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Cluster sampling in Pandas is a type of sampling method used when it is difficult to identify and select all members from a population. This method involves randomly selecting clusters of individuals, such as households or schools, and then randomly selecting individuals from the clusters. This method is useful for sampling large populations. In Pandas, it can be done using the sklearn.cluster package, which includes tools for generating and sampling clusters. Examples of this method can be found online, including tutorials and code snippets.
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 cluster sampling, in which a population is split into clusters and all members of some clusters are chosen to be included in the sample.
This tutorial explains how to perform cluster sampling on a pandas DataFrame in Python.
Example: Cluster Sampling in Pandas
Suppose a company that gives city tours wants to survey its customers. Out of ten tours they give one day, they randomly select four tours and ask every customer to rate their experience on a scale of 1 to 10.
The following code shows how to create a pandas DataFrame to work with:
import pandas as pd import numpy as np #make this example reproducible np.random.seed(0) #create DataFrame df = pd.DataFrame({'tour': np.repeat(np.arange(1,11), 20), 'experience': np.random.normal(loc=7, scale=1, size=200)}) #view first six rows of DataFrame df.head() tour experience 1 1 6.373546 2 1 7.183643 3 1 6.164371 4 1 8.595281 5 1 7.329508 6 1 6.179532
And the following code shows how obtain a sample of customers by randomly selecting four tours and including every member in those tours in the sample:
#randomly choose 4 tour groups out of the 10 clusters = np.random.choice(np.arange(1,11), size=4, replace=False) #define sample as all members who belong to one of the 4 tour groups cluster_sample = df[df['tour'].isin(clusters)] #view first six rows of sample cluster_sample.head() tour experience 40 3 5.951447 41 3 5.579982 42 3 5.293730 43 3 8.950775 44 3 6.490348 #find how many observations came from each tour group cluster_sample['tour'].value_counts() 10 20 6 20 5 20 3 20 Name: tour, dtype: int64
From the output we can see that:
- 20 customers from tour group #10 were included in the sample.
- 20 customers from tour group #6 were included in the sample.
- 20 customers from tour group #5 were included in the sample.
- 20 customers from tour group #3 were included in the sample.
Thus, this sample is composed of 80 total customers that came from 4 different tour groups.
Understanding Different Types of Sampling Methods
Stratified Sampling in Pandas
Systematic Sampling in Pandas