Difference between map, applymap and apply methods in Pandas


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!


apply works on a row / column basis of a DataFrame
applymap works element-wise on a DataFrame
map works element-wise on a Series

Straight from Wes McKinney's Python for Data Analysis book, pg. 132 (I highly recommended this book):

Another frequent operation is applying a function on 1D arrays to each column or row. DataFrame’s apply method does exactly this:

In [116]: frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon'])

In [117]: frame Out[117]: b d e Utah -0.029638 1.081563 1.280300 Ohio 0.647747 0.831136 -1.549481 Texas 0.513416 -0.884417 0.195343 Oregon -0.485454 -0.477388 -0.309548

In [118]: f = lambda x: x.max() - x.min()

In [119]: frame.apply(f) Out[119]: b 1.133201 d 1.965980 e 2.829781 dtype: float64

Many of the most common array statistics (like sum and mean) are DataFrame methods, so using apply is not necessary.

Element-wise Python functions can be used, too. Suppose you wanted to compute a formatted string from each floating point value in frame. You can do this with applymap:

In [120]: format = lambda x: '%.2f' % x

In [121]: frame.applymap(format) Out[121]: b d e Utah -0.03 1.08 1.28 Ohio 0.65 0.83 -1.55 Texas 0.51 -0.88 0.20 Oregon -0.49 -0.48 -0.31

The reason for the name applymap is that Series has a map method for applying an element-wise function:

In [122]: frame['e'].map(format)
Utah       1.28
Ohio      -1.55
Texas      0.20
Oregon    -0.31
Name: e, dtype: object

Create a Pandas Dataframe by appending one row at a time

How do I get the row count of a Pandas DataFrame?