Philipp/aki_prj23_transparenzregister
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Executing black over all jupyter notebook (#190)

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Reverting black for the jupyter notebooks gets old. Can we just run
black over all of them?
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Philipp Horstenkamp 2023-10-04 20:03:47 +02:00 committed by GitHub
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15 changed files with 658 additions and 487 deletions
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41
Jupyter/AI-models/Sentiment Analysis/vader.ipynb
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@ -82,7 +82,7 @@
"# Download the lexicon\n", "# Download the lexicon\n",
"nltk.download(\"vader_lexicon\")\n", "nltk.download(\"vader_lexicon\")\n",
"\n", "\n",
"# Import the lexicon \n", "# Import the lexicon\n",
"from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
"\n", "\n",
"# Create an instance of SentimentIntensityAnalyzer\n", "# Create an instance of SentimentIntensityAnalyzer\n",
@ -132,12 +132,22 @@
"text_df = pd.DataFrame(\n", "text_df = pd.DataFrame(\n",
" [\n", " [\n",
" {\"text\": \"Microsoft fails to hit profit expectations.\"},\n", " {\"text\": \"Microsoft fails to hit profit expectations.\"},\n",
" {\"text\": \"Confidence continues to prevail on the stock market, as the performance of the DAX shows.\"},\n", " {\n",
" \"text\": \"Confidence continues to prevail on the stock market, as the performance of the DAX shows.\"\n",
" },\n",
" {\"text\": \"Stocks rallied and the British pound gained.\"},\n", " {\"text\": \"Stocks rallied and the British pound gained.\"},\n",
" {\"text\": \"Meyer Burger now serves Australian market and presents itself at Smart Energy Expo in Sydney.\"},\n", " {\n",
" {\"text\": \"Meyer Burger enters Australian market and exhibits at Smart Energy Expo in Sydney.\"},\n", " \"text\": \"Meyer Burger now serves Australian market and presents itself at Smart Energy Expo in Sydney.\"\n",
" {\"text\": \"J&T Express Vietnam helps local craft villages increase their reach.\"},\n", " },\n",
" {\"text\": \"7 experts recommend the stock for purchase, 1 expert recommends holding the stock.\"},\n", " {\n",
" \"text\": \"Meyer Burger enters Australian market and exhibits at Smart Energy Expo in Sydney.\"\n",
" },\n",
" {\n",
" \"text\": \"J&T Express Vietnam helps local craft villages increase their reach.\"\n",
" },\n",
" {\n",
" \"text\": \"7 experts recommend the stock for purchase, 1 expert recommends holding the stock.\"\n",
" },\n",
" {\"text\": \"Microsoft share falls.\"},\n", " {\"text\": \"Microsoft share falls.\"},\n",
" {\"text\": \"Microsoft share is rising.\"},\n", " {\"text\": \"Microsoft share is rising.\"},\n",
" ]\n", " ]\n",
@ -262,22 +272,21 @@
], ],
"source": [ "source": [
"def format_output(output_dict):\n", "def format_output(output_dict):\n",
" \n", " polarity = \"neutral\"\n",
" polarity = \"neutral\"\n",
"\n", "\n",
" if(output_dict['compound']>= 0.05):\n", " if output_dict[\"compound\"] >= 0.05:\n",
" polarity = \"positive\"\n", " polarity = \"positive\"\n",
"\n", "\n",
" elif(output_dict['compound']<= -0.05):\n", " elif output_dict[\"compound\"] <= -0.05:\n",
" polarity = \"negative\"\n", " polarity = \"negative\"\n",
"\n", "\n",
" return polarity\n", " return polarity\n",
"\n", "\n",
"\n", "\n",
"def predict_sentiment(text):\n", "def predict_sentiment(text):\n",
" \n", " output_dict = sent_analyzer.polarity_scores(text)\n",
" output_dict = sent_analyzer.polarity_scores(text)\n", " return output_dict\n",
" return output_dict\n", "\n",
"\n", "\n",
"# Run the predictions\n", "# Run the predictions\n",
"text_df[\"vader_prediction\"] = text_df[\"text\"].apply(predict_sentiment)\n", "text_df[\"vader_prediction\"] = text_df[\"text\"].apply(predict_sentiment)\n",

22
Jupyter/API-tests/API news_research_Tim.ipynb
View File

@ -31,8 +31,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"import requests\n", "import requests\n",
"url = 'https://www.tagesschau.de/api2/'\n", "\n",
"r = requests.get(url +'homepage')\n", "url = \"https://www.tagesschau.de/api2/\"\n",
"r = requests.get(url + \"homepage\")\n",
"r = r.json()" "r = r.json()"
] ]
}, },
@ -76,10 +77,11 @@
"source": [ "source": [
"# Aggregieren der Titel und Beschreibungen\n", "# Aggregieren der Titel und Beschreibungen\n",
"import pandas as pd\n", "import pandas as pd\n",
"\n",
"data = {\"titles\": [], \"description\": []}\n", "data = {\"titles\": [], \"description\": []}\n",
"for i in range(len(r[\"news\"])): \n", "for i in range(len(r[\"news\"])):\n",
" data[\"titles\"].append(r[\"news\"][i][\"title\"])\n", " data[\"titles\"].append(r[\"news\"][i][\"title\"])\n",
" data[\"description\"].append(r[\"news\"][i][\"content\"][0][\"value\"])\n", " data[\"description\"].append(r[\"news\"][i][\"content\"][0][\"value\"])\n",
"df = pd.DataFrame(data)\n", "df = pd.DataFrame(data)\n",
"print(df.__len__)" "print(df.__len__)"
] ]
@ -19323,7 +19325,7 @@
} }
], ],
"source": [ "source": [
"r = requests.get(url +'news').json()\n", "r = requests.get(url + \"news\").json()\n",
"r" "r"
] ]
}, },
@ -19355,8 +19357,8 @@
], ],
"source": [ "source": [
"data = {\"titles\": []}\n", "data = {\"titles\": []}\n",
"for i in range(len(r[\"news\"])): \n", "for i in range(len(r[\"news\"])):\n",
" data[\"titles\"].append(r[\"news\"][i][\"title\"])\n", " data[\"titles\"].append(r[\"news\"][i][\"title\"])\n",
"# data[\"description\"].append(r[\"news\"][i][\"content\"][0][\"value\"])\n", "# data[\"description\"].append(r[\"news\"][i][\"content\"][0][\"value\"])\n",
"df = pd.DataFrame(data)\n", "df = pd.DataFrame(data)\n",
"print(df)" "print(df)"
@ -19391,7 +19393,7 @@
], ],
"source": [ "source": [
"date = \"230425\"\n", "date = \"230425\"\n",
"r = requests.get(url +'newsfeed-101~_date-{date}.json').json()\n", "r = requests.get(url + \"newsfeed-101~_date-{date}.json\").json()\n",
"r" "r"
] ]
}, },
@ -19989,7 +19991,7 @@
"pageSize = 5\n", "pageSize = 5\n",
"resultPage = 2\n", "resultPage = 2\n",
"print(url)\n", "print(url)\n",
"txt = f'search/?searchText={searchtxt}&pageSize={pageSize}&resultPage={resultPage}'\n", "txt = f\"search/?searchText={searchtxt}&pageSize={pageSize}&resultPage={resultPage}\"\n",
"r = requests.get(url + txt).json()\n", "r = requests.get(url + txt).json()\n",
"r" "r"
] ]

12
Jupyter/API-tests/API research_Tristan.ipynb
View File

@ -51,6 +51,7 @@
], ],
"source": [ "source": [
"from deutschland.bundesanzeiger import Bundesanzeiger\n", "from deutschland.bundesanzeiger import Bundesanzeiger\n",
"\n",
"ba = Bundesanzeiger()\n", "ba = Bundesanzeiger()\n",
"# search term\n", "# search term\n",
"data = ba.get_reports(\"Atos IT-Dienstleistung & Beratung GmbH\")\n", "data = ba.get_reports(\"Atos IT-Dienstleistung & Beratung GmbH\")\n",
@ -73,11 +74,13 @@
], ],
"source": [ "source": [
"# Note: There can be multiple \"Aufsichtsrat\" entries per Company, the API however does only return one because the keys are overwritten\n", "# Note: There can be multiple \"Aufsichtsrat\" entries per Company, the API however does only return one because the keys are overwritten\n",
"jahresabschluss = data['Jahresabschluss zum Geschäftsjahr vom 01.01.2019 bis zum 31.12.2019']\n", "jahresabschluss = data[\n",
" \"Jahresabschluss zum Geschäftsjahr vom 01.01.2019 bis zum 31.12.2019\"\n",
"]\n",
"\n", "\n",
"# Note: Although the report includes the entire text it lacks the formatting that would make extracting information a lot easier as the data is wrapped inside a <table> originally\n", "# Note: Although the report includes the entire text it lacks the formatting that would make extracting information a lot easier as the data is wrapped inside a <table> originally\n",
"with open(\"./jahresabschluss-example.txt\", \"w\") as file:\n", "with open(\"./jahresabschluss-example.txt\", \"w\") as file:\n",
" file.write(jahresabschluss['report'])\n", " file.write(jahresabschluss[\"report\"])\n",
"print(jahresabschluss.keys())" "print(jahresabschluss.keys())"
] ]
}, },
@ -96,6 +99,7 @@
], ],
"source": [ "source": [
"from deutschland.handelsregister import Handelsregister\n", "from deutschland.handelsregister import Handelsregister\n",
"\n",
"hr = Handelsregister()\n", "hr = Handelsregister()\n",
"\n", "\n",
"results = hr.search(keywords=\"BLUECHILLED Verwaltungs GmbH\")\n", "results = hr.search(keywords=\"BLUECHILLED Verwaltungs GmbH\")\n",
@ -128,6 +132,7 @@
"source": [ "source": [
"# SQLite export\n", "# SQLite export\n",
"import sqlite3\n", "import sqlite3\n",
"\n",
"con = sqlite3.connect(\"../data/openregister.db\")" "con = sqlite3.connect(\"../data/openregister.db\")"
] ]
}, },
@ -176,7 +181,7 @@
], ],
"source": [ "source": [
"schema = cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n", "schema = cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
"schema.fetchall()\n" "schema.fetchall()"
] ]
}, },
{ {
@ -414,6 +419,7 @@
], ],
"source": [ "source": [
"import pandas as pd\n", "import pandas as pd\n",
"\n",
"df = pd.read_sql_query(\"SELECT * FROM company LIMIT 100\", con)\n", "df = pd.read_sql_query(\"SELECT * FROM company LIMIT 100\", con)\n",
"df.head()" "df.head()"
] ]

50
Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb
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@ -68,6 +68,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -86,9 +87,9 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"dfEON=pd.read_csv('EON_Data.csv', index_col=0, sep=';') \n", "dfEON = pd.read_csv(\"EON_Data.csv\", index_col=0, sep=\";\")\n",
"dfBASF=pd.read_csv('BASF_Data.csv', index_col=0, sep=';') \n", "dfBASF = pd.read_csv(\"BASF_Data.csv\", index_col=0, sep=\";\")\n",
"dfTELEKOM=pd.read_csv('TELEKOM_Data.csv', index_col=0, sep=';') " "dfTELEKOM = pd.read_csv(\"TELEKOM_Data.csv\", index_col=0, sep=\";\")"
] ]
}, },
{ {
@ -112,7 +113,7 @@
} }
], ],
"source": [ "source": [
"#select a specific year\n", "# select a specific year\n",
"dfTELEKOM.loc[2016]" "dfTELEKOM.loc[2016]"
] ]
}, },
@ -148,33 +149,34 @@
], ],
"source": [ "source": [
"def get_Data(company, metric):\n", "def get_Data(company, metric):\n",
" if company=='BASF':\n", " if company == \"BASF\":\n",
" dfSelect=dfBASF\n", " dfSelect = dfBASF\n",
" print('BASF')\n", " print(\"BASF\")\n",
" if company=='EON':\n", " if company == \"EON\":\n",
" dfSelect=dfEON\n", " dfSelect = dfEON\n",
" print('EON') \n", " print(\"EON\")\n",
" if company=='Telekom':\n", " if company == \"Telekom\":\n",
" dfSelect=dfTELEKOM\n", " dfSelect = dfTELEKOM\n",
" print('Telekom') \n", " print(\"Telekom\")\n",
" fig=dfSelect.plot()\n", " fig = dfSelect.plot()\n",
" fig.show() \n", " fig.show()\n",
" return\n", " return\n",
"\n", "\n",
"W_company=widgets.Dropdown(\n", "\n",
" options=['BASF', 'EON', 'Telekom'],\n", "W_company = widgets.Dropdown(\n",
" value='BASF',\n", " options=[\"BASF\", \"EON\", \"Telekom\"],\n",
" description='Company:',\n", " value=\"BASF\",\n",
" description=\"Company:\",\n",
" disabled=False,\n", " disabled=False,\n",
")\n", ")\n",
"W_metric=widgets.Dropdown(\n", "W_metric = widgets.Dropdown(\n",
" options=['EBIT', 'EBITDA', 'Volume'],\n", " options=[\"EBIT\", \"EBITDA\", \"Volume\"],\n",
" value='Volume',\n", " value=\"Volume\",\n",
" description='Metric:',\n", " description=\"Metric:\",\n",
" disabled=False,\n", " disabled=False,\n",
")\n", ")\n",
"\n", "\n",
"out=widgets.interact(get_Data, company=W_company, metric=W_metric)" "out = widgets.interact(get_Data, company=W_company, metric=W_metric)"
] ]
}, },
{ {

