Cluster Sampling in Pandas

In this article, we will learn how we can perform cluster sampling in Pandas. But before we deep dive into that, let's explore a little about what sampling is in Pandas, as well as how pandas help us to do that.

Sampling

In Pandas, sampling refers to the process of selecting a subset of rows or columns from a DataFrame or Series object. Sampling can be useful in many data analysis tasks, such as data exploration, testing, and validation.

Pandas provides several methods for sampling data, including:

• DataFrame.sample(): This method returns a random sample of rows from a DataFrame. You can specify the number of rows to return, as well as the sampling method (e.g., random, weighted, etc.).

• Series.sample(): This method returns a random sample of values from a Series. You can specify the number of values to return, as well as the sampling method.

• DataFrame.groupby().apply(): This method allows you to group a DataFrame by one or more columns, and then apply a sampling function to each group. For example, you could use this method to select a random sample of rows from each group in a DataFrame.

• DataFrame.resample(): This method is used to resample time−series data at a different frequency (e.g., daily to monthly). It can also be used to sample time−series data randomly or with a specified method (e.g., mean, sum, etc.).

Overall, sampling in Pandas can help you quickly gain insights into your data and make informed decisions about how to proceed with your analysis.

In the above point we talked about the different ways with which we can do sampling in Pandas, now let's discuss cluster sampling in Pandas.

Cluster Sampling

Cluster sampling is a statistical method used to gather data from a population that is too large or too difficult to access as a whole. This method involves dividing the population into smaller subgroups or clusters, and then selecting a random sample of clusters to be included in the study. Once the clusters are selected, data is collected from all individuals within each chosen cluster.

Cluster sampling is often used when the population is geographically dispersed or when it is difficult or impractical to access certain areas of the population. For example, when conducting a survey of households in a city, it may be more efficient to divide the city into neighbourhoods or blocks and select a random sample of these smaller areas for data collection, rather than trying to contact every household in the city.

To perform cluster sampling, the population is first divided into clusters, which should be internally homogenous but externally heterogeneous. This means that individuals within each cluster should be similar to one another, but clusters themselves should be different from one another. This is important because it allows the clusters to be representative of the overall population.

Once the clusters are identified, a random sample of them is selected. In order to ensure that the sample is representative of the population, it is important to use a random selection method, such as simple random sampling or stratified random sampling.

After selecting the clusters, data is collected from all individuals within each chosen cluster. This can be done using various sampling techniques, such as simple random sampling, systematic sampling, or probability proportional to size (PPS) sampling.

One of the main advantages of cluster sampling is that it is more cost−effective and efficient than other sampling methods, such as simple random sampling or stratified sampling. This is because it allows researchers to focus their resources on a smaller portion of the population, rather than trying to collect data from the entire population.

However, cluster sampling has some limitations. One potential disadvantage is that it may introduce sampling bias, as individuals within each chosen cluster may be more similar to one another than to individuals in other clusters. In addition, cluster sampling may lead to increased variability and decreased precision in the estimates, as the sample size within each cluster may be smaller than the sample size in a simple random sample of the same size.

In summary, cluster sampling is a statistical method that involves dividing a population into smaller subgroups or clusters, and then selecting a random sample of clusters for data collection. Cluster sampling is often used when the population is geographically dispersed or when it is difficult or impractical to access certain areas of the population. While it has some advantages over other sampling methods, it also has some limitations and potential sources of bias that should be considered when selecting a sampling method.

Now let's try to work on a few code examples where we will see cluster sampling in action.

To perform cluster sampling on a population of 16 individuals in Python, we can create a Pandas DataFrame with the numbers 1 to 16 and define clusters consisting of groups of 4 individuals. Then, we can randomly select one of the clusters as our sample.

Example

# Import the pandas and numpy libraries
import pandas as pd
import numpy as np

# Create a dictionary containing a range of numbers from 1 to 15
data = {'N_numbers': np.arange(1, 16)}

# Convert the dictionary into a Pandas DataFrame
df = pd.DataFrame(data)

# Take a random sample of 4 numbers from the DataFrame
samples = df.sample(4)

# Print the random sample
print(samples)

Explanation

This code demonstrates how to create a Pandas DataFrame and take a random sample from it using the sample() method.

