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:

Name | Description |
---|---|

start | This represents the end of the interval. The interval does not include this value, except in some cases where the step is not an integer and floating-point round-off affects the length of the output Python NumPy array. |

stop | This represents the end of the interval. The interval does not include this value, except in some cases where the step is not an integer and floating-point round-off affects the length of the output Python NumPy array. |

step | [optional] This represents the spacing between values. The default step value is 1. |

dtype | [optional] The type of the output Python NumPy array. If dtype is not given, the data type of the output array is inferred from the other input arguments. |

### 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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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.