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methods and techniques","data-exploration",11,"pi pi-search","content:pandas:11.data-exploration.md","pandas/11.data-exploration.md","pandas/11.data-exploration",[116,125,134],{"_path":117,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":118,"description":119,"slug":120,"parentId":106,"order":121,"_type":14,"_id":122,"_source":16,"_file":123,"_stem":124,"_extension":19},"/pandas/viewing-your-data","Viewing Your Data","Learn different ways to look at and sample your DataFrame data for exploration and understanding","viewing-your-data",12,"content:pandas:12.viewing-your-data.md","pandas/12.viewing-your-data.md","pandas/12.viewing-your-data",{"_path":126,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":127,"description":128,"slug":129,"parentId":106,"order":130,"_type":14,"_id":131,"_source":16,"_file":132,"_stem":133,"_extension":19},"/pandas/data-shape-and-statistics","Data Shape and Statistics","Learn how to understand your DataFrame's size, structure, and get basic statistical summaries","data-shape-and-statistics",13,"content:pandas:13.data-shape-and-statistics.md","pandas/13.data-shape-and-statistics.md","pandas/13.data-shape-and-statistics",{"_path":135,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":136,"description":137,"slug":138,"parentId":106,"order":139,"_type":14,"_id":140,"_source":16,"_file":141,"_stem":142,"_extension":19},"/pandas/column-information-and-data-types","Column Information and Data Types","Learn how to explore individual columns and understand different data types in your DataFrames","column-information-and-data-types",14,"content:pandas:14.column-information-and-data-types.md","pandas/14.column-information-and-data-types.md","pandas/14.column-information-and-data-types",{"_path":144,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":145,"description":146,"slug":147,"order":148,"isParent":12,"icon":149,"_type":14,"_id":150,"_source":16,"_file":151,"_stem":152,"_extension":19,"children":153,"expanded":12},"/pandas/selecting-data","Selecting Data","Learn how to select specific columns, rows, and subsets of data from your DataFrames","selecting-data",15,"pi pi-filter","content:pandas:15.selecting-data.md","pandas/15.selecting-data.md","pandas/15.selecting-data",[154,163,172],{"_path":155,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":156,"description":157,"slug":158,"parentId":144,"order":159,"_type":14,"_id":160,"_source":16,"_file":161,"_stem":162,"_extension":19},"/pandas/selecting-columns-and-rows","Selecting Columns and Rows","Learn how to 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Conditional Selection","Learn to filter DataFrames using conditions, boolean logic, and multiple criteria for precise data selection","boolean-and-conditional-selection",18,"content:pandas:18.boolean-and-conditional-selection.md","pandas/18.boolean-and-conditional-selection.md","pandas/18.boolean-and-conditional-selection",{"_path":182,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":183,"description":184,"slug":185,"order":186,"isParent":12,"icon":187,"_type":14,"_id":188,"_source":16,"_file":189,"_stem":190,"_extension":19,"children":191,"expanded":12},"/pandas/data-cleaning","Data Cleaning","Learn essential data cleaning techniques to prepare messy data for analysis","data-cleaning",19,"pi 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quality","removing-duplicates",21,"content:pandas:21.removing-duplicates.md","pandas/21.removing-duplicates.md","pandas/21.removing-duplicates",{"_path":211,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":212,"description":213,"slug":214,"parentId":182,"order":215,"_type":14,"_id":216,"_source":16,"_file":217,"_stem":218,"_extension":19},"/pandas/data-type-conversion","Data Type Conversion","Learn to convert data to the right types for proper analysis and calculations","data-type-conversion",22,"content:pandas:22.data-type-conversion.md","pandas/22.data-type-conversion.md","pandas/22.data-type-conversion",{"_path":220,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":221,"description":222,"slug":223,"parentId":182,"order":224,"_type":14,"_id":225,"_source":16,"_file":226,"_stem":227,"_extension":19},"/pandas/string-cleaning-operations","String Cleaning Operations","Learn to clean and standardize text data for consistent analysis using pandas string methods","string-cleaning-operations",23,"content:pandas:23.string-cleaning-operations.md","pandas/23.string-cleaning-operations.md","pandas/23.string-cleaning-operations",{"_path":229,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":230,"description":231,"slug":232,"order":233,"isParent":12,"icon":234,"_type":14,"_id":235,"_source":16,"_file":236,"_stem":237,"_extension":19,"children":238,"expanded":12},"/pandas/data-transformation","Data Transformation","Learn how to modify, add, and