Adventures in Machine Learning

Cluster Sampling: A Powerful Tool for Data-Driven Decisions

Sampling Methods: Understanding

Cluster Sampling

In statistics, selecting a random group of individuals from a larger population is essential in research. By obtaining a representative sample of a population, the data can be used to make generalizations about the population as a whole.

Sampling is the process of selecting a subset of individuals from the population to estimate characteristics of the whole population.

One commonly used sampling method is cluster sampling.

In this article, we will dive deeper into cluster sampling, including its definition, how to apply it using Python, and its benefits.

Cluster Sampling

Cluster sampling is a sampling technique in which a population is divided into groups or clusters. Then, a random sample of clusters is selected, and all individuals within those clusters are included in the study.

This technique is used when the population is too large to sample every individual, but the entire population is still needed for the study.

Typically, clusters are chosen for a reason, such as they share common characteristics, or they are located within the same geographic area.

For example, population clusters for a study could be certain neighborhoods within a city, schools within districts, or specific departments within a company. By selecting clusters, the researcher can reduce travel time and expenses when accessing difficult-to-reach or far-flung populations.

Using cluster sampling saves time and money in comparison to other sampling techniques like simple random sampling. The clusters are homogenous and easier to access than the individuals, so the study may be conducted efficiently.

Cluster sampling enables researchers to work faster and get better results with less sample effort.

Creating a Pandas DataFrame

To illustrate cluster sampling, let’s set up a hypothetical example exploring how a city tour company can examine the demographics of customers who book their walking tours. To begin with, we’ll create a Pandas DataFrame to keep track of each customer’s details.

A Pandas DataFrame is a tool for manipulating data. Follow these steps to create a DataFrame using Python, a prevalent programming language used by data scientists.

First, we must import the Pandas module. We’ll do this using Python’s import function:


import pandas as pd


Next, create the DataFrame:


city_tours_data = pd.DataFrame({

‘customer_id’: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],

‘tour_group’: [‘Group A’, ‘Group C’, ‘Group A’, ‘Group B’, ‘Group C’, ‘Group A’, ‘Group C’, ‘Group B’, ‘Group A’, ‘Group C’],

‘age’: [21, 31, 28, 23, 45, 50, 29, 33, 42, 38],

‘gender’: [‘F’, ‘F’, ‘M’, ‘F’, ‘M’, ‘M’, ‘M’, ‘F’, ‘F’, ‘M’]



To create this DataFrame, you specify a dictionary where each column name is a key and the value lists contain the column values. This DataFrame contains four columns:

– Customer ID: A unique identifier for each customer.

– Tour Group: The tour group name selected by the customer. – Age: The age of the customer in years.

– Gender: The gender of the customer, represented by M or F. Performing

Cluster Sampling with Pandas

After creating the DataFrame, the next step is to apply cluster sampling to the data.

In cluster sampling, we determine the number of individuals that represent each group so that we can prevent bias in the selection. In our example, each tour group will represent one group.

There are various methods of random selection, including Simple Random Sampling, Systematic Sampling, etc. We will use the random module in Python which randomly chooses the tour groups that will represent the cluster.


import random

groups = list(set(city_tours_data[‘tour_group’]))

# Set seed for repeatability


selected_groups = random.sample(groups, 2)


`set(city_tours_data[‘tour_group’])` returns a set of unique values in the tour_group column, which we convert to a list to use the `random.sample()` function. The `random.sample()` function takes two arguments: the first is the population to sample from (in our case, it is the list of unique tour group names), and the second argument is the number of groups we want to select.

Let’s assume that two tour groups are selected as clusters: Group A and Group C. We’ll use these two clusters to extract a sample of customers, but first, we’ll filter the DataFrame based on these clusters using Pandas.


cluster_sample_df = city_tours_data[city_tours_data[‘tour_group’].isin(selected_groups)]


The `isin` method in Pandas checks whether each value in the tour_group column is present in the list of selected_groups. By filtering customers into selected_groups, we’ve created our cluster sample DataFrame.

Benefits of

Cluster Sampling

Cluster sampling is one of the faster and more cost-effective techniques for selecting a sample of individuals from a large population. This sampling method involves less travelling, paperwork, and temporal requirements, compared to other techniques.

When selecting individual sample subjects, it is necessary that each unit is properly accounted for, which is time-consuming and costly but prevented in cluster sampling.

Moreover, given the diversity of complexities in real-life datasets, clustering is often the only way to estimate complex relationships through sampling.

For example, cluster sampling can be applied when selecting data from IoT devices, such as sensors located in buildings or cars spread out across a city.

