Understanding Pandas Stacked Bar Charts with Custom Ordering

Understanding Pandas Stacked Bar Charts and Custom Ordering

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When working with Pandas dataframes and creating stacked bar charts, it is often necessary to impose a custom ordering on the categories in the legend. In this article, we will explore how to achieve this using Python’s Pandas library.

Problem Statement

The question presented explores the issue of custom ordering for categorical values when creating stacked bar charts with Pandas. The user wants to reorder the elements in the chart so that they match their intended logical order (from bottom to top), while still displaying the legend entries in reverse order.

Solution Overview

To solve this problem, we will utilize several key concepts from Pandas:

CategoricalIndex: A data structure that groups categorical values and allows for custom ordering.
groupby and value_counts: Methods used to count the occurrences of each category in a series.
sort_index: A method used to sort the columns (or index) of a dataframe by values.

Step 1: Importing Libraries and Generating Sample Data

First, we need to import the necessary libraries:

import pandas as pd
import numpy as np

# Set a random seed for reproducibility
np.random.seed(2019)

Next, let’s generate some sample data using Pandas’ random module. We’ll create a new dataframe with 100 rows, each with a randomly chosen district and a randomly assigned portion of income.

df_orig = pd.DataFrame({'District': np.random.choice(list('ABCDE'), size=100),
                        'Portion of income': np.random.choice(['unsure', '<25%', '25-50%', '50-75%', '75-100%'], size=100)})

Step 2: Grouping and Transforming the Data

We will group the data by district and count the occurrences of each portion of income using groupby and value_counts. Then, we’ll transform this series to have values between 0 and 100.

df = df_orig.groupby('District')['Portion of income'].value_counts(dropna=False)
df = df.groupby('District').transform(lambda x: 100 * x / sum(x))

Step 3: Dropping NaN Values (if any) and Creating a CategoricalIndex

In the provided example, np.nan values were present in some districts. To handle these, we need to drop them from the dataframe.

df = df.drop(labels='nan', level=1)

Next, let’s create a categorical index with custom ordering for our categories:

categories = ['unsure', '<25%', '25-50%', '50-75%', '75-100%']
df.columns = pd.CategoricalIndex(df.columns.values,
                                 ordered=True,
                                 categories=categories)

Step 4: Sorting the Columns and Plotting the Chart

Now that we have a categorical index with custom ordering, let’s sort the columns by this new ordering:

df = df.sort_index(axis=1)

Finally, we can create our stacked bar chart using plot method.

ax = df.plot.bar(stacked=True, rot=0)
ax.set_ylim(ymax=100)

Step 5: Reversing the Legend Entries

We want to reverse the legend entries so they match our custom ordering. To achieve this, we will use get_legend_handles_labels and reversed functions from Matplotlib:

handles, labels = ax.get_legend_handles_labels()
ax.legend(reversed(handles), reversed(labels))

Putting it all Together

Below is the complete code example that includes all these steps.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(2019)

# Generate some sample data
df_orig = pd.DataFrame({'District': np.random.choice(list('ABCDE'), size=100),
                        'Portion of income': np.random.choice(['unsure', '<25%', '25-50%', '50-75%', '75-100%'], size=100)})

# Grouping and transforming the data
df = df_orig.groupby('District')['Portion of income'].value_counts(dropna=False)
df = df.groupby('District').transform(lambda x: 100 * x / sum(x))

# Dropping NaN values (if any)
df = df.drop(labels='nan', level=1)

# Creating a categorical index with custom ordering
categories = ['unsure', '<25%', '25-50%', '50-75%', '75-100%']
df.columns = pd.CategoricalIndex(df.columns.values,
                                 ordered=True,
                                 categories=categories)

# Sorting the columns by the new categorical ordering
df = df.sort_index(axis=1)

# Plotting the stacked bar chart
ax = df.plot.bar(stacked=True, rot=0)
ax.set_ylim(ymax=100)

# Reversing the legend entries to match our custom order
handles, labels = ax.get_legend_handles_labels()
ax.legend(reversed(handles), reversed(labels))

plt.show()

Conclusion

By utilizing Pandas’ categorical index and sorting capabilities, we have successfully created a stacked bar chart with custom ordering of categories in both the chart itself and its legend. This example showcases an advanced technique for customizing data visualizations using Python’s popular libraries.

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Last modified on 2023-06-17