[Python] Convert pandas Series to dict with type conversion?

I have a file containing miscellaneous parameters, stored as csv. I'm loading it into Python using pandas.read_csv, which very conveniently returns a DataFrame with one column. But I don't need any of the fancy pandas features, so I immediately convert my data to a dict.

My next problem is that the parameters all have different types: some are integers, some may be floats, and some are strings. pandas usually loads them with dtype=object, and they go into the dict as strings. (Except sometimes they're all numeric, so I get a numeric dtype right away.) I wrote a simple function that attempts to identify numeric types and convert them appropriately.

header = {key: typecast(value)
for (key, value) in dict(header['Value']).items()}

def typecast(value):
"""Convert a string to a numeric type if possible

>>> def cast_with_type(s):
... return (result := typecast(s), type(result))
>>> cast_with_type(123)
(123, <class 'int'>)
>>> cast_with_type('foo')
('foo', <class 'str'>)
>>> cast_with_type('123')
(123, <class 'int'>)
>>> cast_with_type('123.4')
(123.4, <class 'float'>)
"""
try:
if '.' in value:
return float(value)
else:
return int(value)
except TypeError:
return value
except ValueError:
return value


Is there a built-in feature that does this better than what I already have?

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