Find Standard Deviation in Python – List & pandas DataFrame (4 Examples)
This tutorial explains how to calculate the standard deviation in Python programming.
Example 1: Calculating the Standard Deviation of a List Object
my_lt = [10, 6, 2, 2, 15, 20, 3, 7, 4] # Constructing a list in Python print(my_lt) # [10, 6, 2, 2, 15, 20, 3, 7, 4] |
my_lt = [10, 6, 2, 2, 15, 20, 3, 7, 4] # Constructing a list in Python print(my_lt) # [10, 6, 2, 2, 15, 20, 3, 7, 4]
import numpy as np # Import NumPy library in Python |
import numpy as np # Import NumPy library in Python
print(np.std(my_lt)) # Computing the standard deviation of a list # 5.9066817155564495 |
print(np.std(my_lt)) # Computing the standard deviation of a list # 5.9066817155564495
Example 2: Calculating the Standard Deviation of the Columns in a pandas DataFrame
import pandas as pd # Import pandas library in Python |
import pandas as pd # Import pandas library in Python
my_df = pd.DataFrame({'A':range(10, 17), # Constructing a pandas DataFrame 'B':[6, 1, 8, 5, 3, 8, 9], 'C':range(2, 9), 'GRP':['gr1', 'gr2', 'gr1', 'gr3', 'gr1', 'gr2', 'gr3']}) print(my_df) # A B C GRP # 0 10 6 2 gr1 # 1 11 1 3 gr2 # 2 12 8 4 gr1 # 3 13 5 5 gr3 # 4 14 3 6 gr1 # 5 15 8 7 gr2 # 6 16 9 8 gr3 |
my_df = pd.DataFrame({'A':range(10, 17), # Constructing a pandas DataFrame 'B':[6, 1, 8, 5, 3, 8, 9], 'C':range(2, 9), 'GRP':['gr1', 'gr2', 'gr1', 'gr3', 'gr1', 'gr2', 'gr3']}) print(my_df) # A B C GRP # 0 10 6 2 gr1 # 1 11 1 3 gr2 # 2 12 8 4 gr1 # 3 13 5 5 gr3 # 4 14 3 6 gr1 # 5 15 8 7 gr2 # 6 16 9 8 gr3
print(my_df.std()) # Computing the Standard Deviation of all columns # A 2.160247 # B 2.927700 # C 2.160247 # dtype: float64 |
print(my_df.std()) # Computing the Standard Deviation of all columns # A 2.160247 # B 2.927700 # C 2.160247 # dtype: float64
Example 3: Calculating the Standard Deviation of the Columns in a pandas DataFrame by Group
print(my_df.groupby('GRP').std()) # Computing the column standard deviations by group # A B C # GRP # gr1 2.000000 2.516611 2.000000 # gr2 2.828427 4.949747 2.828427 # gr3 2.121320 2.828427 2.121320 |
print(my_df.groupby('GRP').std()) # Computing the column standard deviations by group # A B C # GRP # gr1 2.000000 2.516611 2.000000 # gr2 2.828427 4.949747 2.828427 # gr3 2.121320 2.828427 2.121320
Example 4: Calculating the Standard Deviation of the Rows in a pandas DataFrame
print(my_df.std(axis = 1)) # Computing the standard deviation of all rows # 0 4.000000 # 1 5.291503 # 2 4.000000 # 3 4.618802 # 4 5.686241 # 5 4.358899 # 6 4.358899 # dtype: float64 |
print(my_df.std(axis = 1)) # Computing the standard deviation of all rows # 0 4.000000 # 1 5.291503 # 2 4.000000 # 3 4.618802 # 4 5.686241 # 5 4.358899 # 6 4.358899 # dtype: float64