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# Element wise concatenation of two NumPy arrays of string

Python's element-wise union of two NumPy string arrays is a potent method with a wide range of uses. This blog article will go over NumPy's setup and implementation procedures, the syntax for joining two NumPy string arrays together element-wise in Python, and the underlying method. Element-wise concatenation, for instance, is commonly used in data manipulation tasks to combine two data sets.

## Installation and Setup

Simply use **pip **or **conda**. NumPy is a powerful library that provides support for mathematical operations and arrays. Once installed, you can import it into your Python script using the following command −

import numpy as np

## Syntax

Element-wise concatenation of two NumPy arrays of string in Python is done using the **numpy.core.defchararray.add()** function.

numpy.core.defchararray.add(arr1, arr2)

The two NumPy arrays that you want to combine in this case are arr1 and arr2. The add() method will join the components of arr1 and arr2 element by element, so the first element of arr1 will be combined with the first element of arr2, the second element of arr1 with the second element of arr2, and so forth.

## Example

import numpy as np arr1 = np.array(['hello', 'world']) arr2 = np.array(['!', '?']) result = np.core.defchararray.add(arr1, arr2) print(result)

## Output

['hello!' 'world?']

Here, NumPy arrays of string, **arr1 **and **arr2 **are created. Use the **add**() function to concatenate the elements of these arrays element-wise. The resulting array, result, contains the concatenated strings.

## Example

import numpy as np arr1 = np.array(['apple', 'banana', 'cherry']) arr2 = np.array([' pie', ' split', ' tart']) result = np.core.defchararray.add(arr1, arr2) print(result)

## Output

['apple pie' 'banana split' 'cherry tart']

We have two NumPy arrays of string, arr1 and arr2. We then use the add() function to concatenate the elements of these arrays element-wise. The resulting array, result, contains the concatenated strings.

## Applications

This might be a choice if two data sets can be merged based on a field that is comparable to another field, like a product Number. Use element-wise concatenation to merge the numbers from the product ID and another column to create a new column in each data gathering.

**Data Manipulation** − Element-wise concatenation, for instance, is commonly used in data manipulation tasks to combine two data sets. Consider combining two data sets based on a field that is comparable to another field, such as a product ID. Element-wise concatenation can be used to merge the values of the product ID and another column to create a new column in each data gathering.

**Natural Language Processing (NLP)** − Element-wise concatenation is commonly used in NLP tasks like attitude analysis and text classification. The text data in these tasks is frequently presented as a matrix, with each row designating a passage of text or a sentence and each column designating a word or a symbol. Each sentence or text's word embeddings or word vector representations can be merged using element-wise concatenation.

**Data Cleaning** − Likewise, missing values can be replaced or unwanted symbols can be eliminated from data by using element-wise concatenation. For instance, in a data gathering that contains phone numbers, element-wise concatenation can be used to merge the area code and the phone number. Then, using regular expressions, unwanted symbols can be eliminated, including brackets and hyphens.

## Conclusion

An excellent technique that can be used for many tasks, such as data administration, natural language processing, and data cleaning, is the element-wise concatenation of two NumPy text arrays in Python. Two NumPy strings can be combined into one array using the **numpy.core.defchararray.add()** method. When working with big datasets that call for quick and effective procedures, this tool is especially helpful. It also offers freedom when managing text data that may be in a variety of lengths or styles.