pandas add columns to one DF from another without merge-CodePudding

Let's say I have

DF1=pd.DataFrame([[1,2,3],[2,3,4],[3,4,5]], columns=['a','b','c'])
DF2=pd.DataFrame([[9,8,7]],columns=['x','y','z'])

I want to add all the columns of DF2 to DF1 but I want to repeat the rows of DF2 to fill all the rows of DF1

I can think of 3 ways to do it, none of them seem satisfactory.

With a for loop, one column at a time

for col in ['x','y','z']:
    DF1[col]=DF2[col]

Creating a dummy column and then merging the two against that dummy variable

DF1=DF1.assign(dummy=1).merge(DF2.assign(dummy=1)).drop('dummy',axis=1)

which is faster (with my real data) than the for loop but looks really weird.

Creating a new DF2 with rows equal to DF1's rows:

DF1[['x','y','z']]=pd.concat([DF2 for x in range(DF1.shape[0])])

This one errors out saying cannot reindex on an axis with duplicate labels but it doesn't really matter because just the pd.concat([DF2 for x in range(DF1.shape[0])]) is disqualifying slow.

Is there a syntax that's more like this but that actually works and is better than the merge?

DF1['x','y','z']=DF2

CodePudding user response：

Are you asking for a cross join?

DF1.join(DF2, how='cross')

this is roughly equivalent to pd.concat([DF1,DF2], axis=1).ffill().astype(int)

Output

   a  b  c  x  y  z
0  1  2  3  9  8  7
1  2  3  4  9  8  7
2  3  4  5  9  8  7

CodePudding user response：

What about something like this:

DF1[DF2.columns] = DF2
DF1 = DF1.fillna(method = "ffill")

DF1[DF2.columns] = DF2.loc[[0]*3, :].values

You can (dinamically) change the 3 for the size of DF1.