5 Best Ways to Plot a Stacked Horizontal Bar Chart in Python Using Pandas

💡 Problem Formulation: Data visualization is an integral part of data analysis, enabling clear communication of insights. Often, we need to compare parts of a whole across different categories. This is where a stacked horizontal bar chart is useful. The input involves a DataFrame with categorical data and numeric values. The desired output is a stacked horizontal bar chart that displays the proportion of each category’s subgroups.

Method 1: Using matplotlib with pandas

One common method to plot a stacked horizontal bar chart in pandas is by utilizing the built-in plot function with matplotlib as a backend. This method allows for extensive customization and interactivity. The stacked parameter is set to True to enable stacking. The kind parameter is set to ‘barh’ for horizontal bars.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.set_index('Category').plot(kind='barh', stacked=True)
plt.show()

The output is a matplotlib figure showing a stacked bar chart with horizontal bars representing the sum of ‘Value1’ and ‘Value2’ for each category.

This code snippet creates a dataframe from a dictionary, sets the ‘Category’ column as the index, and plots a stacked horizontal bar chart where each stack segment represents a different column in the dataframe. Using matplotlib.pyplot, we can display the plot.

Method 2: Using pandas with seaborn

Combining pandas with seaborn provides an elegant way to plot complex charts with more appealing aesthetics by default. Seaborn operates on long-form data, which might require reshaping a DataFrame using pandas’ melt function before plotting.

Here’s an example:

import pandas as pd
import seaborn as sns

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df_melted = df.melt(id_vars='Category', var_name='Variable', value_name='Value')
sns.barplot(x='Value', y='Category', hue='Variable', data=df_melted, orient='h')

The output will be a seaborn figure with a clear distinction between ‘Value1’ and ‘Value2’ in different colors within each category.

The code reshapes the data so that each variable becomes a separate entry, and then uses seaborn’s barplot function to plot a stacked horizontal bar chart, coloring each variable differently for distinction.

Method 3: Plotting with pandas_bokeh

Pandas now has an extension for integrating with Bokeh, an interactive visualization library. With pandas_bokeh, you can create interactive plots directly from your DataFrame. The DataFrame’s plot_bokeh() method produces interactive charts that can be embedded in web applications.

Here’s an example:

import pandas as pd
import pandas_bokeh

pandas_bokeh.output_notebook()  # For rendering in a Jupyter Notebook, use output_file() for standalone HTML

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

df.plot_bokeh(kind='barh', stacked=True, show_figure=True)

The output will be an interactive Bokeh chart embedded in the Jupyter Notebook or saved as an HTML file, with a stacked horizontal bar chart visual.

This code snippet demonstrates a quick transition from static matplotlib-like plotting to interactive Bokeh charts, making the visualizations more engaging and informative with the same pandas DataFrame.

Method 4: Using plotly for Interactive Charts

Plotly is a powerful library for creating interactive plots. When combined with pandas, it allows for the generation of complex, interactive charts from a DataFrame without extensive boilerplate code. Plotly’s express module provides a simple interface for creating various types of plots, including stacked bar charts.

Here’s an example:

import pandas as pd
import plotly.express as px

df = pd.DataFrame({
    'Category': ['A', 'B', 'C'],
    'Value1': [10, 20, 30],
    'Value2': [20, 10, 40]
})

fig = px.bar(df, x=['Value1', 'Value2'], y='Category', orientation='h', title='Stacked Bar Chart')
fig.show()

The output is an interactive Plotly graph with a horizontal stacked bar chart that users can hover over to see exact values.

This snippet demonstrates the simplicity of creating stacked horizontal bar charts with Plotly, offering users the ability to interact with the data and gain better insights.

Bonus One-Liner Method 5: Quick Plot with df.plot.stacked_bar()

For those needing a quick and effortless method, pandas allows for even more direct plotting via df.plot.stacked_bar(), although this might be less flexible compared to other methods.

Here’s an example:

df.set_index('Category')['Value1', 'Value2'].plot(kind='barh', stacked=True)

Output is similar to Method 1, producing a simple matplotlib stacked horizontal bar chart.

This one-liner takes advantage of pandas integrated plotting with a chain-able function specifically for plotting stacked bar charts, making it an ultra-convenient option for quick data exploration.

Summary/Discussion

• Method 1: matplotlib with pandas. Highly customizable. Can become complex for intricate customizations.
• Method 2: pandas with seaborn. Elegant and aesthetic. Requires reshaping data for long-form compatibility.
• Method 3: pandas_bokeh. Interactive and can be embedded into web apps. Requires additional setup for output configuration.
• Method 4: plotly. Highly interactive and intuitive. Can be more demanding on resources for very large datasets.
• Method 5: Quick plot with df.plot.stacked_bar(). Fast for exploratory analysis. Less feature-rich and customizable.