datastories 0.1.1.8

Data Story Pattern Analysis for LOSD

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

pip install datastories

Requirements will be automatically installed with package

###Import/Usage

import datastories.analytical as patterns

patterns.DataStoryPattern(sparqlendpointurl, jsonmetadata)

Object created allow to query SPARQL endpoint based on JSON meatadat provided

Patterns Description

• Measurement and Counting
• League Table
• Internal Comprison

MCounting

Measurement and Counting Arithemtical operators applied to whole dataset - basic information regarding data

Attributes

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

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

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