Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
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 2 3000 HeadPhone 3 1500 EarPhone 4 3000 HDD 5 4000 SSD Standard Deviation of Price column from DataFrame2 = 2601.28173535
Updated on: 15-Sep-2021
617 Views
Advertisements