Python: Sorting, Filtering, and Grouping in Pandas

1. df[df[col] > 0.5] - Rows where 'col' is greater than 0.5

• Use boolean indexing to filter rows where a specific column (‘col’) meets a certain condition (greater than 0.5).

2. df[(df[col] > 0.5) & (df[col] < 0.7)] - Rows where 0.5 < 'col' < 0.7

• Use logical operators (& for 'and') to filter rows based on multiple conditions for a specific column ('col').

3. df.sort_values('col1') - Sorts by 'col1' in ascending order

• Use sort_values() to sort the DataFrame by a specific column ('col1') in ascending order by default.

4. df.sort_values('col2', ascending=False) - Sorts by 'col2' in descending order

• Use sort_values() with the ascending parameter to sort the DataFrame by a specific column ('col2') in descending order.

5. df.sort_values(['col1', 'col2'], ascending=[True, False]) - Sorts by 'col1' ascending, 'col2' descending

• Use sort_values() with a list of columns and a corresponding list of sorting orders to sort the DataFrame by multiple columns with different sorting directions.

6. df.groupby('col') - Group by values in one column

• Use groupby() to group rows based on unique values in a specific column ('col').