BR_eco_data_web_scraping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e1fee7af-d4c0-447b-a830-578a92921335",
   "metadata": {},
   "source": [
    "# Brazilian economics data web scraping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56572c56-da0a-4ccf-ba98-f54ce0a3c055",
   "metadata": {},
   "source": [
    "Notebook to automate the collecting process of various economics temporal series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4aaf4647-277f-4156-ae79-111f70c0f7b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libs\n",
    "import pandas as pd\n",
    "#import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "from datetime import datetime\n",
    "import calendar\n",
    "from ftplib import FTP\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cc1711-afb5-4648-8b7e-38dfa0b71b45",
   "metadata": {},
   "source": [
    "### CDI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b446f790-df25-479d-9d4c-fa9b536e8b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_cdi = 'http://www.b3.com.br/pt_br/market-data-e-indices/indices/indices-de-segmentos-e-setoriais/serie-historica-do-di.htm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "229a34a6-dd9d-4ea9-ac50-32b308468f4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bbd695-4478-4035-8632-047bad05bef5",
   "metadata": {},
   "source": [
    "### IPCA\n",
    "\n",
    "IPCA (Índice de preços ao consumidor amplo/Broad Consumer Price Index) is the official Brazilian inflation index. It is calculated on a monthly basis by the IBGE (Instituto Brasileiro de Geografia e Estatística/Brazilian Institute of Geography and Statistics - https://www.ibge.gov.br/), it is provided as temporal series since January 1994 in: https://ftp.ibge.gov.br/Precos_Indices_de_Precos_ao_Consumidor/IPCA/Serie_Historica/ipca_SerieHist.zip.\n",
    "\n",
    "Below code downloads and clean-up the series provided by IBGE:\n",
    "\n",
    "Download raw .zip from IBGE FTP -> unzip -> load into a DataFrame -> clean-up -> save the cleaned data in a local repository as .xlsx (MS Excel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5fdc67d5-0475-4550-ab88-8efe11693636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ipca scraper config\n",
    "# url: https://ftp.ibge.gov.br/Precos_Indices_de_Precos_ao_Consumidor/IPCA/Serie_Historica/ipca_SerieHist.zip\n",
    "IPCA_scraper_config = pd.Series({ 'url_ipca' : 'ftp.ibge.gov.br',\n",
    "                                  'username_ipca' : '',\n",
    "                                  'password_ipca' : '',\n",
    "                                  'remote_path_ipca' : '/Precos_Indices_de_Precos_ao_Consumidor/IPCA/Serie_Historica',\n",
    "                                  'remote_file_ipca' : 'ipca_SerieHist.zip',\n",
    "                                  'local_path_ipca' : ''})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "469c225a-36a5-40b7-a4b0-dab152b2361b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a folder\n",
    "def create_folder_if_not_exists(folder_path):\n",
    "    try:\n",
    "        # Check if the folder exists\n",
    "        if not os.path.exists(folder_path):\n",
    "            # If not, create the folder\n",
    "            os.makedirs(folder_path)\n",
    "            print(f\"Folder '{folder_path}' created successfully.\")\n",
    "        else:\n",
    "            print(f\"Folder '{folder_path}' already exists.\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "971c0ffe-741a-44ea-9173-d6b95189f1fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "#download data from FTP\n",
    "def download_ftp_data(url, username, password, remote_path, remote_file, local_path):\n",
    "    try:\n",
    "        # Connect to the FTP server\n",
    "        ftp = FTP(url)\n",
    "        ftp.login(username, password)\n",
    "\n",
    "        # Change to the remote directory\n",
    "        ftp.cwd(remote_path)\n",
    "\n",
    "        # Open a local file for writing\n",
    "        with open(local_path+'/'+remote_file, 'wb') as local_file:\n",
    "            # Retrieve the file from the FTP server\n",
    "            ftp.retrbinary('RETR ' + remote_file, local_file.write)\n",
    "\n",
    "        print(f\"File downloaded successfully to {local_path}\")\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")\n",
    "\n",
    "    finally:\n",
    "        # Close the FTP connection\n",
    "        if ftp:\n",
    "            ftp.quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2ec5a3c1-ea1b-4e13-b366-b286f4171ae3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#unzip a file\n",
    "def unzip(file_path, file_name):\n",
    "    try:\n",
    "        with zipfile.ZipFile(file_path+'/'+file_name, 'r') as zip_ref:\n",
