Merge pull request #106 from aseemanand/aanand_010825

Doc updates - Index sync with Readme and other minor updates

README.md

@@ -18,11 +18,11 @@ pip install pycatcher
 ### Basic Requirements
 * PyCatcher expects a Pandas DataFrame as an input for various outlier detection methods. It can convert Spark DataFrame 
 to Pandas DataFrame at the data processing stage. 
-* First column in the dataframe must be a time period column (date in 'YYYY-MM-DD'/month in 'YYYY-MM'/year in 'YYYY' 
+* First column in the dataframe must be a time period column (date in 'YYYY-MM-DD'; month in 'YYYY-MM'; year in 'YYYY' 
 format) and the last column a numeric column (sum or total count for the time period) to detect outliers using 
 Seasonal Decomposition algorithms.
 * Last column must be a numeric column to detect outliers using Interquartile Range (IQR) and Moving Average algorithms. 
-* There is no need for any labeled observations (ground truth). Outliers are detected solely through 
+* At present, PyCatcher does not depend on labeled observations (ground truth). Outliers are detected solely through 
 underlying algorithms (for example, seasonal-trend decomposition and dispersion methods like MAD or Z-Score).   
 
 <hr style="border:1.25px solid gray">

docs/index.md

@@ -15,10 +15,15 @@ quarter level time-series data.
 pip install pycatcher
 ```
 
-### DataFrame Arguments
-* First column in the dataframe must be a date column ('YYYY-MM-DD') and the last column a numeric column 
-(sum or total count for the time period) to detect outliers using Seasonal Decomposition algorithms.
-* Last column must be a numeric column to detect outliers using Moving Average and Z-score algorithm. 
+### Basic Requirements
+* PyCatcher expects a Pandas DataFrame as an input for various outlier detection methods. It can convert Spark DataFrame 
+to Pandas DataFrame at the data processing stage. 
+* First column in the dataframe must be a time period column (date in 'YYYY-MM-DD'; month in 'YYYY-MM'; year in 'YYYY' 
+format) and the last column a numeric column (sum or total count for the time period) to detect outliers using 
+Seasonal Decomposition algorithms.
+* Last column must be a numeric column to detect outliers using Interquartile Range (IQR) and Moving Average algorithms. 
+* At present, PyCatcher does not depend on labeled observations (ground truth). Outliers are detected solely through 
+underlying algorithms (for example, seasonal-trend decomposition and dispersion methods like MAD or Z-Score).   
 
 <hr style="border:1.25px solid gray">
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pycatcher"
-version = "0.0.62"
+version = "0.0.63"
 description = "This package identifies outlier(s) for a given time-series dataset in simple steps. It supports day, week, month and quarter level time-series data."
 authors = ["Aseem Anand <[email protected]>"]
 maintainers = ["Jagadish Pamarthi <[email protected]>"]