5 Best Ways to Concatenate Pandas DataFrames Without Duplicates

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💡 Problem Formulation: When working with large datasets, it’s common to combine data from various sources. Preserve unique records while concatenating DataFrames in Python using the pandas library. For example, suppose we have two DataFrames with customer details, and we want to merge them into a single DataFrame without duplicate customers based on a unique identifier such as a customer ID.

Method 1: Using concat() and drop_duplicates()

This method involves the use of the pandas concat() function to combine DataFrames, followed by the drop_duplicates() method to eliminate any duplicate rows based on all or a subset of columns. This technique is simple and can be customized to consider all or specific duplicate columns for removal.

Here’s an example:

import pandas as pd

df1 = pd.DataFrame({'customer_id': [1, 2], 'name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'customer_id': [2, 3], 'name': ['Bob', 'Charlie']})

combined_df = pd.concat([df1, df2]).drop_duplicates(subset='customer_id')
print(combined_df)

Output:

   customer_id     name
0            1    Alice
1            2      Bob
1            3  Charlie

The code snippet above first uses pd.concat() to stack the DataFrames vertically and then applies drop_duplicates() with the subset argument specifying the ‘customer_id’ column. This removes any additional instances of customer IDs that occur more than once, ensuring each customer is listed only once in the combined DataFrame.

Method 2: Use merge() with the indicator Option

Method 2 leverages the merge() function with an indicator argument set to True. It allows you to perform a database-style join and then filter the resulting DataFrame to include only unique records. This method is efficient and suitable when dealing with two DataFrames.

Here’s an example:

df1 = pd.DataFrame({'customer_id': [1, 2], 'name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'customer_id': [2, 3], 'name': ['Bob', 'Charlie']})

combined_df = df1.merge(df2, on='customer_id', how='outer', indicator=True)
combined_df = combined_df[combined_df['_merge'] != 'both']
combined_df.drop(columns=['_merge'], inplace=True)
print(combined_df)

Output:

   customer_id     name
0            1    Alice
2            3  Charlie

This block of code initially merges the two DataFrames on the ‘customer_id’ field using an outer join, which includes all records from both. The indicator=True flag adds an extra column with information about the origin of each row. Rows flagged as ‘both’ are duplicates in the context of the ‘customer_id’, and filtering these out, we get a DataFrame with unique customer IDs.

Method 3: DataFrame append() with drop_duplicates()

Appending one DataFrame to another and then removing duplicates is achieved with DataFrame append() method followed by drop_duplicates(). This method is quite intuitive and works well when you have a small number of DataFrames to concatenate.

Here’s an example:

df1 = pd.DataFrame({'customer_id': [1, 2], 'name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'customer_id': [2, 3], 'name': ['Bob', 'Charlie']})

combined_df = df1.append(df2).drop_duplicates(subset='customer_id')
print(combined_df)

Output:

   customer_id     name
0            1    Alice
1            2      Bob
1            3  Charlie

In the above code, the append() method concatenates df2 to df1. The resulting DataFrame might have duplicate entries which are then removed using the drop_duplicates() method. This ensures only unique customer records are retained in the final DataFrame.

Method 4: Custom Function Combining concat() and groupby()

For those needing more control or handling special cases, creating a custom function that uses concat() followed by groupby() and aggregation can provide the desired level of customization. This allows handling of duplicates based on more complex logic than simple row-equivalence.

Here’s an example:

def combine_dataframes_custom(df1, df2, unique_col, agg_dict=None):
    combined_df = pd.concat([df1, df2])

    if agg_dict is None:
        combined_df = combined_df.drop_duplicates(subset=unique_col)
    else:
        combined_df = combined_df.groupby(unique_col, as_index=False).agg(agg_dict)
    
    return combined_df

df1 = pd.DataFrame({'customer_id': [1, 2], 'name': ['Alice', 'Bob']})
df2 = pd.DataFrame({'customer_id': [2, 3], 'name': ['Bob', 'Charlie']})

combined_df = combine_dataframes_custom(df1, df2, 'customer_id')
print(combined_df)

Output:

   customer_id     name
0            1    Alice
1            2      Bob
2            3  Charlie

This custom function accepts two DataFrames and a unique column to determine duplicates. It concatenates the DataFrames and then either drops duplicates or performs group by and aggregation if an aggregation dictionary is provided. This method caters to more complex scenarios where additional manipulation of duplicated data might be necessary.

Bonus One-Liner Method 5: Using pd.concat() with ignore_index and drop_duplicates()

In a one-liner, you can concatenate DataFrames and remove duplicates by passing the result of pd.concat() directly into drop_duplicates(). With ignore_index=True, you’ll also get a nicely ordered index in the resulting DataFrame.

Here’s an example:

combined_df = pd.concat([df1, df2], ignore_index=True).drop_duplicates(subset='customer_id')
print(combined_df)

Output:

   customer_id     name
0            1    Alice
1            2      Bob
3            3  Charlie

This concise line takes advantage of method chaining to append df2 to df1, reindex the result with ignore_index=True, and then immediately remove duplicates. This makes for clean and expressive code that’s easy to read and write.

Summary/Discussion

  • Method 1: Using concat() and drop_duplicates(). Strengths: Straightforward and customizable for specific columns. Weaknesses: May not be the most efficient for very large datasets.
  • Method 2: Use merge() with the indicator Option. Strengths: Offers precision through SQL-like joins. Weaknesses: More complex syntax and not as intuitive as concat().
  • Method 3: DataFrame append() with drop_duplicates(). Strengths: Easy to understand and use for small-scale operations. Weaknesses: Like Method 1, can become inefficient with large datasets.
  • Method 4: Custom Function Combining concat() and groupby(). Strengths: Highly customizable and powerful for complex data manipulation. Weaknesses: Requires more code and is dependent on user-defined logic which can introduce errors if not carefully implemented.
  • Bonus One-Liner Method 5: Using pd.concat() with ignore_index and drop_duplicates(). Strengths: Elegant and compact. Weaknesses: Limited customization compared to other methods.