{"id":3782,"date":"2023-07-13T05:04:48","date_gmt":"2023-07-13T05:04:48","guid":{"rendered":"https:\/\/pickl.ai\/blog\/?p=3782"},"modified":"2024-08-14T09:43:08","modified_gmt":"2024-08-14T09:43:08","slug":"data-manipulation-types-examples","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/","title":{"rendered":"Everything You Need to Know about Data Manipulation"},"content":{"rendered":"<p><b>Summary:<span style=\"font-weight: 400;\"> Data manipulation is a crucial data science process that involves transforming, organising, and cleaning data to extract meaningful insights. It includes techniques like filtering, sorting, aggregation, and joining data from multiple sources. It enhances data quality, enables deeper exploration, and facilitates informed decision-making across various domains.<\/span><\/b><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Getting_Started\" >Getting Started<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#What_is_Data_Manipulation\" >What is Data Manipulation?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Key_Features_of_Data_Manipulation\" >Key Features of Data Manipulation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Filtering\" >Data Filtering<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Sorting\" >Data Sorting<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Aggregation\" >Data Aggregation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Transformation\" >Data Transformation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Joining_and_Merging\" >Data Joining and Merging<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Cleaning\" >Data Cleaning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Reshaping\" >Data Reshaping<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Calculation_and_Derivation\" >Data Calculation and Derivation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Manipulation_Examples\" >Data Manipulation Examples<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Example_1_Filtering_and_Sorting\" >Example 1: Filtering and Sorting<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Example_2_Aggregation_and_Summarisation\" >Example 2: Aggregation and Summarisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Example_3_Joining_and_Merging\" >Example 3: Joining and Merging<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Advantages_of_Data_Manipulation\" >Advantages of Data Manipulation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Improved_Data_Quality\" >Improved Data Quality<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Enhanced_Data_Exploration\" >Enhanced Data Exploration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Customised_Data_Presentation\" >Customised Data Presentation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Efficient_Decision-Making\" >Efficient Decision-Making<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Types_of_Data_Manipulation\" >Types of Data Manipulation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Cleaning-2\" >Data Cleaning<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Transformation-2\" >Data Transformation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Aggregation-2\" >Data Aggregation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Enrichment\" >Data Enrichment<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Manipulation_vs_Data_Modification\" >Data Manipulation vs. Data Modification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Tabular_Representation_of_Data_Manipulation_vs_Data_Modification\" >Tabular Representation of Data Manipulation vs Data Modification<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-28\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Data_Manipulation_Tools\" >Data Manipulation Tools<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-29\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#SQL_Structured_Query_Language\" >SQL (Structured Query Language)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-30\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Python_and_R\" >Python and R<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-31\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Excel\" >Excel<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-32\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Business_Intelligence_BI_Tools\" >Business Intelligence (BI) Tools<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-33\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-34\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Why_is_Data_Manipulation_Important\" >Why is Data Manipulation Important?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-35\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#What_Tools_or_Technologies_are_Commonly_Used_for_Data_Manipulation\" >What Tools or Technologies are Commonly Used for Data Manipulation?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-36\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Can_Data_Manipulation_be_Reversible\" >Can Data Manipulation be Reversible?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-37\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#How_Does_Data_Manipulation_Relate_to_Data_Analysis\" >How Does Data Manipulation Relate to Data Analysis?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-38\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Are_there_any_Best_Practices_for_Data_Manipulation\" >Are there any Best Practices for Data Manipulation?