Merge Dataframes of Different Sizes

Pandas: combine data frames of different sizes

Just perform a left merge on 'product_id' column:

In [12]:

df.merge(df1, on='product_id', how='left')
Out[12]:
   product_id  count_white  total_count
0       12345            4           10
1       23456            7           30
2       34567            1           90

Merge two dataframes of different sizes after a groupby function

We are using inner join to merge both dataframes, since original df has duplicates on merge keys so it was returning duplicate values. drop_duplicates() came in handy to solve that problem.

Code

df_cut.merge(df.drop_duplicates(), on=["COD","TEC","SET", "AZIM"])

Output

    COD        TEC  SET AZIM    STATE   CITY
0   ALAAD_0001  4   1   0       AL      MAC
1   ALAAD_0001  4   2   120     AL      MAC
2   ALAAD_0001  4   3   240     AL      MAC
3   BAPID_0001  2   1   20      BA      SAL
4   BAPID_0001  2   2   100     BA      SAL
5   BAPID_0001  2   3   250     BA      SAL
6   CEMBC_0003  4   1   90      CE      FOR
7   CEMBC_0003  4   2   160     CE      FOR
8   CEMBC_0003  4   3   280     CE      FOR

How to merge two Pandas DataFrames of different size based on condition

Try adding an indicator column to o_type_df:

o_type_df['TypeID'] = 'O'

Then merge left on those columns:

merged = (
    primary_df.merge(o_type_df,
                     left_on=['RCID', 'TypeID'],
                     right_on=['O_ID', 'TypeID'],
                     how='left')
)

merged:

   RCID TypeID   Data   O_ID O_Data
0   777      D  Hello    NaN    NaN
1   777      O    Hey  777.0    Foo
2   778      O    Hey  778.0    Bar
3   779      D  Hello    NaN    NaN

Or with assign:

merged = (
    primary_df.merge(o_type_df.assign(TypeID='O'),
                     left_on=['RCID', 'TypeID'],
                     right_on=['O_ID', 'TypeID'],
                     how='left')
)

merged:

   RCID TypeID   Data   O_ID O_Data
0   777      D  Hello    NaN    NaN
1   777      O    Hey  777.0    Foo
2   778      O    Hey  778.0    Bar
3   779      D  Hello    NaN    NaN

Concatenate two dataframes of different sizes (pandas)

In this case using combine_first

df1.set_index('id').combine_first(df2.set_index('id')).reset_index()
Out[766]: 
   id  metric1  metric2
0   a    123.0      1.0
1   b     22.0      2.0
2   c    356.0      3.0
3   d    412.0      4.0
4   f     54.0      5.0
5   g    634.0      6.0
6   h     72.0      7.0
7   j    812.0      8.0
8   k    129.0      9.0
9   l    110.0     10.0
10  m    200.0     11.0
11  q    812.0      NaN
12  w    110.0      NaN
13  z    129.0      NaN

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