It may sometimes be required to apply certain functions along the axes of a dataframe. The axis can be specified, otherwise the default axis is considered as column-wise, where every column is considered as an array.
If the axis is specified, then the operations are performed row-wise on the data.
The ‘apply’ function can be used in conjunction with the dot operator on the dataframe. Let us see an example −
Example
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])}
print("The dataframe is :")
my_df = pd.DataFrame(my_data)
print(my_df)
print("The description of data is :")
print(my_df.apply(np.mean))Output
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 The description of data is : Age 47.200 value 46.554 dtype: float64
Explanation
The required libraries are imported, and given alias names for ease of use.
Dictionary of series consisting of key and value is created, wherein a value is actually a series data structure.
This dictionary is later passed as a parameter to the ‘Dataframe’ function present in the ‘pandas’ library
The dataframe is printed on the console.
We are looking at getting all the information about the data.
The ‘describe’ function is called on the dataframe.
The description is printed on the console.