you prefer you can type *pip install basedosdados* directly in your Command Prompt.<br>\n",
    "\n",
    "Before using *basedosdados* you have to import it: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import basedosdados as bd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have installed and imported the package it is time to see what data is available. The following command will list all current datasets.<br>\n",
    "<br>\n",
    "Note: the first time you execute a command in *basedosdados* it will prompt you to login with your Google Cloud. This is because results are delivered through BigQuery (which belongs to Google). BigQuery provides a reliable and quick service for managing data. It is free unless you require more than 1TB per month. If this happens to you just follow the on-screen instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "dataset_id: br_abrinq_oca\n",
      "\n",
      "\n",
      "dataset_id: br_ana_atlas_esgotos\n",
      "\n",
      "\n",
      "dataset_id: br_bd_diretorios_brasil\n",
      "\n",
      "\n",
      "dataset_id: br_camara_atividade_legislativa\n",
      "\n",
      "\n",
      "dataset_id: br_camara_dados_abertos\n",
      "\n",
      "\n",
      "dataset_id: br_cgu_servidores_executivo_federal\n",
      "\n",
      "\n",
      "dataset_id: br_geobr_mapas\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_amc\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_censo2010\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_nomes_brasil\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_pib\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_pnad_covid\n",
      "\n",
      "\n",
      "dataset_id: br_ibge_populacao\n",
      "\n",
      "\n",
      "dataset_id: br_imprensa_nacional_dou\n",
      "\n",
      "\n",
      "dataset_id: br_inep_ideb\n",
      "\n",
      "\n",
      "dataset_id: br_inep_indicadores_educacionais\n",
      "\n",
      "\n",
      "dataset_id: br_inep_sinopse_estatistica_educacao_basica\n",
      "\n",
      "\n",
      "dataset_id: br_inpe_prodes\n",
      "\n",
      "\n",
      "dataset_id: br_mapbiomas_estatisticas\n",
      "\n",
      "\n",
      "dataset_id: br_mc_auxilio_emergencial\n",
      "\n",
      "\n",
      "dataset_id: br_mc_indicadores\n",
      "\n",
      "\n",
      "dataset_id: br_me_caged\n",
      "\n",
      "\n",
      "dataset_id: br_me_rais\n",
      "\n",
      "\n",
      "dataset_id: br_me_socios\n",
      "\n",
      "\n",
      "dataset_id: br_mobilidados_indicadores\n",
      "\n",
      "\n",
      "dataset_id: br_ms_sim\n",
      "\n",
      "\n",
      "dataset_id: br_observatorio_crianca_indicadores\n",
      "\n",
      "\n",
      "dataset_id: br_ponte_indicadores\n",
      "\n",
      "\n",
      "dataset_id: br_sp_alesp\n",
      "\n",
      "\n",
      "dataset_id: br_sp_gov_orcamento\n",
      "\n",
      "\n",
      "dataset_id: br_sp_gov_ssp\n",
      "\n",
      "\n",
      "dataset_id: br_tesouro_finbra\n",
      "\n",
      "\n",
      "dataset_id: br_tse_eleicoes\n",
      "\n",
      "\n",
      "dataset_id: br_tse_filiacao_partidaria\n",
      "\n",
      "\n",
      "dataset_id: mundo_onu_adh\n"
     ]
    }
   ],
   "source": [
    "bd.list_datasets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the first time you execute a command in *basedosdados* it will prompt you to login with your Google Cloud. This is because results are delivered through BigQuery (which belongs to Google). BigQuery provides a reliable and quick service for managing data. It is free unless you require more than 1TB per month. If the login screen appears to you just follow the on-screen instructions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check the data you want with the *get_dataset_description*. We will use *br_ibge_pib* as example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dados sobre Produto Interno Bruto do IBGE.\n",
      "\n",
      "\n",
      "Para saber mais acesse:\n",
      "Website: https://basedosdados.org/dataset/br-ibge-pib\n",
      "Github: https://github.com/basedosdados/mais/\n",
      "\n",
      "Ajude a manter o projeto :)\n",
      "Apoia-se: https://apoia.se/basedosdados\n",
      "\n",
      "Instituição (Quem mantém os dados oficiais?)\n",
      "-----------\n",
      "Nome: instituto-brasileiro-de-geografia-e-estatistica\n",
      "\n",
      "Onde encontrar os dados\n",
      "-----------------------\n",
      "- https://www.ibge.gov.br/explica/pib.php\n",
      "\n",
      "Grupos\n",
      "------\n",
      "- economia\n",
      "\n",
      "Idiomas\n",
      "------\n",
      "- portugues\n",
      "\n",
      "Licença\n",
      "-------\n",
      "Nome: MIT\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bd.get_dataset_description('br_ibge_pib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each of the *datasets* has tables inside and to see what are the available tables you can use the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "table_id: municipios\n"
     ]
    }
   ],
   "source": [
    "bd.list_dataset_tables('br_ibge_pib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To analyze which information is in that table you can run the following command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Produto Interno Bruto (PIB) municipal a preços correntes.\n",
      "\n",
      "\n",
      "Para saber mais acesse:\n",
      "Website: \n",
      "Github: https://github.com/basedosdados/mais/\n",
      "\n",
      "Ajude a manter o projeto :)\n",
      "Apoia-se: https://apoia.se/basedosdados\n",
      "\n",
      "Nível da Observação (Colunas que identificam cada linha unicamente)\n",
      "-------------------\n",
      "- municipio\n",
      "- ano\n",
      "\n",
      "Cobertura Temporal\n",
      "------------------\n",
      "- 2002\n",
      "- 2003\n",
      "- 2004\n",
      "- 2005\n",
      "- 2006\n",
      "- 2007\n",
      "- 2008\n",
      "- 2009\n",
      "- 2010\n",
      "- 2011\n",
      "- 2012\n",
      "- 2013\n",
      "- 2014\n",
      "- 2015\n",
      "- 2016\n",
      "- 2017\n",
      "- 2018\n",
      "\n",
      "Cobertura Espacial\n",
      "------------------\n",
      "- brasil\n",
      "\n",
      "Frequencia de Atualização\n",
      "-------------------------\n",
      "1 ano\n",
      "\n",
      "Tratado por\n",
      "-----------\n",
      "Nome: Ricardo Dahis\n",
      "Código: https://github.com/basedosdados/mais/tree/master/bases/br_ibge_pib/code\n",
      "Email: rdahis@basedosdados.org\n",
      "\n",
      "Publicado por\n",
      "-------------\n",
      "Nome: Ricardo Dahis\n",
      "Código: https://github.com/basedosdados/mais/tree/master/bases/br_ibge_pib/code\n",
      "Website: www.ricardodahis.com\n",
      "Email: rdahis@basedosdados.org\n"
     ]
    }
   ],
   "source": [
    "bd.get_table_description('br_ibge_pib', 'municipios')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With these three commands you are set and know what data is available. You are now able to create your dataframe and start your analysis! In this example we will generate a dataframe called *df* with information regarding Brazilian municipalities' GDP.  The data is stored as a dataframe object and it will interact with the same commands used in Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: 100%|██████████████████████████████████████████████████████████| 94616/94616 [00:08<00:00, 10636.02rows/s]\n"
     ]
    }
   ],
   "source": [
    "df = bd.read_table('br_ibge_pib', 'municipios')\n",
    "# Note: depending on how you configured your Google Cloud setup, it might be necessary to add project ID in the request.\n",
    "# Like `bd.read_table('br_ibge_pib', 'municipios', billing_project_id=<YOUR_PROJECT_ID>)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will do quick exploratory example on the dataset we have been using. <br>\n",
    "First by let's define again the table and take a look at it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id_municipio</th>\n",
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       "      <th>PIB</th>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   id_municipio   ano        PIB  impostos_liquidos         VA  \\\n",
       "0       1100015  2002  111290995            7549266  103741729   \n",
       "1       1100023  2002  449592816           42665972  406926845   \n",
       "2       1100031  2002   31767520            1867935   29899585   \n",
       "3       1100049  2002  474443097           49622951  424820146   \n",
       "4       1100056  2002   79173614            8200971   70972643   \n",
       "\n",
       "   VA_agropecuaria  VA_industria  VA_servicos  VA_ADESPSS  \n",
       "0         27013223       9376871     24651113    42700523  \n",
       "1         28590178      99922936    161851549   116562182  \n",
       "2         10883801       1961907      5128876    11925001  \n",
       "3         32810376      98292764    175748703   117968303  \n",
       "4         11283793       6651475     25306009    27731366  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 1: São Paulo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cities are being indexed by their ID You can see the correspondence in the following website: https://www.ibge.gov.br/explica/codigos-dos-municipios.php <br>\n",
    "<br>\n",
    "We will take a look at São Paulo city with the following ID: 3550308<br>\n",
    "Creating a dataframe with São Paulo information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sao_paulo = df[df.id_municipio == 3550308]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the table created we can check that everything is working as expected. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      <td>354089105362</td>\n",
       "      <td>70226124655</td>\n",
       "      <td>283862980706</td>\n",
       "      <td>10567072</td>\n",
       "      <td>41061915989</td>\n",
       "      <td>220108253285</td>\n",
