2024-07-12
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Data visualisationi est notitias in forma graphics ostentare ut homines adiuvent informationes et exemplaria post notitias intuitivas et cito intelligendas. In hodiernae explosionis notitiae aetate, notitia visualizationis maximi momenti est ob has causas:
bqplot est secundumGrammatica Graphica 2D visualizationis bibliothecae, speciatim proIuppiter codicillus design.componitd3.js etipywidgets pluma ordinatur ad efficaces visualizationis facultates d3.js ad Pythonem adducere. Hic sunt notae clavis quaedam bibliothecae bqplot:
Bqplot inaugurari valde simplex est, modo sequi mandatum in terminatione seu imperio promptum;
pip install bqplot
jupyter nbextension enable --py --sys-prefix bqplot
Post feliciter institutionem necessarias bibliothecas in Iupyter codicillos importare ut satus utendo:
import numpy as np
import bqplot.pyplot as plt
Simplex exemplum hic ostendens quomodo Mearum usus bqplot crearet;
# 生成随机数据
data = np.random.randn(100)
# 创建直方图
fig = plt.figure(title='Histogram by bqplot')
hist = plt.hist(sample=data, bins=10, colors=['#01a2d9'])
fig
Per hoc simplex exemplum, videre potes facilitatem usus et potentiae interactive functiones bqplot. In sequentibus capitulis varias chartulas rationes in bqplot inveneris et lineamenta provectas.
Priusquam incipias uti bqplot pro notitia visualisationi, primum opus est ut bibliothecam bqplot in tuo ambitu instituas. bqplot pluribus modis institui potest, plerumque pituita vel conda utens. Hic sunt gradus distincti utendi utriusque modi:
pip install bqplot
conda install -c conda-forge bqplot
Post institutionem perfectam, confirmare potes utrum institutionem bene egerit sequenti mandato:
import bqplot
print(bqplot.__version__)
Post bqplot inauguratis, proximos necessarias bibliothecas importare debes ut opus visualizationis incipias. De more sequenti bibliothecas importare debes:
bqplot
: Interactive charts creare solebat.numpy
: Usus ad calculos numerales et MGE.pandas
: Ad usum notitiarum tractationum et analysis.Exemplar hic est codicem pro his bibliothecis invehendis:
import bqplot.pyplot as plt
import numpy as np
import pandas as pd
Ut omnia recte constituantur, experiri potes ut chartam simplicem creando. Exemplar hic est codicem qui ostendit quomodo necessarias bibliothecas importet et chartam simplicem efficiat:
# 导入必要的库
import bqplot.pyplot as plt
import numpy as np
# 创建一个简单的条形图
fig = plt.figure(title="简单条形图示例")
x = list("ABCDE")
y = np.random.rand(5)
bar = plt.bar(x, y)
fig
In hoc exemplo primo importamusbqplot.pyplot
asplt
Ergo uterenumpy
Notitia temere generate. Deinde chartam simplicem cum quinque vectibus repagulis creavimus et in codicillo Iuppiter ostendimus.
Per gradus superiores bibliothecam bqplot feliciter inauguratus es et primam tuam simplicem chart interactive creasti. Deinde pergere potes rationes graviores et chartula explorare.
Priusquam incipiens ad usum bqplot
Priusquam notitias visualizationis conficere potes, primum opus est ut dataset inquisita importet.Hic sunt aliquae rationes communes dataseae importantes et quomodo utantur illispandas
bibliothecam ad aliquid hoc data.
pandas
Potens est notitia processus bibliothecae quae late pro analysi et praeprocessione utendum est.Ecce quomodo utorpandas
Exemplum tabellae CSV deferendi:
import pandas as pd
# 导入CSV文件
df1 = pd.read_csv("../input/autompg-dataset/auto-mpg.csv")
Praeter CSV imagini,pandas
Etiam notitias inferentes in multis formats sustinet, ut Praecedo lima, JSON imagini, etc. Exempla hic sunt;
# 导入Excel文件
df_excel = pd.read_excel("../input/dataset.xlsx")
# 导入JSON文件
df_json = pd.read_json("../input/dataset.json")
Si data reposita est in database, uti potes pandas
of*read_sql
munus ad importare data. Hic est exemplum.
