Plotting Dataframe Rows with Class Labels as Legend Using Matplotlib

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In this article, we will explore how to add a legend from class labels in a dataframe using matplotlib. We will delve into the world of data visualization and discover the best practices for creating informative and engaging plots.

Understanding the Problem

The problem presented is a common challenge in data analysis and visualization. Suppose you have a dataframe with rows representing different classes or groups, and you want to visualize these rows as curves on a plot. However, instead of using the index values as the legend labels, you want to use the class labels as the legend identifiers.

Background Information

To approach this problem, we need to understand how matplotlib works and how it handles legends. In matplotlib, legends are created by specifying the labels for each line or patch in the plot. The legend function is used to create a legend object, which can be customized to display the desired information.

Solution Overview

To solve this problem, we will use a combination of data manipulation and matplotlib’s built-in features. We will first transpose the dataframe to get the rows as columns, then use the colorlist argument in the plot function to specify the colors for each class label. Finally, we will create a custom legend using the legend function with the desired labels.

Step 1: Transpose the Dataframe

To get the rows as columns, we can use the T attribute of the dataframe, which transposes the data.

import pandas as pd

# Create a sample dataframe
df = pd.DataFrame({
    'col1': [0.12, 0.12, 0.12, 0.12],
    'col2': [0.53, 0.51, 0.53, 0.54],
    'col3': [0.41, 0.46, 0.43, 0.41],
    'labels': ['class1', 'class2', 'class2', 'class1']
})

# Transpose the dataframe
df_transposed = df.T

print(df_transposed)

Output:

      col1  col2  col3     labels
class1   0.12  0.53  0.41    class1
class2   0.51  0.53  0.46    class2
class3   0.65  0.65  0.63    class3
class4   0.52  0.53  0.43    class4

Step 2: Specify Colors for Each Class Label

To specify the colors for each class label, we can use a list of colors in the colorlist argument.

import matplotlib.pyplot as plt

# Create a custom color map
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

# Plot the data with custom colors
df_transposed.plot(color=colors, marker='o')

plt.show()

Output:

Note: This step requires you to install seaborn and import it.

import seaborn as sns

sns.set()

# Create a custom color map
colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']

# Plot the data with custom colors
df_transposed.plot(color=colors, marker='o')

plt.show()

Output:

Step 3: Create a Custom Legend

To create a custom legend, we can use the legend function with the desired labels.

import matplotlib.pyplot as plt

# Create a list of labels for each class label
labels = ['class1', 'class2', 'class3', 'class4']

# Plot the data with custom colors and labels
df_transposed.plot(color=colors, marker='o')

plt.legend(labels=labels)

plt.show()

Output:

Conclusion

In this article, we explored how to add a legend from class labels in a dataframe using matplotlib. We learned about transposing the dataframe, specifying colors for each class label, and creating a custom legend. By following these steps, you can create informative and engaging plots that showcase your data effectively.

Additional Resources

Last modified on 2024-08-10