Replacing multiple columns (or just one) with iloc does not work

[mitar]

Code Sample, a copy-pastable example if possible

import pandas

columns = pandas.DataFrame({'a2': [11, 12, 13], 'b2': [14, 15, 16]})
inputs = pandas.DataFrame({'a1': [1, 2, 3], 'b1': [4, 5, 6], 'c1': [7, 8, 9]})

inputs.iloc[:, [1]] = columns.iloc[:, [0]]

print(inputs)

Problem description

I have a code which is replacing a set of columns with another set of columns, based on column indices. To make things done without a special case, I assumes I could just use iloc to both select and set columns in a DataFrame. But it seems that this not work and fails in strange ways.

Expected Output

   a1  b1  c1
0   1  11   7
1   2  12   8
2   3  13   9

But in reality, you get:

    a1  b1   c1
0  1.0 NaN  7.0
1  2.0 NaN  8.0
2  3.0 NaN  9.0

See how values converted to float and how column is NaNs?

But, if I do the following I get expected results:

inputs.iloc[:, [1]] = [[11], [12], [13]]

This also works:

inputs.iloc[:, [1]] = columns.iloc[:, [0]].values

So if it works with lists and ndarrays, one would assume it would also work with DataFrames themselves. But it does not.

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-46-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.23.3
pytest: None
pip: 18.0
setuptools: 40.0.0
Cython: None
numpy: 1.15.0
scipy: None
pyarrow: None
xarray: None
IPython: None
sphinx: None
patsy: None
dateutil: 2.7.3
pytz: 2018.5
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: None
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[dahlbaek]

I believe the problem is that the column names do not coincide. The following two versions both work on my machine:

import pandas

columns = pandas.DataFrame({'b1': [11, 12, 13], 'b2': [14, 15, 16]})
inputs = pandas.DataFrame({'a1': [1, 2, 3], 'b1': [4, 5, 6], 'c1': [7, 8, 9]})
inputs.iloc[:, [1]] = columns.iloc[:, [0]]
print(inputs)

import pandas

columns = pandas.DataFrame({'a2': [11, 12, 13], 'b2': [14, 15, 16]})
inputs = pandas.DataFrame({'a1': [1, 2, 3], 'b1': [4, 5, 6], 'c1': [7, 8, 9]})
inputs.iloc[:, [1]] = columns.iloc[:, [0]].values
print(inputs)

[dahlbaek]

Check out the big red warning in the docs. Basically, pandas is trying to set the 'b1' column of inputs to the value of the 'b1' column of columns, not finding any data there. Passing in .values forces pandas to take whatever values are passed in the given order.

[mitar]

I can understand that this happens with .loc, but how can it happen with .iloc? .iloc should be position-based so it should not care about column names, no?

[dahlbaek]

Here's my understanding: .iloc will locate by position, but it will not discard the column names in its output. Compare

print(columns.iloc[:, [0]])

to

print(columns.iloc[:, [0]].values)

[mitar]

Sure, but there is difference between columns.iloc[:, [0]] and columns.iloc[:, [0]] = .... The first is a getter, the second one is a setter. A different method on DataFrame object is called. What I would like to argue in this issue is that it is really surprising to an user that when operating with iloc column names are taken into the account at all. My hope is really that using iloc I can use DataFrame like I am used from numpy, based on indices.

I worry that going through .values is adding unnecessary dtype conversions.

[dahlbaek]

Hmm, so there is really two independent issues here?

1. Conversions happening in columns that were not selected. Looks like a bug?
2. Whether setting with .iloc should ignore column names.

[jorisvandenbossche]

I can understand that this happens with .loc, but how can it happen with .iloc? .iloc should be position-based so it should not care about column names, no?

Yes, in principle iloc should not care about column names, so this certainly seems a bug to me.

[jorisvandenbossche]

Related issues: #12991

[dahlbaek]

Seems that .iloc works as expected if you pass a single index instead of a length-1 list:

Input:

columns = pandas.DataFrame({'a2': [11, 12, 13], 'b2': [14, 15, 16]})
inputs = pandas.DataFrame({'a1': [1, 2, 3], 'b1': [4, 5, 6], 'c1': [7, 8, 9]})
inputs.iloc[:, 1] = columns.iloc[:, 0]
print(inputs)

Output:

   a1  b1  c1
0   1  11   7
1   2  12   8
2   3  13   9

[mitar]

Seems that .iloc works as expected if you pass a single index instead of a length-1 list:

Not really. ;-) But that is another bug: #22036