BUG: iloc.__setitem__ with a numpy array throws a TypeError expecting 1-D list

[devin-petersohn]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• (optional) I have confirmed this bug exists on the master branch of pandas.

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Note: Please read this guide detailing how to provide the necessary information for us to reproduce your bug.

Code Sample, a copy-pastable example

# Your code here
import numpy as np
import pandas as pd

dfx = pd.DataFrame({'a': np.ones(10)}) 
dfx.iloc[np.array([0]), np.array([0])] = np.array([[2]]) # works
dfx['a_I'] = dfx['a'].astype('Int64') 
dfx = dfx[["a_I"]] 
dfx.iloc[np.array([0]), np.array([0])] = np.array([[2]]) # doesn't work

Problem description

The second case should set the value to 2.

Expected Output

I have narrowed the problem down to here:

pandas/pandas/core/indexing.py

Line 1768 in dec736f

indexer = maybe_convert_ix(*indexer)

The dimension of the indexer is being increased in that call.

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.6.8.final.0
python-bits : 64
OS : Darwin
OS-release : 19.3.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.0.3
numpy : 1.18.1
pytz : 2018.5
dateutil : 2.7.3
pip : 19.2.3
setuptools : 42.0.2
Cython : 0.29.10
pytest : 5.4.1
hypothesis : None
sphinx : 2.4.4
blosc : None
feather : 0.4.0
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : 2.8.3 (dt dec pq3 ext lo64)
jinja2 : 2.11.1
IPython : 7.0.1
pandas_datareader: None
bs4 : 4.8.2
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.2.0
numexpr : 2.7.1
odfpy : None
openpyxl : 3.0.3
pandas_gbq : 0.13.1
pyarrow : 0.16.0
pytables : None
pytest : 5.4.1
pyxlsb : None
s3fs : 0.4.0
scipy : 1.4.1
sqlalchemy : 1.3.15
tables : 3.6.1
tabulate : None
xarray : 0.15.0
xlrd : 1.2.0
xlwt : None
xlsxwriter : None
numba : 0.48.0

[jorisvandenbossche]

@devin-petersohn thanks for the report!

The increase in dimensionality of indexer might be a problem as well, but the actual error is coming from trying to coerce the LHS values (np.array([[2]])) to an IntegerArray. The integer array constructor expects a 1D array, not a 2D like that.
So when indexing a dataframe, the 2D array is fine, but when passing this value to Block.setitem we should reduce the dimension of the value.

[CloseChoice]

Actually this happens to more than just the IntegerArray:

dfx = pd.DataFrame({'a': np.ones(10)})
dfx['Categorical'] = pd.Categorical(dfx['a'])
dfx[['Categorical']].iloc[np.array([0]), np.array([0])] = np.array([[3]])

The BooleanArray shares the same checks in the coerce function. We should check all other array types to be consistent here.
Maybe implementing a more general coerce function (coerce2d) is the way to go here. Without using masks (and commenting out the dimensionality checks) the following works:

dfx = pd.DataFrame({'a': np.ones(10)})
dfx['Integer'] = dfx['a'].astype('Int64')
dfx[['Integer']].iloc[np.array([0]), np.array([0])] = np.array([[2]])

and gives the correct result:

     a  Integer 
0  1.0        2 
1  1.0        1 
2  1.0        1 
3  1.0        1 
4  1.0        1 
5  1.0        1 
6  1.0        1 
7  1.0        1 
8  1.0        1 
9  1.0        1 

What do you think @jorisvandenbossche @devin-petersohn ?