In summary, cluster sampling is a useful method for obtaining representative samples from larger populations.

By creating representative clusters and randomly sampling from them, researchers can refine their studies, reduce cost and time investment, and obtain meaningful data that can be generalized to the entire population.


In conclusion, hypotheses are based on representative data obtained through different sampling techniques. By relying on the statistics-driven approach, data scientists can make predictions, test them, and arrive at the optimal decision.

With cluster sampling, the subgroups within the population are representative, ensuring that the sample is an accurate representation of the overall population in the research. By utilizing techniques such as Pandas and Python, individuals can apply cluster sampling and make sense of big data practically.

In our example scenario, we analyzed data to understand the demographics of customers who book walking tours. This method is effective in obtaining a random cluster sample of the population and can prevent certain biases.

By keeping up with emerging techniques and tools for data analysis, researchers can create more effective study designs and draw meaningful insights. Results of

Cluster Sampling

In the previous sections, we have established a fundamental understanding of cluster sampling and applied this technique to a hypothetical example involving a city tour company.

In this section, we will explore the results of the sample obtained through cluster sampling. Specifically, we will explore the composition of the sample and the number of observations from each tour group.

Composition of Sample

After applying cluster sampling to the dataset of the city tour company, we were left with a sample that consisted of two tour groups – Group A and Group C. Let’s analyze the composition of the sample further.


cluster_sample_df = city_tours_data[city_tours_data[‘tour_group’].isin(selected_groups)]

print(f”Cluster sample size: {len(cluster_sample_df)}”)


The output of this code will return the number of observations in the cluster sample. In this case, the cluster sample size is 6, which is the total number of customers in tour groups A and C combined.


tour_group_counts = cluster_sample_df.value_counts(‘tour_group’)



This code will return the count of observations in each tour group. In our example, the output shows the following:



Group A 4

Group C 2

dtype: int64


We can see that there are a total of six observations, with four of them belonging to Group A and two belonging to Group C. This distribution implies that the company’s walking tours are more popular among customers in Group A than in Group C.

Number of Observations from Each Tour Group

In our hypothetical example of the city tour company, we obtained a sample of six customers from two tour groups. To see how this sample is helpful to the company’s marketing strategy, let’s explore the number of observations from each tour group in more detail.




This code uses the Pandas groupby() method to group the cluster sample by tour group and calculate statistics for each group. This method will generate a summary of the age and gender of the selected customers from each tour group.


customer_id age

count mean std min 25% 50% 75% max count mean std min 25% 50% 75% max


Group A 4.0 5.0 3.055050 1.0 3.5 5.5 7.0 9.0 4.0 30.250000 10.794512 21.0 25.0 28.5 33.75 42.0

Group C 2.0 3.5 3.535534 1.0 2.2 3.5 4.8 6.0 2.0 39.000000 4.242641 36.0 37.5 39.0 40.50 42.0


The results show that the mean age of customers from Group A is 30.25, with customers’ ages ranging from 21 to 42 years. For Group C, the average customer age is higher at 39 years old, and there is a narrower age range between 36 and 42 years old.

We can use the data about the age and gender of the customers to make strategic marketing decisions. For example, we can use the results from this sample to guide targeted advertisements that would appeal to these groups’ unique characteristics.

This example highlights the practical application of cluster sampling and how it can deliver beneficial insights to guide strategic decision making.


In conclusion, we have examined the results of cluster sampling in a hypothetical example of a city tour company that wants to understand its customers’ demographics. The sample obtained using cluster sampling offers valuable information, including the composition of the sample and the number of observations from each tour group.

Through the evaluation of the sample, the company can gain insights useful in developing marketing strategies intended to generate more bookings from similar groups.

By analyzing the data and considering the results, researchers, businesses, and nonprofit organizations can draw meaningful conclusions about their populations and make evidence-based decisions.

In summary, cluster sampling is a powerful tool in obtaining representative samples from larger populations, allowing us to gain insights that could guide strategic decisions in various fields. In conclusion, this article has explored the concept of cluster sampling, a useful sampling technique for obtaining representative samples from larger populations.

By dividing the population into groups or clusters, researchers can extract a sample while minimizing time and cost expenses. This article also explained how to perform cluster sampling using Python with Pandas, and how to interpret the results obtained through this technique.

Ultimately, cluster sampling is a powerful tool for researchers and businesses to examine the composition of samples and enables them to make informed decisions based on data. The use of cluster sampling and other sampling techniques can provide valuable insights that help in guiding strategic decision making.

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