Pandas dataframe concatenation with axis=1 : lost column names-CodePudding

I'm trying to concatenate two dataframes with these conditions :

for an existing header, append to the column ;
otherwise add a new column.

The code is working but the columns names are lost in case 2. Why? It doesn't seem to be mentioned in Pandas doc. Or I missed something?

How to keep the column names?

The code :

# Testing
# Merge, join, concatenate
# Pandas documentation : https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html

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],
)

df3 = pd.DataFrame(
    {
        "E": ["E0", "E1", "E2", "E3", "E4", "E5"],
    },
    #index=[0, 1, 2, 3, 4 , 5],
)

frames = [df1, df2]
result_1 = pd.concat(frames, ignore_index=True)
print(result_1)

frames = [result_1, df3]
if "E" in df3.columns :
  result_2 = pd.concat(frames, axis=1, ignore_index=True)
  print(result_2)

CodePudding user response：

You requested to drop the index with ignore_index=True. As you are concatenating on axis=1 the index is the columns!

frames = [result_1, df3]
if "E" in df3.columns :
  result_2 = pd.concat(frames, axis=1)
  print(result_2)

Output:

    A   B   C   D    E
0  A0  B0  C0  D0   E0
1  A1  B1  C1  D1   E1
2  A2  B2  C2  D2   E2
3  A3  B3  C3  D3   E3
4  A4  B4  C4  D4   E4
5  A5  B5  C5  D5   E5
6  A6  B6  C6  D6  NaN
7  A7  B7  C7  D7  NaN