Practical tips for numpy arrays: converting from list

Practical tips for converting list to numpy array, specific code examples required

In Python, NumPy (Numerical Python) is a tool for doing science in Python Computing library. It provides an efficient multidimensional array object (ndarray), as well as tools for fast operations on arrays. By converting the list into a NumPy array, we can take advantage of the power of NumPy for data processing and analysis.

Below we will introduce several practical techniques for converting lists to NumPy arrays and give specific code examples.

1. Using the np.array() function

The np.array() function is one of the most commonly used functions in NumPy and can convert a list into a NumPy array. The parameter of this function accepts a list as input and returns a corresponding NumPy array.

Sample code:

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 np.asarray() function

np.asarray() The function function is similar to the np.array() function, which can convert the list into a NumPy array. Unlike np.array(), the np.asarray() function will preserve the type of the input data as much as possible instead of converting it to the default dtype.

Sample code:

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 np.reshape() function

np.reshape() Functions can change the shape of NumPy arrays. By converting the list to a one-dimensional array and then using the np.reshape() function to change the shape, we can get NumPy arrays of different dimensions.

Sample code:

import numpy as np

my_list = [1, 2, 3, 4, 5]
my_array = np.array(my_list)
reshaped_array = np.reshape(my_array, (5, 1))
print(reshaped_array)

Output result:

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

4. Use np.zeros() or np.ones() function

## The #np.zeros() function can create a NumPy array with all 0s, and the np.ones() function can create a NumPy array with all 1s. We can convert a list into a NumPy array by first creating a NumPy array with all 0s or all 1s, and then assigning it a value.

Sample code:

import numpy as np

my_list = [1, 2, 3, 4, 5]
my_array = np.zeros(len(my_list), dtype=int)
for i, item in enumerate(my_list):
    my_array[i] = item
print(my_array)

Output result:

[1 2 3 4 5]

Use np.fromiter() function

np.fromiter() Functions can create a NumPy array from an iterable object (such as a list). Compared with the previous method, the np.fromiter() function is more flexible and can specify the dtype and shape when creating the array.

Sample code:

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]

The above are several practical techniques for converting list to NumPy array. I hope it will be helpful to you. The powerful functions of NumPy can improve the efficiency of data processing and analysis, and converting a list into a NumPy array is the first step in data processing and analysis. By mastering these techniques, you will be able to use NumPy for scientific computing more flexibly.

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