ENH: Support mangle_dupe_cols=False in pd.read_csv()

[gfyoung]

#12935 added full support for duplicate column names (in header or in names) by mangling them. While this has been considered acceptable by users, ideally, we would like to not have to mangle them.

[gfyoung]

@jreback : Given what you said in #17060, is this something we should still pursue ?

[jorisvandenbossche]

Depending on how difficult this is, I would personally still have it as our goal to have mangle_dupe_cols=False implemented some time.

[caniko]

What is the ETA on this issue?

[jreback]

when / if a community pull request happens

[gfyoung]

@caniko2 : This is quite a tricky one given that duplicate column names have unusual behavior in pandas. You are more than welcome to submit a PR to implement it if you like.

[jackzhenguo]

Could anyone help me to check whether current pandas 0.24.2 support "mangle_dupe_cols=False"?

I find docs at http://pandas.pydata.org/pandas-docs/stable/user_guide/io.html, showing : Passing in False will cause data to be overwritten if there are duplicate names in the columns.

Thanks so much!

[gfyoung]

Still no support, as behavior of data handling has proven to be quite non-trivial when there are duplicate column names. You are welcome to give it a shot though!

[grisaitis]

Still no support, as behavior of data handling has proven to be quite non-trivial when there are duplicate column names. You are welcome to give it a shot though!

is this issue still difficult to resolve?

[hepcat72]

Since I cannot set it to False and I cannot otherwise check for duplicated using df.columns.duplicated() on the dataframe returned by read_excel, how do I raise an exception when a duplicate is found - because it definitely causes a problem with other code - and the user needs to rectify it.

[Jaakkonen]

The documentation really shouldn't have this option if it in reality doesn't exist. Or the docs should say that this is to-be-implemented (and has been in that state for almost 6 years already)

[phofl]

The argument was removed, so closing