import sqlite3
# 连接到SQLite数据库
conn = sqlite3.connect('../input/database.db')
# 从数据库中读取数据
df_sql = pd.read_sql('SELECT * FROM table_name', conn)
Cum dataset importatur, solere debes praevidere dataset ad intellegendum structuram et contentum notitiarum.pandas
Varii modi praebentur ut notitiaset praemonstrare.
usus head()
Methodus videndi primos ordines dataset:
# 查看前5行数据
print(df1.head())
usus info()
Modus inspicere potest notitias fundamentales notitiarum copiarum, inclusas generis notitias et valores absentis:
# 查看数据集的基本信息
print(df1.info())
usus describe()
Methodus in statistica notitiarum notitiarum statutorum inspicere potest, inter medium, vexillum deviationis, pretii minimi, pretii maximi, etc.
# 查看数据集的统计信息
print(df1.describe())
usus columns
Possessiones videre potes columnam nomina dataset:
# 查看数据集的列名
print(df1.columns)
Per modos superiores, praecipuam cognitionem habere potes notitiarum inlatarum statutorum, ita fundamentum ponendi operis visualizationis sequentis datae.
{
"title": "bqplot教程:在Jupyter Notebook中进行交互式数据可视化",
"summary": "本文详细介绍了如何使用bqplot库在Jupyter Notebook中进行交互式数据可视化。bqplot是一个基于Grammar of Graphics的2D可视化解决方案,结合了d3.js和ipywidgets的功能,旨在将d3.js的功能带到Python中。",
"content_outline": [
{
"h1": "基本图表类型",
"h2": [
"4.1 散点图",
"4.2 饼图",
"4.3 箱线图",
"4.4 条形图",
"4.5 堆积条形图"
]
}
]
}
Disperge machinatio charta usus est ut relationem inter binas variabiles ostenderet. Disperge insidias tibi permittere ut visibiliter observes distributionem et rationem datarum. in bqplot , creando , dispergere , insidiae valde simplices.
import bqplot as bq
import numpy as np
# 创建数据
x = np.random.rand(100)
y = np.random.rand(100)
# 创建尺度
x_sc = bq.LinearScale()
y_sc = bq.LinearScale()
# 创建散点标记
scatter = bq.Scatter(x=x, y=y, scales={'x': x_sc, 'y': y_sc})
# 创建轴
ax_x = bq.Axis(scale=x_sc, label='X Axis')
ax_y = bq.Axis(scale=y_sc, orientation='vertical', label='Y Axis')
# 创建图表
fig = bq.Figure(marks=[scatter], axes=[ax_x, ax_y], title='Scatter Plot')
# 显示图表
fig
Pie Charta est chartula proportio data ad proponendum. In bqplot, chartis pie creandis aeque facilis est.
import bqplot as bq
# 创建数据
data = [10, 20, 30, 40]
labels = ['A', 'B', 'C', 'D']
# 创建饼图标记
pie = bq.Pie(sizes=data, labels=labels)
# 创建图表
fig = bq.Figure(marks=[pie], title='Pie Chart')
# 显示图表
fig
Arca machinalis chartula usus est ut distributionem notitiarum ostendat. Exhibere potest mediana, quartiles et manor notitiarum.