393
documentations/seminararbeiten/Datenspeicherung/Jupyter/11_Create_Tables_with_SQL-Alchemy.ipynb
View File

@ -44,7 +44,7 @@
" username=\"postgres\",\n", " username=\"postgres\",\n",
" password=\"postgres\",\n", " password=\"postgres\",\n",
" host=\"localhost\",\n", " host=\"localhost\",\n",
" database=\"postgres\"\n", " database=\"postgres\",\n",
")\n", ")\n",
"\n", "\n",
"engine = create_engine(url)" "engine = create_engine(url)"
@ -57,7 +57,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#connect to database\n", "# connect to database\n",
"connection = engine.connect()" "connection = engine.connect()"
] ]
}, },
@ -77,12 +77,13 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#create an object *district_court* which inherits attributes from Base-class\n", "# create an object *district_court* which inherits attributes from Base-class\n",
"Base = declarative_base()\n", "Base = declarative_base()\n",
"\n", "\n",
"\n",
"class DistrictCourt(Base):\n", "class DistrictCourt(Base):\n",
" __tablename__ = 'district_court'\n", " __tablename__ = \"district_court\"\n",
" \n", "\n",
" id = Column(Integer(), primary_key=True)\n", " id = Column(Integer(), primary_key=True)\n",
" city = Column(String(100), nullable=False)\n", " city = Column(String(100), nullable=False)\n",
" name = Column(String(100), nullable=False)" " name = Column(String(100), nullable=False)"
@ -106,10 +107,12 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"class Company(Base):\n", "class Company(Base):\n",
" __tablename__ = 'company'\n", " __tablename__ = \"company\"\n",
"\n", "\n",
" hr = Column(Integer(), nullable=False, primary_key=True)\n", " hr = Column(Integer(), nullable=False, primary_key=True)\n",
" court_id = Column(Integer, ForeignKey(\"district_court.id\"), nullable=False, primary_key=True)\n", " court_id = Column(\n",
" Integer, ForeignKey(\"district_court.id\"), nullable=False, primary_key=True\n",
" )\n",
" name = Column(String(100), nullable=False)\n", " name = Column(String(100), nullable=False)\n",
" street = Column(String(100), nullable=False)\n", " street = Column(String(100), nullable=False)\n",
" zip = Column(Integer(), nullable=False)\n", " zip = Column(Integer(), nullable=False)\n",
@ -117,7 +120,7 @@
" sector = Column(String(100), nullable=False)\n", " sector = Column(String(100), nullable=False)\n",
"\n", "\n",
" __table_args__ = (\n", " __table_args__ = (\n",
" PrimaryKeyConstraint('hr', 'court_id', name='pk_company_hr_court'),\n", " PrimaryKeyConstraint(\"hr\", \"court_id\", name=\"pk_company_hr_court\"),\n",
" )" " )"
] ]
}, },
@ -139,7 +142,7 @@
} }
], ],
"source": [ "source": [
"#check if table-object is created\n", "# check if table-object is created\n",
"Company.__table__" "Company.__table__"
] ]
}, },
@ -151,7 +154,7 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"class Finance(Base):\n", "class Finance(Base):\n",
" __tablename__ = 'finance'\n", " __tablename__ = \"finance\"\n",
"\n", "\n",
" id = Column(Integer, primary_key=True)\n", " id = Column(Integer, primary_key=True)\n",
" company_hr = Column(Integer)\n", " company_hr = Column(Integer)\n",
@ -170,7 +173,9 @@
" company = relationship(\"Company\")\n", " company = relationship(\"Company\")\n",
"\n", "\n",
" __table_args__ = (\n", " __table_args__ = (\n",
" ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", " ForeignKeyConstraint(\n",
" [company_hr, company_court], [Company.hr, Company.court_id]\n",
" ),\n",
" )" " )"
] ]
}, },
@ -181,25 +186,35 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#create own enumeration type and sentiment object\n", "# create own enumeration type and sentiment object\n",
"sentiment_type=Enum(\"employee_voting\",\"sustainability\",\"environmental_aspects\",\"perception\", name=\"sentiment_type\", create_type=False)\n", "sentiment_type = Enum(\n",
" \"employee_voting\",\n",
" \"sustainability\",\n",
" \"environmental_aspects\",\n",
" \"perception\",\n",
" name=\"sentiment_type\",\n",
" create_type=False,\n",
")\n",
"\n",
"\n", "\n",
"class Sentiment(Base):\n", "class Sentiment(Base):\n",
" __tablename__ = 'sentiment'\n", " __tablename__ = \"sentiment\"\n",
"\n", "\n",
" id = Column(Integer(), primary_key=True)\n", " id = Column(Integer(), primary_key=True)\n",
" #company_hr = mapped_column(ForeignKey(\"company.hr\"))\n", " # company_hr = mapped_column(ForeignKey(\"company.hr\"))\n",
" #company_court = mapped_column(ForeignKey(\"company.court_id\"))\n", " # company_court = mapped_column(ForeignKey(\"company.court_id\"))\n",
" company_hr = Column(Integer)\n", " company_hr = Column(Integer)\n",
" company_court = Column(Integer)\n", " company_court = Column(Integer)\n",
" date = Column(DateTime(), default=datetime.now)\n", " date = Column(DateTime(), default=datetime.now)\n",
" type = Column(sentiment_type,nullable=False)\n", " type = Column(sentiment_type, nullable=False)\n",
" value =Column(Float(),nullable=False)\n", " value = Column(Float(), nullable=False)\n",
" source=Column(String(100))\n", " source = Column(String(100))\n",
" \n", "\n",
" sentiment = relationship('Company')\n", " sentiment = relationship(\"Company\")\n",
" __table_args__ = (\n", " __table_args__ = (\n",
" ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", " ForeignKeyConstraint(\n",
" [company_hr, company_court], [Company.hr, Company.court_id]\n",
" ),\n",
" )" " )"
] ]
}, },
@ -210,14 +225,14 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#create person object\n", "# create person object\n",
"class Person(Base):\n", "class Person(Base):\n",
" __tablename__ = 'person'\n", " __tablename__ = \"person\"\n",
"\n", "\n",
" id = Column(Integer(), primary_key=True)\n", " id = Column(Integer(), primary_key=True)\n",
" name=Column(String(100), nullable=False)\n", " name = Column(String(100), nullable=False)\n",
" surname=Column(String(100), nullable=False)\n", " surname = Column(String(100), nullable=False)\n",
" works_for=Column(String(100))" " works_for = Column(String(100))"
] ]
}, },
{ {
@ -227,27 +242,39 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#create own relation type and person_relation object\n", "# create own relation type and person_relation object\n",
"rel_type=Enum(\"Executive\",\"Auditor\",\"Supervisory_Board\",\"Managing_Director\",\"Authorized_Representive\",\"Final_Auditor\", name=\"rel_type\", create_type=False)\n", "rel_type = Enum(\n",
" \"Executive\",\n",
" \"Auditor\",\n",
" \"Supervisory_Board\",\n",
" \"Managing_Director\",\n",
" \"Authorized_Representive\",\n",
" \"Final_Auditor\",\n",
" name=\"rel_type\",\n",
" create_type=False,\n",
")\n",
"\n",
"\n", "\n",
"class Person_Relation(Base):\n", "class Person_Relation(Base):\n",
" __tablename__ = 'person_relation'\n", " __tablename__ = \"person_relation\"\n",
"\n", "\n",
" id = Column(Integer(), primary_key=True)\n", " id = Column(Integer(), primary_key=True)\n",
" #company_hr = mapped_column(ForeignKey(\"company.hr\"))\n", " # company_hr = mapped_column(ForeignKey(\"company.hr\"))\n",
" #company_court = mapped_column(ForeignKey(\"company.court_id\"))\n", " # company_court = mapped_column(ForeignKey(\"company.court_id\"))\n",
" company_hr = Column(Integer)\n", " company_hr = Column(Integer)\n",
" company_court = Column(Integer)\n", " company_court = Column(Integer)\n",
" person_id = mapped_column(ForeignKey(\"person.id\"))\n", " person_id = mapped_column(ForeignKey(\"person.id\"))\n",
" date_from = Column(DateTime(), default=datetime.now)\n", " date_from = Column(DateTime(), default=datetime.now)\n",
" date_to = Column(DateTime(), default=datetime.now) \n", " date_to = Column(DateTime(), default=datetime.now)\n",
" relation=Column(rel_type, nullable=False)\n", " relation = Column(rel_type, nullable=False)\n",
" \n", "\n",
" #company = relationship(\"Company\")\n", " # company = relationship(\"Company\")\n",
" #person = relationship(\"Person\", foreign_keys=[person_id])\n", " # person = relationship(\"Person\", foreign_keys=[person_id])\n",
" #company = relationship('Company', foreign_keys=[company_hr,company_court])\n", " # company = relationship('Company', foreign_keys=[company_hr,company_court])\n",
" __table_args__ = (\n", " __table_args__ = (\n",
" ForeignKeyConstraint([company_hr, company_court], [Company.hr, Company.court_id]),\n", " ForeignKeyConstraint(\n",
" [company_hr, company_court], [Company.hr, Company.court_id]\n",
" ),\n",
" )" " )"
] ]
}, },
@ -258,22 +285,30 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#create own relation type and company_relation object\n", "# create own relation type and company_relation object\n",
"rel_type_comp=Enum(\"participates_with\",\"has_shares_of\",\"is_supplied_by\",\"works_with\", name=\"rel_type_comp\", create_type=False)\n", "rel_type_comp = Enum(\n",
" \"participates_with\",\n",
" \"has_shares_of\",\n",
" \"is_supplied_by\",\n",
" \"works_with\",\n",
" name=\"rel_type_comp\",\n",
" create_type=False,\n",
")\n",
"\n",
"\n", "\n",
"class Company_Relation(Base):\n", "class Company_Relation(Base):\n",
" __tablename__ = 'company_relation'\n", " __tablename__ = \"company_relation\"\n",
"\n", "\n",
" id = Column(Integer(), primary_key=True)\n", " id = Column(Integer(), primary_key=True)\n",
" company1_id = Column(Integer,nullable=False)\n", " company1_id = Column(Integer, nullable=False)\n",
" company2_id= Column(Integer,nullable=False)\n", " company2_id = Column(Integer, nullable=False)\n",
" date_from = Column(DateTime(), default=datetime.now)\n", " date_from = Column(DateTime(), default=datetime.now)\n",
" date_to = Column(DateTime(), default=datetime.now) \n", " date_to = Column(DateTime(), default=datetime.now)\n",
" relation=Column(rel_type_comp, nullable=False)\n", " relation = Column(rel_type_comp, nullable=False)\n",
" \n",
" #company = relationship(\"Company\")\n",
"\n", "\n",
" __table_args__ = {'extend_existing': True}" " # company = relationship(\"Company\")\n",
"\n",
" __table_args__ = {\"extend_existing\": True}"
] ]
}, },
{ {
@ -320,7 +355,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('Amtsgerichte.csv', sep=';') \n" "df = pd.read_csv(\"Amtsgerichte.csv\", sep=\";\")"
] ]
}, },
{ {
@ -331,11 +366,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" court=DistrictCourt( \n", " court = DistrictCourt(city=str(df[\"Stadt\"].iloc[i]), name=str(df[\"Name\"].iloc[i]))\n",
" city = str(df['Stadt'].iloc[i]),\n", "\n",
" name = str(df['Name'].iloc[i]))\n",
" \n",
