Ways to filter Pandas DataFrame by column values - GeeksforGeeks

Ask questions Research chat →

https://www.geeksforgeeks.org/ways-to-filter-pandas-dataframe-by-column-values/ · scraped

data science python

Attachments

Scraped Content

— 207 words · 2026-02-14 17:44:37 UTC ·

Excerpt

--- Output: ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/871f1661-80b8-4d0c-ac3b-2adfc6ff4c66/97104ba6-2968-45cd-9819-2676f732c64f/Screenshot1178-660x173.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIAZI2LB46643QLPN27%2F20260214%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260214T174437Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEEaCXVzLXdlc3QtMiJGMEQCIDVrV6O20VUtuQSv07bljgD8Oh%2FWH2JGRLHNYoJtykwSAiAqmHnInZOxpC8YIuPReyCXGT1K1Ww4Kx4HuUIChdP6HCr%2FAwgKEAAaDDYzNzQyMzE4MzgwNSIM%2BoyxiS6UurwT44paKtwD1QyuPTkSb0Mop13S0%2BmvIX3eqktGN05P5q9ivpZwVmW%2BW0jUsZjkfDtxk1I7GQ47%2FXxLFDX7oweiR%2FbLAoO8ncFnJShNurvPIa1WuHftHr%2F6oHzSiZTSMHFNIbQ5bxLOwglwqpDmg7cAxxuGBXuK1K4IKtnx%2FXPl%2BFlC5EuYH5DyVkad%2B9nmrVCdPSUIjAXjBmw93UaT5aAtJanhGnnI8Ys2Wd6mq70DPbl4nWvVpVN%2BxTYG62J4%2FhCzeG%2F1glXW8Zvk9h9ihqr1QI%2Fqum%2FiLtzT9U0K74t0dNXLLDK7XMdArEKKT1pH67pCly%2FlXj422OYaec5OcUE2jaqthViXN9HEgXyOBKcB%2BjBA95zGfRBY5SP1iwB7k0syCMNuW2UakMQ2XHv1
--- Output: ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/871f1661-80b8-4d0c-ac3b-2adfc6ff4c66/97104ba6-2968-45cd-9819-2676f732c64f/Screenshot1178-660x173.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIAZI2LB46643QLPN27%2F20260214%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260214T174437Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEEaCXVzLXdlc3QtMiJGMEQCIDVrV6O20VUtuQSv07bljgD8Oh%2FWH2JGRLHNYoJtykwSAiAqmHnInZOxpC8YIuPReyCXGT1K1Ww4Kx4HuUIChdP6HCr%2FAwgKEAAaDDYzNzQyMzE4MzgwNSIM%2BoyxiS6UurwT44paKtwD1QyuPTkSb0Mop13S0%2BmvIX3eqktGN05P5q9ivpZwVmW%2BW0jUsZjkfDtxk1I7GQ47%2FXxLFDX7oweiR%2FbLAoO8ncFnJShNurvPIa1WuHftHr%2F6oHzSiZTSMHFNIbQ5bxLOwglwqpDmg7cAxxuGBXuK1K4IKtnx%2FXPl%2BFlC5EuYH5DyVkad%2B9nmrVCdPSUIjAXjBmw93UaT5aAtJanhGnnI8Ys2Wd6mq70DPbl4nWvVpVN%2BxTYG62J4%2FhCzeG%2F1glXW8Zvk9h9ihqr1QI%2Fqum%2FiLtzT9U0K74t0dNXLLDK7XMdArEKKT1pH67pCly%2FlXj422OYaec5OcUE2jaqthViXN9HEgXyOBKcB%2BjBA95zGfRBY5SP1iwB7k0syCMNuW2UakMQ2XHv1ZQNBpXPHe%2BXcLo%2BElOpvvkMccAg%2BvO3zN6isvzEdayWF12wDvt%2BPcnBW28r%2ByFMZrnysbc5SQgfpqJAEFoJvdBYGmsh%2FrxPnj3tG%2B6FEPhWxUM4ivPbhPCQ0qcLJDVJ%2B3rvvzVIDnCXc5T8%2BJwsSk4o%2B1X%2B%2FsK8P5HFqThqsVyE6ib7Shgbj4QpinpTKRKsQozZsBfjcLzMae3kU7IIb2spyT5zBvXw2%2FHgvl%2FarDnKWuA7v%2FcQw%2FdHCzAY6pgH5JQ6Y5VnLJAFMubMC5cpGWsK4r6DDqjdXqM60XBvhTvcxYLRYnNvvlRcRfhYr8txZevQspiftn1atfWtpm8u6HXixTX1T%2Bzr%2BrVzjeYAC9ZA5CIjM79Wky8JHIfqK4gAabp6MA1J0nAGfxfoxBAQyoBk3oBoUMHEe%2FuhjQYZo3QNnkBw51CGrtdXTAqZcsgt2baQGIeGEBJ6M9bYFpp0RSYnD%2BhEO&X-Amz-Signature=4617cdb92edddc362638d3b846526a7a48b049be9cb7cf91768a6e8a72a8064e&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject) Method 1: Selecting rows of Pandas Dataframe based on particular column value using ‘>’, ‘=’, ‘=’, ‘<=’, ‘!