Update exercise4.py

exercises/exercise4.py

@@ -1,56 +1,31 @@
 import pandas as pd
-import numpy as np
 import urllib.request
 import zipfile as ZipFile
+from sqlalchemy import create_engine
 
 
+#Download zip file
+urllib.request.urlretrieve("https://www.mowesta.com/data/measure/mowesta-dataset-20221107.zip", "./exercises/exercise4.zip")
 
-df = urllib.request.urlretrieve("https://www.mowesta.com/data/measure/mowesta-dataset-20221107.zip", 'zipfile')
+zip = zip.ZipFile("./exercises/exercise4.zip")
+zip.extractall('./exercise')
 
-#zipresp = urlopen(zipurl)
-    # Create a new file on the hard drive
-tempzip = open("/tmp/tempfile.zip", "wb")
-    # Write the contents of the downloaded file into the new file
-tempzip.write(df.read())
-    # Close the newly-created file
-tempzip.close()
-    # Re-open the newly-created file with ZipFile()
-zf = ZipFile("/tmp/tempfile.zip")
-    # Extract its contents into <extraction_path>
-    # note that extractall will automatically create the path
-zf.extractall(path = '///.exercises/')
-    # close the ZipFile instance
-zf.close()
-'''#Download csv File
-df = pd.read_csv("data.csv",sep=';', decimal=',')
 
-#Drop Status column
-df = df.drop(['Status'], axis=1)
 
-#Then, drop all rows with invalid values in Verkehr:
-#df = df[df['Verkehr'].isin(['FV','RV','nur DPN'])]
-#Only use the columns
-df = df['Geraet', 'Hersteller','Model','Monat','Temperatur in °C (DWD)','Batterietemperatur in °C (DWD)','Geraet aktiv']
+df = pd.read_csv("./exercises/data.csv",sep=';', decimal=',', index_col=False, 
+                 usecols=["Geraet", "Hersteller", "Model", "Monat", "Temperatur in °C (DWD)", "Batterietemperatur in °C", "Geraet aktiv"])
 
 
+df = df.rename(columns={"Temperatur in °C (DWD)": "Temperatur", "Batterietemperatur in °C": "Batterietemperatur"})
 
+df ['Temperatur']= df['Temperatur'] * 9/5 +32 
+df['Batterietemperatur']=df['Betterietemperatur'] * 9/5 +32
 
-#Transform data
-df = df[(df['Temperatur'] * 9/5 +32 )& ((df['Betterietemperatur'] * 9/5 +32 ) ]
 
-#Valid "IFOPT" values follow this pattern:
-#<exactly two characters>:<any amount of numbers>:<any amount of numbers><optionally another colon followed by any amount of numbers>
-df = df[df['IFOPT'].str.contains(r'^[A-Za-z]{2}:\d*:\d*(?::\d*)?$',na=False)]
+df = df[(df["Geraet"] > 0) & 
+        (df["Monat"] > 0) ]
+     
 
-#Change empty cells to nan
-df.replace('',np.nan, inplace=True)
-
-#Drop nan cells
-df.dropna(inplace=True)
-
-#Convert column 'Betreiber_Nr' to integer
-df['Betreiber_Nr'] = df['Betreiber_Nr'].astype(int)
-
-#Write to sqlite
-df.to_sql('temperatures', 'sqlite:///temperatures.sqlite', if_exists= 'replace', index=False)
-'''
+# write to sqlite database
+engine = create_engine('sqlite:///./temperatures.sqlite', echo=False)
+df.to_sql("temperatures", con=engine, if_exists='replace', index=False)