Munyika yanhasi, kushanda nenguva-yakatevedzana data inyanzvi yakakosha kune mugadziri. Rimwe remabasa akajairwa nderekushandura timestamp kune yakatarwa nguva, senge data revhiki kana remwedzi. Kuvhiya uku kwakakosha pakuongorora kwakasiyana, sekufunda maitiro uye mapatani mune data. Muchinyorwa chino, isu tichaongorora maitiro ekushandura timestamp kuti ive nguva mune yenguva-yakateedzera dataset uchishandisa ine simba Python raibhurari, Pandas. Tichatorawo kunyura mukati mekodhi, kuongorora maraibhurari nemabasa anobatanidzwa mukuita, uye kunzwisisa kukosha kwavo mukugadzirisa dambudziko iri.
Pandas ndeye yakavhurika-sosi data yekuongorora uye manipulation raibhurari, iyo inopa inoshanduka uye yakakwirira-kuita mabasa ekushanda nenguva-yakateedzera data. Inoita kuti basa redu rive nyore, rakarurama, uye rishande.
Mhinduro yekushandura timestamp data kune yakatarwa nguva, senge vhiki nevhiki kana pamwedzi, inosanganisira kushandisa iyo Pandas raibhurari's resampling nzira. Resampling chishandiso chine simba chinogona kushandiswa pane timestamp dhata kana nguva yakatevedzana dhata kuti ingave yekusample kana kuderedza mapoinzi edata. Mune ino kesi, isu tichadzikisa pasi mapoinzi data kugadzira nguva dzinodiwa.
Zvino, ngatitarisei nhanho-ne-nhanho tsananguro yekodhi:
1. Pinza kunze maraibhurari anodiwa:
import pandas as pd import numpy as np
2. Gadzira sampuli yedataframe ine timestamp index:
date_rng = pd.date_range(start='1/1/2020', end='1/10/2020', freq='D')
df = pd.DataFrame(date_rng, columns=['date'])
df['data'] = np.random.randint(0,100,size=(len(date_rng)))
df.set_index('date', inplace=True)
3. Edza zvakare data-yakatevedzana uye shandura iyo timestamp data kuita nguva:
df_period = df.resample('W').sum()
4. Dhinda dataframe yabuda:
print(df_period)
Dataframe yekupedzisira `df_period` ine nhamba yedata rekutanga rakaunganidzwa nesvondo.
** Kunzwisisa Maraibhurari uye Mabasa Anoshandiswa **
Pandas Library
Pandas iraibhurari yePython inoshandiswa zvakanyanya pakubata uye kuongorora data. Inopa yakakwirira-yepamusoro data zvimiro seSeries neDataFrame, ichibvumira vanogadzira kuita mashandiro akadai sekubatanidza, kugadzirazve, uye kuchenesa nekukurumidza uye nemazvo. Kwatiri, Pandas inobatsira kubata timestamp data zvinobudirira uye inopa akakosha mabasa senge resample() kushandura timestamp data kuita nguva.
Resample Basa
The muenzaniso () basa muPandas inzira iri nyore yekushandura frequency uye kudzokorora data yenguva yakatevedzana. Inopa akawanda sarudzo dzekuunganidza data kana kudzika pasi, kusanganisira sum, zvinoreva, median, modhi, uye mamwe mabasa anotsanangurwa nemushandisi. Isu tinoshandisa basa iri kushandura yedu timestamp data kunguva yevhiki nekutsanangura iyo resampling frequency se'W'. Iwe unogona zvakare kushandisa 'M' pamwedzi, 'Q' kwekota, zvichingodaro.
Zvino zvataongorora mashandiro ePandas uye basa resample rekushandura timestamp kuenda kunguva data, isu tinokwanisa kubata data-sensitive data nenzira ine musoro. Nerubatsiro rwezvishandiso izvi, vanogadzira, vanoongorora data, uye nyanzvi dzeSEO vanogona kuvhura ruzivo rwakasiyana kubva kune yavo data, vachivabatsira kuita zvirinani sarudzo uye kufanotaura.
