Isingeniso
I-Python iwulimi lokuhlela oluguquguqukayo futhi olusetshenziswa kabanzi emikhakheni eyahlukene, okuhlanganisa ukuhlaziywa kwedatha, ubuhlakani bokwenziwa, nokuthuthukiswa kwewebhu. Enye yemitapo yolwazi ebalulekile yokuphatha idatha yezinga elikhulu kuPython ngu I-NumPy. I-NumPy ihlinzeka ngento enamandla ye-N-dimensional array, esenza sikwazi ukwenza imisebenzi yezibalo eyinkimbinkimbi kalula. Omunye wemisebenzi ebalulekile ekuhlaziyeni idatha yi- umsebenzi wokuhlukanisa, esetshenziselwa ukuhlukanisa idatha ibe izingxenye ezincane ukuze kuhlaziywe okwengeziwe. Kulesi sihloko, sizongena ku-syntax nasekusetshenzisweni komsebenzi wokuhlukanisa we-NumPy ngokuhlinzeka ngesixazululo esisebenzayo, incazelo yesinyathelo nesinyathelo, nokuxoxa ngemitapo yolwazi nemisebenzi ehlobene.
Isixazululo senkinga:
Ake sithi sinesethi yedatha ekhiqizwe embukisweni wemfashini futhi sifuna ukuhlaziya izitayela ezahlukene, izitayela, nezinhlanganisela zemibala. Umgomo wethu uwukuhlukanisa le dathasethi ibe yiziqephu ezincane ukuze kuhlaziywe okwengeziwe. Ukufeza lokhu, sizosebenzisa i- Umsebenzi wokuhlukanisa we-NumPy.
import numpy as np
# Sample data (styles, trends, and colors)
data = np.array([["Bohemian", "Oversized", "Earthy"],
["Minimalist", "Tailored", "Monochrome"],
["Classic", "Simple", "Neutrals"],
["Romantic", "Flowy", "Pastels"]])
# Split the data into 2 equal parts using NumPy split function
split_data = np.split(data, 2)
Incazelo yesinyathelo ngesinyathelo yekhodi:
1. Siqala nge ukungenisa umtapo wezincwadi we-NumPy, esihlinzeka ngemisebenzi edingekayo yokuphatha idatha yezinga elikhulu.
2. Sibe sesidala a isampula yedathasethi ngezitayela zemfashini ezahlukene, izitayela, nezikimu zemibala. Le dathasethi iwuhlelo lwe-2D NumPy.
3. Ekugcineni, usebenzisa i- Umsebenzi wokuhlukanisa we-NumPy, sihlukanisa idathasethi ibe izingxenye ezimbili ezilinganayo. Okuguquguqukayo 'kwedatha_yedatha' manje kuqukethe amalungu afanayo amabili amancane, ngalinye linengxenye yedathasethi yoqobo.
Ukuqonda i-NumPy nomsebenzi wayo wokuhlukanisa
I-NumPy, emfushane ye-Numeric Python, iwumtapo wezincwadi obalulekile wokwenza imisebenzi yezinombolo ePython. Yaziwa kabanzi ngayo into esebenza kahle ye-N-dimensional, esebenza njengethuluzi elinamandla lekhompyutha yesayensi nokuhlaziya idatha.
The I-NumPy ihlukaniswe umsebenzi usetshenziselwa ukuhlukanisa amalungu afanayo okokufaka abe amalungu afanayo amancane amaningi eduze kwe-eksisi ecacisiwe. Lo msebenzi ungaba usizo ekuhlukaniseni amasethi edatha amakhulu abe izingxenye ezincane, ezilawulekayo, ngaleyo ndlela kube lula ukwenza ukuhlaziya okuqondile ezingxenyeni ezihlukene zedatha.
Eminye imisebenzi ye-NumPy yokukhohlisa idatha
Ngaphandle komsebenzi wokuhlukanisa, iNumPy iphinde inikeze eminye imisebenzi eminingana yokukhohlisa idatha, njenge:
- lungisa kabusha: Lo msebenzi usetshenziselwa ukushintsha umumo wamalungu afanayo anikeziwe ngaphandle kokushintsha idatha engaphansi. Ingasetshenziselwa ukuguqula amalungu afanayo anohlangothi olulodwa lube izingxenye ezimbili-ntathu noma okuphambene nalokho.
- hlanganisa: Lo msebenzi usetshenziselwa ukuhlanganisa amalungu afanayo amabili noma ngaphezulu ku-eksisi ecacisiwe. Kungaba usizo lapho uhlanganisa idatha evela emithonjeni ehlukene.
- hstack: Lo msebenzi usetshenziselwa ukupakisha amalungu afanayo avundlile (ngokuhlakanipha kwekholomu) eduze kwe-eksisi eyodwa. Kuwusizo ukwengeza amakholomu kumalungu afanayo akhona noma ekudaleni amalungu afanayo amasha ngokuhlanganisa amalungu afanayo amaningi ngapha nangapha.
- i-vstack: Ngokufanayo ne-hstack, lo msebenzi usetshenziselwa ukupakisha amalungu afanayo aqondile (ngokuhlakanipha kumugqa) ku-eksisi eyodwa. Kuyinzuzo ukwenezela imigqa kumalungu afanayo akhona noma ukudala amalungu afanayo amasha ngokuhlanganisa amalungu afanayo amaningi phezu kwelinye.
Ekuphetheni, i Umsebenzi wokuhlukanisa we-NumPy iyithuluzi elibalulekile lokuphatha idatha yezinga elikhulu kuPython. Ngokuhlukanisa idathasethi ibe yiziqephu ezincane, singakwazi ukuhlaziya kahle amasethi amancane edatha futhi sikhiphe imininingwane ebalulekile. Ngaphezu kwalokho, ukuqonda imisebenzi ehlobene namalabhulali ku-NumPy kuzoqhubeka kusize ukuthuthukisa amakhono ethu okukhohlisa idatha kuPython.
