Pandas get rows which are NOT in other dataframe

Exploring different methods

Comparing dataframes can be tricky. Here we explore different methods to retrieve unique records, ensuring data alignment, column names, and data types correspond.

Level Up: using merge

When more firepower is needed, merge with the indicator parameter. Ideal for dealing with large datasets and heterogeneous content.

comparison_df = df1.merge(df2, how='left', on='key_column', indicator=True)
exclusive_df1 = comparison_df[comparison_df['_merge'] == 'left_only'].drop('_merge', axis=1)
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Duplicate alerts! 🚨 Duplicate keys in df2 could tweak the uniqueness.

Indexes not aligned? No problem!

If indices are playing hide and seek, count on the isin combo with a negation operator ~.

exclusive_df1 = df1[~df1['key_column'].isin(df2['key_column'])]
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Wait, check for a matching 'key_column' and same data type first. 💁‍♂️

Data types non-matching? We got you!

Data type discrepancies in key columns can cause a hiccup. Verify and sync them by converting if needed.

df1['key_column'] = df1['key_column'].astype(df2['key_column'].dtype)
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For massive datasets

When you are dealing with Moby Dick-size datasets:

• Anchor index with set_index.
• Use MultiIndex.from_tuples for complex indices.

Post-operation cleanup

Post filtering, housekeeping is essential. Remove any temporary columns or indexes. A clean work environment promotes accurate results. 🧹

exclusive_df1 = exclusive_df1.reset_index(drop=True)
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Mind the gap: managing discrepancies

While the main act holds the spotlight, the side performers add the critical touch. Let's dive deeper into some edge cases and best practices.

Beware the clones: duplicate rows

A clone army in df2 may impact results. We need to ensure a row's uniqueness is based on the entire row data, not just a key column. After all, it takes two to tango!

Shell game: handling null values

Null values (NaN) are the magicians of data - they don't equal even themselves! Comparisons involving NaN will always be False, so 'abra-ca-dabra' your nulls away using pd.notna() before making comparisons.

Readability and efficiency: loc and iloc

When it comes to large datasets, loc and iloc are your sweet dreams (or a beautiful nightmare). They make the query more readable and efficient.

exclusive_df1 = df1.loc[~df1['key_column'].isin(df2['key_column'])]  # "Location, location, location!"
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Going down the rabbit hole: multi-indexes

In a world of multi-level indexes and complex filters, take the red pill and venture into the land of advanced indexing:

index = pd.MultiIndex.from_tuples(df1.index.to_list())
exclusive_df1 = df1[~index.isin(df2.index)]  # Feel the power of MultiIndex!
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This offers a more accurate approach to exclude rows.