Kwihlabathi lohlalutyo lwedatha, ukuphatha iiseti zedatha enkulu kunokuba ngumsebenzi onzima. Enye yeendawo ezibalulekileyo zale nkqubo kukucoca idatha ukufumana ulwazi olufanelekileyo. Xa kuziwa kwiPython, ithala leencwadi elinamandla pandas uya kusinceda. Kweli nqaku, siza kuxubusha indlela yokucoca yonke imiqolo kwipandas DataFrame. Siza kuhamba ngesinyathelo-nge-nyathelo inkcazo yekhowudi kwaye sinikeze ukuqonda okunzulu kwamathala eencwadi kunye nemisebenzi engasetyenziselwa iingxaki ezifanayo.
Ukwazisa iipanda
yilayibrari yomthombo ovulekileyo obonelela ngokulula ukusebenzisa izakhiwo zedatha kunye nezixhobo zokuhlalutya idatha kulwimi lweprogram yePython. Idlala indima ebalulekileyo kwi-ecosystem yesayensi yedatha kwaye ibe sisixhobo esimele sibe nayo nayiphi na inzululwazi yedatha okanye umhlalutyi osebenza kunye nePython. Phakathi kweempawu zayo, iipandas zibonelela ngezinto ezimbini eziphambili zedatha: DataFrame kwaye uthotho. I-DataFrame yitheyibhile ene-dimensional ebhalwe ii-axes (iirowu kunye neentsika), ngelixa uthotho lululuhlu olunombhalo onedimensional enye.
Kweli nqaku, siza kugxila ekuhluzeni amaxabiso athile akhoyo nakweyiphi na ikholamu yePandas DataFrame. Ukwenza oku, siya kusebenzisa i-pandas .iphakathi() umsebenzi kunye ne boolean masking.
Ukuhluza isakhelo seDatha
Ukuhluza isakhelo seDatha kwiipanda, landela la manyathelo:
1. Thatha ngaphandle ithala leencwadi le-pandas
2. Yenza i-DataFrame okanye uyilayishe kwifayile
3. Chaza amaxabiso ofuna ukuwahluza
4. Faka isihluzo usebenzisa `.isin()` umsebenzi kunye ne boolean masking
5. Bonisa i-DataFrame ehluziweyo
Masingene kwikhowudi ukuqonda ukuba isebenza njani.
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)
Kulo mzekelo, siqala ukungenisa ilayibrari ye-pandas kwaye senze i-DataFrame kunye neekholamu ezintathu. Sichaza amaxabiso esifuna ukuwahluza (1, 3, 5, kunye 'A') kwaye sisebenzise isihluzo sisebenzisa `.isin()` umsebenzi odityaniswe ne boolean masking. Umsebenzi `Nayiphi na(i-axis=1)` ijonga ukuba naliphi na ixabiso phakathi komqolo lidibana neenqobo zokucoca. Ekugqibeleni, siprinta i-DataFrame ehluziweyo.
I .isin () umsebenzi kunye ne boolean masking
The .iphakathi() umsebenzi kwipanda sisixhobo esisebenza ngeendlela ezininzi sokuhluza idatha esekelwe kuluhlu okanye uluhlu lwamaxabiso. Ibuyisela i-Boolean DataFrame yokwakheka okufanayo njengeyoqobo, ebonisa ukuba zeziphi iziqalelo ezikhoyo kuluhlu olunikiweyo okanye iseti. Kwimeko yethu, sidlula uluhlu lwamaxabiso esifuna ukuwahluza.
I-Boolean masking bubuchule obusetyenziswa kwi-pandas kuhluzo olulumkileyo lwedatha. Iquka ukusebenzisa isigqumathelo se boolean (uluhlu lwamaxabiso ayiNyaniso noBubuxoki) kulwakhiwo lwedatha ukuhluza imiba yayo. Kumxholo wengxaki yethu, sisebenzisa i boolean masking kunye .isin () umsebenzi ukufumana kwakhona imiqolo equlathe amaxabiso afunekayo.
Ngokuqonda okucacileyo kwelayibrari yepandas, izakhiwo zeDathaFrame, kunye .isin () umsebenzi, sinokuhluza ngokufanelekileyo nayiphi na iPandas DataFrame. Ezi ndlela zobugcisa zisivumela ukuba sihlolisise iiseti ezinkulu zedatha kwaye sikhuphe ukuqonda okuxabisekileyo ngokulula, ukwenza i-pandas ibe yilayibrari yokuhlalutya idatha kwiPython.