transform data in your DataFrames for analysis","data-transformation",24,"pi pi-folder","content:pandas:24.data-transformation.md","pandas/24.data-transformation.md","pandas/24.data-transformation",[239,248,257],{"_path":240,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":241,"description":242,"slug":243,"parentId":229,"order":244,"_type":14,"_id":245,"_source":16,"_file":246,"_stem":247,"_extension":19},"/pandas/adding-and-modifying-columns","Adding and Modifying Columns","Learn how to create new columns and update existing ones with calculations and conditional logic","adding-and-modifying-columns",25,"content:pandas:25.adding-and-modifying-columns.md","pandas/25.adding-and-modifying-columns.md","pandas/25.adding-and-modifying-columns",{"_path":249,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":250,"description":251,"slug":252,"parentId":229,"order":253,"_type":14,"_id":254,"_source":16,"_file":255,"_stem":256,"_extension":19},"/pandas/applying-functions","Applying Functions","Use built-in and custom functions to transform your data efficiently","applying-functions",26,"content:pandas:26.applying-functions.md","pandas/26.applying-functions.md","pandas/26.applying-functions",{"_path":258,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":259,"description":260,"slug":261,"parentId":229,"order":262,"_type":14,"_id":263,"_source":16,"_file":264,"_stem":265,"_extension":19},"/pandas/renaming-and-sorting-data","Renaming and Sorting Data","Organize your DataFrame with better column names and logical data ordering","renaming-and-sorting-data",27,"content:pandas:27.renaming-and-sorting-data.md","pandas/27.renaming-and-sorting-data.md","pandas/27.renaming-and-sorting-data",{"_path":267,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":268,"description":269,"slug":270,"order":271,"isParent":12,"icon":234,"_type":14,"_id":272,"_source":16,"_file":273,"_stem":274,"_extension":19,"children":275,"expanded":12},"/pandas/grouping-and-aggregation","Grouping and Aggregation","Learn to group data and calculate summary statistics for powerful data analysis","grouping-and-aggregation",28,"content:pandas:28.grouping-and-aggregation.md","pandas/28.grouping-and-aggregation.md","pandas/28.grouping-and-aggregation",[276,285,294],{"_path":277,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":278,"description":279,"slug":280,"parentId":267,"order":281,"_type":14,"_id":282,"_source":16,"_file":283,"_stem":284,"_extension":19},"/pandas/groupby-operations","GroupBy Operations","Learn the fundamentals of splitting data into groups and basic aggregations","groupby-operations",29,"content:pandas:29.groupby-operations.md","pandas/29.groupby-operations.md","pandas/29.groupby-operations",{"_path":286,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":287,"description":288,"slug":289,"parentId":267,"order":290,"_type":14,"_id":291,"_source":16,"_file":292,"_stem":293,"_extension":19},"/pandas/aggregation-functions","Aggregation Functions","Explore different ways to summarize your grouped data with built-in and custom functions","aggregation-functions",30,"content:pandas:30.aggregation-functions.md","pandas/30.aggregation-functions.md","pandas/30.aggregation-functions",{"_path":295,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":296,"description":297,"slug":298,"parentId":267,"order":299,"_type":14,"_id":300,"_source":16,"_file":301,"_stem":302,"_extension":19},"/pandas/pivot-tables-and-cross-tabulation","Pivot Tables and Cross Tabulation","Create powerful summary tables and cross-tabulations for analysis","pivot-tables-and-cross-tabulation",31,"content:pandas:31.pivot-tables-and-cross-tabulation.md","pandas/31.pivot-tables-and-cross-tabulation.md","pandas/31.pivot-tables-and-cross-tabulation",{"_path":304,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":305,"description":306,"slug":307,"order":308,"isParent":12,"icon":234,"_type":14,"_id":309,"_source":16,"_file":310,"_stem":311,"_extension":19,"children":312,"expanded":12},"/pandas/combining-data","Combining Data","Learn to combine data from multiple DataFrames for comprehensive analysis","combining-data",32,"content:pandas:32.combining-data.md","pandas/32.combining-data.md","pandas/32.combining-data",[313,322],{"_path":314,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":315,"description":316,"slug":317,"parentId":304,"order":318,"_type":14,"_id":319,"_source":16,"_file":320,"_stem":321,"_extension":19},"/pandas/concatenating-dataframes","Concatenating DataFrames","Learn to stack DataFrames vertically and horizontally for simple data combination","concatenating-dataframes",33,"content:pandas:33.concatenating-dataframes.md","pandas/33.concatenating-dataframes.md","pandas/33.concatenating-dataframes",{"_path":323,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":324,"description":325,"slug":326,"parentId":304,"order":327,"_type":14,"_id":328,"_source":16,"_file":329,"_stem":330,"_extension":19},"/pandas/merging-and-join-operations","Merging