Pandas: Keep First Occurrence of Duplicates

To keep the first occurrence of repeated values in the same column while preserving the original DataFrame order and index, you can use several approaches with pandas. The key is to understand how pandas handles order preservation and index maintenance during duplicate removal.

The drop_duplicates() method does preserve order by default, but you may need to use it correctly to maintain both the order and the original index. Here are the recommended approaches:

Contents

• Basic Solution with drop_duplicates()
• Preserving Index Values
• Alternative Methods
• Handling Complex Scenarios
• Performance Considerations
• Best Practices

Basic Solution with drop_duplicates()

The most straightforward approach is to use drop_duplicates() with the keep='first' parameter, which is actually the default behavior:

import pandas as pd

df = pd.DataFrame({
    "id": [1, 2, 3, 4, 5, 6],
    "email": [
        "a@example.com",
        "b@example.com",
        "a@example.com",
        "c@example.com",
        "b@example.com",
        "d@example.com"
    ]
})

# Drop duplicates keeping first occurrence (default behavior)
result = df.drop_duplicates(subset="email", keep="first")
print(result)

This should produce:

   id          email
0   1   a@example.com
1   2   b@example.com
3   4   c@example.com
5   6   d@example.com

According to the pandas documentation, the drop_duplicates() method preserves the original order by default: “Drop duplicates except for the first occurrence.”

Preserving Index Values

If you’re experiencing issues with index preservation, the problem might be related to how you’re handling the results. Here’s how to ensure both order and index are preserved:

# Method 1: Direct drop_duplicates (preserves order and index)
result = df.drop_duplicates(subset="email", keep="first")

# Method 2: Using boolean indexing with duplicated()
# This gives you more control over the selection process
mask = ~df["email"].duplicated(keep="first")
result = df[mask]

print(result)

The duplicated() method returns a boolean Series indicating duplicate values, and by negating it (~), we select only the first occurrence of each unique value. According to the pandas documentation, this approach “keeps the first occurrence for each set of duplicated entries.”

Alternative Methods

Here are several alternative approaches you can use:

Method 1: Using groupby() with first()

# Group by the column and take the first occurrence of each group
result = df.groupby("email", as_index=False).first()

# If you need to preserve the original index order, sort by index and reset
result = result.sort_index().reset_index(drop=True)

As mentioned in the research findings, “The groupby() function coupled with the first() method can group duplicate items and then select the first occurrence from each group.” This approach is beneficial when dealing with a DataFrame and requires preservation of the index.

Method 2: Using sort_values() for last occurrence scenarios

If you need to keep the last occurrence instead of the first:

# Sort in reverse order and keep first (which will be the last in original)
result = df.sort_values("email", ascending=False).drop_duplicates(subset="email").sort_index()

Method 3: For index-specific duplicates

If you’re dealing with duplicate index values specifically:

# Remove rows with duplicate indices, keeping first occurrence
result = df[~df.index.duplicated(keep="first")]

This method was suggested in Stack Overflow as a way to “drop all rows with duplicate index except the first occurrence.”

Handling Complex Scenarios

Multiple columns for duplicate detection

If you need to consider multiple columns when identifying duplicates:

# Drop duplicates based on multiple columns
result = df.drop_duplicates(subset=["email", "id"], keep="first")

Preserving specific data types or additional information

When working with more complex DataFrames, you might want to preserve additional information:

# Keep the first occurrence but also count duplicates
df_with_count = df.copy()
df_with_count["duplicate_count"] = df.groupby("email")["email"].transform("count")
result = df.drop_duplicates(subset="email", keep="first")

Time-based duplicate removal

For time series data, you might want to keep the most recent entry:

# Assuming you have a timestamp column
result = df.sort_values("timestamp").drop_duplicates(subset="email", keep="last")

Performance Considerations

For large datasets, performance can be a concern. Here are some performance considerations:

• drop_duplicates() is generally the most efficient method for simple duplicate removal
• groupby() with first() can be more flexible but may be slower for very large datasets
• Boolean indexing with duplicated() offers good performance and flexibility

# Performance comparison for large datasets
import time

# Method 1: drop_duplicates
start = time.time()
result1 = df.drop_duplicates(subset="email", keep="first")
time1 = time.time() - start

# Method 2: groupby
start = time.time()
result2 = df.groupby("email", as_index=False).first()
time2 = time.time() - start

# Method 3: duplicated with boolean indexing
start = time.time()
result3 = df[~df["email"].duplicated(keep="first")]
time3 = time.time() - start

print(f"drop_duplicates: {time1:.4f}s")
print(f"groupby: {time2:.4f}s")
print f"boolean indexing: {time3:.4f}s")

Best Practices

1. Always verify your results: After removing duplicates, check that you got the expected outcome:

print(f"Original rows: {len(df)}")
print(f"After deduplication: {len(result)}")
print(f"Duplicates removed: {len(df) - len(result)}")

2. Consider creating a backup: Before performing deduplication, especially on important data:

df_backup = df.copy()

3. Document your deduplication logic: Add comments to explain why and how you’re removing duplicates.

4. Handle edge cases: Consider what to do with completely empty rows or rows with NaN values.

5. Test with sample data: Always test your deduplication logic on a sample before applying it to the full dataset.

The key insight is that pandas’ drop_duplicates() method is designed to preserve the original order by default when using keep='first'. If you’re experiencing issues with order preservation, it’s likely due to how you’re handling the results or additional operations being performed on the DataFrame.

Sources

1. pandas.DataFrame.drop_duplicates — pandas 2.3.3 documentation
2. pandas.Series.drop_duplicates — pandas 2.3.3 documentation
3. Remove pandas rows with duplicate indices - Stack Overflow
4. Pandas.Index.drop_duplicates() Explained
5. Drop duplicates in pandas DataFrame
6. Pandas Drop Duplicate Rows - drop_duplicates() function | DigitalOcean

Conclusion

To summarize the key points for keeping the first occurrence of repeated values in pandas:

1. Use drop_duplicates(subset="email", keep="first") - This is the most straightforward and efficient method that preserves both order and index by default.

2. For more control, use boolean indexing - df[~df["email"].duplicated(keep="first")] gives you explicit control over the selection process.

3. Consider groupby().first() for scenarios where you need more complex grouping logic or want to preserve additional information from the first occurrence.

4. Always verify your results by checking the row count and comparing with your expectations.

5. Handle edge cases such as empty rows, NaN values, and completely duplicate rows appropriately.

The pandas library is well-designed to handle duplicate removal while preserving order, so you shouldn’t need to worry about losing the original order when using these methods correctly. If you do encounter order preservation issues, double-check that you’re not inadvertently sorting or reordering your DataFrame in other parts of your code.