Metadata-Version: 2.1
Name: datastories
Version: 0.1.1.8
Summary: Data Story Pattern Analysis for LOSD
Home-page: https://github.com/MaciejJanowski/DataStoryPatternLibrary
Author: Maciej Janowski
Author-email: maciej.janowski@insight-centre.org
License: MIT
Description: # DataStoryPatternLibrabry
        
        Data Story Patterns Library is a repository with pattern analysis designated for Linked Open Statistical Data. Story Patterns were retrieved from literture reserach udenr general subject of "data journalism".
        
        ### Installation
        ```python
        pip install datastories
        ```
        Requirements will be automatically installed with package
        
        ###Import/Usage 
        ```python
        import datastories.analytical as patterns
        
        patterns.DataStoryPattern(sparqlendpointurl, jsonmetadata)
        ```
        Object created allow to query SPARQL endpoint based on JSON meatadat provided
         
        
        # Patterns Description
        <!--ts-->
           * [Measurement and Counting](#MCounting)
           * [League Table](#LTable)
           * [Internal Comprison](#InternalComparison)
        <!--te-->
        # MCounting
        
          Measurement and Counting
          Arithemtical operators applied to whole dataset - basic information regarding data
            
        ### Attributes
         ```python
         MCounting(self,cube="",dims=[],meas=[],hierdims=[],count_type="raw",df=pd.DataFrame() )
         ```
          Parameter                 | Type       | Description   |	
          | :------------------------ |:-------------:| :-------------|
          | cube	       |```	String     ```   | Cube, which dimensions and measures will be investigated
          | dims	       |```	  list[String]     ```   | List of dimensions (from cube) to take into investigation
          | meas	       |	    ```  list[String]  ```      | List of measures (from cube) to take into investigation
          | hierdims	       |```  dict{hierdim:{"selected_level":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation
          | count_type	       |	```String```         | Type of Count to perform
          | df	       |```	DataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint
         
        ### Output
        Based on count_type value
        
        |Count_type                |  Description   |	
          | ------------------------ | -------------|
          | raw| data without any analysis performed|
          | sum| sum across all numeric columns|
          | mean| mean across all numeric columns|
          | min| minimum values from all numeric columns|
          | max| maximum values from all numeric columns|
          | count| amount of records|
        
        
        # LTable
        
          LeagueTable - sorting and extraction specific amount of records
            
        ### Attributes
         ```python
         LTable(self,cube=[],dims=[],meas=[],hierdims=[], columns_to_order="", order_type="asc", number_of_records=20,df=pd.DataFrame())
         ```
          Parameter                 | Type       | Description   |	
          | :------------------------ |:-------------:| :-------------|
          | cube	       |```	String     ```   | Cube, which dimensions and measures will be investigated
          | dims	       |```	  list[String]     ```   | List of dimensions (from cube) to take into investigation
          | meas	       |	    ```  list[String]  ```      | List of measures (from cube) to take into investigation
          | hierdims	       |```  dict{hierdim:{"selected_level":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation
          | columns_to_order	       |	```list[String]```         | Set of columns to order by
          | order_type	       |	```String```         | Type of order (asc/desc)
          | number_of_records	       |	```Integer```         | Amount of records to retrieve
          | df	       |```	DataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint
         
        ### Output
        Based on sort_type value
        
        |Count_type                |  Description   |	
          | ------------------------ | -------------|
          | asc|ascending order based on columns provided in ```columns_to_order```|
          | desc|descending order based on columns provided in ```columns_to_order```|
        
        
        # InternalComparison
        
          InternalComparison - comparison of numeric values related to textual values within one column
            
        ### Attributes
         ```python
         def InternalComparison(self,cube="",dims=[],meas=[],hierdims=[],df=pd.DataFrame(), dim_to_compare="",meas_to_compare="",comp_type=""):
         ```
          Parameter                 | Type       | Description   |	
          | :------------------------ |:-------------:| :-------------|
          | cube	       |```	String     ```   | Cube, which dimensions and measures will be investigated
          | dims	       |```	  list[String]     ```   | List of dimensions (from cube) to take into investigation
          | meas	       |	    ```  list[String]  ```      | List of measures (from cube) to take into investigation
          | hierdims	       |```  dict{hierdim:{"selected_level":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation
          | df	       |```	DataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint
          | dim_to_compare	       |	```String```         | Dimension, which values will be investigated
          | meas_to_compare	       |	```String```         | Measure, which numeric values related to ```dim_to_compare``` will be processed
          | comp_type	       |	```String```         | Type of comparison to perform
         
        ### Output
        Independent from ```comp_type``` selected, output data will have additional column with numerical column ```meas_to_compare``` processed in specific way.
        
        Available types of comparison ```comp_type```
        |Comp_type                |  Description   |	
          | ------------------------ | -------------|
          | diffmax| difference with max value related to specific textual value|
          | diffmean| difference with arithmetic mean related to specific textual values|
          | diffmin|difference with minimum value related to specific textual value|
        
        
        
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