What is the difference between FacetGrid.map and FacetGrid.map_dataframe in seaborn?

Question

The seaborn documentation is pretty unclear about the differences, and I can't figure them out. It seems like they have very similar, if not identical, functionality.

seaborn.FacetGrid.map

seaborn.FacetGrid.map_dataframe

What exactly are the differences, and when do you use one vs. the other? The seaborn documentation about map_dataframe says "Unlike the map method, a function used here must “understand” Pandas objects." That is the only difference in the documentation of map_dataframe vs. map. What kind of objects are sent to the function in map then if not a dataframe and why does it matter? I also don't really understand the color argument that the target functions have to accept. What information is in that color argument?

Answer 1

When you use FacetGrid.map(func, "col1", "col2", ...), the function func is passed the values of the columns "col1" and "col2" (an more if needed) as parameters 1 and 2 (args[0], args[1], ...). In addition, the function always receives a keyword argument named color=.

When you use FacetGrid.map_dataframe(func, "col1", "col2", ...), the function func is passed the names "col1" and "col2" (an more if needed) as parameters 1 and 2 (args[0], args[1], ...), and the filtered dataframe as keyword argument data=. In addition, the function always receives a keyword argument named color=.

Maybe this demonstration would help:

N=4
df = pd.DataFrame({'col1': np.random.random(N), 'col2':np.random.random(N), 'cat':np.random.choice([True,False], size=N)})

|    |     col1 |      col2 | cat   |
|---:|---------:|----------:|:------|
|  0 | 0.651592 | 0.631109  | True  |
|  1 | 0.981403 | 0.550882  | False |
|  2 | 0.467846 | 0.997084  | False |
|  3 | 0.119726 | 0.0452547 | False |

• using FacetGrid.map():

code:

def test(*args, **kwargs):
    print(">>> content of ARGS:")
    print(args)
    print(">>> content of KWARGS:")
    print(kwargs)


g = sns.FacetGrid(df, col='cat')
g.map(test, 'col1', 'col2')

output:

>>> content of ARGS:
(1    0.981403
2    0.467846
3    0.119726
Name: col1, dtype: float64, 1    0.550882
2    0.997084
3    0.045255
Name: col2, dtype: float64)
>>> content of KWARGS:
{'color': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765)}
>>> content of ARGS:
(0    0.651592
Name: col1, dtype: float64, 0    0.631109
Name: col2, dtype: float64)
>>> content of KWARGS:
{'color': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765)}

• using map_dataframe

code:

g.map_dataframe(test, 'col1', 'col2')

output:

>>> content of ARGS:
('col1', 'col2')
>>> content of KWARGS:
{'color': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765), 
 'data':        col1      col2    cat
         1  0.981403  0.550882  False
         2  0.467846  0.997084  False
         3  0.119726  0.045255  False}
>>> content of ARGS:
('col1', 'col2')
>>> content of KWARGS:
{'color': (0.12156862745098039, 0.4666666666666667, 0.7058823529411765), 
 'data':        col1      col2   cat
         0  0.651592  0.631109  True}