# Solved: NumPy left_shift Code When inputs and bit shift are numbers

NumPy is an essential library for any Python programmer working with mathematical or numerical data. It provides powerful array and matrix manipulation capabilities. One of the lesser-known functions that NumPy offers is the ability to perform bitwise operations on array elements. This includes the bitwise left shift operation, which can be applied to numbers within a NumPy array. In this article, we’ll explore the NumPy left_shift operation and provide a step-by-step tutorial on how to use this handy function.

To begin, let’s discuss the problem we intend to solve. Given two integer inputs, we want to perform a bitwise left shift operation on each number, effectively shifting the bits in each number a specified number of positions to the left. In Python, NumPy provides the `left_shift()` function for this purpose, and we’ll examine how to use it effectively.

## Understanding the NumPy left_shift() Function

The `NumPy left_shift()` function takes two arguments: an array containing the numbers to be shifted and the number of positions to shift the bits. The function then returns a new array where each element has been bitwise left shifted by the specified number of positions. The general syntax of the function is as follows:

```import numpy as np
result = np.left_shift(array, shift_positions)
```

Now, let’s dive into a step-by-step tutorial on how to implement this code for solving our problem.

## Step-by-step Tutorial: Bitwise Left Shift with NumPy

1. First, we need to import the NumPy library. This can be done by adding the following line of code:

```   import numpy as np
```

2. Next, we’ll create a NumPy array containing the integer numbers that we wish to perform the bitwise left shift operation on. This can be done as follows:

```   input_numbers = np.array([1, 2, 3, 4, 5])
```

3. Now, we can perform the bitwise left shift operation using the `left_shift()` function. In this example, we’ll shift the bits in each number two positions to the left:

```   result = np.left_shift(input_numbers, 2)
```

4. Finally, let’s print the resulting array to see the results of the left shift operation:

```   print("Input numbers: ", input_numbers)
print("Result after left shift: ", result)
```

The output will be:

“`
Input numbers: [1, 2, 3, 4, 5]
Result after left shift: [ 4, 8, 12, 16, 20]
“`

## Applications and Related Functions

Bitwise operations like the NumPy left_shift function are useful in many areas, such as digital signal processing, image processing, and cryptography. They enable programmers to manipulate and analyze data at a binary level, which can be essential for optimizing performance in certain applications.

In addition to the left_shift function, NumPy also provides a variety of other bitwise operations, including right shift, bitwise AND, bitwise OR, and bitwise XOR. These functions can be similarly applied to numerical data, offering a flexible suite of tools for working with binary representations.

In conclusion, the `NumPy left_shift()` function is a powerful tool for performing bitwise left shift operations on numerical data. By understanding its syntax and usage, Python programmers can expand their skill set and tackle complex numerical problems with ease.

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