import bqplot as bq
import numpy as np
# 创建数据
data = [np.random.normal(0, 1, 100), np.random.normal(3, 1, 100), np.random.normal(6, 1, 100)]
# 创建尺度
x_sc = bq.OrdinalScale()
y_sc = bq.LinearScale()
# 创建箱线图标记
boxplot = bq.Boxplot(x=data, scales={'x': x_sc, 'y': y_sc})
# 创建轴
ax_x = bq.Axis(scale=x_sc, label='Groups')
ax_y = bq.Axis(scale=y_sc, orientation='vertical', label='Values')
# 创建图表
fig = bq.Figure(marks=[boxplot], axes=[ax_x, ax_y], title='Box Plot')
# 显示图表
fig
Charta vectis est chartula ad comparationes inter categoricas datas exhibendas. Altitudo cuiuslibet vectis significat valorem notitiae illius generis.
import bqplot as bq
# 创建数据
x_labels = ['A', 'B', 'C', 'D']
y_values = [30, 20, 40, 10]
# 创建尺度
x_sc = bq.OrdinalScale()
y_sc = bq.LinearScale()
# 创建条形图标记
bar = bq.Bars(x=x_labels, y=y_values, scales={'x': x_sc, 'y': y_sc})
# 创建轴
ax_x = bq.Axis(scale=x_sc, label='Categories')
ax_y = bq.Axis(scale=y_sc, orientation='vertical', label='Values')
# 创建图表
fig = bq.Figure(marks=[bar], axes=[ax_x, ax_y], title='Bar Chart')
# 显示图表
fig
Charta vectis acervus est chartula ad comparationes inter multiplices notitias categoricas exhibendas. Altitudo cuiuslibet vectis significat valorem notitiae illius categoriae, et quaelibet vectis in segmenta dividi potest, quodlibet segmentum subcategorium repraesentans.
import bqplot as bq
# 创建数据
x_labels = ['A', 'B', 'C', 'D']
y_values = [
[30, 20],
[20, 30],
[40, 10],
[10, 40]
]
# 创建尺度
x_sc = bq.OrdinalScale()
y_sc = bq.LinearScale()
# 创建堆积条形图标记
stacked_bar = bq.Bars(x=x_labels, y=y_values, scales={'x': x_sc, 'y': y_sc}, type='stacked')
# 创建轴
ax_x = bq.Axis(scale=x_sc, label='Categories')
ax_y = bq.Axis(scale=y_sc, orientation='vertical', label='Values')
# 创建图表
fig = bq.Figure(marks=[stacked_bar], axes=[ax_x, ax_y], title='Stacked Bar Chart')
# 显示图表
fig
Mearum genus chartularum genus est ut distributionem notitiarum ostendat.existbqplot
in, possunt esseplt.hist
munus creare Mearum. En simplex exemplum:
import numpy as np
import bqplot.pyplot as plt
# 生成随机数据
data = np.random.randn(1000)
# 创建直方图
fig = plt.figure()
hist = plt.hist(data, bins=30)
plt.title('Histogram of Random Data')
plt.xlabel('Value')
plt.ylabel('Frequency')
fig
In hoc exemplo primum puncta data temere 1000 generavimus et deinde usi sumusplt.hist
Munus mearum cum 30 bins creat. Chartam tuam clariorem ac faciliorem reddere potes per titulos et axem pitta- corum constituendo.
Lineae chartulae communes sunt generis chartulae ut ostendant trends in notitia super tempus vel alias variabiles continuas.existbqplot
in, possunt esseplt.plot
munus facere linea chart. Hic est exemplum.
import numpy as np
import bqplot.pyplot as plt
# 生成数据
x = np.linspace(0, 10, 100)
y = np.sin(x)
# 创建折线图
fig = plt.figure()
line = plt.plot(x, y)
plt.title('Line Chart of Sine Function')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
fig
In hoc exemplo generavimus sine functione data cum punctis 100 data et deinde adhibitisplt.plot
Munus facit lineam chart. Chartam tuam clariorem ac faciliorem reddere potes per titulos et axem pitta- corum constituendo.
Candelabrum chartis (etiam notae ut chartis candelabrum) typum chartularum ad oeconomicos notas exhibendas, sicut pretia stirpis.existbqplot
in, possunt esseplt.candle
munus facere candelabrum chartis. Hic est exemplum.
import numpy as np
import bqplot.pyplot as plt
# 生成随机金融数据
n = 100
open_prices = np.random.randn(n)
high_prices = open_prices + np.random.rand(n)
low_prices = open_prices - np.random.rand(n)
close_prices = open_prices + np.random.randn(n) * 0.5
# 创建蜡烛图
fig = plt.figure()
candle = plt.candle(open_prices, high_prices, low_prices, close_prices)
plt.title('Candlestick Chart of Random Financial Data')
plt.xlabel('Time')
plt.ylabel('Price')
fig
In hoc exemplo temere apertas, altas, humiles et proximas notitias pretiosorum generamus et deinde utimurplt.candle
Munus candelabrum creat chart. Chartam tuam clariorem ac faciliorem reddere potes per titulos et axem pitta- corum constituendo.
Tabula caloris est genus chartulae ad densitatem seu intensionem distributionis duarum dimensivarum notitiarum exhibendam.existbqplot
in, possunt esseplt.heatmap
munus facere calidum tabula. Hic est exemplum.
import numpy as np
import bqplot.pyplot as plt
# 生成随机数据
data = np.random.rand(10, 10)
# 创建热力图
fig = plt.figure()
heatmap = plt.heatmap(data)
plt.title('Heatmap of Random Data')
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
fig
In hoc exemplo generavimus 10x10 matrix notitiarum incerti et deinde ususplt.heatmap
Munus mappam caloris creat. Chartam tuam clariorem ac faciliorem reddere potes per titulos et axem pitta- corum constituendo.
Chart geographica typum chartis geographicis adhibitis ad ostentationem notae geographicae, sicut tabula geographica.existbqplot
in, possunt esseplt.geo
munus facere maps geographicas. Hic est exemplum.
import bqplot.pyplot as plt
# 加载地理数据
map_data = 'World'
# 创建地理图
fig = plt.figure()
geo = plt.geo(map_data)
plt.title('Geographical Map')
fig
In hoc exemplo, notitias geographicas mundi oneremus et deinde utimurplt.geo
Munus mappam geographicam gignit. Titulum ponendo, chartulam tuam clariorem ac faciliorem reddere potes.
In data visualizationis, functiones interactivas clavis sunt ad experientiam usoris meliorandam et ad explorandas facultates datas.bqplot
Copiosas interactivas partes praebet ut utentes ad explorationem dynamicam et responsivam perducant in Comentario Iuppiter. Partes interactive haec includunt sed non limitantur ad:
Haec interactive components sunt bqplot
of*Interactions
Modulus exsecutionis users dat instrumenta explorationis intuitivae et validae.
Zooming and panning are the basic interactive functions in data visualization.bqplot
Praebet constructum-in zoom et sartagine munera, utentes zoom per rotam murem et sartagine per murem trahere possunt. En simplex exemplum:
import bqplot.pyplot as plt
import numpy as np
# 创建数据
x = np.linspace(-10, 10, 100)
y = np.sin(x)
# 创建图表
fig = plt.figure(title="Zoom and Pan Example")
plt.plot(x, y)
plt.show()
In hoc exemplo, usor potest zoom in vel e chartula utendo rota muris et sartagine chartulam trahendo murem.
Facultates selectae selectae et peniculus deligendae permittunt utentes ut puncta vel areas specificae intra chartulam analysi ulterioris analysi eligant.bqplot
providitBrushSelector
etLassoSelector
components ad effectum deducendi hanc functionality.Hoc est ususBrushSelector
Exemplum:
from bqplot import BrushSelector
# 创建选择器
brush = BrushSelector(x_scale=x_scale, y_scale=y_scale)
# 将选择器添加到图表
fig.interaction = brush
In hoc exemplo, usor potest eligere aream rectangulam in chart trahendo murem, et puncta delectabilia illustrabuntur.
