create multiple columns from 1 column pandas

Question

I am trying to duplicate a column multiple time from a df such as

df.head()

                      close
date                       
2015-09-23 17:00:00  1.3324
2015-09-23 17:01:00  1.3325
2015-09-23 17:02:00  1.3323
2015-09-23 17:03:00  1.3323
2015-09-23 17:04:00  1.3323

from a certain list of name, I want to duplicate that colum as many time as there is name in my list:

list =['a','b','c']

and get

  df.head()

                      close    a     b      c
date                       
2015-09-23 17:00:00  1.3324 1.3324 1.3324 1.3324
2015-09-23 17:01:00  1.3325 1.3325 1.3325 1.3325
2015-09-23 17:02:00  1.3323 1.3323 1.3323 1.3323
2015-09-23 17:03:00  1.3323 1.3323 1.3323 1.3323
2015-09-23 17:04:00  1.3323 1.3323 1.3323 1.3323

I tried

df[list] = df

but columns must be same length as key. Thanks for your help!

Answer 1

The simplest way would be to iterate through your list and create a new column for each key (side note: you should probably avoid using list as the name of a variable, since you'll overwrite the native list):

keys = ['a','b','c']
for k in keys:
    df[k] = df['close']

If you want to do it in one line, without a loop, you could do the following:

keys = ['a','b','c']
df = df.join(pd.concat([df.close]*len(keys), keys=keys))

Moving outwards from the middle, [df.close]*len(keys) creates a list with as many copies of the original dataframe column as there are keys in your list. These are then combined into one dataframe using pd.concat(), with the column names being set with your list (keys=keys). Now that you have a dataframe with your duplicate columns, you can add it to the original dataframe using df.join().

Answer 2

You can use concat:

li = ['a','b','c']

df1 = pd.concat([df['close']]*(len(li)+1), axis=1, keys=['close'] + li)
print (df1)
                      close       a       b       c
date                                               
2015-09-23 17:00:00  1.3324  1.3324  1.3324  1.3324
2015-09-23 17:01:00  1.3325  1.3325  1.3325  1.3325
2015-09-23 17:02:00  1.3323  1.3323  1.3323  1.3323
2015-09-23 17:03:00  1.3323  1.3323  1.3323  1.3323
2015-09-23 17:04:00  1.3323  1.3323  1.3323  1.3323