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        "author": "Maciej Janowski",
        "author_email": "maciej.janowski@insight-centre.org",
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        "description": "# DataStoryPatternLibrabry\n\nData 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\".\n\n### Installation\n```python\npip install datastories\n```\nRequirements will be automatically installed with package\n\n###Import/Usage \n```python\nimport datastories.analytical as patterns\n\npatterns.DataStoryPattern(sparqlendpointurl, jsonmetadata)\n```\nObject created allow to query SPARQL endpoint based on JSON meatadat provided.\n\n# JSON Template\n```json\n{\n    \"cube_key\" : {\n\t\t\"title\":\"title of cube\",\n\t\t\"dataset_structure\":\"URI for cube structure\",\n        \"dimensions\":{\n            \"dimension_key\":{\n                \"dimension_title\":\"Title of diemnsion\",\n                \"dimension_url\":\"URI for dimension\",\n                \"dimension_prefix\":\"URI for dimension's values\"\n            },\n            \"dimension_key\":{\n                \"dimension_title\":\"Title of diemnsion\",\n                \"dimension_url\":\"URI for dimension\",\n                \"dimension_prefix\":\"URI for dimension's values\"\n            }\n\t\t},\n\t\t\"hierarchical_dimensions\":{\n\t\t\t\"dimension_key\":{\n                \"dimension_title\":\"Title of diemnsion\",\n                \"dimension_url\":\"URI for dimension\",\n                \"dimension_prefix\":\"URI for dimension's values\",\n\t\t\t\t\"dimension_levels\":\n\t\t\t\t{\n\t\t\t\t\t\"level_key\":\"integer(granularity level)\",\n\t\t\t\t\t\"level_key\":\"integer(granularity level)\"\n\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\t\t\"measures\":{\n\t\t\t\"measure_key\":{\n\t\t\t\"measure_title\":\"Title of measure\",\n\t\t\t\"measure_url\":\"URI for measure\"\n\t\t\t}\n\n\t\t}\n    }\n}\n```\n \n\n# Patterns Description\n<!--ts-->\n   * [Measurement and Counting](#MCounting)\n   * [League Table](#LTable)\n   * [Internal Comprison](#InternalComparison)\n   * [Profile Outliers](#ProfileOutliers)\n   * [Dissect Factors](#DissectFactors)\n   * [Highlight Contrast](#HighlightContrast)\n   * [Start Big Drill Down](#StartBigDrillDown)\n   * [Start Small Zoom Out](#StartSmallZoomOut)\n   * [Analysis By Category](#AnalysisByCategory)\n   * [Explore Intersection](#ExploreIntersection)\n   * [Narrating Change Over Time](#NarratingChangeOverTime)\n<!--te-->\n# MCounting\n\n  Measurement and Counting\n  Arithemtical operators applied to whole dataset - basic information regarding data\n    \n### Attributes\n ```python\n def MCounting(self,cube=\"\",dims=[],meas=[],hierdims=[],count_type=\"raw\",df=pd.DataFrame() )\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | count_type\t       |\t```String```         | Type of Count to perform\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n \n### Output\nBased on count_type value\n\n|Count_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | raw| data without any analysis performed|\n  | sum| sum across all numeric columns|\n  | mean| mean across all numeric columns|\n  | min| minimum values from all numeric columns|\n  | max| maximum values from all numeric columns|\n  | count| amount of records|\n\n\n# LTable\n\n  LeagueTable - sorting and extraction specific amount of records\n    \n### Attributes\n ```python\n def LTable(self,cube=[],dims=[],meas=[],hierdims=[], columns_to_order=\"\", order_type=\"asc\", number_of_records=20,df=pd.DataFrame())\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | columns_to_order\t       |\t```list[String]```         | Set of columns to order by\n  | order_type\t       |\t```String```         | Type of order (asc/desc)\n  | number_of_records\t       |\t```Integer```         | Amount of records to retrieve\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n \n### Output\nBased on sort_type value\n\n|Sort_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | asc|ascending order based on columns provided in ```columns_to_order```|\n  | desc|descending order based on columns provided in ```columns_to_order```|\n\n\n# InternalComparison\n\n  InternalComparison - comparison of numeric values related to textual values within one column\n    \n### Attributes\n ```python\n