Matplotlib iraibhurari ine simba yekuronga inoshandiswa muPython programing mutauro. Inopa API yakanangana nechinhu chekumisikidza zvirongwa mumashandisirwo anoshandisa general-chinangwa GUI maturusi seTkinter, wxPython, kana Qt. Chimwe chezvishandiso zvakakosha zvakapihwa naMatplotlib kugona kwekugadzira yekuvimba nguva yekuronga.
Nguva yeruvimbo, sezwi rezviverengero, rinoreva chiyero chechokwadi munzira yesampling. Chiyero chekuvimba chinokuudza kuti ungave nechokwadi sei, chinoratidzwa sechikamu. Semuenzaniso, 99% yekuvimba nhanho inoratidza kuti imwe neimwe yefungidziro yako ingangove yakarurama 99% yenguva.
Kugadzira Chivimbo Kupindirana Plot Uchishandisa Matplotlib
Kugadzira yekuvimbika nguva yekuronga muMatplotlib kunosanganisira akati wandei matanho. Ngationgororei tsananguro yeinoenderana Python kodhi kuti tiite aya matanho:
Kutanga, isu tinofanirwa kuunza kunze ma library anodiwa:
import matplotlib.pyplot as plt import numpy as np from scipy.stats import sem, t from scipy import mean
Iye zvino, tinogona kuverenga nguva yekuvimba tichitevera matanho aya.
1. Sarudza dhatabheti isina kurongeka yatichaverengera nguva yekuvimba.
2. Verenga zvinoreva uye zvakajairwa kukanganisa kwedataset.
3. Sarudza muganho wekukanganisa wenguva yekuvimba.
4. Pakupedzisira, verenga huwandu hwenguva yekuvimba.
Heino kodhi yePython inoenderana nematanho aya.
confidence = 0.95 data = np.random.rand(100) n = len(data) m = mean(data) std_err = sem(data) h = std_err * t.ppf((1 + confidence) / 2, n - 1) start = m - h end = m + h
Musiyano wekuti 'ruvimbo' idanho rekuvimba rinoratidzwa sechikamu, uye 'data' rine dataset isina kurongeka. Izvo zvinoreva uye zvakajairwa kukanganisa zvinoverengerwa neiyo 'zvinoreva' uye 'sem' basa reSciPy raibhurari zvichiteerana. Muganho wekukanganisa 'h' unotariswa nekuwanza chikanganiso chakajairwa net-score, yatinotora kubva ku-t-kugovera tichishandisa 'ppf' basa. Pakupedzisira, tinoverenga huwandu hwenguva yekuvimba.
Kuronga Nguva Yekuvimba muMatplotlib
Muchikamu chino chekupedzisira chekodhi, tiri kushandisa Matplotlib kuona nguva yekuvimba.
plt.figure(figsize=(9,6))
plt.bar(np.arange(len(data)), data)
plt.fill_between(np.arange(len(data)), start, end, color='b', alpha=0.1)
plt.title('Confidence Interval')
plt.show()
Inoshandisa bar plot kuratidza iyo data uye 'fill_between' nzira yekumiririra nguva yekuvimba. Basa re'nhamba' rinotanga chimiro chitsva uye 'show' basa rinopa chiitiko.
Kugadzira chimiro chenguva yekuvimba muMatplotlib inzira iri nyore yekuongorora nekuona data rako, kunyanya data rinosanganisira kuongororwa kwenhamba. Ichi chishandiso chine simba chinopa nzira iri nyore uye intuitive kupa data yakaoma muchimiro chinogona kududzirwa zviri nyore, zvichiita kuti ive yakakosha toolkit kune chero python data analyst kana musayendisiti. Nekunzwisisa maitiro ekugadzirisa nekushandisa izvi, tinogona kuita kuti nzira yekududzira data inyatsoshanda uye yakarurama.
