Systematic Sampling in Pandas (With Examples)

Researchers often take samples from a population and use the data from the sample to draw conclusions about the population as a whole.

One commonly used sampling method is systematic sampling, which is implemented with a simple two step process:

1. Place each member of a population in some order.

2. Choose a random starting point and select every nth member to be in the sample.

This tutorial explains how to perform systematic sampling on a pandas DataFrame in Python.

Example: Systematic Sampling in Pandas

Suppose a teacher wants to obtain a sample of 100 students from a school that has 500 total students. She chooses to use systematic sampling in which she places each student in alphabetical order according to their last name, randomly chooses a starting point, and picks every 5th student to be in the sample.

The following code shows how to create a fake data frame to work with in Python:

import pandas as pd
import numpy as np
import string
import random

#make this example reproducible

#create simple function to generate random last names
def randomNames(size=6, chars=string.ascii_uppercase):
    return ''.join(random.choice(chars) for _ in range(size))

#create DataFrame
df = pd.DataFrame({'last_name': [randomNames() for _ in range(500)],
                   'GPA': np.random.normal(loc=85, scale=3, size=500)})

#view first six rows of DataFrame

last_name	GPA
0	PXGPIV	86.667888
1	JKRRQI	87.677422
2	TRIZTC	83.733056
3	YHUGIN	85.314142
4	ZVUNVK	85.684160

And the following code shows how to obtain a sample of 100 students through systematic sampling:

#obtain systematic sample by selecting every 5th row
sys_sample_df = df.iloc[::5]

#view first six rows of DataFrame

   last_name      gpa
3      ORJFW 88.78065
8      RWPSB 81.96988
13     RACZU 79.21433
18     ZOHKA 80.47246
23     QJETK 87.09991
28     JTHWB 83.87300

#view dimensions of data frame

(100, 2)

Notice that the first member included in the sample was in the first row of the original data frame. Each subsequent member in the sample is located 5 rows after the previous member.

And from using shape() we can see that the systematic sample we obtained is a data frame with 100 rows and 2 columns.

Additional Resources

Types of Sampling Methods
Cluster Sampling in Pandas
Stratified Sampling in Pandas

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