I-Pandas iwumtapo wezincwadi wePython onamandla wokuhlaziya idatha kanye nokukhohlisa, osetshenziswa kabanzi ezizindeni ezahlukahlukene, kufaka phakathi umhlaba wemfashini. Ngokusebenzisa ama-Panda, ochwepheshe bemfashini nabathuthukisi bangabona okuthrendayo, amaphethini, nemininingwane ngokuhlaziya amasethi edatha ahlobene nemboni yemfashini. Kulesi sihloko, sizocubungula imisebenzi yePandas enamandla, Kusho futhi isamba, kanye nezicelo zabo ekuhlaziyeni idatha yemfashini.
Le misebenzi ingaba usizo kakhulu ekutholeni ulwazi olubalulekile mayelana nezinto zemfashini njengokuthengisa, amathrendi amanani, ukukala umkhiqizo, nokuningi. Ngokubala incazelo kanye nesamba sezibaluli ezihlukahlukene, singathola imininingwane ebalulekile ukuze senze izinqumo ezinolwazi mayelana nesitayela namathrendi emfashini.
Isixazululo senkinga
Ukubonisa ukusetshenziswa kwe-pandas Kusho futhi isamba imisebenzi, ake sicabange ukuthi sinesethi yedatha equkethe imininingwane emayelana nezinto ezihlukene zemfashini njengesitayela sazo, imibala, intengo, nokulinganisa. Sizongenisa le dathasethi ku-pandas DataFrame futhi siqale ukuhlaziya kwethu sisebenzisa isilinganiso kanye nemisebenzi yesamba.
import pandas as pd
# Read data from a CSV file and load it into a DataFrame
data = pd.read_csv('fashion_items.csv')
# Calculate mean and sum of the price column
mean_price = data['price'].mean()
sum_price = data['price'].sum()
print('Mean price:', mean_price)
print('Total price:', sum_price)
Incazelo yesinyathelo ngesinyathelo sekhodi
- Okokuqala, singenisa umtapo wezincwadi we-pandas ngegama elithi 'pd'.
- Okulandelayo, sifunda idatha efayeleni le-CSV elibizwa ngokuthi 'fashion_items.csv' futhi siyilayishe kuFrame Yedatha ebizwa ngokuthi 'idatha' sisebenzisa umsebenzi we-pd.read_csv. Isethi yedatha iqukethe ulwazi mayelana nezinto zemfashini ezihlukahlukene.
- Bese, sibala inani lentengo yazo zonke izinto zemfashini sisebenzisa umsebenzi othi mean() osetshenziswa kukholomu 'yentengo' ye-DataFrame. Leli nani ligcinwe kokuhlukile okuqanjwe ngokuthi 'mean_price'.
- Ngokufanayo, sibala inani lentengo yazo zonke izinto zemfashini ngokubiza isamba () umsebenzi kukholomu 'yentengo'. Leli nani ligcinwe kokuhlukile okuqanjwe ngokuthi 'sum_price'.
- Ekugcineni, siphrinta isilinganiso esibaliwe kanye nezintengo eziphelele zezinto zemfashini.
Amalabhulali ahlobene nemisebenzi ku-Pandas
Kunenqwaba yemitapo yolwazi nemisebenzi ehambisana nokusetshenziswa kwama-panda ekuhlaziyeni idatha embonini yemfashini. Eminye yale misebenzi ewusizo ngaphandle kwalokho Kusho futhi isamba zihlanganisa:
Umsebenzi wePandas groupby
The iqembuby umsebenzi uwusizo ikakhulukazi ekuhlanganiseni idatha ngokusekelwe kumakholomu athile. Isibonelo, uma sifuna ukuhlaziya inani nenani eliphelele lezinto zemfashini zesitayela ngasinye esikhona kudathasethi yethu.
# Group data by style and calculate mean and sum of the price
grouped_data = data.groupby('style')['price'].agg(['mean', 'sum'])
print(grouped_data)
I-Pandas ihlanganisa umsebenzi
The hlanganisa umsebenzi usivumela ukuthi sihlanganise ama-DataFrames amabili ngokusekelwe kukholomu evamile. Ngokwesibonelo, ake sithi sinedathasethi ehlukile equkethe ulwazi mayelana nokuduma kwesitayela ngasinye. Ngokuhlanganisa womabili ama-DataFrames, singaguqula lolu lwazi lube imininingwane ebalulekile.
# Import data related to style popularity
style_popularity_data = pd.read_csv('style_popularity.csv')
# Merge the original data and style_popularity_data based on the 'style' column
merged_data = pd.merge(data, style_popularity_data, on='style')
print(merged_data.head())
Ngokuqonda nokusebenzisa le misebenzi enamandla ngaphakathi kwelabhulali ye-Pandas, ochwepheshe bemfashini nonjiniyela bangenza izinqumo ezinolwazi futhi bahlaziye amathrendi nezitayela zakamuva kalula.
