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cubestories-0.3.9
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CuebStories
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cubestories-0.3.9 |
| نام | cubestories |
| نسخه کتابخانه | 0.3.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Maciej Janowski |
| ایمیل نویسنده | maciej.janowski@insight-centre.org |
| آدرس صفحه اصلی | https://github.com/MaciejJanowski/CubeStories |
| آدرس اینترنتی | https://pypi.org/project/cubestories/ |
| مجوز | MIT |
# CubeStories
CubeStories allows querying Linked Open Statistical by Providing parameters in a form of Python dictionaries(JSON).
Reserach paper underpinning the implementation [Research paper](https://www.researchgate.net/publication/333227519_Mediating_Open_Data_Consumption_-_Identifying_Story_Patterns_for_Linked_Open_Statistical_Data)
High-level encapsulation of datastories librabry [DataStories repo](https://github.com/MaciejJanowski/DataStoryPatternLibrary)
Test sripts are available at: [Test repor](https://github.com/MaciejJanowski/CubeStoriesTesting)
### Installation
```python
pip install cubestories
```
Requirements will be automatically installed with package
### Import/Usage
```python
from CubeStories import *
```
# Usage
Library implements 3 artifacts required for Data analysis
* Metadata Parameters - metadata required for SPARQL queries
```json
{
"sparqlEndPointUrl":"[SPARQL ENDPOINT URL]",
"jsonMetaDataFile":"[directory of JSON file with metadata]"
}
```
* Cube Parameters - what properties of cube to be retrieved from endpoint(based on JSON file provided in Metadata Parameters).
Values highlighted as: ```--- --- `` has to be specified by a user - replaced to value only
```json
{
"cube":"---Key of Cube ---",
"dimensions":["---List of dimensions---"],
"measures":["---List of Measures---"],
"hierdimensions":
{"---DimKey---":{
"selected_level":"---levelkey---"
}
}
```
* Analysis Pipeline - JSON-based list of pattern analysis to be performed. Each Pattern will have such template provided
```json
{
"---PatternName---": {
"parameter1":["---list of values---"],
"parameter2":"---value---"
},
"---PatternName----":{
"parameter1":"---pattern1 value---",
"parameter2":["---list of values---"]
}
}
```
# JSON Template - one of the metadata parameters
```json
{
"---cube_key---" : {
"title":"---title of cube---",
"dataset_structure":"---URI for cube structure---",
"dimensions":{
"---dimension_key---":{
"dimension_title":"---Title of diemnsion---",
"dimension_url":"---URI for dimension---",
"dimension_prefix":"---URI for dimension's values---"
},
"---dimension_key---":{
"dimension_title":"---Title of diemnsion---",
"dimension_url":"---URI for dimension---",
"dimension_prefix":"---URI for dimension's values---"
}
},
"hierarchical_dimensions":{
"---dimension_key---":{
"dimension_title":"---Title of diemnsion---",
"dimension_url":"---URI for dimension---",
"dimension_prefix":"---URI for dimension's values---",
"dimension_levels":
{
"---level_key---":{
"description":"---description of granularity level---",
"granularity":"---integer level of granularity---"
},
"---level_key---":{
"description":"---description of granularity level---",
"granularity":"---integer level of granularity---"
}
}
}
},
"measures":{
"---measure_key---":{
"measure_title":"---Title of measure---",
"measure_url":"---URI for measure---"
}
}
}
}
```
# Patterns Description
## Comments after ## are just for descriptive purposes. REMOVE THEM WHEN SPECIFYING PIPELINE
<!--ts-->
* [Measurement and Counting](#MCounting)
* [League Table](#LTable)
* [Internal Comprison](#InternalComparison)
* [Profile Outliers](#ProfileOutliers)
* [Dissect Factors](#DissectFactors)
* [Highlight Contrast](#HighlightContrast)
* [Start Big Drill Down](#StartBigDrillDown)
* [Start Small Zoom Out](#StartSmallZoomOut)
* [Analysis By Category](#AnalysisByCategory)
* [Explore Intersection](#ExploreIntersection)
* [Narrating Change Over Time](#NarratingChangeOverTime)
<!--te-->
# MCounting
Measurement and Counting
Arithemtical operators applied to whole dataset - basic information regarding data
### Attributes
```json
"MeasCount":{
"count_type":"count value"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| count_type | ```String``` | Type of Count to perform
### 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
```json
"LeagueTab":{
"columns_to_order":["list of columns to order by"],
"order_type":"type of order by",
"number_of_records":5
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| 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
### Output
Based on sort_type value
|Sort_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
```json
"IntComp":{
"dim_to_compare":"dimension to compare",
"meas_to_compare":"measure to compare",
"comp_type":"comparison type"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| 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|
# ProfileOutliers
ProfileOutliers - detection of unusual values within data (anomalies)
### Attributes
```json
"ProfOut":{
"display_type":"Gender"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| display_type | ```String``` | What information display are bound to display (with/without anomalies)
### Output
Pattern analysis using ```python scipy``` library will perform quick exploration in serach of unusual values within data.
