Lambda functions are just functions but typically very simple ones. It's the fact that makes them really easy to declare that makes them extremely expressive as well and in this series of videos we'd like to demonstrate not just how they work but also why they're nice to reason about.
Note that for this code to run you need to install both numpy and pandas beforehand.
import numpy as np import pandas as pd df = pd.DataFrame(np.random.normal(0, 1, (10, 2))) df.columns = ['column_a', 'column_b'] df.loc[lambda d: d['column_b'] > 0]
Note that again here the
loc tells us what we're going to be
taking a subset of the original dataframe while the
tell us how we're going to select which rows can stay. It can be
somewhat complicated to fully appreciate how this works
under the hood though. We'll add a chapter on method chains in the future
to dive more in depth into this phenomenon.
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