Say I have two lists:
header = ['a', 'b', 'c', 'd']
data_type = ['str', 'str', 'float64', 'float64']
How do I get a combined list like this:
data_type = {'a':str, 'b':str, 'c':float64, 'd':float64}
This is used to define dtype in pd.read_csv method.
Fastest:
header = ['a', 'b', 'c', 'd']
data_type = ['str', 'str', 'float64', 'float64']
dict(zip(header, data_type))
the idea is:
two lists merged with zip function(https://docs.python.org/2/library/functions.html#zip) and than produced tuple of tuples converted to dictionary with dict function.
pandas questions are sometimes harsh for pure python answerers.zip that's really a good investment. My guess is that there are a lot of pandas specialists out there that don't know a lot of useful basic python stuff. Congrats.From your question it seems you actually want to return a datatype (python keyword) and not a string of a datatype, so I would use a dict comprehension with eval, this is how I would do it:
from numpy import float64
{header[i]:eval(data_type[i]) for i in range(len(header))}
{'a': str, 'b': str, 'c': numpy.float64, 'd': numpy.float64}
Also note that the dict comprehension option is slightly slower, at 1.91 µs per loop versus 1.62 µs per loop for the the dict+zip option
{k: data_type[i] for (i, k) in enumerate(header)}. Using enumerate here for me looks more pythonic - and a little bit more transparent
str, without quotes?? (valid, but suspicious)float64without quotes? valid?? maybe in pandas?