Comparing two dataframes of different length row by row and adding columns for each row with equal value

Question

I have two dataframes of different length in python pandas like this:

df1:                                 df2:

      Column1  Column2 Column3            ColumnA ColumnB 
    0    1       a       r              0    1       a
    1    2       b       u              1    1       d
    2    3       c       k              2    1       e
    3    4       d       j              3    2       r
    4    5       e       f              4    2       w
                                        5    3       y 
                                        6    3       h

What I am trying to do now is comparing Column1 of df1 and ColumnA of df2. For each "hit", where a row in ColumnA in df2 has the same value as a row in Column1 in df1, I want to append a column to df1 with the vaule ColumnB of df2 has for the row where the "hit" was found, so that my result looks like this:

df1:

   Column1  Column2  Column3  Column4 Column5  Column6
0     1        a        r        a       d        e
1     2        b        u        r       w
2     3        c        k        y       h
3     4        d        j
4     5        e        f

What I have tried so far was:

for row in df1, df2:
   if df1[Column1] == df2[ColumnA]:
      print 'yey!'

which gave me an error saying I could not compare two dataframes of different length. So I tried:

for row in df1, df2:
    if def2[def2['ColumnA'].isin(def1['column1'])]:
        print 'lalala' 
    else:
        print 'Nope'

Which "works" in terms that I get an output, but I do not think it iterates over the rows and compares them, since it only prints 'lalala' two times. So I researched some more and found a way to iterate over each row of the dataframe, which is:

for index, row in df1.iterrows():
    print row['Column1]

But I do not know how to use this to compare the columns of the two dataframes and get the output I desire.

Any help on how to do this would be really appreciated.

Answer 1

I recommend you to use DataFrame API which allows to operate with DF in terms of join, merge, groupby, etc. You can find my solution below:

import pandas as pd

df1 = pd.DataFrame({'Column1': [1,2,3,4,5], 
    'Column2': ['a','b','c','d','e'], 
    'Column3': ['r','u','k','j','f']})

df2 = pd.DataFrame({'Column1': [1,1,1,2,2,3,3], 'ColumnB': ['a','d','e','r','w','y','h']})

dfs = pd.DataFrame({})
for name, group in df2.groupby('Column1'):
    buffer_df = pd.DataFrame({'Column1': group['Column1'][:1]})
    i = 0
    for index, value in group['ColumnB'].iteritems():
        i += 1
        string = 'Column_' + str(i)
        buffer_df[string] = value

    dfs = dfs.append(buffer_df)

result = pd.merge(df1, dfs, how='left', on='Column1')
print(result)

The result is:

   Column1 Column2 Column3 Column_0 Column_1 Column_2
0        1       a       r        a        d        e
1        2       b       u        r        w      NaN
2        3       c       k        y        h      NaN
3        4       d       j      NaN      NaN      NaN
4        5       e       f      NaN      NaN      NaN

P.s. More details:

1) for df2 I produce groups by 'Column1'. The single group is a data frame. Example below:

   Column1 ColumnB
0        1       a
1        1       d
2        1       e

2) for each group I produce data frame buffer_df:

   Column1 Column_0 Column_1 Column_2
0        1        a        d        e

3) after that I create DF dfs:

   Column1 Column_0 Column_1 Column_2
0        1        a        d        e
3        2        r        w      NaN
5        3        y        h      NaN

4) in the end I execute left join for df1 and dfs obtaining needed result.

2)* buffer_df is produced iteratively:

step0 (buffer_df = pd.DataFrame({'Column1': group['Column1'][:1]})):
            Column1
         5       3

step1 (buffer_df['Column_0'] = group['ColumnB'][5]):      
            Column1 Column_0
         5       3       y

step2 (buffer_df['Column_1'] = group['ColumnB'][5]):      
            Column1 Column_0 Column_1
         5       3       y       h