How to find the standard deviation of specific columns in a dataframe in Pandas Python?

Standard deviation measures how spread out values are in a dataset and indicates how far individual values are from the arithmetic mean. In Pandas, you can calculate the standard deviation of specific columns using the std() function.

When working with DataFrames, you often need to find the standard deviation of particular numeric columns. The std() function can be applied to individual columns by indexing the DataFrame with the column name.

Example

Let's create a DataFrame and calculate the standard deviation of specific columns ?

import pandas as pd

my_data = {
    'Name': pd.Series(['Tom', 'Jane', 'Vin', 'Eve', 'Will']),
    '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("\nThe standard deviation of column 'Age' is:")
print(my_df['Age'].std())

print("\nThe standard deviation of column 'Value' is:")
print(my_df['Value'].std())

The dataframe is:
   Name  Age   Value
0   Tom   45    8.79
1  Jane   67   23.24
2   Vin   89   31.98
3   Eve   12   78.56
4  Will   23   90.20

The standard deviation of column 'Age' is:
31.499206339207976

The standard deviation of column 'Value' is:
35.747101700697364

Multiple Columns at Once

You can also calculate standard deviation for multiple columns simultaneously ?

import pandas as pd

my_data = {
    'Name': pd.Series(['Tom', 'Jane', 'Vin', 'Eve', 'Will']),
    'Age': pd.Series([45, 67, 89, 12, 23]),
    'Value': pd.Series([8.79, 23.24, 31.98, 78.56, 90.20])
}

my_df = pd.DataFrame(my_data)

print("Standard deviation of numeric columns:")
print(my_df[['Age', 'Value']].std())

Standard deviation of numeric columns:
Age      31.499206
Value    35.747102
dtype: float64

Using Column Index

You can also access columns by their integer index position ?

import pandas as pd

my_data = {
    'Name': pd.Series(['Tom', 'Jane', 'Vin', 'Eve', 'Will']),
    'Age': pd.Series([45, 67, 89, 12, 23]),
    'Value': pd.Series([8.79, 23.24, 31.98, 78.56, 90.20])
}

my_df = pd.DataFrame(my_data)

print("Standard deviation using column index:")
print("Age column (index 1):", my_df.iloc[:, 1].std())
print("Value column (index 2):", my_df.iloc[:, 2].std())

Standard deviation using column index:
Age column (index 1): 31.499206339207976
Value column (index 2): 35.747101700697364

Conclusion

Use df['column_name'].std() to find the standard deviation of a specific column. For multiple columns, use df[['col1', 'col2']].std() or access by index with df.iloc[:, index].std().