NumPy.arange() function in Python [7 use cases]

In this NumPy tutorial, I will explain what the numpy.arange() function in Python, by exploring its syntax, parameters, use cases, and some examples to help you grasp how to utilize it effectively in your numerical computing tasks through Python. I will also explain the difference between the linspace() and arange() functions in Python NumPy.

NumPy, short for Numerical Python, is one of the most popular libraries in Python for numerical computations. It provides a high-performance multidimensional array object and tools for working with these arrays in Python. One of the foundational functions in NumPy Python is arrange().

The numpy.arange() function in Python is a function in the NumPy library that generates arrays with regularly incrementing values. The function is similar to the built-in Python function range(), but it returns an array rather than a list or an iterable.

NumPy.arange() function in Python

The numpy.arange() function in Python, provided by the NumPy library, generates sequences of numbers in arrays. Similar to Python’s built-in range() function, which returns sequences of numbers in lists, the arange() function creates NumPy arrays in Python, which are more suitable for numerical and matrix operations. Unlike Python lists, operations on NumPy arrays are optimized and can be executed faster, especially with large amounts of data.

To utilize the arange() function, you first need to import the NumPy library, typically aliased as np(import numpy as np). Once you have NumPy imported, you can generate a sequence of numbers by calling the np.arange() function in Python and specifying the start, stop, and step values.

Syntax of arange Python NumPy function

Here is the syntax of numpy.arange() function in Python:

``numpy.arange([start, ]stop, [step, ], dtype=None)``

Parameters: The function takes the following parameters:

Return values of the numpy.arange() function in Python

The np.arange() function in NumPy Python is used to generate arrays with sequences of numbers. The function returns values based on the arguments passed to it.

Example:

Let’s take an example to check what the arrange() function returns in Python.

``````import numpy as np

a = np.arange(2,10)
print(a)``````

Here is the Screenshot of the following given Python code:

The np.arange Python function use cases

Let’s take some different cases to generate a Python NumPy array using the np.arange() function.

Case 1: np.arange() function with only stop value is provided.

• In this case, the NumPy Python array starts from 0 and goes up to (but does not include) the provided stop value, as we provide as a parameter in the arrange function.
• The step parameter is assumed to be 1.
``````import numpy as np

numbers = np.arange(5)
print(numbers)
print(type(numbers))``````

Output: Here, is the output of the pile of Python code and the screenshot to prove that it returns a Python NumPy array.

``````[0 1 2 3 4]
<class 'numpy.ndarray'>``````

Case 2: NumPy arrange function in Python with both start and stop values.

• In this case, the NumPy Python array starts from the provided start value and goes up to (but does not include) the provided stop value.
• The step parameter is assumed to be 1.
``````import numpy as np

numbers = np.arange(2, 7)
print(numbers)
print(type(numbers))``````

Output: Here, is the output of the pile of Python code and the screenshot to prove that it returns a Python NumPy array.

``````[2 3 4 5 6]
<class 'numpy.ndarray'>``````

This we can use both the start and stop parameters in numpy.arange() function in Python to generate an array.

Case 3: Python arange() numpy function with start, stop, and step values are all provided

• Starts from the provided start value and goes up to (but not including) the provided stop value, incrementing by the step value.
• The step can also be negative, which would mean you’re generating a sequence in decreasing order or floating value to get the sequence with float values.
``````import numpy as np

numbers = np.arange(10, 2, -2)
print(numbers)
print(type(numbers))``````

Output: The screenshots with various kinds of step parameters are presented below.

``````[10  8  6  4]
<class 'numpy.ndarray'>``````

Or, we can use the step parameters as: The first one is the one where we have provided a positive step value and the second one is where we have provided a float value as a step parameter.

Case 4: Arange in NumPy Python with specifying data type

• The dtype parameter allows you to specify the data type of the output NumPy array.
• In this case, we will specify the type of the array and then will generate the array with the specified datatype.
``````import numpy as np

numbers = np.arange(10, dtype='float64')
print(numbers)
print(type(numbers))``````

Output: Here, I have defined the dtype parameters with float64, to generate an array with all float values in Python NumPy.

``````[0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]
<class 'numpy.ndarray'>``````

Case 5: Python NumPy arange 2d array

• In this section, we will learn about Python NumPy arange 2d array.
• A two-dimensional array in Python means the collection of homogenous data or numbers in lists of a list. It is also known as a NumPy matrix. In a 2Dimension NumPy array, you have to use two square brackets which is why it is called a list of lists in Python.
• In NumPy arange 2d, we can easily use a function that is np.reshape().
• This np.reshape() function gives a new shape and size to a NumPy array without changing its data.
• To use the arange function, we will declare a new script with the NumPy library in Python.

Syntax:

Here is the syntax of NumPy arange 2D in Python

``````numpy.arange(
[start],
stop,
[step],
dtype=None
)``````

Example:

``````import numpy as np

a = np.arange(2,6).reshape(2,2)
print(a)``````

Here is the Screenshot of the following given Python code.

Case 6: Python NumPy arange reshape

• In this section, we will learn and discuss Python NumPy arange reshape.
• By reshaping we can add or delete dimensions or change the number of values in each dimension.
• To use the numpy.arange() method and numpy.reshape() function we will create a new script with the NumPy library imported as np in Python.

Syntax:

Here is the syntax of NumPy arange reshape in Python:

``````numpy.arange(
[start],
stop,
[step],
dtype=None
)
reshape()``````

Example:

``````import numpy as np

b = np.arange(2,8).reshape(3,2)
print(b)``````

Here is the Screenshot of the following given Python code.

Case 7: Python numpy arange datetime

• In this section, we will learn about Python NumPy arange datetime.
• The Numpy arange function generates a NumPy array with evenly spaced values based on the start and stop intervals specified upon declaration.
• The datetime type works with many common NumPy, for example, arange can be used to generate a range of date functions.
• To use the arange function, we will create a new script with the NumPy library imported as np.

Example:

``````import numpy as np

a = np.arange('2009-02', '2009-03', dtype='datetime64[D]')
print(a)``````

Here is the Screenshot of the following given Python code

Difference between Python NumPy arange and linspace

• In this section, we will learn about Python NumPy arange vs linspace.
• The numpy.linspace() and numpy.arange() methods are mostly similar because the np.linspace() method also declares an iterable sequence of evenly spaced values within a given interval in Python.
• It also gives values in the specified given interval and the elements are evenly spaced like numpy.arange() function.
• The np.linspace() function in Python NumPy will return an iterable sequence of evenly spaced values on that specific interval.

Syntax:

``````np.linspace(
start,
stop,
endpoint=True,
dtype=None,
axis=0
)``````

Example:

``````import numpy as np

a = np.arange(2,10,2)
b = np.linspace(0,10,5)
print(a)
print(b)``````

Here is the Screenshot of the following given Python code:

Conclusion

The numpy.arange() function in Python is a versatile and powerful tool for generating sequences of numbers in Python. Its ability to handle float numbers and specify data types, coupled with its simplicity and efficiency, makes it a go-to function for array creation in scientific computing and data analysis through the NumPy library.

By understanding its parameters and various use cases, users can leverage numpy.arange() to streamline their numerical computations and enhance the performance of their Python programs. Also knowing the difference between the linspace() and arange() functions we can solve many of our problems.

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