Instrumenta detailed informationes ostendere possunt cum usor murem super punctum datae oberrat.bqplot
providitTooltip
componentes ad consequi hanc functionality. En simplex exemplum:
from bqplot import Tooltip
# 创建工具提示
tooltip = Tooltip(fields=['x', 'y'], formats=['.2f', '.2f'])
# 将工具提示添加到图表
scatter.tooltip = tooltip
In hoc exemplo, cum usura inertit insidias notitias dispersas in punctum, the x
ety
pretii.
Renovatio dynamica pluma permittit ut chartis dynamice renovatio fundatur in usoris initus vel data mutationes.bqplot
providitinteracts
moduli ad efficiendum hoc functionality. En simplex exemplum:
from ipywidgets import IntSlider
# 创建滑块
slider = IntSlider(value=50, min=0, max=100, step=1)
# 定义更新函数
def update_plot(change):
new_value = change['new']
scatter.x = np.linspace(0, new_value, 100)
# 绑定滑块到更新函数
slider.observe(update_plot, names='value')
# 显示滑块和图表
slider
fig
In hoc exemplo, usor potest dynamice datas renovare in chartula componendo valorem lapsus.
Interactive dashboards magna applicatione in notitia visualizationis sunt, quae utentes dynamice explorare permittunt per elementa interactive ut profundiorem cognitionem informationum post notitias acquirerent. bqplot valida lineamenta praebet ad creandum dashboards interactive. Hic simplex exemplum est ostendens quomodo creare ashboardday plures chartas et interactivas partes continere.
Importare necessarias bibliothecas:
import bqplot as bq
import ipywidgets as widgets
from bqplot import pyplot as plt
import numpy as np
Para data:
x = np.arange(100)
y = np.random.randn(100).cumsum()
Create chart component:
line_chart = plt.plot(x, y, 'Line Chart')
bar_chart = plt.bar(x, y, 'Bar Chart')
Create interactive components:
dropdown = widgets.Dropdown(
options=['Line Chart', 'Bar Chart'],
value='Line Chart',
description='Chart Type:'
)
Define commercium logicae:
def on_change(change):
if change['new'] == 'Line Chart':
plt.clear()
plt.plot(x, y, 'Line Chart')
elif change['new'] == 'Bar Chart':
plt.clear()
plt.bar(x, y, 'Bar Chart')
dropdown.observe(on_change, names='value')
Composita components:
dashboard = widgets.VBox([dropdown, plt.figure])
display(dashboard)
Per gradus superiores simplicem interactivum ashboardday creare possumus ubi users de diversis chartis generibus per gutta-down menu eligere potest ut dynamicam datam visualizationem consequantur.
Datorum selectores magni ponderis sunt in bqplot pro notitia eliquatione et commercio. Per selectorem datam, utentes notitias directe eligere et operari possunt in chartula, ut subtiliores analysin consequantur.
Importare necessarias bibliothecas:
import bqplot as bq
import ipywidgets as widgets
from bqplot import pyplot as plt
import numpy as np
Para data:
x = np.arange(100)
y = np.random.randn(100).cumsum()
Create chart:
scatter_chart = plt.scatter(x, y, 'Scatter Chart')
Crea data electrix:
selector = bq.interacts.BrushSelector(x_scale=scatter_chart.scales['x'], y_scale=scatter_chart.scales['y'])
scatter_chart.interaction = selector
Define selectio logicae:
def on_selection(change):
selected_data = scatter_chart.selected
print(f"Selected Data: {selected_data}")
selector.observe(on_selection, names='selected')
Ostende chart:
display(plt.figure)
Per gradus superiores, electrix notitias in dispersos insidias creare potest. Usor potest eligere data puncta trahendo murem et output notitias electas in consolatorio.
bqplot non solum 2D chartis fundamentalibus sustinet, sed etiam munera chart geographica validissima praebet quae varias tabulas proiectiones creare possunt et chartis geographicis provectis.
