接下来你可以继续测试一下你的vega_datasets库是否安装成功,vega_datasets库里面具有多个数据集,小编这里就用其中的stocks数据集做一个测试吧(谁让小编是一多家上市公司的董事呢)。 如果你安装成功并且你没有输入错误的话,你会得到: pdvega_line测试图 是不是都开始怀疑人生了,这么简单就可以做出在Excel中至少需要...
import plotly.express as px from vega_datasets import data df = data.disasters() df = df[df.Year > 1990] fig = px.bar(df, y="Entity", x="Deaths", animation_frame="Year", orientation='h', range_x=[0, df.Deaths.max()], color="Entity") # improve aesthetics (size, grids etc....
复制代码 import altair as alt from vega_datasets import data # 准备数据 source = data.cars() # 绘制散点图 chart = alt.Chart(source).mark_circle(size=60).encode( x="Horsepower", y="Miles_per_Gallon", color="Origin", tooltip=["Name", "Origin"] ).interactive() chart.show() 小贴士...
import plotly.express as px from vega_datasets import data import pandas as pd df = data.movies() df = df.dropna() df['Genre_id'] = df.Major_Genre.factorize()[0] fig = px.parallel_categories( df, dimensions=['MPAA_Rating', 'Creative_Type', 'Major_Genre'], color="Genre_id", ...
fromvega_datasetsimportdata df = data.disasters df = df[df.Year >1990] fig = px.bar(df, y="Entity", x="Deaths", animation_frame="Year", orientation='h', range_x=[0, df.Deaths.max], color="Entity") # improve aesthetics (size, grids etc.) ...
import altair as alt from vega_datasets import data # 准备数据 source = data.cars() # 绘制散点图 chart = alt.Chart(source).mark_circle(size=60).encode( x="Horsepower", y="Miles_per_Gallon", color="Origin", tooltip=["Name", "Origin"] ).interactive() chart.show()小贴士：Altair生成...
vega-datasets并非必须安装,但Altair官网案例数据集基本来自这个数据集,为了便于学习,建议大家安装这个数据包。安装成功后,就可以利用Altair来实现数据可视化了。 Altair可视化样例 因为Altair利用JavaScript实现数据可视化,之前配置在Excel内jupyter开发环境,这次研究Altair发挥了用武之地。
pip install vega_datasets pip install vega_datasets 1. 2. 3. 然后打开anaconda安装目录,打开Navigation 打开JupyterLab 新建一个notebook,运行示例代码,OK! Altair图形语法 Chart有三个基本方法:数据(data)、标记(mark)和编码(encode),使用它们的格式如下:alt.Chart(data).mark_point().encode( ...
from vega_datasets import data source = data.unemployment_across_industries.url alt.Chart(source).mark_area().encode( alt.X('yearmonth(date):T', axis=alt.Axis(format='%Y', domain=False, tickSize=0) ), alt.Y('sum(count):Q', stack='center', axis=None), ...
from vega_datasets import data source = data.unemployment_across_industries.url alt.Chart(source).mark_area().encode( alt.X('yearmonth(date):T', axis=alt.Axis(format='%Y', domain=False, tickSize=0) ), alt.Y('sum(count):Q', stack='center', axis=None), ...