python dictionary to create new data frame column

Question

I created a dictionary consisting of unique values from two columns in two separate files. These two columns have equivalent values that are formatted differently and therefore will not merge correctly.

dataframe A:

A  B
1  dfg 
2  srg
3  sgf
4  sfh
3  srg
6  srg
1  sfg

d={1: 1.102832,
   2: 2.102832,
   3: 3.102832,
   4: 4.102832,
   5: 5.102832,
   6: 6.102832,
   7: 7.102832}

Final product should look like this:

A  B     C
1  dfg  1.102832
2  srg  2.102832
3  sgf  3.102832
4  sfh  4.102832
3  srg  3.102832
6  srg  6.102832
1  sfg  1.102832

I attempted to use pandas.Series.map as follows:

s["C"]=s["A"].map(dictionary)

Unfortunately my result looks like this:

A  B     C
1  dfg  NaN
2  srg  NaN
3  sgf  NaN
4  sfh  NaN
3  srg  NaN
6  srg  NaN
1  sfg  NaN

What am I missing?

Answer 1

for key,value in d.iteritems():
    A.ix[A['A']==key,'C'] = value 

Assuming your first dataframe is called 'A' en your dictionary is called 'd'

Answer 2

In theory what you tried should work. However, you have to pay careful attention to the types of the values in s['A'] and the keys in d lest they do not compare equally. For example, if s['A'] contains strings, while d.keys() contains ints, then

import pandas as pd
d = {1: 1.102832,
     2: 2.102832,
     3: 3.102832,
     4: 4.102832,
     5: 5.102832,
     6: 6.102832,
     7: 7.102832}

s = pd.DataFrame({
    'A':'1 2 3 4 3 6 1'.split(),
    'B':'dfg srg sgf sfh srg srg sfg'.split()})

s['C'] = s['A'].map(d)
print(s)

yields

   A    B   C
0  1  dfg NaN
1  2  srg NaN
2  3  sgf NaN
3  4  sfh NaN
4  3  srg NaN
5  6  srg NaN
6  1  sfg NaN

whereas if you convert s['A'] to numerical values:

s['A'] = pd.to_numeric(s['A'], errors='coerce')
s['C'] = s['A'].map(d)
print(s)

then you get the desired result:

   A    B         C
0  1  dfg  1.102832
1  2  srg  2.102832
2  3  sgf  3.102832
3  4  sfh  4.102832
4  3  srg  3.102832
5  6  srg  6.102832
6  1  sfg  1.102832