What will you learn?

Discover how to merge dataframes in Python based on specific iteration numbers, using the powerful pandas library. This tutorial will equip you with the skills to efficiently combine multiple datasets, making your data manipulation tasks more effective and streamlined.

Introduction to the Problem and Solution

Imagine having several dataframes, each containing different information, that need to be merged based on a particular iteration number. The challenge is to develop a solution that seamlessly integrates these dataframes using Python’s pandas library.

To tackle this, we will start by loading individual dataframes and then iterating through them to extract data corresponding to a specified iteration number. Subsequently, we will consolidate these extracted datasets into a single dataframe for comprehensive analysis.


# Import necessary libraries
import pandas as pd

# Load individual dataframes (df1, df2, df3,...)
# Iterate through the dataframes and select rows based on a specific iteration number (e.g., 2)
# Merge selected rows into one consolidated dataframe

# Example code snippet:
selected_data = []
dfs = [df1, df2, df3]  # List of individual dataframes

for df in dfs:
    selected_data.append(df[df['iteration'] == 2])  # Selecting rows where 'iteration' column value is 2

consolidated_df = pd.concat(selected_data)  # Merging selected rows into one dataframe

# Further processing or printing of the consolidated_df as needed

# Credits: For more Python help visit PythonHelpDesk.com

# Copyright PHD


  • Import Libraries: Importing pandas as pd for efficient data handling.
  • Load Data: Loading separate dataframes containing diverse information.
  • Iteration & Selection: Iterating through each dataframe to filter rows based on a specified iteration number.
  • Merge Data: Combining the selected rows from all iterations into a unified dataframe using pd.concat().

This method ensures that only relevant details from each dataframe are included in the final consolidated dataset.

    How can I specify a different iteration number for selection?

    You can modify the comparison value in df[‘iteration’] == X, where X represents your desired iteration number.

    Can I apply additional filters while selecting rows?

    Yes, you can incorporate additional conditions within the square brackets when filtering rows during selection.

    Is it possible to handle missing values during merging?

    Pandas offers options like fillna() or dropna() to address missing values before or after merging datasets.

    What if my dataframes have different column names?

    Ensure consistency in column names across all datasets before merging to facilitate seamless consolidation.

    Can I merge more than three datasets using this method?

    Certainly! Extend this solution by adding more individual dataframes to your list of dfs for processing and merging accordingly.


    Mastering the art of combining multiple data frames based on specific iteration numbers using pandas in Python empowers you with robust analytical capabilities. By following the outlined coding practices, you can efficiently merge diverse datasets for enhanced insights and informed decision-making processes. For further guidance or inquiries regarding Python programming concepts, visit PythonHelpDesk.

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