JIYIK CN >

This tutorial explains axisthe meaning of the parameters 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 DataFrame to explain how to use parameters empl_dfin Pandas methods .axis

axisUsing parameters in Pandas methods

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

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

Example: Using in Pandas methodsaxis=0

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 empl_dfthe column-wise mean of a DataFrame. The mean is calculated only for columns that have values.

If we set axis=0, it will calculate the mean of each column by averaging the row values ​​of that column.

Example using Pandas methodaxis=1

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 empl_dfthe row mean of the DataFrame, in other words, it will calculate the mean of each row by averaging the values ​​of the numeric columns of that row. We will get a single column at the end for the mean of each row.