Map dataFrame values to another DataFrame

Question

I have these two dataFrames

data1 = [[1,'A'],[2,'B'],[3,'C'],[4,'D'],[5,'E']]
data2 = [1,1,1,1,2,5,4,3]
df1 = pd.DataFrame(data1,columns = ['one','two'])
df2 = pd.DataFrame(data2,columns = ['one'])

I want to map all values of df2 of column one with df1 of column two. In simple terms i want to use df1 as a dictionary. I want output like this for df2

   one
0    A
1    A
2    A
3    A
4    B
5    E
6    D
7    C
  

I am doing this

df2['one']= df2['one'].apply(lambda x: df1.two[df1.one == x])

Which gives me output

   one
0    A
1    A
2    A
3    A
4  NaN
5  NaN
6  NaN
7  NaN

All A is correct but why latter all are NaN?

Answer 1

Try this, much better syntax and functionality over using apply with a lambda function:

df2['one'].map(df1.set_index('one')['two'])

Output:

0    A
1    A
2    A
3    A
4    B
5    E
6    D
7    C
Name: one, dtype: object

---

Why your method doesn't work.... Look at the output of :

df2['one'].apply(lambda x: df1.two[df1.one == x])

Output:

     0    1    2    3    4
0    A  NaN  NaN  NaN  NaN
1    A  NaN  NaN  NaN  NaN
2    A  NaN  NaN  NaN  NaN
3    A  NaN  NaN  NaN  NaN
4  NaN    B  NaN  NaN  NaN
5  NaN  NaN  NaN  NaN    E
6  NaN  NaN  NaN    D  NaN
7  NaN  NaN    C  NaN  NaN

Because of index alignment in pandas only the first column, 0. get assigned. Here, you are using pd.Series.apply where you are applying the lambda function over the elements of a series and assigning it back to a dataFrame causing your mal-alignment issues.

Answer 2

dict df1 columns and map to df2.

df2.one=df2.one.map(dict(zip(df1.one,df1.two)))

  one
0   A
1   A
2   A
3   A
4   B
5   E
6   D
7   C