" session.add(court)\n", " session.add(court)\n",
" session.commit()" " session.commit()"
] ]
@ -355,7 +388,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('01_Stammdaten_Unternehmen_HR2.csv', sep=';',encoding=\"ISO-8859-1\")" "df = pd.read_csv(\"01_Stammdaten_Unternehmen_HR2.csv\", sep=\";\", encoding=\"ISO-8859-1\")"
] ]
}, },
{ {
@ -406,15 +439,16 @@
], ],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" comp=Company( \n", " comp = Company(\n",
" hr= int(df['HR'].iloc[i]),\n", " hr=int(df[\"HR\"].iloc[i]),\n",
" court_id= int(df['Amtsgericht'].iloc[i]),\n", " court_id=int(df[\"Amtsgericht\"].iloc[i]),\n",
" name = str(df['Name'].iloc[i]),\n", " name=str(df[\"Name\"].iloc[i]),\n",
" street = str(df['Strasse'].iloc[i]),\n", " street=str(df[\"Strasse\"].iloc[i]),\n",
" zip = int(df['PLZ'].iloc[i]),\n", " zip=int(df[\"PLZ\"].iloc[i]),\n",
" city = str(df['Stadt'].iloc[i]),\n", " city=str(df[\"Stadt\"].iloc[i]),\n",
" sector=str(df['Branche'].iloc[i]))\n", " sector=str(df[\"Branche\"].iloc[i]),\n",
" )\n",
" session.add(comp)\n", " session.add(comp)\n",
" session.commit()" " session.commit()"
] ]
@ -445,7 +479,7 @@
} }
], ],
"source": [ "source": [
"df=pd.read_csv('BASF_Data.csv', sep=';', decimal=\",\",encoding=\"ISO-8859-1\") \n", "df = pd.read_csv(\"BASF_Data.csv\", sep=\";\", decimal=\",\", encoding=\"ISO-8859-1\")\n",
"df.columns" "df.columns"
] ]
}, },
@ -459,21 +493,22 @@
"from datetime import datetime\n", "from datetime import datetime\n",
"\n", "\n",
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" fin=Finance( \n", " fin = Finance(\n",
" company_hr = int(df['Company_HR'].iloc[i]),\n", " company_hr=int(df[\"Company_HR\"].iloc[i]),\n",
" company_court = int(df['Company_Court'].iloc[i]),\n", " company_court=int(df[\"Company_Court\"].iloc[i]),\n",
" date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", " date=datetime.strptime(str(df[\"Jahr\"].iloc[i]), \"%Y\"),\n",
" total_volume=str(df['Umsatz'].iloc[i]),\n", " total_volume=str(df[\"Umsatz\"].iloc[i]),\n",
" ebit=str(df['Ebit'].iloc[i]) ,\n", " ebit=str(df[\"Ebit\"].iloc[i]),\n",
" ebitda=str(df['EBITDA'].iloc[i]),\n", " ebitda=str(df[\"EBITDA\"].iloc[i]),\n",
" ebit_margin=null(),\n", " ebit_margin=null(),\n",
" total_balance=null(),\n", " total_balance=null(),\n",
" equity=null(),\n", " equity=null(),\n",
" debt=null(),\n", " debt=null(),\n",
" return_on_equity=null(),\n", " return_on_equity=null(),\n",
" capital_turnover_rate=null())\n", " capital_turnover_rate=null(),\n",
" \n", " )\n",
"\n",
" session.add(fin)\n", " session.add(fin)\n",
" session.commit()" " session.commit()"
] ]
@ -504,7 +539,7 @@
} }
], ],
"source": [ "source": [
"df=pd.read_csv('Telekom_Data.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", "df = pd.read_csv(\"Telekom_Data.csv\", sep=\";\", decimal=\",\", encoding=\"ISO-8859-1\")\n",
"df.columns" "df.columns"
] ]
}, },
@ -516,21 +551,22 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" fin=Finance( \n", " fin = Finance(\n",
" company_hr = int(df['Company_HR'].iloc[i]),\n", " company_hr=int(df[\"Company_HR\"].iloc[i]),\n",
" company_court = int(df['Company_Court'].iloc[i]), \n", " company_court=int(df[\"Company_Court\"].iloc[i]),\n",
" date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", " date=datetime.strptime(str(df[\"Jahr\"].iloc[i]), \"%Y\"),\n",
" total_volume=str(df['Umsatz'].iloc[i]),\n", " total_volume=str(df[\"Umsatz\"].iloc[i]),\n",
" ebit=str(df['Ebit'].iloc[i]) ,\n", " ebit=str(df[\"Ebit\"].iloc[i]),\n",
" ebitda=str(df['EBITDA'].iloc[i]),\n", " ebitda=str(df[\"EBITDA\"].iloc[i]),\n",
" ebit_margin=null(),\n", " ebit_margin=null(),\n",
" total_balance=null(),\n", " total_balance=null(),\n",
" equity=null(),\n", " equity=null(),\n",
" debt=null(),\n", " debt=null(),\n",
" return_on_equity=null(),\n", " return_on_equity=null(),\n",
" capital_turnover_rate=null())\n", " capital_turnover_rate=null(),\n",
" \n", " )\n",
"\n",
" session.add(fin)\n", " session.add(fin)\n",
" session.commit()" " session.commit()"
] ]
@ -561,7 +597,7 @@
} }
], ],
"source": [ "source": [
"df=pd.read_csv('EON_Data.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", "df = pd.read_csv(\"EON_Data.csv\", sep=\";\", decimal=\",\", encoding=\"ISO-8859-1\")\n",
"df.columns" "df.columns"
] ]
}, },
@ -575,21 +611,22 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" fin=Finance( \n", " fin = Finance(\n",
" company_hr = int(df['Company_HR'].iloc[i]),\n", " company_hr=int(df[\"Company_HR\"].iloc[i]),\n",
" company_court = int(df['Company_Court'].iloc[i]),\n", " company_court=int(df[\"Company_Court\"].iloc[i]),\n",
" date=datetime.strptime(str(df['Jahr'].iloc[i]), '%Y'),\n", " date=datetime.strptime(str(df[\"Jahr\"].iloc[i]), \"%Y\"),\n",
" total_volume=str(df['Umsatz'].iloc[i]),\n", " total_volume=str(df[\"Umsatz\"].iloc[i]),\n",
" ebit=str(df['Ebit'].iloc[i]) ,\n", " ebit=str(df[\"Ebit\"].iloc[i]),\n",
" ebitda=str(df['EBITDA'].iloc[i]),\n", " ebitda=str(df[\"EBITDA\"].iloc[i]),\n",
" ebit_margin=null(),\n", " ebit_margin=null(),\n",
" total_balance=null(),\n", " total_balance=null(),\n",
" equity=null(),\n", " equity=null(),\n",
" debt=null(),\n", " debt=null(),\n",
" return_on_equity=null(),\n", " return_on_equity=null(),\n",
" capital_turnover_rate=null())\n", " capital_turnover_rate=null(),\n",
" \n", " )\n",
"\n",
" session.add(fin)\n", " session.add(fin)\n",
" session.commit()" " session.commit()"
] ]
@ -628,7 +665,7 @@
} }
], ],
"source": [ "source": [
"df=pd.read_csv('person1000.csv', sep=';',decimal=',',encoding=\"ISO-8859-1\") \n", "df = pd.read_csv(\"person1000.csv\", sep=\";\", decimal=\",\", encoding=\"ISO-8859-1\")\n",
"df.columns" "df.columns"
] ]
}, },
@ -640,12 +677,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" per=Person( \n", " per = Person(name=str(df[\"Name\"].iloc[i]), surname=str(df[\"Surname\"].iloc[i]))\n",
" name = str(df['Name'].iloc[i]),\n", "\n",
" surname = str(df['Surname'].iloc[i])\n",
")\n",
" \n",
" session.add(per)\n", " session.add(per)\n",
" session.commit()" " session.commit()"
] ]
@ -667,53 +701,70 @@
"source": [ "source": [
"import random\n", "import random\n",
"\n", "\n",
"relation=['Executive',\n", "relation = [\n",
"'Auditor',\n", " \"Executive\",\n",
"'Supervisory_Board',\n", " \"Auditor\",\n",
"'Managing_Director',\n", " \"Supervisory_Board\",\n",
"'Authorized_Representive',\n", " \"Managing_Director\",\n",
"'Final_Auditor'\n", " \"Authorized_Representive\",\n",
" \"Final_Auditor\",\n",
"]\n", "]\n",
"\n", "\n",
"hr_court=[\n", "hr_court = [\n",
"(12334,2),\n", " (12334, 2),\n",
"(64566,2),\n", " (64566, 2),\n",
"(5433,3),\n", " (5433, 3),\n",
"(12334,4),\n", " (12334, 4),\n",
"(12336,5),\n", " (12336, 5),\n",
"(555,6),\n", " (555, 6),\n",
"(555,7),\n", " (555, 7),\n",
"(12384,8),\n", " (12384, 8),\n",
"(64345,9),\n", " (64345, 9),\n",
"(4344,1),\n", " (4344, 1),\n",
"(866,1),\n", " (866, 1),\n",
"(9875,1)\n", " (9875, 1),\n",
"]\n", "]\n",
"\n", "\n",
"edges=[]\n", "edges = []\n",
"\n", "\n",
"#create amount of combinations\n", "# create amount of combinations\n",
"for i in range(2000):\n", "for i in range(2000):\n",
" rand_comp=random.randint(0,11)\n", " rand_comp = random.randint(0, 11)\n",
" comp_hr=hr_court[rand_comp][0]\n", " comp_hr = hr_court[rand_comp][0]\n",
" comp_court=hr_court[rand_comp][1]\n", " comp_court = hr_court[rand_comp][1]\n",
" \n", "\n",
" rand_person=random.randint(1,999)\n", " rand_person = random.randint(1, 999)\n",
" rand_relation=random.randint(0,5)\n", " rand_relation = random.randint(0, 5)\n",
" rand_year_start=random.randint(2005,2023)\n", " rand_year_start = random.randint(2005, 2023)\n",
" if rand_year_start<2023:\n", " if rand_year_start < 2023:\n",
" year_to=rand_year_start+1\n", " year_to = rand_year_start + 1\n",
" else:\n", " else:\n",
" pass\n", " pass\n",
" # year_to=None\n", " # year_to=None\n",
" \n", "\n",
" #edges.append((rand_company,df['Name'].iloc[rand_person],rand_year_start,year_to,relation[rand_relation])) \n", " # edges.append((rand_company,df['Name'].iloc[rand_person],rand_year_start,year_to,relation[rand_relation]))\n",
" edges.append((comp_hr,comp_court,rand_person,int(rand_year_start),year_to,relation[rand_relation])) \n", " edges.append(\n",
" \n", " (\n",
"#edges.to_csv('edges.csv')\n", " comp_hr,\n",
"col=['Company_HR','Company_Court','Person_ID','Year_From','Year_To','Relation_Type']\n", " comp_court,\n",
"dfEdges=pd.DataFrame(edges,columns=col)\n", " rand_person,\n",
"dfEdges.to_csv('edges.csv')" " int(rand_year_start),\n",
" year_to,\n",
" relation[rand_relation],\n",
" )\n",
" )\n",
"\n",
"# edges.to_csv('edges.csv')\n",
"col = [\n",
" \"Company_HR\",\n",
" \"Company_Court\",\n",
" \"Person_ID\",\n",
" \"Year_From\",\n",
" \"Year_To\",\n",
" \"Relation_Type\",\n",
"]\n",
"dfEdges = pd.DataFrame(edges, columns=col)\n",
"dfEdges.to_csv(\"edges.csv\")"
] ]
}, },
{ {
@ -912,7 +963,7 @@
} }
], ],
"source": [ "source": [
"df=pd.read_csv('edges.csv', sep=',') \n", "df = pd.read_csv(\"edges.csv\", sep=\",\")\n",
"df" "df"
] ]
}, },
@ -924,16 +975,16 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" edges=Person_Relation( \n", " edges = Person_Relation(\n",
" company_hr = int(df['Company_HR'].iloc[i]),\n", " company_hr=int(df[\"Company_HR\"].iloc[i]),\n",
" company_court = int(df['Company_Court'].iloc[i]),\n", " company_court=int(df[\"Company_Court\"].iloc[i]),\n",
" person_id = int(df['Person_ID'].iloc[i]),\n", " person_id=int(df[\"Person_ID\"].iloc[i]),\n",
" date_from=datetime.strptime(str(df['Year_From'].iloc[i]), '%Y'),\n", " date_from=datetime.strptime(str(df[\"Year_From\"].iloc[i]), \"%Y\"),\n",
" date_to=datetime.strptime(str(df['Year_To'].iloc[i]), '%Y'),\n", " date_to=datetime.strptime(str(df[\"Year_To\"].iloc[i]), \"%Y\"),\n",
" relation = str(df['Relation_Type'].iloc[i])\n", " relation=str(df[\"Relation_Type\"].iloc[i]),\n",
")\n", " )\n",
" \n", "\n",
" session.add(edges)\n", " session.add(edges)\n",
" session.commit()" " session.commit()"
] ]
@ -1084,7 +1135,7 @@
"\n", "\n",
"print(\"total_volume|ebit|ebitda\")\n", "print(\"total_volume|ebit|ebitda\")\n",
"for n in Fin:\n", "for n in Fin:\n",
" print(n.total_volume, n.ebit, n.ebitda)\n" " print(n.total_volume, n.ebit, n.ebitda)"
] ]
}, },
{ {
@ -1094,7 +1145,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"users = session.query(User).filter(User.name == 'John').all()" "users = session.query(User).filter(User.name == \"John\").all()"
] ]
}, },
{ {