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-39\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"getting-started\"><span class=\"ez-toc-section\" id=\"Getting_Started\"><\/span><b>Getting Started<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">We live in a world where data drives decisions and transformative changes are made across business nations. Data manipulation in Data Science is the fundamental process in data analysis.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It helps extract meaningful insights that help meet specific organisational goals. Data professionals use different techniques and operations to derive valuable information from raw and unstructured data. The objective is to enhance the data quality and prepare the data sets for analysis.<\/span><\/p>\n<h2 id=\"what-is-data-manipulation\"><span class=\"ez-toc-section\" id=\"What_is_Data_Manipulation\"><\/span><b>What is Data Manipulation?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data manipulation in data science is a crucial step that helps unfold patterns that eventually help make informed decisions. Simply, it refers to modifying, transforming, or reorganising data to extract meaningful insights, prepare it for analysis, or meet specific requirements.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Moreover, it also enables data professionals to gain a deeper insight into complex datasets. With the ability to manipulate data efficiently, companies can unlock their true potential, which can eventually help boost their productivity and gain a competitive edge.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Since data manipulation in data science has become integral for organisations, this blog has focused on discussing its key features and providing data manipulation examples that will help you understand the significance of this technique.<\/span><\/p>\n<p><b>Also Check Out:\u00a0<\/b><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/5-common-data-science-challenges-and-effective-solutions\/\"><span style=\"font-weight: 400;\">5 Common Data Science Challenges and Effective Solutions<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/data-science-cheat-sheet-business-leaders\/\"><span style=\"font-weight: 400;\">Data Science Cheat Sheet for Business Leaders<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"key-features-of-data-manipulation\"><span class=\"ez-toc-section\" id=\"Key_Features_of_Data_Manipulation\"><\/span><b>Key Features of Data Manipulation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter size-full wp-image-8973\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1.jpg\" alt=\"\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image5-1-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Now, you will look at the key features of data manipulation, which are essential in extracting valuable insights from raw data. From filtering and sorting to aggregation and cleaning, these functions enable efficient organisation, transformation, and analysis, facilitating informed decision-making processes in data science and beyond.<\/span><\/p>\n<h3 id=\"data-filtering\"><span class=\"ez-toc-section\" id=\"Data_Filtering\"><\/span><b>Data Filtering<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/www.indeed.com\/career-advice\/career-development\/what-is-data-filtering\"><span style=\"font-weight: 400;\">Data filtering<\/span><\/a><span style=\"font-weight: 400;\"> is crucial for manipulating data and extracting pertinent insights from raw datasets. Filtering streamlines analysis processes by selectively isolating specific data points or patterns, which enhances efficiency. It ensures that only relevant information contributes to informed decision-making and insightful discoveries.<\/span><\/p>\n<h3 id=\"data-sorting\"><span class=\"ez-toc-section\" id=\"Data_Sorting\"><\/span><b>Data Sorting<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Sorting data or structuring it into columns and rows enhances readability and comprehension. Analysts can quickly identify patterns, outliers, and trends by organising data logically, streamlining the analysis process. This structured presentation aids in extracting meaningful insights and making informed decisions based on a clear understanding of the data.<\/span><\/p>\n<h3 id=\"data-aggregation\"><span class=\"ez-toc-section\" id=\"Data_Aggregation\"><\/span><b>Data Aggregation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Data_aggregation\"><span style=\"font-weight: 400;\">Aggregation<\/span><\/a><span style=\"font-weight: 400;\">, a vital data manipulation feature, condenses multiple records into a concise summary in data science. It encompasses computing averages, summations, accounting totals, and identifying maximum or minimum values. It streamlines analysis processes and yields actionable insights from complex datasets.<\/span><\/p>\n<h3 id=\"data-transformation\"><span class=\"ez-toc-section\" id=\"Data_Transformation\"><\/span><b>Data Transformation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Transformation facilitates data refinement through diverse operations such as mathematical calculations, type conversions, and unit standardisation. It empowers users to convert data types, normalise values, and execute computations. It enhances data compatibility and analytical accuracy for comprehensive insights and informed decision-making.