'How to convert each row of a dataframe to new column use concat in python

If I have dataframes,

df1 = pd.DataFrame(
{
    "A": ["A0", "A1", "A2", "A3"],
    "B": ["B0", "B1", "B2", "B3"],
    "C": ["C0", "C1", "C2", "C3"],
    "D": ["D0", "D1", "D2", "D3"],
},
index=[0, 1, 2, 3],)
df2 = pd.DataFrame(
{
    "A": ["A4", "A5", "A6", "A7"],
    "B": ["B4", "B5", "B6", "B7"],
    "C": ["C4", "C5", "C6", "C7"],
    "D": ["D4", "D5", "D6", "D7"],
},
index=[4,5,6,7],)

I want to use pd.concat to combine these two dataframes as

dfnew = pd.concat([df1.loc[0],
               df1.loc[1],
                df1.loc[2],
               df1.loc[3],
              df2.loc[4],
              df2.loc[5],
              df2.loc[6],
              df2.loc[7]],
             axis=0,sort=False)
dfnew = dfnew.to_frame().transpose()

dfnew is a 1row x 32 columns dataframe. But how about I have many rows in df1 and df2, or I want to combine different number of rows of df1 and df2 in a loop? What can I do for the concat .loc[] part? Or is there another way to do this?

Thank you ahead.

Solution 1:^[1]

IIUC, you could stack the individual dataframes, concat and reshape:

dfnew = pd.concat([df1.stack(), df2.stack()]).droplevel(0).to_frame().T

output:

    A   B   C   D   A   B   C   D   A   B   C   D   A   B   C   D   A   B   C   D   A   B   C   D   A   B   C   D   A   B   C   D
0  A0  B0  C0  D0  A1  B1  C1  D1  A2  B2  C2  D2  A3  B3  C3  D3  A4  B4  C4  D4  A5  B5  C5  D5  A6  B6  C6  D6  A7  B7  C7  D7

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Solution 1	mozway

'How to convert each row of a dataframe to new column use concat in python

Solution 1:[1]

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Related Questions

Solution 1:^[1]