46
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/01_Connect_to_Database.ipynb
View File

@ -7,7 +7,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#pip install psycopg2" "# pip install psycopg2"
] ]
}, },
{ {
@ -17,7 +17,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#pip install --user ipython-sql" "# pip install --user ipython-sql"
] ]
}, },
{ {
@ -55,10 +55,8 @@
], ],
"source": [ "source": [
"conn = psycopg2.connect(\n", "conn = psycopg2.connect(\n",
" host=\"localhost\",\n", " host=\"localhost\", database=\"transparenz\", user=\"postgres\", password=\"postgres\"\n",
" database=\"transparenz\",\n", ")\n",
" user=\"postgres\",\n",
" password=\"postgres\")\n",
"\n", "\n",
"print(\"Database connected successfully\")" "print(\"Database connected successfully\")"
] ]
@ -89,11 +87,12 @@
], ],
"source": [ "source": [
"conn.autocommit = True\n", "conn.autocommit = True\n",
"#create a table\n", "# create a table\n",
"cur = conn.cursor() # creating a cursor\n", "cur = conn.cursor() # creating a cursor\n",
" \n", "\n",
"# executing queries to create table\n", "# executing queries to create table\n",
"cur.execute(\"\"\"\n", "cur.execute(\n",
" \"\"\"\n",
"CREATE TABLE company\n", "CREATE TABLE company\n",
"(\n", "(\n",
" ID SERIAL PRIMARY KEY NOT NULL,\n", " ID SERIAL PRIMARY KEY NOT NULL,\n",
@ -102,12 +101,13 @@
" ZIP INT NOT NULL,\n", " ZIP INT NOT NULL,\n",
" CITY VARCHAR(100) NOT NULL,\n", " CITY VARCHAR(100) NOT NULL,\n",
" SECTOR VARCHAR(200) NOT NULL)\n", " SECTOR VARCHAR(200) NOT NULL)\n",
"\"\"\")\n", "\"\"\"\n",
" \n", ")\n",
"\n",
"# commit the changes\n", "# commit the changes\n",
"conn.commit() # <--- makes sure the change is shown in the database\n", "conn.commit() # <--- makes sure the change is shown in the database\n",
"#conn.close()\n", "# conn.close()\n",
"#cur.close()\n", "# cur.close()\n",
"print(\"Table Created successfully\")" "print(\"Table Created successfully\")"
] ]
}, },
@ -135,11 +135,12 @@
], ],
"source": [ "source": [
"conn.autocommit = True\n", "conn.autocommit = True\n",
"#create a table\n", "# create a table\n",
"cur = conn.cursor() # creating a cursor\n", "cur = conn.cursor() # creating a cursor\n",
" \n", "\n",
"# executing queries to create table\n", "# executing queries to create table\n",
"cur.execute(\"\"\"\n", "cur.execute(\n",
" \"\"\"\n",
"CREATE TABLE finance\n", "CREATE TABLE finance\n",
"(\n", "(\n",
" FINANCE_ID SERIAL PRIMARY KEY NOT NULL,\n", " FINANCE_ID SERIAL PRIMARY KEY NOT NULL,\n",
@ -147,12 +148,13 @@
" KIND_OF VARCHAR(50) NOT NULL,\n", " KIND_OF VARCHAR(50) NOT NULL,\n",
" DATE DATE NOT NULL,\n", " DATE DATE NOT NULL,\n",
" SUM FLOAT NOT NULL)\n", " SUM FLOAT NOT NULL)\n",
"\"\"\")\n", "\"\"\"\n",
" \n", ")\n",
"\n",
"# commit the changes\n", "# commit the changes\n",
"conn.commit() # <--- makes sure the change is shown in the database\n", "conn.commit() # <--- makes sure the change is shown in the database\n",
"#conn.close()\n", "# conn.close()\n",
"#cur.close()\n", "# cur.close()\n",
"print(\"Table Created successfully\")" "print(\"Table Created successfully\")"
] ]
}, },

33
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/02_Connect_to_Database_publish_Company_Data.ipynb
View File

@ -10,6 +10,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -20,7 +21,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('01_Stammdaten_Unternehmen.csv', sep=';') " "df = pd.read_csv(\"01_Stammdaten_Unternehmen.csv\", sep=\";\")"
] ]
}, },
{ {
@ -240,10 +241,8 @@
], ],
"source": [ "source": [
"conn = psycopg2.connect(\n", "conn = psycopg2.connect(\n",
" host=\"localhost\",\n", " host=\"localhost\", database=\"transparenz\", user=\"postgres\", password=\"postgres\"\n",
" database=\"transparenz\",\n", ")\n",
" user=\"postgres\",\n",
" password=\"postgres\")\n",
"\n", "\n",
"print(\"Database connected successfully\")" "print(\"Database connected successfully\")"
] ]
@ -267,18 +266,18 @@
"\n", "\n",
"\n", "\n",
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" name=str(df['Name'].iloc[i])\n", " name = str(df[\"Name\"].iloc[i])\n",
" street=str(df['Straße'].iloc[i])\n", " street = str(df[\"Straße\"].iloc[i])\n",
" zipcode=int(df['PLZ'].iloc[i])\n", " zipcode = int(df[\"PLZ\"].iloc[i])\n",
" city=str(df['Stadt'].iloc[i])\n", " city = str(df[\"Stadt\"].iloc[i])\n",
" sector=str(df['Branche'].iloc[i])\n", " sector = str(df[\"Branche\"].iloc[i])\n",
" \n", "\n",
" postgres_insert_query = \"\"\" INSERT INTO company (NAME,STREET, ZIP, CITY,SECTOR) VALUES (%s,%s,%s,%s,%s)\"\"\" \n", " postgres_insert_query = \"\"\" INSERT INTO company (NAME,STREET, ZIP, CITY,SECTOR) VALUES (%s,%s,%s,%s,%s)\"\"\"\n",
" \n", "\n",
" record_to_insert = (name,street,zipcode,city,sector)\n", " record_to_insert = (name, street, zipcode, city, sector)\n",
" cur.execute(postgres_insert_query, record_to_insert) \n", " cur.execute(postgres_insert_query, record_to_insert)\n",
" \n", "\n",
"conn.commit()\n", "conn.commit()\n",
"conn.close()" "conn.close()"
] ]

33
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-1_Publish_Finance_Testdata_BASF.ipynb
View File

@ -23,6 +23,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -33,7 +34,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('BASF_Data_NewOrder.csv', sep=';', decimal=\",\") " "df = pd.read_csv(\"BASF_Data_NewOrder.csv\", sep=\";\", decimal=\",\")"
] ]
}, },
{ {
@ -528,10 +529,8 @@
], ],
"source": [ "source": [
"conn = psycopg2.connect(\n", "conn = psycopg2.connect(\n",
" host=\"localhost\",\n", " host=\"localhost\", database=\"transparenz\", user=\"postgres\", password=\"postgres\"\n",
" database=\"transparenz\",\n", ")\n",
" user=\"postgres\",\n",
" password=\"postgres\")\n",
"\n", "\n",
"print(\"Database connected successfully\")" "print(\"Database connected successfully\")"
] ]
@ -553,20 +552,22 @@
"source": [ "source": [
"cur = conn.cursor()\n", "cur = conn.cursor()\n",
"\n", "\n",
"PK_ID=8 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", "PK_ID = 8 # BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n",
"\n", "\n",
"\n", "\n",
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" kind_of=str(df['Metrik'].iloc[i])\n", " kind_of = str(df[\"Metrik\"].iloc[i])\n",
" date=str(df['Datum'].iloc[i])\n", " date = str(df[\"Datum\"].iloc[i])\n",
" amount=float(df['Summe [Milliarden €]'].iloc[i])\n", " amount = float(df[\"Summe [Milliarden €]\"].iloc[i])\n",
" \n", "\n",
" postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", " postgres_insert_query = (\n",
" record_to_insert = (PK_ID,kind_of,date,amount)\n", " \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\"\n",
" cur.execute(postgres_insert_query, record_to_insert) \n", " )\n",
" #print(postgres_insert_query, record_to_insert)\n", " record_to_insert = (PK_ID, kind_of, date, amount)\n",
" \n", " cur.execute(postgres_insert_query, record_to_insert)\n",
" # print(postgres_insert_query, record_to_insert)\n",
"\n",
"conn.commit()\n", "conn.commit()\n",
"conn.close()" "conn.close()"
] ]

33
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish-Finance_Testdata_Telekom.ipynb
View File

@ -10,6 +10,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -20,7 +21,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('Telekom_Data_NewOrder.csv', sep=';',decimal=',') " "df = pd.read_csv(\"Telekom_Data_NewOrder.csv\", sep=\";\", decimal=\",\")"
] ]
}, },
{ {
@ -403,10 +404,8 @@
], ],
"source": [ "source": [
"conn = psycopg2.connect(\n", "conn = psycopg2.connect(\n",
" host=\"localhost\",\n", " host=\"localhost\", database=\"transparenz\", user=\"postgres\", password=\"postgres\"\n",
" database=\"transparenz\",\n", ")\n",
" user=\"postgres\",\n",
" password=\"postgres\")\n",
"\n", "\n",
"print(\"Database connected successfully\")" "print(\"Database connected successfully\")"
] ]
@ -428,20 +427,22 @@
"source": [ "source": [
"cur = conn.cursor()\n", "cur = conn.cursor()\n",
"\n", "\n",
"PK_ID=5 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", "PK_ID = 5 # BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n",
"\n", "\n",
"\n", "\n",
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" kind_of=str(df['Metrik'].iloc[i])\n", " kind_of = str(df[\"Metrik\"].iloc[i])\n",
" date=str(df['Datum'].iloc[i])\n", " date = str(df[\"Datum\"].iloc[i])\n",
" amount=float(df['Summe [Milliarden €]'].iloc[i])\n", " amount = float(df[\"Summe [Milliarden €]\"].iloc[i])\n",
" \n", "\n",
" postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", " postgres_insert_query = (\n",
" record_to_insert = (PK_ID,kind_of,date,amount)\n", " \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\"\n",
" cur.execute(postgres_insert_query, record_to_insert) \n", " )\n",
" #print(postgres_insert_query, record_to_insert)\n", " record_to_insert = (PK_ID, kind_of, date, amount)\n",
" \n", " cur.execute(postgres_insert_query, record_to_insert)\n",
" # print(postgres_insert_query, record_to_insert)\n",
"\n",
"conn.commit()\n", "conn.commit()\n",
"conn.close()" "conn.close()"
] ]

