How to do RANDOM sample in a Pandas DataFrame in Python

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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.



 

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