       "      <td>22682244361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42764</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2009</td>\n",
       "      <td>389816220416</td>\n",
       "      <td>73196101277</td>\n",
       "      <td>316620119139</td>\n",
       "      <td>13817967</td>\n",
       "      <td>45880789159</td>\n",
       "      <td>244712355652</td>\n",
       "      <td>26013156360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48329</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2010</td>\n",
       "      <td>450491987985</td>\n",
       "      <td>85320191365</td>\n",
       "      <td>365171796620</td>\n",
       "      <td>14050283</td>\n",
       "      <td>57881158639</td>\n",
       "      <td>280383326528</td>\n",
       "      <td>26893261170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53894</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2011</td>\n",
       "      <td>501964421296</td>\n",
       "      <td>97490926050</td>\n",
       "      <td>404473495246</td>\n",
       "      <td>16254598</td>\n",
       "      <td>62577555051</td>\n",
       "      <td>312593327364</td>\n",
       "      <td>29286358233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59459</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2012</td>\n",
       "      <td>538877121413</td>\n",
       "      <td>101430123726</td>\n",
       "      <td>437446997686</td>\n",
       "      <td>16999200</td>\n",
       "      <td>63105175747</td>\n",
       "      <td>342783373334</td>\n",
       "      <td>31541449405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65025</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2013</td>\n",
       "      <td>582079726489</td>\n",
       "      <td>106469218771</td>\n",
       "      <td>475610507718</td>\n",
       "      <td>28540943</td>\n",
       "      <td>66882689993</td>\n",
       "      <td>372699138120</td>\n",
       "      <td>36000138662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70595</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2014</td>\n",
       "      <td>621917371650</td>\n",
       "      <td>111044209474</td>\n",
       "      <td>510873162176</td>\n",
       "      <td>39808513</td>\n",
       "      <td>66893050096</td>\n",
       "      <td>406109012608</td>\n",
       "      <td>37831290958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76165</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2015</td>\n",
       "      <td>653646991153</td>\n",
       "      <td>113823596248</td>\n",
       "      <td>539823394905</td>\n",
       "      <td>40989565</td>\n",
       "      <td>66082751683</td>\n",
       "      <td>432441078055</td>\n",
       "      <td>41258575602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81735</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2016</td>\n",
       "      <td>683066696839</td>\n",
       "      <td>117106590019</td>\n",
       "      <td>565960106820</td>\n",
       "      <td>43867060</td>\n",
       "      <td>60646691270</td>\n",
       "      <td>463376974425</td>\n",
       "      <td>41892574065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87305</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2017</td>\n",
       "      <td>698952189373</td>\n",
       "      <td>122526061015</td>\n",
       "      <td>576426128358</td>\n",
       "      <td>37648699</td>\n",
       "      <td>58642822734</td>\n",
       "      <td>474787043038</td>\n",
       "      <td>42958613887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92875</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2018</td>\n",
       "      <td>714683362463</td>\n",
       "      <td>127154298214</td>\n",
       "      <td>587529064249</td>\n",
       "      <td>36419777</td>\n",
       "      <td>58054689156</td>\n",
       "      <td>485428812952</td>\n",
       "      <td>44009142363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_municipio   ano           PIB  impostos_liquidos            VA  \\\n",
       "3826        3550308  2002  188706118970        33670931377  155035187593   \n",
       "9386        3550308  2003  206365287729        37331157576  169034130153   \n",
       "14946       3550308  2004  219862351904        41506499883  178355852021   \n",
       "20507       3550308  2005  254874224343        47336795924  207537428419   \n",
       "26071       3550308  2006  283840191745        51501316759  232338874986   \n",
       "31635       3550308  2007  321826046595        56759767093  265066279502   \n",
       "37199       3550308  2008  354089105362        70226124655  283862980706   \n",
       "42764       3550308  2009  389816220416        73196101277  316620119139   \n",
       "48329       3550308  2010  450491987985        85320191365  365171796620   \n",
       "53894       3550308  2011  501964421296        97490926050  404473495246   \n",
       "59459       3550308  2012  538877121413       101430123726  437446997686   \n",
