Description
Code Sample, a copy-pastable example if possible
import pandas as pd
a = pd.DataFrame({'one':['foo','bar','bar'],'two':[1,2,3],'three':[1,3,4]})
b = pd.DataFrame({'one':['baz','bar','plob'],'two':[2,2,1],'three':[5,3,1]})
a['one'] = a['one'].astype('category')
b['one'] = b['one'].astype('category')
c = a.set_index('one')
d = b.set_index('one')
print(pd.concat([a, b]))
one three two
0 foo 1 1
1 bar 3 2
2 bar 4 3
0 baz 5 2
1 bar 3 2
2 plob 1 1
print(pd.concat([c, d]))
raise TypeError("categories must match existing categories "
TypeError: categories must match existing categories when appending
Problem description
While concatenating categorical data with non-identical categories is now support for data columns by upcasting the data to an appropriate dtype it is still throwing an error when attempting to do the same thing with categorical indexes.
I think it would be convenient for users to implement the same behaviour for data columns and indexes.
Expected Output
print(pd.concat([c, d]))
three two
one
foo 1 1
bar 3 2
bar 4 3
baz 5 2
bar 3 2
plob 1 1
print(pd.concat([c, d]).index.dtype)
dtype('O')
Output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Darwin
OS-release: 15.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_GB.UTF-8
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
pandas: 0.20.3
pytest: 3.2.1
pip: None
setuptools: None
Cython: 0.26
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: None
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.1.0
xlwt: 1.3.0
xlsxwriter: 0.9.8
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None