Filter Pandas DataFrame Based on Index

Filter Pandas DataFrame Based on Index

Filtering a pandas DataFrame based on its index can be done using various methods, depending on the specific filtering criteria. Below are some common examples:

1. Filter by Index Value:

To filter rows by explicit index values, you can use the .loc[] accessor.

import pandas as pd df = pd.DataFrame({ 'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8] }, index=['a', 'b', 'c', 'd']) # Filter rows where index is 'a' or 'd' filtered_df = df.loc[['a', 'd']] print(filtered_df)

2. Filter by Index Position:

To filter rows by their positional index, you can use the .iloc[] accessor.

# Filter first and third rows filtered_df = df.iloc[[0, 2]] print(filtered_df)

3. Filter by Index Range:

For filtering by a range of index values or positions:

# Filter rows with index values between 'b' and 'd' filtered_df = df.loc['b':'d'] print(filtered_df) # Filter rows from position 1 up to (but not including) position 3 filtered_df = df.iloc[1:3] print(filtered_df)

4. Filter Using Boolean Indexing:

You can also filter rows based on a condition applied to the index.

# Filter rows where index is longer than 1 character filtered_df = df[df.index.str.len() > 1] print(filtered_df)

5. Filter Using the isin() Method:

The isin() method is useful when checking for membership in a list.

# Filter rows where the index is in a specified list filtered_df = df[df.index.isin(['a', 'c'])] print(filtered_df)

You can choose the method that best fits your filtering criteria and requirements.

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