How to Use axis=0 and axis=1 in Pandas?

In Pandas, the axis parameter controls the direction of operations on DataFrames and Series. Understanding axis=0 and axis=1 is essential for performing row-wise and column-wise operations efficiently.

Understanding Axis in Pandas

A Pandas DataFrame is a two-dimensional table with rows and columns. The axis parameter determines how operations are applied:

• Axis 0 (rows) ? Operations are applied along rows, working down vertically. Results are computed across rows for each column.

• Axis 1 (columns) ? Operations are applied along columns, working across horizontally. Results are computed across columns for each row.

Using axis=0 (Row Operations)

When axis=0, operations are performed down the rows, producing results for each column.

Syntax

result = df.operation(axis=0)

Example

Let's sum values along axis=0 to get column totals:

import pandas as pd
import numpy as np

# Create sample DataFrame
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                   columns=['A', 'B', 'C'])
print("DataFrame:")
print(df)

# Sum along axis=0 (down rows)
row_sum = df.sum(axis=0)
print("\nSum along axis=0 (column totals):")
print(row_sum)

DataFrame:
   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9

Sum along axis=0 (column totals):
A    12
B    15
C    18
dtype: int64

Using axis=1 (Column Operations)

When axis=1, operations are performed across columns, producing results for each row.

Syntax

result = df.operation(axis=1)

Example

Let's sum values along axis=1 to get row totals:

import pandas as pd
import numpy as np

# Create sample DataFrame
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
                   columns=['A', 'B', 'C'])
print("DataFrame:")
print(df)

# Sum along axis=1 (across columns)
column_sum = df.sum(axis=1)
print("\nSum along axis=1 (row totals):")
print(column_sum)

DataFrame:
   A  B  C
0  1  2  3
1  4  5  6
2  7  8  9

Sum along axis=1 (row totals):
0     6
1    15
2    24
dtype: int64

Combining Operations

You can chain operations with different axis values to perform complex calculations:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), 
                  columns=['A', 'B', 'C'])

# Find maximum value in each row (axis=1)
row_max = df.max(axis=1)
print("Maximum value in each row:")
print(row_max)

# Sum all maximum values (axis=0)
total_max = row_max.sum()
print("\nSum of maximum values:")
print(total_max)

Maximum value in each row:
0    3
1    6
2    9
dtype: int64

Sum of maximum values:
18

Common Operations with Axis

Conclusion

Understanding axis=0 and axis=1 is crucial for DataFrame operations. Remember: axis=0 works down rows (column-wise results), while axis=1 works across columns (row-wise results). Practice with different operations to master this fundamental concept.