如何处理numpy数组中的混合数据类型

Question

Stuck in this Numpy Problem

country=['India','USA']
​gdp=[22,33]

import numpy as np
a=np.column_stack((country,gdp))

array([['India', '22'],
       ['USA', '33']], dtype='<U11')

I have an NDArray and I want to find the maximum of the 2nd column. I tried the below

print(a.max(axis=1)[1])
print(a[:,1].max())

It threw this error: TypeError: cannot perform reduce with flexible type

Tried converting the type

datatype=([('country',np.str_,64),('gross',np.float32)])

new=np.array(a,dtype=datatype)

But got the below error

could not convert string to float: 'India'.

Answer 1

The error is due to the string data in your array, which makes the dtype to be Unicode(indicated by U11 ie, 11-character unicode) string. If you wish to store data in the numerical format, then use structured arrays . However, if you only wish to compute the maximum of the numerical column, use

print(a[:, 1].astype(np.int).max())
// 33

You may choose to use other numerical dtypes such as np.float inplace of np.int based on the nature of data in the specific column.

Answer 2

Consider using numpy structured arrays for mixed types. You will have no issues if you explicitly set data types.

This is often necessary, and certainly advisable, with numpy .

import numpy as np

country = ['India','USA','UK']
gdp = [22,33,4]

a = np.array(list(zip(country, gdp)),
             dtype=[('Country', '|S11'), ('Number', '<i8')])

res_asc = np.sort(a, order='Number')

# array([(b'UK', 4), (b'India', 22), (b'USA', 33)], 
#       dtype=[('Country', 'S11'), ('Number', '<i8')])

res_desc = np.sort(a, order='Number')[::-1]

# array([(b'USA', 33), (b'India', 22), (b'UK', 4)], 
#       dtype=[('Country', 'S11'), ('Number', '<i8')])