Description
Code Sample, a copy-pastable example if possible
import numpy as np
import pandas as pd
from itertools import combinations
columns_list = [
pd.MultiIndex.from_product([['a'], range(2)]),
pd.MultiIndex.from_product([['a'], np.arange(2.)]),
pd.MultiIndex.from_product([['b'], ['A', 'B']]),
pd.MultiIndex.from_product([['c'], pd.date_range(start='2017', end='2018', periods=2)])]
dfs = [pd.DataFrame(np.zeros((1, len(columns))), columns=columns) for columns in columns_list]
for i, df in enumerate(dfs):
print('dataframe:', i)
print(df)
print()
for i, j in combinations(range(len(dfs)), 2):
print('combining:', i, j)
try:
temp = pd.concat((dfs[i], dfs[j]), axis=1)
print(temp)
except TypeError as ex:
print(repr(ex))
print()Output
dataframe: 0
a
0 1
0 0.0 0.0
dataframe: 1
a
0.0 1.0
0 0.0 0.0
dataframe: 2
b
A B
0 0.0 0.0
dataframe: 3
c
2017-01-01 2018-01-01
0 0.0 0.0
combining: 0 1
a
0.0 1.0 0.0 1.0
0 0.0 0.0 0.0 0.0
combining: 0 2
a b
0 1 A B
0 0.0 0.0 0.0 0.0
combining: 0 3
TypeError("'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.",)
combining: 1 2
a b
0.0 1.0 A B
0 0.0 0.0 0.0 0.0
combining: 1 3
TypeError("'values' is not ordered, please explicitly specify the categories order by passing in a categories argument.",)
combining: 2 3
b c
A B 2017-01-01 00:00:00 2018-01-01 00:00:00
0 0.0 0.0 0.0 0.0
Problem description
I expect that you should be able to combine any MultiIndex of any type, as long as the number of levels matches. It turns out you can combine DatetimeIndex with str, but if you try to combine it with with int or float a baffling TypeError is raised.
At the moment the only workaround I can see is to convert numeric indices to strings.
Expected Output
dataframe: 0
a
0 1
0 0.0 0.0
dataframe: 1
a
0.0 1.0
0 0.0 0.0
dataframe: 2
b
A B
0 0.0 0.0
dataframe: 3
c
2017-01-01 2018-01-01
0 0.0 0.0
combining: 0 1
a
0 1 0.0 1.0
0 0.0 0.0 0.0 0.0
combining: 0 2
a b
0 1 A B
0 0.0 0.0 0.0 0.0
combining: 0 3
a c
0 1 2017-01-01 00:00:00 2018-01-01 00:00:00
0 0.0 0.0 0.0 0.0
combining: 1 2
a b
0.0 1.0 A B
0 0.0 0.0 0.0 0.0
combining: 1 3
a c
0.0 1.0 2017-01-01 00:00:00 2018-01-01 00:00:00
0 0.0 0.0 0.0 0.0
combining: 2 3
b c
A B 2017-01-01 00:00:00 2018-01-01 00:00:00
0 0.0 0.0 0.0 0.0
Output of pd.show_versions()
pandas: 0.23.4
pytest: 3.7.4
pip: 9.0.1
setuptools: 36.3.0
Cython: None
numpy: 1.14.2
scipy: 1.0.0
pyarrow: None
xarray: None
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.5.0
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.4
feather: None
matplotlib: 2.2.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: 4.0.0
bs4: None
html5lib: 0.999999999
sqlalchemy: 1.1.13
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None