Convert other data into numpy arrays e.g. asarray

The goal

numpy.asarray

numpy.asarray(a, dtype=None, order=None, *, like=None)

The importance of asarray:

Convert the input to an array.

import numpy as np


a_list = [[1, 2], [3, 4]]
a_np = np.asarray(a_list)

w = np.asarray(1)

print(w.sum())  # -> 1
print(a_np.sum())  # -> 10
print(a_np > 2)

print(1.sum()) # SyntaxError: invalid decimal literal
print(a_list.sum())  # AttributeError: 'list' object has no attribute 'sum'
print(a_list > 2) # TypeError: '>' not supported between instances of 'list' and 'int'

Output:

[[False False]
 [ True  True]]

numpy.fromiter

This is an optional topic!

numpy.fromiter(iter, dtype, count=-1, *, like=None)

Create a new 1-dimensional array from an iterable object.

numpy.fromfunction

This is an optional topic!

numpy.fromfunction(function, shape, *, dtype=<class 'float'>, like=None, **kwargs)[source]

Construct an array by executing a function over each coordinate.

The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z).

Don’t confuse asarray with array. array can be used to put a numpy structure around data. asarray converts the data into a numpy array. (As far as I understand…). Thus normally you don’t need to touch array.

numpy.array(object, dtype=None, *, copy=True, order='K', subok=False, ndmin=0, like=None)

Create an array.

The source code is Open Source and can be found on GitHub.