How to Plot a Stacked Bar Chart with Pandas When Data is Separated into Multiple Columns?

Plotting a Stacked Bar Chart with Pandas

In Python, we can use Pandas and Matplotlib to create stacked bar charts. A common challenge is structuring the data for the chart.

For instance, consider the task of creating a stacked bar graph with data separated into multiple columns. The given example shows a spreadsheet with site names and abuse/NFF counts. To plot this data:

1. Import Libraries: Begin by importing Pandas and Matplotlib.
2. Create Data Frame: Create a Pandas DataFrame from your CSV data.
3. Restructure Data: Use the groupby() and unstack() functions to restructure the data into a format suitable for the bar chart. In the example, the data is grouped by Site Name and Abuse/NFF, and then the counts are unstacked.
4. Create Bar Chart: Use the plot() function with the kind='bar' and stacked=True arguments to create the stacked bar chart.
5. Label Axes: Remember to label the x and y axes.

Example Code:

import pandas as pd
import matplotlib.pyplot as plt

# Create DataFrame from CSV data
df = pd.read_csv('data.csv')

# Restructure data
df2 = df.groupby(['Site Name', 'Abuse/NFF'])['Site Name'].count().unstack('Abuse/NFF').fillna(0)

# Create bar chart
df2[['abuse', 'nff']].plot(kind='bar', stacked=True)
plt.xlabel('Site Name')
plt.ylabel('Count')
plt.title('Stacked Bar Chart of Abuse and NFF')
plt.show()

The above is the detailed content of How to Plot a Stacked Bar Chart with Pandas When Data is Separated into Multiple Columns?. For more information, please follow other related articles on the PHP Chinese website!