Data Visualization: How was inflation in Brazil for the past presidents?

Inflation in Brazil has been growing alarmingly

Photo by Jack Prichett on Unsplash

Introduction

This project was developed and inspired by the Dataquest guide project. In it, the average monthly IPCA data from 2003 to 2022 was selected in order to analyze its variation for each president in that period.

The IPCA (“Índice de Preço ao Consumidor Amplo”, Broad Consumer Price Index) aims to measure the inflation of a set of products and services sold in retail, referring to the personal consumption of families. Currently (2022) Brazil is facing one of the worst inflation rates, so I produce this article to analyze the inflation values for the last presidents.

Photo by Allef Vinicius on Unsplash

Database

All data was taken from the IBGE (“Instituto Brasileiro de Geografia e Estatística”, Brazilian Institute of Geography and Statistics) constitutes the main provider of data and information in the country, and meets the needs of the most diverse segments of civil society, as well as federal, state and municipal government agencies.

Pre-processing and cleaning

Important libraries for the program

import logging
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
from matplotlib import style

Reading the data

def read_data(file_path):
try:
 assert isinstance(file_path, str)
    df_func = pd.read_csv(file_path, sep=";")
    shape = df_func.shape[0]
   logging.info(f"SUCCESS: There are {shape} rows in your dataframe")
    return df_func
except FileNotFoundError:
    logging.error(f"ERROR: we were not able to find {file_path}")
    return None
except AssertionError:
    logging.error("ERROR: Error type")
    return None

df_ipca = read_data('ipca.csv')

hard to understand

Cleaning columns

df_ipca.rename(
columns={
'IPCA dessazonalizado - Variação mensal (%)': 'IPCA',},inplace=True)
df_ipca['IPCA'] = df_ipca['IPCA'].str.replace(',', '.')
df_ipca['IPCA'] = df_ipca['IPCA'].astype(float)
df_ipca['date'] = df_ipca['Mês'].str.split(' ').str[1]
df_ipca['date'] = pd.to_datetime(df_ipca['date'], format='%Y')

Data visualization

Graph of inflation in Brazil

To see the graphic production code in detail, you can access my GitHub which will contain all the repository and documentation necessary to generate the graphic.

Conclusion

As we can see, the governments of president Lula and Temer, had a tendency to reduce inflation, on the other hand, the government of Dilma had an increase at the end of her term, most likely caused by her impeachment and, last but not least, we have the current government (Bolsonaro)which since 2021 has been increasing in inflation values