A duniyar kimiyyar bayanai da shirye-shirye, Python ya zama sanannen harshe cikin sauri saboda sauƙi, karantawa, da kuma iyawa. A cikin wannan labarin, za mu nutse cikin zurfin ciki Python NumPy ɗakin karatu da ƙarfinsa matsi aiki. Za mu tattauna yadda za mu yi amfani da fa'idodinsa don sarrafa bayanai da kuma bincikar bayanai ba tare da wahala ba. Ci gaba da karantawa don gano yadda zaku iya magance matsaloli masu rikitarwa ta amfani da NumPy matsi aiki tare da misalai, gami da bayanin mataki-mataki na lambar.
Don taimakawa misalta wannan batu, bari mu yi tunani game da yanayin kyan gani na zamani. A matsayin ƙwararren ƙwararren salon, kun san yadda yake da mahimmanci a zaɓi ingantattun kayan da za su burge masu sauraro, wakiltar jituwa na salo, kamanni, da kuma abubuwan da ke faruwa a cikin gungu ɗaya.
Fahimtar Laburaren NumPy
- NumPy (Python Lambobi) babban ɗakin karatu ne mai buɗe ido wanda ke da fa'ida sosai don aiwatar da ayyukan lissafi da ma'ana akan manya, tsararru da matrices.
- Yana ba da kyakkyawan tallafi don ayyuka daban-daban na lissafi, ayyukan ƙididdiga, da algebra na yau da kullun.
- Tsarin NumPy yayi kama da jerin Python, amma yana aiki da sauri kuma yana buƙatar ƙarancin ƙwaƙwalwar ajiya.
Kamar yadda haɗe-haɗe na tufafi, launuka, da tarihin salon salon ke tasiri salon kayan sawa, ɗakunan karatu da ayyuka a Python suna taka muhimmiyar rawa wajen magance ƙalubalen shirye-shirye.
Ayyukan Matsi na NumPy
A cikin duniyar salon, salon da ya dace shine duk game da sanya sassan su dace da juna. Hakazalika, da NumPy matsi Aiki yana ba mu damar cire shigarwar mai-girma ɗaya daga siffar tsararrun shigarwa.
import numpy as np sample_array = np.array([[[0], [1], [2]]]) squeezed_array = np.squeeze(sample_array) print(squeezed_array)
Snippet lambar da ke sama tana cire shigarwar mai girma ɗaya daga sifar samfurin_array, yana haifar da tsararru mai girma ɗaya.
Fahimtar Axis a cikin Ayyukan Matsi na NumPy
Wani muhimmin al'amari na aikin matsi na NumPy shine amfani da na'urar axis siga. Yana ba mu damar zaɓar waɗanne girma dabam don matsi, maimakon cire duk shigarwar mai girma ɗaya.
Don samun kyakkyawar fahimtar manufar, bari mu sake yin tunaninsa dangane da salo da salo. Tufafin zai iya ƙunshi yadudduka da kayan haɗi waɗanda aka haɗa tare da takamaiman gatari ko kwatance (sama zuwa ƙasa, gaba-da-baya). Hakazalika, lokacin aiki tare da matsi aiki, zamu iya tunanin kowane axis yana wakiltar wani bangare na sifar tsararru.
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)
A cikin wannan misali, ƙayyadewa axis=1 yana sa aikin cire kawai shigarwar mai-girma ɗaya tare da axis na biyu. Wannan zaɓin cire girma ya yi daidai da zaɓin takamaiman yadudduka na kayan ba tare da tarwatsa sauran matakan ba.
A ƙarshe, fahimtar da Laburare NumPy kuma mai karfi matsi Ayyukan yana da yuwuwar haɓaka ƙwarewar shirye-shiryen Python ɗinku sosai a cikin sarrafa bayanai da bincike. Kamar dai yadda ƙwararren ƙwararren salon ke karɓar salo iri-iri, kamannuna, da abubuwan da suke faruwa, ƙwararren mai haɓakawa yana rungumar juzu'in dakunan karatu na Python da ayyuka don ƙirƙirar ingantacciyar mafita da kyawu.
