Comparing map, applymap and apply: Context Matters

The major differences are:

Definition

• map is defined on Series only
• applymap is defined on DataFrames only
• apply is defined on both

Input argument

• map accepts dict, Series, or callable
• applymap and apply accept callable only

Behavior

• map is elementwise for Series
• applymap is elementwise for DataFrames
• apply also works elementwise but is suited to more complex operations and aggregation. The behaviour and return value depends on the function.

Use case (the most important difference)

• 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

1. 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.

2. applymap in more recent versions has been optimised for some operations. You will find applymap slightly faster than apply in some cases. My suggestion is to test them both and use whatever works better.

3. 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.

4. Series.apply returns a scalar for aggregating operations, Series otherwise. Similarly for DataFrame.apply. Note that apply also has fastpaths when called with certain NumPy functions such as mean, sum, etc.