Covid-19 Data Visualization using matplotlib in Python

Visualizing COVID-19 Data with Matplotlib in Python

Understanding the spread of COVID-19 is crucial for public health planning and response. Visualizing this data can provide clear insights into trends and patterns. In this guide, we'll explore how to create informative plots using Python's Matplotlib library.

Understanding the Dataset

We will use the 'case_time_series.csv' dataset, which contains daily records of confirmed, recovered, and deceased COVID-19 cases. The dataset includes the following columns:

• Date: The date of the record
• Daily Confirmed: Number of confirmed cases on that day
• Daily Recovered: Number of recoveries on that day
• Daily Deceased: Number of deaths on that day

By analyzing these columns, we can visualize the progression of the pandemic over time.

Loading and Preparing the Data

First, we'll import the necessary libraries and load the dataset:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Load the dataset
data = pd.read_csv('case_time_series.csv')

# Extract relevant columns
dates = data.iloc[61:, 0].values
confirmed = data.iloc[61:, 1].values
recovered = data.iloc[61:, 3].values
deceased = data.iloc[61:, 5].values

Here, we skip the first 61 rows as they contain metadata, focusing on the actual time series data.

Creating a Line Plot

To visualize the trends in confirmed, recovered, and deceased cases over time, we can create a line plot:

plt.figure(figsize=(10, 6))
plt.plot(dates, confirmed, label='Confirmed Cases', color='blue')
plt.plot(dates, recovered, label='Recovered Cases', color='green')
plt.plot(dates, deceased, label='Deceased Cases', color='red')

plt.title('COVID-19 Cases Over Time')
plt.xlabel('Date')
plt.ylabel('Number of Cases')
plt.xticks(rotation=45)
plt.legend()
plt.tight_layout()
plt.show()

This plot will display the trends of each category, allowing us to observe patterns and make comparisons.

Enhancing the Visualization

To improve the readability and aesthetics of the plot, consider the following enhancements:

• Gridlines: Add gridlines to make it easier to read values.
• Annotations: Highlight significant events or peaks in the data.
• Color Palette: Use a consistent color palette for clarity.

Implementing these enhancements can make the visualization more informative and visually appealing.

Conclusion

Visualizing COVID-19 data using Matplotlib in Python allows for an intuitive understanding of the pandemic's progression. By analyzing trends in confirmed, recovered, and deceased cases, we can gain valuable insights to inform public health decisions.