Based on ```display_type``` parameter data will be displayed with/without ddetected unusual values.
Available types of displaying ```display_type```
|display_type | Description |
| ------------------------ | -------------|
| outliers_only|returns rows from dataset where unusual values were detected |
| without_outliers|returns dataset with excluded rows where unusual values were detected |
# DissectFactors
DissectFactors - decomposition of data based on values in dim_to_dissect
### Attributes
```json
"DissFact":{
"dim_to_dissect":"dimension to dissect"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| dim_to_dissect | ```String``` | Based on which dimension data should be decomposed
### Output
As an output, data will be decomposed in a form of a dictionary, where each subset have values only related to specific value.
Dictionary of subdataset will be constructed as a series of paiers where key per each susbet will values from ```dim_to_dissect```
and this key value will be data, where yhis key value was occurring.
# HighlightContrast
HighlightContrast - partial difference within values related to one textual column
### Attributes
```json
"HighCont":{
"dim_to_contrast":"dimension to contrast",
"meas_to_contrast":"measure to contrast",
"contrast_type":"type of contrast"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| dim_to_contrast | ```String``` | Textual column, from which values will be contrasted
| meas_to_contrast | ```String``` | Numerical column, which values are contrasted
| contrast_type | ```String``` | Type of contrast to present
### Output
Independent from ```contrast_type``` selected, output data will have additional column with numerical column ```meas_to_contrast``` processed in specific way.
Available types of comparison ```contrast_type```
|Contrast_type | Description |
| ------------------------ | -------------|
| partofwhole| difference with max value related to specific textual value|
| partofmax| difference with arithmetic mean related to specific textual values|
| partofmin|difference with minimum value related to specific textual value|
# StartBigDrillDown
StartBigDrillDown - data retrieval from multiple hierachical levels.
This pattern can be only applied to data not stored already in DataFrame
### Attributes
```json
"StBigDrillDown":{
"hierdim_drill_down":{
"Key of hierarchical dimension":["dimlevel1key","dimlevel2key","dimlevel3key"]
}
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| hierdim_drill_down |``` dict{hierdim:list[str]} ``` | Hierarchical dimension with list of hierarchy levels to inspect
### Output
As 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.
# StartSmallZoomOut
StartSmallZoomOut - data retrieval from multiple hierachical levels.
This pattern can be only applied to data not stored already in DataFrame
### Attributes
```json
"StSmallZoomOut":{
"hierdim_zoom_out":{
"Key of hierarchical dimension":["dimlevel1key","dimlevel2key","dimlevel3key"]
}
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| hierdim_zoom_out |``` dict{hierdim:list[str]} ``` | Hierarchical dimension with list of hierarchy levels to inspect
### Output
As 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.
# AnalysisByCategory
AnalysisByCategory - ecomposition of data based on values in dim_for_category with analysis performed on each susbet
### Attributes
```json
"AByCategory":{
"dim_for_category":"dimension for categorisation",
"meas_to_analyse":"measure to perform analysis",
"analysis_type":"type of analysis"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| dim_for_category | ```String``` | Dimension, based on which input data will be categorised
| meas_to_analyse | ```String``` | Measure, which will be analysed
| analysis_type | ```String``` | Type of analysis to perform
### Output
As 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
Available types of analysis ```analysis_type```
|Analysis_type | Description |
| ------------------------ | -------------|
| min| Minimum per each category|
| max| Maximum per each category|
| mean|Arithmetical mean per each category|
| sum|Total value from each category|
# ExploreIntersection
### Attributes
```json
"ExpInt":{
"dim_to_explore":"dimension to explroe across cubes"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| dim_to_explore |``` String ``` | Dimension, which existence within enpoint is going to be investigated
### Output
Pattern will return series of datasets, where each will represent occurence of ```dim_to_explore``` in one cube
# NarratingChangeOverTime
Presenting difference between 2 numerical properties of data
### Attributes
```json
"NarrChangeOT":{
"meas_to_narrate":["list of two dimensions to present change"],
"narr_type":"type of narration"
}
```
Parameter | Type | Description |
| :------------------------ |:-------------:| :-------------|
| meas_to_narrate | ```String``` | Set of 2 measures, which change will be narrated
| narr_type | ```String``` | Type of narration to perform
### Output
Independent from ```narr_type``` selected, output data will have additional column with numerical values processed in specific way.
Available types of analysis ```narr_type```
|Narr_type | Description |
| ------------------------ | -------------|
| percchange| Percentage change between first nad second property|
| diffchange| Quantitive change between first and second property|
نحوه نصب
نصب پکیج whl cubestories-0.3.9:
pip install cubestories-0.3.9.whl
نصب پکیج tar.gz cubestories-0.3.9:
pip install cubestories-0.3.9.tar.gz