Deep Dive into Pandas query() for Powerful DataFrame Filtering

The tutorial introduces the query function of the Pandas library as a concise way to filter DataFrames, especially when many conditions are involved.

It starts by importing the dataset with import pandas as pd and shows the basic syntax: df.query("Quantity == 95"), which returns rows where the expression evaluates to true.

Multiple conditions are combined using logical operators and and or, e.g., df.query("Quantity == 95 and `UnitPrice(USD)` == 182"). Column names containing special characters must be wrapped in back‑ticks.

The article demonstrates negation with df.query("not(Quantity == 95)") and shows how to use other comparison operators ( !=, >, <, ≥, ≤).

Text filtering is performed by comparing string literals, for example df.query("Status == 'Not Shipped'"). The or keyword can be replaced by the pipe symbol |.

Mathematical calculations are allowed inside the query string, such as df.query("Shipping_Cost*2 < 50") or more complex expressions like df.query("Quantity**2 + Shipping_Cost**2 < 500"). Built‑in Python functions (e.g., sqrt, abs) can also be used: df.query("sqrt(UnitPrice) > 15").

For datetime columns, the column must be of type dateTime64[ns]. After converting strings with pd.to_datetime, the dt accessor enables filtering, e.g., df.query("OrderDate.dt.month == 8") or by date range

df.query("OrderDate >= '2021-08-15' and OrderDate <= '2021-08-31'")

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The function returns a new DataFrame by default; setting inplace=True modifies the original object, which should be used with caution.

Finally, the article encourages readers to practice the query() method to become fluent in DataFrame filtering.