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2 Commits
| Author | SHA1 | Date |
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Falko
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746fd8c267 | |
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Falko
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40a121a122 |
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3.11
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id,price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd,a,b,c,d,e,f,g,h,i,j,k,l,m
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BB-108899,99.95,_,_,_,_,_,Listenpreis:99.95 EUR (7.2 ct/Teil),"DetailsVon: BlueBrixxEAN: 4060904014783Release: 2026Kategorien: Gebäude, Kinder, PopkulturHersteller-Kategorien: BBPlay, The Three Investigators",Inhalt1393 Teile,PreiseListenpreis:99.95 EUR (7.2 ct/Teil),"EU:BlueBrixx - 99,95 €¹"
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BB-108899,99.95,_,_,_,_,_,Listenpreis:99.95 EUR (7.2 ct/Teil),"DetailsVon: BlueBrixxEAN: 4060904014783Release: 2026Kategorien: Gebäude, Kinder, PopkulturHersteller-Kategorien: BBPlay, The Three Investigators",Inhalt1393 Teile,PreiseListenpreis:99.95 EUR (7.2 ct/Teil),"EU:BlueBrixx - 99,95 €¹"
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BB-108569,29.95,_,_,_,_,_,Listenpreis:29.95 EUR (5.2 ct/Teil),DetailsVon: BlueBrixxEAN: 4060904023020Steine von: XingbaoRelease: 2026Kategorie: TiereHersteller-Kategorie: BBPro,Inhalt579 TeileMaße: 25.5 x 24.3 x 59.6 cm (B x H x T),PreiseListenpreis:29.95 EUR (5.2 ct/Teil),"EU:BlueBrixx - 29,95 €¹"
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Can't render this file because it is too large.
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# %% [markdown]
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# Build the Lego Knowledge Graph using the sources in `/data`.
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# %%
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from rdflib import Graph, Namespace, XSD, OWL, RDF, RDFS, SKOS, URIRef, Literal
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import pandas as pd
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from datetime import datetime
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# %% [markdown]
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# Setup the requirements for building a knowledge graph
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# %%
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g = Graph()
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thm = Namespace("https://thm.de/")
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THM = Namespace("https://thm.de/ont/")
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# %% [markdown]
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# # Rebrickable
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# %% [markdown]
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# 
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# %%
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re_colors = pd.read_csv("data/rebrickable/colors.csv")
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re_elements = pd.read_csv("data/rebrickable/elements.csv")
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re_inventories = pd.read_csv("data/rebrickable/inventories.csv")
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re_inventory_minifigs = pd.read_csv("data/rebrickable/inventory_minifigs.csv")
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re_inventory_parts = pd.read_csv("data/rebrickable/inventory_parts.csv")
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re_inventory_sets = pd.read_csv("data/rebrickable/inventory_sets.csv")
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re_minifigs = pd.read_csv("data/rebrickable/minifigs.csv")
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re_part_categories = pd.read_csv("data/rebrickable/part_categories.csv")
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re_part_relationships = pd.read_csv("data/rebrickable/part_relationships.csv")
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re_parts = pd.read_csv("data/rebrickable/parts.csv")
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re_sets = pd.read_csv("data/rebrickable/sets.csv")
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re_themes = pd.read_csv("data/rebrickable/themes.csv")
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# %% [markdown]
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# Colors
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# %%
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for color in re_colors.itertuples(index=False):
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color_ref = thm[f"colors/{color.id}"]
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g.add((color_ref, RDFS.label, Literal(color.name, lang="en")))
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g.add((color_ref, THM.color, Literal(color.rgb)))
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g.add((color_ref, THM.is_transparent, Literal(color.is_trans, datatype=XSD.boolean)))
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if not pd.isna(color.y1):
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# First appearance
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g.add((color_ref, THM.first_year, Literal(datetime(year = int(color.y1), month=1, day=1))))
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if not pd.isna(color.y2):
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# Last appearance
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g.add((color_ref, THM.last_year, Literal(datetime(year = int(color.y2), month=1, day=1))))
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# %% [markdown]
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# Part Categories
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# %%
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for part_category in re_part_categories.itertuples(index=False):
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part_category_ref = thm[f"part_category/{part_category.id}"]
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g.add((part_category_ref, RDFS.label, Literal(part_category_ref, lang="en")))
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# %% [markdown]
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# Parts
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# %%
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for part in re_parts.itertuples(index=False):
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part_ref = thm[f"part/{part.part_num}"]
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g.add((part_ref, RDFS.label, Literal(part.name, lang="en")))
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g.add((part_ref, THM.part_category, thm[f"part_category/{part.part_cat_id}"]))
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g.add((part_ref, THM.part_material, Literal(part.part_material)))
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# %% [markdown]
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# Elements
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# %%
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for element in re_elements.itertuples(index=False):
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part_ref = thm[f"part/{element.part_num}"]
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color_ref = thm[f"colors/{element.color_id}"]
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g.add((part_ref, THM.has_color, color_ref))
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# %% [markdown]
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# Part Relationships
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# %%
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for part_relationship in re_part_relationships.itertuples(index=False):
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part_ref_parent = thm[f"part/{part_relationship.parent_part_num}"]
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part_ref_child = thm[f"part/{part_relationship.child_part_num}"]
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g.add((part_ref_parent, THM.has_child, part_ref_child))
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# %% [markdown]
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# Themes
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# %%
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for theme in re_themes.itertuples(index=False):
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theme_ref = thm[f"theme/{int(theme.id)}"]
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g.add((theme_ref, RDFS.label, Literal(theme.name, lang="en")))
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if not pd.isna(theme.parent_id):
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g.add((theme_ref, THM.parent_theme, thm[f"theme/{int(theme.parent_id)}"]))
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# %% [markdown]
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# Sets
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# %%
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for lego_set in re_sets.itertuples(index=False):
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set_ref = thm[f"set/lego/{lego_set.set_num}"]
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g.add((set_ref, RDFS.label, Literal(lego_set.name, lang="en")))
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g.add((set_ref, THM.year, Literal(datetime(int(lego_set.year), 1, 1))))
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g.add((set_ref, THM.theme, thm[f"theme/{int(lego_set.theme_id)}"]))
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g.add((set_ref, THM.num_parts, Literal(int(lego_set.num_parts), datatype=XSD.integer)))
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g.add((set_ref, THM.brand, Literal("Lego")))
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# %% [markdown]
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# Minifigures
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# %%
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for minifig in re_minifigs.itertuples(index=False):
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minifig_ref = thm[f"minifig/{minifig.fig_num}"]
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g.add((set_ref, RDFS.label, Literal(minifig.name, lang="en")))
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g.add((set_ref, THM.num_parts, Literal(int(minifig.num_parts), datatype=XSD.integer)))
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# %% [markdown]
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# Now the ugly part: Inventories
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# %%
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for inventory in re_inventories.itertuples(index=False):
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inventory_ref = thm[f"inventory/{inventory.id}"]
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g.add((inventory_ref, THM.set, thm[f"sets/lego/{inventory.set_num}"]))
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# %% [markdown]
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# Inventories relate sets, minifigures and parts to each other, creating a kind of "top level set"
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# (this takes a lot of time)
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# %%
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for inventory_part in re_inventory_parts.itertuples(index=False):
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inventory_part_ref = thm[f"inventory_part/{inventory_part.inventory_id}/{inventory_part.part_num}"]
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inventory_ref = thm[f"inventory/{inventory_part.inventory_id}"]
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part_ref = thm[f"part/{inventory_part.part_num}"]
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g.add((inventory_part_ref, RDFS.domain, inventory_ref))
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g.add((inventory_part_ref, RDFS.range, part_ref))
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g.add((inventory_part_ref, RDF.type, RDF.Property))
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g.add((inventory_part_ref, THM.quantity, Literal(int(inventory_part.quantity), datatype=XSD.integer)))
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g.add((inventory_part_ref, THM.is_spare, Literal(inventory_part.is_spare, datatype=XSD.boolean)))
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g.add((inventory_part_ref, THM.color, thm[f"color/{inventory_part.color_id}"]))
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# %%
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for inventory_set in re_inventory_sets.itertuples(index=False):
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inventory_set_ref = thm[f"inventory_set/{inventory_set.inventory_id}/{inventory_set.set_num}"]
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inventory_ref = thm[f"inventory/{inventory_set.inventory_id}"]
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set_ref = thm[f"set/lego/{inventory_set.set_num}"]
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g.add((inventory_set_ref, RDFS.domain, inventory_ref))
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g.add((inventory_set_ref, RDFS.range, set_ref))
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g.add((inventory_set_ref, RDF.type, RDF.Property))
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g.add((inventory_set_ref, THM.quantity, Literal(int(inventory_set.quantity), datatype=XSD.integer)))
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# %%
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for inventory_minifig in re_inventory_minifigs.itertuples(index=False):
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inventory_minifig_ref = thm[f"inventory_minifig/{inventory_minifig.inventory_id}/{inventory_minifig.fig_num}"]
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inventory_ref = thm[f"inventory/{inventory_minifig.inventory_id}"]
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minifig_ref = thm[f"minifig/lego/{inventory_minifig.fig_num}"]
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g.add((inventory_minifig_ref, RDFS.domain, inventory_ref))
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g.add((inventory_minifig_ref, RDFS.range, minifig_ref))
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g.add((inventory_minifig_ref, RDF.type, RDF.Property))
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g.add((inventory_minifig_ref, THM.quantity, Literal(int(inventory_minifig.quantity), datatype=XSD.integer)))
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# %% [markdown]
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# Serialize the graph in turtle format
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# %% [markdown]
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# ```
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# ___-------___
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# _-~~ ~~-_
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# _-~ /~-_
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# /^\__/^\ /~ \ / \
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# /| O|| O| / \_______________/ \
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# | |___||__| / / \ \
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# | \ / / \ \
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# | (_______) /______/ \_________ \
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# | / / \ / \
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# \ \^\\ \ / \ /
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# \ || \______________/ _-_ //\__//
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# \ ||------_-~~-_ ------------- \ --/~ ~\ || __/
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# ~-----||====/~ |==================| |/~~~~~
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# (_(__/ ./ / \_\ \.
