- I have a pandas series which values are numpy array. For simplicity, say
series = pd.Series([np.array([1,2,3,4]), np.array([5,6,7,8]), np.array([9,10,11,12])], index=['file1', 'file2', 'file3'])
file1 [1, 2, 3, 4]
file2 [5, 6, 7, 8]
file3 [9, 10, 11, 12]
How can I expand it to a dataframe of the form df_concatenated:
0 1 2 3
file1 1 2 3 4
file2 5 6 7 8
file3 9 10 11 12
- A wider version of the same problem. Actually the
seriesis obtained from a different dataframe of the form:
DataFrame:
0 1
file slide
file1 1 1 2
2 3 4
file2 1 5 6
2 7 8
file3 1 9 10
2 11 12
by grouping on 'file' index with concatenation of columns.
def concat_sublevel(data):
return np.concatenate(data.values)
series = data.groupby(level=[0]).apply(concat_sublevel)
May be somebody see a better way to come from dataframe data to df_concatenated.
Caveat. slide sub-index can have different number of values for different file values. In such a case I need to repeat one of the rows to get the same dimensions in all resulting rows