       "65025       3550308  2013  582079726489       106469218771  475610507718   \n",
       "70595       3550308  2014  621917371650       111044209474  510873162176   \n",
       "76165       3550308  2015  653646991153       113823596248  539823394905   \n",
       "81735       3550308  2016  683066696839       117106590019  565960106820   \n",
       "87305       3550308  2017  698952189373       122526061015  576426128358   \n",
       "92875       3550308  2018  714683362463       127154298214  587529064249   \n",
       "\n",
       "       VA_agropecuaria  VA_industria   VA_servicos   VA_ADESPSS  \n",
       "3826           8534692   27010713107  116949858849  11066080946  \n",
       "9386           9199665   29354085959  126754015882  12916828648  \n",
       "14946          8990852   32122629562  132237766074  13986465534  \n",
       "20507          9445707   36062543096  156031303988  15434135628  \n",
       "26071         10450623   35544663880  179516995546  17266764937  \n",
       "31635         12613897   39817653016  205237080080  19998932510  \n",
       "37199         10567072   41061915989  220108253285  22682244361  \n",
       "42764         13817967   45880789159  244712355652  26013156360  \n",
       "48329         14050283   57881158639  280383326528  26893261170  \n",
       "53894         16254598   62577555051  312593327364  29286358233  \n",
       "59459         16999200   63105175747  342783373334  31541449405  \n",
       "65025         28540943   66882689993  372699138120  36000138662  \n",
       "70595         39808513   66893050096  406109012608  37831290958  \n",
       "76165         40989565   66082751683  432441078055  41258575602  \n",
       "81735         43867060   60646691270  463376974425  41892574065  \n",
       "87305         37648699   58642822734  474787043038  42958613887  \n",
       "92875         36419777   58054689156  485428812952  44009142363  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sao_paulo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has some graph functions built in and we can use them to further visualize data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ano'>"
      ]
     },
     "execution_count": 13,
     "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": [
    "sao_paulo.plot(x='ano', y='PIB')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the tables are in nominal prices it might be interesting to see results in logarithmic form. For that we can use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "#Adding new columns:\n",
    "df['log_pib'] = df.PIB.apply(np.log10)\n",
    "\n",
    "#Separating Sao Paulo again, now it will come with the new columns:\n",
    "sao_paulo = df[df.id_municipio == 3550308]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyzing data the way we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_municipio</th>\n",
       "      <th>ano</th>\n",
       "      <th>PIB</th>\n",
       "      <th>impostos_liquidos</th>\n",
       "      <th>VA</th>\n",
       "      <th>VA_agropecuaria</th>\n",
       "      <th>VA_industria</th>\n",
       "      <th>VA_servicos</th>\n",
       "      <th>VA_ADESPSS</th>\n",
       "      <th>log_pib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3826</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2002</td>\n",
       "      <td>188706118970</td>\n",
       "      <td>33670931377</td>\n",
       "      <td>155035187593</td>\n",
       "      <td>8534692</td>\n",
       "      <td>27010713107</td>\n",
       "      <td>116949858849</td>\n",
       "      <td>11066080946</td>\n",
       "      <td>11.275786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9386</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2003</td>\n",
       "      <td>206365287729</td>\n",
       "      <td>37331157576</td>\n",
       "      <td>169034130153</td>\n",
       "      <td>9199665</td>\n",
       "      <td>29354085959</td>\n",
       "      <td>126754015882</td>\n",
       "      <td>12916828648</td>\n",
       "      <td>11.314637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14946</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2004</td>\n",
       "      <td>219862351904</td>\n",
       "      <td>41506499883</td>\n",
       "      <td>178355852021</td>\n",
       "      <td>8990852</td>\n",
       "      <td>32122629562</td>\n",
       "      <td>132237766074</td>\n",
       "      <td>13986465534</td>\n",
       "      <td>11.342151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20507</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2005</td>\n",
       "      <td>254874224343</td>\n",
       "      <td>47336795924</td>\n",
       "      <td>207537428419</td>\n",
       "      <td>9445707</td>\n",
       "      <td>36062543096</td>\n",
       "      <td>156031303988</td>\n",
       "      <td>15434135628</td>\n",
       "      <td>11.406326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26071</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2006</td>\n",
