Panda's DataFrame - renaming multiple identically named columns

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

I was looking to find a solution within Pandas more than a general Python solution. Column's get_loc() function returns a masked array if it finds duplicates with 'True' values pointing to the locations where duplicates are found. I then use the mask to assign new values into those locations. In my case, I know ahead of time how many dups I'm going to get and what I'm going to assign to them but it looks like df.columns.get_duplicates() would return a list of all dups and you can then use that list in conjunction with get_loc() if you need a more generic dup-weeding action

'''UPDATED AS-OF SEPT 2020'''

cols=pd.Series(df.columns)
for dup in df.columns[df.columns.duplicated(keep=False)]: 
    cols[df.columns.get_loc(dup)] = ([dup + '.' + str(d_idx) 
                                     if d_idx != 0 
                                     else dup 
                                     for d_idx in range(df.columns.get_loc(dup).sum())]
                                    )
df.columns=cols

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

New Better Method (Update 03Dec2019)

This code below is better than above code. Copied from another answer below (@SatishSK):

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

# you just need the following 4 lines to rename duplicates
# df is the dataframe that you want to rename duplicated columns

cols=pd.Series(df.columns)

for dup in cols[cols.duplicated()].unique(): 
    cols[cols[cols == dup].index.values.tolist()] = [dup + '.' + str(i) if i != 0 else dup for i in range(sum(cols == dup))]

# rename the columns with the cols list.
df.columns=cols

df

Output:

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

You could use this:

def df_column_uniquify(df):
    df_columns = df.columns
    new_columns = []
    for item in df_columns:
        counter = 0
        newitem = item
        while newitem in new_columns:
            counter += 1
            newitem = "{}_{}".format(item, counter)
        new_columns.append(newitem)
    df.columns = new_columns
    return df

Then

import numpy as np
import pandas as pd

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

so that df:

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

then

df = df_column_uniquify(df)

so that df:

   blah  blah2  blah3  blah_1  blah_2
0     0      1      2       3       4
1     5      6      7       8       9