    "            zip_ref.extractall(file_path)\n",
    "        print(f\"File unziped successfully to {file_path}\")\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99e01897-779b-48f4-a68d-d31c96eee496",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scraper to colect, clean and save the ipca data\n",
    "def scrap_IPCA(config):\n",
    "    #get the timestamp and create a folder to store the downloaded raw data\n",
    "    timestamp = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
    "    config.local_path_ipca = 'raw_data/'+'ipca_raw_'+timestamp\n",
    "    create_folder_if_not_exists('raw_data')\n",
    "    create_folder_if_not_exists(config.local_path_ipca)\n",
    "    \n",
    "    #download the ipca raw data (.zip) from IBGE FTP to local_path folder\n",
    "    download_ftp_data(config.url_ipca, config.username_ipca, config.password_ipca, config.remote_path_ipca, config.remote_file_ipca, config.local_path_ipca)\n",
    "    \n",
    "    #unzip the file (.xls file expected)\n",
    "    unzip(config.local_path_ipca, config.remote_file_ipca)\n",
    "    \n",
    "    #load excel file as a DataFrame\n",
    "    try:\n",
    "        files = os.listdir(config.local_path_ipca)\n",
    "        # Find the Excel file (assuming there's only one)\n",
    "        excel_file = next((file for file in files if file.endswith(('.xls', '.xlsx'))), None)\n",
    "        if excel_file:\n",
    "            # If there's an Excel file, open it with pandas\n",
    "            excel_file_path = os.path.join(config.local_path_ipca, excel_file)\n",
    "            df = pd.read_excel(excel_file_path)\n",
    "            print(\"Excel file successfully loaded.\")\n",
    "        else:\n",
    "            print(f\"No excel file found in {local_path}\")\n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")\n",
    "    \n",
    "    #clean-up the data loaded\n",
    "    df_ok = df.copy().rename(columns={'Unnamed: 0':'ref_year', 'Unnamed: 1':'ref_month' , 'Unnamed: 3':'in_month', 'Unnamed: 4':'in_3_months', 'Unnamed: 5':'in_6_months', 'Unnamed: 6':'in_year', 'Unnamed: 7':'in_12_months'}).drop(columns='Unnamed: 2')\n",
    "    df_ok = df_ok.replace({'JAN':1, 'FEV':2, 'MAR':3, 'ABR':4, 'MAI':5, 'JUN':6, 'JUL':7, 'AGO':8, 'SET':9, 'OUT':10, 'NOV':11, 'DEZ':12}).replace({' ':pd.NA})\n",
    "    df_ok['ref_year'] = df_ok['ref_year'].fillna(axis='index', method='ffill')\n",
    "    df_ok = df_ok.dropna(how='any').reset_index(drop = True)\n",
    "    #pd.set_option('display.max_rows', None)\n",
    "\n",
    "    #adding some useful columns\n",
    "    df_ok.insert(2,'ref_last_day', df_ok.apply(lambda row: calendar.monthrange(row['ref_year'], row['ref_month'])[1], axis=1))\n",
    "    df_ok.insert(3, 'ref_date', pd.to_datetime(df_ok['ref_last_day'].astype(str) + '-' + df_ok['ref_month'].astype(str) + '-' + df_ok['ref_year'].astype(str), dayfirst=True, format='%d-%m-%Y'))\n",
    "    df_ok = df_ok.set_index(['ref_year','ref_month'])\n",
    "    print(\"DataFrame cleaned.\")\n",
    "    \n",
    "    #store the cleaned DF in a MS Excel file\n",
    "    try:\n",
    "        df_ok.to_excel(config.local_path_ipca + '/' + \"ipca_hist_series.xlsx\", merge_cells=False)  \n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error: {e}\")\n",
    "    \n",
    "    return df_ok"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a480f18-18a8-442d-aac6-2a0668337c4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Folder 'raw_data' already exists.\n",
      "Folder 'raw_data/ipca_raw_20231125_140536' created successfully.\n",
      "File downloaded successfully to raw_data/ipca_raw_20231125_140536\n",
      "File unziped successfully to raw_data/ipca_raw_20231125_140536\n",
      "Excel file successfully loaded.\n",
      "DataFrame cleaned.\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ref_last_day</th>\n",
       "      <th>ref_date</th>\n",
       "      <th>in_month</th>\n",
       "      <th>in_3_months</th>\n",
       "      <th>in_6_months</th>\n",
       "      <th>in_year</th>\n",
       "      <th>in_12_months</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ref_year</th>\n",
       "      <th>ref_month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1994</th>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-01-31</td>\n",
       "      <td>41.31</td>\n",
       "      <td>162.13</td>\n",
       "      <td>533.33</td>\n",
       "      <td>41.31</td>\n",
       "      <td>2693.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1994-02-28</td>\n",
       "      <td>40.27</td>\n",
       "      <td>171.24</td>\n",
       "      <td>568.17</td>\n",
       "      <td>98.22</td>\n",
