Create exercise4.py

exercises/exercise4.py

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+import pandas as pd
+import numpy as np
+import urllib.request
+import zipfile as ZipFile
+
+
+
+df = urllib.request.urlretrieve("https://www.mowesta.com/data/measure/mowesta-dataset-20221107.zip", 'zipfile')
+
+#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'])]
+
+#>= or > is enough
+df = df[(df['Laenge']>= -90 )& (df['Laenge']<= 90) & (df['Breite']>= -90 )& (df['Breite']<= 90) ]
+
+#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)]
+
+#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('trainstops', 'sqlite:///trainstops.sqlite', if_exists= 'replace', index=False)
+'''