First, the pandas and numpy libraries are imported using the import statements. Pandas is a popular data analysis library in Python that provides powerful tools for working with tabular data, while NumPy is a library that provides support for working with arrays and matrices.

Next, a dictionary data is created using NumPy's arange() function to generate a range of numbers from 1 to 15. This dictionary has a single key−value pair, where the key is the string 'N_numbers' and the value is a NumPy array containing the numbers.

The dictionary is then passed to the pd.DataFrame() function, which creates a Pandas DataFrame object with a single column labeled 'N_numbers'. The numbers generated by np.arange() are used to populate this column.

The sample() method is then called on the DataFrame object df with a parameter of 4. This method takes a random sample of n rows from the DataFrame, where n is the parameter passed to the method. In this case, a sample of 4 rows is taken randomly from the DataFrame, and the resulting sample is stored in the variable samples.

Finally, the resulting sample is printed to the console using the print() function. The output will be a Pandas DataFrame containing 4 randomly selected rows from the original DataFrame, with the same column structure. The rows and their contents will be different each time the code is run, as the sample() method returns a different random sample each time it is called.

To run the code, we first need to make sure that we have pandas and numpy installed, and if not then we can run the command shown below.

Command

pip3 install pandas numpy

Now run the above code with the command shown below.

Command

python3 main.py

If we run the above command, we should get an output similar to the one shown below.

Output

N_numbers
0      	1
8      	9
9     	10
1      	2

Let's explore one more example.

Example

# Import the pandas and numpy libraries
import pandas as pd
import numpy as np

# Create a dictionary of data containing employee IDs and their corresponding values
data = {'employee_id': np.arange(1, 21), 'value': np.random.randn(20)}

# Create a Pandas DataFrame from the dictionary
df = pd.DataFrame(data)

# Print the resulting DataFrame to the console
print(df)

Explanation

This code creates a Pandas DataFrame object from a dictionary of data containing employee IDs and their corresponding values. It then prints the resulting DataFrame to the console.

First, the pandas and numpy libraries are imported using the import statements. Pandas is a library for data manipulation and analysis, while NumPy is a library for scientific computing in Python.

A dictionary data is created containing two key−value pairs, where the keys are 'employee_id' and 'value', and the values are arrays of length 20 generated by NumPy's arange() and random.randn() functions, respectively.

The dictionary is then passed to the pd.DataFrame() function, which creates a Pandas DataFrame object with two columns labeled 'employee_id' and 'value' containing the corresponding data from the dictionary.

Finally, the resulting DataFrame is printed to the console using the print() function. The output will be a table with two columns and 20 rows, containing the employee IDs and their corresponding values. The values will be random, as they are generated by the random.randn() function.

Now run the above code with the command shown below.

Command

python3 main.py

If we run the above command, we should get an output similar to the one shown below.

Output

	employee_id 	value
0         	1  0.579512
1         	2 -0.646034
2         	3  1.315528
3         	4 -1.073037
4         	5 -1.456259
5         	6  0.208272
6         	7 -0.431192
7         	8 -2.046502
8         	9 -1.571820
9        	10  0.436177
10       	11 -0.987235
11       	12  0.266647
12       	13 -0.386446
13       	14 -0.558013
14       	15 -2.427465
15       	16  0.535111
16       	17  0.007998
17       	18 -0.376771
18       	19 -0.403859
19       	20  0.524652

Conclusion

To sum up, cluster sampling is a really useful method for carrying out surveys and research in large populations. It saves time and money by grouping people with similar traits and then picking a selection of those groups for the study. In Python, there are a bunch of libraries like Pandas and Scikit−learn that you can use to easily apply cluster sampling techniques. These libraries help researchers analyze data and draw accurate conclusions while reducing sampling bias. All in all, cluster sampling in Python is a powerful tool that can make surveys and research studies much more efficient and precise.