       "      <td>283840191745</td>\n",
       "      <td>51501316759</td>\n",
       "      <td>232338874986</td>\n",
       "      <td>10450623</td>\n",
       "      <td>35544663880</td>\n",
       "      <td>179516995546</td>\n",
       "      <td>17266764937</td>\n",
       "      <td>11.453074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31635</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2007</td>\n",
       "      <td>321826046595</td>\n",
       "      <td>56759767093</td>\n",
       "      <td>265066279502</td>\n",
       "      <td>12613897</td>\n",
       "      <td>39817653016</td>\n",
       "      <td>205237080080</td>\n",
       "      <td>19998932510</td>\n",
       "      <td>11.507621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37199</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2008</td>\n",
       "      <td>354089105362</td>\n",
       "      <td>70226124655</td>\n",
       "      <td>283862980706</td>\n",
       "      <td>10567072</td>\n",
       "      <td>41061915989</td>\n",
       "      <td>220108253285</td>\n",
       "      <td>22682244361</td>\n",
       "      <td>11.549113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42764</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2009</td>\n",
       "      <td>389816220416</td>\n",
       "      <td>73196101277</td>\n",
       "      <td>316620119139</td>\n",
       "      <td>13817967</td>\n",
       "      <td>45880789159</td>\n",
       "      <td>244712355652</td>\n",
       "      <td>26013156360</td>\n",
       "      <td>11.590860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48329</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2010</td>\n",
       "      <td>450491987985</td>\n",
       "      <td>85320191365</td>\n",
       "      <td>365171796620</td>\n",
       "      <td>14050283</td>\n",
       "      <td>57881158639</td>\n",
       "      <td>280383326528</td>\n",
       "      <td>26893261170</td>\n",
       "      <td>11.653687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53894</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2011</td>\n",
       "      <td>501964421296</td>\n",
       "      <td>97490926050</td>\n",
       "      <td>404473495246</td>\n",
       "      <td>16254598</td>\n",
       "      <td>62577555051</td>\n",
       "      <td>312593327364</td>\n",
       "      <td>29286358233</td>\n",
       "      <td>11.700673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59459</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2012</td>\n",
       "      <td>538877121413</td>\n",
       "      <td>101430123726</td>\n",
       "      <td>437446997686</td>\n",
       "      <td>16999200</td>\n",
       "      <td>63105175747</td>\n",
       "      <td>342783373334</td>\n",
       "      <td>31541449405</td>\n",
       "      <td>11.731490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65025</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2013</td>\n",
       "      <td>582079726489</td>\n",
       "      <td>106469218771</td>\n",
       "      <td>475610507718</td>\n",
       "      <td>28540943</td>\n",
       "      <td>66882689993</td>\n",
       "      <td>372699138120</td>\n",
       "      <td>36000138662</td>\n",
       "      <td>11.764982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70595</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2014</td>\n",
       "      <td>621917371650</td>\n",
       "      <td>111044209474</td>\n",
       "      <td>510873162176</td>\n",
       "      <td>39808513</td>\n",
       "      <td>66893050096</td>\n",
       "      <td>406109012608</td>\n",
       "      <td>37831290958</td>\n",
       "      <td>11.793733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76165</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2015</td>\n",
       "      <td>653646991153</td>\n",
       "      <td>113823596248</td>\n",
       "      <td>539823394905</td>\n",
       "      <td>40989565</td>\n",
       "      <td>66082751683</td>\n",
       "      <td>432441078055</td>\n",
       "      <td>41258575602</td>\n",
       "      <td>11.815343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81735</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2016</td>\n",
       "      <td>683066696839</td>\n",
       "      <td>117106590019</td>\n",
       "      <td>565960106820</td>\n",
       "      <td>43867060</td>\n",
       "      <td>60646691270</td>\n",
       "      <td>463376974425</td>\n",
       "      <td>41892574065</td>\n",
       "      <td>11.834463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87305</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2017</td>\n",
       "      <td>698952189373</td>\n",
       "      <td>122526061015</td>\n",
       "      <td>576426128358</td>\n",
       "      <td>37648699</td>\n",
       "      <td>58642822734</td>\n",
       "      <td>474787043038</td>\n",
       "      <td>42958613887</td>\n",