<\/span><\/p>\n<h3 id=\"data-joining-and-merging\"><span class=\"ez-toc-section\" id=\"Data_Joining_and_Merging\"><\/span><b>Data Joining and Merging<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data Joining and Merging in data manipulation enable seamless data integration from diverse sources or tables, leveraging standard fields or keys. This integration enhances analytical depth, offering a comprehensive perspective for refined insights and facilitating complex analyses crucial for informed decision-making in various data science and business operations domains.<\/span><\/p>\n<h3 id=\"data-cleaning\"><span class=\"ez-toc-section\" id=\"Data_Cleaning\"><\/span><b>Data Cleaning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/what-is-data-cleaning-in-machine-learning\/\"><span style=\"font-weight: 400;\">Data cleaning<\/span><\/a><span style=\"font-weight: 400;\"> is pivotal in data manipulation, offering tools to rectify errors, eliminate duplicates, and handle outliers. This demanding process safeguards data quality, strengthening the precision of analyses. By removing inconsistencies and refining datasets, data cleaning lays a strong foundation for informed decision-making and extraction of reliable insights.<\/span><\/p>\n<h3 id=\"data-reshaping\"><span class=\"ez-toc-section\" id=\"Data_Reshaping\"><\/span><b>Data Reshaping<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data reshaping techniques like pivoting, melting, and transposing offer versatility in restructuring data, catering to diverse analytical needs. Converting data formats enhances suitability for specific analyses or visualisations. These operations are pivotal in refining datasets for deeper insights and more precise presentations in data-driven decision-making processes.<\/span><\/p>\n<h3 id=\"data-calculation-and-derivation\"><span class=\"ez-toc-section\" id=\"Data_Calculation_and_Derivation\"><\/span><b>Data Calculation and Derivation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It allows you to create new calculated columns or derived variables based on existing data. This feature enables you to perform calculations, apply formulas, or generate new insights from the available data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It provides powerful tools and techniques for manipulating, organising, and transforming data, allowing for efficient data analysis and decision-making processes.<\/span><\/p>\n<h2 id=\"data-manipulation-examples\"><span class=\"ez-toc-section\" id=\"Data_Manipulation_Examples\"><\/span><b>Data Manipulation Examples<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">It includes various operations that Data Scientists can perform on data. Let&#8217;s explore some common examples to understand how it works in practice: filtering and sorting to organise data, aggregation for summarisation, cleaning to enhance data quality, and joining to integrate diverse datasets. These operations facilitate insightful analysis and decision-making.<\/span><\/p>\n<h3 id=\"example-1-filtering-and-sorting\"><span class=\"ez-toc-section\" id=\"Example_1_Filtering_and_Sorting\"><\/span><b>Example 1: Filtering and Sorting<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">One fundamental data manipulation task is filtering and sorting. It involves selecting specific rows or columns based on certain criteria and arranging the data in order. For instance, in a customer database, you might filter the records only to include customers who purchased in the last month and then sort them based on their total spending.<\/span><\/p>\n<h3 id=\"example-2-aggregation-and-summarisation\"><span class=\"ez-toc-section\" id=\"Example_2_Aggregation_and_Summarisation\"><\/span><b>Example 2: Aggregation and Summarisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Another essential aspect of data manipulation is aggregating and summarising data. It involves calculating summary statistics, such as a specific variable&#8217;s average, sum, minimum, or maximum values. For instance, in sales data, you might aggregate the total revenue generated per product category or calculate the monthly average sales.<\/span><\/p>\n<h3 id=\"example-3-joining-and-merging\"><span class=\"ez-toc-section\" id=\"Example_3_Joining_and_Merging\"><\/span><b>Example 3: Joining and Merging<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It also combines data from multiple sources by joining and merging operations. It allows you to integrate information from different datasets into a unified view. For example, in an e-commerce setting, you might merge customer data with order data to gain insights into customer behaviour and preferences.<\/span><\/p>\n<h2 id=\"advantages-of-data-manipulation\"><span class=\"ez-toc-section\" id=\"Advantages_of_Data_Manipulation\"><\/span><b>Advantages of Data Manipulation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-8972\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2.jpg\" alt=\"\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image3-2-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Effective data manipulation offers several advantages, such as improving data analysis and decision-making. Now, you will learn how it helps you gain comprehensive insights and informed decisions crucial for successful outcomes in various fields of data science and beyond.<\/span><\/p>\n<p><b>Must See:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/understanding-data-science-and-data-analysis-life-cycle\/\"><span style=\"font-weight: 400;\">Understanding Data Science and Data Analysis Life Cycle<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h3 id=\"improved-data-quality\"><span class=\"ez-toc-section\" id=\"Improved_Data_Quality\"><\/span><b>Improved Data Quality<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">This technique enable you to clean and transform raw data into a more refined and usable format. By removing inconsistencies, errors, and duplicates, you enhance the quality and reliability of the data, leading to more accurate analyses and insights.