5.9. Exploratory Data Analysis with Matplotlib and Seaborn: Creating Line Plots for Time Series

5.9. Exploratory Data Analysis with Matplotlib and Seaborn: Creating Line Plots for Time Series

Exploratory data analysis (EDA) is a fundamental step in the machine learning and deep learning process. It allows data scientists and analysts to better understand trends, patterns, and relationships within data. In particular, for time series, EDA is crucial for understanding how values ​​change over time and identifying seasonal behaviors or long-term trends.

In this context, the Matplotlib and Seaborn libraries in Python are powerful tools for data visualization. Both offer a wide range of chart types and styles that can be customized to meet the specific needs of any analysis. We'll focus on creating line charts, which are particularly useful for visualizing time series.

Line Plots with Matplotlib

Matplotlib is a 2D plotting library in Python that produces publication-quality figures in a variety of print formats and interactive environments across all platforms. Line plots with Matplotlib are created using the plot() function, which connects data points with lines.

To get started, you need to import the Matplotlib library and then prepare your time series data. Data can be in any structure that can be converted to Python lists or arrays, such as lists, NumPy arrays, or Pandas DataFrames. Here's a basic example of how to create a simple line chart:

    import matplotlib.pyplot as plt

    # Suppose we have two lists: one for time and one for values
    time = ['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04']
    values ​​= [10, 20, 15, 25]

    plt.plot(time, values)
    plt.xlabel('Time')
    plt.ylabel('Values')
    plt.title('Simple Time Series Line Plot')
    plt.show()
    

This code will produce a basic line chart, but you will often need to customize the chart to make it more informative. For example, you might want to format dates on the x-axis so they are more readable, or add markers to each data point to make them stand out.

Customization with Matplotlib

Customizing the line chart can include adding a grid, changing the line color, adding markers, setting axis limits, and more. Here are some examples of how you can customize your line chart:

    plt.plot(time, values, color='green', marker='o', linestyle='--')
    plt.grid(True)
    plt.xlim('2021-01-01', '2021-01-04')
    plt.ylim(0, 30)
    

In the example above, we changed the line color to green, added circular markers to each data point, and used a dashed line. We also activate the grid and set the limits for the x and y axes.

Line Charts with Seaborn

Seaborn is a Matplotlib-based data visualization library that offers a high-level interface for drawing attractive statistical graphs. For time series, Seaborn's lineplot() function is a great option as it offers additional functionality such as automatically calculated confidence intervals.

Just like with Matplotlib, you need to import the Seaborn library and prepare your data. Below is an example of how to create a line chart with Seaborn:

    import seaborn as sns

    # Using the same data set as the previous example
    sns.lineplot(x=time, y=values)
    plt.xlabel('Time')
    plt.ylabel('Values')
    plt.title('Time Series Line Chart with Seaborn')
    plt.show()
    

Seaborn automatically improves chart aesthetics and provides a more polished visualization with less code. Additionally, it allows easy integration with Pandas DataFrames, which is very useful when working with time series.

Customization with Seaborn

Customizing line charts in Seaborn is just as easy as in Matplotlib. You can modify the color palette, add titles, labels and more. Additionally, Seaborn works well with the Matplotlib style context, allowing you to use Matplotlib commands to further customize the plot. Here is an example of customization with Seaborn:

    sns.lineplot(x=time, y=values, color='purple', marker='s')
    plt.grid(True)
    plt.xticks(rotation=45) # Rotate x-axis labels for better readability
    plt.tight_layout() #Automatically adjusts subplot parameters
    

In this example, we changed the line color to purple, added square markers, and rotated the x-axis labels to improve readability. Using tight_layout() is a good practice to ensure that nothing is cut off when saving or displaying the chart.

Conclusion

Exploratory data analysis is a critical step in the machine learning and deep learning process, and data visualization plays a key role in this analysis. Line charts for time series are an essential tool for understanding how data varies over time. Both Matplotlib and Seaborn are powerful libraries that offer robust functionality for creating informative, custom line plots. By mastering these libraries, you can extract valuable insights from your data and communicate your findings effectively.