参考文章：

python大佬的可视化工具-Altair_qq_21478261的博客-CSDN博客_altair python

官方参考文章：

Example Gallery — Altair 4.2.0 documentation

Overview | Vega-Litepaaas

 安装轮子：

$ pip install altair vega_datasets

官网部分案列：

# Altair, datasets are most commonly provided as a Dataframe
import pandas as pd
import altair as alt
'''https://altair-viz.github.io/user_guide/encoding.html'''
'''https://vega.github.io/vega-lite/docs/'''
from vega_datasets import data
import numpy as np
pd.set_option('display.max_columns', 10)#显示列数
pd.set_option('display.max_rows', 10)#显示行数
pd.set_option('display.width',	5000)#显示宽度

df = data.seattle_weather()
print(df)
scale = alt.Scale(
    domain=['sun', 'fog', 'drizzle', 'rain', 'snow'],
    range=['#e7ba52', '#c7c7c7', '#aec7e8', '#1f77b4', '#9467bd'])
brush = alt.selection(type='interval')
points = alt.Chart().mark_point().encode(
    alt.X('temp_max:Q', title='Maximum Daily Temperature (C)'),
    alt.Y('temp_range:Q', title='Daily Temperature Range (C)'),
    color=alt.condition(brush,
                        'weather:N',
                        alt.value('lightgray'),
                        scale=scale),
    size=alt.Size('precipitation:Q',
                  scale=alt.Scale(range=[1, 200]))).transform_calculate(
    "temp_range",
    "datum.temp_max - datum.temp_min").properties(
    width=600, height=400).add_selection(brush)

bars = alt.Chart().mark_bar().encode(
    x='count()',
    y='weather:N',
    color=alt.Color('weather:N', scale=scale),
).transform_calculate(
    "temp_range",
    "datum.temp_max - datum.temp_min").transform_filter(brush).properties(
    width=600)

chart_w=alt.vconcat(points, bars, data=df)
chart_w.save("altair-weather.html")

print("-------------- interactive scatter plot--------------")
cars = data.cars()
print("cars:\n",cars)
'''
cars:
                           Name  Miles_per_Gallon  Cylinders  Displacement  Horsepower  Weight_in_lbs  Acceleration       Year  Origin
0    chevrolet chevelle malibu              18.0          8         307.0       130.0           3504          12.0 1970-01-01     USA
1            buick skylark 320              15.0          8         350.0       165.0           3693          11.5 1970-01-01     USA
2           plymouth satellite              18.0          8         318.0       150.0           3436          11.0 1970-01-01     USA
3                amc rebel sst              16.0          8         304.0       150.0           3433          12.0 1970-01-01     USA
4                  ford torino              17.0          8         302.0       140.0           3449          10.5 1970-01-01     USA
'''
#The key idea is that you are declaring links between data columns
# and visual encoding channels, such as the x-axis, y-axis, color, etc.
chart_car=alt.Chart(cars).mark_point().encode(
    x='Horsepower',#马力
    y='Miles_per_Gallon',#油耗
    # y='average(Miles_per_Gallon)',#油耗
    color='Origin',#来自哪里
).interactive()
chart_car.save("altair-cars_y=Miles_per_Gallon.html")


print("-------------- mark_bar--------------")
data_bar = pd.DataFrame({'a': list('CCCDDDEEE'),
                     'b': [2, 7, 4, 1, 2, 6, 8, 4, 7],
                     'b_des': [3, 8, 4, 1, 4, 4, 7, 4, 7]
                         })
chart_bar_xy=alt.Chart(data_bar).mark_bar().encode(
    x='a',
    y="average(b)"
)
chart_bar_yx=alt.Chart(data_bar).mark_bar(color='firebrick').encode(
    alt.X('b', type='quantitative', aggregate='average',title='avg(b) by category'),
    alt.Y('a', type='nominal',title='category')
)
#画目标线
tick = alt.Chart(data_bar).mark_tick(
    color='red',
    thickness=2,
    size=20 * 0.9,  # controls width of tick.
).encode(
    x='a',
    y='average(b_des)'
)
(chart_bar_xy+tick).save("chart_bar_xy_tick.html")
chart_bar_yx.save("chart_bar_yx.html")

source = data.wheat()
#画bar图
chart_bar_wheat=alt.Chart(source).mark_bar().encode(
    y="wheat:Q",
    x='year:O',
    # The highlight will be set on the result of a conditional statement
    #根据条件设定bar的颜色值
    color=alt.condition(
        alt.datum.year < 1610,  # If the year is 1810 this test returns True,
        alt.value('orange'),     # which sets the bar orange.
        alt.value('steelblue')   # And if it's not true it sets the bar steelblue.
    )
)
#加上数量
text = chart_bar_wheat.mark_text(
    align='center',
    baseline='middle',
    dy=-7  # Nudges text to right so it doesn't appear on top of the bar
).encode(
    text='wheat:Q'
)
# 增加均值线
rule = alt.Chart(source).mark_rule(color='red').encode(
    y='mean(wheat):Q')