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# (_(___/ \_____)_)
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# ```
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# %%
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g.bind("thmont", THM)
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g.serialize("lego_graph_rebrickable.ttl", format="turtle")
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@ -10,14 +10,15 @@
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},
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{
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"cell_type": "code",
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"execution_count": 257,
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"execution_count": 2,
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"id": "90209948",
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"metadata": {},
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"outputs": [],
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"source": [
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"from rdflib import Graph, Namespace, XSD, OWL, RDF, RDFS, SKOS, URIRef, Literal\n",
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"import pandas as pd\n",
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"import numpy as np"
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"from datetime import datetime\n",
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"import os"
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]
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},
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{
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@ -30,7 +31,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 258,
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"execution_count": 3,
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"id": "8e573135",
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"metadata": {},
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"outputs": [],
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@ -40,6 +41,61 @@
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"THM = Namespace(\"https://thm.de/ont/\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "class_hierarchy",
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"metadata": {},
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"outputs": [
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||||
{
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"data": {
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"text/plain": [
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||||
"<Graph identifier=Ne25ca93489ee450c8158409bd5d2a548 (<class 'rdflib.graph.Graph'>)>"
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]
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||||
},
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"execution_count": 4,
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||||
"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Class hierarchy\n",
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"g.add((THM.LegoEntity, RDF.type, OWL.Class))\n",
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"g.add((THM.LegoEntity, RDFS.subClassOf, OWL.Thing))\n",
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"\n",
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"g.add((THM.PhysicalItem, RDF.type, OWL.Class))\n",
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"g.add((THM.PhysicalItem, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.Set, RDF.type, OWL.Class))\n",
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"g.add((THM.Set, RDFS.subClassOf, THM.PhysicalItem))\n",
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"g.add((THM.Part, RDF.type, OWL.Class))\n",
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"g.add((THM.Part, RDFS.subClassOf, THM.PhysicalItem))\n",
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"g.add((THM.Minifigure, RDF.type, OWL.Class))\n",
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"g.add((THM.Minifigure, RDFS.subClassOf, THM.PhysicalItem))\n",
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"\n",
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"g.add((THM.CatalogEntry, RDF.type, OWL.Class))\n",
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"g.add((THM.CatalogEntry, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.Color, RDF.type, OWL.Class))\n",
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"g.add((THM.Color, RDFS.subClassOf, THM.CatalogEntry))\n",
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"g.add((THM.PartCategory, RDF.type, OWL.Class))\n",
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"g.add((THM.PartCategory, RDFS.subClassOf, THM.CatalogEntry))\n",
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"g.add((THM.Theme, RDF.type, OWL.Class))\n",
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"g.add((THM.Theme, RDFS.subClassOf, THM.CatalogEntry))\n",
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"\n",
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"g.add((THM.Inventory, RDF.type, OWL.Class))\n",
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"g.add((THM.Inventory, RDFS.subClassOf, THM.LegoEntity))\n",
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"g.add((THM.InventoryEntry, RDF.type, OWL.Class))\n",
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"g.add((THM.InventoryEntry, RDFS.subClassOf, THM.LegoEntity))\n",
|
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"g.add((THM.InventoryPart, RDF.type, OWL.Class))\n",
|
||||
"g.add((THM.InventoryPart, RDFS.subClassOf, THM.InventoryEntry))\n",
|
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"g.add((THM.InventorySet, RDF.type, OWL.Class))\n",
|
||||
"g.add((THM.InventorySet, RDFS.subClassOf, THM.InventoryEntry))\n",
|
||||
"g.add((THM.InventoryMinifig, RDF.type, OWL.Class))\n",
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||||
"g.add((THM.InventoryMinifig, RDFS.subClassOf, THM.InventoryEntry))\n",
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||||
"\n",
|
||||
"g.add((THM.Producer, RDF.type, OWL.Class))\n",
|
||||
"g.add((THM.Producer, RDFS.subClassOf, THM.LegoEntity))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d56199d5",
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||||
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@ -58,7 +114,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 259,
|
||||
"execution_count": 5,
|
||||
"id": "d8a1fe84",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -87,7 +143,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 260,
|
||||
"execution_count": 6,
|
||||
"id": "ae505704",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -95,17 +151,15 @@
|
|||
"for color in re_colors.itertuples(index=False):\n",
|
||||
" color_ref = thm[f\"color/{color.id}\"]\n",
|
||||
"\n",
|
||||
" g.add((color_ref, RDF.type, THM.Color ))\n",
|
||||
" g.add((color_ref, RDF.type, THM.Color))\n",
|
||||
" g.add((color_ref, RDFS.label, Literal(color.name, lang=\"en\")))\n",
|
||||
" g.add((color_ref, THM.rgbcolor, Literal(color.rgb)))\n",
|
||||
" g.add((color_ref, THM.color, Literal(color.rgb)))\n",
|
||||
" g.add((color_ref, THM.is_transparent, Literal(color.is_trans, datatype=XSD.boolean)))\n",
|
||||
" \n",
|
||||
" if not pd.isna(color.y1):\n",
|
||||
" # First appearance\n",
|
||||
" g.add((color_ref, THM.first_year, Literal(int(color.y1), datatype=XSD.integer)))\n",
|
||||
" g.add((color_ref, THM.first_year, Literal(datetime(year = int(color.y1), month=1, day=1))))\n",
|
||||
" if not pd.isna(color.y2):\n",
|
||||
" # Last appearance\n",
|
||||
" g.add((color_ref, THM.last_year, Literal(int(color.y2), datatype=XSD.integer)))\n"
|
||||
" g.add((color_ref, THM.last_year, Literal(datetime(year = int(color.y2), month=1, day=1))))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -118,7 +172,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 261,
|
||||
"execution_count": 7,
|
||||
"id": "fb9e17d6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -126,8 +180,8 @@
|
|||
"for part_category in re_part_categories.itertuples(index=False):\n",
|
||||
" part_category_ref = thm[f\"part_category/{part_category.id}\"]\n",
|
||||
"\n",
|
||||
" g.add((part_category_ref, RDF.type, THM.PartCategory ))\n",
|
||||
" g.add((part_category_ref, RDFS.label, Literal(part_category_ref, lang=\"en\")))"
|
||||
" g.add((part_category_ref, RDF.type, THM.PartCategory))\n",
|
||||
" g.add((part_category_ref, RDFS.label, Literal(part_category.name, lang=\"en\")))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -140,7 +194,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 262,
|
||||
"execution_count": 8,
|
||||
"id": "8fdb080e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -164,24 +218,16 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 263,
|
||||
"execution_count": 9,
|
||||
"id": "579b1d67",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for element in re_elements.itertuples(index=False):\n",
|
||||
" element_ref = thm[f\"element/{element.element_id}\"]\n",
|
||||
" part_ref = thm[f\"part/{element.part_num}\"]\n",
|
||||
" color_ref = thm[f\"color/{element.color_id}\"]\n",
|
||||
"\n",
|
||||
" g.add((element_ref, RDF.type, RDF.Property))\n",
|
||||
" g.add((element_ref, RDF.type, THM.Element))\n",
|
||||
"\n",
|
||||
" g.add((element_ref, RDFS.domain, THM.Part))\n",
|
||||
" g.add((element_ref, RDFS.range, THM.Color))\n",
|
||||
"\n",
|
||||
" g.add((element_ref, THM.color, color_ref))\n",
|
||||
" g.add((part_ref, THM.part, part_ref))"
|
||||
" g.add((part_ref, THM.has_color, color_ref))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -194,7 +240,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 264,
|
||||
"execution_count": 10,
|
||||
"id": "00db079a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -203,8 +249,7 @@
|
|||
" part_ref_parent = thm[f\"part/{part_relationship.parent_part_num}\"]\n",
|
||||
" part_ref_child = thm[f\"part/{part_relationship.child_part_num}\"]\n",
|
||||
"\n",
|
||||
" g.add((part_ref_parent, THM.has_child, part_ref_child))\n",
|
||||
" g.add((part_ref_child, THM.has_parent, part_ref_parent))"
|
||||
" g.add((part_ref_parent, THM.has_child, part_ref_child))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -217,7 +262,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 265,
|
||||
"execution_count": 11,
|
||||
"id": "1a529aae",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -242,7 +287,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 266,
|
||||
"execution_count": 12,
|
||||
"id": "29b357ef",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -252,7 +297,7 @@
|
|||
"\n",
|
||||
" g.add((set_ref, RDF.type, THM.Set))\n",
|
||||
" g.add((set_ref, RDFS.label, Literal(lego_set.name, lang=\"en\")))\n",
|
||||
" g.add((set_ref, THM.year, Literal(int(lego_set.year), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, THM.year, Literal(datetime(int(lego_set.year), 1, 1))))\n",
|
||||
" g.add((set_ref, THM.theme, thm[f\"theme/{int(lego_set.theme_id)}\"]))\n",
|
||||
" g.add((set_ref, THM.num_parts, Literal(int(lego_set.num_parts), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, THM.brand, Literal(\"Lego\")))"
|
||||
|
|
@ -268,7 +313,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 267,
|
||||
"execution_count": 13,
|
||||
"id": "a67b3e70",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -276,7 +321,7 @@
|
|||
"for minifig in re_minifigs.itertuples(index=False):\n",
|
||||
" minifig_ref = thm[f\"minifig/{minifig.fig_num}\"]\n",
|
||||
"\n",
|
||||
" g.add((minifig_ref, RDF.type, THM.Minifig))\n",
|
||||
" g.add((minifig_ref, RDF.type, THM.Minifigure))\n",
|
||||
" g.add((minifig_ref, RDFS.label, Literal(minifig.name, lang=\"en\")))\n",
|
||||
" g.add((minifig_ref, THM.num_parts, Literal(int(minifig.num_parts), datatype=XSD.integer)))"
|
||||
]
|
||||
|
|
@ -291,7 +336,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 268,
|
||||
"execution_count": 14,
|
||||
"id": "0c97dc4d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -314,47 +359,25 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 269,
|
||||
"execution_count": 15,
|
||||
