[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-292606-105":59,"doc-detail-292606-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","data-quality-check-and-summary-statistics","Data Quality Check and Summary Statistics","","This document provides a comprehensive framework for assessing data quality and generating summary statistics for analytical datasets. It outlines essential procedures including the verification of row and column structures, identification of data types and formats, and rigorous handling of missing values. Furthermore, it details techniques for detecting duplications and analyzing data distributions using various visualizations such as histograms, boxplots, and scatter plots. The guide also covers key numerical summarization metrics including central tendency, dispersion, skewness, and kurtosis, alongside methods for examining correlations and distances between data points, ensuring robustness for data-driven decision-making processes.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/data-quality-check-and-summary-statistics/292606/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/data-quality-check-and-summary-statistics/292606.png","ImageObject",300,407,{"name":92,"@type":93},"Melati","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What aspects should be checked when reviewing rows and columns in a dataset?","Question",{"text":113,"@type":114},"Reviewers should clarify the meaning of each row (e.g., shipment vs. combined shipment) and analyze column counts, specifically the number of rows and unique values.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What methods are recommended for analyzing data distributions?",{"text":118,"@type":114},"The document suggests using value count bar plots for discrete data, and histograms, kernel density estimation (KDE), boxplots, scatter plots, or contour plots for continuous data.",{"name":120,"@type":111,"acceptedAnswer":121},"How can missing values be identified and represented in data?",{"text":122,"@type":114},"Missing values can be identified using tools like the 'missingno' python package and should be accounted for in various representations, such as 'NaN', 'NaT', 'None', 'null', or specific coded values like '-1'.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},292606,1789653145,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81,"preview_page_count":4},962085570644,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","| 􀃸 Data Quality |\n| --- |\n| 􀃸 Rowsand Columns\u003Cbr>􀃸 Rows\u003Cbr>Descriptions\u003Cbr>What does the row mean? Is the row fora shipment,, combined shipment, etc?\u003Cbr>􀃸 Columns\u003Cbr>\u003Cbr>Count\u003Cbr>How many rows? How many unique valllues?\u003Cbr>\u003Cbr>\u003Cbr>􀃸 Types and Formats\u003Cbr>\u003Cbr>􀃸 Data Formats\u003Cbr>\u003Cbr>Are the dates loaded as dates?\u003Cbr>Are the numbers loaded as numbers? Are they strings?\u003Cbr>Are the¦nancial values correct? str or numbers? EU format, US format?\u003Cbr>\u003Cbr>􀃸 Missing Values\u003Cbr>Are there missing values in each column\u003Cbr>\u003Cbr>Percentage\u003Cbr>Visualizations\u003Cbr>e... .....g...., missingno python package\u003Cbr>􀃸 Diferent types of missing values\u003Cbr>Standard missing values nan, nat, None, na, nullllll.....................\u003Cbr>Represented with aspeci¦c value ----1,0,, MISSING, .......................\u003Cbr>\u003Cbr>􀃸 Duplications\u003Cbr>Are there duplications of rows/columns?\u003Cbr>Duplications of¦elds when the documentation says they are unique\u003Cbr>\u003Cbr>􀃸 Distributions\u003Cbr>􀃸 What is the generation process\u003Cbr>Is ita histogram analysis of another row?\u003Cbr>Is ita linear combination of other rows?\u003Cbr>...\u003Cbr>...........\u003Cbr>􀃸 Visualize the distribution of the values\u003Cbr>Value count bar plot\u003Cbr>For descrete data, lllist allllll possiblllevalllues and counts\u003Cbr>Histogram and KDE\u003Cbr>for continuous data, use histograms or KDE....\u003Cbr>Boxplot\u003Cbr>Boxplllotis easier to understand for business peopllle\u003Cbr>Scater plot\u003Cbr>Gut feellling of where the data points are lllocated\u003Cbr>Contour plot\u003Cbr>􀃸 Numerical Summarization\u003Cbr>Locations\u003Cbr>Mean, median, quartillles,, mode...................\u003Cbr>Spreads\u003Cbr>range, variance, standard deviation, IQR\u003Cbr>Skewness\u003Cbr>asymmetries\u003Cbr>Kurtosis\u003Cbr>\u003Cbr>􀃸 Correlations, Similarities Pairplot\u003Cbr>􀃸 Correlations\u003Cbr>Pearson\u003Cbr>Kendal Tau Correlation\u003Cbr>􀃸 Distances\u003Cbr>Euclidean distance Mahalanobis distance Minkowski distance Jaccard distance\u003Cbr>\u003Cbr>\u003Cbr>|\n|  |\n\ncreated with  \nData Quality Check and Summary Statistics","cbCaicMReH0BP79H","https://ap.wps.com/l/cbCaicMReH0BP79H","pdf",332230,"English","# Data Quality\n## Rows and Columns\n## Types and Formats\n## Missing Values\n## Duplications\n## Distributions\n## Numerical Summarization\n## Correlations and Similarities","[{\"question\":\"What aspects should be checked when reviewing rows and columns in a dataset?\",\"answer\":\"Reviewers should clarify the meaning of each row (e.g., shipment vs. combined shipment) and analyze column counts, specifically the number of rows and unique values.\"},{\"question\":\"What methods are recommended for analyzing data distributions?\",\"answer\":\"The document suggests using value count bar plots for discrete data, and histograms, kernel density estimation (KDE), boxplots, scatter plots, or contour plots for continuous data.\"},{\"question\":\"How can missing values be identified and represented in data?\",\"answer\":\"Missing values can be identified using tools like the 'missingno' python package and should be accounted for in various representations, such as 'NaN', 'NaT', 'None', 'null', or specific coded values like '-1'.\"}]","Data Quality Check and Summary Statistics | PDF"]