33
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/03-2_Publish_Finance_Testdata_EON.ipynb
View File

@ -10,6 +10,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -20,7 +21,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"df=pd.read_csv('EON_Data_NewOrder.csv', sep=';',decimal=',') " "df = pd.read_csv(\"EON_Data_NewOrder.csv\", sep=\";\", decimal=\",\")"
] ]
}, },
{ {
@ -340,10 +341,8 @@
], ],
"source": [ "source": [
"conn = psycopg2.connect(\n", "conn = psycopg2.connect(\n",
" host=\"localhost\",\n", " host=\"localhost\", database=\"transparenz\", user=\"postgres\", password=\"postgres\"\n",
" database=\"transparenz\",\n", ")\n",
" user=\"postgres\",\n",
" password=\"postgres\")\n",
"\n", "\n",
"print(\"Database connected successfully\")" "print(\"Database connected successfully\")"
] ]
@ -365,20 +364,22 @@
"source": [ "source": [
"cur = conn.cursor()\n", "cur = conn.cursor()\n",
"\n", "\n",
"PK_ID=9 #BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n", "PK_ID = 9 # BASF hat den PK 8, deshalb wird dieser manuell hinzugefügt\n",
"\n", "\n",
"\n", "\n",
"for i in range(len(df)):\n", "for i in range(len(df)):\n",
" #get data from dataframe\n", " # get data from dataframe\n",
" kind_of=str(df['Metrik'].iloc[i])\n", " kind_of = str(df[\"Metrik\"].iloc[i])\n",
" date=str(df['Datum'].iloc[i])\n", " date = str(df[\"Datum\"].iloc[i])\n",
" amount=float(df['Summe [Milliarden €]'].iloc[i])\n", " amount = float(df[\"Summe [Milliarden €]\"].iloc[i])\n",
" \n", "\n",
" postgres_insert_query = \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\" \n", " postgres_insert_query = (\n",
" record_to_insert = (PK_ID,kind_of,date,amount)\n", " \"\"\" INSERT INTO finance (company_id,kind_of, date, sum) VALUES (%s,%s,%s,%s)\"\"\"\n",
" cur.execute(postgres_insert_query, record_to_insert) \n", " )\n",
" #print(postgres_insert_query, record_to_insert)\n", " record_to_insert = (PK_ID, kind_of, date, amount)\n",
" \n", " cur.execute(postgres_insert_query, record_to_insert)\n",
" # print(postgres_insert_query, record_to_insert)\n",
"\n",
"conn.commit()\n", "conn.commit()\n",
"conn.close()" "conn.close()"
] ]

3
documentations/seminararbeiten/Datenspeicherung/Jupyter/Notebooks_with_SQL_and_preliminary_data/04_First_Query.ipynb
View File

@ -17,6 +17,7 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"import ipywidgets as widgets\n", "import ipywidgets as widgets\n",
"\n",
"pd.options.plotting.backend = \"plotly\"" "pd.options.plotting.backend = \"plotly\""
] ]
}, },
@ -48,7 +49,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"#load sql extension\n", "# load sql extension\n",
"%load_ext sql" "%load_ext sql"
] ]
}, },

162
documentations/seminararbeiten/Datenvisualisierung/Diagramme_plotly/Visualisierung_plotly.ipynb
View File

@ -79,16 +79,20 @@
"import numpy as np\n", "import numpy as np\n",
"\n", "\n",
"# create sample data for one company\n", "# create sample data for one company\n",
"data = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],\n", "data = {\n",
" 'Umsatz': [19, 23, 30, 42, 37, 45]}\n", " \"Jahr\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"],\n",
" \"Umsatz\": [19, 23, 30, 42, 37, 45],\n",
"}\n",
"# save as pandas dataframe\n", "# save as pandas dataframe\n",
"df = pd.DataFrame.from_dict(data)\n", "df = pd.DataFrame.from_dict(data)\n",
"\n", "\n",
"# create sample data for a second company\n", "# create sample data for a second company\n",
"data2 = {'Jahr': ['2017', '2018', '2019', '2020', '2021', '2022'],\n", "data2 = {\n",
" 'Umsatz': [15, 21, 33, 28, 27, 30]}\n", " \"Jahr\": [\"2017\", \"2018\", \"2019\", \"2020\", \"2021\", \"2022\"],\n",
" \"Umsatz\": [15, 21, 33, 28, 27, 30],\n",
"}\n",
"# save as pandas dataframe\n", "# save as pandas dataframe\n",
"df2 = pd.DataFrame.from_dict(data2)\n" "df2 = pd.DataFrame.from_dict(data2)"
] ]
}, },
{ {
@ -1006,12 +1010,12 @@
], ],
"source": [ "source": [
"# create bar plot\n", "# create bar plot\n",
"fig_saeule = px.bar(df, y = 'Umsatz', labels = {'index': '', 'Umsatz': ''})\n", "fig_saeule = px.bar(df, y=\"Umsatz\", labels={\"index\": \"\", \"Umsatz\": \"\"})\n",
"# set color\n", "# set color\n",
"fig_saeule.update_traces(marker_color = '#00509b')\n", "fig_saeule.update_traces(marker_color=\"#00509b\")\n",
"\n", "\n",
"# save as image\n", "# save as image\n",
"fig_saeule.write_image('Saeule.png')\n", "fig_saeule.write_image(\"Saeule.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_saeule.show()" "fig_saeule.show()"
] ]
@ -1924,12 +1928,12 @@
], ],
"source": [ "source": [
"# create horizontal bar plot\n", "# create horizontal bar plot\n",
"fig_balken = px.bar(df, x = 'Umsatz', labels = {'index': '', 'Umsatz': ''}, orientation='h')\n", "fig_balken = px.bar(df, x=\"Umsatz\", labels={\"index\": \"\", \"Umsatz\": \"\"}, orientation=\"h\")\n",
"# set color\n", "# set color\n",
"fig_balken.update_traces(marker_color = '#00509b')\n", "fig_balken.update_traces(marker_color=\"#00509b\")\n",
"\n", "\n",
"# save as image\n", "# save as image\n",
"fig_balken.write_image('Balken.png')\n", "fig_balken.write_image(\"Balken.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_balken.show()" "fig_balken.show()"
] ]
@ -2841,14 +2845,19 @@
} }
], ],
"source": [ "source": [
"\n",
"# sreate bar plot with named labels and title\n", "# sreate bar plot with named labels and title\n",
"fig_saeule_titel = px.bar(df, x = 'Jahr', y = 'Umsatz', labels = {'Umsatz': 'Umsatz in Mio.€'}, title = 'Umsatzentwicklung von Unternehmen A')\n", "fig_saeule_titel = px.bar(\n",
" df,\n",
" x=\"Jahr\",\n",
" y=\"Umsatz\",\n",
" labels={\"Umsatz\": \"Umsatz in Mio.€\"},\n",
" title=\"Umsatzentwicklung von Unternehmen A\",\n",
")\n",
"# set color\n", "# set color\n",
"fig_saeule_titel.update_traces(marker_color = '#00509b')\n", "fig_saeule_titel.update_traces(marker_color=\"#00509b\")\n",
"\n", "\n",
"# save as image\n", "# save as image\n",
"fig_saeule_titel.write_image('Saeule_Titel.png')\n", "fig_saeule_titel.write_image(\"Saeule_Titel.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_saeule_titel.show()" "fig_saeule_titel.show()"
] ]
@ -3764,27 +3773,31 @@
], ],
"source": [ "source": [
"# create figure\n", "# create figure\n",
"fig_saeule_zwei= go.Figure()\n", "fig_saeule_zwei = go.Figure()\n",
"\n", "\n",
"# add trace for company 1\n", "# add trace for company 1\n",
"fig_saeule_zwei.add_trace(go.Bar(x = df['Jahr'], y = df['Umsatz'], name = 'A', marker_color=\"#00509b\"))\n", "fig_saeule_zwei.add_trace(\n",
" go.Bar(x=df[\"Jahr\"], y=df[\"Umsatz\"], name=\"A\", marker_color=\"#00509b\")\n",
")\n",
"\n", "\n",
"# add trace for company 2\n", "# add trace for company 2\n",
"fig_saeule_zwei.add_trace(go.Bar(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', marker_color = \"#6f7072\"))\n", "fig_saeule_zwei.add_trace(\n",
" go.Bar(x=df2[\"Jahr\"], y=df2[\"Umsatz\"], name=\"B\", marker_color=\"#6f7072\")\n",
")\n",
"\n", "\n",
"# update layout to grouped\n", "# update layout to grouped\n",
"fig_saeule_zwei.update_layout(barmode='group')\n", "fig_saeule_zwei.update_layout(barmode=\"group\")\n",
"\n", "\n",
"# set title and labels\n", "# set title and labels\n",
"fig_saeule_zwei.update_layout(\n", "fig_saeule_zwei.update_layout(\n",
" title = \"Vergleich der Umsatzentwicklung\",\n", " title=\"Vergleich der Umsatzentwicklung\",\n",
" xaxis_title = \"Jahr\",\n", " xaxis_title=\"Jahr\",\n",
" yaxis_title = \"Umsatz in Mio.€\",\n", " yaxis_title=\"Umsatz in Mio.€\",\n",
" legend_title = \"Unternehmen\",\n", " legend_title=\"Unternehmen\",\n",
")\n", ")\n",
"\n", "\n",
"# save as image\n", "# save as image\n",
"fig_saeule_zwei.write_image('Saeule_Zwei.png')\n", "fig_saeule_zwei.write_image(\"Saeule_Zwei.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_saeule_zwei.show()" "fig_saeule_zwei.show()"
] ]
@ -4697,9 +4710,9 @@
], ],
"source": [ "source": [
"# create line plot\n", "# create line plot\n",
"fig_line = px.line(df, y = df['Umsatz'], labels = {'index':'', 'Umsatz': ''})\n", "fig_line = px.line(df, y=df[\"Umsatz\"], labels={\"index\": \"\", \"Umsatz\": \"\"})\n",
"# set color\n", "# set color\n",
"fig_line.update_traces(line_color = '#00509b')\n", "fig_line.update_traces(line_color=\"#00509b\")\n",
"# save as image\n", "# save as image\n",
"fig_line.write_image(\"Linie.png\")\n", "fig_line.write_image(\"Linie.png\")\n",
"# show in network\n", "# show in network\n",
@ -5617,15 +5630,31 @@
"# create figure\n", "# create figure\n",
"fig_line = go.Figure()\n", "fig_line = go.Figure()\n",
"# add trace for company 1\n", "# add trace for company 1\n",
"fig_line.add_trace(go.Scatter(x = df['Jahr'], y = df['Umsatz'], name = 'A', line_color = '#00509b', marker_color = '#00509b'))\n", "fig_line.add_trace(\n",
" go.Scatter(\n",
" x=df[\"Jahr\"],\n",
" y=df[\"Umsatz\"],\n",
" name=\"A\",\n",
" line_color=\"#00509b\",\n",
" marker_color=\"#00509b\",\n",
" )\n",
")\n",
"# add trace for company 2\n", "# add trace for company 2\n",
"fig_line.add_trace(go.Scatter(x = df2['Jahr'], y = df2['Umsatz'], name = 'B', line_color = '#6f7072', marker_color = '#6f7072'))\n", "fig_line.add_trace(\n",
" go.Scatter(\n",
" x=df2[\"Jahr\"],\n",
" y=df2[\"Umsatz\"],\n",
" name=\"B\",\n",
" line_color=\"#6f7072\",\n",
" marker_color=\"#6f7072\",\n",
" )\n",
")\n",
"# set title and labels\n", "# set title and labels\n",
"fig_line.update_layout(\n", "fig_line.update_layout(\n",
" title = \"Vergleich der Umsatzentwicklung\",\n", " title=\"Vergleich der Umsatzentwicklung\",\n",
" xaxis_title = \"Jahr\",\n", " xaxis_title=\"Jahr\",\n",
" yaxis_title = \"Umsatz in Mio.€\",\n", " yaxis_title=\"Umsatz in Mio.€\",\n",
" legend_title = \"Unternehmen\",\n", " legend_title=\"Unternehmen\",\n",
")\n", ")\n",
"# save as image\n", "# save as image\n",
"fig_line.write_image(\"Linie_Vergleich.png\")\n", "fig_line.write_image(\"Linie_Vergleich.png\")\n",
@ -6534,13 +6563,15 @@
], ],
"source": [ "source": [
"# create sample data\n", "# create sample data\n",
"x = [1,2,2,3,5,5,6,6,6,7,8,8,8,10,10,10,7,4,3,4,9,6]\n", "x = [1, 2, 2, 3, 5, 5, 6, 6, 6, 7, 8, 8, 8, 10, 10, 10, 7, 4, 3, 4, 9, 6]\n",
"y = [1,2,3,3,3,4,5,5,6,6,7,7,8,8,9,10,2,10,8,6,6,4]\n", "y = [1, 2, 3, 3, 3, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 10, 2, 10, 8, 6, 6, 4]\n",
"\n", "\n",
"# create scatter plot\n", "# create scatter plot\n",
"scatter = go.Figure(data=go.Scatter(x=x, y=y, mode='markers', marker = {'color': '#00509b'}))\n", "scatter = go.Figure(\n",
" data=go.Scatter(x=x, y=y, mode=\"markers\", marker={\"color\": \"#00509b\"})\n",
")\n",
"# save as image\n", "# save as image\n",
"scatter.write_image('Streudiagramm.png')\n", "scatter.write_image(\"Streudiagramm.png\")\n",
"# show in netbook\n", "# show in netbook\n",
"scatter.show()" "scatter.show()"
] ]
@ -7445,13 +7476,15 @@
"source": [ "source": [
"# create sample data\n", "# create sample data\n",
"sentiment = [0.1, 0.3, 0.6]\n", "sentiment = [0.1, 0.3, 0.6]\n",
"scores = ['negativ', 'neutral', 'positiv']\n", "scores = [\"negativ\", \"neutral\", \"positiv\"]\n",
"# create pie chart\n", "# create pie chart\n",
"fig_kreis = px.pie(values=sentiment, color = scores, color_discrete_map={'negativ':'black',\n", "fig_kreis = px.pie(\n",
" 'neutral':'#6f7072',\n", " values=sentiment,\n",
" 'positiv':'#00509b'})\n", " color=scores,\n",
" color_discrete_map={\"negativ\": \"black\", \"neutral\": \"#6f7072\", \"positiv\": \"#00509b\"},\n",
")\n",
"# save as image\n", "# save as image\n",
"fig_kreis.write_image('Kreis.png')\n", "fig_kreis.write_image(\"Kreis.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_kreis.show()" "fig_kreis.show()"
] ]
@ -8356,14 +8389,22 @@
} }
], ],
"source": [ "source": [
"# create figure \n", "# create figure\n",
"fig_sentiment = px.pie(values=sentiment, names=scores, color = scores, color_discrete_map={'negativ':'lightcoral',\n", "fig_sentiment = px.pie(\n",
" 'neutral':'moccasin',\n", " values=sentiment,\n",
" 'positiv':'darkseagreen'}, title = 'Stimmungsanalyse basierend auf Nachrichtenartikel X')\n", " names=scores,\n",
" color=scores,\n",
" color_discrete_map={\n",
" \"negativ\": \"lightcoral\",\n",
" \"neutral\": \"moccasin\",\n",
" \"positiv\": \"darkseagreen\",\n",
" },\n",
" title=\"Stimmungsanalyse basierend auf Nachrichtenartikel X\",\n",
")\n",
"# change line color\n", "# change line color\n",
"fig_sentiment.update_traces(marker = dict(line=dict(color='#000000', width=2)))\n", "fig_sentiment.update_traces(marker=dict(line=dict(color=\"#000000\", width=2)))\n",
"# save as image\n", "# save as image\n",
"fig_sentiment.write_image('Kreis_Sentiment.png')\n", "fig_sentiment.write_image(\"Kreis_Sentiment.png\")\n",
"# show in notebook\n", "# show in notebook\n",
"fig_sentiment.show()" "fig_sentiment.show()"
] ]
@ -8408,34 +8449,39 @@
"compound = 0.75\n", "compound = 0.75\n",
"angle = (compound * 100 - 50) * 1.8\n", "angle = (compound * 100 - 50) * 1.8\n",
"\n", "\n",
"x_length = (np.sin(np.radians(angle))*1.2)/2\n", "x_length = (np.sin(np.radians(angle)) * 1.2) / 2\n",
"y_length = (np.cos(np.radians(angle))*1.2)/2\n", "y_length = (np.cos(np.radians(angle)) * 1.2) / 2\n",
"\n", "\n",
"if compound < 0.5:\n", "if compound < 0.5:\n",
" y_tail = y_length\n", " y_tail = y_length\n",
" y_head = -y_length\n", " y_head = -y_length\n",
" x_tail = x_length\n", " x_tail = x_length\n",
" x_head = - x_length\n", " x_head = -x_length\n",
"else:\n", "else:\n",
" y_tail = -y_length\n", " y_tail = -y_length\n",
" y_head = y_length\n", " y_head = y_length\n",
" x_tail = - x_length\n", " x_tail = -x_length\n",
" x_head = x_length\n", " x_head = x_length\n",
"\n", "\n",
"\n", "\n",
"dx = x_head - x_tail\n", "dx = x_head - x_tail\n",
"dy = y_head - y_tail\n", "dy = y_head - y_tail\n",
"\n", "\n",
"fig, ax = plt.subplots() \n", "fig, ax = plt.subplots()\n",
"arrow = patches.FancyArrowPatch((x_tail, y_tail), (x_head, y_head),\n", "arrow = patches.FancyArrowPatch(\n",
" mutation_scale=100, ec = 'darkseagreen', fc = 'darkseagreen')\n", " (x_tail, y_tail),\n",
" (x_head, y_head),\n",
" mutation_scale=100,\n",
" ec=\"darkseagreen\",\n",
" fc=\"darkseagreen\",\n",
")\n",
"ax.add_patch(arrow)\n", "ax.add_patch(arrow)\n",
"plt.axis('off')\n", "plt.axis(\"off\")\n",
"ax.set_xlim([-1, 1])\n", "ax.set_xlim([-1, 1])\n",
"ax.set_ylim([-1, 1])\n", "ax.set_ylim([-1, 1])\n",
"ax.set_title('Aktueller Stimmungstrend', fontsize=20)\n", "ax.set_title(\"Aktueller Stimmungstrend\", fontsize=20)\n",
"fig.suptitle(' ', fontsize=24)\n", "fig.suptitle(\" \", fontsize=24)\n",
"fig.savefig('Pfeil.png')\n" "fig.savefig(\"Pfeil.png\")"
] ]
} }
], ],

