Is Covid-19 Still A Threat? A Python Data Analysis

How many Covid-19 cases are there today?

Is Covid-19 Still A Threat? Image by Ajay Kumar Singh from Pixabay.

Each individual COVID-19 case is dramatic and each death is a tragedy, but is it still a threat to the major parts of our society? Welcome to Python Data Science December #17.

We will explore the COVID-19 cases & deaths over time for different countries in Europe using Python Pandas, Plotly & Matplotlib. All resources, datasets, required Python libraries & installations are listed at the end of the story, in the chapter Summary & Resources.

📅 The Data

I came across a COVID-19 dataset for the countries in the European Union from Eurostat. We will use it as the baseline for our article today.

I am using Python Jupyter notebook to examine the data, but you can also just take a regular Python script. To get a first impression of the data, let’s install & import Pandas and read the file into a Pandas DataFrame.

import pandas as pd
df = pd.read_csv('data.csv')

First, let us explore the dataset running df.info().

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9486 entries, 0 to 9485
Data columns (total 11 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   country           9486 non-null   object 
 1   country_code      9180 non-null   object 
 2   continent         9486 non-null   object 
 3   population        9486 non-null   int64  
 4   indicator         9486 non-null   object 
 5   weekly_count      9078 non-null   float64
 6   year_week         9486 non-null   object 
 7   rate_14_day       8997 non-null   float64
 8   cumulative_count  9078 non-null   float64
 9   source            9486 non-null   object 
 10  note              0 non-null      float64
dtypes: float64(4), int64(1), object(6)
memory usage: 815.3+ KB

We have 9486 rows of data structured into 11 columns. Let’s have a closer look at the first rows of the dataset and some random sample rows.

df.head()     #returns the first 5 rows

df.sample(n=10)     #returns a random sample of 10 rows