Everything You Need to Know About numpy.array()

Today we will take a look at the numpy.array()function. It is a function in the NumPy library that creates an array in Python. The array() function takes an iterable object (such as a list, tuple, or ndarray) and converts it to an ndarray.

“The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are similar to lists in Python, except that every element of an array must be of the same type, typically a numeric type like float or int.” (NumPy documentation, p. 5)

Here is an example of creating a NumPy ndarray with the array() function:

The array() function can take many optional parameters, including dtype (data type of the elements), copy (whether to make a copy of the input object), order (the memory layout of the array), and others. For example, to create an array of integers, the following code can be used:

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

This will output:

   [1 2 3 4]

The numpy.array() is an essential function for working with arrays in NumPy. It is the primary method for creating NumPy arrays and is used extensively in data science applications. It allows for easy conversion of iterable objects to ndarray, which can then be manipulated and processed using NumPy’s extensive library of functions.

Here are two more examples of numpy.array() with explanations of each line of the code:

Example 1 – Creating a 1D array from a list of integers

   import numpy as np

   # create a list of integers
   my_list = [1, 2, 3, 4, 5]

   # create a NumPy array from the list
   my_array = np.array(my_list)

   print(my_array)

Explanation:

• The import numpy as np statement imports the NumPy library and renames it to np.
• The my_list variable is created as a list of integers.
• The np.array(my_list) function creates a NumPy array from the my_list variable and assigns it to the my_array variable.
• The print(my_array) statement prints the resulting NumPy array.

Output:

   [1 2 3 4 5]

Example 2 – Creating a 2D array from a list of lists

   import numpy as np

   # create a list of lists
   my_list = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

   # create a NumPy array from the list of lists
   my_array = np.array(my_list)

   print(my_array)

Explanation:

• The import numpy as np statement imports the NumPy library and renames it to np.
• The my_list variable is created as a list of lists, where each inner list represents a row in the resulting 2D array.
• The np.array(my_list) function creates a NumPy array from the my_list variable and assigns it to the my_array variable.
• The print(my_array) statement prints the resulting NumPy array.

Output:

   [[1 2 3]
    [4 5 6]
    [7 8 9]]

The highly rated book that discusses numpy.array() is “Python Data Science Handbook” by Jake VanderPlas. This book is a comprehensive guide to using Python for data analysis, and it covers topics such as NumPy arrays, Pandas dataframes, Matplotlib visualizations, and Scikit-learn machine learning algorithms.

The book devotes an entire chapter to NumPy, including detailed explanations of the numpy.array() function and its various methods for manipulating arrays. It also covers advanced topics such as broadcasting, indexing, and vectorization.

“Python Data Science Handbook” is highly regarded in the data science community and has received positive reviews for its clear explanations and practical examples. It is suitable for both beginners and experienced programmers who want to learn more about data analysis using Python and NumPy.

Here are three insightful quotes from “Python Data Science Handbook” regarding numpy.array():

1. “In many ways, the NumPy array is like a simpler, more stripped-down version of the Pandas DataFrame object with which we will become familiar in the next chapter” (p. 47).

This quote highlights the relationship between NumPy arrays and Pandas dataframes, which are both essential tools for data analysis in Python. It also emphasizes the simplicity and versatility of NumPy arrays.

2. “NumPy arrays are more compact than lists for storing large amounts of data” (p. 49).

This quote emphasizes one of the key advantages of using NumPy arrays over lists: their memory efficiency. This can be especially important when working with large datasets.

3. “NumPy arrays also support vectorized operations, which allow you to perform computations on entire arrays at once” (p. 50).

This quote highlights another key feature of NumPy arrays: their ability to perform fast and efficient calculations on large arrays. This can save a significant amount of time and effort compared to performing the same operations on lists or other data structures.