Pandas DataFrame DataFrame.plot.bar() Function

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Minahil Noor Jan 30, 2023
Pandas Pandas DataFrame Pandas DataFrame Plot
  1. Syntax of pandas.DataFrame.plot.bar()
  2. Example Codes: DataFrame.plot.bar()
  3. Example Codes: DataFrame.plot.bar() With Multiple Data Columns
  4. Example Codes: DataFrame.plot.bar() With subplots=True to Create Subplots
  5. Example Codes: DataFrame.plot.bar() to Plot Single Data Column
  6. Example Codes: DataFrame.plot.bar() With the Specified Colors
Pandas DataFrame DataFrame.plot.bar() Function

Python Pandas DataFrame.plot.bar() function plots a bar graph along the specified axis. It plots the graph in categories. The categories are given on the x-axis and the values are given on the y-axis.

Syntax of pandas.DataFrame.plot.bar()

DataFrame.sample(x=None, y=None, **kwds)

Parameters
x This is the axis where categories will be plotted. If it is not specified, then the index of the DataFrame is used.
y It represents the values that are plotted against the categories. If it is not specified, then it plots all the numerical columns of the DataFrame against the categories.
**kwds These are the additional keyword arguments to customize the plotted graph. You can check these here.

Return

It returns a N-dimensional array. If subplots=True, then it returns N-dimensional array with matplotlib.axes.Axes per column.

Example Codes: DataFrame.plot.bar()

Let’s first understand this function using a simple DataFrame.

import pandas as pd
dataframe = pd.DataFrame({'Value':[100, 200, 300]})
print(dataframe)

Our DataFrame is as below.

 Value
0  100
1  200
2  300

All the parameters of this function are optional. If we execute this function without passing any parameter then it takes the index as x-axis and numeric data columns as y-axis.

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame({"Value": [100, 200, 300]})
axis = dataframe.plot.bar(rot=0)
print(axis)
plt.show()

Output:

AxesSubplot(0.125,0.125;0.775x0.755)

Pandas DataFrame.plot.bar Basic

The parameter rot is an additional keyword parameter. It changes the rotation of the categories names on the x-axis.

The plot will look like below if we don’t set rot.

Pandas DataFrame.plot.bar Basic - not set rot

Example Codes: DataFrame.plot.bar() With Multiple Data Columns

Now, we will change our DataFrame to a complex one.

import pandas as pd

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
print(dataframe)

Our DataFrame looks like

        Age  Avg Age in Family
Olivia   23                 70
John     17                 65
Laura    40                 80
Ben      38                 55
Kevin    24                 60
Robin    12                 63
Elsa     45                 90

Plot this DataFrame using DataFrame.plot.bar() function

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axis = dataframe.plot.bar(rot=0)
print(axis)
plt.show()

Output:

AxesSubplot(0.125,0.125;0.775x0.755)

Pandas DataFrame.plot.bar with multiple columns

It generates a bar graph containing two bars of numerical data of each category. It helps in analyzing data efficiently.

Example Codes: DataFrame.plot.bar() With subplots=True to Create Subplots

If subplots=True, then the function returns an N-dimensional array with matplotlib.axes.Axes per column. Using this, we can separate our data columns into different subplots instead of one single plot.

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axes = dataframe.plot.bar(rot=0, subplots=True)
print(axes)
plt.show()

Output:

[<matplotlib.axes._subplots.AxesSubplot object at 0x0000029A89B06DC8>
 <matplotlib.axes._subplots.AxesSubplot object at 0x0000029A89B4B2C8>]

Pandas DataFrame.plot.bar Subplots

Example Codes: DataFrame.plot.bar() to Plot Single Data Column

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axis = dataframe.plot.bar(y="Age", rot=0)
print(axis)
plt.show()

Output:

AxesSubplot(0.125,0.125;0.775x0.755)

Pandas DataFrame.plot.bar Plot Single Column

We can also plot any data column against other columns instead of plotting indexes as categories.

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axis = dataframe.plot.bar(x="Age", rot=0)
print(axis)
plt.show()

Output:

AxesSubplot(0.125,0.125;0.775x0.755)

Pandas DataFrame.plot.bar - Use One Column As X-axis

Example Codes: DataFrame.plot.bar() With the Specified Colors

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axis = dataframe.plot.bar(rot=0, color="m")
plt.show()

Pandas DataFrame.plot.bar - Single Color For Different Columns

It specifies the color m for all columns in the DataFrame.

import pandas as pd
import matplotlib.pyplot as plt

dataframe = pd.DataFrame(
    {
        "Age": [23, 17, 40, 38, 24, 12, 45],
        "Avg Age in Family": [70, 65, 80, 55, 60, 63, 90],
    },
    index=["Olivia", "John", "Laura", "Ben", "Kevin", "Robin", "Elsa"],
)
axis = dataframe.plot.bar(rot=0, color=["r", "b"])
print(axis)
plt.show()

Pandas DataFrame.plot.bar - Different Colors For Different Columns

We could also specify different colors for different columns in the DataFrame by giving a color list to the color parameter.

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