Datenbereinigung in Python
Adel Nehme
VP of AI Curriculum, DataCamp
Alle Spalten haben die gleichen Werte.
| Vorname | Nachname | Adresse | Größe | Gewicht |
|---|---|---|---|---|
| Justin | Saddlemyer | Boulevard du Jardin Botanique 3, Brüssel | 193 cm | 87 kg |
| Justin | Saddlemyer | Boulevard du Jardin Botanique 3, Brüssel | 193 cm | 87 kg |
Die meisten Spalten haben die gleichen Werte.
| Vorname | Nachname | Adresse | Größe | Gewicht |
|---|---|---|---|---|
| Justin | Saddlemyer | Boulevard du Jardin Botanique 3, Brüssel | 193 cm | 87 kg |
| Justin | Saddlemyer | Boulevard du Jardin Botanique 3, Brüssel | 194 cm | 87 kg |



# Print the header
height_weight.head()
first_name last_name address height weight
0 Lane Reese 534-1559 Nam St. 181 64
1 Ivor Pierce 102-3364 Non Road 168 66
2 Roary Gibson P.O. Box 344, 7785 Nisi Ave 191 99
3 Shannon Little 691-2550 Consectetuer Street 185 65
4 Abdul Fry 4565 Risus St. 169 65
# Get duplicates across all columns
duplicates = height_weight.duplicated()
print(duplicates)
1 False
... ....
22 True
23 False
... ...
# Get duplicate rows
duplicates = height_weight.duplicated()
height_weight[duplicates]
first_name last_name address height weight
100 Mary Colon 4674 Ut Rd. 179 75
101 Ivor Pierce 102-3364 Non Road 168 88
102 Cole Palmer 8366 At, Street 178 91
103 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 196 83
Die Methode .duplicated()
subset: Liste der Spaltennamen, die auf Duplikate überprüft werden sollen.
keep: Soll man die ersten ('first'), die letzten ('last') oder alle (False) doppelten Werte behalten?
# Column names to check for duplication
column_names = ['first_name','last_name','address']
duplicates = height_weight.duplicated(subset = column_names, keep = False)
# Output duplicate values
height_weight[duplicates]
first_name last_name address height weight
1 Ivor Pierce 102-3364 Non Road 168 66
22 Cole Palmer 8366 At, Street 178 91
28 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 195 83
37 Mary Colon 4674 Ut Rd. 179 75
100 Mary Colon 4674 Ut Rd. 179 75
101 Ivor Pierce 102-3364 Non Road 168 88
102 Cole Palmer 8366 At, Street 178 91
103 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 196 83
# Output duplicate values
height_weight[duplicates].sort_values(by = 'first_name')
first_name last_name address height weight
22 Cole Palmer 8366 At, Street 178 91
102 Cole Palmer 8366 At, Street 178 91
28 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 195 83
103 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 196 83
1 Ivor Pierce 102-3364 Non Road 168 66
101 Ivor Pierce 102-3364 Non Road 168 88
37 Mary Colon 4674 Ut Rd. 179 75
100 Mary Colon 4674 Ut Rd. 179 75
# Output duplicate values
height_weight[duplicates].sort_values(by = 'first_name')

# Output duplicate values
height_weight[duplicates].sort_values(by = 'first_name')

# Output duplicate values
height_weight[duplicates].sort_values(by = 'first_name')

Die Methode .drop_duplicates()
subset: Liste der Spaltennamen, die auf Duplikate überprüft werden sollen.
keep: Soll man die ersten ('first'), die letzten ('last') oder alle (False) doppelten Werte behalten?
inplace: Duplizierte Zeilen direkt im DataFrame löschen, ohne ein neues Objekt zu erstellen (True).
# Drop duplicates
height_weight.drop_duplicates(inplace = True)
# Output duplicate values
column_names = ['first_name','last_name','address']
duplicates = height_weight.duplicated(subset = column_names, keep = False)
height_weight[duplicates].sort_values(by = 'first_name')
first_name last_name address height weight
28 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 195 83
103 Desirae Shannon P.O. Box 643, 5251 Consectetuer, Rd. 196 83
1 Ivor Pierce 102-3364 Non Road 168 66
101 Ivor Pierce 102-3364 Non Road 168 88
# Output duplicate values
column_names = ['first_name','last_name','address']
duplicates = height_weight.duplicated(subset = column_names, keep = False)
height_weight[duplicates].sort_values(by = 'first_name')

Die Methoden .groupby() und .agg()
# Group by column names and produce statistical summaries column_names = ['first_name','last_name','address'] summaries = {'height': 'max', 'weight': 'mean'} height_weight = height_weight.groupby(by = column_names).agg(summaries).reset_index()# Make sure aggregation is done duplicates = height_weight.duplicated(subset = column_names, keep = False) height_weight[duplicates].sort_values(by = 'first_name')
first_name last_name address height weight
Datenbereinigung in Python