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Pandas DataFrame - max() function



The Pandas DataFrame - max() function returns the maximum of the values over the specified axis. The syntax for using this function is mentioned below:

Syntax

DataFrame.max(axis=None, skipna=None, level=None, numeric_only=None)

Parameters

axis Optional. Specify {0 or 'index', 1 or 'columns'}. If 0 or 'index' maximum of the values are generated for each column. If 1 or 'columns' maximum of the values are generated for each row. Default: 0
skipna Optional. Specify True to exclude NA/null values when computing the result. Default is True.
level Optional. Specify level (int or str). If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series. A str specifies the level name.
numeric_only Optional. Specify True to include only float, int or boolean data. Default: False

Return Value

Returns maximum of the values of Series or DataFrame if a level is specified.

Example: Using max() column-wise on whole DataFrame

In the example below, a DataFrame df is created. The max() function is used to get the maximum of values for each column.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#maximum of values of all entries column-wise
print("\ndf.max() returns:")
print(df.max())

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.max() returns:
Bonus      5
Salary    65
dtype: int64

Example: Using max() row-wise on whole DataFrame

To get the row-wise sum, the axis parameter can set to 1.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#maximum of values of all entries row-wise
print("\ndf.max(axis=1) returns:")
print(df.max(axis=1))

The output of the above code will be:

The DataFrame is:
       Bonus  Salary
John       5      60
Marry      3      62
Sam        2      65
Jo         4      59

df.max(axis=1) returns:
John     60
Marry    62
Sam      65
Jo       59
dtype: int64

Example: Using max() on selected column

Instead of whole data frame, the max() function can be applied on selected columns. Consider the following example.

import pandas as pd
import numpy as np

df = pd.DataFrame({
  "Bonus": [5, 3, 2, 4],
  "Last Salary": [58, 60, 63, 57],
  "Salary": [60, 62, 65, 59]},
  index= ["John", "Marry", "Sam", "Jo"]
)

print("The DataFrame is:")
print(df)

#maximum of values of single column
print("\ndf['Salary'].max() returns:")
print(df["Salary"].max())

#maximum of values of multiple columns
print("\ndf[['Salary', 'Bonus']].max() returns:")
print(df[["Salary", "Bonus"]].max())

The output of the above code will be:

The DataFrame is:
       Bonus  Last Salary  Salary
John       5           58      60
Marry      3           60      62
Sam        2           63      65
Jo         4           57      59

df['Salary'].max() returns:
65

df[['Salary', 'Bonus']].max() returns:
Salary    65
Bonus      5
dtype: int64

❮ Pandas DataFrame - Functions

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