pd.Series.map never maps NAs through a dictionary

[Kodiologist]

Code Sample

import pandas as pd, numpy as np
print(pd.Series([1, 2, np.nan]).map({1: "a", 2: "b", np.nan: "c"}))

This prints:

0      a
1      b
2    NaN
dtype: object

Problem description

The parameter na_action of pd.Series.map is ignored if the first argument of map is a dictionary. Rather than mapping NAs if na_action is None and passing them through unaltered if it's "ignore", pandas always passes NAs through unaltered, as if na_action were "ignore". (The default is None.)

Expected Output

0      a
1      b
2      c
dtype: object

Output of pd.show_versions()

commit: None
python: 3.6.1.final.0
python-bits: 64
OS: Linux
OS-release: 4.10.0-33-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.20.3
pytest: 3.0.7
pip: 9.0.1
setuptools: 36.0.1
Cython: 0.25.2
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: None
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: None
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.5
s3fs: None
pandas_gbq: None
pandas_datareader: None

[jreback]

np.nan is a bit odd to use as a dictionary key. any reason you don't want to use .fillna()?

e.g.

In [9]: s.map(pd.Series({1: "a", 2: "b", np.nan: "c"})).fillna('c')
Out[9]: 
0    a
1    b
2    c
dtype: object

# or replace
In [12]: s.replace({1: "a", 2: "b", np.nan: "c"})
Out[12]: 
0    a
1    b
2    c
dtype: object

[Kodiologist]

In my case, I have some series I got out of a Stata dataset that I'm turning into Categoricals based on a codebook. What appears as an NA in the original series is often given a descriptive label by the codebook, analogously to the non-missing values. And I want to preserve the order of categories and the presence of categories with no corresponding values. So I wrote

  v = v.map(codes).astype("category", categories = val_labels)

But replace seems to work fine, so I'll use that instead. Thanks. (The documentation for a dict to_replace in Series.replace looks weird, by the way. Perhaps it was incorrectly copied from DataFrame.replace.)

[jreback]

@Kodiologist ok, so pls submit a PR for the docs issue. always best from a user perspective.

I'll mark this as a bug in any event, its a bit tricky though. welcome to have a look.

[jreback]

xref #14210

[mroeschke]

Looks to work on master. Could use a test.

In [59]: print(pd.Series([1, 2, np.nan]).map({1: "a", 2: "b", np.nan: "c"}))
    ...:
0    a
1    b
2    c
dtype: object

In [60]: pd.__version__
Out[60]: '0.26.0.dev0+593.g9d45934af'