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Python Pandas copying column from df to another if values ​​are the same

I have two data frames:

DF ONE:

ID     A    B    C
 1     x    y    z
 1     x    y    z
 2     x    y    z
 2     x    y    z
 2     x    y    z
 3     x    y    z

DF TWO:

ID     D    E    F
 1     a    b    c1
 2     a    b    c2
 3     a    b    c3

I want to take a column E

for example from DF TWO and put it in DF ONE if the id is the same, so after getting this result:

ID     A    B    C    F
 1     x    y    z    c1
 1     x    y    z    c1
 2     x    y    z    c2
 2     x    y    z    c2
 2     x    y    z    c2
 3     x    y    z    c3

thank you for your help

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3 answers


You can use :map

dict 

d = df2.set_index('ID')['F'].to_dict()
print (d)
{1: 'c1', 2: 'c2', 3: 'c3'}

df1['F'] = df1['ID'].map(d)
print (df1)
   ID  A  B  C   F
0   1  x  y  z  c1
1   1  x  y  z  c1
2   2  x  y  z  c2
3   2  x  y  z  c2
4   2  x  y  z  c2
5   3  x  y  z  c3

Another solution is map

to Series

:



s = df2.set_index('ID')['F']
print (s)
ID
1    c1
2    c2
3    c3
Name: F, dtype: object

df1['F'] = df1['ID'].map(s)
print (df1)
   ID  A  B  C   F
0   1  x  y  z  c1
1   1  x  y  z  c1
2   2  x  y  z  c2
3   2  x  y  z  c2
4   2  x  y  z  c2
5   3  x  y  z  c3

Delay

#[60000 rows x 5 columns]
df1 = pd.concat([df1]*10000).reset_index(drop=True)

In [115]: %timeit pd.merge(df1, df2[['ID', 'F']],how='left')
100 loops, best of 3: 11.1 ms per loop

In [116]: %timeit df1['ID'].map(df2.set_index('ID')['F'])
100 loops, best of 3: 3.18 ms per loop

In [117]: %timeit df1['ID'].map(df2.set_index('ID')['F'].to_dict())
100 loops, best of 3: 3.36 ms per loop

In [118]: %timeit df1['ID'].map({k:v for k, v in df2[['ID', 'F']].as_matrix()})
100 loops, best of 3: 3.44 ms per loop

In [119]: %%timeit 
     ...: df2.index = df2['ID']
     ...: df1['F1'] = df1['ID'].map(df2['F'])
     ...: 
100 loops, best of 3: 3.33 ms per loop
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You need to create maps from df2

and you can do it like this:

mapping = {k:v for k, v in df2[['ID', 'F']].as_matrix()}

Then just apply them to df1

:



df1['F'] = df1['ID'].map(mapping)

Or you can use:

df1 = pd.merge(df1, df2[['ID', 'F']],how='left')
+2


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You can use map

, taking care of setting ID

as an index of your TWO dataframe with df2.index = df2['ID']

:

In [10]: df1['F'] = df1['ID'].map(df2['F'])

In [11]: df1
Out[11]: 
   ID  A  B  C   F
0   1  x  y  z  c1
1   1  x  y  z  c1
2   2  x  y  z  c2
3   2  x  y  z  c2
4   2  x  y  z  c2
5   3  x  y  z  c3
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