<\/span><\/p>\n<h3 id=\"enhanced-data-exploration\"><span class=\"ez-toc-section\" id=\"Enhanced_Data_Exploration\"><\/span><b>Enhanced Data Exploration<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Manipulating data allows you to explore and examine the information from different angles. By filtering, sorting, and summarising the data, you can quickly identify patterns, trends, and outliers, enabling deeper exploration and understanding of the underlying phenomena.<\/span><\/p>\n<h3 id=\"customised-data-presentation\"><span class=\"ez-toc-section\" id=\"Customised_Data_Presentation\"><\/span><b>Customised Data Presentation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">It provides the flexibility to tailor the presentation of data according to specific requirements. You can aggregate and summarise data in various ways, create custom reports and visualisations, and highlight the most relevant insights, making it easier for stakeholders to comprehend and make informed decisions.<\/span><\/p>\n<h3 id=\"efficient-decision-making\"><span class=\"ez-toc-section\" id=\"Efficient_Decision-Making\"><\/span><b>Efficient Decision-Making<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">By manipulating data effectively, you can extract valuable insights quickly, enabling timely and informed decision-making. This drives strategic actions, achieves desired outcomes, identifies market trends, optimises business processes, or targets customer segments.<\/span><\/p>\n<h2 id=\"types-of-data-manipulation\"><span class=\"ez-toc-section\" id=\"Types_of_Data_Manipulation\"><\/span><b>Types of Data Manipulation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img decoding=\"async\" class=\"aligncenter size-full wp-image-8974\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image1-1.jpg\" alt=\"\" width=\"1000\" height=\"333\" \/><\/p>\n<p><span style=\"font-weight: 400;\">This techniques are categorised based on the operations they perform. Let&#8217;s explore some common types mentioned below. Each plays a crucial role in extracting insights, ensuring data quality, and facilitating informed decision-making processes in various domains.<\/span><\/p>\n<h3 id=\"data-cleaning-2\"><span class=\"ez-toc-section\" id=\"Data_Cleaning-2\"><\/span><b>Data Cleaning<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data cleaning involves identifying and rectifying dataset errors, inconsistencies, and missing values. This process ensures data accuracy and integrity by removing or correcting problematic entries, standardising formats, and resolving inconsistencies.<\/span><\/p>\n<h3 id=\"data-transformation-2\"><span class=\"ez-toc-section\" id=\"Data_Transformation-2\"><\/span><b>Data Transformation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data transformation refers to converting data from one format or structure to another. It involves normalisation, scaling, encoding, or converting data types to facilitate analysis and improve compatibility with different systems or models.<\/span><\/p>\n<h3 id=\"data-aggregation-2\"><span class=\"ez-toc-section\" id=\"Data_Aggregation-2\"><\/span><b>Data Aggregation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data aggregation combines individual data points into groups or summaries. It involves operations like calculating totals, averages, or percentages across specific dimensions or categories, providing a high-level view of the data.<\/span><\/p>\n<h3 id=\"data-enrichment\"><span class=\"ez-toc-section\" id=\"Data_Enrichment\"><\/span><b>Data Enrichment<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/www.edq.com\/data-quality-platform\/data-enrichment\/#null\"><span style=\"font-weight: 400;\">Data enrichment<\/span><\/a><span style=\"font-weight: 400;\"> involves enhancing existing data by adding additional information from external sources. It can include appending demographic, geographic, or social media data to enrich the existing dataset and gain deeper insights.<\/span><\/p>\n<h2 id=\"data-manipulation-vs-data-modification\"><span class=\"ez-toc-section\" id=\"Data_Manipulation_vs_Data_Modification\"><\/span><b>Data Manipulation vs. Data Modification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">While data manipulation and data modification may sound similar, they have distinct meanings in the context of data management:<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data manipulation refers to transforming and organising data to facilitate analysis and decision-making. As discussed earlier, it involves filtering, sorting, summarising, joining, and aggregating data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, data modification refers to altering the structure or schema of a database. It involves changing the database&#8217;s structure, such as adding or deleting tables, modifying column definitions, or altering relationships between tables.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Both data manipulation and modification are essential in <\/span><a href=\"https:\/\/pickl.ai\/blog\/data-management-guide\/\"><span style=\"font-weight: 400;\">data management<\/span><\/a><span style=\"font-weight: 400;\">. However, they serve different purposes and address various aspects of working with data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It&#8217;s essential to note that while these two concepts are distinct, they can interconnect in specific scenarios. Data manipulation may involve temporary modifications for analysis purposes. However, it typically focuses on creating derived data representations rather than permanently changing them. On the other hand, data modification involves direct and permanent changes to the data itself.