I have two Pandas DataFrames whose data from different sources, but both DataFrames have the same column names. When combined only one column will keep the name.

Like this:

speed_df = pd.DataFrame.from_dict({'ts':  [0,1,3,4],'val': [5,4,2,1]})temp_df = pd.DataFrame.from_dict({'ts':  [0,1,2],'val': [9,8,7]})

And I need to have a result like this:

final_df = pd.DataFrame.from_dict({'ts':    [0,1,2,3,4],'speed': [5,4,NaN,1],'temp':  [9,8,7,NaN,NaN]})

Later I will deal with empty cells (here filled with NaN) by copying the values of the previous valid value. And get something like this:

final_df = pd.DataFrame.from_dict({'ts':    [0,1,2,3,4],'speed': [5,4,4,1],'temp':  [9,8,7,7,7]})

Use pd.merge

In [406]: (pd.merge(speed_df, temp_df, how='outer', on='ts').rename(columns={'val_x': 'speed','val_y': 'temp'}).sort_values(by='ts'))
Out[406]:ts  speed  temp
0   0    5.0   9.0
1   1    4.0   8.0
4   2    NaN   7.0
2   3    2.0   NaN
3   4    1.0   NaNIn [407]: (pd.merge(speed_df, temp_df, how='outer', on='ts').rename(columns={'val_x': 'speed', 'val_y': 'temp'}).sort_values(by='ts').ffill())
Out[407]:ts  speed  temp
0   0    5.0   9.0
1   1    4.0   8.0
4   2    4.0   7.0
2   3    2.0   7.0
3   4    1.0   7.0