"id": "dc2ba03e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\nfor inventory_part in re_inventory_parts.itertuples(index=False):\\n inventory_part_ref = thm[f\"inventory_part/{inventory_part.inventory_id}/{inventory_part.part_num}\"]\\n\\n inventory_ref = thm[f\"inventory/{inventory_part.inventory_id}\"]\\n part_ref = thm[f\"part/{inventory_part.part_num}\"]\\n\\n g.add((inventory_part_ref, RDF.type, THM.PartInv))\\n g.add((inventory_part_ref, RDF.type, RDF.Property))\\n\\n g.add((inventory_part_ref, RDFS.domain, THM.Inventory))\\n g.add((inventory_part_ref, RDFS.range, THM.Part))\\n\\n g.add((inventory_ref, THM.contains, inventory_part_ref))\\n g.add((part_ref, THM.belongs, inventory_part_ref))\\n\\n g.add((inventory_part_ref, THM.quantity, Literal(int(inventory_part.quantity), datatype=XSD.integer)))\\n g.add((inventory_part_ref, THM.is_spare, Literal(inventory_part.is_spare, datatype=XSD.boolean)))\\n g.add((inventory_part_ref, THM.color, thm[f\"color/{inventory_part.color_id}\"]))\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 269,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
"for inventory_part in re_inventory_parts.itertuples(index=False):\n",
|
||||
" inventory_part_ref = thm[f\"inventory_part/{inventory_part.inventory_id}/{inventory_part.part_num}\"]\n",
|
||||
" \n",
|
||||
" inventory_ref = thm[f\"inventory/{inventory_part.inventory_id}\"]\n",
|
||||
" part_ref = thm[f\"part/{inventory_part.part_num}\"]\n",
|
||||
"\n",
|
||||
" g.add((inventory_part_ref, RDF.type, THM.PartInv))\n",
|
||||
" g.add((inventory_part_ref, RDF.type, RDF.Property))\n",
|
||||
"\n",
|
||||
" g.add((inventory_part_ref, RDFS.domain, THM.Inventory))\n",
|
||||
" g.add((inventory_part_ref, RDFS.range, THM.Part))\n",
|
||||
" \n",
|
||||
" g.add((inventory_ref, THM.contains, inventory_part_ref))\n",
|
||||
" g.add((part_ref, THM.belongs, inventory_part_ref))\n",
|
||||
"\n",
|
||||
" g.add((inventory_part_ref, RDF.type, THM.InventoryPart))\n",
|
||||
" g.add((inventory_part_ref, THM.inventory, thm[f\"inventory/{inventory_part.inventory_id}\"]))\n",
|
||||
" g.add((inventory_part_ref, THM.part, thm[f\"part/{inventory_part.part_num}\"]))\n",
|
||||
" g.add((inventory_part_ref, THM.quantity, Literal(int(inventory_part.quantity), datatype=XSD.integer)))\n",
|
||||
" g.add((inventory_part_ref, THM.is_spare, Literal(inventory_part.is_spare, datatype=XSD.boolean)))\n",
|
||||
" g.add((inventory_part_ref, THM.color, thm[f\"color/{inventory_part.color_id}\"]))\n",
|
||||
"\"\"\""
|
||||
" g.add((inventory_part_ref, THM.color, thm[f\"color/{inventory_part.color_id}\"]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 270,
|
||||
"execution_count": 16,
|
||||
"id": "8715a1cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -362,24 +385,15 @@
|
|||
"for inventory_set in re_inventory_sets.itertuples(index=False):\n",
|
||||
" inventory_set_ref = thm[f\"inventory_set/{inventory_set.inventory_id}/{inventory_set.set_num}\"]\n",
|
||||
"\n",
|
||||
" inventory_ref = thm[f\"inventory/{inventory_set.inventory_id}\"]\n",
|
||||
" set_ref = thm[f\"set/lego/{inventory_set.set_num}\"]\n",
|
||||
"\n",
|
||||
" g.add((inventory_set_ref, RDF.type, THM.SetInv))\n",
|
||||
" g.add((inventory_set_ref, RDF.type, RDF.Property))\n",
|
||||
"\n",
|
||||
" g.add((inventory_set_ref, RDFS.domain, THM.Inventory))\n",
|
||||
" g.add((inventory_set_ref, RDFS.range, THM.Set))\n",
|
||||
"\n",
|
||||
" g.add((inventory_ref, THM.contains, inventory_set_ref))\n",
|
||||
" g.add((set_ref, THM.belongs, inventory_set_ref))\n",
|
||||
" \n",
|
||||
" g.add((inventory_set_ref, RDF.type, THM.InventorySet))\n",
|
||||
" g.add((inventory_set_ref, THM.inventory, thm[f\"inventory/{inventory_set.inventory_id}\"]))\n",
|
||||
" g.add((inventory_set_ref, THM.set, thm[f\"set/lego/{inventory_set.set_num}\"]))\n",
|
||||
" g.add((inventory_set_ref, THM.quantity, Literal(int(inventory_set.quantity), datatype=XSD.integer)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 271,
|
||||
"execution_count": 17,
|
||||
"id": "08c2c580",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -387,18 +401,9 @@
|
|||
"for inventory_minifig in re_inventory_minifigs.itertuples(index=False):\n",
|
||||
" inventory_minifig_ref = thm[f\"inventory_minifig/{inventory_minifig.inventory_id}/{inventory_minifig.fig_num}\"]\n",
|
||||
"\n",
|
||||
" inventory_ref = thm[f\"inventory/{inventory_minifig.inventory_id}\"]\n",
|
||||
" minifig_ref = thm[f\"minifig/{inventory_minifig.fig_num}\"]\n",
|
||||
"\n",
|
||||
" g.add((inventory_minifig_ref, RDF.type, THM.MinifigInv))\n",
|
||||
" g.add((inventory_minifig_ref, RDF.type, RDF.Property))\n",
|
||||
"\n",
|
||||
" g.add((inventory_minifig_ref, RDFS.domain, THM.Inventory))\n",
|
||||
" g.add((inventory_minifig_ref, RDFS.range, THM.Minifig))\n",
|
||||
"\n",
|
||||
" g.add((inventory_ref, THM.contains, inventory_minifig_ref))\n",
|
||||
" g.add((minifig_ref, THM.belongs, inventory_minifig_ref))\n",
|
||||
" \n",
|
||||
" g.add((inventory_minifig_ref, RDF.type, THM.InventoryMinifig))\n",
|
||||
" g.add((inventory_minifig_ref, THM.inventory, thm[f\"inventory/{inventory_minifig.inventory_id}\"]))\n",
|
||||
" g.add((inventory_minifig_ref, THM.minifig, thm[f\"minifig/{inventory_minifig.fig_num}\"]))\n",
|
||||
" g.add((inventory_minifig_ref, THM.quantity, Literal(int(inventory_minifig.quantity), datatype=XSD.integer)))"
|
||||
]
|
||||
},
|
||||
|
|
@ -420,26 +425,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 272,
|
||||
"execution_count": 18,
|
||||
"id": "1e0ac437",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"bs_sets = pd.read_csv(\"./data/brickset/sets.csv\")\n",
|
||||
"bs_parts = pd.read_csv(\"./data/brickset/parts.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d120c079",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Add Set prices"
|
||||
"bs_sets = pd.read_csv(\"./data/brickset/sets.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 273,
|
||||
"execution_count": 19,
|
||||
"id": "fd944ccb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -449,40 +445,14 @@
|
|||
" set_ref = thm[f\"set/lego/{num}\"]\n",
|
||||
"\n",
|
||||
" if (set_ref, None, None) in g:\n",
|
||||
" #brickset prices already in euro\n",
|
||||
" #choose the cheapest price since the usual customer wont choose the highest price\n",
|
||||
" options = [bs_set.USRetailPrice, bs_set.DERetailPrice, bs_set.UKRetailPrice, bs_set.CARetailPrice]\n",
|
||||
" options = [int(opt) for opt in options if not pd.isna(opt)]\n",
|
||||
"\n",
|
||||
" if len(options) >= 1:\n",
|
||||
" cheapest = min(options)\n",
|
||||
" g.add((set_ref, THM.price_new, Literal(cheapest, datatype=XSD.float)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cbd69fa6",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Only concrete elements (parts considering their shape, color and print) can have prices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 274,
|
||||
"id": "307a3210",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"for bs_element in bs_parts.itertuples(index=False):\n",
|
||||
" element_ref = thm[f\"element/{bs_element.ElementID}\"]\n",
|
||||
" \n",
|
||||
" if (element_ref, None, None) in g:\n",
|
||||
" if not pd.isna(bs_element.BrickLinkSoldPriceNew):\n",
|
||||
" g.add((element_ref, THM.price_new, Literal(bs_element.BrickLinkSoldPriceNew, datatype=XSD.float)))\n",
|
||||
" if not pd.isna(bs_element.BrickLinkSoldPriceUsed):\n",
|
||||
" g.add((element_ref, THM.price_used, Literal(bs_element.BrickLinkSoldPriceUsed, datatype=XSD.float)))"
|
||||
" if not pd.isna(bs_set.USRetailPrice):\n",
|
||||
" g.add((set_ref, THM.us_retail_price, Literal(bs_set.USRetailPrice, datatype=XSD.float)))\n",
|
||||
" if not pd.isna(bs_set.DERetailPrice):\n",
|
||||
" g.add((set_ref, THM.de_retail_price, Literal(bs_set.DERetailPrice, datatype=XSD.float)))\n",
|
||||
" if not pd.isna(bs_set.UKRetailPrice):\n",
|
||||
" g.add((set_ref, THM.us_retail_price, Literal(bs_set.UKRetailPrice, datatype=XSD.float)))\n",
|
||||
" if not pd.isna(bs_set.CARetailPrice):\n",
|
||||
" g.add((set_ref, THM.ca_retail_price, Literal(bs_set.CARetailPrice, datatype=XSD.float)))"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -497,7 +467,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 275,
|
||||
"execution_count": 20,
|
||||
"id": "a8beb593",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -509,7 +479,17 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 276,
|
||||
"execution_count": 21,
|
||||
"id": "b14e6e89",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"additional_entries = 0"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "bbf5462b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -518,9 +498,11 @@
|
|||
" set_ref = thm[f\"set/lego/{bl_set.set_id}\"]\n",
|
||||
"\n",
|
||||
" if not (set_ref, None, None) in g:\n",
|
||||
" g.add((set_ref, RDFS.label, Literal(lego_set.name, lang=\"en\")))\n",
|
||||
" additional_entries += 1\n",
|
||||
" g.add((set_ref, RDF.type, THM.Set))\n",
|
||||
" g.add((set_ref, RDFS.label, Literal(bl_set.set_name, lang=\"en\")))\n",
|
||||
" if not pd.isna(bl_set.year) and str(bl_set.year).isnumeric():\n",
|
||||
" g.add((set_ref, THM.year, Literal(int(bl_set.year))))\n",
|
||||
" g.add((set_ref, THM.first_year, Literal(datetime(int(bl_set.year), 1, 1))))\n",
|
||||
" if not pd.isna(bl_set.parts) and str(bl_set.parts).isnumeric():\n",
|
||||
" g.add((set_ref, THM.num_parts, Literal(int(bl_set.parts), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, THM.brand, Literal(\"Lego\")))"
|
||||
|
|
@ -528,139 +510,129 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 277,
|
||||
"execution_count": 23,
|
||||
"id": "ef52582e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\nfor bl_part in bl_parts.itertuples(index=False):\\n part_ref = thm[f\"part/{bl_part.part_id}\"]\\n\\n if not (part_ref, None, None) in g:\\n additional_entries += 1\\n g.add((part_ref, RDFS.label, Literal(bl_part.part_name, lang=\"en\")))\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 277,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
"for bl_part in bl_parts.itertuples(index=False):\n",
|
||||
" part_ref = thm[f\"part/{bl_part.part_id}\"]\n",
|
||||
"\n",
|
||||
" if not (part_ref, None, None) in g:\n",
|
||||
" additional_entries += 1\n",
|
||||
" g.add((part_ref, RDFS.label, Literal(bl_part.part_name, lang=\"en\")))\n",
|
||||
"\"\"\""
|
||||
" g.add((part_ref, RDF.type, THM.Part))\n",
|
||||
" g.add((part_ref, RDFS.label, Literal(bl_part.part_name, lang=\"en\")))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 278,
|
||||
"execution_count": 24,
|
||||
"id": "8bf0ffeb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for bl_minifig in bl_minifigs.itertuples(index=False):\n",
|
||||
" minifig_ref = thm[f\"minifig/{bl_minifig.minifig_id}\"]\n",
|
||||
"\n",
|
||||
" if not (minifig_ref, None, None) in g:\n",
|
||||
" additional_entries += 1\n",
|
||||
" g.add((minifig_ref, RDF.type, THM.Minifigure))\n",
|
||||
" g.add((minifig_ref, RDFS.label, Literal(bl_minifig.minifig_name, lang=\"en\")))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "3491b032",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'\\nfor bl_minifig in bl_minifigs.itertuples(index=False):\\n minifig_ref = thm[f\"minfig/{bl_minifig.minifig_id}\"]\\n\\n if not (minifig_ref, None, None) in g:\\n g.add((minifig_ref, RDFS.label, Literal(bl_minifig.minifig_name, lang=\"en\")))\\n'"
|
||||
]
|
||||
},
|
||||
"execution_count": 278,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Added 107748 items\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\"\"\"\n",
|
||||
"for bl_minifig in bl_minifigs.itertuples(index=False):\n",
|
||||
" minifig_ref = thm[f\"minfig/{bl_minifig.minifig_id}\"]\n",
|
||||
"\n",
|
||||
" if not (minifig_ref, None, None) in g:\n",
|
||||
" g.add((minifig_ref, RDFS.label, Literal(bl_minifig.minifig_name, lang=\"en\")))\n",
|
||||
"\"\"\""
|
||||
"print(f\"Added {additional_entries} items\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e73471b9",
|
||||
"id": "6eca7f24",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Merlins Steine"
|
||||
"# Merlin"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 279,
|
||||
"id": "ab1ec488",
|
||||
"execution_count": 26,
|
||||
"id": "c1e9ff32",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"me_sets = pd.read_csv(\"./data/merlin/others.csv\")"
|
||||
"merlin_sets = pd.read_csv(\"./data/merlin/others.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 293,
|
||||
"id": "9bcd2956",
|
||||
"execution_count": 27,
|
||||
"id": "217dc4d2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t = me_sets[me_sets[\"brand\"] == \"Pantasy\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 294,
|
||||
"id": "9ab21460",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t[\"ratio\"] = t[\"price_eur\"] / t[\"num_parts\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 295,
|
||||
"id": "459c3a4c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"np.float64(0.43016261640379705)"
|
||||
]
|
||||
},
|
