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datastories-0.3.9
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مشاهده بیشترتوضیحات
Data Story Pattern Analysis for LOSD
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | datastories-0.3.9 |
| نام | datastories |
| 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/DataStoryPatternLibrary |
| آدرس اینترنتی | https://pypi.org/project/datastories/ |
| مجوز | MIT |
# 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.
# JSON Template
```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":"integer(granularity level)",
"level_key":"integer(granularity level)"
}
}
},
"measures":{
"measure_key":{
"measure_title":"Title of measure",
"measure_url":"URI for measure"
}
}
}
}
```
# Patterns Description
<!--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
```python
def 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
def 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
|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
```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|
# ProfileOutliers
ProfileOutliers - detection of unusual values within data (anomalies)
### Attributes
```python
def ProfileOutliers(self,cube=[],dims=[],meas=[],hierdims=[],df=pd.DataFrame(), displayType="outliers_only")
```
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
| 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
```python
def DissectFactors(self,cube="",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_to_dissect="")
```
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_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
```python
def HighlightContrast(self,cube="",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_to_contrast="",contrast_type="",meas_to_contrast="")
```
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_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
```python
def StartBigDrillDown(self,cube="",dims=[],meas=[],hierdim_drill_down=[])
```
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
| 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
```python
def StartSmallZoomOut(self,cube="",dims=[],meas=[],hierdim_zoom_out=[])
```
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
| 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
```python
def AnalysisByCategory(self,cube="",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),dim_for_category="",meas_to_analyse="",analysis_type="min"):
```
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_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
```python
def ExploreIntersection(self, dim_to_explore=""):
```
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
### Attributes
```python
def NarrChangeOT(self,cube="",dims=[],meas=[],hierdims=[],df=pd.DataFrame(),meas_to_narrate="",narr_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
| 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 datastories-0.3.9:
pip install datastories-0.3.9.whl
نصب پکیج tar.gz datastories-0.3.9:
pip install datastories-0.3.9.tar.gz