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How to Concatenate Pandas DataFrames by Column Values

Learn how to efficiently concatenate multiple pandas DataFrames based on column values in Python. Complete guide with code examples for combining DataFrames that share the same fruit or category values.

#data-concatenation#data-manipulation#pandas-dataframes#pandas-tutorial#python-data-analysis#python-programming
Question

How to concatenate multiple pandas DataFrames based on column values in Python? I have a collection of DataFrames stored in a list called Data_Tables, all with identical columns. I need to combine DataFrames that share the same value in a specific column (e.g., ‘fruit’). For example, if I have 10 DataFrames where 3 contain ‘apple’, 2 contain ‘banana’, and 2 contain ‘lemon’, I want to create separate concatenated DataFrames for each fruit type. I also have lists identifying which fruits have multiple DataFrames (Multiple_Fruit) and their counts (Num_Dups_per_Fruit). My current approach involves nested loops to identify matching DataFrames, but I’m having trouble with the concatenation step. How can I efficiently combine DataFrames based on column values while handling an arbitrary number of DataFrames and fruit types?

NeuroAgent

To concatenate multiple pandas DataFrames based on column values, you can use pandas’ groupby() or concat() functions along with filtering. The most efficient approach involves first extracting the fruit column values from each DataFrame, grouping them by these values, then concatenating DataFrames that share the same fruit values. This creates separate concatenated DataFrames for each fruit type while handling any number of DataFrames and fruit types automatically.

Contents

Basic Concatenation Approach

The foundation of this solution lies in pandas’ concat() function, which allows you to combine multiple DataFrames along a particular axis. When you have DataFrames with identical columns, simple concatenation works well:

python
import pandas as pd

# Basic concatenation of all DataFrames
combined_df = pd.concat(Data_Tables, ignore_index=True)

However, this approach combines all DataFrames without considering your fruit column values. To group by fruit values, you need a more sophisticated method that considers the specific column values.

Grouping by Column Values

To group DataFrames by column values, you need to first identify which DataFrames contain each fruit type. This can be done by iterating through your Data_Tables list and checking the fruit column:

python
# Dictionary to store DataFrames by fruit type
fruit_groups = {}

for df in Data_Tables:
    # Get the fruit value from each DataFrame
    fruit_value = df['fruit'].iloc[0]  # Assuming all rows in a DataFrame have the same fruit
    
    if fruit_value not in fruit_groups:
        fruit_groups[fruit_value] = []
    fruit_groups[fruit_value].append(df)

This creates a dictionary where each key is a fruit type and each value is a list of DataFrames containing that fruit.

Creating Separate DataFrames for Each Fruit

Once you have your DataFrames grouped by fruit type, you can concatenate them for each group:

python
# Dictionary to store concatenated DataFrames for each fruit
concatenated_fruits = {}

for fruit, dfs in fruit_groups.items():
    # Concatenate all DataFrames for this fruit
    concatenated_fruits[fruit] = pd.concat(dfs, ignore_index=True)

This will give you separate DataFrames for each fruit type, properly concatenated while maintaining all the original data.

Optimized Solution Using Your Lists

Given that you already have Multiple_Fruit and Num_Dups_per_Fruit lists, you can optimize the process by focusing only on fruits that have multiple DataFrames:

python
def concatenate_by_fruit(data_tables, multiple_fruit, num_dups_per_fruit):
    """
    Concatenate DataFrames based on column values efficiently.
    
    Args:
        data_tables: List of DataFrames to concatenate
        multiple_fruit: List of fruits that have multiple DataFrames
        num_dups_per_fruit: List of counts of DataFrames per fruit
        
    Returns:
        Dictionary of concatenated DataFrames by fruit type
    """
    # Create dictionary for easy lookup of DataFrame counts
    fruit_counts = dict(zip(multiple_fruit, num_dups_per_fruit))
    
    # Initialize dictionary to store DataFrames by fruit
    fruit_groups = {fruit: [] for fruit in multiple_fruit}
    
    # Process each DataFrame
    for df in data_tables:
        fruit_value = df['fruit'].iloc[0]  # Get fruit value
        
        if fruit_value in fruit_counts:
            fruit_groups[fruit_value].append(df)
    
    # Concatenate DataFrames for each fruit
    concatenated_results = {}
    for fruit, dfs in fruit_groups.items():
        if len(dfs) > 0:  # Only concatenate if we have DataFrames for this fruit
            concatenated_results[fruit] = pd.concat(dfs, ignore_index=True)
    
    return concatenated_results

Handling Edge Cases

Several edge cases should be considered in your implementation:

  1. Empty DataFrames: Handle cases where DataFrames might be empty
  2. Mixed Fruit Values: If a DataFrame contains multiple fruit values
  3. Missing Fruit Column: Handle cases where the fruit column might not exist
  4. Case Sensitivity: Consider if ‘Apple’ and ‘apple’ should be treated as the same
python
def safe_concatenate_by_fruit(data_tables, multiple_fruit, num_dups_per_fruit, fruit_column='fruit'):
    """
    Safe concatenation function with error handling.
    """
    try:
        # Create lookup dictionary
        fruit_counts = dict(zip(multiple_fruit, num_dups_per_fruit))
        
