DOC: applymap calling on first column twice

[uperetz]

Code

import pandas as pd
df=pd.DataFrame([[1,2,3],[4,5,6]])
df.index=['a','b'] #I was suspicious of the index
def bla(x):
    print('Applying to:',x)
    return x+1

df.applymap(bla)

Output

    Applying to: 1
    Applying to: 4
    Applying to: 1
    Applying to: 4
    Applying to: 2
    Applying to: 5
    Applying to: 3
    Applying to: 6
       0  1  2
    a  2  3  4
    b  5  6  7

Problem description

Seems applymap is calling on the first column twice. There is no effect in the end result, just a run-time hit as far as I can tell. This affects functions with side effects, as well as runtime.

Expected Output

    Applying to: 1
    Applying to: 4
    Applying to: 2
    Applying to: 5
    Applying to: 3
    Applying to: 6
       0  1  2
    a  2  3  4
    b  5  6  7

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-8-amd64
machine: x86_64
processor:
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 36.0.1
Cython: None
numpy: 1.12.1
scipy: 0.18.1
statsmodels: None
xarray: None
IPython: 6.1.0
sphinx: None
patsy: None
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.1
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.3
html5lib: 0.999999999
httplib2: 0.9.2
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
boto: None
pandas_datareader: None

[chris-b1]

See the note in the apply docstring - though applymap should also apparently be updated to include this note.

Notes

In the current implementation apply calls func twice on the first column/row to decide whether it can ?> take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will > take effect twice for the first column/row.

[uperetz]

Thanks, I did not think to look at the apply help, guess this answers that.