Plotting a Hierarchically Indexing Pandas DataFrame

In this article, we’ll explore how to create and plot a pandas DataFrame with a hierarchically indexing MultiIndex. We’ll also dive into why you might see an “None, None” entry in your legend and provide two solutions to eliminate it.

Creating the DataFrame

To start, let’s import the necessary libraries and create our DataFrame:

import pandas as pd

# Create a list of column indices
col_indices = pd.MultiIndex.from_product([[1, 2], ['a', 'b']])

# Create a list of row indices
row_indices = [1, 2, 3]

# Create the DataFrame with hierarchically indexing MultiIndex
df = pd.DataFrame(index=row_indices, columns=col_indices)

In this example, we’re creating a DataFrame with two levels of index: row_indices and col_indices. The resulting DataFrame will have an “index” column that contains our row indices and a “columns” column that contains our column indices.

Understanding the Legend

When plotting a DataFrame, matplotlib creates a legend to display the labels for each series in your plot. In this case, we’re only plotting a single line of data, but we still see an entry in the legend labeled “None, None”. This is because the None value represents an empty string or a missing label.

Solution 1: Removing the Legend Entry

One solution to eliminate the “None, None” entry from your legend is to simply remove it by setting the columns.names attribute of your DataFrame to empty strings:

df.columns.names = ['', '']

Alternatively, you can set the column labels to anything else that makes sense for your plot. For example:

df.columns.names = ['name1', 'name2']

By doing so, we’re effectively telling matplotlib that there are no additional labels to display in our legend.

Solution 2: Using `plt.legend()`

Another solution is to add a call to plt.legend() after your plotting command. This will explicitly tell matplotlib to display the legend for your plot:

import matplotlib.pyplot as plt

# Create the DataFrame
df = pd.DataFrame(index=row_indices, columns=col_indices)

# Plot the data
plt.plot(df)

# Display the legend
plt.legend()

In this example, we’re creating a simple line plot of our data and then calling plt.legend() to display the legend.

Example Use Cases

Let’s consider an example use case where we want to plot the values in our DataFrame against each column index. We can do this using a simple loop:

import matplotlib.pyplot as plt

# Create the DataFrame
df = pd.DataFrame(index=row_indices, columns=col_indices)

# Plot the data
for col_name in df.columns:
    plt.plot(df[col_name], label=col_name)

# Display the legend
plt.legend()

In this example, we’re creating a plot where each column index is represented by a separate line of data. We’re using the label argument to specify the label for each line, which corresponds to the column index.

Conclusion

Plotting a hierarchically indexing pandas DataFrame can be a bit tricky, but with the right techniques and strategies, you can create beautiful and informative plots that showcase your data. By removing or customizing the “None, None” entry from your legend, you can create more effective visualizations that communicate your message to your audience.

Additional Tips

When working with hierarchically indexing DataFrames, it’s essential to understand how the index and column levels interact with each other.
Using plt.legend() explicitly can help ensure that your plot is displayed correctly, especially when dealing with complex or custom labels.
Consider using a consistent naming convention for your DataFrame columns to make it easier to interpret your data in the legend.

Code Snippet

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

# Create a list of column indices
col_indices = pd.MultiIndex.from_product([[1, 2], ['a', 'b']])

# Create a list of row indices
row_indices = [1, 2, 3]

# Create the DataFrame with hierarchically indexing MultiIndex
df = pd.DataFrame(index=row_indices, columns=col_indices)

# Plot the data
for col_name in df.columns:
    plt.plot(df[col_name], label=col_name)

# Display the legend
plt.legend()

# Save the plot to a file
plt.savefig('hierarchically_indexing_plot.png')

Last modified on 2024-01-19