Emhlabeni wesayensi yedatha nohlelo, iPython isiphenduke ulimi oludumile ngokushesha ngenxa yobulula bayo, ukufundeka, kanye nokuhlukahluka. Kulesi sihloko, sizocwilisa sijule I-Python NumPy umtapo wolwazi namandla awo cindezela umsebenzi. Sizoxoxa ngokuthi singazisebenzisa kanjani ngokunenzuzo izici zayo ukuze silawule futhi sihlaziye idatha kalula. Funda ukuze uthole ukuthi ungazixazulula kanjani izinkinga eziyinkimbinkimbi usebenzisa i- NumPy cindezela sebenza ngezibonelo, kufaka phakathi incazelo yesinyathelo nesinyathelo yekhodi.
Ukusiza ukukhombisa lesi sihloko, ake sicabange ngesimo sesimanje se-catwalk. Njengengcweti yezemfashini, uyazi ukuthi kubaluleke kangakanani ukukhetha ingubo ephelele ezoheha izethameli, emele ukuvumelana kwezitayela, ukubukeka, kanye nezitayela eqenjini elilodwa.
Ukuqonda i-NumPy Library
- I-NumPy (Numerical Python) iwumthombo wolwazi ovulekile owusizo ngendlela emangalisayo ekwenzeni imisebenzi yezibalo nenengqondo kuma-arrays amakhulu, ahlukahlukene kanye nakumatikuletsheni.
- Inikeza ukusekelwa okuhle kakhulu kwemisebenzi yezibalo eyahlukene, ukusebenza kwezibalo, kanye nemigqa ye-algebra eqondile.
- I-syntax yeNumPy ifana kakhulu nohlu lwePython, kodwa isebenza ngokushesha futhi idinga inkumbulo encane.
Njengoba nje inhlanganisela yezingubo, imibala, nomlando wemfashini kuthonya isitayela sempahla, imitapo yolwazi nemisebenzi e-Python idlala indima ebalulekile ekuxazululeni izinselele zohlelo.
Umsebenzi Wokukhama we-NumPy
Ezweni lemfashini, isitayela esihle kakhulu simayelana nokwenza izingcezu zihlangane ngaphandle komthungo. Ngokufanayo, i- NumPy cindezela umsebenzi usivumela ukuthi sisuse okufakiwe okunohlangothi olulodwa kumumo wohlelo lokokufaka.
import numpy as np sample_array = np.array([[[0], [1], [2]]]) squeezed_array = np.squeeze(sample_array) print(squeezed_array)
Amazwibela ekhodi angenhla asusa okufakiwe kwe- single-dimensional kumumo we- uhlu_lwesampula, okuholela kumalungu afanayo anohlangothi olulodwa.
Ukuqonda i-Axis ku-NumPy Squeeze Function
Isici esibalulekile somsebenzi wokuminyanisa we-NumPy ukusetshenziswa kwe i-axis ipharamitha. Kusivumela ukuthi sicacise ngokukhetha ukuthi yiziphi izilinganiso okufanele ziminywe, esikhundleni sokususa konke okufakiwe okunohlangothi olulodwa.
Ukuze siqonde kangcono umqondo, ake siphinde sicabange ngawo ngokwesitayela nemfashini. Ingubo ingaqukatha izendlalelo nezinto ezisetshenziswayo ezihlanganiswe ngezimbazo noma izikhombisi-ndlela ezithile (phezulu ukuya phansi, phambili ukuya emuva). Ngokufanayo, lapho usebenza ne- cindezela umsebenzi, singacabanga i-eksisi ngayinye imele isici esithile somumo wamalungu afanayo.
import numpy as np sample_array_2 = np.array([[[1], [2], [3]], [[4], [5], [6]]]) squeezed_array_axis = np.squeeze(sample_array_2, axis=1) print(squeezed_array_axis)
Kulesi sibonelo, ukucacisa i-eksisi=1 ibangela ukuthi umsebenzi ukhiphe kuphela okufakiwe okunohlangothi olulodwa ku-eksisi yesibili. Lokhu kususwa okukhethiwe kobukhulu kufana nokukhetha izendlalelo ezithile zengubo ngaphandle kokuphazamisa obunye ubukhulu.
Sengiphetha, ukuqonda i- Umtapo wolwazi we-NumPy namandla ayo cindezela umsebenzi unamandla okuthuthukisa kakhulu amakhono akho wokuhlela wePython ekukhohliseni nasekuhlaziyeni idatha. Njengoba nje uchwepheshe wemfashini emukela izitayela ezihlukahlukene, ukubukeka, namathrendi, umthuthukisi onekhono wamukela ukuguquguquka kwemitapo yolwazi yePython kanye nemisebenzi ukuze adale izixazululo ezisebenza kahle nezinhle.
