# 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 2nd 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
Standard Deviation of Price column from DataFrame2 = 2601.28173535