Pandas Axis Meaning

Pandas Axis Meaning

This tutorial explains the meaning of the axis parameter used in various methods of Pandas objects like DataFrames and Series.

import pandas as pd

empl_df = pd.DataFrame({
    'Name': ["Jon", "Willy", "Mike", "Luna", "Sam", "Aliza"],
    'Age': [30, 33, 35, 30, 30, 31],
    'Weight(KG)': [75, 75, 80, 70, 73, 70],
    'Height(meters)': [1.7, 1.7, 1.85, 1.75, 1.8, 1.75],
    'Salary($)': [3300, 3500, 4000, 3050, 3500, 3700]

})

print(empl_df)

Output:

    Name  Age  Weight(KG)  Height(meters)  Salary($)
0    Jon   30          75            1.70       3300
1  Willy   33          75            1.70       3500
2   Mike   35          80            1.85       4000
3   Luna   30          70            1.75       3050
4    Sam   30          73            1.80       3500
5  Aliza   31          70            1.75       3700

We use the DataFrame empl_df to explain how to use the axis parameter in Pandas methods.

Use of axis Parameter in Pandas Methods

The axis parameter specifies the direction along which a particular method or function is applied in a DataFrame. axis=0 represents the function is applied column-wise, and axis=1 means that the function is applied row-wise on the DataFrame.

If we apply a function column-wise, we will get a result with a single row; if we apply a function row-wise, we will get a DataFrame with a single column.

Example: Use axis=0 in Pandas Methods

import pandas as pd

empl_df = pd.DataFrame({
    'Name': ["Jon", "Willy", "Mike", "Luna", "Sam", "Aliza"],
    'Age': [30, 33, 35, 30, 30, 31],
    'Weight(KG)': [75, 75, 80, 70, 73, 70],
    'Height(meters)': [1.7, 1.7, 1.85, 1.75, 1.8, 1.75],
    'Salary($)': [3300, 3500, 4000, 3050, 3500, 3700]

})
print("The Employee DataFrame is:")
print(empl_df,"\n")

print("The DataFrame with mean values of each column is:")
print(empl_df.mean(axis=0))

Output:

The Employee DataFrame is:
    Name  Age  Weight(KG)  Height(meters)  Salary($)
0    Jon   30          75            1.70       3300
1  Willy   33          75            1.70       3500
2   Mike   35          80            1.85       4000
3   Luna   30          70            1.75       3050
4    Sam   30          73            1.80       3500
5  Aliza   31          70            1.75       3700

The DataFrame with mean values of each column is:
Age                 31.500000
Weight(KG)          73.833333
Height(meters)       1.758333
Salary($)         3508.333333
dtype: float64

It calculates the column-wise mean of the DataFrame empl_df. The mean is calculated only for columns with numerical values.

If we set axis=0, it will calculate each column’s mean by averaging the row values for that particular column.

Example: Use axis=1 in Pandas Methods

import pandas as pd

empl_df = pd.DataFrame({
    'Name': ["Jon", "Willy", "Mike", "Luna", "Sam", "Aliza"],
    'Age': [30, 33, 35, 30, 30, 31],
    'Weight(KG)': [75, 75, 80, 70, 73, 70],
    'Height(meters)': [1.7, 1.7, 1.85, 1.75, 1.8, 1.75],
    'Salary($)': [3300, 3500, 4000, 3050, 3500, 3700]

})
print("The Employee DataFrame is:")
print(empl_df,"\n")

print("The DataFrame with mean values of each row is:")
print(empl_df.mean(axis=1))

Output:

The Employee DataFrame is:
    Name  Age  Weight(KG)  Height(meters)  Salary($)
0    Jon   30          75            1.70       3300
1  Willy   33          75            1.70       3500
2   Mike   35          80            1.85       4000
3   Luna   30          70            1.75       3050
4    Sam   30          73            1.80       3500
5  Aliza   31          70            1.75       3700

The DataFrame with mean values of each row is:
0     851.6750
1     902.4250
2    1029.2125
3     787.9375
4     901.2000
5     950.6875
dtype: float64

It calculates row-wise mean for the DataFrame empl_df, in other words, it will calculate the mean value for each row by averaging the column values of numeric type for that row. We will get a single column at the end with the average value for each row.

Related Article - Pandas DataFrame Row

  • Get the Row Count of a Pandas DataFrame
  • Randomly Shuffle DataFrame Rows in Pandas
  • Filter Dataframe Rows Based on Column Values in Pandas
  • Iterate Through Rows of a DataFrame in Pandas
  • Get Index of All Rows Whose Particular Column Satisfies Given Condition in Pandas
  • Find Duplicate Rows in a DataFrame Using Pandas
  • Related Article - Pandas DataFrame Column

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  • Delete Pandas DataFrame Column
  • Convert Pandas Column to Datetime
  • Get the Sum of Pandas Column
  • Change the Order of Pandas DataFrame Columns
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