Description
Code Sample, a copy-pastable example if possible
In:
M = pd.DataFrame([
[1, 'a', 'A'],
[1, 'b', 'B'],
[1, 'c', None]],
columns=['x', 'y', 'z'])
P = M.pivot_table(values='x', index='y', columns='z', aggfunc='sum', fill_value=0, margins=True, dropna=False)
P
Out:
| z | A | B | All |
|---|---|---|---|
| y | |||
| a | 1.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 3.0 |
Expected Output
Either
| z | A | B | All |
|---|---|---|---|
| y | |||
| a | 1.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 2.0 |
or
| z | A | B | None | All |
|---|---|---|---|---|
| y | ||||
| a | 1.0 | 0.0 | 0.0 | 1.0 |
| b | 0.0 | 1.0 | 0.0 | 1.0 |
| c | 0.0 | 0.0 | 1.0 | 1.0 |
| All | 1.0 | 1.0 | 1.0 | 3.0 |
depending on dropna's meaning for this case
output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.2.0-36-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: 1.3.1
pip: 8.1.2
setuptools: 22.0.5
Cython: 0.20.1post0
numpy: 1.11.0
scipy: 0.17.1
statsmodels: None
xarray: None
IPython: 4.2.0
sphinx: None
patsy: 0.4.1
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: 2.6.0
matplotlib: 1.5.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.5.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: None