CODEWITHSUNDEEP
python-3.xnumpymultidimensional-arraytype-conversiondtype
user10121139
user10121139

Reputation:

How to convert dtype of numpy subarray?

I am trying to read data from a csv file into a numpy array. Since the csv file contains empty fields, I read all of the data into an array of dtype=str, and plan to convert rows/columns into appropriate numerical types. The example below is my unsuccessful at converting these array dtypes.

import numpy as np

x = np.array([
['name', 'property', 'value t0', 'value t1', 'value t2'],
['a', 0.5, 1, 2, 3],
['b', 0.2, 5, 10, 100],
['c', 0.7, 3, 6, 9],
], dtype=str)

First, let's view the original array.

# print("\n .. x (shape={}, dtype={}):\n{}\n".format(x.shape, x.dtype, x))

[['name' 'property' 'value t0' 'value t1' 'value t2']
 ['a' '0.5' '1' '2' '3']
 ['b' '0.2' '5' '10' '100']
 ['c' '0.7' '3' '6' '9']]

Then, let's make sure the numerical entries (taken from the first row down and second column right) can be converted into type <int>.

# print(x[1:, 2:].astype(int))

[[  1   2   3]
 [  5  10 100]
 [  3   6   9]]

So, I tried to put these concepts together.

# # x[1:, 2:] = x[1:, 2:].astype(int)
# x[1:, 2:] = np.array(x[1:, 2:], dtype=int)

print(x)

[['name' 'property' 'value t0' 'value t1' 'value t2']
 ['a' '0.5' '1' '2' '3']
 ['b' '0.2' '5' '10' '100']
 ['c' '0.7' '3' '6' '9']]

Why are the selected entries remaining strings? I saw similar questions posted, for which the accepted solution appears to be using named-fields. But, I prefer numerical indexing to named-fields for my use-case.

Upvotes: 1

Views: 748

Answers (1)

hpaulj
hpaulj

Reputation: 231335

In [83]: alist = [ 
    ...: ['name', 'property', 'value t0', 'value t1', 'value t2'], 
    ...: ['a', 0.5, 1, 2, 3], 
    ...: ['b', 0.2, 5, 10, 100], 
    ...: ['c', 0.7, 3, 6, 9], 
    ...: ]                                                                                                           
In [84]: alist                                                                                                       
Out[84]: 
[['name', 'property', 'value t0', 'value t1', 'value t2'],
 ['a', 0.5, 1, 2, 3],
 ['b', 0.2, 5, 10, 100],
 ['c', 0.7, 3, 6, 9]]
In [85]: np.array(alist)                                                                                             
Out[85]: 
array([['name', 'property', 'value t0', 'value t1', 'value t2'],
       ['a', '0.5', '1', '2', '3'],
       ['b', '0.2', '5', '10', '100'],
       ['c', '0.7', '3', '6', '9']], dtype='<U8')

object array:

In [87]: np.array(alist, dtype=object)                                                                               
Out[87]: 
array([['name', 'property', 'value t0', 'value t1', 'value t2'],
       ['a', 0.5, 1, 2, 3],
       ['b', 0.2, 5, 10, 100],
       ['c', 0.7, 3, 6, 9]], dtype=object)

structured array:

In [88]: np.array([tuple(row) for row in alist[1:]], dtype='U1,f,i,i,i')                                             
Out[88]: 
array([('a', 0.5, 1,  2,   3), ('b', 0.2, 5, 10, 100),
       ('c', 0.7, 3,  6,   9)],
      dtype=[('f0', '<U1'), ('f1', '<f4'), ('f2', '<i4'), ('f3', '<i4'), ('f4', '<i4')])

pandas:

In [90]: import pandas as pd                                                                                         
In [91]: pd.DataFrame(alist[1:], columns=alist[0])                                                                   
Out[91]: 
  name  property  value t0  value t1  value t2
0    a       0.5         1         2         3
1    b       0.2         5        10       100
2    c       0.7         3         6         9

Upvotes: 2

Related Questions