Data Manipulation – Understanding Data Manipulation | HTML KICK

Data Manipulation

Data Manipulation: Data is changing the way corporations operate today. This is from business decision-making to daily operations because everything depends on data. None of this remains possible minus changing raw information to useful data, particularly when a big amount of data & disparate sources are being involved. Data manipulation helps in translating data to the required format thus it can be simply cleaned & mapped to remove insights

What’s Data Manipulation?

This process of altering or changing information to make it organized and more readable. For instance, one can arrange information alphabetically to improve the process of getting important information. Another good example of data manipulation is website management. Here website owners use web-server logs for finding traffic sources, most viewed web pages, and much more. Also, stockbrokers utilize data manipulation in forecasting stock market trends.

Clients in the accounting field or any other similar fields normally manipulate information for figuring out product costs, potential tax obligations, or sales trends. Stock market experts are normally utilizing data manipulation for predicting trends in the stock market & the way stocks may do in the future.

For one to achieve data manipulation, DML is used. DML refers to Data Manipulation Language. This is a programming language that is capable of removing, adding, or changing databases. This includes changing information to anything readable.

The common SQL statement used in the data manipulation process

  • Select: This statement enables one to choose the information you need to manipulate from a database. Precisely, it commands the database which data to select & where to get the data.
  • Insert: This statement enables one in relocating information within a database. Precisely, it commands the database where data is presently located & where the information requires to be moved.
  • Update: This statement enables one in updating existing data in the database. Precisely, it can tell the database which data requires to be updated, where new data needs to be input & whether it needs to add information records one at a moment or together.
  • Delete: This statement allows one in removing information from the database. Precisely, it informs the database which information to delete & where it can get the data.

Importance of Data Manipulation

Manipulation of data is important for any business operations & optimization because it enables the data organization for better clarity & readability. It allows you to operate with the information that you require. It’s important for a business due to the factors below.

  • Data Projection

Manipulation of data makes it likely to utilize historical data in projecting the future & offering in-depth analysis, particularly when it gets to financial transactions.

  • Data Consistency

Data consistency enables it to be understood & read. Data from different sources may not have a unified view. Though, manipulation of data makes it simple for organizing & keeping it. Data manipulation offers one surety that data is well-structured, organized & stored consistently.

  • Remove redundant data

Information can have useless figures. Manipulation of data enables one to eliminate unusable data. Washing your records is probable using data manipulation.

  • Create significance from data

Manipulation of data allows one to edit, insert, delete or change data. Thus, one can achieve more with any available information. When one understands how to utilize data, it gets better for the business.

Data Manipulation Steps

Some significant steps which help you in getting started with data manipulation include

  • First, of it all, manipulation of data is possible just if you’ve data. Therefore, you’re needed to form a database that’s generated from some data sources.
  • The knowledge requires restructuring & reorganization, which can be done with manipulation of data which assists one in cleansing your information.
  • After that, you require to import a database & create it to start working with data.
  • With the assistance of data manipulation, one can edit, merge, delete, or combine data.
  • Data analysis gets to be easier at the period of manipulating information.

How Data is manipulated In Excel?

Data manipulation in Python and data manipulation in R remain important aspects of data manipulation. Before going via the more intense principles of manipulation of data in Python & R, let’s now comprehend data manipulation in excel.

Tips to assist you in manipulating Excel information

Autofill in Excel

When one needs to use a similar equation across numerous cells, the feature is important. A way of doing this is through retyping the formula. A different way is by dragging the cursor to a cell’s lower right corner & then downwards. This will assist one in concurrently applying a similar formula to numerous rows.

  • Formulas & functions

Subtraction, addition, division & multiplication are some of the basic math functions in Excel. You are required to understand how to utilize Excel-critical features.

  • Sort & Filter

The clients save more time when analyzing information through sorting & filtering options in Excel.

  • Column merging, splitting & merging

Rows or columns in Excel are normally removed or added. Data organization normally needs splitting, integrating, or combining numerous datasheets.

