sofiapessoaamorim / life_expectancy

Life Expectancy Data Cleaning

This project is designed to load and clean life expectancy data from a provided CSV file. The main purpose of the script is to process the data, convert it into a more usable format, and save the cleaned data as a CSV file. The structure of this project is as it follows:

life_expectancy
├── data 
├── tests
├── cleaning.py        
├── pyproject.toml                           
└── README.md          

The cleaning.py script focuses on the data for a specific region.

Table of Contents

• Introduction
• Usage
• Functionality
• Requirements
• Installation

Introduction

Life expectancy data often comes in messy formats, and it's important to preprocess and clean the data before performing any analysis. This script provides a solution for loading, cleaning, and saving life expectancy data for a specific region. It handles tasks like handling missing values, converting data types, and reshaping the data for easier analysis.

Usage

To use the script, follow these steps:

1. Make sure you have the necessary requirements installed (see Requirements).
2. Download the raw life expectancy data file (eu_life_expectancy_raw.tsv) and place it in the data directory.
3. Open a terminal or command prompt.
4. Navigate to the project directory.
5. Run the script using the following command, replacing REGION_NAME with the desired region's name (e.g., "PT" for Portugal):

python cleaning.py --region REGION_NAME

The cleaned and processed data will be saved as pt_life_expectancy.csv in the data directory.

Functionality

The script performs the following tasks:

1. Loading Data: The script loads the raw life expectancy data from a provided CSV file (eu_life_expectancy_raw.tsv).

2. Data Cleaning: It preprocesses the data by splitting a combined column and cleaning column names.

3. NaN Handling and Conversion: The script identifies NaN-like values in specified columns and converts them to floats. It also removes any rows with missing data.

4. Data Reshaping: The script melts the DataFrame to turn all year columns into a single year column for easier analysis.

5. Region Selection: It filters the data to retain only the rows corresponding to the specified region.

6. Saving Data: The cleaned and reshaped data is saved as pt_life_expectancy.csv in the data directory.

Requirements

• Python 3.x
• pandas
• numpy

Installation

1. Clone or download this repository.

git clone https://github.com/sofiapessoaamorim/life_expectancy.git