=’ operator. Example 1: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 75 using [ ]. Output: ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/871f1661-80b8-4d0c-ac3b-2adfc6ff4c66/ecbe954d-d10a-4080-bde5-6cf491fa984e/Screenshot1179-660x179.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIAZI2LB46643QLPN27%2F20260214%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260214T174437Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEEaCXVzLXdlc3QtMiJGMEQCIDVrV6O20VUtuQSv07bljgD8Oh%2FWH2JGRLHNYoJtykwSAiAqmHnInZOxpC8YIuPReyCXGT1K1Ww4Kx4HuUIChdP6HCr%2FAwgKEAAaDDYzNzQyMzE4MzgwNSIM%2BoyxiS6UurwT44paKtwD1QyuPTkSb0Mop13S0%2BmvIX3eqktGN05P5q9ivpZwVmW%2BW0jUsZjkfDtxk1I7GQ47%2FXxLFDX7oweiR%2FbLAoO8ncFnJShNurvPIa1WuHftHr%2F6oHzSiZTSMHFNIbQ5bxLOwglwqpDmg7cAxxuGBXuK1K4IKtnx%2FXPl%2BFlC5EuYH5DyVkad%2B9nmrVCdPSUIjAXjBmw93UaT5aAtJanhGnnI8Ys2Wd6mq70DPbl4nWvVpVN%2BxTYG62J4%2FhCzeG%2F1glXW8Zvk9h9ihqr1QI%2Fqum%2FiLtzT9U0K74t0dNXLLDK7XMdArEKKT1pH67pCly%2FlXj422OYaec5OcUE2jaqthViXN9HEgXyOBKcB%2BjBA95zGfRBY5SP1iwB7k0syCMNuW2UakMQ2XHv1ZQNBpXPHe%2BXcLo%2BElOpvvkMccAg%2BvO3zN6isvzEdayWF12wDvt%2BPcnBW28r%2ByFMZrnysbc5SQgfpqJAEFoJvdBYGmsh%2FrxPnj3tG%2B6FEPhWxUM4ivPbhPCQ0qcLJDVJ%2B3rvvzVIDnCXc5T8%2BJwsSk4o%2B1X%2B%2FsK8P5HFqThqsVyE6ib7Shgbj4QpinpTKRKsQozZsBfjcLzMae3kU7IIb2spyT5zBvXw2%2FHgvl%2FarDnKWuA7v%2FcQw%2FdHCzAY6pgH5JQ6Y5VnLJAFMubMC5cpGWsK4r6DDqjdXqM60XBvhTvcxYLRYnNvvlRcRfhYr8txZevQspiftn1atfWtpm8u6HXixTX1T%2Bzr%2BrVzjeYAC9ZA5CIjM79Wky8JHIfqK4gAabp6MA1J0nAGfxfoxBAQyoBk3oBoUMHEe%2FuhjQYZo3QNnkBw51CGrtdXTAqZcsgt2baQGIeGEBJ6M9bYFpp0RSYnD%2BhEO&X-Amz-Signature=e018b56c5fee02244a04c7adf3a141044cc506cc3218d97e6cbf0f0f5e46b264&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject) Example 2: Selecting all the rows from the given Dataframe in which ‘Percentage’ is greater than 70 using loc[ ]. Output: Method 2: Selecting those rows of Pandas Dataframe whose column value is present in the list using isin() method of the dataframe. Example 1: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using [ ]. Output: Example 2: Selecting all the rows from the given dataframe in which ‘Stream’ is present in the options list using loc[ ]. Output: ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/871f1661-80b8-4d0c-ac3b-2adfc6ff4c66/1d2eb98f-f548-4fbe-8f31-41bbb4334152/Screenshot1180-660x130.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIAZI2LB46643QLPN27%2F20260214%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260214T174437Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEEaCXVzLXdlc3QtMiJGMEQCIDVrV6O20VUtuQSv07bljgD8Oh%2FWH2JGRLHNYoJtykwSAiAqmHnInZOxpC8YIuPReyCXGT1K1Ww4Kx4HuUIChdP6HCr%2FAwgKEAAaDDYzNzQyMzE4MzgwNSIM%2BoyxiS6UurwT44paKtwD1QyuPTkSb0Mop13S0%2BmvIX3eqktGN05P5q9ivpZwVmW%2BW0jUsZjkfDtxk1I7GQ47%2FXxLFDX7oweiR%2FbLAoO8ncFnJShNurvPIa1WuHftHr%2F6oHzSiZTSMHFNIbQ5bxLOwglwqpDmg7cAxxuGBXuK1K4IKtnx%2FXPl%2BFlC5EuYH5DyVkad%2B9nmrVCdPSUIjAXjBmw93UaT5aAtJanhGnnI8Ys2Wd6