How to do RANDOM sample in a Pandas DataFrame in Python

Hits: 83

How to do RANDOM sample in a Pandas DataFrame in Python

Taking a random sample from a Pandas DataFrame in Python can be done by using the sample() function. This function allows you to select a random subset of rows from a DataFrame.

First, you need to import the Pandas library and create a DataFrame. For example, you can create a DataFrame with some sample data.

import pandas as pd

data = {'product': ['Apple', 'Banana', 'Cherry', 'Date', 'Eggplant'],

              'price': [1.2, 2.3, 2.5, 1.7, 2.0]}

df = pd.DataFrame(data)

Next, you can use the sample() function to take a random sample of rows from the DataFrame. The sample() function takes two parameters: the number of rows you want to select, and whether or not you want the sample to be selected randomly.

For example, to select a random sample of 2 rows from the DataFrame, you can use the following code:

random_sample = df.sample(n=2, random_state = 1)

You can also use frac parameter instead of n, it is used when you want to take a sample in terms of percentage or fraction. For example, to take 25% of the dataframe as sample.

random_sample = df.sample(frac = 0.25, random_state = 1)

You can also use replace = True parameter to allow duplicates and sample with replacement.

random_sample = df.sample(n=2, replace = True, random_state = 1)

By using the sample() function, you can easily take a random sample from a Pandas DataFrame in Python. This can be useful for data exploration and analysis, especially when working with large datasets. It can also be used for machine learning algorithms where, you use a sample data for the training set and the rest for testing, this is known as cross validation


In this Learn through Codes example, you will learn: How to do RANDOM sample in a Pandas DataFrame in Python.


Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners