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Python: Data Cleaning And Preprocessing

50 exercises3 free
  1. 01Strip Whitespace From Strings
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  2. 02Convert Column To Datetime
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  3. 03Replace Missing With Mean
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  4. 04Remove Duplicate Rows
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  5. 05Drop Rows With NaN
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  6. 06Normalize A Numeric Column 0-1
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  7. 07Encode Categorical With Get_dummies
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  8. 08Rename Columns
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  9. 09Filter Out Outliers
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  10. 10Convert Strings To Lowercase
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  11. 11Forward Fill Missing Values
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  12. 12Extract Substring From A Column
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  13. 13Replace A Pattern In Strings
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  14. 14Convert Multiple Columns To Numeric
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  15. 15Discretize A Numeric Column
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  16. 16Group And Sum
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  17. 17Sort By Multiple Columns
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  18. 18Drop Columns With All NaN, {'A':[1,None,3],'Empty':[None,None,None]}
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  19. 19Cumulative Sum Of A Column, {'B':[10,20,30]}
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  20. 20Pivot Data From Long To Wide
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  21. 21Downcast Numeric Columns
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  22. 22Reverse A String Column
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  23. 23Replace Zero Values With NaN
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  24. 24Reindex DataFrame To Fill Missing Indices
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  25. 25Map Numeric Values To Categories
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  26. 26Merge Two DataFrames With A Custom Suffix
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  27. 27Drop Rows With Too Many NaN
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  28. 28Keep Only Numeric Columns
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  29. 29Keep Top N Rows After Sorting
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  30. 30Clip Values Outside A Range
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  31. 31Randomly Sample Rows
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  32. 32Identify Duplicated Rows
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  33. 33Convert Column To Categorical
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  34. 34Interpolate Missing Values
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  35. 35Shift Column To Align Data
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  36. 36Convert Time String To Minutes
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  37. 37Trim To Largest Group Only
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  38. 38Boolean Mask To Remove Special Characters
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  39. 39Evaluate A Numeric Expression In Strings
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  40. 40Combine Columns Into One String
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  41. 41Expand Dictionary Column
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  42. 42Identify Numeric Columns With Non-Numeric Data
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  43. 43Fix Typos In A Categorical Column
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  44. 44Split Column With Delimiter
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  45. 45Replace Negative Values With Zero
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  46. 46Standardize A Column (Z-Score)
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  47. 47Drop Rows Outside A Date Range
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  48. 48Convert Wide Data To Long
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  49. 49Categorize Numeric Column By Percentile
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  50. 50Apply Function Element-Wise With Applymap
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