{"id":200684,"date":"2024-11-10T08:03:09","date_gmt":"2024-11-10T08:03:09","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/how-to-filter-rows-based-on-column-value-pandas\/"},"modified":"2024-11-10T08:03:09","modified_gmt":"2024-11-10T08:03:09","slug":"how-to-filter-rows-based-on-column-value-pandas","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-to-filter-rows-based-on-column-value-pandas\/","title":{"rendered":"How to filter rows based on column value Pandas?"},"content":{"rendered":"

In Pandas, you can filter rows based on column value by using boolean indexing. This involves creating a boolean mask that selects rows based on the condition you specify.<\/strong> Here’s a step-by-step guide on how to do it:<\/p>\n

1. Load the Pandas library: First, you need to import the Pandas library to use its functions.
\n2. Load your dataset: Use the Pandas `read_csv()` function to load your dataset into a DataFrame.
\n3. Create a boolean mask: Define a condition that you want to filter your rows on. For example, `mask = df[‘column_name’] > value` will create a boolean mask that selects rows where the column value is greater than `value`.
\n4. Apply the boolean mask: Use the boolean mask to filter the rows in your DataFrame. For example, `filtered_df = df[mask]` will create a new DataFrame containing only the rows that meet the condition.<\/p>\n

By following these steps, you can easily filter rows based on column value in Pandas.<\/p>\n

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