Displaying the Row Index Number Based on Conditions

What will you learn?

Discover how to efficiently identify and display row index numbers in a Python DataFrame that satisfy specific conditions using Pandas.

Introduction to the Problem and Solution

In this scenario, we encounter the task of pinpointing row index numbers in a DataFrame that meet certain criteria. To tackle this challenge effectively, we rely on the robust capabilities of Python’s Pandas library. By harnessing Pandas functions and conditional statements, we can seamlessly extract the desired information from our dataset.

To achieve our objective, it is essential to grasp the concept of filtering data based on conditions with Pandas. Through techniques like boolean indexing, we can select rows that fulfill our specified conditions. This method empowers us to precisely locate rows within our DataFrame that adhere to the defined criteria.


# Import necessary libraries
import pandas as pd

# Create a sample DataFrame (replace this with your actual DataFrame)
data = {'A': [10, 20, 30, 40],
        'B': [15, 25, 35, 45]}
df = pd.DataFrame(data)

# Display row index numbers where column 'A' values are greater than 20
result_indices = df[df['A'] > 20].index.tolist()

# For more complex conditions involving multiple columns:
# result_indices = df[(df['A'] > 20) & (df['B'] < 40)].index.tolist()

# Note: Replace 'df' with your actual DataFrame variable name

# Credits: Visit PythonHelpDesk.com for more Python tips and solutions.

# Copyright PHD


In the provided code snippet: – We import Pandas as pd for efficient data manipulation. – A sample DataFrame df is created for illustration purposes. – Boolean indexing (df[‘A’] > 20) filters rows based on a condition. – The .index.tolist() method retrieves matching index labels as row indices. – Retrieved row indices are either printed or utilized further as needed.

    1. How can I check multiple conditions while filtering rows? You can combine multiple conditions using logical operators like & (AND), | (OR), and parentheses for grouping.

    2. Can I display specific columns alongside row indices that meet my criteria? Yes! Once you have identified relevant row indices, you can extract corresponding values from other columns by referencing those indices in your DataFrame.

    3. Is it possible to reset row indices after filtering data? Certainly! You can easily reset or reassign row indices post-filtering by utilizing methods like .reset_index() provided by Pandas.

    4. What if there are missing values in my dataset? Will they impact filtering results? Missing values may affect comparisons when filtering data based on specific criteria. It’s advisable to handle missing data appropriately before applying filters.

    5. How do I handle cases where text-based conditions are involved instead of numerical values? For text-based conditions or string comparisons within DataFrames, consider using methods like .str.contains() combined with appropriate regex patterns for efficient filtering.


Mastering the art of filtering rows based on specific conditions is pivotal when working with tabular data in Python utilizing libraries such as Pandas. These skills are indispensable for conducting robust data analysis tasks efficiently across diverse domains like finance, research, and machine learning.

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