Data Manipulation: Definition, Examples, and Uses

Have you ever wondered how data enthusiasts turn raw, messy data into meaningful insights that can change the world (or at least, a business)? Imagine you're given a huge, jumbled-up puzzle. Each piece is a data point, and the picture on the puzzle is the information you want to uncover. Data manipulation is like sorting, arranging, and connecting those puzzle pieces to reveal the bigger picture.

Data Manipulation is one of the initial processes done in Data Analysis. It involves arranging or rearranging data points to make it easier for users/data analysts to perform necessary insights or business directives. Data Manipulation encompasses a broad range of tools and languages, which may include coding and non-coding techniques. It is not only used extensively by Data Analysts but also by business people and accountants to view the budget of a certain project.

It also has its programming language, DML (Data Manipulation Language) which is used to alter data in databases. Let's know what exactly Data manipulation is.

What is Data Manipulation?

Data Manipulation is the process of manipulating (creating, arranging, deleting) data points in a given data to get insights much easier. We know that about 90% of the data we have are unstructured. Data manipulation is a fundamental step in data analysis, data mining, and data preparation for machine learning and is essential for making informed decisions and drawing conclusions from raw data.

To make use of these data points, we perform data manipulation. It involves:

• Creating a database
• SQL for structured data manipulation
• NoSQL languages like MongoDB for unstructured data manipulation.

Steps Required to Perform Data Manipulation

The steps we perform in Data Manipulation are:

• Mine the data and create a database: The data is first mined from the internet, either with API requests or Web Scraping, and these data points are structured into a database for further processing.
• Perform data preprocessing: The Data acquired from mining is still a little rough and may have incorrect values, missing values, and some outliers. In this step, all these problems are taken care of, either by deleting the rows or, by adding the mean values in all missing areas (Note: This is only in the case of numerical data.)
• Arrange the data: After the data has been preprocessed, it is arranged accordingly to make analysis of data easier.
• Transform the data: The data in question is transformed, either by changing datatypes or transposing data in some cases.
• Perform Data Analysis: Work with the data to view the result. Create visualizations or an output column to view the output.

We’ll see more on each of these steps in detail below.

Tools Used in Data Manipulation

Many tools are used in Data Manipulation. Some most popularly known tools with no-code/code Data manipulation functionalities are:

• MS Excel - MS Excel is one of the most popular tools used for data manipulation. It provides a huge array/ variety for freedom/ manipulation of data.
• Power BI - It is a tool used to create interactive dashboards easily. It is provided by Microsoft and can be coded into it.
• Tableau - Tableau has a similar functionality as Power BI, but it is also a data analysis tool where you can manipulate data to create stunning visualizations.

Operations of Data Manipulation

Data Manipulation follows the 4 main operations, CRUD (Create, Read, Update and Delete). It is used in many industries to improve the overall output.

In most DML, there is some version of the CRUD operations where:

• Create: To create a new data point or database.
• Read: Read the data to understand where we need to perform data manipulation.
• Update: Update missing/wrong data points with the correct ones to encourage data to be streamlined.
• Delete: Deletes the rows with missing data points/ erroneous/ misclassified data.

These 4 main operations are performed in different ways seen below:

• Data Preprocessing: Most of the raw data that is mined may contain errors, missing values and mislabeled data. This will hamper the final output if it is not dealt with in the initial stages.
• Structuring data (if it is unstructured): If there’s any sort of data available in the database which can be structured into a table to query them effectively, we sort those data into tables for greater efficiency.
• Reduce the number of features: As we know, data analysis is inherently computationally intensive. As a result, one of the reasons to perform data manipulation is to find out the optimum number of features needed for getting the result, while discarding the other features. Some techniques used here are, Principal Component Analysis (PCA), Discrete Wavelet Transform and so on.
• Clean the data: Delete unnecessary data points or outliers which may affect the final output. This is done to streamline the output.
• Transforming data: Some insights into data can be improved by transforming the data. This may involve transposing data, and arranging/rearranging them.

Example of Data Manipulation

Let us see a basic example of Data manipulation in more detail. We can see that there are examples of Data Manipulation that can be used as a baseline. First of all, Import the data, load it and display it.

Considering you have a dataset, you’ll need to load it and display it.

The Iris dataset is viewed below:

This reads the Iris Dataset and prints the last 5 values of the Dataset.

import pandas as pd
df=pd.read_csv("Iris.csv")
print(df.tail())

Output:

Use of Data Manipulation

In today’s world where every business has become competitive and undergoing digital transformation, the right data is paramount for all decision-making abilities. Hence, to achieve our results easier and faster, we implement data manipulation.

There are many reasons why we need to manipulate our data. They are:

• Increased Efficiency.
• Less Room for Error.
• Easier to Analyze data.
• Fewer chances for unexpected results.

Conclusion

Due to unrestricted globalization, and near-digitization of all industries, there is a greater need for correct data for good business insights. This calls for even more rigorous Data Manipulation Techniques in both the coding sphere and the lowcode/nocode spheres. Various programming languages and tools, such as Python with libraries like pandas, R, SQL, and Excel, are commonly used for data manipulation tasks. Data Manipulation may be hard if the data mined is unreliable. Hence there are even more regulations on data mining, Data Manipulation and Data Analysis.