130
documentations/seminararbeiten/Datenvisualisierung/Diagramme_pyvis/Visualisierung_networkx_pyvis.ipynb
View File

@ -92,39 +92,40 @@
"G = nx.MultiGraph()\n", "G = nx.MultiGraph()\n",
"\n", "\n",
"# create list of nodes with attributes as a dictionary\n", "# create list of nodes with attributes as a dictionary\n",
"nodes = [(1, {'label': 'Firma 1', 'branche': 'Branche 1', 'land': 'Land 1'}), \n", "nodes = [\n",
" (2, {'label': 'Firma 2', 'branche': 'Branche 1', 'land': 'Land 2'}),\n", " (1, {\"label\": \"Firma 1\", \"branche\": \"Branche 1\", \"land\": \"Land 1\"}),\n",
" (3, {'label': 'Firma 3', 'branche': 'Branche 1', 'land': 'Land 3'}),\n", " (2, {\"label\": \"Firma 2\", \"branche\": \"Branche 1\", \"land\": \"Land 2\"}),\n",
" (4, {'label': 'Firma 4', 'branche': 'Branche 2', 'land': 'Land 4'}),\n", " (3, {\"label\": \"Firma 3\", \"branche\": \"Branche 1\", \"land\": \"Land 3\"}),\n",
" (5, {'label': 'Firma 5', 'branche': 'Branche 2', 'land': 'Land 1'}),\n", " (4, {\"label\": \"Firma 4\", \"branche\": \"Branche 2\", \"land\": \"Land 4\"}),\n",
" (6, {'label': 'Firma 6', 'branche': 'Branche 2', 'land': 'Land 3'}),\n", " (5, {\"label\": \"Firma 5\", \"branche\": \"Branche 2\", \"land\": \"Land 1\"}),\n",
" (7, {'label': 'Firma 7', 'branche': 'Branche 3', 'land': 'Land 3'}),\n", " (6, {\"label\": \"Firma 6\", \"branche\": \"Branche 2\", \"land\": \"Land 3\"}),\n",
" (8, {'label': 'Firma 8', 'branche': 'Branche 3', 'land': 'Land 2'}),\n", " (7, {\"label\": \"Firma 7\", \"branche\": \"Branche 3\", \"land\": \"Land 3\"}),\n",
" (9, {'label': 'Firma 9', 'branche': 'Branche 4', 'land': 'Land 1'}),\n", " (8, {\"label\": \"Firma 8\", \"branche\": \"Branche 3\", \"land\": \"Land 2\"}),\n",
" (10, {'label': 'Firma 10', 'branche': 'Branche 4', 'land': 'Land 4'}),\n", " (9, {\"label\": \"Firma 9\", \"branche\": \"Branche 4\", \"land\": \"Land 1\"}),\n",
" ]\n", " (10, {\"label\": \"Firma 10\", \"branche\": \"Branche 4\", \"land\": \"Land 4\"}),\n",
"]\n",
"\n", "\n",
"# create list of edges with attributes as a dictionary\n", "# create list of edges with attributes as a dictionary\n",
"edges = [\n", "edges = [\n",
" (1, 2, {'label': 'beziehung1'}), \n", " (1, 2, {\"label\": \"beziehung1\"}),\n",
" (5, 2, {'label': 'beziehung2'}), \n", " (5, 2, {\"label\": \"beziehung2\"}),\n",
" (1, 3, {'label': 'beziehung3'}), \n", " (1, 3, {\"label\": \"beziehung3\"}),\n",
" (2, 4, {'label': 'beziehung3'}), \n", " (2, 4, {\"label\": \"beziehung3\"}),\n",
" (2, 6, {'label': 'beziehung4'}), \n", " (2, 6, {\"label\": \"beziehung4\"}),\n",
" (2, 5, {'label': 'beziehung4'}),\n", " (2, 5, {\"label\": \"beziehung4\"}),\n",
" (8, 10, {'label': 'beziehung4'}),\n", " (8, 10, {\"label\": \"beziehung4\"}),\n",
" (9, 10, {'label': 'beziehung3'}), \n", " (9, 10, {\"label\": \"beziehung3\"}),\n",
" (3, 7, {'label': 'beziehung2'}), \n", " (3, 7, {\"label\": \"beziehung2\"}),\n",
" (6, 8, {'label': 'beziehung1'}), \n", " (6, 8, {\"label\": \"beziehung1\"}),\n",
" (6, 9, {'label': 'beziehung1'}), \n", " (6, 9, {\"label\": \"beziehung1\"}),\n",
" (1, 6, {'label': 'beziehung2'})\n", " (1, 6, {\"label\": \"beziehung2\"}),\n",
" ]\n", "]\n",
"\n", "\n",
"# add nodes to the graph\n", "# add nodes to the graph\n",
"G.add_nodes_from(nodes)\n", "G.add_nodes_from(nodes)\n",
"\n", "\n",
"# add edges to the graph, to hide arrow heads of the edges use option arrows = 'false'\n", "# add edges to the graph, to hide arrow heads of the edges use option arrows = 'false'\n",
"G.add_edges_from(edges, arrows = 'false')" "G.add_edges_from(edges, arrows=\"false\")"
] ]
}, },
{ {
@ -147,7 +148,9 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"for node in G.nodes:\n", "for node in G.nodes:\n",
" G.nodes[node]['title'] = G.nodes[node]['label'] + '\\n' + 'Anzahl Verbindungen: ' + str(G.degree[node])" " G.nodes[node][\"title\"] = (\n",
" G.nodes[node][\"label\"] + \"\\n\" + \"Anzahl Verbindungen: \" + str(G.degree[node])\n",
" )"
] ]
}, },
{ {
@ -206,16 +209,16 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# scaling the size of the nodes by 5*degree\n", "# scaling the size of the nodes by 5*degree\n",
"scale = 5 \n", "scale = 5\n",
"\n", "\n",
"# getting all nodes and their number of connections\n", "# getting all nodes and their number of connections\n",
"d = dict(G.degree)\n", "d = dict(G.degree)\n",
"\n", "\n",
"# updating dict\n", "# updating dict\n",
"d.update((x, scale*(y+1)) for x, y in d.items())\n", "d.update((x, scale * (y + 1)) for x, y in d.items())\n",
"\n", "\n",
"# setting size attribute according to created dictionary\n", "# setting size attribute according to created dictionary\n",
"nx.set_node_attributes(G,d,'size')" "nx.set_node_attributes(G, d, \"size\")"
] ]
}, },
{ {
@ -236,10 +239,17 @@
"from pyvis.network import Network\n", "from pyvis.network import Network\n",
"\n", "\n",
"# create network, 'directed = true' allows multiple edges between nodes\n", "# create network, 'directed = true' allows multiple edges between nodes\n",
"nt = Network('1000px', '1000px', neighborhood_highlight=True, notebook=True, cdn_resources='in_line', directed=True)\n", "nt = Network(\n",
" \"1000px\",\n",
" \"1000px\",\n",
" neighborhood_highlight=True,\n",
" notebook=True,\n",
" cdn_resources=\"in_line\",\n",
" directed=True,\n",
")\n",
"\n", "\n",
"# populates the nodes and edges data structures\n", "# populates the nodes and edges data structures\n",
"nt.from_nx(G)\n" "nt.from_nx(G)"
] ]
}, },
{ {
@ -275,25 +285,28 @@
"outputs": [], "outputs": [],
"source": [ "source": [
"# define new function that sets the color of the nodes\n", "# define new function that sets the color of the nodes\n",
"def color_type (net, type):\n", "def color_type(net, type):\n",
" ''' color_type sets the color of a network depending on an attribute of the nodes\n", " \"\"\"color_type sets the color of a network depending on an attribute of the nodes\n",
" net: network\n", " net: network\n",
" type: 'branche' or 'land' '''\n", " type: 'branche' or 'land'\"\"\"\n",
"\n", "\n",
" colormap = {'Branche 1': '#87CEEB',\n", " colormap = {\n",
" 'Branche 2': '#0f4c81',\n", " \"Branche 1\": \"#87CEEB\",\n",
" 'Branche 3': '#B2FFFF', \n", " \"Branche 2\": \"#0f4c81\",\n",
" 'Branche 4': '#191970',\n", " \"Branche 3\": \"#B2FFFF\",\n",
" 'Land 1': '#F8D568', \n", " \"Branche 4\": \"#191970\",\n",
" 'Land 2': '#F58025', \n", " \"Land 1\": \"#F8D568\",\n",
" 'Land 3': '#CC5500', \n", " \"Land 2\": \"#F58025\",\n",
" 'Land 4': '#C0362C'}\n", " \"Land 3\": \"#CC5500\",\n",
" \"Land 4\": \"#C0362C\",\n",
" }\n",
" for node in net.nodes:\n", " for node in net.nodes:\n",
" node['color'] = colormap[node[type]]\n", " node[\"color\"] = colormap[node[type]]\n",
" return net\n", " return net\n",
"\n", "\n",
"\n",
"# set color based on attribute\n", "# set color based on attribute\n",
"nt = color_type(nt, 'branche')" "nt = color_type(nt, \"branche\")"
] ]
}, },
{ {
@ -310,8 +323,8 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"# set all edge colors \n", "# set all edge colors\n",
"nt.options.edges.color = 'grey'" "nt.options.edges.color = \"grey\""
] ]
}, },
{ {
@ -360,13 +373,20 @@
], ],
"source": [ "source": [
"# activate physics options to try out different solver\n", "# activate physics options to try out different solver\n",
"#nt.show_buttons(filter_=['physics'])\n", "# nt.show_buttons(filter_=['physics'])\n",
"\n", "\n",
"# set physics options\n", "# set physics options\n",
"nt.barnes_hut(gravity=-8000, central_gravity=0.3, spring_length=200, spring_strength=0.1, damping=0.09, overlap=0)\n", "nt.barnes_hut(\n",
" gravity=-8000,\n",
" central_gravity=0.3,\n",
" spring_length=200,\n",
" spring_strength=0.1,\n",
" damping=0.09,\n",