       "      <td>11.844447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92875</th>\n",
       "      <td>3550308</td>\n",
       "      <td>2018</td>\n",
       "      <td>714683362463</td>\n",
       "      <td>127154298214</td>\n",
       "      <td>587529064249</td>\n",
       "      <td>36419777</td>\n",
       "      <td>58054689156</td>\n",
       "      <td>485428812952</td>\n",
       "      <td>44009142363</td>\n",
       "      <td>11.854114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_municipio   ano           PIB  impostos_liquidos            VA  \\\n",
       "3826        3550308  2002  188706118970        33670931377  155035187593   \n",
       "9386        3550308  2003  206365287729        37331157576  169034130153   \n",
       "14946       3550308  2004  219862351904        41506499883  178355852021   \n",
       "20507       3550308  2005  254874224343        47336795924  207537428419   \n",
       "26071       3550308  2006  283840191745        51501316759  232338874986   \n",
       "31635       3550308  2007  321826046595        56759767093  265066279502   \n",
       "37199       3550308  2008  354089105362        70226124655  283862980706   \n",
       "42764       3550308  2009  389816220416        73196101277  316620119139   \n",
       "48329       3550308  2010  450491987985        85320191365  365171796620   \n",
       "53894       3550308  2011  501964421296        97490926050  404473495246   \n",
       "59459       3550308  2012  538877121413       101430123726  437446997686   \n",
       "65025       3550308  2013  582079726489       106469218771  475610507718   \n",
       "70595       3550308  2014  621917371650       111044209474  510873162176   \n",
       "76165       3550308  2015  653646991153       113823596248  539823394905   \n",
       "81735       3550308  2016  683066696839       117106590019  565960106820   \n",
       "87305       3550308  2017  698952189373       122526061015  576426128358   \n",
       "92875       3550308  2018  714683362463       127154298214  587529064249   \n",
       "\n",
       "       VA_agropecuaria  VA_industria   VA_servicos   VA_ADESPSS    log_pib  \n",
       "3826           8534692   27010713107  116949858849  11066080946  11.275786  \n",
       "9386           9199665   29354085959  126754015882  12916828648  11.314637  \n",
       "14946          8990852   32122629562  132237766074  13986465534  11.342151  \n",
       "20507          9445707   36062543096  156031303988  15434135628  11.406326  \n",
       "26071         10450623   35544663880  179516995546  17266764937  11.453074  \n",
       "31635         12613897   39817653016  205237080080  19998932510  11.507621  \n",
       "37199         10567072   41061915989  220108253285  22682244361  11.549113  \n",
       "42764         13817967   45880789159  244712355652  26013156360  11.590860  \n",
       "48329         14050283   57881158639  280383326528  26893261170  11.653687  \n",
       "53894         16254598   62577555051  312593327364  29286358233  11.700673  \n",
       "59459         16999200   63105175747  342783373334  31541449405  11.731490  \n",
       "65025         28540943   66882689993  372699138120  36000138662  11.764982  \n",
       "70595         39808513   66893050096  406109012608  37831290958  11.793733  \n",
       "76165         40989565   66082751683  432441078055  41258575602  11.815343  \n",
       "81735         43867060   60646691270  463376974425  41892574065  11.834463  \n",
       "87305         37648699   58642822734  474787043038  42958613887  11.844447  \n",
       "92875         36419777   58054689156  485428812952  44009142363  11.854114  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sao_paulo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ano'>"
      ]
     },
     "execution_count": 16,
     "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": [
    "sao_paulo.plot(x='ano',y='log_pib')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Example 2: 2015"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to check all value added to GDP by the services sector in the year 2015 you could use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'log_VA_servicos'}>]], dtype=object)"
      ]
     },
     "execution_count": 17,
     "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": [
    "# First separating 2015 data only:\n",
    "municipios2015 = df[df.ano == 2015]\n",
    "\n",
    "#Adding logarithmic column:\n",
    "municipios2015['log_VA_servicos'] = municipios2015.VA_servicos.apply(np.log10)\n",
    "\n",
    "#Visualizing dataframe as histogram:\n",
    "municipios2015[municipios2015['log_VA_servicos'] > 0].hist('log_VA_servicos')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the last piece of code we are using conditional statement '> 0' because it might be the case of a city with value of zero in the *VA_servicos* and when we calculate log it is equal to -inf."
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}