Executing black over all jupyter notebook (#190)

Reverting black for the jupyter notebooks gets old. Can we just run
black over all of them?

Jupyter/AI-models/Sentiment Analysis/vader.ipynb

@@ -82,7 +82,7 @@
     "# Download the lexicon\n",
     "nltk.download(\"vader_lexicon\")\n",
     "\n",
-    "# Import the lexicon \n",
+    "# Import the lexicon\n",
     "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
     "\n",
     "# Create an instance of SentimentIntensityAnalyzer\n",
@@ -132,12 +132,22 @@
     "text_df = pd.DataFrame(\n",
     "    [\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\": \"Meyer Burger now serves Australian market and presents itself at Smart Energy Expo in Sydney.\"},\n",
-    "        {\"text\": \"Meyer Burger enters Australian market and exhibits at Smart Energy Expo in Sydney.\"},\n",
-    "        {\"text\": \"J&T Express Vietnam helps local craft villages increase their reach.\"},\n",
-    "        {\"text\": \"7 experts recommend the stock for purchase, 1 expert recommends holding the stock.\"},\n",
+    "        {\n",
+    "            \"text\": \"Meyer Burger now serves Australian market and presents itself at Smart Energy Expo in Sydney.\"\n",
+    "        },\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 is rising.\"},\n",
     "    ]\n",
@@ -262,22 +272,21 @@
    ],
    "source": [
     "def format_output(output_dict):\n",
-    "  \n",
-    "  polarity = \"neutral\"\n",
+    "    polarity = \"neutral\"\n",
     "\n",
-    "  if(output_dict['compound']>= 0.05):\n",
-    "    polarity = \"positive\"\n",
+    "    if output_dict[\"compound\"] >= 0.05:\n",
+    "        polarity = \"positive\"\n",
     "\n",
-    "  elif(output_dict['compound']<= -0.05):\n",
-    "    polarity = \"negative\"\n",
+    "    elif output_dict[\"compound\"] <= -0.05:\n",
+    "        polarity = \"negative\"\n",
     "\n",
-    "  return polarity\n",
+    "    return polarity\n",
     "\n",
     "\n",
     "def predict_sentiment(text):\n",
-    "  \n",
-    "  output_dict = sent_analyzer.polarity_scores(text)\n",
-    "  return output_dict\n",
+    "    output_dict = sent_analyzer.polarity_scores(text)\n",
+    "    return output_dict\n",
+    "\n",
     "\n",
     "# Run the predictions\n",
     "text_df[\"vader_prediction\"] = text_df[\"text\"].apply(predict_sentiment)\n",

Jupyter/API-tests/API news_research_Tim.ipynb

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

Jupyter/API-tests/API research_Tristan.ipynb

@@ -51,6 +51,7 @@
    ],
    "source": [
     "from deutschland.bundesanzeiger import Bundesanzeiger\n",
+    "\n",
     "ba = Bundesanzeiger()\n",
     "# search term\n",
     "data = ba.get_reports(\"Atos IT-Dienstleistung & Beratung GmbH\")\n",
@@ -73,11 +74,13 @@
    ],
    "source": [
     "# 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",
     "# 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",
-    "    file.write(jahresabschluss['report'])\n",
+    "    file.write(jahresabschluss[\"report\"])\n",
     "print(jahresabschluss.keys())"
    ]
   },
@@ -96,6 +99,7 @@
    ],
    "source": [
     "from deutschland.handelsregister import Handelsregister\n",
+    "\n",
     "hr = Handelsregister()\n",
     "\n",
     "results = hr.search(keywords=\"BLUECHILLED Verwaltungs GmbH\")\n",
@@ -128,6 +132,7 @@
    "source": [
     "# SQLite export\n",
     "import sqlite3\n",
+    "\n",
     "con = sqlite3.connect(\"../data/openregister.db\")"
    ]
   },
@@ -176,7 +181,7 @@
    ],
    "source": [
     "schema = cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\n",
-    "schema.fetchall()\n"
+    "schema.fetchall()"
    ]
   },
   {
@@ -414,6 +419,7 @@
    ],
    "source": [
     "import pandas as pd\n",
+    "\n",
     "df = pd.read_sql_query(\"SELECT * FROM company LIMIT 100\", con)\n",
     "df.head()"
    ]

Jupyter/Timeseries/01_Transparenzregister_Zeitdaten.ipynb

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