👀 Views: 48 💬 Answers: 1 📅 Created: 2025-08-30

pandas dataframe filtering python

I'm trying to configure I'm stuck on something that should probably be simple. I'm trying to create a dynamic filtering function for a Pandas DataFrame that allows users to specify multiple conditions for filtering rows. Ideally, I want to accommodate various column types and handle cases where some columns are optional in the filter criteria. For example, I have a DataFrame with the following structure: ```python import pandas as pd data = { 'name': ['Alice', 'Bob', 'Charlie', 'David'], 'age': [24, 30, 22, 35], 'city': ['New York', 'Los Angeles', 'New York', 'Chicago'], 'salary': [70000, 80000, 120000, 90000] } df = pd.DataFrame(data) ``` I want to implement a function like this: ```python def dynamic_filter(df, **kwargs): # implementation goes here ``` Where `kwargs` could include different filters, such as `age`, `city`, or `salary`. So, for instance, if a user wants to filter by `age` greater than 25 and from `New York`, I want the function to return only the relevant rows. However, I’m running into issues when trying to apply multiple conditions, especially when some conditions may not be provided. Here's what I have tried: ```python filtered_df = df if 'age' in kwargs: filtered_df = filtered_df[filtered_df['age'] > kwargs['age']] if 'city' in kwargs: filtered_df = filtered_df[filtered_df['city'] == kwargs['city']] ``` This works for single filters, but how can I extend this to handle any number of filters dynamically? Additionally, I want to ensure that if no filters are provided, the original DataFrame is returned. I also tried using `query()` method, but I found it less flexible because of the need to format the string for conditions. I would really appreciate any insights or best practices on how to effectively implement this filtering logic without running into performance issues or complex code. This is part of a larger web app I'm building. Am I missing something obvious? My team is using Python for this web app.