ai prompts for Transforming and cleaning data

Sure! Here are 50 examples of prompts related to data analysis of transforming and cleaning data:

1. Identify and remove duplicate records in a dataset.
2. Handle missing values by imputing with mean, median, or mode.
3. Standardize date formats across different columns.
4. Normalize numerical data to a specific range.
5. Convert categorical variables into numerical values using one-hot encoding.
6. Detect and handle outliers in a dataset.
7. Merge multiple datasets based on a common key.
8. Split a dataset into training and testing sets.
9. Transform skewed data using logarithmic or square root transformations.
10. Standardize text data by converting to lowercase.
11. Remove special characters from text data.
12. Fill missing categorical values with the most frequent category.
13. Create new features based on existing data (feature engineering).
14. Aggregate data by specific groups (e.g., sum, mean).
15. Pivot data to transform rows into columns.
16. Unpivot data to transform columns into rows.
17. Remove leading and trailing spaces from text data.
18. Convert data types (e.g., string to integer).
19. Handle inconsistent data entries (e.g., 'NY' vs. 'New York').
20. Scale numerical features using standardization or normalization.
21. Encode ordinal categorical variables.
22. Combine multiple columns into a single column.
23. Extract specific information from text data (e.g., extract year from date).
24. Remove stop words from text data.
25. Tokenize text data into individual words.
26. Identify and correct data entry errors.
27. Transform data to meet specific business rules.
28. Create dummy variables for categorical features.
29. Handle imbalanced datasets using resampling techniques.
30. Apply data smoothing techniques to reduce noise.
31. Detect and remove irrelevant features.
32. Transform data to a long or wide format.
33. Apply data binning to group continuous variables.
34. Remove rows with missing values.
35. Replace missing values with a specific value.
36. Identify and handle multicollinearity in features.
37. Apply feature scaling to ensure all features contribute equally.
38. Transform data using polynomial features.
39. Apply principal component analysis (PCA) for dimensionality reduction.
40. Detect and handle anomalies in the dataset.
41. Apply data augmentation techniques to increase dataset size.
42. Standardize numerical features to have zero mean and unit variance.
43. Convert text data into numerical vectors using TF-IDF.
44. Apply clustering techniques to group similar data points.
45. Transform data using Fourier transform for frequency analysis.
46. Apply data imputation techniques for missing values.
47. Detect and handle seasonality in time series data.
48. Apply data normalization to ensure consistent scale.
49. Transform categorical data using label encoding.
50. Apply data cleaning techniques to remove noise and inconsistencies.