Limited rows selection with given column in Pandas | Python

Limited rows selection with given column in Pandas | Python

If you want to select a limited number of rows based on the values in a specific column in a Pandas DataFrame, you can use the .nlargest() or .nsmallest() methods, or you can use traditional indexing with .loc[].

Here's how you can do this:

  1. Using nlargest and nsmallest:

    If you want to select the top n rows based on the values in a specific column:

    import pandas as pd # Sample data data = { 'A': [1, 2, 3, 4, 5], 'B': [5, 4, 3, 2, 1] } df = pd.DataFrame(data) # Select top 3 rows based on column 'A' top_3_A = df.nlargest(3, 'A') print(top_3_A) # Select bottom 3 rows based on column 'A' bottom_3_A = df.nsmallest(3, 'A') print(bottom_3_A)

  2. Using traditional indexing with .loc[]:

    If you want to apply custom conditions or a combination of conditions:

    # Select top 3 rows based on column 'A' top_3_A = df.sort_values(by='A', ascending=False).loc[:2] print(top_3_A) # Select rows where column 'A' is greater than 3 condition_A = df[df['A'] > 3] print(condition_A)

    Note: In the example above, .loc[:2] is used to select the first three rows. This is positional based indexing. If your DataFrame has a custom index, you might want to use .iloc[] instead to ensure you're selecting by position.

Remember, the method you use might depend on the specific task. If you're just trying to get the top or bottom n rows based on a column's values, nlargest and nsmallest are very concise. For more complex selections, traditional indexing might be more appropriate.

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