def InternalComparison(self,cube=\"\",dims=[],meas=[],hierdims=[],df=pd.DataFrame(), dim_to_compare=\"\",meas_to_compare=\"\",comp_type=\"\")\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | dim_to_compare\t       |\t```String```         | Dimension, which values will be investigated\n  | meas_to_compare\t       |\t```String```         | Measure, which numeric values related to ```dim_to_compare``` will be processed\n  | comp_type\t       |\t```String```         | Type of comparison to perform\n \n### Output \nIndependent from ```comp_type``` selected, output data will have additional column with numerical column ```meas_to_compare``` processed in specific way.\n\nAvailable types of comparison ```comp_type```\n\n|Comp_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | diffmax|difference with max value related to specific textual value|\n  | diffmean|difference with arithmetic mean related to specific textual values|\n  | diffmin|difference with minimum value related to specific textual value|\n\n# ProfileOutliers\n\n  ProfileOutliers - detection of unusual values within data (anomalies)\n    \n### Attributes\n ```python\n def ProfileOutliers(self,cube=[],dims=[],meas=[],hierdims=[],df=pd.DataFrame(), displayType=\"outliers_only\")\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | display_type\t       |\t```String```         | What information display are bound to display (with/without anomalies)\n\n### Output \nPattern analysis using ```python scipy``` library will perform quick exploration in serach of unusual values within data.\n\nBased on ```display_type``` parameter data will be displayed with/without ddetected unusual values.\n\nAvailable types of displaying ```display_type```\n\n|display_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | outliers_only|returns rows from dataset where unusual values were detected |\n  | without_outliers|returns dataset with excluded rows where unusual values were detected |\n\n\n# DissectFactors\n\n  DissectFactors - decomposition of data based on values in dim_to_dissect\n    \n### Attributes\n ```python\n def DissectFactors(self,cube=\"\",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_to_dissect=\"\")\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | dim_to_dissect\t       |\t```String```         | Based on which dimension data should be decomposed\n\n### Output \nAs an output, data will be decomposed in a form of a dictionary, where each subset have values only related to specific value.\nDictionary of subdataset will be constructed as a series of paiers where key per each susbet will values from ```dim_to_dissect```\nand this key value will be data, where yhis key value was occurring.\n\n\n# HighlightContrast\n\n  HighlightContrast - partial difference within values related to one textual column\n    \n### Attributes\n ```python\n def HighlightContrast(self,cube=\"\",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_to_contrast=\"\",contrast_type=\"\",meas_to_contrast=\"\")\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | dim_to_contrast\t       |\t```String```         | Textual column, from which values will be contrasted\n  | meas_to_contrast\t       |\t```String```         | Numerical column, which values are contrasted\n  | contrast_type\t       |\t```String```         | Type of contrast to present\n \n### Output \nIndependent from ```contrast_type``` selected, output data will have additional column with numerical column ```meas_to_contrast``` processed in specific way.\n\nAvailable types of comparison ```contrast_type```\n\n|Contrast_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | partofwhole| difference with max value related to specific textual value|\n  | partofmax| difference with arithmetic mean related to specific textual values|\n  | partofmin|difference with minimum value related to specific textual value|\n\n\n\n\n# StartBigDrillDown\n\n  StartBigDrillDown - data retrieval from multiple hierachical levels.