Student That Spent a Week Crying and Digging FIFA Data Finally Pulls Out Something Nobody Expected.
Hello and welcome to Data Analysis with Python: Zero to Pandas, the final course project for Jovian.ai. In the next 10 minutes, we'll briefly explore the results of FIFA's first division male international football (soccer) matches ranging from 1872 to 2020 and then, we'll focus on how well Argentina -my home country- fared through the years against other international teams.
The dataset for the FIFA's football results was obtained from https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017 and the geo and topographic data was extracted from the European Union's Urban Centre DataBase A2019, available here: https://ghsl.jrc.ec.europa.eu/ucdb2018Overview.php.
For this exploratory analysis (EDA), I used the packages Numpy and Pandas to extract, clean and process the information and to visualize and explore the relationship between datapoints, Matplotlib and Seaborn.
Downloading the Dataset
Lets start by installing all the tools we'll be using in this course:
# Lets begin installing the libraries I'd be using:
!pip install jovian opendatasets --upgrade --quiet
!pip install numpy --upgrade --quiet
!pip install pandas --upgrade --quiet
!pip install matplotlib --upgrade --quiet
!pip install seaborn --upgrade --quiet# Then importing the modules:
import opendatasets as od
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns