Duplicate a Column in Data Frame and Rename It to Another Column Name

Duplicate a column in data frame and rename it to another column name

_{Answered with help of user @thelatemail.}

df = read.table(sep="",
                header=T, 
                text="Name    Age    Rate
                      Aira     23     90
                      Ben      32     98
                      Cat      27     95")

df$Rate2 = df$Rate #create column 'Rate2' and make it equal to 'Rate' (duplicate).

Another option to duplicate, triplicate or 'n plicate':

#use ?replicate function, which replicates elements over vectors and lists. 
n = 3 #replicate 3 new columns
df3 = cbind(df, replicate(n,df$Rate)) #replicate from column "Rate" in the df object
df3 #plot df3 output

   Name Age Rate 1  2  3
1  Aira 23  90   90 90 90
2  Ben  32  98   98 98 98
3  Cat  27  95   95 95 95

Renaming columns in a Pandas dataframe with duplicate column names?

X_R.columns = ['Retail','Cost']

Rename duplicate column name by order in Pandas

You could use an itertools.count() counter and a list expression to create new column headers, then assign them to the data frame.

For example:

>>> import itertools
>>> df = pd.DataFrame([[1, 2, 3]], columns=["Nice", "Nice", "Hello"])
>>> df
   Nice  Nice  Hello
0     1     2      3
>>> count = itertools.count(1)
>>> new_cols = [f"Nice{next(count)}" if col == "Nice" else col for col in df.columns]
>>> df.columns = new_cols
>>> df
   Nice1  Nice2  Hello
0      1      2      3

(Python 3.6+ required for the f-strings)

EDIT: Alternatively, per the comment below, the list expression can replace any label that may contain "Nice" in case there are unexpected spaces or other characters:

new_cols = [f"Nice{next(count)}" if "Nice" in col else col for col in df.columns]

Python - Pandas - Copy column names to new dataframe without bringing data

You could do it like this:

new_df = df.copy()
new_df[['5:10', '6:10', '7:10']] = ''

or more concise:

new_df = df.copy()
new_df[new_df.columns[1:]] = ''

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But why not just create a new dataframe with new_df = df.copy() and then perform your computations without blanking the dataframe? I don't think you need to do that, and it just adds time to the process.

Pandas copy column names from one dataframe to another

Just like you have used columns from the dataframe with column names, you can use values from the dataframe without column names:

new_df_with_col_names = pd.DataFrame(data=no_col_names_df.values, columns=col_names_df.columns)

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In [4]: new_df_with_col_names = pd.DataFrame(data=no_col_names_df, columns=col_names_df.columns)

In [5]: new_df_with_col_names
Out[5]:
   col1  col2  col3
0   NaN   NaN   NaN
1   NaN   NaN   NaN
2   NaN   NaN   NaN

In [6]: new_df_with_col_names = pd.DataFrame(data=no_col_names_df.values, columns=col_names_df.columns)

In [7]: new_df_with_col_names
Out[7]:
   col1  col2  col3
0     1     2     3
1     4     5     6
2     7     8     9

Panda's DataFrame - renaming multiple identically named columns

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

Merging multiple data frames causing duplicate column names

You can do

s = pd.concat([x.set_index('key') for x in df_list],axis = 1,keys=range(len(df_list)))
s.columns = s.columns.map('{0[1]}_{0[0]}'.format)
s = s.reset_index()
s
Out[236]: 
  key   value_0   value_1   value_2   value_3
0   A -1.957968       NaN -0.852135 -0.976960
1   B  1.545932 -0.276838       NaN  0.197615
2   C -2.149727       NaN -0.364382  0.349993
3   D  0.524990 -0.476655       NaN       NaN
4   E       NaN -2.135870  0.798782       NaN
5   F       NaN  1.456544 -0.255705  0.447279

Is there a built in Python/pandas function to rename duplicate columns in a pandas.DataFrame?

but I could not find a handy way to do this to a DataFrame object already

To an existing dataframe we have to resort to some code, there is no builtin;

s = pd.Series(df.columns)
df.columns= df.columns+s.groupby(s).cumcount().replace(0,'').astype(str)

   x  x1  y
0  2   5  1
1  1   9  3
2  4   1  2

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