||||
"execution_count": 295,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"t[\"ratio\"].mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 282,
|
||||
"id": "bfcf2840",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for me_set in me_sets.itertuples(index=False):\n",
|
||||
" if not pd.isna(me_set.brand) and not pd.isna(me_set.id):\n",
|
||||
" set_ref = thm[f\"set/{me_set.brand}/{me_set.id}\"]\n",
|
||||
"additional_entries = 0\n",
|
||||
"\n",
|
||||
"for merlin_set in merlin_sets.itertuples(index=False):\n",
|
||||
" set_ref = thm[f\"set/lego/{merlin_set.id}\"]\n",
|
||||
" producer_ref = thm[f\"producer/{merlin_set.producer}\"]\n",
|
||||
"\n",
|
||||
" if not (producer_ref, None, None) in g:\n",
|
||||
" g.add((producer_ref, RDF.type, THM.Producer))\n",
|
||||
" g.add((producer_ref, RDFS.label, Literal(merlin_set.producer)))\n",
|
||||
"\n",
|
||||
" if not (set_ref, None, None) in g:\n",
|
||||
" additional_entries += 1\n",
|
||||
" g.add((set_ref, RDF.type, THM.Set))\n",
|
||||
" g.add((set_ref, RDFS.label, Literal(me_set.name, lang=\"en\")))\n",
|
||||
" if not pd.isna(me_set.release):\n",
|
||||
" g.add((set_ref, THM.year, Literal(int(me_set.release), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, RDFS.label, Literal(merlin_set.name)))\n",
|
||||
" g.add((set_ref, THM.brand, producer_ref))\n",
|
||||
" if not pd.isna(merlin_set.year):\n",
|
||||
" g.add((set_ref, THM.first_year, Literal(int(merlin_set.year), datatype=XSD.integer)))\n",
|
||||
" if not pd.isna(merlin_set.parts):\n",
|
||||
" g.add((set_ref, THM.num_parts, Literal(int(merlin_set.parts), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, THM.size_word, Literal(merlin_set.size)))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"id": "54eaa79e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"merlin_prices = pd.read_csv(\"./data/merlin/prices.csv\")\n",
|
||||
"\n",
|
||||
" if not pd.isna(me_set.num_parts):\n",
|
||||
" g.add((set_ref, THM.num_parts, Literal(int(me_set.num_parts), datatype=XSD.integer)))\n",
|
||||
" g.add((set_ref, THM.brand, Literal(me_set.brand)))\n",
|
||||
" if not pd.isna(me_set.price_eur):\n",
|
||||
" g.add((set_ref, THM.price_new, Literal(me_set.price_eur, datatype=XSD.float)))"
|
||||
"for merlin_price in merlin_prices.itertuples(index=False):\n",
|
||||
" set_ref = thm[f\"set/lego/{merlin_price.id}\"]\n",
|
||||
" if merlin_price.price_eur not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.eur_price, Literal(merlin_price.price_eur, datatype=XSD.float)))\n",
|
||||
" #usd (data src not clean)\n",
|
||||
" if merlin_price._3 not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.usd_price, Literal(merlin_price._3, datatype=XSD.float)))\n",
|
||||
" # cn \n",
|
||||
" if merlin_price._2 not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.cn_price, Literal(merlin_price._2, datatype=XSD.float)))\n",
|
||||
" # best price euro \n",
|
||||
" if merlin_price._4 not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.eur_bestprice, Literal(merlin_price._4, datatype=XSD.float)))\n",
|
||||
" # best price cn\n",
|
||||
" if merlin_price._5 not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.cn_bestprice, Literal(merlin_price._5, datatype=XSD.float)))\n",
|
||||
" # best price usd\n",
|
||||
" if merlin_price._6 not in [None, \"_\"]:\n",
|
||||
" g.add((set_ref, THM.usd_bestprice, Literal(merlin_price._6, datatype=XSD.float)))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
@ -697,31 +669,67 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 283,
|
||||
"execution_count": 29,
|
||||
"id": "1a30bff8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<Graph identifier=Naee4bab906a6444290a3659ffe0fbd45 (<class 'rdflib.graph.Graph'>)>"
|
||||
"<Graph identifier=Ne25ca93489ee450c8158409bd5d2a548 (<class 'rdflib.graph.Graph'>)>"
|
||||
]
|
||||
},
|
||||
"execution_count": 283,
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"g.bind(\"thm\", THM)\n",
|
||||
"g.bind(\"thmont\", THM)\n",
|
||||
"\n",
|
||||
"g.serialize(\"lego_graph.ttl\", format=\"turtle\")"
|
||||
"g.serialize(\"lego_graph_rebrickable.ttl\", format=\"turtle\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "7b9c3bcf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/3626cpr3530'), rdflib.term.Literal('Minifig Head Chitauri, Gold Armor, Lime Eyes, Large Open Mouth with Teeth Print', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n",
|
||||
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/970c12'), rdflib.term.Literal('Hips and Dark Bluish Gray Legs', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n",
|
||||
"(rdflib.term.URIRef('https://thm.de/inventory_part/102116/973c14h14pr5739'), rdflib.term.Literal('Torso Armor, Light Bluish Gray and Olive Green Panels Print, Light Bluish Gray Arms and Hands', lang='en'), rdflib.term.Literal('1', datatype=rdflib.term.URIRef('http://www.w3.org/2001/XMLSchema#integer')))\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import rdflib\n",
|
||||
" \n",
|
||||
"query = \"\"\"\n",
|
||||
"SELECT ?inventory_part ?part_name ?quantity\n",
|
||||
"WHERE {\n",
|
||||
" ?inventory thmont:set <https://thm.de/set/lego/fig-011397> .\n",
|
||||
" ?inventory_part thmont:inventory ?inventory ;\n",
|
||||
" thmont:part ?part ;\n",
|
||||
" thmont:quantity ?quantity .\n",
|
||||
" ?part rdfs:label ?part_name .\n",
|
||||
"}\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"qres = g.query(query)\n",
|
||||
"\n",
|
||||
"for row in qres:\n",
|
||||
" print(row)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "venv (3.14.4)",
|
||||
"display_name": "kgr",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
@ -735,7 +743,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.14.4"
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
@ -30,7 +30,7 @@
|
|||
a4paper,margin=25mm
|
||||
}
|
||||
|
||||
\title{\huge{Knowledge Graph - Lego}}
|
||||
\title{\huge{Knowledgegraphen - Lego}}
|
||||
\date{\today}
|
||||
\author{
|
||||
\begin{tabular}{ccc}
|
||||
|
|
@ -49,7 +49,7 @@
|
|||
%... then configure it.
|
||||
\fancyhead{} % clear all header fields
|
||||
\fancyhead[L]{Lego}
|
||||
\fancyhead[R]{KGR - Knowledge Graphen}
|
||||
\fancyhead[R]{KGR - Knowledgegraphen}
|
||||
\fancyfoot{} % clear all footer fields
|
||||
\fancyfoot[LE,RO]{\thepage}
|
||||
|
||||
|
|
@ -74,16 +74,16 @@
|
|||
\begin{enumerate}
|
||||
\item \label{item:min_set_count} Was ist die minimale Anzahl an Sets, die benötigt wird um ein anderes Set zusammenzubauen?
|
||||
\item \label{item:min_set_price} Was ist der geringste Preis einer Auswahl an Sets um ein anderes Set zusammenzubauen?
|
||||
\item \label{item:set_span} Welche anderen Sets, können mit Sets, die sich schon im eigenen Besitz befinden zusammengebaut werden?
|
||||
\item Sind Sets von anderen Herstellern im Vergleich zu Lego Sets, auf den durchschnittlichen Teilepreis betrachtet billiger?
|
||||
\item Haben neuere Sets im Vergleich zu älteren Sets eine geringere Teileanzahl, da auf eine grössere Anzahl an speziell angefertigten Teilen zugegriffen werden kann?
|
||||
\item Haben Sets mit höherer Teileanzahl eine höhere Anzahl an Minifiguren?
|
||||
\item \label{item:set_span} Welche anderen Sets, können mit Sets, die sich schon im eigenen Besitz befinden zusammengebaut werden?
|
||||
\item \label{item:equivalent_part} Welche Lego-Teile besitzen äquivalente Teile von anderen Marken?
|
||||
\end{enumerate}
|
||||
|
||||
\subsection{Nutzen}
|
||||
|
||||
Wird ein Set an Klemmbausteinen nicht mehr vertrieben, an welchem dennoch Nachfrage besteht, existieren folgende Möglichkeiten:
|
||||
Wird ein Set an Klemmbausteinen nicht mehr vertrieben und man möchte das Set dennoch haben, so ergeben sich mehrere Möglichkeiten:
|
||||
\begin{itemize}
|
||||
\item Man kauft das Set von einem Zweitanbieter
|
||||
\item Man stellt sich die benötigten Teile des Sets selbst zusammen. Dies geschieht entweder indem die Teile einzeln von Zweitanbietern gekauft werden oder durch den Erwerb von anderen Sets, welche die benötigten Teile enthalten. Siehe Fragen: \ref{item:min_set_count} und \ref{item:min_set_price}.
|
||||
|
|
@ -114,7 +114,6 @@
|
|||
\begin{figure}[H]
|
||||
\includegraphics[width=\columnwidth]{bilder/downloads_schema_v3.png}
|
||||
\caption{Datenbankschema \textit{Rebrickable} \cite{FreeLEGOCatalog}}
|
||||
\label{fig:rebrickable_scheme}
|
||||
\end{figure}
|
||||
|
||||
Der Datensatz konnte über die URL \url{https://rebrickable.com/downloads/} erhalten werden.
|
||||
|
|
@ -128,7 +127,7 @@
|
|||
\toprule
|
||||
& Brickset \\ \midrule
|
||||
URL & \url{https://brickset.com/}\\
|
||||
Beschaffung & CSV-Download \\
|
||||
Beschaffung & Webscraping/CSV-Download \\
|
||||
Lizenz & nicht spezifiziert \\
|
||||
Erhalt & 23.04.2026 \\ \bottomrule
|
||||
\end{tabularx}
|
||||
|
|
@ -180,17 +179,14 @@
|
|||
|
||||
\subsection{Integrationsprozess}
|
||||
|
||||
Jedes von Lego veröffentlichte Teil besitzt der Form zugrunde eine eindeutige Teile-Nummer, auch Design-ID genannt. Die Teilenummer wird nur aufgrund der Form eines Legosteins vergeben und kann auf dem Lego-Stein abgelesen werden. Üblicherweise besitzt eine Design-ID 4-5 Stellen. Abhängig von der Form, Farbe und des Drucks besitzt jeder Lego-Stein eine 6-7 stellige Element-ID. Die Element-IDs von Teilen eines Lego-Sets befindet sich als Auflistung aller Teile in der Bauanleitung eines Lego-Sets.\\
|
||||
Sets besitzen ebenfalls eine Set-Nummer. Einige Set-Nummern werden mit einem Suffix bspw. \textit{-1} angegeben. Dieser Suffix gibt Aufschluss über die Version des Sets. Allerdings gibt es spezielle Lego-Sets, welche in Teil-Sets aufgegliedert werden oder mehrere Bauvarianten besitzen \cite{FreeLEGOCatalog}. Diese Art von Sets wird mithilfe von Inventaren modelliert (Siehe \ref{fig:rebrickable_scheme}). Ein Inventar kann als übergeordnetes Set verstanden werden. Ein Inventar kann somit Set-, Minifiguren- und Teile-Inventare besitzen, die angeben in welcher Stückzahl ein Teil-Set, eine Minifigur oder ein Teil vorhanden ist\\
|
||||
Lego-Minifiguren erhalten durch Lego keine eindeutige Identifikationsnummer. Zur eindeutigen Identifikation von Minifiguren wird die von \textit{Rebrickable} vergebene ID verwendet. Auf Minifiguren-Seiten von \textit{Rebrickable} sind Referenzen zu IDs derselben Minifigur auf anderen Seiten enthalten. Diese Referenzen sind innerhalb des bereitgestellten Datensatzes nicht abgebildet.\\
|
||||
Da die einzige Quelle für andere Hersteller nur \textit{Merlins Steine} ist und diese nur Sets enthält, wird der Hersteller in der IRI nur für Sets miteinbezogen.
|
||||
Jedes von Lego veröffentlichte Teil besitzt der Form zugrunde eine eindeutige Teile-Nummer, auch Design-ID genannt. Die Teilenummer wird nur aufgrund der Form eines Legosteins vergeben und kann auf dem Lego-Stein abgelesen werden. Üblicherweise besitzt eine Design-ID 4-5 Stellen. Abhängig von der Form, Farbe und des Drucks besitzt jeder Lego-Stein eine 6-7 stellige Element-ID. Diese Element-ID lässt sich Lego-Sets besitzen ebenfalls eine Set-Nummer. Allerdings gibt es spezielle Lego-Sets, welche in Teil-Sets aufgegliedert werden oder mehrere Bauvarianten besitzen \cite{FreeLEGOCatalog}. Diese sind in der Modellierung dieser Arbeit nicht weiter berücksichtigt. Stattdessen wird das zugrundeliegende Hauptset betrachtet. Lego-Minifiguren erhalten durch Lego keine eindeutige Identifikationsnummer. Zur eindeutigen Identifikation von Minifiguren wird die von \textit{Rebrickable} vergebene ID verwendet.\\
|
||||
Da die einzige Quelle für andere Hersteller nur \textit{Merlins Steine} ist und diese nur Sets enthält, wird der Hersteller in der IRI miteinbezogen.
|
||||
\begin{verbatim}
|
||||
https://thm.de/set/{brand}/{id}
|
||||
https://thm.de/set/{brand}/{id}
|
||||
\end{verbatim}
|
||||
Um die Dateigrösse des Graph zu reduzieren wurde \texttt{thm}, statt \texttt{th-mannheim} verwendet.
|
||||
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=0.8\columnwidth]{bilder/example_part_number.png}
|
||||
\includegraphics[width=\columnwidth]{bilder/example_part_number.png}
|
||||
\caption{Lego Stein mit Teile-Nummer (Design-ID) 41769 \cite{cunninghamSellLEGOBricklink2018}}
|
||||
\label{fig:lego_example_part_number}
|
||||
\end{figure}
|
||||
|
|
@ -203,170 +199,22 @@ https://thm.de/set/{brand}/{id}
|
|||
|
||||
\subsection{Pipeline}
|
||||
|
||||
Die Datensätze von \textit{Bricklink} und \textit{Merlins Steine} wurden durch Webscraping erhoben. Entstandene Fehler durch Ausnahmefälle mussten manuell bereinigt werden. Demnach ist dieser Teil nicht automatisierbar. Abbildung \ref{fig:pipeline} zeigt die Pipeline zur Erstellung des Knowledge Graph.
