Line Plots & Time Series

Purpose

Line plots are ideal for showing trends over time or continuous sequences. They're particularly effective for:

• Time series data (stock prices, temperature, passenger counts).
• Showing changes and trends across ordered categories.
• Comparing multiple time series on the same axes.

Example in the Notebook

The notebook uses the flights dataset to demonstrate:

1. Raw monthly passenger counts as a line.
2. A 12-month rolling average (smoothed trend).
3. Confidence bands (rolling mean ± standard deviation).

Key Code Snippet

ts = fl.set_index('date')['passengers'].sort_index()
# Use .to_numpy() for compatibility with modern matplotlib type hints
ax.plot(ts.index.to_numpy(), ts.to_numpy(), label='passengers', color='C0')
rolling = ts.rolling(window=12).mean()
ax.plot(rolling.index.to_numpy(), rolling.to_numpy(), label='12-month rolling mean', color='C1')
rolling_std = ts.rolling(window=12).std()
ax.fill_between(ts.index.to_numpy(), (rolling - rolling_std).to_numpy(), (rolling + rolling_std).to_numpy(),
                color='C1', alpha=0.2)

Customization Tips

• Adjust rolling window: Change window=12 to a different period (e.g., 6 for half-yearly).
• Confidence levels: Modify the band width (e.g., use 2× standard deviation for ~95% CI).
• Multiple series: Plot multiple lines on the same axes with different colors and labels.
• Markers: Add marker='o' to ax.plot() to show data points.

When to Use

• Always: for time-indexed data.
• Consider: when you have many repeated measurements and want to show a trend.
• Avoid: for categorical data without a natural order (use bar plots instead).

See Also

• Matplotlib documentation: plt.plot()
• Pandas rolling: Series.rolling()