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[FreeCoursesOnline.Me] [Packt] Data Wrangling with Python 3.x [FCO]

文件类型 收录时间 最后活跃 资源热度 文件大小 文件数量
视频 2019-2-27 19:48 2024-6-12 08:35 113 1.34 GB 38
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FreeCoursesOnline Me Packt Data Wrangling with Python 3 x FCO

文件列表
  1. 01.Gathering and Parsing Data/0101.The Course Overview.mp430.8MB
  2. 01.Gathering and Parsing Data/0102.Installing Anaconda Navigator on WindowsLinux.mp453.54MB
  3. 01.Gathering and Parsing Data/0103.Importing and Parsing CSV in Python.mp428.48MB
  4. 01.Gathering and Parsing Data/0104.Importing and Parsing JSON in Python.mp422.82MB
  5. 01.Gathering and Parsing Data/0105.Scraping Data from Public Web – Part 1.mp420.9MB
  6. 01.Gathering and Parsing Data/0106.Scraping Data from Public Web – Part 2.mp449.36MB
  7. 02.Working with Data from Excel and PDF Files/0201.Importing and Parsing Excel Files – Part 1.mp420.32MB
  8. 02.Working with Data from Excel and PDF Files/0202.Importing and Parsing Excel Files – Part 2.mp4205.67MB
  9. 02.Working with Data from Excel and PDF Files/0203.Manipulating PDF Files in Python – Part 1.mp4125.34MB
  10. 02.Working with Data from Excel and PDF Files/0204.Manipulating PDF Files in Python – Part 2.mp4126.67MB
  11. 03.Storing Data in Persistent Storage/0301.Difference between Relational and Non-Relational Databases.mp414.69MB
  12. 03.Storing Data in Persistent Storage/0302.Storing Data in SQLite Databases.mp433.35MB
  13. 03.Storing Data in Persistent Storage/0303.Storing Data in MongoDB.mp425.17MB
  14. 03.Storing Data in Persistent Storage/0304.Storing Data in Elasticsearch.mp427.53MB
  15. 03.Storing Data in Persistent Storage/0305.Comparative Study of Databases for Storage.mp45.02MB
  16. 04.Cleaning Structured Data/0401.The Most Important Step in Data Analysis.mp411.88MB
  17. 04.Cleaning Structured Data/0402.ViewingInspecting DataFrames.mp426MB
  18. 04.Cleaning Structured Data/0403.RenamingAddingRemoving the DataFrame Columns.mp426.72MB
  19. 04.Cleaning Structured Data/0404.Dropping Duplicate Rows.mp426.24MB
  20. 04.Cleaning Structured Data/0405.Indexing DataFrame to Retrieve Specific Columns and Rows.mp428.16MB
  21. 04.Cleaning Structured Data/0406.MergingConcatenatingJoining DataFrames.mp431.45MB
  22. 04.Cleaning Structured Data/0407.Dealing with Missing Values.mp433.56MB
  23. 05.More Data Cleaning and Transformation/0501.Filtering and Sorting of DataFrame.mp425.93MB
  24. 05.More Data Cleaning and Transformation/0502.EncodingMapping Existing Values – Part 1.mp423.51MB
  25. 05.More Data Cleaning and Transformation/0503.EncodingMapping Existing Values – Part 2.mp418.85MB
  26. 05.More Data Cleaning and Transformation/0504.RescaleStandardize Column Values.mp429.42MB
  27. 05.More Data Cleaning and Transformation/0505.Common Cleaning Operations.mp427.8MB
  28. 05.More Data Cleaning and Transformation/0506.Exporting Datasets for Future Use.mp422.07MB
  29. 06.Performing Statistical Analysis/0601.Different Uses of Packages (Pandas, NumPy, SciPy, and Matplotlib).mp43.12MB
  30. 06.Performing Statistical Analysis/0602.Types of Column NamesFeaturesAttributes in Structured Data.mp44.36MB
  31. 06.Performing Statistical Analysis/0603.Split-Apply-Combine (Performing Group By Operation).mp424.38MB
  32. 06.Performing Statistical Analysis/0604.Descriptive Statistics Using Python – Part 1.mp423MB
  33. 06.Performing Statistical Analysis/0605.Descriptive Statistics Using Python – Part 2.mp422.35MB
  34. 07.Let the Visualizations Tell the Story/0701.Using Visualizations.mp43.76MB
  35. 07.Let the Visualizations Tell the Story/0702.Cool Visualization of Real-World Datasets of World Population Evolution.mp47.12MB
  36. 07.Let the Visualizations Tell the Story/0703.Visualizations in Python – Part 1.mp426.3MB
  37. 07.Let the Visualizations Tell the Story/0704.Visualizations in Python – Part 2.mp495.74MB
  38. 07.Let the Visualizations Tell the Story/0705.Exploring an Online Visualization Tool (RAWGraphs).mp436.73MB
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