|
||||
|
||||
\begin{figure}[H]
|
||||
\includegraphics[width=\columnwidth]{./bilder/kgr_pipeline1.drawio.png}
|
||||
\caption{Pipeline Erstellung Knowledge Graph}
|
||||
\label{fig:pipeline}
|
||||
\end{figure}
|
||||
|
||||
\section{Evaluation}
|
||||
|
||||
\subsection{Ergebnis}
|
||||
|
||||
Das Projekt kann unter der URL: \url{https://gitty.informatik.hs-mannheim.de/2211275/kgr} betrachtet werden.
|
||||
Der resultierende Knowledge-Graph ist über 300 MB gross. Die Dateigrösse lässt sich auf die Zuordnungen von Teilen zu Inventaren zurückführen.
|
||||
|
||||
\subsection{Beispiel-Queries}
|
||||
Erhalten der Gesamtheit aller Lego Star Wars Minifiguren:
|
||||
\begin{verbatim}
|
||||
SELECT DISTINCT ?name
|
||||
WHERE {
|
||||
?set thmont:theme ?theme.
|
||||
?theme rdf:type thmont:Theme.
|
||||
?set rdf:type thmont:Set.
|
||||
?theme rdfs:label "Star Wars"@en.
|
||||
?inventory thmont:set ?set.
|
||||
?inventory rdf:type thmont:Inventory.
|
||||
?inventory thmont:contains ?minifig_inv.
|
||||
?minifig_inv rdf:type thmont:MinifigInv.
|
||||
?minifig thmont:belongs ?minifig_inv.
|
||||
?minifig rdfs:label ?name.
|
||||
}
|
||||
\end{verbatim}
|
||||
Anzahl aller Minifiguren enthalten in allen Lego-Sets gruppiert nach Figur.
|
||||
\begin{verbatim}
|
||||
SELECT
|
||||
(SUM(?quantity) as ?sum) ?minifig ?name
|
||||
WHERE {
|
||||
?minifig rdf:type thm:Minifig.
|
||||
?minifig_inv rdf:type thm:MinifigInv.
|
||||
?minifig thm:belongs ?minifig_inv.
|
||||
?minifig_inv thm:quantity ?quantity.
|
||||
?minifig rdfs:label ?name.
|
||||
}
|
||||
GROUP BY ?minifig ?name
|
||||
ORDER BY DESC(?sum)
|
||||
\end{verbatim}
|
||||
Durchschnittliche Anzahl an Teilen je Set gruppiert nach Jahren.
|
||||
\begin{verbatim}
|
||||
SELECT ?year (AVG(?part_count) as ?avgp)
|
||||
WHERE {
|
||||
?set thm:year ?year.
|
||||
?set thm:num_parts ?part_count.
|
||||
}
|
||||
GROUP BY ?year
|
||||
ORDER BY DESC(?avgp)
|
||||
\end{verbatim}
|
||||
Durchschnittlicher Teilepreis gruppiert nach Marken. \label{verb:ppp_query}
|
||||
\begin{verbatim}
|
||||
SELECT ?brand (AVG(?price)/AVG(?num) as ?t)
|
||||
WHERE {
|
||||
?set thm:num_parts ?num.
|
||||
?set rdfs:label ?name.
|
||||
?set rdf:type thm:Set.
|
||||
?set thm:brand ?brand.
|
||||
?set thm:price_new ?price.
|
||||
FILTER (?num > 0)
|
||||
}
|
||||
GROUP BY ?brand
|
||||
ORDER BY DESC(?t)
|
||||
\end{verbatim}
|
||||
|
||||
\subsection{Abdeckung}
|
||||
|
||||
Tabelle \ref{tab:coverage} zeigt einen Überblick welche der Prädikate (Graph, Name, Kategorie, Preise, Jahr) der Knowledge Graph für Minifiguren (Figs), Teile und Sets abdeckt (X=enthalten,-=Fehlt). Das Prädikat \textit{Graph} spiegelt wider, ob das Prädikat im Graph vorhanden ist.
|
||||
\begin{table}[H]
|
||||
\centering
|
||||
\begin{tabular}{@{}lllllll@{}}
|
||||
\toprule
|
||||
& \multicolumn{3}{l}{Lego} & \multicolumn{3}{l}{Andere Marken} \\ \midrule
|
||||
& Figs & Teile & Sets & Figs & Teile & Sets \\ \midrule
|
||||
Graph & X & X & X & - & - & X \\
|
||||
Name & X & X & X & - & - & X\\
|
||||
Kateg. & - & X & X & - & - & - \\
|
||||
Preise & - & X & X & - & - & X \\
|
||||
Jahr & - & - & X & - & - & X \\ \bottomrule
|
||||
\end{tabular}
|
||||
\caption{Abdeckung des Graphen für Lego und weitere Klemmbausteinmarken}
|
||||
\label{tab:coverage}
|
||||
\end{table}
|
||||
Da Lego keine IDs für Minifiguren vergibt ist das erkennen zweier gleichartiger Figuren schwieriger. Die Preise von \textit{Brickset} konnten nicht den Minifiguren aus \textit{Rebrickable} zugeordnet werden. Da \textit{Rebrickable} die Zuordnung von Minifiguren zu Sets liefert, wurde die Entscheidung getroffen auf die Preiszuordnung zu verzichten. Für Figuren und Teile weiterer Marken, waren eine Zuordnung nur schwer bis gar nicht möglich. Diese Zuordnung wäre beispielsweise durch Bilderkennung, anhand vom Hersteller bereitgestellte Bauanleitungen in Form von .PDF-Dateien möglich.
|
||||
\subsection{Konsistenz}
|
||||
|
||||
\subsection{Qualität}
|
||||
|
||||
Es wird betrachtet, ob die ursprünglichen Fragestellungen mithilfe des Knowledge Graphen beantwortet werden können.
|
||||
|
||||
\begin{enumerate}
|
||||
\item Was ist die minimale Anzahl an Sets, die benötigt wird um ein anderes Set zusammenzubauen?
|
||||
\item Was ist der geringste Preis einer Auswahl an Sets um ein anderes Set zusammenzubauen?
|
||||
\item Welche anderen Sets, können mit Sets, die sich schon im eigenen Besitz befinden zusammengebaut werden?\\
|
||||
\textit{Der Knowledge Graph beinhaltet die Daten, sodass die Fragestellungen 1., 2., 3. beantwortet werden können. Eine effiziente Softwarelösung besitzt eine höhere geschätzte Komplexität und liegt ausserhalb des Rahmens dieser Arbeit.}
|
||||
\item Sind Sets von anderen Herstellern im Vergleich zu Lego Sets, auf den durchschnittlichen Teilepreis betrachtet billiger?\\
|
||||
\textit{Diese Frage kann mithilfe der letzten Beispiel-Query \ref{verb:ppp_query} beantwortet werden. Abbildung \ref{fig:ppp} zeigt die Teile-Preise je Hersteller. Lego hat den höchsten Teilepreis mit 0.096 \texteuro und BlueBrixx den niedrigsten mit 0.0437 \texteuro.
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=\columnwidth]{./bilder/diagram_avg_part_price_brand.png}
|
||||
\caption{Hersteller sortiert nach durchschnittlichem Teile-Preis in \texteuro}
|
||||
\label{fig:ppp}
|
||||
\end{figure}
|
||||
}
|
||||
\item Haben neuere Sets im Vergleich zu älteren Sets eine geringere Teileanzahl, da auf eine grössere Anzahl an speziell angefertigten Teilen zugegriffen werden kann?\\
|
||||
\textit{Es ist das Gegenteil zu erkennen. Eine Erklärung dafür könnte sein, dass die Klemmbaustein-Marken mehrheitlich Sets für \ac{AFOL} herausbringen im Vergleich zu vorherigen Jahren. Diese Sets sind meistens komplexer und besitzen demnach eine höhere Teileanzahl.}
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=\columnwidth]{./bilder/diagram_avg_parts_per_year.png}
|
||||
\caption{Durchschnittliche Teileanzahl nach Jahren aller Klemmbausteinmarken}
|
||||
\label{fig:avg_parts_per_year}
|
||||
\end{figure}
|
||||
\begin{verbatim}
|
||||
SELECT (AVG(?parts) as ?total) ?year
|
||||
WHERE {
|
||||
?set rdf:type thm:Set.
|
||||
?set thm:brand ?brand.
|
||||
?set thm:year ?year.
|
||||
?set rdfs:label ?name.
|
||||
?set thm:num_parts ?parts.
|
||||
}
|
||||
GROUP BY ?year
|
||||
ORDER BY ASC(?total)
|
||||
\end{verbatim}
|
||||
\item Haben Sets mit höherer Teileanzahl eine höhere Anzahl an Minifiguren?\\
|
||||
\textit{Zwischen der Anzahl Minifiguren $M$ und der Anzahl an Teilen $T$ eines Sets kann eine mittelstarke Korrelation $\rho_{M,T} \approx 0.5926$ festgestellt werden. Hier gab es mehrere interessante Ausreisser: \href{https://rebrickable.com/sets/BIGBOX-1/the-ultimate-battle-for-chima}{BIGBOX-1,The Ultimate Battle for Chima}, \href{https://rebrickable.com/sets/2000409-2/window-exploration-bag/}{2000409-2, Window Exploration Bag 2} und \href{https://rebrickable.com/sets/2000409-1/window-exploration-bag/}{2000409-1, Window Exploration Bag 1}.}
|
||||
\textit{
|
||||
\begin{figure}[H]
|
||||
\centering
|
||||
\includegraphics[width=\columnwidth]{./bilder/diagram_correlation_parts_figs.png}
|
||||
\caption{Anzahl Minifiguren und Teile}
|
||||
\label{fig:scatter_parts_figs}
|
||||
\end{figure}
|
||||
}
|
||||
Der Datensatz konnte mithilfe folgender SPARQL-Query erhalten werden. Hier werden Sets mit 0 Teilen herausgefiltert.
|
||||
\begin{verbatim}
|
||||
SELECT ?part_num (SUM(?qty) as ?total)
|
||||
WHERE {
|
||||
?set rdf:type thm:Set.
|
||||
?set thm:brand ?brand.
|
||||
?set rdfs:label ?name.
|
||||
?set thm:num_parts ?part_num.
|
||||
?inv thm:set ?set.
|
||||
?inv rdf:type thm:Inventory.
|
||||
?inv thm:contains ?fig_inv.
|
||||
?fig_inv thm:quantity ?qty.
|
||||
FILTER(?part_num > 0).
|
||||
}
|
||||
GROUP BY ?set ?part_num ?name ?inv
|
||||
\end{verbatim}
|
||||
\item Welche Lego-Teile besitzen äquivalente Teile von anderen Marken?\\
|
||||
\textit{Der Knowledge Graph bietet keine Möglichkeit dies zu beantwortet, da keine Datensätze über Teile, die nicht von Lego produziert worden sind, in die Erstellung des Graph eingeflossen sind.}
|
||||
\end{enumerate}
|
||||
|
||||
\section{Ausblick}
|
||||
\begin{itemize}
|
||||
\item Es fehlen tiefer greifende Daten zu anderen Klemmbausteinmarken, neben Lego. Ein erhalten der Daten wäre nur durch direkte Anfrage beim Hersteller oder durch aufwändige Methodik, wie Bilderkennung möglich.
|
||||
\item identische Minifiguren könnten mit höherem Aufwand gegenseitig zugeordnet werden. So würde eine Verbindung zwischen der Inventarisierung durch \textit{Rebrickable } und Preisen von \textit{Brickset} entstehen.
|
||||
\item Zur Beantwortung der Fragestellungen 1,2,3 ist eine eigene Softwarelösung und eine Erweiterung des Graphen vonnöten. \textit{Rebrickable} bietet selber Werkzeuge \url{https://rebrickable.com/help/build-engine/} an, um diese Fragestellung zu beantworten.
