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Python Pandas DataFrame.plot.bar() function draws a bar chart along the specified axis. It plots the graph categorically. The categories Xare given on the axis and the values Y​​are given on the axis.

pandas.DataFrame.plot.bar()grammar

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

parameter

Return Value

It returns an N-dimensional array, if subplots=True, then it returns an N-dimensional array with each column having matplotlib.axes.Axes. If subplots=True, then it returns an N-dimensional array with each column having matplotlib.axes.Axes.

Sample code:DataFrame.plot.bar()

Let's first use a simple DataFrameto understand this function.

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

Ours DataFrameis,

 Value
0  100
1  200
2  300

All the parameters of this function are optional. If we do not pass any parameters while executing this function, then it takes the index as Xthe axis and the numeric data columns as Ythe axes.

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)

The parameter rotis an additional keyword argument that changes the rotation of the category names on the x-axis.

If we don't set it rot, the following situation will appear in the figure.

Example code: DataFrame.plot.bar()Relationships with multiple columns of data

Now, we will change our DataFrameto a complex data.

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)

Ours DataFrameis,

        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

Use DataFrame.plot.bar()the function to plot this 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)
print(axis)
plt.show()

Output:

AxesSubplot(0.125,0.125;0.775x0.755)

It generates a bar chart of numerical data containing two bars for each category. It helps in analyzing data effectively.

Example code: Creating a subgraph DataFrame.plot.bar()withsubplots=True

If subplots=True, then the function returns an N-dimensional array with for each column matplotlib.axes.Axes. Using this function, we can separate the data columns into different sub-graphs instead of a single graph.

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>]

Example code: DataFrame.plot.bar()To plot a single column of data

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)

We can also plot any data column against any other column instead of plotting the index 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)

Example code: DataFrame.plot.bar()with 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()

It DataFrameassigns colors to all columns in m.

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()

We can also specify different colors colorfor different columns in by providing a list of colors to the parameter .DataFrame