使用pdvega绘制折线图可以说相对简便,仅仅需要一条语句就可以完成,前提条件是你已经将你的数据整理成了数据表的形式(当然pdvega也可以很方便地处理费数据表形式的数据,但是作为一个与数据和图表打交道的人,个人建议还是要养成一种保持干净整洁数据的习惯)。为了后面作图的需要,本期文章中会先后引用三个数据表,这三...
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....
importplotly.expressaspx 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.) fig.update_layo...
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", ...
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() ...
# load a sample dataset as a pandas DataFrame from vega_datasets import data cars = data.cars() # make the chart alt.Chart(cars).mark_point().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ).interactive() Altair 相比于Matplotlib和Seaborn的散点图,Altair不需要使用者花费...
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...
from vega_datasets import data source = data.cars() chart = alt.Chart(source).mark_circle().encode( x='Horsepower', y='Miles_per_Gallon', color='Origin', ).interactive() tab1, tab2 = st.tabs(["Streamlit theme (default)", "Altair native theme"]) ...
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), ...
import altair as altfrom vega_datasets import datasource = data.iris()alt.Chart(source).mark_circle().encode( alt.X('sepalLength').scale(zero=False), alt.Y('sepalWidth').scale(zero=False, padding=1), color='species', size='petalWidth')4. Bokeh Bokeh主打web交互式可视化，...