|
||||
\end{itemize}
|
||||
|
||||
\section*{Abkürzungsverzeichnis}
|
||||
\begin{acronym}[Abkürzungsverzeichnis]
|
||||
\acro{MOC}{My Own Creation}
|
||||
|
|
|
|||
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<mxCell id="Hu6JBMkSkJOX-7uFVOw6-3" parent="1" style="rounded=1;whiteSpace=wrap;html=1;" value="Datensätze als .CSV" vertex="1">
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|
||||
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|
||||
</mxCell>
|
||||
</root>
|
||||
</mxGraphModel>
|
||||
</diagram>
|
||||
</mxfile>
|
||||
Binary file not shown.
|
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|
@ -1 +0,0 @@
|
|||
,Lambda/Roman,Lambda,03.05.2026 11:29,file:///C:/Users/Roman/AppData/Roaming/LibreOffice/4;
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
"brand","t"
|
||||
"Lego","0.09687904"
|
||||
"Cobi","0.07586302"
|
||||
"Pantasy","0.05728256"
|
||||
"MouldKing","0.05323224"
|
||||
"BlueBrixx","0.043733075"
|
||||
|
Binary file not shown.
|
|
@ -1,78 +0,0 @@
|
|||
"total","year"
|
||||
"0.0","2027"
|
||||
"1.0","1950"
|
||||
"100.263888888888888888888889","1973"
|
||||
"101.025641025641025641025641","1972"
|
||||
"103.288135593220338983050847","1985"
|
||||
"103.794871794871794871794872","1974"
|
||||
"104.323741007194244604316547","1992"
|
||||
"104.347058823529411764705882","1994"
|
||||
"105.636363636363636363636364","1980"
|
||||
"106.891608391608391608391608","1997"
|
||||
"113.566666666666666666666667","1967"
|
||||
"12.435897435897435897435897","1955"
|
||||
"12.5","1953"
|
||||
"122.129411764705882352941176","1988"
|
||||
"123.9125295508274231678487","1998"
|
||||
"124.003865979381443298969072","2012"
|
||||
"124.326375711574952561669829","2014"
|
||||
"125.840579710144927536231884","1993"
|
||||
"126.522070015220700152207002","2011"
|
||||
"127.65641025641025641025641","1995"
|
||||
"129.071428571428571428571429","1968"
|
||||
"131.587837837837837837837838","1991"
|
||||
"132.5","2013"
|
||||
"134.275735294117647058823529","2003"
|
||||
"134.780487804878048780487805","1975"
|
||||
"139.038461538461538461538462","2004"
|
||||
"14.25","1959"
|
||||
"140.74025974025974025974026","1978"
|
||||
"142.662337662337662337662338","1976"
|
||||
"147.16195121951219512195122","2015"
|
||||
"148.229656419529837251356239","2009"
|
||||
"159.889","2016"
|
||||
"16.0","1956"
|
||||
"161.840396753832281334535618","2017"
|
||||
"163.051724137931034482758621","1990"
|
||||
"165.224489795918367346938776","1996"
|
||||
"165.62577962577962577962578","2007"
|
||||
"166.35746606334841628959276","2006"
|
||||
"174.373493975903614457831325","2005"
|
||||
"175.333333333333333333333333","1960"
|
||||
"179.64218455743879472693032","2010"
|
||||
"18.015151515151515151515152","1958"
|
||||
"183.630669546436285097192225","2008"
|
||||
"204.192013593882752761257434","2018"
|
||||
"277.083064516129032258064516","2019"
|
||||
"34.291666666666666666666667","1957"
|
||||
"341.934300993124522536287242","2021"
|
||||
"391.574585635359116022099448","2020"
|
||||
"40.868421052631578947368421","1964"
|
||||
"403.468056489576328177538668","2022"
|
||||
"43.819672131147540983606557","1966"
|
||||
"475.79951690821256038647343","2023"
|
||||
"48.964285714285714285714286","1965"
|
||||
"500.052801724137931034482759","2026"
|
||||
"593.916924664602683178534572","2024"
|
||||
"612.001367365542388331814038","2025"
|
||||
"64.955056179775280898876404","1982"
|
||||
"65.820833333333333333333333","1987"
|
||||
"67.295081967213114754098361","1963"
|
||||
"71.92","1969"
|
||||
"75.561224489795918367346939","1984"
|
||||
"77.122807017543859649122807","1977"
|
||||
"8.357142857142857142857143","1954"
|
||||
"80.690476190476190476190476","1970"
|
||||
"85.846153846153846153846154","1962"
|
||||
"87.706521739130434782608696","2001"
|
||||
"88.775362318840579710144928","1989"
|
||||
"91.280701754385964912280702","1971"
|
||||
"92.6796875","1999"
|
||||
"93.075980392156862745098039","2000"
|
||||
"93.593939393939393939393939","1986"
|
||||
"93.785714285714285714285714","1981"
|
||||
"93.853333333333333333333333","1983"
|
||||
"94.418524871355060034305317","2002"
|
||||
"94.88","1961"
|
||||
"96.4","1949"
|
||||
"96.913978494623655913978495","1979"
|
||||
|
Binary file not shown.
File diff suppressed because it is too large
Load Diff
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|
|
@ -0,0 +1,93 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7cce205d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Querries"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "34c85d02",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 8\u001b[39m\n\u001b[32m 4\u001b[39m \n\u001b[32m 5\u001b[39m \n\u001b[32m 6\u001b[39m g = rdflib.Graph()\n\u001b[32m 7\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m8\u001b[39m g.parse(filename, format=\u001b[33m'ttl'\u001b[39m)\n\u001b[32m 9\u001b[39m \n\u001b[32m 10\u001b[39m query = \"\"\"\n\u001b[32m 11\u001b[39m \n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/graph.py:1660\u001b[39m, in \u001b[36mGraph.parse\u001b[39m\u001b[34m(self, source, publicID, format, location, file, data, **args)\u001b[39m\n\u001b[32m 1657\u001b[39m parser = plugin.get(\u001b[38;5;28mformat\u001b[39m, Parser)()\n\u001b[32m 1658\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 1659\u001b[39m \u001b[38;5;66;03m# TODO FIXME: Parser.parse should have **kwargs argument.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1660\u001b[39m \u001b[30;43mparser\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mparse\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msource\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1661\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mSyntaxError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m se:\n\u001b[32m 1662\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m could_not_guess_format:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:2034\u001b[39m, in \u001b[36mTurtleParser.parse\u001b[39m\u001b[34m(self, source, graph, encoding, turtle)\u001b[39m\n\u001b[32m 2032\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m stream:\n\u001b[32m 2033\u001b[39m stream = source.getByteStream()\n\u001b[32m-> \u001b[39m\u001b[32m2034\u001b[39m \u001b[30;43mp\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mloadStream\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstream\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 2036\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m prefix, namespace \u001b[38;5;129;01min\u001b[39;00m p._bindings.items():\n\u001b[32m 2037\u001b[39m graph.bind(prefix, namespace)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:492\u001b[39m, in \u001b[36mSinkParser.loadStream\u001b[39m\u001b[34m(self, stream)\u001b[39m\n\u001b[32m 491\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mloadStream\u001b[39m(\u001b[38;5;28mself\u001b[39m, stream: Union[IO[\u001b[38;5;28mstr\u001b[39m], IO[\u001b[38;5;28mbytes\u001b[39m]]) -> Optional[Formula]:\n\u001b[32m--> \u001b[39m\u001b[32m492\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mloadBuf\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstream\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mread\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:498\u001b[39m, in \u001b[36mSinkParser.loadBuf\u001b[39m\u001b[34m(self, buf)\u001b[39m\n\u001b[32m 495\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Parses a buffer and returns its top level formula\"\"\"\u001b[39;00m\n\u001b[32m 496\u001b[39m \u001b[38;5;28mself\u001b[39m.startDoc()\n\u001b[32m--> \u001b[39m\u001b[32m498\u001b[39m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfeed\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mbuf\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 499\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.endDoc()\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:524\u001b[39m, in \u001b[36mSinkParser.feed\u001b[39m\u001b[34m(self, octets)\u001b[39m\n\u001b[32m 521\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j < \u001b[32m0\u001b[39m:\n\u001b[32m 522\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m524\u001b[39m i = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mdirectiveOrStatement\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43ms\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mj\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 525\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i < \u001b[32m0\u001b[39m:\n\u001b[32m 526\u001b[39m \u001b[38;5;66;03m# print(\"# next char: %s\" % s[j])\u001b[39;00m\n\u001b[32m 527\u001b[39m \u001b[38;5;28mself\u001b[39m.BadSyntax(s, j, \u001b[33m\"\u001b[39m\u001b[33mexpected directive or statement\u001b[39m\u001b[33m\"\u001b[39m)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:543\u001b[39m, in \u001b[36mSinkParser.directiveOrStatement\u001b[39m\u001b[34m(self, argstr, h)\u001b[39m\n\u001b[32m 540\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j >= \u001b[32m0\u001b[39m:\n\u001b[32m 541\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.checkDot(argstr, j)\n\u001b[32m--> \u001b[39m\u001b[32m543\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mstatement\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 544\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j >= \u001b[32m0\u001b[39m:\n\u001b[32m 545\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.checkDot(argstr, j)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:791\u001b[39m, in \u001b[36mSinkParser.statement\u001b[39m\u001b[34m(self, argstr, i)\u001b[39m\n\u001b[32m 788\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i < \u001b[32m0\u001b[39m:\n\u001b[32m 789\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m i\n\u001b[32m--> \u001b[39m\u001b[32m791\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mproperty_list\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mr\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 793\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j < \u001b[32m0\u001b[39m:\n\u001b[32m 794\u001b[39m \u001b[38;5;28mself\u001b[39m.BadSyntax(argstr, i, \u001b[33m\"\u001b[39m\u001b[33mexpected propertylist\u001b[39m\u001b[33m\"\u001b[39m)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1153\u001b[39m, in \u001b[36mSinkParser.property_list\u001b[39m\u001b[34m(self, argstr, i, subj)\u001b[39m\n\u001b[32m 1150\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m i \u001b[38;5;66;03m# void but valid\u001b[39;00m\n\u001b[32m 1152\u001b[39m objs: typing.List[Any] = []\n\u001b[32m-> \u001b[39m\u001b[32m1153\u001b[39m i = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mobjectList\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mj\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mobjs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1154\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i < \u001b[32m0\u001b[39m:\n\u001b[32m 1155\u001b[39m \u001b[38;5;28mself\u001b[39m.BadSyntax(argstr, j, \u001b[33m\"\u001b[39m\u001b[33mobjectList expected\u001b[39m\u001b[33m\"\u001b[39m)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1203\u001b[39m, in \u001b[36mSinkParser.objectList\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 1202\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mobjectList\u001b[39m(\u001b[38;5;28mself\u001b[39m, argstr: \u001b[38;5;28mstr\u001b[39m, i: \u001b[38;5;28mint\u001b[39m, res: MutableSequence[Any]) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1203\u001b[39m i = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mobject\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1204\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m i < \u001b[32m0\u001b[39m:\n\u001b[32m 1205\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m -\u001b[32m1\u001b[39m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1500\u001b[39m, in \u001b[36mSinkParser.object\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 1494\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mobject\u001b[39m(\n\u001b[32m 1495\u001b[39m \u001b[38;5;28mself\u001b[39m,\n\u001b[32m 1496\u001b[39m argstr: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m 1497\u001b[39m i: \u001b[38;5;28mint\u001b[39m,\n\u001b[32m 1498\u001b[39m res: MutableSequence[Any],\n\u001b[32m 1499\u001b[39m ) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1500\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msubject\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1501\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j >= \u001b[32m0\u001b[39m:\n\u001b[32m 1502\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m j\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:798\u001b[39m, in \u001b[36mSinkParser.subject\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 797\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msubject\u001b[39m(\u001b[38;5;28mself\u001b[39m, argstr: \u001b[38;5;28mstr\u001b[39m, i: \u001b[38;5;28mint\u001b[39m, res: MutableSequence[Any]) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m798\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mitem\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:890\u001b[39m, in \u001b[36mSinkParser.item\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 889\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mitem\u001b[39m(\u001b[38;5;28mself\u001b[39m, argstr: \u001b[38;5;28mstr\u001b[39m, i, res: MutableSequence[Any]) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m--> \u001b[39m\u001b[32m890\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:897\u001b[39m, in \u001b[36mSinkParser.path\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 895\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpath\u001b[39m(\u001b[38;5;28mself\u001b[39m, argstr: \u001b[38;5;28mstr\u001b[39m, i: \u001b[38;5;28mint\u001b[39m, res: MutableSequence[Any]) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m 896\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Parse the path production.