and Join Operations","Master different types of joins to combine related data from multiple sources","merging-and-join-operations",34,"content:pandas:34.merging-and-join-operations.md","pandas/34.merging-and-join-operations.md","pandas/34.merging-and-join-operations",{"_path":332,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":333,"description":334,"slug":335,"order":336,"isParent":12,"icon":234,"_type":14,"_id":337,"_source":16,"_file":338,"_stem":339,"_extension":19,"children":340,"expanded":12},"/pandas/working-with-dates","Working with Dates","Master date and time operations for time-based analysis and reporting","working-with-dates",35,"content:pandas:35.working-with-dates.md","pandas/35.working-with-dates.md","pandas/35.working-with-dates",[341,350],{"_path":342,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":343,"description":344,"slug":345,"parentId":332,"order":346,"_type":14,"_id":347,"_source":16,"_file":348,"_stem":349,"_extension":19},"/pandas/datetime-operations","DateTime Operations","Learn to convert, manipulate, and extract information from dates and times","datetime-operations",36,"content:pandas:36.datetime-operations.md","pandas/36.datetime-operations.md","pandas/36.datetime-operations",{"_path":351,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":352,"description":353,"slug":354,"parentId":332,"order":355,"_type":14,"_id":356,"_source":16,"_file":357,"_stem":358,"_extension":19},"/pandas/time-series-and-resampling","Time Series and Resampling","Discover how to analyze data over time periods and aggregate by different time intervals","time-series-and-resampling",37,"content:pandas:37.time-series-and-resampling.md","pandas/37.time-series-and-resampling.md","pandas/37.time-series-and-resampling",{"_path":360,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":361,"description":362,"slug":363,"order":364,"isParent":12,"icon":234,"_type":14,"_id":365,"_source":16,"_file":366,"_stem":367,"_extension":19,"children":368,"expanded":12},"/pandas/saving-your-work","Saving Your Work","Learn to export your analyzed data to different formats for sharing and reporting","saving-your-work",38,"content:pandas:38.saving-your-work.md","pandas/38.saving-your-work.md","pandas/38.saving-your-work",[369,378],{"_path":370,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":371,"description":372,"slug":373,"parentId":360,"order":374,"_type":14,"_id":375,"_source":16,"_file":376,"_stem":377,"_extension":19},"/pandas/exporting-data","Exporting Data (CSV, Excel, JSON)","Learn to save your DataFrames to common file formats for sharing and reporting","exporting-data",39,"content:pandas:39.exporting-data.md","pandas/39.exporting-data.md","pandas/39.exporting-data",{"_path":379,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":380,"description":381,"slug":382,"parentId":360,"order":383,"_type":14,"_id":384,"_source":16,"_file":385,"_stem":386,"_extension":19},"/pandas/database-export","Database Export","Discover how to save data back to databases for integration with other systems","database-export",40,"content:pandas:40.database-export.md","pandas/40.database-export.md","pandas/40.database-export",[58],{"_path":59,"_dir":23,"_draft":7,"_partial":7,"_locale":6,"title":60,"description":61,"slug":62,"parentId":5,"order":63,"body":389,"_type":14,"_id":64,"_source":16,"_file":65,"_stem":66,"_extension":19},{"type":390,"children":391,"toc":3944},"root",[392,401,407,458,465,470,949,954,959,1363,1369,1374,1692,1698,1703,2040,2046,2051,2392,2398,2403,2842,2848,3087,3093,3098,3697,3703,3907,3913,3918,3933,3938],{"type":393,"tag":394,"props":395,"children":397},"element","h1",{"id":396},"ℹ️-basic-dataframe-info",[398],{"type":399,"value":400},"text","ℹ️ Basic DataFrame Info",{"type":393,"tag":402,"props":403,"children":404},"p",{},[405],{"type":399,"value":406},"Before analyzing data, you need to understand what you're working with! Pandas provides simple methods to quickly explore your DataFrame and understand its structure, size, and content.",{"type":393,"tag":408,"props":409,"children":413},"info-box",{"emoji":410,"title":411,"type":412},"🔍","Why Explore Your Data First?","info",[414,423,448],{"type":393,"tag":402,"props":415,"children":416},{},[417],{"type":393,"tag":418,"props":419,"children":420},"strong",{},[421],{"type":399,"value":422},"Before diving into analysis, always check:",{"type":393,"tag":424,"props":425,"children":426},"ul",{},[427,433,438,443],{"type":393,"tag":428,"props":429,"children":430},"li",{},[431],{"type":399,"value":432},"How big is your dataset? (rows and columns)",{"type":393,"tag":428,"props":434,"children":435},{},[436],{"type":399,"value":437},"What types of data do you have? 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