        # Initialize storage
        fruit_groups = {fruit: [] for fruit in multiple_fruit}
        
        # Process DataFrames with error handling
        for df in data_tables:
            if df.empty or fruit_column not in df.columns:
                continue  # Skip empty DataFrames or those without fruit column
                
            # Handle multiple fruit values by taking the first
            fruit_value = df[fruit_column].iloc[0] if not df[fruit_column].empty else None
            
            if fruit_value is not None and fruit_value in fruit_counts:
                fruit_groups[fruit_value].append(df)
        
        # Concatenate results
        concatenated_results = {}
        for fruit, dfs in fruit_groups.items():
            if len(dfs) > 0:
                concatenated_results[fruit] = pd.concat(dfs, ignore_index=True)
        
        return concatenated_results
        
    except Exception as e:
        print(f"Error during concatenation: {e}")
        return {}

Complete Example

Here’s a complete working example demonstrating the entire process:

python
import pandas as pd
import numpy as np

# Sample data setup
np.random.seed(42)

# Create sample DataFrames
apple_df1 = pd.DataFrame({
    'fruit': ['apple', 'apple', 'apple'],
    'color': ['red', 'green', 'yellow'],
    'weight': [150, 120, 130]
})

apple_df2 = pd.DataFrame({
    'fruit': ['apple', 'apple'],
    'color': ['red', 'green'],
    'weight': [160, 110]
})

banana_df1 = pd.DataFrame({
    'fruit': ['banana', 'banana'],
    'color': ['yellow', 'yellow'],
    'weight': [200, 210]
})

lemon_df1 = pd.DataFrame({
    'fruit': ['lemon', 'lemon', 'lemon'],
    'color': ['yellow', 'yellow', 'yellow'],
    'weight': [80, 85, 90]
})

lemon_df2 = pd.DataFrame({
    'fruit': ['lemon', 'lemon'],
    'color': ['yellow', 'yellow'],
    'weight': [75, 82]
})

# Setup the user's lists
Data_Tables = [apple_df1, apple_df2, banana_df1, lemon_df1, lemon_df2]
Multiple_Fruit = ['apple', 'lemon']  # Fruits with multiple DataFrames
Num_Dups_per_Fruit = [2, 2]  # Count of DataFrames per fruit

# Optimized concatenation function
def concatenate_by_fruit_optimized(data_tables, multiple_fruit, num_dups_per_fruit):
    """
    Optimized function to concatenate DataFrames by fruit values.
    """
    # Create dictionary for efficient lookup
    fruit_to_count = dict(zip(multiple_fruit, num_dups_per_fruit))
    
    # Group DataFrames by fruit
    fruit_dfs = {fruit: [] for fruit in multiple_fruit}
    
    for df in data_tables:
        if df.empty or 'fruit' not in df.columns:
            continue
            
        fruit_value = df['fruit'].iloc[0]
        
        if fruit_value in fruit_to_count:
            fruit_dfs[fruit_value].append(df)
    
    # Concatenate DataFrames for each fruit
    result = {}
    for fruit, dfs in fruit_dfs.items():
        if dfs:  # Only concatenate if we have DataFrames
            result[fruit] = pd.concat(dfs, ignore_index=True)
    
    return result

# Execute the concatenation
concatenated_results = concatenate_by_fruit_optimized(Data_Tables, Multiple_Fruit, Num_Dups_per_Fruit)

# Display results
print("Concatenated Results:")
for fruit, df in concatenated_results.items():
    print(f"\n{fruit.upper()} DataFrame:")
    print(df)
    print(f"Shape: {df.shape}")
    print(f"Number of original DataFrames: {len([d for d in Data_Tables if d['fruit'].iloc[0] == fruit])}")

This solution efficiently handles your requirements by:

  1. Using your existing Multiple_Fruit and Num_Dups_per_Fruit lists for targeted processing
  2. Creating separate concatenated DataFrames for each fruit type
  3. Maintaining all original data while ignoring row indexes
  4. Handling edge cases gracefully
  5. Providing clear, maintainable code that scales with your data

The approach eliminates the need for nested loops by using dictionary-based grouping, making it both efficient and easy to understand.

Sources

  1. Pandas Documentation - concat()
  2. Pandas Documentation - groupby()
  3. Pandas Documentation - DataFrame.iloc

Conclusion

  • Use dictionary-based grouping instead of nested loops for better performance and readability
  • Leverage your existing lists (Multiple_Fruit and Num_Dups_per_Fruit) to focus processing only on fruits with multiple DataFrames
  • Handle edge cases like empty DataFrames or missing columns to make your solution robust
  • Consider memory efficiency when working with very large datasets by processing DataFrames in chunks
  • Test with sample data first to ensure your concatenation logic works as expected before applying to your full dataset

This approach provides a clean, efficient solution that scales well with the number of DataFrames and fruit types while maintaining all the original data integrity.

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