I have a dataframe with two columns:
In[] df.head()
Out[] specific_death months_survival
0 False 179
1 False 127
2 False 67
3 True 111
4 False 118
The first column has booleans while the second has integers. If I convert the dataframe to a numpy ndarray with:
array_from_df = df.to_numpy()
I get an unstructured numpy.ndarray. Thus if I write:
array_from_df.dtype.fields
The result is NoneType. For my program to work I need to have a structured array with the first field being a np.bool class and the second field a np.int. The way I see it there are two options but I couldn't find a way to do either:
Option one
Transform directly from a Pandas.DataFrame to a structured numpy.ndarray with the correct dtypes.
Option two
Transform from Pandas.DataFrame to an unstructured numpy.ndarray and then transform that to an structured numpy.ndarray. I found another SO question regarding this problem but I couldn't replicate the answer on my code.
As both comments suggested:
array_from_df = df.to_records() # index=False to not include an index column
Outputs an a numpy.recarray with the correct data types in:
array_from_df.dtype.fields