The act of manipulating data is a process of changing or transforming of data to make the data more insightful, more useful and more suitable for analysis.
Data manipulation can involve various actions such as restructuring, cleaning, combining and analyzing of data.
Essentially the data manipulation is a process of preparing data and modifying of data in order to extract the valuable information and also insights.
Data manipulation involves cleaning and removing of errors, inconsistencies, and information that is irrelevant from the data.
Data manipulation also involves restructuring and changing the format or structure of the data to make the data easier to work with and also combining and merging of data from different sources in order to create a more comprehensive dataset.
Data manipulation also involves analyzing and applying of statistical or other techniques to extract the patterns, trends and the relationships from the data.
Transforming and converting of data from one format to another, like changing text to numerical data or dates is also another process involved in manipulation of data.
Manipulation of data is important it is improved data quality and ensures the data is consistent, accurate and reliable and also enhances insights.
By cleaning and transforming of the data, it also becomes easier to identify patterns, trends and also anomalies which may be hidden in the raw data.
Manipulated data is also good for better decision making and provides a clearer picture of the situation and leads to more informed and effective decisions.
Data manipulation also streamlines the process of analysis by making data more accessible and organized and results in more efficient analysis.
And data manipulation also provides support for machine learning as many machine learning algorithms also require data to be in s specific format and data manipulation is essential in preparing the data for these models.
Examples of data manipulation include filtering and selecting only the data which meets the specific criteria.
Sorting and arranging of data in a particular order.
Aggregating and summarizing of data, like calculating the total or average.
And joining and combining of data from multiple tables or datasets that are based on a shared attribute.