Importare necessarias bibliothecas:
import bqplot as bq
import ipywidgets as widgets
from bqplot import pyplot as plt
import numpy as np
Para orbis terrarum notitia:
import json
with open('world.json') as f:
world_data = json.load(f)
Orbis Terrarum creare:
map_chart = bq.Map(
map_data=bq.topo_load('world.json'),
scales={'projection': bq.AlbersUSA()}
)
Create interactive components:
dropdown = widgets.Dropdown(
options=['AlbersUSA', 'Mercator', 'Orthographic'],
value='AlbersUSA',
description='Projection:'
)
Define commercium logicae:
def on_change(change):
if change['new'] == 'AlbersUSA':
map_chart.scales['projection'] = bq.AlbersUSA()
elif change['new'] == 'Mercator':
map_chart.scales['projection'] = bq.Mercator()
elif change['new'] == 'Orthographic':
map_chart.scales['projection'] = bq.Orthographic()
dropdown.observe(on_change, names='value')
Composita components:
map_dashboard = widgets.VBox([dropdown, map_chart])
display(map_dashboard)
Per gradus superiores tabulam geographicam creare possumus quae plures tabulas proiectiones sustinet. Users varias proiectionis modos per gutta-down tabulas eligere possumus ad ostentationem dynamicam.
Per has provectas functiones et applicationes, bqplot praebet utentes valida instrumenta visualizationis data, analysin interactiva faciens in Iupytero Comentario commodiorem et efficientem.
bqplot
praebet aliquid similematplotlib
of*pyplot
API utentes utentes celeriter creare et chartas ostentare permittit.Sunt quidam communiterpyplot
Munera et exempla:
figure()
: Novam graphicam crea.plot()
: ducatur chart.scatter()
: dispergat insidias trahere.bar()
: ducatur talea chart.pie()
: trahe chartulam pie.hist()
: Mearum trahe.Sample signum:
from bqplot import pyplot as plt
import numpy as np
# 创建数据
x = np.arange(10)
y = x ** 2
# 创建图形
fig = plt.figure()
# 绘制折线图
plt.plot(x, y)
# 显示图形
plt.show()
bqplot
Obiectum exemplar in Grammaticis graphicis nititur, ut flexibiliorem et distinctiorem rationem praebeat chartis customizandis. Hic sunt quaedam objecta et exempla nuclei:
Figure
: Continens graphi, continens omnia figmenta et secures.Mark
: Imprimis elementa graphice, ut lineae, puncta, vectes, etc.Axis
: axis.Scale
: Data ad graphi destinata.Sample signum:
from bqplot import Figure, Axis, Scale, Lines
import numpy as np
# 创建数据
x = np.arange(10)
y = x ** 2
# 创建比例尺
x_scale = Scale(min=0, max=10)
y_scale = Scale(min=0, max=100)
# 创建轴
x_axis = Axis(scale=x_scale, label='X Axis')
y_axis = Axis(scale=y_scale, label='Y Axis', orientation='vertical')
# 创建标记
line = Lines(x=x, y=y, scales={'x': x_scale, 'y': y_scale})
# 创建图形
fig = Figure(marks=[line], axes=[x_axis, y_axis])
# 显示图形
fig
bqplot
Instrumenta instrumentorum et instrumentorum instrumentalium locupletes praebet functiones, permittens utentes cum graphicis commodius correspondeant.
Tooltip
: Praestare notitias informationes cum muris volitatur.Toolbar
: Interactive munera praebet ut zooming et panning.Sample signum:
from bqplot import Tooltip, Toolbar
# 创建工具提示
tooltip = Tooltip(fields=['x', 'y'], formats=['.2f', '.2f'])
# 创建工具栏
toolbar = Toolbar(figure=fig)
# 添加到图形
line.tooltip = tooltip
fig.toolbar = toolbar
# 显示图形
fig
bqplot
Interactive functiones complexas sustinet ut lectio, zooming, panning, etc. etiam,bqplot
Etiam adhiberi potest ad visualizationes provectas creandas sicut mappas mercatus.
