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In advance: Sorry, the title is a bit fuzzy

PYTHON

I have two tables. In one there are unique names for example 'A', 'B', 'C' and in the other table there is a Time series with months example 10/2021, 11/2021, 12/2021. I want to join the tables now that I have all TimeStemps for each name. So the final data should look like this:

Month Name
10/2021 A
11/2021 A
12/2021 A
10/2021 B
11/2021 B
12/2021 B
10/2021 C
11/2021 C
12/2021 C
Anentropic
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zimon
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2 Answers2

1

If you are only trying to get the cartesian product of the values - you can do it using itertools.product

import pandas as pd
from itertools import product
df1 = pd.DataFrame(list('abcd'), columns=['letters'])
df2 = pd.DataFrame(list('1234'), columns=['numbers'])

df_combined = pd.DataFrame(product(df1['letters'], df2['numbers']), columns=['letters', 'numbers'])

output

   letters numbers
0        a       1
1        a       2
2        a       3
3        a       4
4        b       1
5        b       2
6        b       3
7        b       4
8        c       1
9        c       2
10       c       3
11       c       4
12       d       1
13       d       2
14       d       3
15       d       4
Mortz
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  • Thanks for your suggestion Mortz. However I go with hpchavaz' solution since I don't want to install new packages :) – zimon May 20 '22 at 15:45
  • Oh you don't have to install anything - `itertools` is part of the standard library, like `math` or `string` - see [here](https://docs.python.org/3/library/itertools.html) – Mortz May 20 '22 at 16:54
1

from cartesian product in pandas

df1 = pd.DataFrame([1, 2, 3], columns=['A'])
df2 = pd.DataFrame(["a", "b", "c"], columns=['B'])

df = (df1.assign(key=1)
         .merge(df2.assign(key=1), on="key")
         .drop("key", axis=1)
)

   A  B
0  1  a
1  1  b
2  1  c
3  2  a
4  2  b
5  2  c
6  3  a
7  3  b
8  3  c
hpchavaz
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