The tolist()
converts a NumPy array or similar data structure into a Python list.
Key takeaways:
Use
numpy.array()
ornumpy.asarray()
to convert lists to arrays in Python.
numpy.array()
creates a copy of the list by default, whilenumpy.asarray()
does not.
numpy.asarray()
is more memory-efficient as it avoids unnecessary copying.
During programming, there will be instances when we will need to convert existing lists to arrays to perform certain operations on them (arrays enable mathematical operations to be performed on them in ways that lists do not).
Lists can be converted to arrays using the built-in functions in the Python
numpy
library.
numpy
provides us with two functions to use when converting a list into an array:
numpy.array()
numpy.asarray()
numpy.array()
This function of the numpy
library takes a list as an argument and returns an array that contains all the elements of the list. Let’s see the example below:
import numpy as npmy_list = [2,4,6,8,10]my_array = np.array(my_list)# printing my_arrayprint my_array# printing the type of my_arrayprint type(my_array)
numpy.asarray()
This function calls the numpy.array()
function inside itself. See the definition below:
def asarray(a, dtype=None, order=None):
return array(a, dtype, copy=False, order=order)
The main difference between
np.array()
andnp.asarray()
is that thecopy
flag isFalse
fornp.asarray()
, whereas it isTrue
by default fornp.array()
.
This means that np.array()
will make a copy of the object (by default) and convert that to an array, while np.asarray()
will not.
The code below illustrates the usage of np.asarray()
:
import numpy as npmy_list = [2,4,6,8,10]my_array = np.asarray(my_list)# Printing my_arrayprint my_array# Printing the type of my_arrayprint type(my_array)
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Converting lists to arrays is essential for numerical operations, and Python’s numpy
library offers two methods: numpy.array()
and numpy.asarray()
. The key difference is that numpy.array()
creates a copy by default, while numpy.asarray()
avoids copying for better performance, making choosing the right function for your needs is important.
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