Can you tell me when to use these vectorization methods with basic examples?
I see that map
is a Series
method whereas the rest are DataFrame
methods. I got confused about apply
and applymap
methods though. Why do we have two methods for applying a function to a DataFrame? Again, simple examples which illustrate the usage would be great!
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Answer
Comparing map
, applymap
and apply
: Context Matters
First major difference: DEFINITION
map
is defined on Series ONLYapplymap
is defined on DataFrames ONLYapply
is defined on BOTH
Second major difference: INPUT ARGUMENT
map
acceptsdict
s,Series
, or callableapplymap
andapply
accept callables only
Third major difference: BEHAVIOR
map
is elementwise for Seriesapplymap
is elementwise for DataFramesapply
also works elementwise but is suited to more complex operations and aggregation. The behaviour and return value depends on the function.
Fourth major difference (the most important one): USE CASE
map
is meant for mapping values from one domain to another, so is optimised for performance (e.g.,df['A'].map({1:'a', 2:'b', 3:'c'})
)applymap
is good for elementwise transformations across multiple rows/columns (e.g.,df[['A', 'B', 'C']].applymap(str.strip)
)apply
is for applying any function that cannot be vectorised (e.g.,df['sentences'].apply(nltk.sent_tokenize)
).
Also see When should I (not) want to use pandas apply() in my code? for a writeup I made a while back on the most appropriate scenarios for using apply
(note that there aren’t many, but there are a few— apply is generally slow).
Summarising
Footnotes
map
when passed a dictionary/Series will map elements based on the keys in that dictionary/Series. Missing values will be recorded as NaN in the output.
applymap
in more recent versions has been optimised for some operations. You will findapplymap
slightly faster thanapply
in some cases. My suggestion is to test them both and use whatever works better.
map
is optimised for elementwise mappings and transformation. Operations that involve dictionaries or Series will enable pandas to use faster code paths for better performance.
Series.apply
returns a scalar for aggregating operations, Series otherwise. Similarly forDataFrame.apply
. Note thatapply
also has fastpaths when called with certain NumPy functions such asmean
,sum
, etc.