Welcome to the final lesson of our journey into mastering data visualization with Python. By now, you've become comfortable with using FacetGrid and line plots in Seaborn for creating organized and insightful visualizations. In this lesson, we'll leverage this powerful tool to explore patterns over multiple years within our time-series context.

To uncover these patterns, we'll harness the capabilities of FacetGrid from Seaborn to construct faceted line plots. Each subplot will represent data from a different year, enabling us to make effective comparisons across time periods.

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1import matplotlib.pyplot as plt
2import seaborn as sns
3
4# Load the flights dataset
5flights = sns.load_dataset('flights')
6
7# Create subplots for each year
8g = sns.FacetGrid(
9    flights,        # Dataset to plot
10    col="year",     # Facet columns by 'year'
11    col_wrap=4,     # Wrap after every 4 plots
12    height=3,       # Height of each subplot
13    aspect=1.5      # Aspect ratio of each subplot
14)

In this FacetGrid, we utilize parameters such as col_wrap, height, and aspect to customize the layout and appearance of our faceted plots.

• col_wrap: Decides how many plots fit in one row before starting a new row, giving us neat rows and avoiding clutter.

• height: Sets how tall each plot is, ensuring that the data is presented in a legible manner.

• aspect: Controls the plot's shape, allowing us to make each plot wider and optimize the use of space.

By carefully selecting these parameters, we ensure that the resulting visualization is clear and effective, allowing us to easily identify and compare trends across different years.

To delve deeper into the data, we'll map 'month' versus 'passengers' on each subplot using line plots, offering a detailed view of passenger numbers across months for each year. This approach simplifies the interpretation of monthly trends over multiple years.

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1# Plot 'month' vs 'passengers' in each subplot
2g.map(sns.lineplot, "month", "passengers")
3
4# Display the plot
5plt.show()

Utilizing line plots within this context boosts our ability to visualize and compare data trends over time, making complex datasets more understandable and revealing valuable insights into temporal patterns.

The resulting visualization showcases line plots with monthly passenger numbers for each specific year:

By faceting the data by year with four columns, we leverage FacetGrid to easily compare monthly trends across different years. Each subplot provides a clear and comparative view of annual passenger trends.

In this lesson, you harnessed the power of FacetGrid and line plots to visualize yearly patterns in time series data. You discovered how faceted plots can break down complex datasets into yearly subplots, illuminating intricate temporal trends.

As you move on to the practice exercises, I encourage you to experiment further with customizations and apply these skills in other data contexts. Well done on completing this course, and I wish you success in all your future data visualization endeavors.