I have a DataFrame like this:
A B
----------
c d
e f
I'd like to introduce a third column, made up of a concatenation of A, B and the index, so that the DataFrame becomes:
A B C
---------------
c d cd0
e f ef1
I'd like to do that like so:
df['C'] = df['A'] + df['B'] + # and here I don't know how to reference the row index.
How can I do this?
Option 1
For better scalability, use assign + agg:
df['C'] = df.assign(index=df.index.astype(str)).agg(''.join, 1)
df
A B C
0 c d cd0
1 e f ef1
Or, using np.add.reduce in a similar fashion:
df['C'] = np.add.reduce(df.assign(index=df.index.astype(str)), axis=1)
df
A B C
0 c d cd0
1 e f ef1
Option 2
A less scalable option using vectorised string concatenation:
df['C'] = df['A'] + df['B'] + df.index.astype(str)
df
A B C
0 c d cd0
1 e f ef1
With pd.DataFrame.itertuples
Python 3.6
df.assign(C=[f'{a}{b}{i}' for i, a, b in df.itertuples()])
A B C
0 c d cd0
1 e f ef1
With pd.Series.str.cat
df.assign(C=df.A.str.cat(df.B).str.cat(df.index.astype(str)))
A B C
0 c d cd0
1 e f ef1
Mish/Mash
from operator import add
from functools import reduce
from itertools import chain
df.assign(C=reduce(add, chain((df[c] for c in df), [df.index.astype(str)])))
A B C
0 c d cd0
1 e f ef1
Summation
df.assign(C=df.sum(1) + df.index.astype(str))
A B C
0 c d cd0
1 e f ef1
df['C'] = df['A'].astype(str) + df['B'].astype(str) + np.array(map(str, df.index.values))
Basically you access the df index with df.index, and to turn that into a numpy array you add the .values, and to convert that into a string (to easily add to the previous columns, which are strings), you can use a map function.
Edit: added .astype(str) to columns A and B, to convert them to strings. If they are already strings, this won't be necessary.
TypeError: must be str, not map
astype when performing type conversions in numpy/pandas. Also, your code works only in python2 only, unless you collect the output of map to a list.