See the Story Behind the Numbers (2024)

Exploring Data Analytics with COVID-19 Data

Welcome to another exciting edition of our newsletter! In this post, we’re diving into the world of data analytics using a real-world dataset: COVID-19 data. This project will help you understand how to manipulate, analyse, and visualise data to extract meaningful insights. By the end of this tutorial, you'll have a strong grasp of the fundamental concepts of data analytics, making your learning journey both interesting and motivating.

What You'll Learn:

Let’s get started!

Step 1: Setup Your Environment

Before we dive into the data, make sure you have the necessary tools installed. We’ll use Python along with the following libraries:

You can install these libraries using pip

pip install pandas numpy matplotlib seaborn

Step 2: Load the Dataset

import pandas as pd

# Load the dataset

url = "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"

data = pd.read_csv(url)

# Display the first few rows

print(data.head())

About the COVID-19 Dataset

The COVID-19 dataset used in this tutorial is sourced from the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). This comprehensive dataset tracks the global spread of the COVID-19 virus, providing detailed information on confirmed cases, deaths, and recoveries across different countries and regions.

Key Features

This dataset is widely used by researchers, analysts, and public health officials to monitor the spread of the virus, analyse trends, and inform policy decisions. Its detailed and regularly updated nature makes it an invaluable resource for anyone looking to understand the impact of COVID-19 on a global scale.

Output

Step 3: Explore the Data

Understanding the structure and content of your dataset is crucial. Let’s take a closer look at the data.

# Display basic information about the dataset

print(data.info())

# Show basic statistics

print(data.describe())

# Display the column names

print(data.columns)

Output

Step 4: Data Cleaning

Real-world data often requires cleaning. We’ll handle missing values and transform the data into a more usable format.

# Check for missing values

print(data.isnull().sum())

# Drop columns with missing values if necessary (Example: data.dropna(axis=1, inplace=True))

# Melt the dataset to have a better structure for analysis

data_melted = data.melt(id_vars=["Province/State", "Country/Region", "Lat", "Long"], var_name="Date", value_name="Confirmed")

data_melted["Date"] = pd.to_datetime(data_melted["Date"], format='%m/%d/%y')

# Display the first few rows of the cleaned data

print(data_melted.head())

Understanding data.melt

The data.melt function is used to convert a wide-format DataFrame into a long-format DataFrame. In wide format, each subject or entity has its own column. In long format, the data is stacked so that each row is a single observation. This is particularly useful for time series data or data that needs to be grouped and aggregated.

Syntax

data.melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None, ignore_index=True)

Parameters

Melt the COVID-19 dataset:

Output

Step 5: Data Analysis

Now that our data is clean, we can start analyzing it. Let’s look at the global spread of COVID-19 over time.

# Group by Date to see the global confirmed cases over time

global_cases = data_melted.groupby("Date")["Confirmed"].sum().reset_index()

# Display the first few rows

print(global_cases.head())

Output

Step 6: Data Visualization

Visualizing data helps in understanding trends and patterns. We’ll create some basic plots to visualize the spread of COVID-19.

import matplotlib.pyplot as plt

import seaborn as sns

# Plot the global confirmed cases over time

plt.figure(figsize=(10, 6))

plt.plot(global_cases["Date"], global_cases["Confirmed"], marker='o', linestyle='-')

plt.title('Global COVID-19 Confirmed Cases Over Time')

plt.xlabel('Date')

plt.ylabel('Confirmed Cases')

plt.grid(True)

plt.show()

Output

Additional Analysis: Country-Specific Trends

We can also analyse the data for specific countries. Let’s see the trend for a specific country, like the United States.

# Filter data for the United States

us_data = data_melted[data_melted["Country/Region"] == "US"]

# Group by Date to see the confirmed cases over time for the US

us_cases = us_data.groupby("Date")["Confirmed"].sum().reset_index()

# Plot the confirmed cases over time for the US

plt.figure(figsize=(10, 6))

plt.plot(us_cases["Date"], us_cases["Confirmed"], marker='o', linestyle='-', color='red')

plt.title('COVID-19 Confirmed Cases Over Time in the US')

plt.xlabel('Date')

plt.ylabel('Confirmed Cases')

plt.grid(True)

plt.show()

Output

Conclusion

Congratulations! You've just completed a basic data analytics project using COVID-19 data. Here’s what we covered:

By working through these steps, you’ve gained valuable skills in data manipulation, analysis, and visualisation. Keep exploring different datasets and applying these techniques to uncover more insights. Data analytics is a powerful tool, and you’re well on your way to mastering it!

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