mq70DPbl4nWvVpVN%2BxTYG62J4%2FhCzeG%2F1glXW8Zvk9h9ihqr1QI%2Fqum%2FiLtzT9U0K74t0dNXLLDK7XMdArEKKT1pH67pCly%2FlXj422OYaec5OcUE2jaqthViXN9HEgXyOBKcB%2BjBA95zGfRBY5SP1iwB7k0syCMNuW2UakMQ2XHv1ZQNBpXPHe%2BXcLo%2BElOpvvkMccAg%2BvO3zN6isvzEdayWF12wDvt%2BPcnBW28r%2ByFMZrnysbc5SQgfpqJAEFoJvdBYGmsh%2FrxPnj3tG%2B6FEPhWxUM4ivPbhPCQ0qcLJDVJ%2B3rvvzVIDnCXc5T8%2BJwsSk4o%2B1X%2B%2FsK8P5HFqThqsVyE6ib7Shgbj4QpinpTKRKsQozZsBfjcLzMae3kU7IIb2spyT5zBvXw2%2FHgvl%2FarDnKWuA7v%2FcQw%2FdHCzAY6pgH5JQ6Y5VnLJAFMubMC5cpGWsK4r6DDqjdXqM60XBvhTvcxYLRYnNvvlRcRfhYr8txZevQspiftn1atfWtpm8u6HXixTX1T%2Bzr%2BrVzjeYAC9ZA5CIjM79Wky8JHIfqK4gAabp6MA1J0nAGfxfoxBAQyoBk3oBoUMHEe%2FuhjQYZo3QNnkBw51CGrtdXTAqZcsgt2baQGIeGEBJ6M9bYFpp0RSYnD%2BhEO&X-Amz-Signature=61b9641300b65af4d24f5e9fc3b7bc8f4f90368e64febb2a92976ab8e98bc3da&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject) Method 3: Selecting rows of Pandas Dataframe based on multiple column conditions using ‘&’ operator. Example1: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using [ ]. Output: ![](https://prod-files-secure.s3.us-west-2.amazonaws.com/871f1661-80b8-4d0c-ac3b-2adfc6ff4c66/5b65af0e-b99b-4e6f-9182-f6c052bf52b2/Screenshot1181.png?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIAZI2LB46643QLPN27%2F20260214%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Date=20260214T174437Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEEEaCXVzLXdlc3QtMiJGMEQCIDVrV6O20VUtuQSv07bljgD8Oh%2FWH2JGRLHNYoJtykwSAiAqmHnInZOxpC8YIuPReyCXGT1K1Ww4Kx4HuUIChdP6HCr%2FAwgKEAAaDDYzNzQyMzE4MzgwNSIM%2BoyxiS6UurwT44paKtwD1QyuPTkSb0Mop13S0%2BmvIX3eqktGN05P5q9ivpZwVmW%2BW0jUsZjkfDtxk1I7GQ47%2FXxLFDX7oweiR%2FbLAoO8ncFnJShNurvPIa1WuHftHr%2F6oHzSiZTSMHFNIbQ5bxLOwglwqpDmg7cAxxuGBXuK1K4IKtnx%2FXPl%2BFlC5EuYH5DyVkad%2B9nmrVCdPSUIjAXjBmw93UaT5aAtJanhGnnI8Ys2Wd6mq70DPbl4nWvVpVN%2BxTYG62J4%2FhCzeG%2F1glXW8Zvk9h9ihqr1QI%2Fqum%2FiLtzT9U0K74t0dNXLLDK7XMdArEKKT1pH67pCly%2FlXj422OYaec5OcUE2jaqthViXN9HEgXyOBKcB%2BjBA95zGfRBY5SP1iwB7k0syCMNuW2UakMQ2XHv1ZQNBpXPHe%2BXcLo%2BElOpvvkMccAg%2BvO3zN6isvzEdayWF12wDvt%2BPcnBW28r%2ByFMZrnysbc5SQgfpqJAEFoJvdBYGmsh%2FrxPnj3tG%2B6FEPhWxUM4ivPbhPCQ0qcLJDVJ%2B3rvvzVIDnCXc5T8%2BJwsSk4o%2B1X%2B%2FsK8P5HFqThqsVyE6ib7Shgbj4QpinpTKRKsQozZsBfjcLzMae3kU7IIb2spyT5zBvXw2%2FHgvl%2FarDnKWuA7v%2FcQw%2FdHCzAY6pgH5JQ6Y5VnLJAFMubMC5cpGWsK4r6DDqjdXqM60XBvhTvcxYLRYnNvvlRcRfhYr8txZevQspiftn1atfWtpm8u6HXixTX1T%2Bzr%2BrVzjeYAC9ZA5CIjM79Wky8JHIfqK4gAabp6MA1J0nAGfxfoxBAQyoBk3oBoUMHEe%2FuhjQYZo3QNnkBw51CGrtdXTAqZcsgt2baQGIeGEBJ6M9bYFpp0RSYnD%2BhEO&X-Amz-Signature=3620aebd519377060389bfd91f016916d58fd14e0f38f8802be374dbf445c599&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject) Example 2: Selecting all the rows from the given Dataframe in which ‘Age’ is equal to 22 and ‘Stream’ is present in the options list using loc[ ]. Output:

Visibility

Visible to everyone

Reading Status

Related Bookmarks

My Note


Saved!

Annotations

Export as Markdown
+ Annotate selection

Add Annotation