       "      <td>3035.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-03-31</td>\n",
       "      <td>42.75</td>\n",
       "      <td>182.96</td>\n",
       "      <td>602.93</td>\n",
       "      <td>182.96</td>\n",
       "      <td>3417.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1994-04-30</td>\n",
       "      <td>42.68</td>\n",
       "      <td>185.71</td>\n",
       "      <td>648.92</td>\n",
       "      <td>303.73</td>\n",
       "      <td>3828.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-05-31</td>\n",
       "      <td>44.03</td>\n",
       "      <td>193.36</td>\n",
       "      <td>695.71</td>\n",
       "      <td>481.49</td>\n",
       "      <td>4331.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-08-31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.23</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ref_last_day   ref_date in_month in_3_months in_6_months  \\\n",
       "ref_year ref_month                                                             \n",
       "1994     1                    31 1994-01-31    41.31      162.13      533.33   \n",
       "         2                    28 1994-02-28    40.27      171.24      568.17   \n",
       "         3                    31 1994-03-31    42.75      182.96      602.93   \n",
       "         4                    30 1994-04-30    42.68      185.71      648.92   \n",
       "         5                    31 1994-05-31    44.03      193.36      695.71   \n",
       "...                          ...        ...      ...         ...         ...   \n",
       "2023     6                    30 2023-06-30    -0.08        0.76        2.87   \n",
       "         7                    31 2023-07-31     0.12        0.27        2.45   \n",
       "         8                    31 2023-08-31     0.23        0.27        1.83   \n",
       "         9                    30 2023-09-30     0.26        0.61        1.38   \n",
       "         10                   31 2023-10-31     0.24        0.73           1   \n",
       "\n",
       "                   in_year in_12_months  \n",
       "ref_year ref_month                       \n",
       "1994     1           41.31      2693.84  \n",
       "         2           98.22      3035.71  \n",
       "         3          182.96      3417.39  \n",
       "         4          303.73      3828.49  \n",
       "         5          481.49      4331.19  \n",
       "...                    ...          ...  \n",
       "2023     6            2.87         3.16  \n",
       "         7            2.99         3.99  \n",
       "         8            3.23         4.61  \n",
       "         9             3.5         5.19  \n",
       "         10           3.75         4.82  \n",
       "\n",
       "[358 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#executing the scraper\n",
    "ipca_df = scrap_IPCA(IPCA_scraper_config)\n",
    "ipca_df\n",
    "#example of filter to get an specific value from the DF: ipca_df['in_12_months'].loc[2003,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "697c7555-224c-489c-bbf0-d8e4d51c3efe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>ref_date</th>\n",
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       "  <tbody>\n",
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       "      <th rowspan=\"5\" valign=\"top\">1994</th>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-01-31</td>\n",
       "      <td>41.31</td>\n",
       "      <td>162.13</td>\n",
       "      <td>533.33</td>\n",
       "      <td>41.31</td>\n",
       "      <td>2693.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1994-02-28</td>\n",
       "      <td>40.27</td>\n",
       "      <td>171.24</td>\n",
       "      <td>568.17</td>\n",
       "      <td>98.22</td>\n",
       "      <td>3035.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-03-31</td>\n",
       "      <td>42.75</td>\n",
       "      <td>182.96</td>\n",
       "      <td>602.93</td>\n",
       "      <td>182.96</td>\n",
       "      <td>3417.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>1994-04-30</td>\n",
       "      <td>42.68</td>\n",
       "      <td>185.71</td>\n",
       "      <td>648.92</td>\n",
       "      <td>303.73</td>\n",
       "      <td>3828.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>1994-05-31</td>\n",
       "      <td>44.03</td>\n",
       "      <td>193.36</td>\n",
       "      <td>695.71</td>\n",
       "      <td>481.49</td>\n",
       "      <td>4331.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2023</th>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>2023-06-30</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.76</td>\n",
       "      <td>2.87</td>\n",
       "      <td>2.87</td>\n",
