Python Create Combinations from Multiple Data Frames

Question

Basically, I would like to create a new data frame from some existing data frames by creating all the possible column combinations. This is quite easy in SAS (or expand.grid function in R):

create table combine_var as
select *
from var_1, avar_2;

But I am not sure, what is the equalent way in Python. For instance, my data frame looks like:

var_1= pd.DataFrame.from_items([('val_1', [0.00789, 0.01448, 0.03157])])
var_2= pd.DataFrame.from_items([('val_2', [0.5, 1.0])])

And I expect the output is:

val_1   val_2
0.00789 0.5
0.00789 1.0
0.01448 0.5
0.01448 1.0
0.03157 0.5
0.03157 1.0

Community · Accepted Answer · 2017-05-23 12:23:43Z

4

You could use expand_grid which is mentioned in docs cookbook:

def expand_grid(data_dict):
  rows = itertools.product(*data_dict.values())
  return pd.DataFrame.from_records(rows, columns=data_dict.keys())

expand_grid({'val_1': [0.00789, 0.01448, 0.03157], 'val_2' : [0.5, 1.0]})

In [107]: expand_grid({'val_1': [0.00789, 0.01448, 0.03157], 'val_2' : [0.5, 1.0]})
Out[107]:
     val_1  val_2
0  0.00789    0.5
1  0.00789    1.0
2  0.01448    0.5
3  0.01448    1.0
4  0.03157    0.5
5  0.03157    1.0

EDIT

For existing dataframes you first will need to create one dictionary from your dataframes. You could combine to one with one of the answers to that question. Example for your case:

expand_grid(dict(var_1.to_dict('list'), **var_2.to_dict('list')))

In [122]: expand_grid(dict(var_1.to_dict('list'), **var_2.to_dict('list')))
Out[122]:
     val_1  val_2
0  0.00789    0.5
1  0.00789    1.0
2  0.01448    0.5
3  0.01448    1.0
4  0.03157    0.5
5  0.03157    1.0

edited May 23, 2017 at 12:23

CommunityBot

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answered Dec 1, 2015 at 7:26

Anton Protopopov

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4 Comments

TTT Over a year ago

Yes. I saw that code. But it needs to have a dictionary first. Just wondering if there exists a function which directly takes in data frames

Anton Protopopov Over a year ago

you could convert them to dict and combine to one and pass to that function

TTT Over a year ago

Thanks for your solution! It seems like at this moment there is no build-in function in 'Pandas' to achieve this...

Anton Protopopov Over a year ago

At least I don't know that. It's probably good to write a feature request for pandas package

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