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Python Pandas - Bar Plots

A bar plot is a graphical representation of categorical data using rectangular bars, where the length of each bar is proportional to the value it represents. Bar plots are commonly used to compare discrete categories. The bars can be displayed either vertically or horizontally −

• Vertical bar plot: Categories are displayed along the x-axis, and the values are represented on the y-axis.

• Horizontal bar plot: Categories are along the y-axis, and the values are represented on the x-axis.

In Pandas, the plot.bar() and plot.barh() methods allow you to create vertical and horizontal bar plots, respectively. In this tutorial, we will learn about how to use these Pandas methods for creating various types of bar plots, including stacked, grouped, bar plot customization, and more.

The Pandas library provides two efficient methods called plot.bar() and plot.barh() for creating bar plots directly from Series or DataFrame objects. These methods internally use Matplotlib and return either a matplotlib.axes.Axes object or NumPy array np.ndarray of Axes when subplots parameter is set to True.

The plot.bar() method creates the vertical bar plots, while plot.barh() creates horizontal bar plots.

Following is the syntax of the plot.bar() Method −

DataFrame.plot.bar(x=None, y=None, **kwargs)

Following is the syntax of the plot.barh() Method −

DataFrame.plot.barh(x=None, y=None, **kwargs)

Where,

• x: Specifies the column for the categorical values. If not specified, then the DataFrame index is used.

• y: Specifies the column or index label for the y-axis. If not specified, all numerical columns are used.

• color: Sets colors for bars. Accepts a single color, a sequence of colors, or a dictionary mapping columns to colors.

• **kwargs: Additional arguments to customize the plot.

You can use the DataFrame.plot.bar() method for creating the basic vertical bar plot. In this categories are displayed along the x-axis, while the heights of these bars along the y-axis represents the values.

This example creates a simple vertical bar plot using the DataFrame.plot.bar() method.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Create a DataFrame with sample data
df = pd.DataFrame({'Category': ['A', 'B', 'C'], 'Values': [10, 30, 20]})

# Vertical bar plot
ax = df.plot.bar(x='Category', y='Values')

plt.title("Vertical Bar Plot")
plt.show()

After executing the above code, we get the following output −

To create a horizontal bar chart, we can use plot.barh() method. A horizontal bar plot flips the orientation of the bars, where y-axis displays the categories, and the heights of these bars along the x-axis represents the values.

The following example demonstrates how to create a horizontal bar plot using the DataFrame.plot.barh() method.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Create a DataFrame with sample data
df = pd.DataFrame({'Category': ['A', 'B', 'C'], 'Values': [10, 30, 20]})

# Horizontal bar plot
ax = df.plot.barh(x='Category', y='Values')

plt.title("Horizontal Bar Plot")
plt.show()

Following is the output of the above code −

Stacked bar plots display multiple numerical columns in a single bar, showing cumulative values for each category. To create stacked bars, set the Stacked parameter to True.

This example creates a stacked bar plot by setting the stacked parameter to True.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Sample DataFrame
data = {'Category': ['Col1', 'Col2', 'Col3'], 'Value1': [10, 15, 20], 'Value2': [5, 10, 15]}
df = pd.DataFrame(data)

# Stacked bar plot
ax = df.plot.bar(x='Category', stacked=True)

plt.title("Stacked Bar Plot")
plt.show()

On executing the above code we will get the following output −

This example creates a stacked horizontal bar plot by adjusting the stacked parameter value of the plot.barh() method.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Sample DataFrame
data = {'Category': ['Col1', 'Col2', 'Col3'], 'Value1': [10, 15, 20], 'Value2': [5, 10, 15]}
df = pd.DataFrame(data)

# Stacked horizontal bar plot
ax = df.plot.barh(x='Category', stacked=True)

plt.title("Stacked Horizontal Bar Plot")
plt.show()

After executing the above code, we get the following output −

You can customize bar plots using the various parameters available in Matplotlib, such as colors, colormaps, labels, gridlines, and more.

This example demonstrates using the Python dictionary object for mapping column names to colors customization.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Sample DataFrame
data = {'Category': ['Col1', 'Col2', 'Col3'], 'Value1': [10, 15, 20], 'Value2': [5, 10, 15]}
df = pd.DataFrame(data)

# Assign specific colors to columns
ax = df.plot.bar(color={'Value1': 'green', 'Value2': 'black'})

plt.title("Customizing Bar Colors")
plt.show()

Following is the output of the above code −

You can split multiple columns of a DataFrame into separate plots using the subplots=True parameter. The following example demonstrates the same.

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

plt.rcParams["figure.figsize"] = [7, 4]

# Sample DataFrame
data = {'Category': ['Col1', 'Col2', 'Col3'], 'Value1': [10, 15, 20], 'Value2': [5, 10, 15]}
df = pd.DataFrame(data)

# Subplots for each column
axes = df.plot.bar(subplots=True, color=['cyan', 'magenta'])
plt.show()

On executing the above code we will get the following output −

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