How to merge dataframes and fill values

user_13886807 0
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I am trying to merge the 2 DataFrames below to get an output where each code is listed on each date and the quantity is filled as 0 if the code was not in the original dataframe on that date. I have put an example of my input and desired output below but my live data will have over a years worth of dates and over 20,000 codes.

Input data:

df1

    date
0   2021-05-03
1   2021-05-04
2   2021-05-05
3   2021-05-06
4   2021-05-07
5   2021-05-08
6   2021-05-09
7   2021-05-10

df2

    date      code  qty
0   2021-05-03  A   2
1   2021-05-06  A   5
2   2021-05-07  A   4
3   2021-05-08  A   5
4   2021-05-10  A   6
5   2021-05-04  B   1
6   2021-05-08  B   4

Desired Output:

date      code  qty
03/05/2021  A   2
03/05/2021  B   0
04/05/2021  A   0
04/05/2021  B   1
05/05/2021  A   0
05/05/2021  B   0
06/05/2021  A   5
06/05/2021  B   0
07/05/2021  A   4
07/05/2021  B   0
08/05/2021  A   5
08/05/2021  B   4
09/05/2021  A   0
09/05/2021  B   0
10/05/2021  A   6
10/05/2021  B   0

I have tried the below merge but the output I get does not seem to be as desired:

df_new = df1.merge(df2, how='left', on='date')


    date      code  qty
0   2021-05-03  A   2.0
1   2021-05-04  B   1.0
2   2021-05-05  NaN NaN
3   2021-05-06  A   5.0
4   2021-05-07  A   4.0
5   2021-05-08  A   5.0
6   2021-05-08  B   4.0
7   2021-05-09  NaN NaN
8   2021-05-10  A   6.0
pandas dataframe merge python
3 Answers

This is better suited for a reindex. You create all combinations, set the index, reindex to all of those combinations, fillna and then reset the index.

import pandas as pd

idx = pd.MultiIndex.from_product([df1.date, df2['code'].unique()],
                                 names=['date', 'code'])

df2 = (df2.set_index(['date', 'code'])
          .reindex(idx)
          .fillna(0, downcast='infer')
          .reset_index())

          date code  qty
0   2021-05-03    A    2
1   2021-05-03    B    0
2   2021-05-04    A    0
3   2021-05-04    B    1
4   2021-05-05    A    0
5   2021-05-05    B    0
6   2021-05-06    A    5
7   2021-05-06    B    0
8   2021-05-07    A    4
9   2021-05-07    B    0
10  2021-05-08    A    5
11  2021-05-08    B    4
12  2021-05-09    A    0
13  2021-05-09    B    0
14  2021-05-10    A    6
15  2021-05-10    B    0
user_43333590

One option with pivot and stack:

(df2.pivot_table(index='date', columns='code', fill_value=0)
    .reindex(df1.date, fill_value=0)
    .stack('code')
    .reset_index()
)

Output:

          date code  qty
0   2021-05-03    A    2
1   2021-05-03    B    0
2   2021-05-04    A    0
3   2021-05-04    B    1
4   2021-05-05    A    0
5   2021-05-05    B    0
6   2021-05-06    A    5
7   2021-05-06    B    0
8   2021-05-07    A    4
9   2021-05-07    B    0
10  2021-05-08    A    5
11  2021-05-08    B    4
12  2021-05-09    A    0
13  2021-05-09    B    0
14  2021-05-10    A    6
15  2021-05-10    B    0
user_42384080

Do a cross-join between df1 and unique vals of code. Then use df.fillna():

In [480]: x = pd.DataFrame(df2.code.unique())

In [483]: y = df1.assign(key=1).merge(x.assign(key=1), on='key').drop('key', 1).rename(columns={0: 'code'})

In [486]: res = y.merge(df2, how='left').fillna(0)

In [487]: res
Out[487]: 
          date code  qty
0   2021-05-03    A  2.0
1   2021-05-03    B  0.0
2   2021-05-04    A  0.0
3   2021-05-04    B  1.0
4   2021-05-05    A  0.0
5   2021-05-05    B  0.0
6   2021-05-06    A  5.0
7   2021-05-06    B  0.0
8   2021-05-07    A  4.0
9   2021-05-07    B  0.0
10  2021-05-08    A  5.0
11  2021-05-08    B  4.0
12  2021-05-09    A  0.0
13  2021-05-09    B  0.0
14  2021-05-10    A  6.0
15  2021-05-10    B  0.0
user_58208140
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