Can you merge elements of Pandas dataframes into tuples?-CodePudding

If you have two Pandas dataframes in Python with identical axes, is there a function to merge the elements as tuples so that they maintain their positions? If there is a better way to combine these dataframes without duplicating the number of indices or columns, that works as well.

Expected logic:

Image of what I'm trying to do

CodePudding user response：

You can do this in pure pandas:

(pd.concat([df1,df2])
   .stack()
   .groupby(level=[0,1])
   .apply(tuple)
   .unstack()
 )

Output:

        A        B
0  (1, 7)  (4, 10)
1  (2, 8)  (5, 11)
2  (3, 9)  (6, 12)

Input:

import pandas as pd 
df1 = pd.DataFrame({"A":[1,2,3],"B":[4,5,6]})
df2 = pd.DataFrame({"A":[7,8,9],"B":[10,11,12]})

CodePudding user response：

The operation you're looking for seems like "zip". That is, match elements of two sequences together into a sequence of tuples. If you look at each column in your dataframes and zip them together you will have a result that is a list of lists of tuples - what you want to be in your result dataframe. You can then construct a dataframe with the same columns and index out of that data. In code, that looks like this:

data = [list(zip(df1[col], df2[col])) for col in df1]
pd.DataFrame(data, index=[1,2,3], columns=["A", "B", "C"])

CodePudding user response：

You can maybe use something like this to achieve what you want.

df3 = pd.DataFrame({x: zip(df1[x], df2[x]) for x in df1.columns})

CodePudding user response：

df1 = pd.DataFrame({"A" : [1,2,3], "B":[4,5,6]})
df2 = pd.DataFrame({"A" : [7,8,9], "B":[10,11,12]})

def add_dfs(df1, df2):
    for col in df1.columns:
        df1[col] = df1[col].apply(lambda x: (x,))
    for col in df2.columns:
        df2[col] = df2[col].apply(lambda x: (x,))
    df = df1   df2 # using   operator , satisfies answer technically
    return df

df = add_dfs(df1, df2)