https://stackoverflow.com › questions › 5674960

asarray(x) is almost always the best choice for any array-like object. array and fromiter are slow because they perform a copy. Using asarray allows this copy to be elided: >>> import array >>> import numpy as np >>> test = array.array('d', [0]*12000000)

http://python-simple.com › python-numpy › manipulation-array.php

Manipulation des arrays. Généralités : a = numpy.array ( [ [1, 2, 3], [4, 5, 6]]); a.shape : permet d'avoir la dimension de l'array, ici (2, 3). les arrays 2d sont remplies d'abord par ligne, et les index sont par ligne, puis par colonne. Accès aux éléments par index :

https://numpy.org › doc › stable › reference › generated › numpy.asarray.html

numpy.asarray(a, dtype=None, order=None, *, device=None, copy=None, like=None) #. Convert the input to an array. Parameters: aarray_like. Input data, in any form that can be converted to an array.

https://numpy.org › doc › stable › user › basics.creation.html

There are 6 general mechanisms for creating arrays: Conversion from other Python structures (i.e. lists and tuples) Intrinsic NumPy array creation functions (e.g. arange, ones, zeros, etc.) Replicating, joining, or mutating existing arrays. Reading arrays from disk, either from standard or custom formats

https://numpy.org › doc › stable › reference › generated › numpy.array.html

Learn how to create an array from any array-like object, specify the data-type, memory layout, and minimum dimensions. See examples of different parameters and options for numpy.array function.

https://www.geeksforgeeks.org › convert-python-list-to-numpy-arrays

In Python, the simplest way to convert a list to a NumPy array is by using numpy.array() function. It takes an argument and returns a NumPy array as a result. It creates a new copy in memory and returns a new array.

https://pandas.pydata.org › docs › reference › api › pandas.DataFrame.to_numpy.html

DataFrame. to_numpy (dtype=None, copy=False, na_value=<no_default>) [source] # Convert the DataFrame to a NumPy array. By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame.

https://pandas.pydata.org › pandas-docs › stable › reference › api › pandas.Series.to_numpy.html

A NumPy ndarray representing the values in this Series or Index. Parameters: dtypestr or numpy.dtype, optional. The dtype to pass to numpy.asarray(). copybool, default False. Whether to ensure that the returned value is not a view on another array. Note that copy=False does not ensure that to_numpy() is no-copy.

https://stackoverflow.com › questions › 5951135

First of all, I'd recommend you to go through NumPy's Quickstart tutorial, which will probably help with these basic questions. You can directly create an array from a list as: import numpy as np. a = np.array( [2,3,4] ) Or from a from a nested list in the same way: import numpy as np. a = np.array( [[2,3,4], [3,4,5]] )