Randomly Selecting Columns from Pandas DataFrame
Pandas dataframe is a widely used data structure in Python for data processing. Often, one may encounter a scenario where they need to perform operations on randomly selected columns from a given dataframe.
This article will cover techniques for randomly selecting columns in a pandas dataframe, along with examples to illustrate the methods.
Techniques for Randomly Selecting Columns
The following are techniques that can be used for randomly selecting columns from a pandas dataframe:

Using Numpy’s random.choice method: This method randomly selects columns using a probability distribution based on the input array’s weights.

Using Pandas’ sample method: This method allows you to select a random sample of columns from a dataframe.
The fraction parameter can be used to specify the percentage of columns to be selected randomly. 3.

Using the random module of Python: This module can also be used to select a random column from a pandas dataframe by generating a random index and selecting the column corresponding to that index.
Example of Randomly Selecting Columns
Let’s assume we have a dataframe df with columns ‘a’,’b’,’c’,’d’,’e’,’f’. The following code examples will illustrate how to randomly select columns from this dataframe.
Using Numpy’s random.choice method
import numpy as np
columns = df.columns.to_list()
selected_columns = np.random.choice(columns, size=3, replace=False)
In the above code, we are using Numpy’s random.choice method to select three columns randomly without replacement. The size parameter specifies the number of columns to be selected, and the replace parameter is set to False to avoid selecting the same column multiple times.
Using Pandas’ sample method
selected_columns = df.sample(frac=0.4, axis='columns')
Using the sample method, we can select 40% of the columns randomly along the column axis. The axis parameter specifies that columns should be sampled instead of rows.
Using the random module of Python
import random
columns = df.columns.to_list()
selected_column = random.choice(columns)
In this example, we are using Python’s random module to generate a random index and using that index to select a random column from the dataframe.
Case 1: Randomly Selecting a Single Column
In some cases, we may only need to randomly select a single column from a dataframe.
The following examples demonstrate how to achieve this.
Procedure for Randomly Selecting a Single Column

Get a list of all columns in the dataframe.

Use Python’s random module to generate a random index.

Select the column corresponding to the random index.
Example of Randomly Selecting a Single Column
import random
columns = df.columns.to_list()
selected_column = df[columns[random.randint(0, len(columns)1)]]
In this example, we are using Python’s random module to generate a random index and using this index to select a random column from the dataframe. In conclusion, randomly selecting columns from a pandas dataframe is useful when working on large datasets.
In this article, we have discussed various techniques for randomly selecting columns and provided examples to illustrate these techniques. By using these methods, we can easily perform operations on a random subset of columns from a dataframe.
Continuing from the previous section, this article expansion will cover two additional cases of randomly selecting columns from a pandas dataframe.
Case 2: Randomly Selecting a Specified Number of Columns
Sometimes, we may need to select a specific number of columns from a dataframe randomly.
The following steps can be followed to achieve this:
Procedure for Randomly Selecting a Specified Number of Columns

Get a list of all columns in the dataframe.

Use Python’s random.sample method to randomly select a specific number of columns from the list.

Create a new dataframe with the selected columns.
Example of Randomly Selecting a Specified Number of Columns
import random
columns = df.columns.to_list()
selected_columns = random.sample(columns, k=3)
new_df = df[selected_columns]
In this example, we are using Python’s random.sample method to randomly select 3 columns from the list of all columns and creating a new dataframe with only these selected columns.
Case 3: Allowing Random Selection of Same Column More Than Once
In some cases, we may need to allow the random selection of the same column more than once.
The following steps can be followed to achieve this:
Procedure for Allowing Random Selection of Same Column More Than Once

Get a list of all columns in the dataframe.

Use Python’s random.choices method to randomly select columns from the list, with replacement.

Create a new dataframe with the selected columns.
Example of Allowing Random Selection of Same Column More Than Once
import random
columns = df.columns.to_list()
selected_columns = random.choices(columns, k=5)
new_df = df[selected_columns]
In this example, we are using Python’s random.choices method to randomly select 5 columns from the list of all columns with replacement, meaning the same column can be selected multiple times. We then create a new dataframe with only the selected columns.
While randomly selecting columns can be a useful tool, it is important to ensure that the columns selected are representative of the data and do not introduce bias. Therefore, it is essential to consider the context and purpose of the operation and choose an appropriate technique accordingly.
In conclusion, randomly selecting columns from a pandas dataframe can be helpful in data analysis and processing. This article has provided various techniques and examples of randomly selecting columns, including selecting a specific number of columns and allowing the random selection of the same column more than once.
These methods can be employed based on the context and requirements of the operation. In addition to the cases discussed previously, there may be situations where we need to randomly select a specific fraction of the total number of columns available in a pandas dataframe.
In such cases, we can use a combination of Pandas’ column selection methods and Python’s randomization techniques to achieve our desired outcome. This article will discuss the procedure for randomly selecting a specified fraction of total number of columns, along with an example.
Case 4: Randomly Selecting a Specified Fraction of Total Number of Columns
Suppose we have a dataframe ‘df’ that contains 15 columns. We want to randomly select 30% of the columns to work with.
The following steps can be followed to achieve this:
Procedure for Randomly Selecting a Specified Fraction of Total Number of Columns

Calculate the total number of columns in the dataframe with the .shape attribute.

Determine the number of columns to select by multiplying the total number of columns with the desired fraction.

Generate the indices of the columns to select using Python’s random.sample method.

Use these indices to select the desired columns from the dataframe using Pandas’ .iloc operator.
Example of Randomly Selecting a Specified Fraction of Total Number of Columns
Consider the following example:
import pandas as pd
import numpy as np
import random
# Creating a dataframe with 15 columns
df = pd.DataFrame(np.random.randn(100, 15), columns=list('abcdefghijklmno'))
# Specifying fraction and calculating number of columns to select
fraction = 0.3
num_cols = int(round(df.shape[1] * fraction))
# Selecting columns randomly
col_indices = random.sample(range(df.shape[1]), num_cols)
selected_columns = df.iloc[:, col_indices]
In this example, we first create a dataframe ‘df’ with 15 columns. We then specify the desired fraction of 0.3 and calculate the number of columns to select by rounding up the result of the multiplication between the number of columns and the fraction.
Using random.sample, we generate a list of column indices that represent the selection of a specific number of columns randomly. Finally, we use these indices to extract the selected columns from the original dataframe using the .iloc operator.
Note that when randomly selecting a fraction of total number of columns in a pandas dataframe, we must ensure that the fraction selected is not too small. Selecting a very small fraction, especially when the data is very large, may cause the sample to be too small to be representative of the population.
In conclusion, randomly selecting a specific fraction of total number of columns can be a useful technique when working with pandas dataframes. By following the above procedure, one can randomly extract the desired amount of columns from a dataframe and perform relevant operations on the extracted data.
However, it is crucial to consider the proportion of the columns selected and ensure that the chosen fraction effectively represents a representative sample of the dataset. In summary, this article has discussed various techniques for randomly selecting columns from a pandas dataframe in Python.
We covered different cases, including randomly selecting a single column, selecting a specific number of columns, allowing the random selection of the same column more than once, and randomly selecting a specified fraction of total columns. By utilizing these techniques, we can easily perform operations on a random subset of columns from a dataframe.
However, we must ensure that the columns selected are representative of the data and do not introduce bias. Randomly selecting columns can be a useful tool in data analysis and processing, and appropriate context and purpose considerations should be exercised when choosing a suitable technique.