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

Standard deviation measures how spread out values are from the mean. In Pandas, you can calculate the standard deviation of a DataFrame column using the std() method.

Syntax

To calculate standard deviation of a specific column ?

dataframe['column_name'].std()

Creating Sample DataFrames

First, let's create sample DataFrames with numerical data ?

import pandas as pd

# Create DataFrame1 with car sales data
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("DataFrame1:")
print(dataFrame1)

DataFrame1:
      Car  Units
0     BMW    100
1   Lexus    150
2    Audi    110
3   Tesla     80
4  Bentley    110
5  Jaguar     90

Calculating Standard Deviation

Use the std() method to calculate standard deviation of the "Units" column ?

import pandas as pd

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

# Calculate standard deviation of Units column
std_units = dataFrame1['Units'].std()
print("Standard Deviation of Units column:", std_units)

Standard Deviation of Units column: 24.22120283277228

Example with Multiple Columns

You can calculate standard deviation for different columns in separate DataFrames ?

import pandas as pd

# DataFrame1 - Car sales
dataFrame1 = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Tesla', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

# DataFrame2 - Product prices
dataFrame2 = pd.DataFrame({
    "Product": ['TV', 'PenDrive', 'HeadPhone', 'EarPhone', 'HDD', 'SSD'],
    "Price": [8000, 500, 3000, 1500, 3000, 4000]
})

print("DataFrame1:")
print(dataFrame1)
print("\nStandard Deviation of Units column:", dataFrame1['Units'].std())

print("\nDataFrame2:")
print(dataFrame2)
print("\nStandard Deviation of Price column:", dataFrame2['Price'].std())

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: 24.22120283277228

DataFrame2:
    Product  Price
0        TV   8000
1  PenDrive    500
2  HeadPhone   3000
3  EarPhone   1500
4       HDD   3000
5       SSD   4000

Standard Deviation of Price column: 2601.281735352477

Parameters

The std() method accepts optional parameters ?

import pandas as pd

data = pd.DataFrame({
    "Values": [10, 20, 30, 40, 50]
})

# Default: sample standard deviation (ddof=1)
print("Sample std (ddof=1):", data['Values'].std())

# Population standard deviation (ddof=0)
print("Population std (ddof=0):", data['Values'].std(ddof=0))

Sample std (ddof=1): 15.811388300841898
Population std (ddof=0): 14.142135623730951

Conclusion

Use dataframe['column'].std() to calculate standard deviation of a Pandas DataFrame column. By default, it calculates sample standard deviation with ddof=1. Set ddof=0 for population standard deviation.