Emhlabeni wokuhlaziya idatha, ukuphatha amasethi edatha amakhulu kungaba umsebenzi onzima. Enye yezingxenye ezibalulekile zale nqubo ukuhlunga idatha ukuze uthole ulwazi olufanele. Uma kukhulunywa ngePython, umtapo wolwazi onamandla pandas uyeza ukuzosisiza. Kulesi sihloko, sizoxoxa uwahlunga kanjani wonke amakholomu ku-pandas DataFrame. Sizodlula encazelweni yesinyathelo nesinyathelo sekhodi futhi sinikeze ukuqonda okujulile kwemitapo yolwazi nemisebenzi engasetshenziswa ezinkingeni ezifanayo.
Sethula ama-panda
iyilabhulali yomthombo ovulekile ehlinzeka ngezakhiwo zedatha okulula ukuzisebenzisa namathuluzi okuhlaziya idatha olimi lokuhlela lwePython. Idlala indima ebalulekile ku-ecosystem yesayensi yedatha futhi isiyithuluzi okufanele libe nalo kunoma yimuphi usosayensi wedatha noma umhlaziyi osebenza nePython. Phakathi kwezici zayo, ama-panda anikela ngezakhiwo zedatha ezimbili eziyinhloko: IdathaFrame futhi Series. I-DataFrame iyithebula elinezinhlangothi ezimbili elinezimbazo ezilebulwe (imigqa namakholomu), kanti uchungechunge luwuhlelo olunelebula olunohlangothi olulodwa.
Kulesi sihloko, sizogxila ekuhlungeni amanani athile akhona kunoma iyiphi ikholomu ye-pandas DataFrame. Ukuze wenze lokhu, sizosebenzisa ama-pandas .isin() ukusebenza kanye ne-boolean masking.
Ukuhlunga i-DataFrame
Ukuhlunga i-DataFrame kuma-panda, landela lezi zinyathelo:
1. Ngenisa umtapo wezincwadi we-pandas
2. Dala i-DataFrame noma uyilayishe efayelini
3. Chaza amanani ofuna ukuwahlunga
4. Faka isihlungi usebenzisa umsebenzi `.isin()` kanye nokufihla okuphusile
5. Bonisa i-DataFrame ehlungiwe
Ake singene kukhodi ukuze siqonde ukuthi isebenza kanjani.
import pandas as pd
# Creating a DataFrame
data = {'Column1': [1, 2, 3, 4, 5],
'Column2': [10, 20, 30, 40, 50],
'Column3': ['A', 'B', 'A', 'B', 'A']}
df = pd.DataFrame(data)
# Define the values to filter
filter_values = [1, 3, 5, 'A']
# Apply the filter using .isin() and boolean masking
filtered_df = df[df.isin(filter_values).any(axis=1)]
# Display the filtered DataFrame
print(filtered_df)
Kulesi sibonelo, siqala ukungenisa umtapo wezincwadi we-pandas futhi sakhe i-DataFrame enamakholomu amathathu. Sichaza amanani esifuna ukuwahlunga (1, 3, 5, kanye no-'A') futhi sisebenzise isihlungi sisebenzisa umsebenzi othi `.isin()` ohlanganiswe ne-boolean masking. Umsebenzi `noma iyiphi(i-axis=1)` ihlola ukuthi noma yiliphi inani phakathi komugqa liyahlangabezana nemibandela yokuhlunga. Ekugcineni, siphrinta i-DataFrame ehlungiwe.
Umsebenzi we-.isin() kanye ne-boolean masking
The .isin() function in pandas iyithuluzi eliguquguqukayo lokuhlunga idatha ngokusekelwe kuhlu noma isethi yamanani. Ibuyisela i-boolean DataFrame yomumo ofanayo nowokuqala, okubonisa ukuthi yiziphi izici ezikhona ohlwini olunikeziwe noma isethi. Esimweni sethu, sidlula uhlu lwamanani esifuna ukuwahlunga.
I-boolean masking iyindlela esetshenziswa kuma-panda ukuze kuhlungwe idatha ngendlela ehlakaniphile. Kuqukethe ukusebenzisa imaski ephusile (uhlu lwamavelu Iqiniso Namanga) esakhiweni sedatha ukuze kuhlungwe izici zayo. Kumongo wenkinga yethu, sisebenzisa i-boolean masking kanye nomsebenzi othi .isin() ukubuyisa imigqa equkethe amanani afunekayo.
Ngokuqonda okucacile komtapo we-pandas, izakhiwo ze-DataFrame, nomsebenzi we-.isin(), singakwazi ukuhlunga ngempumelelo noma iyiphi i-pandas DataFrame. Lawa maqhinga asivumela ukuthi sihlole amasethi edatha amakhulu futhi sikhiphe imininingwane ebalulekile kalula, okwenza ama-panda abe umtapo wolwazi wokuhlaziya idatha kuPython.
