Python Matplotlib - Bar Plot - Sort Descending

Python Matplotlib - Bar Plot - Sort Descending

Sorting a bar plot in descending order can make your data visualization more effective by highlighting the most significant values. In this tutorial, we will cover how to sort bar plots in descending order using Python's Matplotlib library.

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Sorting Data for Bar Plot

To create a bar plot in descending order, the data must first be sorted. You can use Python's sorted() function or Pandas library for sorting.

Example 1: Sorting Using sorted()

import matplotlib.pyplot as plt

# Data for the bar plot
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values = [10, 25, 15, 20]

# Sort the data in descending order
sorted_data = sorted(zip(values, categories), reverse=True)
sorted_values, sorted_categories = zip(*sorted_data)

# Create the bar plot
plt.bar(sorted_categories, sorted_values, color='skyblue', edgecolor='black')

# Add labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot Sorted in Descending Order')

# Show the plot
plt.show()

Explanation

1. The zip() function combines values and categories into pairs.
2. The sorted() function sorts the data in descending order using reverse=True.
3. The sorted data is unpacked into sorted_values and sorted_categories for plotting.

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Sorting Data Using Pandas

If your data is in a Pandas DataFrame, sorting can be done directly using the sort_values() method.

Example 2: Sorting Using Pandas

import matplotlib.pyplot as plt
import pandas as pd

# Create a DataFrame
data = {'Categories': ['Category A', 'Category B', 'Category C', 'Category D'],
        'Values': [10, 25, 15, 20]}
df = pd.DataFrame(data)

# Sort the DataFrame in descending order by 'Values'
df_sorted = df.sort_values('Values', ascending=False)

# Create the bar plot
plt.bar(df_sorted['Categories'], df_sorted['Values'], color='orange', edgecolor='black')

# Add labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Bar Plot Sorted in Descending Order (Pandas)')

# Show the plot
plt.show()

Explanation

1. The data is stored in a Pandas DataFrame for easy manipulation.
2. The sort_values() method sorts the DataFrame by the 'Values' column in descending order.
3. The sorted DataFrame is used to create the bar plot.

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Customizing Sorted Bar Plot

After sorting, you can further customize the bar plot by changing colors, adding gridlines, and formatting labels.

Example 3: Customized Sorted Bar Plot

import matplotlib.pyplot as plt

# Data for the bar plot
categories = ['Category A', 'Category B', 'Category C', 'Category D']
values = [10, 25, 15, 20]

# Sort the data in descending order
sorted_data = sorted(zip(values, categories), reverse=True)
sorted_values, sorted_categories = zip(*sorted_data)

# Create the bar plot
plt.bar(sorted_categories, sorted_values, color=['#FF5733', '#33FF57', '#3357FF', '#FFC300'], edgecolor='black')

# Add labels and title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Customized Bar Plot Sorted in Descending Order')

# Add gridlines
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Show the plot
plt.show()

Explanation

1. Each bar is assigned a unique color using a list of hex color codes.
2. Gridlines are added to the y-axis for better visual reference using plt.grid().
3. The sorted data is displayed in a visually enhanced bar plot.

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Summary

In this tutorial, we covered:

• Sorting bar plot data in descending order using sorted() and Pandas.
• Creating bar plots with sorted data.
• Customizing bar plots to improve aesthetics and readability.

Sorting bar plots helps emphasize the most significant data points and makes visualizations more intuitive. Experiment with these techniques to enhance your data presentations.