How to merge pandas dataframes with different column names-CodePudding

Can someone please tell me how I can achieve results like the image above, but with the following differences:

# Note the column names
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],
                   )

# Note the column names
df2 = pd.DataFrame({"AA": ["A4", "A5", "A6", "A7"],
                    "BB": ["B4", "B5", "B6", "B7"],
                    "CC": ["C4", "C5", "C6", "C7"],
                    "DD": ["D4", "D5", "D6", "D7"],
                   },
                   index = [4, 5, 6, 7],
                  )

# Note the column names
df3 = pd.DataFrame({"AAA": ["A8", "A9", "A10", "A11"],
                    "BBB": ["B8", "B9", "B10", "B11"],
                    "CCC": ["C8", "C9", "C10", "C11"],
                    "DDD": ["D8", "D9", "D10", "D11"],
                   },
                   index = [8, 9, 10, 11],
                  )

Every kind of merge I do results in this:

Here's what I'm trying to accomplish:

I'm doing my Capstone Project, and the use case uses the SpaceX data set. I've web-scraped the tables found here: SpaceX Falcon 9 Wikipedia,

Now I'm trying to combine them into one large table. However, there are slight differences in the column names, between each table, and so I have to do more logic to merge properly. There are 10 tables in total, I've checked 5. 3 have unique column names, so the simple merging doesn't work.
I've searched around at the other questions, but the use case is different than mine, so I haven't found an answer that works for me.

I'd really appreciate someone's help, or pointing me where I can find more info on the subject. So far I've had no luck in my searches.

CodePudding user response：

Let us just do np.concatenate

out = pd.DataFrame(np.concatenate([df1.values,df2.values,df3.values]),columns=df1.columns)
Out[346]: 
      A    B    C    D
0    A0   B0   C0   D0
1    A1   B1   C1   D1
2    A2   B2   C2   D2
3    A3   B3   C3   D3
4    A4   B4   C4   D4
5    A5   B5   C5   D5
6    A6   B6   C6   D6
7    A7   B7   C7   D7
8    A8   B8   C8   D8
9    A9   B9   C9   D9
10  A10  B10  C10  D10
11  A11  B11  C11  D11

CodePudding user response：

IIUC, you could just modify the column names and concatenate:

df2.columns = df2.columns.str[0]
df3.columns = df3.columns.str[0]
out = pd.concat([df1, df2, df3])

or if you're into one-liners, you could do:

out = pd.concat([df1, df2.rename(columns=lambda x:x[0]), df3.rename(columns=lambda x:x[0])])

Output:

      A    B    C    D
0    A0   B0   C0   D0
1    A1   B1   C1   D1
2    A2   B2   C2   D2
3    A3   B3   C3   D3
4    A4   B4   C4   D4
5    A5   B5   C5   D5
6    A6   B6   C6   D6
7    A7   B7   C7   D7
8    A8   B8   C8   D8
9    A9   B9   C9   D9
10  A10  B10  C10  D10
11  A11  B11  C11  D11