“Vectorization” in NumPy

ryan — Fri, 21 Mar 2008 14:10:39 +0000

How do you get Matlab-like vectorization when using NumPy? The key is using parentheses when using logical operators. Here’s an example:

from numpy import *

a = array([1,2,3,4,5])
b = array([5,4,3,2,1])
c = array([1,2,2,2,1])

# ===The right way===

(a<5) & (b>3) & (c==2)
# array([ False,  True, False, False, False], dtype=bool)

# ===The wrong way===

a<5 & b>3 & c==2
# ValueError:
# The truth value of an array with more than
# one element is ambiguous. Use a.any() or a.all()

Alternatively, use NumPy’s logical_and and logical_or functions . . . but these functions only take two arguments at a time.

Check the type of array in Numpy

ryan — Mon, 17 Mar 2008 00:55:39 +0000

from numpy import array
a = array([1,2,3])
a.dtype  # dtype('int32')
a.dtype.kind  # 'i', for 'integer'

s = array(['a','b','c'])
s.dtype  # dtype('|S1')
s.dtype.kind  # 'S' for 'string'

f = array([1., 2., 3.])
f.dtype  # dtype('float64')
f.dtype.kind  # 'f' for 'float'

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“Vectorization” in NumPy

Check the type of array in Numpy