A duniyar shirye-shirye, Python ya zama sanannen yare da aka sani don sauƙin amfani, karantawa, da sassauci. Daga cikin ɗakunan karatu da yawa, NumPy ya fito a matsayin ɗayan kayan aiki mafi ƙarfi don sarrafa bayanan lambobi, wanda ke da aikace-aikace da yawa a fannoni daban-daban, gami da salo. A cikin wannan labarin, za mu zurfafa cikin aikin NumPy Shape, tattaunawa game da ma'anar ta da kuma samar da mafita mai amfani ga matsala da ta shafi nazarin yanayin salon. A kan hanyar, za mu kuma bincika dakunan karatu da ayyuka masu alaƙa. Don haka, bari mu fara!
Aikin NumPy Siffar kayan aiki ne mai mahimmanci don nazarin tsarin tsararru. A wasu kalmomi, yana ba mu damar samun ma'auni na tsararru kuma mu sarrafa shi da kyau. Don amfani da wannan aikin, da farko muna buƙatar shigo da ɗakin karatu na NumPy kamar haka:
import numpy as np
Bayan shigo da ɗakin karatu, bari mu yi la'akari da matsala mai amfani: nazarin bayanan yanayin salon tarihi don fahimtar salo daban-daban da kamannun da suka bayyana akan lokaci. A ce muna da kundin bayanai da ke ɗauke da bayanai kan kayan tufafi daban-daban, launukansu, da shekarar da suka yi kyau.
Fahimtar Ayyukan Siffar NumPy
Ayyukan siffa a cikin NumPy aikin ginannen aiki ne wanda ke dawo da girman tsararrun da aka bayar. Don samun damar wannan aikin, kawai kira shi ta amfani da siffar sifa ta kayan tsararru, kamar haka:
array_shape = array_name.shape
Misali, bari mu ɗauka muna da tsararru masu zuwa waɗanda ke ɗauke da bayanan ƙirar mu:
fashion_data = np.array([[2000, "red", "skirt"],
[2001, "blue", "jeans"],
[2002, "green", "jacket"]])
fashion_data_shape = fashion_data.shape
print(fashion_data_shape) # Output: (3, 3)
A cikin wannan misali, aikin siffa ya dawo da tuple (3, 3), yana nuna cewa saitin bayanan mu yana da layuka uku da ginshiƙai uku.
Bincika Yanayin Kayayyaki tare da NumPy
Tare da cikakkiyar fahimtar aikin siffar, yanzu za mu iya tattauna yadda za a iya amfani da shi a cikin mahallin nazarin yanayin salon. A ce muna son yin nazarin shahararrun launuka da kayan tufafi na kowace shekara a cikin bayanan mu. Don yin haka, za mu yi amfani da aikin siffa don maimaitawa ta hanyar tsararru da samun damar bayanai masu dacewa.
Da farko, mun sami adadin layuka (shekaru) a cikin bayanan mu:
num_years = fashion_data_shape[0]
Na gaba, za mu iya yin madauki ta cikin layuka kuma mu fitar da launi da kayan tufafi na kowace shekara:
for i in range(num_years):
trend_year = fashion_data[i, 0]
trend_color = fashion_data[i, 1]
trend_item = fashion_data[i, 2]
print(f"In {trend_year}, {trend_color} {trend_item} were fashionable.")
Wannan snippet code zai fitar da wani abu kamar haka:
““
A cikin 2000, ja siket sun kasance gaye.
A 2001, blue jeans sun kasance gaye.
A cikin 2002, jaket ɗin kore sun kasance gaye.
““
Ta hanyar amfani da aikin siffar NumPy, mun sami damar samun damar bayanai masu dacewa daga saitin bayananmu da kuma nuna salo, kamanni, da abubuwan da ke faruwa a cikin shekaru daban-daban.
Maɓallin Takeaways
A cikin wannan labarin, mun bincika Ayyukan Siffar NumPy da ma'anarsa, nutsewa cikin misali mai amfani na nazari fashion trends data. Mun nuna yadda ake amfani da aikin siffa don samun dama ga abubuwa daban-daban a cikin tsarin bayanai, yana ba mu damar yin nazari da kyau da kuma nuna salo da yanayi daban-daban na tsawon lokaci. A ƙarshe, aikin siffar kayan aiki ne mai ƙarfi don aiki tare da bayanan ƙididdiga, tare da aikace-aikace masu yawa a fannoni daban-daban, ciki har da fashion da kuma style bincike.