  • Eliminating duplicates

There are chances of data replication in the procedure of collecting & data assimilation. In Excel, the Delete Duplicate feature helps in removing replica spreadsheet entries.

R Data Manipulation

Manipulation of data in R allows users to organize information better for it to be suitable for analysis. Simple data manipulation methods in R includes

Sub-setting

The objective of forming subsets is to assist in data analysis. This process consists of the division of data into small-sized samples. This procedure of forming samples is understood as sub-setting.

Ways of sub-setting in R

  1. A) $ – The dollar sign is important for choosing distinct data elements.
  2. B) [[ – Double brackets-operator at R is also significant for returning a single component. Besides, it’s also flexible the reason being it refers to elements through position instead of by name. Thus, it’s important for data frames & lists.
  3. C) [ – This function in R returns numerous aspects of information. The indices in single square brackets are a character vector, a numeric vector, or a logical vector.

For instance – The command below is utilized for retrieving all columns & five rows of an already-built dataset ‘iris’.

  1. a) > data (iris)
  2. b) > iris [1:5, ]

Merging & sorting in R

It’s done so in three ways

  1. A) Adding columns in R using cbind() – This makes sense only when both sets have a similar set of rows & the order of the rows is identical. The cbind() or data.frame function helps one in doing so.
  2. B) Adding rows in R using rbind() – The process is the same as that of adding columns. When two datasets have similar columns, one can add rows at the bottom with the help of the rbind() function.
  3. C) Merging data with various shapes in R using merge() – This feature combines information based on common columns and rows.

Traversing Data in R with Apply() function

Apply() function assist in traversing data in R

  1. A) Matrix or Array – Apply() function go across the columns or rows while using a function to every resulting vector & earns a vector of summarized results.
  2. B) List – Lapply() function traverses a list through applying a function to every element & returns results list.

Data Manipulation in Python

Ways of data manipulation with Python

  1. Pivot Table – With the help of pandas, one produces MS Excel-style pivot tables at Python.
  2. Boolean Indexing – The function is important when clients need to filter values of a certain column dependent on conditions from various columns set.
  3. Apply Function – Apply Function is among the more commonly utilized functions for creating new variables & operating with data
  4. Crosstab – The crosstab function allows the user to have an original view of data. This also offers scope for legalizing some important hypotheses.
  5. Merge data-frames – When a user requires relating information from different sources, the Merge data-frames function gets in handy.
  6. Sort data-frames – The function sort_values() is important when a user needs to sort panda’s data frame using one or numerous columns. Also, the function sort_index() assists in sorting the panda’s data frame using row index.

Tips for utilizing data manipulation

  1. Understand the needs & emphases before starting this process
  2. Determine some specific data you require for the precise business focuses
  3. Research various manipulation tools
  4. Utilize resources to assist and guide you via the process
  5. Consider taking benefits of automation tools
  6. Comprehend mathematical functions & how one assist you to mix data
  7. Utilize various formulas to assist you in gaining more niche results that suit your needs
  8. Filter data to get specific results
  9. Use auto-fill functions for normally used equations or formulas
  10. Update data manipulation as required to address the business focus
  11. Use data visualization tools to represent or present manipulated data

Benefits of Data Manipulation

It’s important for data analytics. Below are some advantages of data management.

  • The user specifies which data is required and which isn’t.
  • It allows humans to interrelate with the system easily.
  • Data-Manipulation Language statements modify the information stored in a database.
  • The language contains various flavors & capabilities between the database vendors.

Disadvantages of Data Manipulation

  • It’s not possible to delete or create tables & columns via the language.
  • One can’t utilize data manipulation language in changing the structure of database

Terry White

Terry White is a professional Drupal & WordPress developer, Web developer, Web designer, Software Engineer, and Blogger. He strives for pixel-perfect design, clean robust code, and a user-friendly interface. If you have a project in mind and like his work, feel free to contact him

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