Python Pandas - Concat dataframes with different columns ignoring column names

Question

I have two pandas.DataFrames which I would like to combine into one. The dataframes have the same number of columns, in the same order, but have column headings in different languages. How can I efficiently combine these dataframes?

df_ger
index  Datum   Zahl1   Zahl2
0      1-1-17  1       2
1      2-1-17  3       4

df_uk
index  Date    No1     No2
0      1-1-17  5       6
1      2-1-17  7       8

desired output
index  Datum   Zahl1   Zahl2
0      1-1-17  1       2
1      2-1-17  3       4
2      1-1-17  5       6
3      2-1-17  7       8

The only approach I came up with so far is to rename the column headings and then use pd.concat([df_ger, df_uk], axis=0, ignore_index=True). However, I hope to find a more general approach.

Answer 1

If the columns are always in the same order, you can mechanically rename the columns and the do an append like:

Code:

new_cols = {x: y for x, y in zip(df_uk.columns, df_ger.columns)}
df_out = df_ger.append(df_uk.rename(columns=new_cols))

Test Code:

df_ger = pd.read_fwf(StringIO(
    u"""
        index  Datum   Zahl1   Zahl2
        0      1-1-17  1       2
        1      2-1-17  3       4"""),
    header=1).set_index('index')

df_uk = pd.read_fwf(StringIO(
    u"""
        index  Date    No1     No2
        0      1-1-17  5       6
        1      2-1-17  7       8"""),
    header=1).set_index('index')

print(df_uk)
print(df_ger)

new_cols = {x: y for x, y in zip(df_uk.columns, df_ger.columns)}
df_out = df_ger.append(df_uk.rename(columns=new_cols))

print(df_out)

Results:

         Date  No1  No2
index                  
0      1-1-17    5    6
1      2-1-17    7    8

        Datum  Zahl1  Zahl2
index                      
0      1-1-17      1      2
1      2-1-17      3      4

        Datum  Zahl1  Zahl2
index                      
0      1-1-17      1      2
1      2-1-17      3      4
0      1-1-17      5      6
1      2-1-17      7      8

Answer 2

Provided you can be sure that the structures of the two dataframes remain the same, I see two options:

1. Keep the dataframe column names of the chosen default language (I assume en_GB) and just copy them over:

   df_ger.columns = df_uk.columns
   df_combined = pd.concat([df_ger, df_uk], axis=0, ignore_index=True)
   

   This works whatever the column names are. However, technically it remains renaming.

2. Pull the data out of the dataframe using numpy.ndarrays, concatenate them in numpy, and make a dataframe out of it again:

   np_ger_data = df_ger.as_matrix()
   np_uk_data = df_uk.as_matrix()
   np_combined_data = numpy.concatenate([np_ger_data, np_uk_data], axis=0)
   df_combined = pd.DataFrame(np_combined_data, columns=["Date", "No1", "No2"])
   

   This solution requires more resources, so I would opt for the first one.

Answer 3

I am not sure if this will be simpler than what you had in mind, but if the main goal is for something general then this should be fine with one assumption: The columns in the two files match for example if date is the first column, the translated version will also be the first column.

# number of columns
n_columns = len(df_ger.columns)

# save final columns names
columns = df_uk.columns

# rename both columns to numbers
df_ger.columns = range(n_columns)
df_uk.columns = range(n_columns)

# concat columns
df_out = pd.concat([df_ger, df_uk], axis=0, ignore_index=True)

# rename columns in new dataframe
df_out.columns = columns