Create the Mean and Standard Deviation of the Data of a Pandas Series - Coding

Standard Deviation is the square root of the Variance. The Standard Deviation denoted by sigma is a measure of the spread of numbers. In pandas, the std() function is used to find the standard Deviation of the series.
The mean can be simply defined as the average of numbers. In pandas, the mean() function is used to find the mean of the series.

Example 1 : Finding the mean and Standard Deviation of a Pandas Series.

# importing the module 
import pandas as pd 
  
# creating a series 
s = pd.Series(data = [5, 9, 8, 5, 7, 8, 1, 2, 3, 
                      4, 5, 6, 7, 8, 9, 5, 3]) 
  
# displaying the series 
print(s) 

Output :

Finding the mean of the series using the mean() function.

# finding the mean 
print(s.mean()) 

Output :

Finding the standard deviation of the series using the std() function.

# finding the Standard deviation 
print(s.std()) 

Output :

Example 2 : Finding the mean and Standard Deviation of a Pandas DataFrame.

# importing the module 
import pandas as pd 
  
# creating a dataframe  
df = pd.DataFrame({'ID':[114, 345, 157788, 5626], 
                   'Product':['shirt', 'trousers', 'tie', 'belt'], 
                   'Color':['White', 'Black', 'Red', 'Brown'], 
                   'Discount':[10, 10, 10, 10]}) 
  
# displaying the DataFrame 
print(df) 

Output :

Finding the mean of the DataFrame using the mean() function.

# finding the mean 
print(df.mean()) 

Output :

Finding the standard deviation of the DataFrame using the std() function.

# finding the Standard deviation 
print(df.std()) 

Output :

Reffered: https://www.geeksforgeeks.org

Python

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