Pandas: implementing Efficiently Filtering Rows Based on Multiple Conditions with OR Logic
I'm wondering if anyone has experience with I'm working with a DataFrame in pandas version 1.5.2, and I'm trying to filter rows based on multiple conditions that involve an OR logic. I have a DataFrame that looks like this: ```python import pandas as pd data = { 'Name': ['Alice', 'Bob', 'Charlie', 'David'], 'Age': [25, 30, 35, 40], 'City': ['New York', 'Los Angeles', 'New York', 'Chicago'], } df = pd.DataFrame(data) ``` I want to filter this DataFrame to include rows where the 'City' is either 'New York' or the 'Age' is greater than 30. I tried using the following approach: ```python filtered_df = df[(df['City'] == 'New York') | (df['Age'] > 30)] ``` However, I am surprised to see that it returns the following output: ``` Name Age City 0 Alice 25 New York 2 Charlie 35 New York 3 David 40 Chicago ``` While the output looks correct, I am concerned about performance, especially with a much larger dataset (over a million rows). I've read that using `query()` can sometimes be more efficient, but I'm unsure how to apply it in this situation. Would it provide any performance benefits, and how would I rewrite the above condition using `query()`? Also, are there any best practices I should follow when filtering large DataFrames to ensure optimal performance? Any insights or recommendations would be greatly appreciated! This issue appeared after updating to Python LTS. I'm open to any suggestions.