How to measure Variance and Standard Deviation for DataFrame columns in Pandas?
Measure Variance and Standard Deviation:
import pandas as pd df = pd.DataFrame([[10, 20, 30, 40], [7, 14, 21, 28], [55, 15, 8, 12], [15, 14, 1, 8], [7, 1, 1, 8], [5, 4, 9, 2]], columns=['Apple', 'Orange', 'Banana', 'Pear'], index=['Basket1', 'Basket2', 'Basket3', 'Basket4', 'Basket5', 'Basket6']) print("\n----------- Calculate Mean -----------\n") print(df.mean()) print("\n----------- Calculate Median -----------\n") print(df.median()) print("\n----------- Calculate Mode -----------\n") print(df.mode())
C:\pandas>python example.py ----------- Measure Variance ----------- Apple 367.900000 Orange 52.666667 Banana 134.266667 Pear 211.866667 dtype: float64 ----------- Standard Deviation ----------- Apple 19.180719 Orange 7.257180 Banana 11.587349 Pear 14.555640 dtype: float64 C:\pandas>
2018-11-06T01:33:19+05:30
2018-11-06T01:33:19+05:30
Amit Arora
Amit Arora
Python Programming Tutorial
Python
Practical Solution