\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m897\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mnodeOrLiteral\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 898\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j < \u001b[32m0\u001b[39m:\n\u001b[32m 899\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m j \u001b[38;5;66;03m# nope\u001b[39;00m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1528\u001b[39m, in \u001b[36mSinkParser.nodeOrLiteral\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 1527\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mnodeOrLiteral\u001b[39m(\u001b[38;5;28mself\u001b[39m, argstr: \u001b[38;5;28mstr\u001b[39m, i: \u001b[38;5;28mint\u001b[39m, res: MutableSequence[Any]) -> \u001b[38;5;28mint\u001b[39m:\n\u001b[32m-> \u001b[39m\u001b[32m1528\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mnode\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1529\u001b[39m startline = \u001b[38;5;28mself\u001b[39m.lines \u001b[38;5;66;03m# Remember where for error messages\u001b[39;00m\n\u001b[32m 1530\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j >= \u001b[32m0\u001b[39m:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1115\u001b[39m, in \u001b[36mSinkParser.node\u001b[39m\u001b[34m(self, argstr, i, res, subjectAlready)\u001b[39m\n\u001b[32m 1112\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m j\n\u001b[32m 1114\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m subj \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: \u001b[38;5;66;03m# If this can be a named node, then check for a name.\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1115\u001b[39m j = \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43muri_ref2\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43margstr\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mi\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mres\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m 1116\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m j >= \u001b[32m0\u001b[39m:\n\u001b[32m 1117\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m j\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/plugins/parsers/notation3.py:1254\u001b[39m, in \u001b[36mSinkParser.uri_ref2\u001b[39m\u001b[34m(self, argstr, i, res)\u001b[39m\n\u001b[32m 1252\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1253\u001b[39m \u001b[38;5;28mself\u001b[39m.BadSyntax(argstr, i, \u001b[33m'\u001b[39m\u001b[33mPrefix \u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m:\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m not bound\u001b[39m\u001b[33m'\u001b[39m % (pfx))\n\u001b[32m-> \u001b[39m\u001b[32m1254\u001b[39m symb = \u001b[38;5;28mself\u001b[39m._store.newSymbol(\u001b[30;43mns\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m+\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mln\u001b[39;49m)\n\u001b[32m 1255\u001b[39m res.append(\u001b[38;5;28mself\u001b[39m._variables.get(symb, symb))\n\u001b[32m 1256\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m j\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/term.py:359\u001b[39m, in \u001b[36mURIRef.__add__\u001b[39m\u001b[34m(self, other)\u001b[39m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__add__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other) -> URIRef:\n\u001b[32m--> \u001b[39m\u001b[32m359\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m__class__\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mstr\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m+\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mother\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/th/kgr/.venv/lib/python3.11/site-packages/rdflib/term.py:286\u001b[39m, in \u001b[36mURIRef.__new__\u001b[39m\u001b[34m(cls, value, base)\u001b[39m\n\u001b[32m 283\u001b[39m \u001b[34m__neg__\u001b[39m: Callable[[URIRef], NegatedPath]\n\u001b[32m 284\u001b[39m \u001b[34m__truediv__\u001b[39m: Callable[[URIRef, Union[URIRef, Path]], SequencePath]\n\u001b[32m--> \u001b[39m\u001b[32m286\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, value: \u001b[38;5;28mstr\u001b[39m, base: Optional[\u001b[38;5;28mstr\u001b[39m] = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 287\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m base \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 288\u001b[39m ends_in_hash = value.endswith(\u001b[33m\"\u001b[39m\u001b[33m#\u001b[39m\u001b[33m\"\u001b[39m)\n",
|
||||
"\u001b[31mKeyboardInterrupt\u001b[39m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import rdflib\n",
|
||||
" \n",
|
||||
"filename = \"lego_graph_rebrickable.ttl\"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"g = rdflib.Graph()\n",
|
||||
"\n",
|
||||
"g.parse(filename, format='ttl')\n",
|
||||
"\n",
|
||||
"query = \"\"\"\n",
|
||||
"\n",
|
||||
"SELECT ?person\n",
|
||||
"WHERE {\n",
|
||||
" ?person thmont:color <https://thm.de/color/19>\n",
|
||||
"\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"qres = g.query(query)\n",
|
||||
"\n",
|
||||
"for row in qres :\n",
|
||||
" print(\"%s\" % row)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "kgr",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 3,
|
||||
"id": "ad994162",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -14,12 +14,13 @@
|
|||
"import pandas as pd\n",
|
||||
"import time\n",
|
||||
"import random\n",
|
||||
"import re"
|
||||
"import re\n",
|
||||
"import pprint"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"execution_count": 4,
|
||||
"id": "b5536e8c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -29,33 +30,45 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "6d109e8a",
|
||||
"execution_count": null,
|
||||
"id": "a5daea73",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "FileNotFoundError",
|
||||
"evalue": "[Errno 2] No such file or directory: 'bluebrixx.json'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mFileNotFoundError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[11]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33m\"./others.csv\"\u001b[39m, mode=\u001b[33m\"w+\"\u001b[39m, encoding=\u001b[33m\"utf8\"\u001b[39m, newline=\u001b[33m\"\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m producerfile:\n\u001b[32m 2\u001b[39m writer = csv.writer(producerfile)\n\u001b[32m 3\u001b[39m writer.writerow([\u001b[33m\"id\"\u001b[39m, \u001b[33m\"producer\"\u001b[39m, \u001b[33m\"name\"\u001b[39m, \u001b[33m\"size\"\u001b[39m, \u001b[33m\"parts\"\u001b[39m, \u001b[33m\"year\"\u001b[39m])\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m producer \u001b[38;5;28;01min\u001b[39;00m producers:\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m open(\u001b[33mf\"{producer}.json\"\u001b[39m, mode=\u001b[33m\"r\"\u001b[39m, encoding=\u001b[33m\"utf8\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m sourcefile:\n\u001b[32m 6\u001b[39m data = json.loads(sourcefile.read())\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;28;01min\u001b[39;00m data[\u001b[33m\"data\"\u001b[39m]:\n\u001b[32m 8\u001b[39m _, id, _, name, rating, _, _, size, parts, year, _ = row\n",
|
||||
"\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'bluebrixx.json'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"id_to_name = dict()\n",
|
||||
"for producer in producers:\n",
|
||||
" with open(f\"../data/merlin/{producer}.json\", mode=\"r\", encoding=\"utf8\") as prodfile:\n",
|
||||
" listings = json.load(prodfile)[\"data\"]\n",
|
||||
"with open(\"./others.csv\", mode=\"w+\", encoding=\"utf8\", newline=\"\") as producerfile:\n",
|
||||
" writer = csv.writer(producerfile)\n",
|
||||
" writer.writerow([\"id\", \"producer\", \"name\", \"size\", \"parts\", \"year\"])\n",
|
||||
" for producer in producers:\n",
|
||||
" with open(f\"data/merlin/{producer}.json\", mode=\"r\", encoding=\"utf8\") as sourcefile:\n",
|
||||
" data = json.loads(sourcefile.read())\n",
|
||||
" for row in data[\"data\"]:\n",
|
||||
" _, id, _, name, rating, _, _, size, parts, year, _ = row\n",
|
||||
"\n",
|
||||
" for listing in listings:\n",
|
||||
" name = listing[3] \n",
|
||||
" id = listing[1]\n",
|
||||
"\n",
|
||||
" id_to_name[id] = name"
|
||||
" writer.writerow([id, producer, name, size, parts, year])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"execution_count": 16,
|
||||
"id": "ab997198",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# uvp preise bestimmen :(\n",
|
||||
"def get_all_ids() -> list[str]:\n",
|
||||
" df = pd.read_csv(\"../data/merlin/others.csv\")\n",
|
||||
" df = pd.read_csv(\"./others.csv\")\n",
|
||||
" return df[\"id\"].to_list()"
|
||||
]
|
||||
},
|
||||
|
|
@ -66,18 +79,19 @@
|
|||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"' with open(\"../data/merlin/prices.csv\", mode=\"a+\", encoding=\"utf8\", newline=\"\") as pricefile:\\n for idx, id in enumerate(get_all_ids()[3663:]):\\n try:\\n small_id = id.lower()\\n\\n response = rq.get(f\"https://www.merlinssteine.de/sets/{small_id}\")\\n soup = bs4.BeautifulSoup(response.text)\\n\\n # Prices\\n price_eur = soup.find(id=\"listprice_eur\")\\n price_usd = soup.find(id=\"listprice_usd\")\\n price_cn = soup.find(id=\"listprice_cn\")\\n bestprice_eur = soup.find(id=\"bestprice_eur\")\\n bestprice_usd = soup.find(id=\"bestprice_usd\")\\n bestprice_cn = soup.find(id=\"bestprice_cn\")\\n\\n all_prices = [price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd]\\n\\n #categories\\n other_dump = [description.text.replace(\"\\n\", \"\") for description in soup.find_all(class_=\"setpage_ct\")]\\n writer = csv.writer(pricefile)\\n\\n all_prices = [p.text if p != None else \"_\" for p in all_prices]\\n writer.writerow([id, *all_prices, *other_dump])\\n time.sleep(random.randint(2, 3))\\n except Exception as e:\\n print(e) '"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
"ename": "KeyboardInterrupt",
|
||||
"evalue": "",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mKeyboardInterrupt\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[18]\u001b[39m\u001b[32m, line 26\u001b[39m\n\u001b[32m 22\u001b[39m \n\u001b[32m 23\u001b[39m \u001b[38;5;66;03m#all_prices = [p.text if p != None else \"_\" for p in all_prices]\u001b[39;00m\n\u001b[32m 24\u001b[39m writer.writerow([id, price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd])\n\u001b[32m 25\u001b[39m time.sleep((random.randint(\u001b[32m2\u001b[39m, \u001b[32m3\u001b[39m))/\u001b[32m10\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Exception \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 27\u001b[39m print(e)\n",
|
||||
"\u001b[31mKeyboardInterrupt\u001b[39m: "
