Customizing Line Colors in Subplots with Matplotlib and Pandas

When working with time series plots and multiple subplots, it’s common to want to customize the appearance of each subplot. In this article, we’ll explore how to change the color of lines within a subplot using matplotlib and pandas.

Introduction to Matplotlib and Pandas

Before diving into customizing line colors, let’s quickly review the basics of matplotlib and pandas.

Matplotlib is a popular Python library for creating static, animated, and interactive visualizations in python. It provides a comprehensive set of tools for creating high-quality 2D and 3D plots, charts, and graphs.

Pandas is another powerful Python library used for data manipulation and analysis. It provides data structures and functions designed to make working with structured data (e.g., tabular) easy and efficient.

In this article, we’ll use matplotlib’s plotting capabilities in conjunction with pandas’ data manipulation features to create a time series plot with customizable line colors.

Understanding the Plotting Function

The plot() function in pandas is just a thin wrapper around matplotlib’s plotting methods. When you call df[['column1', 'column2']].plot(), pandas is actually calling matplotlib.pyplot.plot() under the hood.

Any non-consumed keyword arguments passed to pandas’ plot() function are then passed on to matplotlib’s plotting functions, allowing for fine-grained control over plot appearance.

Customizing Line Colors with Matplotlib

To change the color of lines within a subplot, you can use matplotlib’s color argument. However, this argument is not specifically designed for subplots. Instead, you’ll need to create a separate line for each subplot using the plot() function from matplotlib.

Let’s take a closer look at how to do this:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create sample data
df = pd.DataFrame()
df['diet'] = np.random.random_sample(100)
df['rolling_mean_diet'] = np.random.random_sample(100) / 10 + 0.5

df['gym'] = np.random.random_sample(100)
df['rolling_mean_gym'] = np.random.random_sample(100) / 10 + 0.5

# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(13,10));

# Plot data in subplots
axes[0].plot(df['diet'], color='red')
axes[0].plot(df['rolling_mean_diet'], color='green')

axes[1].plot(df['gym'], color='purple')
axes[1].plot(df['rolling_mean_gym'], color='red')

In this example, we’re using the plot() function to create separate lines for each subplot. We’ve also used the color argument to specify the desired colors.

However, if you want to customize line colors across multiple subplots in a single call to plot(), you’ll need to use the colorlist argument instead of providing individual color values.

Using Color Lists with Matplotlib

Color lists are arrays that contain one or more colors. When you pass a color list to matplotlib’s plotting functions, it will automatically assign each element of the list to a different line in your plot.

Here’s an example of how to use color lists:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create sample data
df = pd.DataFrame()
df['diet'] = np.random.random_sample(100)
df['rolling_mean_diet'] = np.random.random_sample(100) / 10 + 0.5

df['gym'] = np.random.random_sample(100)
df['rolling_mean_gym'] = np.random.random_sample(100) / 10 + 0.5

# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(13,10));

# Plot data in subplots using color list
axes[0].plot(df['diet'], df['rolling_mean_diet'], color=['red', 'green'])
axes[1].plot(df['gym'], df['rolling_mean_gym'], color=['purple', 'red'])

In this example, we’ve used a single call to plot() with a color list. Matplotlib automatically assigns the first element of the color list ('red') to the line for df['diet'] and the second element ('green') to the line for df['rolling_mean_diet'].

If you want to customize multiple lines across different subplots, you can create separate color lists for each subplot. Here’s an example:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Create sample data
df = pd.DataFrame()
df['diet'] = np.random.random_sample(100)
df['rolling_mean_diet'] = np.random.random_sample(100) / 10 + 0.5

df['gym'] = np.random.random_sample(100)
df['rolling_mean_gym'] = np.random.random_sample(100) / 10 + 0.5

# Create subplots
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(13,10));

# Plot data in subplots using separate color lists
axes[0].plot(df['diet'], df['rolling_mean_diet'], color=['red'])
axes[0].plot(df['gym'], df['rolling_mean_gym'], color=['green'])

axes[1].plot(df['diet'], df['rolling_mean_diet'], color=['purple'])
axes[1].plot(df['gym'], df['rolling_mean_gym'], color=['red'])

In this example, we’ve created separate color lists for each subplot using the color argument. Matplotlib automatically assigns the corresponding colors from each list to the desired lines.

By understanding how to use color lists with matplotlib’s plotting functions, you can create customized line plots across multiple subplots in a single call.

Conclusion

In this article, we’ve explored how to customize line colors in matplotlib plots using separate plot() calls and color lists. By leveraging these features, you can create fine-grained control over plot appearance, making your visualizations more engaging and effective.

Whether you’re working with pandas data or other libraries, understanding the basics of matplotlib’s plotting functions is essential for creating high-quality visualizations that effectively communicate insights and trends in your data.

Last modified on 2023-06-12