" overlap=0,\n",
")\n",
"\n", "\n",
"# create html and save in same folder\n", "# create html and save in same folder\n",
"nt.show('Netzwerk_Verflechtungsanalyse.html')" "nt.show(\"Netzwerk_Verflechtungsanalyse.html\")"
] ]
}, },
{ {
@ -416,16 +436,16 @@
"from pyvis.network import Network\n", "from pyvis.network import Network\n",
"\n", "\n",
"sn = nx.Graph()\n", "sn = nx.Graph()\n",
"sn_nodes = [1,2,3,4,5,6,7]\n", "sn_nodes = [1, 2, 3, 4, 5, 6, 7]\n",
"sn_edges = [(1,4),(2,4),(3,4),(4,5),(5,6),(5,7)]\n", "sn_edges = [(1, 4), (2, 4), (3, 4), (4, 5), (5, 6), (5, 7)]\n",
"\n", "\n",
"sn.add_nodes_from(sn_nodes, color = '#00509b')\n", "sn.add_nodes_from(sn_nodes, color=\"#00509b\")\n",
"sn.add_edges_from(sn_edges)\n", "sn.add_edges_from(sn_edges)\n",
"\n", "\n",
"net = Network('1000px', '1000px', notebook=True, cdn_resources='in_line')\n", "net = Network(\"1000px\", \"1000px\", notebook=True, cdn_resources=\"in_line\")\n",
"\n", "\n",
"net.from_nx(sn)\n", "net.from_nx(sn)\n",
"net.show('Netzwerk.html')\n" "net.show(\"Netzwerk.html\")"
] ]
} }
], ],

63
documentations/seminararbeiten/Datenvisualisierung/Erstes_Beispiel_Netzwerk/networkx_pyvis.ipynb
View File

@ -82,19 +82,25 @@
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"# create dataframe based on the sample data\n", "# create dataframe based on the sample data\n",
"df_nodes = pd.read_csv('nodes.csv', sep = ';')\n", "df_nodes = pd.read_csv(\"nodes.csv\", sep=\";\")\n",
"\n", "\n",
"# define shape based on the type\n", "# define shape based on the type\n",
"node_shape = {'Company': 'dot', 'Person': 'triangle'}\n", "node_shape = {\"Company\": \"dot\", \"Person\": \"triangle\"}\n",
"df_nodes['shape'] = df_nodes['type'].map(node_shape)\n", "df_nodes[\"shape\"] = df_nodes[\"type\"].map(node_shape)\n",
"\n", "\n",
"# define color based on branche\n", "# define color based on branche\n",
"node_color = {'Branche 1': ' #f3e8eeff', 'Branche 2': '#bacdb0ff', 'Branche 3': '#729b79ff', 'Branche 4': '#475b63ff', 'Branche 5': '#2e2c2fff'}\n", "node_color = {\n",
"df_nodes['color'] = df_nodes['branche'].map(node_color)\n", " \"Branche 1\": \" #f3e8eeff\",\n",
" \"Branche 2\": \"#bacdb0ff\",\n",
" \"Branche 3\": \"#729b79ff\",\n",
" \"Branche 4\": \"#475b63ff\",\n",
" \"Branche 5\": \"#2e2c2fff\",\n",
"}\n",
"df_nodes[\"color\"] = df_nodes[\"branche\"].map(node_color)\n",
"\n", "\n",
"# add information column that can be used for the mouse over in the graph\n", "# add information column that can be used for the mouse over in the graph\n",
"df_nodes = df_nodes.fillna('')\n", "df_nodes = df_nodes.fillna(\"\")\n",
"df_nodes['title'] = df_nodes['label'] + '\\n' + df_nodes['branche']\n", "df_nodes[\"title\"] = df_nodes[\"label\"] + \"\\n\" + df_nodes[\"branche\"]\n",
"\n", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_nodes.head())" "print(df_nodes.head())"
@ -127,7 +133,7 @@
], ],
"source": [ "source": [
"# create dataframe based on the sample data\n", "# create dataframe based on the sample data\n",
"df_edges = pd.read_csv('edges.csv', sep = ';')\n", "df_edges = pd.read_csv(\"edges.csv\", sep=\";\")\n",
"\n", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_edges.head())" "print(df_edges.head())"
@ -157,10 +163,10 @@
"graph = nx.MultiGraph()\n", "graph = nx.MultiGraph()\n",
"\n", "\n",
"# create edges from dataframe\n", "# create edges from dataframe\n",
"graph = nx.from_pandas_edgelist(df_edges, source = 'from', target = 'to', edge_attr= 'label')\n", "graph = nx.from_pandas_edgelist(df_edges, source=\"from\", target=\"to\", edge_attr=\"label\")\n",
"\n", "\n",
"# update node attributes from dataframe\n", "# update node attributes from dataframe\n",
"nodes_attr = df_nodes.set_index('id').to_dict(orient = 'index')\n", "nodes_attr = df_nodes.set_index(\"id\").to_dict(orient=\"index\")\n",
"nx.set_node_attributes(graph, nodes_attr)" "nx.set_node_attributes(graph, nodes_attr)"
] ]
}, },
@ -193,11 +199,11 @@
" # create empty dictionary\n", " # create empty dictionary\n",
" dict = {}\n", " dict = {}\n",
" # get node id\n", " # get node id\n",
" dict['id'] = node\n", " dict[\"id\"] = node\n",
" # get k-neighbours for k=1,2,3, subtract -1 since output of single_source_shortest_path_length contains node itself\n", " # get k-neighbours for k=1,2,3, subtract -1 since output of single_source_shortest_path_length contains node itself\n",
" dict['k=1'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=1))-1\n", " dict[\"k=1\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=1)) - 1\n",
" dict['k=2'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=2))-1\n", " dict[\"k=2\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=2)) - 1\n",
" dict['k=3'] = len(nx.single_source_shortest_path_length(graph, node, cutoff=3))-1\n", " dict[\"k=3\"] = len(nx.single_source_shortest_path_length(graph, node, cutoff=3)) - 1\n",
" # append list for each node\n", " # append list for each node\n",
" k_neighbours.append(dict)\n", " k_neighbours.append(dict)\n",
"\n", "\n",
@ -225,40 +231,45 @@
"from pyvis.network import Network\n", "from pyvis.network import Network\n",
"\n", "\n",
"# initiate network\n", "# initiate network\n",
"net = Network(directed=False, neighborhood_highlight=True, bgcolor = \"white\", font_color=\"black\")\n", "net = Network(\n",
" directed=False, neighborhood_highlight=True, bgcolor=\"white\", font_color=\"black\"\n",
")\n",
"\n", "\n",
"# pass networkx graph to pyvis\n", "# pass networkx graph to pyvis\n",
"net.from_nx(graph)\n", "net.from_nx(graph)\n",
"\n", "\n",
"# set edge options \n", "# set edge options\n",
"net.inherit_edge_colors(False)\n", "net.inherit_edge_colors(False)\n",
"net.set_edge_smooth('dynamic')\n", "net.set_edge_smooth(\"dynamic\")\n",
"\n", "\n",
"# chose size format\n", "# chose size format\n",
"size_type = 'edges' # select 'edges' or 'eigen'\n", "size_type = \"edges\" # select 'edges' or 'eigen'\n",
"\n", "\n",
"adj_list = net.get_adj_list()\n", "adj_list = net.get_adj_list()\n",
"\n", "\n",
"if size_type == 'eigen':\n", "if size_type == \"eigen\":\n",
" eigenvector = nx.eigenvector_centrality(graph)\n", " eigenvector = nx.eigenvector_centrality(graph)\n",
"\n", "\n",
"# calculate and update size of the nodes depending on their number of edges\n", "# calculate and update size of the nodes depending on their number of edges\n",
"for node_id, neighbors in adj_list.items():\n", "for node_id, neighbors in adj_list.items():\n",
" if size_type == 'edges':\n", " if size_type == \"edges\":\n",
" size = len(neighbors)*5\n", " size = len(neighbors) * 5\n",
" if size_type == 'eigen':\n", " if size_type == \"eigen\":\n",
" size = eigenvector[node_id]*200\n", " size = eigenvector[node_id] * 200\n",
" next((node.update({'size': size}) for node in net.nodes if node['id'] == node_id), None)\n", " next(\n",
" (node.update({\"size\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
" None,\n",
" )\n",
"\n", "\n",
"# set the node distance and spring lenght using repulsion\n", "# set the node distance and spring lenght using repulsion\n",
"net.repulsion(node_distance=250, spring_length=150)\n", "net.repulsion(node_distance=250, spring_length=150)\n",
"\n", "\n",
"# activate physics buttons to further explore the available solvers:\n", "# activate physics buttons to further explore the available solvers:\n",
"# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n", "# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n",
"net.show_buttons(filter_=['physics'])\n", "net.show_buttons(filter_=[\"physics\"])\n",
"\n", "\n",
"# save graph as HTML\n", "# save graph as HTML\n",
"net.save_graph('networkx_pyvis.html')\n" "net.save_graph(\"networkx_pyvis.html\")"
] ]
}, },
{ {

