Fix for Matplot issue

README.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.
+### 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. 
+* There is no need for any 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 = "0.0.61"
+version = "0.0.62"
 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]>"]

src/pycatcher/catch.py

@@ -492,7 +492,7 @@ def detect_outliers_classic(df: pd.DataFrame) -> Union[pd.DataFrame, str]:
 
     Args:
         df (pd.DataFrame): A Pandas DataFrame with time-series data.
-            First column must be a date column ('YYYY-MM-DD')
+            First column must be a date ('YYYY-MM-DD) /month (YYYY-MM) /year (YYYY) column
             and last column should be a count/feature column.
 
     Returns:

src/pycatcher/diagnostics.py

@@ -144,7 +144,7 @@ def build_seasonal_plot_classic(df) -> str:
         logging.info("Duplicate date index values. Check your data.")
 
 
-def generate_seasonal_plot_classic(df, detected_period) -> Union[str, plt]:
+def generate_seasonal_plot_classic(df, detected_period) -> str:
     """
     Build seasonal plot for a given dataframe using classic seasonal decomposition
     Args: