Gabatarwa
Python harshe ne na shirye-shirye iri-iri kuma ana amfani da shi sosai a fagage daban-daban, gami da nazarin bayanai, basirar wucin gadi, da ci gaban yanar gizo. Ɗaya daga cikin mahimman ɗakunan karatu don sarrafa manyan bayanai a cikin Python shine Lambobi. NumPy yana ba da wani abu mai ƙarfi N-dimensional array, wanda ke ba mu damar yin hadaddun ayyukan lissafin lissafi cikin sauƙi. Ɗaya daga cikin mahimman ayyuka a cikin nazarin bayanai shine tsaga aiki, wanda ake amfani dashi don rarraba bayanai zuwa ƙananan sassa don ƙarin bincike. A cikin wannan labarin, za mu nutse cikin ƙayyadaddun kalmomi da amfani da aikin rarrabawar NumPy ta hanyar samar da mafita mai amfani, bayanin mataki-mataki, da tattaunawa game da ɗakunan karatu da ayyuka masu alaƙa.
Maganin matsalar:
A ce muna da bayanan da aka samar daga wasan kwaikwayo na salon kuma muna son yin nazarin salo daban-daban, abubuwan da ke faruwa, da haɗin launuka daban-daban. Burinmu shine mu raba wannan saitin bayanai zuwa ƙananan guntu don ƙarin bincike. Don cimma wannan, za mu yi amfani da Aikin raba 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)
Bayanin mataki-mataki na lambar:
1. Mu fara da shigo da ɗakin karatu na NumPy, wanda ke ba mu ayyukan da ake buƙata don sarrafa manyan bayanai.
2. Sai mu ƙirƙira a samfurin dataset tare da salo daban-daban, salo, da tsarin launi. Wannan saitin bayanai tsararru ce ta 2D NumPy.
3. A ƙarshe, amfani da Aikin raba NumPy, muna raba bayanan zuwa kashi biyu daidai. Maballin 'Split_data' yanzu yana ƙunshe da ƙananan tsararraki biyu, kowannensu yana da rabin ainihin bayanan.
Fahimtar NumPy da aikin raba shi
NumPy, gajere don Numeric Python, babban ɗakin karatu ne don yin ayyukan lambobi a Python. An san shi sosai don ta ingantaccen N-dimensional array abu, wanda ke aiki azaman kayan aiki mai ƙarfi don ƙididdige ƙididdiga da ƙididdigar bayanai.
The NumPy raba Ana amfani da aikin don raba tsararrun shigarwa zuwa ɗimbin ƙananan tsararru tare da ƙayyadadden axis. Wannan aikin na iya zama da fa'ida don wargaza manyan bayanan bayanai zuwa ƙananan sassa, mafi sauƙin sarrafawa, don haka yana sauƙaƙa yin takamaiman nazari akan sassa daban-daban na bayanan.
Sauran ayyukan NumPy don sarrafa bayanai
Baya ga aikin raba, NumPy kuma yana ba da wasu ayyuka da yawa don sarrafa bayanai, kamar:
- sake fasalin: Ana amfani da wannan aikin don canza siffar tsararrun da aka bayar ba tare da canza bayanan da ke ƙasa ba. Ana iya amfani da shi don sauya tsararru mai girma ɗaya zuwa tsararru mai girma biyu ko akasin haka.
- hade: Ana amfani da wannan aikin don haɗa tsararraki biyu ko fiye tare da ƙayyadadden axis. Zai iya zama taimako lokacin haɗa bayanai daga tushe daban-daban.
- hstack: Ana amfani da wannan aikin don tara jeri-jeri a kwance (shafi-hikima) tare da axis guda ɗaya. Yana da amfani don haɗa ginshiƙai zuwa tsararrun da ke akwai ko ƙirƙirar sabon tsararru ta hanyar haɗa jeri da yawa gefe da gefe.
- vstack: Mai kama da hstack, ana amfani da wannan aikin don tara jeri a tsaye (jere-hikima) tare da axis guda ɗaya. Yana da fa'ida don haɗa layuka zuwa tsararrun da ke akwai ko ƙirƙirar sabon tsararru ta hanyar haɗa jeri-jeri da yawa a saman juna.
A ƙarshe, da Aikin raba NumPy kayan aiki ne mai mahimmanci don sarrafa manyan bayanai a cikin Python. Ta hanyar rarraba saitin bayanai zuwa ƙananan gungu, za mu iya yin nazari sosai game da ƙayyadaddun rukunonin bayanai da fitar da bayanai masu mahimmanci. Bugu da ƙari, fahimtar ayyuka masu alaƙa da ɗakunan karatu a cikin NumPy zai ƙara taimakawa haɓaka iyawar sarrafa bayanan mu a Python.