91
documentations/seminararbeiten/Verflechtungsanalyse/mockup_verflechtungsanalyse_with_networkx.ipynb
View File

@ -169,19 +169,25 @@
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"# create dataframe based on the sample data\n", "# create dataframe based on the sample data\n",
"df_nodes = pd.read_csv('companies.csv', sep = ';')\n", "df_nodes = pd.read_csv(\"companies.csv\", sep=\";\")\n",
"\n", "\n",
"# define shape based on the type\n", "# define shape based on the type\n",
"node_shape = {'Company': 'dot', 'Person': 'triangle'}\n", "node_shape = {\"Company\": \"dot\", \"Person\": \"triangle\"}\n",
"df_nodes['shape'] = df_nodes['type'].map(node_shape)\n", "df_nodes[\"shape\"] = df_nodes[\"type\"].map(node_shape)\n",
"\n", "\n",
"# define color based on branche\n", "# define color based on branche\n",
"node_color = {'Automobilhersteller': ' #729b79ff', 'Automobilzulieferer': '#475b63ff', 'Branche 3': '#f3e8eeff', 'Branche 4': '#bacdb0ff', 'Branche 5': '#2e2c2fff'}\n", "node_color = {\n",
"df_nodes['color'] = df_nodes['branche'].map(node_color)\n", " \"Automobilhersteller\": \" #729b79ff\",\n",
" \"Automobilzulieferer\": \"#475b63ff\",\n",
" \"Branche 3\": \"#f3e8eeff\",\n",
" \"Branche 4\": \"#bacdb0ff\",\n",
" \"Branche 5\": \"#2e2c2fff\",\n",
"}\n",
"df_nodes[\"color\"] = df_nodes[\"branche\"].map(node_color)\n",
"\n", "\n",
"# add information column that can be used for the mouse over in the graph\n", "# add information column that can be used for the mouse over in the graph\n",
"df_nodes = df_nodes.fillna('')\n", "df_nodes = df_nodes.fillna(\"\")\n",
"df_nodes['title'] = df_nodes['label'] + '\\n' + df_nodes['branche']\n", "df_nodes[\"title\"] = df_nodes[\"label\"] + \"\\n\" + df_nodes[\"branche\"]\n",
"\n", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_nodes.head())" "print(df_nodes.head())"
@ -215,7 +221,7 @@
], ],
"source": [ "source": [
"# create dataframe based on the sample data\n", "# create dataframe based on the sample data\n",
"df_edges = pd.read_csv('relations.csv', sep = ';')\n", "df_edges = pd.read_csv(\"relations.csv\", sep=\";\")\n",
"\n", "\n",
"# show first five entries of the dataframe\n", "# show first five entries of the dataframe\n",
"print(df_edges.head())" "print(df_edges.head())"
@ -263,23 +269,25 @@
"import networkx as nx\n", "import networkx as nx\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"\n", "\n",
"fig = plt.figure(figsize=(12,12))\n", "fig = plt.figure(figsize=(12, 12))\n",
"ax = plt.subplot(111)\n", "ax = plt.subplot(111)\n",
"ax.set_title('Graph - Shapes', fontsize=10)\n", "ax.set_title(\"Graph - Shapes\", fontsize=10)\n",
"\n", "\n",
"# initiate graph\n", "# initiate graph\n",
"graph = nx.MultiGraph()\n", "graph = nx.MultiGraph()\n",
"\n", "\n",
"# create edges from dataframe\n", "# create edges from dataframe\n",
"graph = nx.from_pandas_edgelist(df_edges, source = 'from', target = 'to', edge_attr= 'label')\n", "graph = nx.from_pandas_edgelist(df_edges, source=\"from\", target=\"to\", edge_attr=\"label\")\n",
"\n", "\n",
"# update node attributes from dataframe\n", "# update node attributes from dataframe\n",
"nodes_attr = df_nodes.set_index('id').to_dict(orient = 'index')\n", "nodes_attr = df_nodes.set_index(\"id\").to_dict(orient=\"index\")\n",
"nx.set_node_attributes(graph, nodes_attr)\n", "nx.set_node_attributes(graph, nodes_attr)\n",
"\n", "\n",
"\n", "\n",
"pos = nx.spring_layout(graph)\n", "pos = nx.spring_layout(graph)\n",
"nx.draw(graph, pos, node_size=1500, node_color='yellow', font_size=8, font_weight='bold')\n", "nx.draw(\n",
" graph, pos, node_size=1500, node_color=\"yellow\", font_size=8, font_weight=\"bold\"\n",
")\n",
"\n", "\n",
"plt.tight_layout()\n", "plt.tight_layout()\n",
"plt.show()\n", "plt.show()\n",
@ -307,16 +315,19 @@
"# visualize using pyvis\n", "# visualize using pyvis\n",
"from pyvis.network import Network\n", "from pyvis.network import Network\n",
"\n", "\n",
"\n",
"def create_centrality_graph(df, measure_type, save_path):\n", "def create_centrality_graph(df, measure_type, save_path):\n",
" # initiate network\n", " # initiate network\n",
" net = Network(directed=False, neighborhood_highlight=True, bgcolor = \"white\", font_color=\"black\")\n", " net = Network(\n",
" directed=False, neighborhood_highlight=True, bgcolor=\"white\", font_color=\"black\"\n",
" )\n",
"\n", "\n",
" # pass networkx graph to pyvis\n", " # pass networkx graph to pyvis\n",
" net.from_nx(graph)\n", " net.from_nx(graph)\n",
"\n", "\n",
" # set edge options \n", " # set edge options\n",
" net.inherit_edge_colors(False)\n", " net.inherit_edge_colors(False)\n",
" net.set_edge_smooth('dynamic')\n", " net.set_edge_smooth(\"dynamic\")\n",
"\n", "\n",
" adj_list = net.get_adj_list()\n", " adj_list = net.get_adj_list()\n",
"\n", "\n",
@ -341,28 +352,33 @@
" measure_vector = nx.average_degree_connectivity(graph)\n", " measure_vector = nx.average_degree_connectivity(graph)\n",
" # df[\"average_degree\"] = measure_vector.values()\n", " # df[\"average_degree\"] = measure_vector.values()\n",
" print(measure_vector.values())\n", " print(measure_vector.values())\n",
" \n",
"\n", "\n",
" # calculate and update size of the nodes depending on their number of edges\n", " # calculate and update size of the nodes depending on their number of edges\n",
" for node_id, neighbors in adj_list.items():\n", " for node_id, neighbors in adj_list.items():\n",
" \n", " # df[\"edges\"] = measure_vector.values()\n",
" # df[\"edges\"] = measure_vector.values()\n", "\n",
" \n",
" if measure_type == \"edges\":\n", " if measure_type == \"edges\":\n",
" size = 10 #len(neighbors)*5 \n", " size = 10 # len(neighbors)*5\n",
" else:\n", " else:\n",
" size = measure_vector[node_id]*50 \n", " size = measure_vector[node_id] * 50\n",
" next((node.update({'size': size}) for node in net.nodes if node['id'] == node_id), None)\n", " next(\n",
" (\n",
" node.update({\"size\": size})\n",
" for node in net.nodes\n",
" if node[\"id\"] == node_id\n",
" ),\n",
" None,\n",
" )\n",
"\n", "\n",
" # set the node distance and spring lenght using repulsion\n", " # set the node distance and spring lenght using repulsion\n",
" net.repulsion(node_distance=150, spring_length=50)\n", " net.repulsion(node_distance=150, spring_length=50)\n",
"\n", "\n",
" # activate physics buttons to further explore the available solvers:\n", " # activate physics buttons to further explore the available solvers:\n",
" # barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n", " # barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n",
" net.show_buttons(filter_=['physics'])\n", " net.show_buttons(filter_=[\"physics\"])\n",
"\n", "\n",
" # save graph as HTML\n", " # save graph as HTML\n",
" net.save_graph(save_path)\n" " net.save_graph(save_path)"
] ]
}, },
{ {
@ -593,33 +609,36 @@
} }
], ],
"source": [ "source": [
"net = Network(directed=False, neighborhood_highlight=True, bgcolor = \"white\", font_color=\"black\")\n", "net = Network(\n",
" directed=False, neighborhood_highlight=True, bgcolor=\"white\", font_color=\"black\"\n",
")\n",
"\n", "\n",
"# pass networkx graph to pyvis\n", "# pass networkx graph to pyvis\n",
"net.from_nx(s)\n", "net.from_nx(s)\n",
"\n", "\n",
"# set edge options \n", "# set edge options\n",
"net.inherit_edge_colors(False)\n", "net.inherit_edge_colors(False)\n",
"net.set_edge_smooth('dynamic')\n", "net.set_edge_smooth(\"dynamic\")\n",
"\n", "\n",
"adj_list = net.get_adj_list()\n", "adj_list = net.get_adj_list()\n",
"\n", "\n",
"# calculate and update size of the nodes depending on their number of edges\n", "# calculate and update size of the nodes depending on their number of edges\n",
"for node_id, neighbors in adj_list.items():\n", "for node_id, neighbors in adj_list.items():\n",
" \n", " # df[\"edges\"] = measure_vector.values()\n",
" # df[\"edges\"] = measure_vector.values()\n", "\n",
" \n", " size = 10 # len(neighbors)*5\n",
" \n", "\n",
" size = 10 #len(neighbors)*5 \n", " next(\n",
" \n", " (node.update({\"size\": size}) for node in net.nodes if node[\"id\"] == node_id),\n",
" next((node.update({'size': size}) for node in net.nodes if node['id'] == node_id), None)\n", " None,\n",
" )\n",
"\n", "\n",
"# set the node distance and spring lenght using repulsion\n", "# set the node distance and spring lenght using repulsion\n",
"net.repulsion(node_distance=150, spring_length=50)\n", "net.repulsion(node_distance=150, spring_length=50)\n",
"\n", "\n",
"# activate physics buttons to further explore the available solvers:\n", "# activate physics buttons to further explore the available solvers:\n",
"# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n", "# barnesHut, forceAtlas2Based, repulsion, hierarchicalRepulsion\n",
"net.show_buttons(filter_=['physics'])\n", "net.show_buttons(filter_=[\"physics\"])\n",
"\n", "\n",
"# save graph as HTML\n", "# save graph as HTML\n",
"net.save_graph(\"./metrics/connected_components_networkx.html\")" "net.save_graph(\"./metrics/connected_components_networkx.html\")"