What's the difference between these 2 methods of Series filtering? (with or without lambda)

Question

I have a data Series called Snow (the amount of snow in different months of the year).

These two lines of code produce the same results (at least seems so!)

So I just wanted to know the difference.

import pandas as pd

snow.loc[(snow.index.month==1) & (snow>0)]
snow.loc[lambda s: (s.index.month==1) & (s>0)]

I edited a bit the question to focus it on the important part (matplotlib is not really relevant there, nor the fact that the dataset is named "Snow") — mozway
– mozway, Commented Apr 25, 2023 at 11:24

mozway · Accepted Answer · 2023-04-25 09:17:49Z

1

There is no difference between the two lines provided you're not running chained commands. Using a function/lambda in loc is a way to ensure that you're referencing the current Series/DataFrame.

It would be different with chained commands.

Example:

snow = pd.Series([0, 1, 0, 1], index=pd.to_datetime(['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))

(snow
 .add(2)
 # here we reference the series independently
 # of the previous chained commands
 .loc[(snow.index.month==1) & (snow>0)]
)

# 2023-01-15    3
# dtype: int64

(snow
 .add(2)
 # here we reference the current state of the Series
 .loc[lambda s: (s.index.month==1) & (s>0)]
)

# 2023-01-01    2
# 2023-01-15    3
# dtype: int64

answered Apr 25, 2023 at 9:17

mozway

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