python - Panda's DataFrame - renaming multiple identically named columns

Question

I have several columns named the same in a df. I need to rename them but the problem is that the df.rename method renames them all the same way. How I can rename the below blah(s) to blah1, blah4, blah5?

df = pd.DataFrame(np.arange(2*5).reshape(2,5))
df.columns = ['blah','blah2','blah3','blah','blah']
df

#     blah  blah2  blah3  blah  blah
# 0   0     1      2      3     4
# 1   5     6      7      8     9

Here is what happens when using the df.rename method:

df.rename(columns={'blah':'blah1'})

#     blah1  blah2  blah3  blah1  blah1
# 0   0      1      2      3      4
# 1   5      6      7      8      9

Answer 1

Starting with Pandas 0.19.0 pd.read_csv() has improved support for duplicate column names

So we can try to use the internal method:

In [137]: pd.io.parsers.ParserBase({'names':df.columns})._maybe_dedup_names(df.columns)
Out[137]: ['blah', 'blah2', 'blah3', 'blah.1', 'blah.2']

This is the "magic" function:

def _maybe_dedup_names(self, names):
    # see gh-7160 and gh-9424: this helps to provide
    # immediate alleviation of the duplicate names
    # issue and appears to be satisfactory to users,
    # but ultimately, not needing to butcher the names
    # would be nice!
    if self.mangle_dupe_cols:
        names = list(names)  # so we can index
        counts = {}

        for i, col in enumerate(names):
            cur_count = counts.get(col, 0)

            if cur_count > 0:
                names[i] = '%s.%d' % (col, cur_count)

            counts[col] = cur_count + 1

    return names