Pandas: How to drop columns whose sum is less than a threshold

Updated: February 20, 2024 By: Guest Contributor Post a comment


Working with data often involves cleaning and preprocessing to ensure that it is in the right format for analysis or modeling. One common task during this process is dropping columns from a DataFrame, especially those that do not meet certain criteria, such as having a sum below a specified threshold. This article will guide you through the process of identifying and removing such columns using the Pandas library in Python. We’ll start with basic examples and progressively move to more advanced scenarios, ensuring you have the tools to handle various data manipulation tasks.

Getting Started

Before we dive into the specifics, let’s ensure we have the Pandas library installed and imported. If you haven’t already, you can install Pandas using pip install pandas. Once installed, import it in your Python script or notebook.

import pandas as pd

Basic Example

Let’s begin with a simple example. Suppose we have a DataFrame with various columns, and we want to drop columns whose sum is less than 10. First, we need to create a sample DataFrame:

import pandas as pd

df = pd.DataFrame({
    'A': [5, 5, 5, 5],
    'B': [1, 0, 2, 3],
    'C': [0, 0, 0, 0],
    'D': [10, 10, 10, 10]

To identify and drop columns with a sum less than 10, we use the following approach:

columns_to_drop = df.columns[df.sum() < 10]
df.drop(columns=columns_to_drop, inplace=True)


This code snippet calculates the sum of each column, identifies columns with a sum less than 10, and then drops them from the DataFrame. The inplace=True parameter modifies the original DataFrame instead of returning a new one. Here’s the resulting DataFrame:

A    D
0  5    10
1  5    10
2  5    10
3  5    10

Working with Conditions

Now, let’s explore a more complex example where we might want to drop columns based on multiple conditions. Suppose we wish to drop columns not only based on their sum but also if they contain a specific value, say 0, more than twice. We combine conditions to achieve this:

columns_to_drop = df.columns[(df.sum() < 10) | (df.isin([0]).sum() > 2)]
df.drop(columns=columns_to_drop, inplace=True)



   A   D
0  5  10
1  5  10
2  5  10
3  5  10

In this scenario, we’re using the bitwise OR operator | to combine conditions. We’re checking if the column sum is less than 10 or if a column contains the value 0 more than twice. The result will be a DataFrame that excludes columns based on these combined criteria.

Applying to Real-world Data

Let’s apply these techniques to a more realistic dataset. For this, we can use any dataset that suits your interest. For demonstration purposes, we’ll work with a publicly available dataset from Kaggle or any similar platform.

After loading the dataset, the procedure remains somewhat similar. Here, however, we might face a larger number of columns, and writing individual conditions for each can be tedious. Instead, we can use loop constructs or vectorized operations in Pandas to efficiently process all columns.

df = pd.read_csv('your_dataset.csv')
columns_to_drop = df.columns[df.sum(axis=0) < your_threshold]
df.drop(columns=columns_to_drop, inplace=True)

The axis=0 parameter ensures we’re summing over columns. Adjust your_threshold as necessary based on your specific requirement.

Advanced Techniques

For larger datasets with numerous columns, the process could become resource-intensive. To optimize, consider splitting your DataFrame into subsets or leveraging the power of Dask, a parallel computing Python library that extends Pandas to large datasets.

Another advanced technique involves using lambda functions and the apply method to customize column dropping logic. For instance:

import numpy as np
import pandas as pd

df = pd.DataFrame(
    {"A": [5, 5, 5, 5], "B": [1, 0, 2, 3], "C": [0, 0, 0, 0], "D": [10, 10, 10, 10]}

custom_condition = lambda col: (col.sum() < 10) or (col.value_counts().get(0, 0) > 2)

# Use apply() to evaluate which columns should be dropped, resulting in a boolean Series
columns_to_keep = df.apply(lambda col: not custom_condition(col))

# Filter the DataFrame based on the boolean Series
df_filtered = df.loc[:, columns_to_keep]


This method provides granular control over the process, enabling complex logic that might be hard to express using standard operations.


Dropping columns in Pandas based on their sum being less than a certain threshold can greatly simplify your data preparation process. By starting with simple methods and progressively adopting more complex techniques, you can efficiently handle various data cleaning tasks. Remember, the key to effective data manipulation lies in understanding your data and applying the appropriate method to achieve your goal.