


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:
- Import Libraries: Begin by importing Pandas and Matplotlib.
- Create Data Frame: Create a Pandas DataFrame from your CSV data.
- 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.
- Create Bar Chart: Use the plot() function with the kind='bar' and stacked=True arguments to create the stacked bar chart.
- 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()
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