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Python Biblotheken - Coggle Diagram

- - - - conn =psycopg2.connect(host=" ", dbname=" ", user = " ", passwort =" ")
    - - cur = conn.cursor()
    - - cur.execute()
    - - rows = cur.fetchall()
  - - - text()
      - execute()
        
        conn.execute(text("Select ..."))
      - Session()
      - MetaData()
      - create_engine()
        
        engine=create_engine("postgresql+osycopg2://user:pass@localhost:5432/mydb")
        
        engine.connect()
        
        with engine.connect() as conn
        
        connect()
      - Table()
  - - - delete()
      - json()
      - put()
      - status_code
      - post()
      - headers
      - get()
        
        "https://api.example.com/data", params={"limit":100}, headers= {"Authorization": "Bearer TOKEN"}
      - Session()
    - - get()
      - post()
      - AsyncClient()
        
        async with httpx.AsyncClient() as client : r = await client.get(url) return r.json
      - Json()
        
        return r.json()
      - asyncio.run(fetch())
- - - - filter(pl.col("revenue") > 1000)
  - - - df.merge(kunden, bestellungen, on="kundenID")
    - - df.groupby("region"),["umsatz"]
    - - pd.read_sql(sql, connection)
    - - pd.read_csv("cvs")
      - df(Data Frame)
        
        df.head()
        
        df.tail()
        
        df.info()
        
        df.discribe()
        
        df.columns
        
        df[["name","Alter"}]
        
        df[df["Alter"] > 18]
        
        sum()
        
        .mean()
        
        .count()
        
        .mean
        
        .max()
        
        reset_index()
        
        sort_values("Umsatz", ascending=False)
        
        df["Gewinn"] = df["Umsatz"] - df["Kosten"]
        
        dropna()
        
        rename(columns={"umsatz":"revenue"}
        
        drop_duplicates()
        
        drop_duplicates
        
        conncat([df1, df2])
    - - df["Preis"].apply(lambda x: x * 1.19)