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Suppose I have a recarray such as the following:
import numpy as np
# example data from @unutbu's answer
recs = [('Bill', '31', 260.0), ('Fred', 15, '145.0')]
r = np.rec.fromrecords(recs, formats = 'S30,i2,f4', names = 'name, age, weight')
print(r)
# [('Bill', 31, 260.0) ('Fred', 15, 145.0)]
Say I want to convert certain columns to floats. How do I do this? Should I change to an ndarray and them back to a recarray?
800
We have a method called astype(data_type) to change the data type of a numpy array. If we have a numpy array of type float64, then we can change it to int32 by giving the data type to the astype() method of numpy array. We can check the type of numpy array using the dtype class.
The data type can be specified using a string, like 'f' for float, 'i' for integer etc. or you can use the data type directly like float for float and int for integer.
Use NumPy array indexing to extract specific columsUse the syntax array[:, [i, j]] to extract the i and j indexed columns from array . Like lists, NumPy arrays use zero-based indexes. Use array[:, i:j+1] to extract the i through j indexed columns from array .
Can an array store different data types? Yes, a numpy array can store different data String, Integer, Complex, Float, Boolean.
Here is an example using astype to perform the conversion:
import numpy as np
recs = [('Bill', '31', 260.0), ('Fred', 15, '145.0')]
r = np.rec.fromrecords(recs, formats = 'S30,i2,f4', names = 'name, age, weight')
print(r)
# [('Bill', 31, 260.0) ('Fred', 15, 145.0)]
The age is of dtype <i2:
print(r.dtype)
# [('name', '|S30'), ('age', '<i2'), ('weight', '<f4')]
We can change that to <f4 using astype:
r = r.astype([('name', '|S30'), ('age', '<f4'), ('weight', '<f4')])
print(r)
# [('Bill', 31.0, 260.0) ('Fred', 15.0, 145.0)]
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