Covid-19 Data Analysis & Visualization

Goal

Build a small Covid-19 analysis project:

• Load time series case data
• Clean/parse dates
• Plot trends and moving averages
• Compare regions

Analysis pipeline

diagram Covid-19 trend analysis pipeline mermaid

  flowchart LR
  A["Raw time series
(daily cases)"] --> B["Clean
(parse dates, filter country)"]
  B --> C["Smooth
(7-day rolling average)"]
  C --> D["Visualize
(trend, region comparison)"]
  D --> E["Conclude
(peaks, seasonality)"]

From noisy daily case counts to a clear, comparable trend.

Data sources

Possible sources:

• Our World in Data (OWID)
• Johns Hopkins dataset

Step 1: Load and inspect

Load data

import pandas as pd
 
df = pd.read_csv("data/covid.csv")
print(df.shape)
print(df.head())

Step 2: Parse dates and subset

Parse date

import pandas as pd
 
if "date" in df.columns:
    df["date"] = pd.to_datetime(df["date"], errors="coerce")

Step 3: Trend plot

Trend

import matplotlib.pyplot as plt
 
country = "India"
sub = df[df["location"] == country].sort_values("date")
 
plt.figure(figsize=(10, 4))
plt.plot(sub["date"], sub["new_cases"], label="new_cases")
plt.title(f"New cases over time - {country}")
plt.xlabel("Date")
plt.ylabel("New cases")
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

From the book

McKinney’s US baby names chapter reveals real trends only after grouping by year and looking at the shape over decades — a single year tells you almost nothing. Daily case counts are the same: one noisy day means little. A rolling window (.rolling(7).mean()) smooths the noise so the real trend underneath can show through.

Step 4: Moving average

Rolling average

sub = sub.copy()
sub["new_cases_ma7"] = sub["new_cases"].rolling(7).mean()
 
plt.figure(figsize=(10, 4))
plt.plot(sub["date"], sub["new_cases"], alpha=0.4, label="daily")
plt.plot(sub["date"], sub["new_cases_ma7"], label="7-day avg")
plt.title(f"New cases (7-day avg) - {country}")
plt.legend()
plt.tight_layout()
plt.show()

Step 5: Compare regions

Compare countries

import seaborn as sns
import matplotlib.pyplot as plt
 
countries = ["India", "United States", "Brazil"]
sub = df[df["location"].isin(countries)].copy()
sub = sub.sort_values("date")
 
plt.figure(figsize=(10, 4))
sns.lineplot(data=sub, x="date", y="new_cases", hue="location")
plt.title("New cases comparison")
plt.xticks(rotation=20)
plt.tight_layout()
plt.show()

Visualize it

sketch Daily cases vs 7-day average p5.js Pause Restart

The faint line is noisy daily data; the bold amber line is the smoothed trend.

Deliverable

Write a short report:

• When did peaks occur?
• Is the trend seasonal?
• What data quality issues exist (missing dates/values)?

🧪 Try It Yourself

Exercise 1 – Parse dates

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Exercise 2 – 7-day rolling average

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Exercise 3 – Filter one country’s data

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