Sample signum:
from bqplot import MarketMap
import pandas as pd
# 创建数据
data = pd.DataFrame({
'label': ['A', 'B', 'C', 'D'],
'values': [100, 200, 150, 300],
'color': ['red', 'green', 'blue', 'yellow']
})
# 创建市场地图
market_map = MarketMap(names=data['label'], values=data['values'], colors=data['color'])
# 创建图形
fig = Figure(marks=[market_map])
# 显示图形
fig
Per introductionem documentorum API superiorum, utentes melius intelligere et uti possunt bqplot
bibliotheca creare dives, interactive notitia visualizations.
Sunt quaedam provocationes, quae incepta migrantes ex aliis bibliothecis visualisationi datas possunt occurrere ut Matplotlib vel Plotly ad bqplot. Hic sunt gradus quidam key et considerationes ut tibi auxilium navigandi processus migrationis.
Priusquam migrationem incipias, primum debes notiones fundamentales bqplot comprehendere, quas possidet:
Certa notitia tua parata est et ad formas quae ab bqplot requiruntur facile converti possunt. De more, notitia in Pandas DataFrame condi potest, quae dat faciles notitias manipulationes et visualizationes.
Importare necessarias bibliothecas:
import bqplot as bq
import pandas as pd
import numpy as np
Create squamas et secures:
x_sc = bq.LinearScale()
y_sc = bq.LinearScale()
ax_x = bq.Axis(scale=x_sc, label='X Axis')
ax_y = bq.Axis(scale=y_sc, label='Y Axis', orientation='vertical')
Create tag:
data = pd.DataFrame(np.random.randn(100, 2), columns=['X', 'Y'])
scatter = bq.Scatter(x=data['X'], y=data['Y'], scales={'x': x_sc, 'y': y_sc})
Create chart:
fig = bq.Figure(axes=[ax_x, ax_y], marks=[scatter])
Ostende chart:
display(fig)
bqplot praebet munera interactiva dives, quae obtineri possunt ex diversis proprietatibus disponendis. Exempli gratia, ut zoom et sartaginem muneris efficiant:
scatter.enable_move = True
scatter.enable_zoom = True
bqplot permittit ut stylum chartæ tuae domicilii, incluso colore, stilo, linea stilo, etc. Exempli gratia, speciem dispertire insidiarum;
scatter = bq.Scatter(x=data['X'], y=data['Y'], scales={'x': x_sc, 'y': y_sc}, colors=['blue'], default_size=20, marker='triangle-up', stroke='black')
bqplot fons aperta est propositi et communitatis membra grata sunt ut codicem, documentum et exempla conferant. Hic sunt quaedam normae conlationes ad auxilium quod incipias conferendi ad consilium bqplot.
Clone repositio:
git clone https://github.com/bqplot/bqplot.git
cd bqplot
install clientelas:
pip install -r requirements.txt
Install development version:
pip install -e .
Cum codice conferente, has vias sequi placet:
Facere ramum:
git checkout -b my-new-feature
Committere mutationes:
git add .
git commit -m "Add some feature"
dis ramus:
git push origin my-new-feature
Create Request excute:
Novam petitionem excute in GitHub tuas mutationes et causam describens.
Sequentes has normas, magnum momentum conferre potes ad consilium bqplot et adiuvandum promovere interactive notitias visualizationis in communitate Pythonis.
bqplot est secundumGrammatica Graphica Visualization systema 2D, dispositoIuppiter codicillus design. Habet sequentia commoda significantes;
Licet bqplot validam functionem et flexibilitatem praebet, etiam limitationes nonnullas habet;
Ut principium activum apertum incepti, bqplot's progressus futurae trends attentionem merentur:
In summa, bqplot, ut instrumentum visualizationis interactive notitia valida, in campo notitiarum scientiarum prospectus late patet. Continua optimizatione et evolutionis lineamentis, plura possibilitates praebere perget analysi et visualizationis.