       "      <td>3.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.27</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>3.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-08-31</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.83</td>\n",
       "      <td>3.23</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.38</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>31</td>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.75</td>\n",
       "      <td>4.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>358 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    ref_last_day   ref_date  in_month  in_3_months  \\\n",
       "ref_year ref_month                                                   \n",
       "1994     1                    31 1994-01-31     41.31       162.13   \n",
       "         2                    28 1994-02-28     40.27       171.24   \n",
       "         3                    31 1994-03-31     42.75       182.96   \n",
       "         4                    30 1994-04-30     42.68       185.71   \n",
       "         5                    31 1994-05-31     44.03       193.36   \n",
       "...                          ...        ...       ...          ...   \n",
       "2023     6                    30 2023-06-30     -0.08         0.76   \n",
       "         7                    31 2023-07-31      0.12         0.27   \n",
       "         8                    31 2023-08-31      0.23         0.27   \n",
       "         9                    30 2023-09-30      0.26         0.61   \n",
       "         10                   31 2023-10-31      0.24         0.73   \n",
       "\n",
       "                    in_6_months  in_year  in_12_months  \n",
       "ref_year ref_month                                      \n",
       "1994     1               533.33    41.31       2693.84  \n",
       "         2               568.17    98.22       3035.71  \n",
       "         3               602.93   182.96       3417.39  \n",
       "         4               648.92   303.73       3828.49  \n",
       "         5               695.71   481.49       4331.19  \n",
       "...                         ...      ...           ...  \n",
       "2023     6                 2.87     2.87          3.16  \n",
       "         7                 2.45     2.99          3.99  \n",
       "         8                 1.83     3.23          4.61  \n",
       "         9                 1.38     3.50          5.19  \n",
       "         10                1.00     3.75          4.82  \n",
       "\n",
       "[358 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the recently saved clean database from excel\n",
    "df = pd.read_excel(IPCA_scraper_config.local_path_ipca + '/' + \"ipca_hist_series.xlsx\").set_index(['ref_year','ref_month'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17ecf441-5ffd-4989-a33e-65865a78bf2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ploting the DataFrame\n",
    "#ploting from '96 to avoid the \"94'-ish pollution\" \n",
    "\n",
    "#fig_size = plt.rcParams[\"figure.figsize\"]\n",
    "#fig_size = (20,50)\n",
    "\n",
    "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(nrows = 5, ncols = 1, sharex = True)\n",
    "\n",
    "start = 1997\n",
    "end = 2023\n",
    "\n",
    "ax1.plot(ipca_df.ref_date.loc[start:end], ipca_df.in_month.loc[start:end], label = 'in the month')\n",
    "ax2.plot(ipca_df.ref_date.loc[start:end], ipca_df.in_year.loc[start:end], label = 'in the year')\n",
    "ax3.plot(ipca_df.ref_date.loc[start:end], ipca_df.in_3_months.loc[start:end], label = 'in 3 months')\n",
    "ax4.plot(ipca_df.ref_date.loc[start:end], ipca_df.in_6_months.loc[start:end], label = 'in 6 months')\n",
    "ax5.plot(ipca_df.ref_date.loc[start:end], ipca_df.in_12_months.loc[start:end], label = 'in 12 months')\n",
    "\n",
    "#x axis settings\n",
    "ax1.set_title('Inflation')\n",
    "ax5.set_xlabel('Period')\n",
    "\n",
    "#y axis settings\n",
    "ax1.set_ylabel('[%]')\n",
    "ax2.set_ylabel('[%]')\n",
    "ax3.set_ylabel('[%]')\n",
    "ax4.set_ylabel('[%]')\n",
    "ax5.set_ylabel('[%]')\n",
    "\n",
    "ax1.legend()\n",
    "ax2.legend()\n",
    "ax3.legend()\n",
    "ax4.legend()\n",
    "ax5.legend()\n",
    "\n",
    "#plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32efb38d-9545-45ee-8d12-c8c46e32ecb0",
   "metadata": {},
   "source": [
    "### IGP-M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75bc8169-befa-430d-bab4-aabbc65130ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "url_igpm = 'https://portal.fgv.br/noticias/igpm-resultados-2022'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1220451-1ef8-4b7a-9b00-de27af95ab1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}