Pandas: Not working set_index with drop

thx, it looks to be a bug. if input is a Series sliced from original, corresponding column should be dropped.

works fine if we pass column name.

dtf.set_index('DATE_TIME', drop=True, inplace=True)
dtf.columns
# Index(['A'], dtype='object')

not a bug - this violates the guarantees of set_index

it's not valid to pass an actual column here -

its not the same as actually assigning the index

there is a PR where try to make this work - but it's inherently ambiguous

not even sure you could warn about this
(though it IS an error to use inplace and drop I think)

not a bug - this violates the guarantees of set_index

Could you elaborate what guarantee that is of set_index? I find it confusing if I specifically use drop=True and get no error when for some reason dropping is not allowed or possible.

@michaelaye

when you pass a list for the keys, it is by-definition setting the index. However, one possibly could think that [58] is the actual result of [57].

In [55]: df = pd.DataFrame({'A':range(2),'B':range(2),'C':range(2)})

In [56]: df
Out[56]: 
   A  B  C
0  0  0  0
1  1  1  1

In [57]: df.set_index(['A','B'])
Out[57]: 
     C
A B   
0 0  0
1 1  1

In [58]: df.index=['A','B']

In [59]: df
Out[59]: 
   A  B  C
A  0  0  0
B  1  1  1

In [54]: DataFrame.set_index?
Signature: DataFrame.set_index(self, keys, drop=True, append=False, inplace=False, verify_integrity=False)
Docstring:
Set the DataFrame index (row labels) using one or more existing
columns. By default yields a new object.

Parameters
----------
keys : column label or list of column labels / arrays
drop : boolean, default True
    Delete columns to be used as the new index
append : boolean, default False
    Whether to append columns to existing index
inplace : boolean, default False
    Modify the DataFrame in place (do not create a new object)
verify_integrity : boolean, default False
    Check the new index for duplicates. Otherwise defer the check until
    necessary. Setting to False will improve the performance of this
    method

Examples
--------
>>> indexed_df = df.set_index(['A', 'B'])
>>> indexed_df2 = df.set_index(['A', [0, 1, 2, 0, 1, 2]])
>>> indexed_df3 = df.set_index([[0, 1, 2, 0, 1, 2]])

Returns
-------
dataframe : DataFrame

any plans to fix this?