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"\"\"\" with open(\"../data/merlin/prices.csv\", mode=\"a+\", encoding=\"utf8\", newline=\"\") as pricefile:\n",
|
||||
"with open(\"./prices2.csv\", mode=\"a+\", encoding=\"utf8\", newline=\"\") as pricefile:\n",
|
||||
" for idx, id in enumerate(get_all_ids()[3663:]):\n",
|
||||
" try:\n",
|
||||
" small_id = id.lower()\n",
|
||||
|
|
@ -93,22 +107,22 @@
|
|||
" bestprice_usd = soup.find(id=\"bestprice_usd\")\n",
|
||||
" bestprice_cn = soup.find(id=\"bestprice_cn\")\n",
|
||||
"\n",
|
||||
" all_prices = [price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd]\n",
|
||||
" #all_prices = [price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd]\n",
|
||||
" \n",
|
||||
" #categories\n",
|
||||
" other_dump = [description.text.replace(\"\\n\", \"\") for description in soup.find_all(class_=\"setpage_ct\")]\n",
|
||||
" #other_dump = [description.text.replace(\"\\n\", \"\") for description in soup.find_all(class_=\"setpage_ct\")]\n",
|
||||
" writer = csv.writer(pricefile)\n",
|
||||
" \n",
|
||||
" all_prices = [p.text if p != None else \"_\" for p in all_prices]\n",
|
||||
" writer.writerow([id, *all_prices, *other_dump])\n",
|
||||
" time.sleep(random.randint(2, 3))\n",
|
||||
" #all_prices = [p.text if p != None else \"_\" for p in all_prices]\n",
|
||||
" writer.writerow([id, price_eur, price_cn, price_usd, bestprice_eur, bestprice_cn, bestprice_usd])\n",
|
||||
" time.sleep((random.randint(2, 3))/10)\n",
|
||||
" except Exception as e:\n",
|
||||
" print(e) \"\"\""
|
||||
" print(e)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"execution_count": null,
|
||||
"id": "4a10a1e3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -128,7 +142,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"execution_count": null,
|
||||
"id": "9c00f188",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -177,82 +191,79 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"id": "9b44a0e5",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def rm_epsilon(l : list[str]) ->list[str]:\n",
|
||||
" return list(filter(lambda s : len(s) > 0, l))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"execution_count": null,
|
||||
"id": "ae53869e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'Listenpreis:': '14.95 EUR (7.3 ct/Teil) (7.9 ct/g)',\n",
|
||||
" 'DetailsVon:': 'BlueBrixx',\n",
|
||||
" 'EAN:': '4060904003671',\n",
|
||||
" 'Steine von:': 'Qunlong',\n",
|
||||
" 'Kategorie:': 'EisenbahnHersteller-',\n",
|
||||
" 'Kategorien:': 'BBSpecial, BRIX',\n",
|
||||
" 'Anleitung': 'Ohne Bauabschnitte',\n",
|
||||
" 'Bewertungen': 'Bewerten',\n",
|
||||
" 'Hersteller-Videos': 'video-1',\n",
|
||||
" 'Inhalt': '205 Teile',\n",
|
||||
" 'Gewicht': ': 190 g',\n",
|
||||
" 'Keine Aufkleber': '',\n",
|
||||
" 'Keine Drucke': '',\n",
|
||||
" 'Farbverteilung': '',\n",
|
||||
" 'TeilelistenBrickLink': 'XMLRebrickable CSVLEGO PaB CSVSetDB CSV',\n",
|
||||
" 'PreiseListenpreis:': '14.95 EUR (7.3 ct/Teil) (7.9 ct/g)'}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"me_details = pd.DataFrame({\n",
|
||||
" \"id\" : [],\n",
|
||||
" \"name\" : [],\n",
|
||||
" \"price_eur\" : [],\n",
|
||||
" \"price_cn\" : [],\n",
|
||||
" \"price_us\" : [],\n",
|
||||
" \"brand\" : [],\n",
|
||||
" \"ean\" : [],\n",
|
||||
" \"producer\" : [],\n",
|
||||
" \"release\" : [],\n",
|
||||
" \"category\" : [],\n",
|
||||
" \"producer_category\" : [],\n",
|
||||
" \"num_parts\" : [],\n",
|
||||
" })\n",
|
||||
"details = {\n",
|
||||
" \"id\" : [],\n",
|
||||
" \"listprice_eur\" : [],\n",
|
||||
" \"listprice_cn\" : [],\n",
|
||||
" \"listprice_usd\" : [],\n",
|
||||
" \"bestprice_eur\" : [],\n",
|
||||
" \"bestprice_cn\" : [],\n",
|
||||
" \"bestprice_usd\" : [],\n",
|
||||
" \"brand\" : [],\n",
|
||||
" \"ean\" : [],\n",
|
||||
" \"producer\" : [],\n",
|
||||
" \"release\" : [],\n",
|
||||
" \"scale\" : [],\n",
|
||||
" \"category\" : [],\n",
|
||||
" \"producer_category\" : [],\n",
|
||||
" \"num_parts\" : [],\n",
|
||||
" \"width\" : [],\n",
|
||||
" \"height\" : [],\n",
|
||||
" \"depth\" : [],\n",
|
||||
" \"designer\" : [],\n",
|
||||
" \"weight\" : [],\n",
|
||||
" \"age\" : []\n",
|
||||
"}\n",
|
||||
"import random\n",
|
||||
"\n",
|
||||
"me_details = pd.DataFrame(details)\n",
|
||||
"\n",
|
||||
"with open(\"../data/merlin/prices.csv\", mode=\"r\", encoding=\"utf8\") as price_file:\n",
|
||||
" reader = csv.reader(price_file)\n",
|
||||
"\n",
|
||||
" for row in reader:\n",
|
||||
" id, lp_eur, lp_cn, lp_usd, bp_eur, bp_cn, bp_usd, *other = row\n",
|
||||
" other = filter(lambda s: not \"Wikipedia\" in s, other)\n",
|
||||
" # for row in reader:\n",
|
||||
" # id, lp_eur, lp_cn, lp_usd, bp_eur, bp_cn, bp_usd, *other = row\n",
|
||||
" \n",
|
||||
" # me_details.loc[-1] = [id, lp_eur, lp_cn, lp_usd, bp_eur, bp_cn, bp_usd] + list(range(0, 12))\n",
|
||||
" # me_details.index = me_details.index + 1\n",
|
||||
" id, lp_eur, lp_cn, lp_usd, bp_eur, bp_cn, bp_usd, *other = [row for row in reader][random.randint(0, 4500)]\n",
|
||||
" other = filter(lambda s: not \"Wikipedia\" in s, other)\n",
|
||||
"\n",
|
||||
" retrieved = split_by_keywords(\"\".join(other), keywords)\n",
|
||||
"\n",
|
||||
" brand = retrieved.get(\"DetailsVon:\", \"\").replace(\" \", \"\")\n",
|
||||
" ean = retrieved.get(\"EAN:\", \"\")\n",
|
||||
" producer = retrieved.get(\"Steine von:\", \"\")\n",
|
||||
" age = retrieved.get(\"Altersempfehlung:\", \"\")\n",
|
||||
" release = retrieved.get(\"Release:\", \"\").split(\" \")[-1]\n",
|
||||
" num_parts = retrieved.get(\"Inhalt\", \"\").split(\"Teile\")[0].replace(\"Ein Teil\", \"1\").replace(\"Preise\", \"\").replace(\"Mit Fernsteuerung / Elektrik\", \"1\").replace(\"Eine Minifigur\", \"1\").replace(\"Minifiguren\", \"\").strip()\n",
|
||||
"\n",
|
||||
" category = retrieved.get(\"Kategorie:\", \"\").strip().split(\",\")\n",
|
||||
" categories = \",\".join(rm_epsilon(retrieved.get(\"Kategorien:\", \"\") .split(\",\") + category)).replace(\"Hersteller\", \"\")\n",
|
||||
" producer_category = retrieved.get(\"Hersteller-Kategorie:\", \"\").split(\",\")\n",
|
||||
" producer_categories = \",\".join(rm_epsilon(retrieved.get(\"Hersteller-Kategorien:\", \"\").split(\",\") + producer_category))\n",
|
||||
"\n",
|
||||
" if brand == \"\":\n",
|
||||
" continue\n",
|
||||
" me_extra = pd.DataFrame({\n",
|
||||
" \"id\" : [id],\n",
|
||||
" \"name\" : [id_to_name.get(id, \"\")],\n",
|
||||
" \"price_eur\" : [lp_eur.replace(\"_\", \"\")],\n",
|
||||
" \"price_us\" : [lp_usd.replace(\"_\", \"\")],\n",
|
||||
" \"price_cn\" : [lp_cn.replace(\"_\", \"\")],\n",
|
||||
" \"brand\" : [brand],\n",
|
||||
" \"ean\" : [ean],\n",
|
||||
" \"producer\" : [producer],\n",
|
||||
" \"release\" : [release],\n",
|
||||
" \"category\" : [categories],\n",
|
||||
" \"producer_category\" : [producer_categories],\n",
|
||||
" \"num_parts\" : [num_parts],\n",
|
||||
" })\n",
|
||||
"\n",
|
||||
" me_details = pd.concat([me_details, me_extra])"
|
||||
" pprint.pp(split_by_keywords(\"\".join(other), keywords))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"id": "1b5bcea6",
|
||||
"execution_count": null,
|
||||
"id": "b83aa413",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
|
|
@ -277,53 +288,55 @@
|
|||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>id</th>\n",
|
||||
" <th>name</th>\n",
|
||||
" <th>price_eur</th>\n",
|
||||
" <th>price_cn</th>\n",
|
||||
" <th>price_us</th>\n",
|
||||
" <th>listprice_eur</th>\n",
|
||||
" <th>listprice_cn</th>\n",
|
||||
" <th>listprice_usd</th>\n",
|
||||
" <th>bestprice_eur</th>\n",
|
||||
" <th>bestprice_cn</th>\n",
|
||||
" <th>bestprice_usd</th>\n",
|
||||
" <th>brand</th>\n",
|
||||
" <th>ean</th>\n",
|
||||
" <th>producer</th>\n",
|
||||
" <th>release</th>\n",
|
||||
" <th>...</th>\n",
|
||||
" <th>scale</th>\n",
|
||||
" <th>category</th>\n",
|
||||
" <th>producer_category</th>\n",
|
||||
" <th>num_parts</th>\n",
|
||||
" <th>width</th>\n",
|
||||
" <th>height</th>\n",
|
||||
" <th>depth</th>\n",
|
||||
" <th>designer</th>\n",
|
||||
" <th>weight</th>\n",
|
||||
" <th>age</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>0 rows × 21 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"Empty DataFrame\n",
|
||||
"Columns: [id, name, price_eur, price_cn, price_us, brand, ean, producer, release, category, producer_category, num_parts]\n",
|
||||
"Index: []"
|
||||
"Columns: [id, listprice_eur, listprice_cn, listprice_usd, bestprice_eur, bestprice_cn, bestprice_usd, brand, ean, producer, release, scale, category, producer_category, num_parts, width, height, depth, designer, weight, age]\n",
|
||||
"Index: []\n",
|
||||
"\n",
|
||||
"[0 rows x 21 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 57,
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"me_details[\"Mit Fernsteuerung / Elektrik\" == me_details[\"num_parts\"]]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"id": "0fb65dec",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"me_details.to_csv(\"../data/merlin/others.csv\", index=False)"
|
||||
"me_details"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "venv (3.14.4)",
|
||||
"display_name": "kgr",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
@ -337,7 +350,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.14.4"
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -0,0 +1,13 @@
|
|||
[project]
|
||||
name = "kgr"
|
||||
version = "0.1.0"
|
||||
description = "Add your description here"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
dependencies = [
|
||||
"bs4>=0.0.2",
|
||||
"pandas>=3.0.2",
|
||||
"rdflib>=7.6.0",
|
||||
"requests>=2.33.1",
|
||||
"sparqlwrapper>=2.0.0",
|
||||
]
|
||||
|
|
@ -0,0 +1,408 @@
|
|||
version = 1
|
||||
revision = 3
|
||||
requires-python = ">=3.11"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version >= '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'win32'",
|
||||
"python_full_version < '3.14' and sys_platform == 'emscripten'",
|
||||
"python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "beautifulsoup4"
|
||||
version = "4.14.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "soupsieve" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c3/b0/1c6a16426d389813b48d95e26898aff79abbde42ad353958ad95cc8c9b21/beautifulsoup4-4.14.3.tar.gz", hash = "sha256:6292b1c5186d356bba669ef9f7f051757099565ad9ada5dd630bd9de5fa7fb86", size = 627737, upload-time = "2025-11-30T15:08:26.084Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "bs4"
|
||||
version = "0.0.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "beautifulsoup4" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/c9/aa/4acaf814ff901145da37332e05bb510452ebed97bc9602695059dd46ef39/bs4-0.0.2.tar.gz", hash = "sha256:a48685c58f50fe127722417bae83fe6badf500d54b55f7e39ffe43b798653925", size = 698, upload-time = "2024-01-17T18:15:47.371Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/51/bb/bf7aab772a159614954d84aa832c129624ba6c32faa559dfb200a534e50b/bs4-0.0.2-py2.py3-none-any.whl", hash = "sha256:abf8742c0805ef7f662dce4b51cca104cffe52b835238afc169142ab9b3fbccc", size = 1189, upload-time = "2024-01-17T18:15:48.613Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "certifi"
|
||||
version = "2026.4.22"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/25/ee/6caf7a40c36a1220410afe15a1cc64993a1f864871f698c0f93acb72842a/certifi-2026.4.22.tar.gz", hash = "sha256:8d455352a37b71bf76a79caa83a3d6c25afee4a385d632127b6afb3963f1c580", size = 137077, upload-time = "2026-04-22T11:26:11.191Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/22/30/7cd8fdcdfbc5b869528b079bfb76dcdf6056b1a2097a662e5e8c04f42965/certifi-2026.4.22-py3-none-any.whl", hash = "sha256:3cb2210c8f88ba2318d29b0388d1023c8492ff72ecdde4ebdaddbb13a31b1c4a", size = 135707, upload-time = "2026-04-22T11:26:09.372Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "charset-normalizer"
|
||||
version = "3.4.7"
|
||||
source = { registry = "https://pypi.org/simple" }
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{ url = "https://files.pythonhosted.org/packages/39/08/aaaad47bc4e9dc8c725e68f9d04865dbcb2052843ff09c97b08904852d84/urllib3-2.6.3-py3-none-any.whl", hash = "sha256:bf272323e553dfb2e87d9bfd225ca7b0f467b919d7bbd355436d3fd37cb0acd4", size = 131584, upload-time = "2026-01-07T16:24:42.685Z" },
|
||||
]
|
||||
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Reference in New Issue