How to convert list to numpy

How to convert list to numpy: 1. Use numpy.array() function. The first parameter of this function is a list object, which can be a one-dimensional or multi-dimensional list; 2. Use numpy.asarray() function, this function will try to use the data type of the input list; 3. Use the numpy.reshape() function to convert a one-dimensional list into a multi-dimensional NumPy array; 4. Use the numpy.fromiter() function, the function's One parameter is an iterable object.

Operating system for this tutorial: Windows 10 system, Python version 3.11.4, Dell G3 computer.

In Python, we often use lists and NumPy arrays to store and process data. A list is an ordered, mutable container that can store any type of data. A NumPy array is a multidimensional numeric array object used for storing and processing large data sets.

Converting a list to a NumPy array can bring many benefits, such as:

NumPy arrays operate faster: NumPy is written in C language at the bottom and can handle large amounts of data efficiently than Python. Lists are faster.

NumPy array operations are more concise: NumPy provides many convenient functions and methods to process arrays, making the code more concise and readable.

NumPy arrays are more powerful: NumPy provides a large number of mathematical functions and statistical functions, which can facilitate data analysis and scientific calculations.

The following are several ways to convert a list into a NumPy array:

1. Use the numpy.array() function: The numpy.array() function can convert a list into a NumPy array. The first parameter of this function is a list object, which can be a one-dimensional or multi-dimensional list. For example:

import numpy as np
my_list = [1, 2, 3, 4, 5]
my_array = np.array(my_list)
print(my_array)

Output result:

[1 2 3 4 5]

2. Use the numpy.asarray() function: The numpy.asarray() function can convert the list into a NumPy array. Unlike the numpy.array() function, the numpy.asarray() function will try to use the data type of the input list. For example:

import numpy as np
my_list = [1, 2, 3, 4, 5]
my_array = np.asarray(my_list)
print(my_array)

Output result:

[1 2 3 4 5]

3. Use the numpy.reshape() function: The numpy.reshape() function can change the dimensions of the array and convert a one-dimensional list into a multi-dimensional one. NumPy array. For example:

import numpy as np
my_list = [1, 2, 3, 4, 5]
my_array = np.reshape(my_list, (5, 1))
print(my_array)

Output result:

[[1]
 [2]
 [3]
 [4]
 [5]]

4. Use the numpy.fromiter() function: The numpy.fromiter() function can create a NumPy array from an iterable object. The first parameter of this function is an iterable object, such as a list, tuple, etc. For example:

import numpy as np
my_list = [1, 2, 3, 4, 5]
my_array = np.fromiter(my_list, dtype=int)
print(my_array)

Output result:

[1 2 3 4 5]

Summary: The above are several ways to convert a list into a NumPy array. According to actual needs, choosing an appropriate method for conversion can improve the efficiency and readability of the code. The functionality and performance of NumPy arrays make it one of the important tools for data processing and scientific computing.

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