How can a specific operation be applied row wise or column wise in Pandas Python?

In Pandas, you can apply operations to a DataFrame either row-wise or column-wise using the apply() function. By default, operations are applied column-wise (axis=0), but you can specify the axis parameter to control the direction.

Column-wise Operations (Default)

When no axis is specified, operations are applied to each column ?

import pandas as pd
import numpy as np

my_data = {'Age': pd.Series([45, 67, 89, 12, 23]),
           'value': pd.Series([8.79, 23.24, 31.98, 78.56, 90.20])}

my_df = pd.DataFrame(my_data)
print("The dataframe is:")
print(my_df)

print("\nColumn-wise mean:")
print(my_df.apply(np.mean))

The dataframe is:
   Age  value
0   45   8.79
1   67  23.24
2   89  31.98
3   12  78.56
4   23  90.20

Column-wise mean:
Age      47.200
value    46.554
dtype: float64

Row-wise Operations

To apply operations row-wise, set axis=1 ?

import pandas as pd
import numpy as np

my_data = {'Age': pd.Series([45, 67, 89, 12, 23]),
           'value': pd.Series([8.79, 23.24, 31.98, 78.56, 90.20])}

my_df = pd.DataFrame(my_data)
print("The dataframe is:")
print(my_df)

print("\nRow-wise mean:")
print(my_df.apply(np.mean, axis=1))

print("\nRow-wise sum:")
print(my_df.apply(np.sum, axis=1))

The dataframe is:
   Age  value
0   45   8.79
1   67  23.24
2   89  31.98
3   12  78.56
4   23  90.20

Row-wise mean:
0    26.895
1    45.120
2    60.490
3    45.280
4    56.600
dtype: float64

Row-wise sum:
0     53.79
1     90.24
2    120.98
3     90.56
4    113.20
dtype: float64

Custom Functions

You can also apply custom functions using lambda or defined functions ?

import pandas as pd
import numpy as np

my_data = {'Age': pd.Series([45, 67, 89, 12, 23]),
           'value': pd.Series([8.79, 23.24, 31.98, 78.56, 90.20])}

my_df = pd.DataFrame(my_data)

# Custom function to find range (max - min)
def find_range(x):
    return x.max() - x.min()

print("Column-wise range:")
print(my_df.apply(find_range))

print("\nRow-wise range:")
print(my_df.apply(lambda x: x.max() - x.min(), axis=1))

Column-wise range:
Age      77.00
value    81.41
dtype: float64

Row-wise range:
0    36.21
1    43.76
2    57.02
3    66.56
4    67.20
dtype: float64

Axis Parameter Summary

Conclusion

Use apply() with axis=0 for column-wise operations and axis=1 for row-wise operations. This flexibility allows you to perform statistical operations or custom functions across either dimension of your DataFrame.