<\/span><\/p>\n<h2 id=\"tabular-representation-of-data-manipulation-vs-data-modification\"><span class=\"ez-toc-section\" id=\"Tabular_Representation_of_Data_Manipulation_vs_Data_Modification\"><\/span><b>Tabular Representation of Data Manipulation vs Data Modification<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">It\u2019s important to note that while these two concepts are distinct, they can be interconnected in specific scenarios. It may involve temporary modifications for analysis purposes, but it typically focuses on creating derived representations of the data rather than permanently changing it.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">On the other hand, data modification involves direct and permanent changes to the data itself.<\/span><\/p>\n<h2 id=\"data-manipulation-tools\"><span class=\"ez-toc-section\" id=\"Data_Manipulation_Tools\"><\/span><b>Data Manipulation Tools<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">A diverse array of tools and technologies exist to streamline data manipulation. Let&#8217;s delve into a selection of relevant tools extensively employed across industries, each tailored to accommodate specific needs and preferences. These tools empower users to efficiently manage, analyse, and derive insights from vast datasets.<\/span><\/p>\n<h3 id=\"sql-structured-query-language\"><span class=\"ez-toc-section\" id=\"SQL_Structured_Query_Language\"><\/span><b>SQL (Structured Query Language)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">SQL is a standard language for managing relational databases. Also, it provides powerful commands for querying, filtering, sorting, and aggregating data. Data professionals commonly utilise SQL for data manipulation tasks, particularly with structured datasets.&#8221;<\/span><\/p>\n<h3 id=\"python-and-r\"><span class=\"ez-toc-section\" id=\"Python_and_R\"><\/span><b>Python and R<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Programming languages like Python and R offer rich libraries and frameworks for data manipulation. Moreover, Pandas in Python and Dplyr in R libraries provide intuitive and efficient functions for various data manipulation operations. Data scientists and analysts widely use these languages in their data science and analytics workflows.<\/span><\/p>\n<h3 id=\"excel\"><span class=\"ez-toc-section\" id=\"Excel\"><\/span><b>Excel<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Excel is a popular spreadsheet software that also offers basic data manipulation capabilities.\u00a0 It allows users to filter and sort data, perform calculations, and create simple visualisations. While Excel is not as powerful as specialised data manipulation tools, it is widely accessible and user-friendly.<\/span><\/p>\n<h3 id=\"business-intelligence-bi-tools\"><span class=\"ez-toc-section\" id=\"Business_Intelligence_BI_Tools\"><\/span><b>Business Intelligence (BI) Tools<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">BI tools like <\/span><a href=\"https:\/\/pickl.ai\/blog\/importance-of-tableau-for-data-science\/\"><span style=\"font-weight: 400;\">Tableau<\/span><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/pickl.ai\/blog\/power-bi-tutorial\/\"><span style=\"font-weight: 400;\">Power BI<\/span><\/a><span style=\"font-weight: 400;\">, and QlikView provide comprehensive data manipulation features and advanced visualisation capabilities. These tools enable users to connect to various data sources, manipulate and transform data visually, and create interactive dashboards and reports.<\/span><b>\u00a0<\/b><\/p>\n<p><b>Read More:<\/b><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/business-intelligence-decision-making\/\"><span style=\"font-weight: 400;\">How does business intelligence help in Decision-making?<\/span><\/a><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/business-intelligence-vs-business-analytics\/\"><span style=\"font-weight: 400;\">Business Intelligence vs Business Analytics<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"why-is-data-manipulation-important\"><span class=\"ez-toc-section\" id=\"Why_is_Data_Manipulation_Important\"><\/span><b>Why is Data Manipulation Important?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data manipulation is crucial for several reasons. It allows analysts and professionals to extract relevant information from raw data, enhance data quality, and prepare datasets for analysis. By manipulating data effectively, organisations can derive valuable insights, make informed decisions, and gain a deeper understanding of their data.<\/span><\/p>\n<h3 id=\"what-tools-or-technologies-are-commonly-used-for-data-manipulation\"><span class=\"ez-toc-section\" id=\"What_Tools_or_Technologies_are_Commonly_Used_for_Data_Manipulation\"><\/span><b>What Tools or Technologies are Commonly Used for Data Manipulation?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Various tools and technologies are available for data manipulation depending on the specific requirements and context. Commonly used tools include SQL (Structured Query Language) for database operations, programming languages like Python or R with libraries such as Pandas or dplyr, spreadsheet software like Microsoft Excel or Google Sheets, and data manipulation frameworks like Apache Spark.