Data Manipulation Description

Data manipulation is the process of changing data to make it easier to read or be more organized. For example, a log of data could be organized in alphabetical order, making individual entries easier to locate. Data manipulation is often used on web server logs to allow a website owner to view their most popular pages as well as their traffic sources.

Users in the accounting field or similar fields often manipulate data to figure out product costs, sales trends, or potential tax obligations. Stock market analysts are frequently using data manipulation to predict trends in the stock market and how stocks might perform in the near future.

Computers may also use data manipulation to display information to users in a more meaningful way, based on code in a software program, web page, or data formatting defined by a user.

data-manipulation

Purpose of Data Manipulation

Data manipulation is crucial for business operations and optimization, as it allows for the organization of data for better clarity and readability. It lets you work with the data in which you need it. It is beneficial for the business because of the following factors.

Data Consistency – Consistency in data allows it to be read and understood. Data from various sources may not have a unified view. However, data manipulation makes it easy for organizing and storing it.

Data Projection – Data manipulation makes it possible to use historical data for projecting the future and providing in-depth analysis, especially when it comes to financial transactions.

Create value from data – Data manipulation enables you to edit, delete, insert, or transform data. Thus, you can do more with the available data. When you know how to use data, it becomes better for your business.

Remove unnecessary data – Data can contain useless figures. Data manipulation allows you to remove unusable data. Cleansing your records is possible with data manipulation.

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Steps for Manipulating Data

Here are the steps you should take if you want to get started with data manipulation.

  • Data manipulation is possible only if you have data to do so. Thus, you need a database that is created from data sources.
  • This data needs rearrangement and restructuring. Data manipulation allows you to cleanse your data.
  • Import and build a database for you to work on.
  • Data manipulation lets you combine, delete, or merge information.
  • Data analysis becomes easy when you manipulate data.
  • data-manipulation

    Data Manipulation in Python

    We have seen various types of data manipulation in R. Similarly, Python is also one of the preferred languages for data scientists. Here are some methods of data manipulation in Python that can help users to analyze data better.

  • Pivot Table – Using pandas, you can produce MS Excel-style pivot tables in Python.
  • Boolean Indexing – This function is useful when the user wants to filter the values of a specific column depending on the conditions from another set of columns.
  • Apply Function – The Apply Function is one of the most commonly used functions for building new variables and working with data
  • Crosstab – The Crosstab function enables the user to get an original view of the data. It also provides scope for validating some fundamental hypotheses.
  • Merge data frames – If the user needs relating data from various sources, the Merge data frames function comes in handy.
  • Sort data frames – The sort_values() function is useful when the user wants to sort the panda’s data frame by one or multiple columns. Similarly, the sort_index() function helps sort the panda’s data frame by row index.
  • We have seen six different types of data manipulation in Python. There are some more such as plotting, cut function, impute missing values, and so on.

    In this article, we have discussed different types of data manipulation in Excel, R, and Python. We have also seen its definition and understood its significance. However, many people confuse data manipulation and data modification. We shall now examine the primary differences between data modification and data manipulation.

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