\n\n  This pattern can be only applied to data not stored already in DataFrame\n    \n### Attributes\n ```python\n def StartBigDrillDown(self,cube=\"\",dims=[],meas=[],hierdim_drill_down=[])\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdim_drill_down\t       |```  dict{hierdim:list[str]} ```        | Hierarchical dimension with list of hierarchy levels to inspect\n  \n\n### Output \nAs an output, data will be retrieved in a form of a dictionary, where each dataset will be retrieved from different hierachy level. List will be provided in```hierdim_drill_down```. Hierachy levels provided by in parameter will automatically sorted in order from most general to most detailed level based on metadata provided.\n\n\n# StartSmallZoomOut\n\n  StartSmallZoomOut - data retrieval from multiple hierachical levels.\n\n  This pattern can be only applied to data not stored already in DataFrame\n    \n### Attributes\n ```python\n def StartSmallZoomOut(self,cube=\"\",dims=[],meas=[],hierdim_zoom_out=[])\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdim_zoom_out\t       |```  dict{hierdim:list[str]} ```        | Hierarchical dimension with list of hierarchy levels to inspect\n  \n\n### Output \nAs an output, data will be retrieved in a form of a dictionary, where each dataset will be retrieved from different hierachy level. List will be provided in```hierdim_zoom_out```. Hierachy levels provided by in parameter will automatically sorted in order from most detaile to most general level based on metadata provided.\n\n\n# AnalysisByCategory\n\n  AnalysisByCategory - ecomposition of data based on values in dim_for_category with analysis performed on each susbet\n    \n### Attributes\n ```python\n def AnalysisByCategory(self,cube=\"\",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_for_category=\"\",meas_to_analyse=\"\",analysis_type=\"min\"):\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | dim_for_category\t       |\t```String```         |  Dimension, based on which input data will be categorised\n  | meas_to_analyse\t       |\t```String```         | Measure, which will be analysed\n  | analysis_type\t       |\t```String```         | Type of analysis to perform\n \n### Output \nAs an output, data will be decomposed in a form of a dictionary, where each subset have values only related to specific value. Such subset will get analysed based on ```analysis_type``` parameter\n\nAvailable types of analysis ```analysis_type```\n\n|Analysis_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | min| Minimum per each category|\n  | max| Maximum per each category|\n  | mean|Arithmetical mean per each category|\n  | sum|Total value from each category|\n\n\n# ExploreIntersection\n\n\n### Attributes\n ```python\n def ExploreIntersection(self, dim_to_explore=\"\"):\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | dim_to_explore\t       |```\tString     ```   |  Dimension, which existence within enpoint is going to be investigated\n \n### Output \nPattern will return series of datasets, where each will represent occurence of ```dim_to_explore``` in one cube\n\n# NarratingChangeOverTime\n### Attributes\n ```python\n def NarrChangeOT(self,cube=\"\",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),meas_to_narrate=\"\",narr_type=\"\")\n ```\n  Parameter                 | Type       | Description   |\t\n  | :------------------------ |:-------------:| :-------------|\n  | cube\t       |```\tString     ```   | Cube, which dimensions and measures will be investigated\n  | dims\t       |```\t  list[String]     ```   | List of dimensions (from cube) to take into investigation\n  | meas\t       |\t    ```  list[String]  ```      | List of measures (from cube) to take into investigation\n  | hierdims\t       |```  dict{hierdim:{\"selected_level\":[value]}}  ```        | Hierarchical Dimesion with selected hierarchy level to take into investigation\n  | df\t       |```\tDataFrame      ```    |  DataFrame object, if data is already retrieved from endpoint\n  | meas_to_narrate\t       |\t```String```         |  Set of 2 measures, which change will be narrated\n  | narr_type\t       |\t```String```         | Type of narration to perform\n\n### Output \nIndependent from ```narr_type``` selected, output data will have additional column with numerical values processed in specific way.\n\nAvailable types of analysis ```narr_type```\n\n|Narr_type                |  Description   |\t\n  | ------------------------ | -------------|\n  | percchange| Percentage change between first nad second property|\n  | diffchange| Quantitive change between first and second property|",
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