<\/span><\/p>\n<h3 id=\"can-data-manipulation-be-reversible\"><span class=\"ez-toc-section\" id=\"Can_Data_Manipulation_be_Reversible\"><\/span><b>Can Data Manipulation be Reversible?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data manipulation creates new representations or summaries without permanently changing the original data. The outcome depends on the operations&#8217; execution and whether the original data remains preserved or backed up.<\/span><\/p>\n<h3 id=\"how-does-data-manipulation-relate-to-data-analysis\"><span class=\"ez-toc-section\" id=\"How_Does_Data_Manipulation_Relate_to_Data_Analysis\"><\/span><b>How Does Data Manipulation Relate to Data Analysis?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data manipulation is a critical step in data analysis. However, before proceeding with the analysis step, it is integral to prepare the data. Thus, Data Manipulation comes into the picture. It cleanses the data and certifies it to be fit for analysis.\u00a0<\/span><\/p>\n<h3 id=\"are-there-any-best-practices-for-data-manipulation\"><span class=\"ez-toc-section\" id=\"Are_there_any_Best_Practices_for_Data_Manipulation\"><\/span><b>Are there any Best Practices for Data Manipulation?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Data manipulation entails adhering to several best practices, including thorough documentation, employing suitable data structures, cautious handling of missing values and outliers, validation of results, ensuring data integrity and security, utilising version control, and fostering collaboration among team members for effective outcomes.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Data Manipulation empowers individuals and organisations to extract valuable insights, improve data quality, and drive informed actions, making it a unique skill set. Moreover, by understanding the various techniques, examples, advantages, and tools associated with data manipulation, you can harness its power to unlock your data&#8217;s true potential.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this article, we have discussed some of the critical aspects of Data Manipulation that will help you harness this technique for accurate data analysis.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Now that you have a comprehensive understanding of data manipulation, it&#8217;s time to apply these insights in practice and leverage the power of data to gain a competitive edge in your field.<\/span><\/p>\n<h2 id=\"\"><\/h2>\n","protected":false},"excerpt":{"rendered":"Data manipulation is essential for data analysis, enabling data cleaning, transformation, aggregation, and integration to derive valuable insights and drive informed decisions.\n","protected":false},"author":17,"featured_media":13768,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[46],"tags":[1211,1210,1214,1213,1215,1212],"ppma_author":[2184,2180],"class_list":{"0":"post-3782","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"tag-data-manipulation-examples","9":"tag-data-manipulation-in-data-science","10":"tag-data-manipulation-language","11":"tag-data-manipulation-vs-data-modification","12":"tag-types-of-data-manipulation","13":"tag-what-is-data-manipulation"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>A Complete Guide to Data Manipulation - Pickl.AI<\/title>\n<meta name=\"description\" content=\"Elevate your data manipulation skills with Pickl.ai. the power of data manipulation techniques to extract and analyse information.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Everything You Need to Know about Data Manipulation\" \/>\n<meta property=\"og:description\" content=\"Elevate your data manipulation skills with Pickl.ai. the power of data manipulation techniques to extract and analyse information.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.pickl.ai\/blog\/data-manipulation-types-examples\/\" \/>\n<meta property=\"og:site_name\" content=\"Pickl.AI\" \/>\n<meta property=\"article:published_time\" content=\"2023-07-13T05:04:48+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-08-14T09:43:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/07\/image4-11.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"628\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Anubhav Jain, Tarun Chaturvedi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Anubhav Jain\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"11 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/\"},\"author\":{\"name\":\"Anubhav Jain\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/#\\\/schema\\\/person\\\/673bb4bf041ebfd7718397ca92e6845e\"},\"headline\":\"Everything You Need to Know about Data Manipulation\",\"datePublished\":\"2023-07-13T05:04:48+00:00\",\"dateModified\":\"2024-08-14T09:43:08+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/\"},\"wordCount\":2146,\"image\":{\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/wp-content\\\/uploads\\\/2023\\\/07\\\/image4-11.jpg\",\"keywords\":[\"data manipulation examples\",\"data manipulation in data science\",\"data manipulation language\",\"Data manipulation vs. data modification\",\"types of data manipulation\",\"What is data manipulation\"],\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/\",\"url\":\"https:\\\/\\\/www.pickl.ai\\\/blog\\\/data-manipulation-types-examples\\\/\",\"name\":\"A Complete Guide to Data Manipulation - 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