Python - Calculate the standard deviation of a column in a Pandas DataFrame

To calculate the standard deviation, use the std() method of the Pandas. At first, import the required Pandas library −

import pandas as pd

Now, create a DataFrame with two columns −

dataFrame1 = pd.DataFrame(    {       "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],       "Units": [100, 150, 110, 80, 110, 90] } ) 

Finding the standard deviation of “Units” column value using std() −

print"Standard Deviation of Units column from DataFrame1 = ",dataFrame1['Units'].std()

In the same way, we have calculated the standard deviation from the 2^nd DataFrame.

Example

Following is the complete code −

# # Python - Calculate the Standard Deviation of column values of a Pandas DataFrame # import pandas as pd # Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } ) print"DataFrame1 ...\n",dataFrame1 # Finding Standard Deviation of "Units" column values print"Standard Deviation of Units column from DataFrame1 = ",dataFrame1['Units'].std() # Create DataFrame2 dataFrame2 = pd.DataFrame( { "Product": ['TV', 'PenDrive', 'HeadPhone', 'EarPhone', 'HDD', 'SSD'], "Price": [8000, 500, 3000, 1500, 3000, 4000] } ) print"\nDataFrame2 ...\n",dataFrame2 # Finding Standard Deviation of "Price" column values print"Standard Deviation of Price column from DataFrame2 = ",dataFrame2['Price'].std() 

Output

This will produce the following output −

DataFrame1 ...        Car   Units 0      BMW    100 1    Lexus    150 2     Audi    110 3    Tesla     80 4  Bentley    110 5   Jaguar     90 Standard Deviation of Units column from DataFrame1 = 24.2212028328 DataFrame2 ...     Price   Product 0   8000       TV 1   500 PenDrive 2   3000 HeadPhone 3   1500 EarPhone 4   3000 HDD 5   4000 SSD Standard Deviation of Price column from DataFrame2 = 2601.28173535