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Axis in Pandas: Understanding Its Meaning
In Pandas, the axis keyword parameter in functions such as mean() defines along which axis the operation is performed.
Consider the following code:
import pandas as pd import numpy as np dff = pd.DataFrame(np.random.randn(1,2),columns=list('AB'))
This creates a dataframe:
+------------+---------+--------+ | | A | B | +------------+---------+--------- | 0 | 0.626386| 1.52325| +------------+---------+--------+
Now, let's calculate the mean along the rows (axis=1):
dff.mean(axis=1)
This gives the following result:
0 1.074821 dtype: float64
Counterintuitively, the expected result is:
A 0.626386 B 1.523255 dtype: float64
Understanding the Axis Parameter
The axis parameter specifies the direction in which the operation is performed.
In the given example, the mean is calculated along the columns (axis=1), resulting in a single value for each row.
Visualizing the Axis
To visualize the axis, consider the following diagram:
+------------+---------+--------+ | | A | B | +------------+---------+--------- | 0 | 0.626386| 1.52325|----axis=1-----> +------------+---------+--------+ | | | axis=0 | ↓ ↓
The red arrow represents axis=1, which operates along the columns. The green arrow represents axis=0, which operates along the rows.
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