#增加一个变量,同Y坐标
line = chart_bar_wheat.mark_line(color='red').encode(
    y='wages:Q'
)
#增加平均值的趋势
line2= alt.Chart(source).mark_line(color='red').transform_window(
    # The field to average
    rolling_mean='mean(wheat)',
    # The number of values before and after the current value to include.
    frame=[-9, 0]
).encode(
    x='year:O',
    y='rolling_mean:Q'
)
chart_bar_wheat_text_rule_line=(chart_bar_wheat+text+rule+line+line2).properties(height=900,width=600)
chart_bar_wheat_text_rule_line.save("chart_bar_wheat_text_rule_line.html")

print("-------------- mark_bar:叠加图--------------")
source = data.seattle_weather()
print("source:\n",source[:6])
'''
source:
         date  precipitation  temp_max  temp_min  wind  weather
0 2012-01-01            0.0      12.8       5.0   4.7  drizzle
1 2012-01-02           10.9      10.6       2.8   4.5     rain
2 2012-01-03            0.8      11.7       7.2   2.3     rain
'''
mark_bar_round=alt.Chart(source).mark_bar(
    cornerRadiusTopLeft=3,#矩形倒角
    cornerRadiusTopRight=3,#矩形倒角
    opacity=0.8
).encode(
    x='month(date):O',
    y='count(weather):Q',#weather的计数，根据color中的属性值计数

    color='weather:N'
)
mark_bar_round.save("mark_bar_round.html")

print("-------------- mark_tick:标记图--------------")
chart_tick=alt.Chart(cars).mark_tick().encode(
    x='Horsepower:Q',
    y='Cylinders:O'#作为标记

)
chart_tick.save("chart_tick.html")

print("-------------- mark_bar:joinaggregate transform--------------")

source = pd.DataFrame({'Activity': ['Sleeping', 'Eating', 'TV', 'Work', 'Exercise'],
                           'Time': [8, 2, 4, 8, 2]})

chart_bar_joinaggregate=alt.Chart(source).transform_joinaggregate(
    TotalTime='sum(Time)',
).transform_calculate(
    PercentOfTotal="datum.Time / datum.TotalTime"
).mark_bar().encode(
    alt.X('PercentOfTotal:Q', axis=alt.Axis(format='.0%')),
    y='Activity:N')
chart_bar_joinaggregate.save("chart_bar_joinaggregate.html")


print("-------------- mark_bar:mark_bar_sum--------------")
source2 = data.barley()
print(source2)
mark_bar_sum1=alt.Chart(source2).mark_bar().encode(
    x=alt.X('sum(yield)',  stack='normalize'),
    y='variety',
    color=alt.Color('site')
)
text1 = alt.Chart(source2).mark_text(dx=-15, dy=3, color='white').encode(
    x=alt.X('sum(yield):Q', stack='normalize'),
    y=alt.Y('variety:N'),
    detail='site:N',
    text=alt.Text('sum(yield):Q', format='.1f')
)
mark_bar_sum2=alt.Chart(source2).mark_bar().encode(
    column='year',
    x='sum(yield):Q',
    y=alt.Y('site:N', sort='x'),
    color='variety',
    order=alt.Order(
      # Sort the segments of the bars by this field
      'variety',
      sort='ascending'
    )
)

text2 = alt.Chart(source2).mark_text(dx=-15, dy=3, color='white').encode(
    column='year',
    x=alt.X('sum(yield):Q', stack='zero'),
    y=alt.Y('site:N'),
    detail='variety:N',
    text=alt.Text('sum(yield):Q', format='.1f')
)
mark_bar_sum1_text=mark_bar_sum1+text1
mark_bar_sum2_text=mark_bar_sum2
mark_bar_sum=(mark_bar_sum1_text & mark_bar_sum2_text)
mark_bar_sum.save("mark_bar_sum.html")


print("-------------- mark_line:with Confidence Interval Band--------------")
line = alt.Chart(cars).mark_line().encode(
    x='Year',
    y='mean(Miles_per_Gallon)'
)

band = alt.Chart(cars).mark_errorband(extent='ci').encode(
    x='Year',
    y=alt.Y('Miles_per_Gallon', title='Miles/Gallon'),
)

mark_line=band + line
mark_line.save("mark_line.html")

print("-------------- mark_area--------------")
source = data.sp500.url
brush = alt.selection(type='interval', encodings=['x'])
base = alt.Chart(source).mark_area().encode(
    x = 'date:T',
    y = 'price:Q'
).properties(
    width=600,
    height=200
)

upper = base.encode(
    alt.X('date:T', scale=alt.Scale(domain=brush))
)

lower = base.properties(
    height=60
).add_selection(brush)
mark_area_A=upper & lower
mark_area_A.save("mark_area_A.html")

source_iowa_electricity = data.iowa_electricity()
print("data.iowa_electricity():\n",source_iowa_electricity)
mark_area_B=alt.Chart(source_iowa_electricity).mark_area(opacity=0.3).encode(
    x="year:T",
    y=alt.Y("net_generation:Q", stack=None),
    color="source:N"
)
mark_area_B.save("mark_area_B.html")

mark_area_B_row=alt.Chart(source_iowa_electricity).mark_area().encode(
    x="year:T",
    y="net_generation:Q",
    color="source:N",
    row="source:N"
).properties(
    height=100
)
mark_area_B_row.save("mark_area_B_row.html")

source_iris = data.iris()
print("data.iris():\n",source_iris)
'''
data.iris():
      sepalLength  sepalWidth  petalLength  petalWidth    species
0            5.1         3.5          1.4         0.2     setosa
1            4.9         3.0          1.4         0.2     setosa
... 
146          6.3         2.5          5.0         1.9  virginica
147          6.5         3.0          5.2         2.0  virginica
'''
mark_area_C=alt.Chart(source_iris).transform_fold(
    ['petalWidth',
     'petalLength',
     'sepalWidth',
     'sepalLength'],
    as_ = ['Measurement_type', 'value']
).transform_density(
    density='value',
    bandwidth=0.3,
    groupby=['Measurement_type'],
    extent= [0, 8],
    counts = True,
    steps=200
).mark_area().encode(
    alt.X('value:Q'),
    alt.Y('density:Q', stack='zero'),
    alt.Color('Measurement_type:N')
).properties(width=400, height=100)
mark_area_C.save("mark_area_C.html")

source_stocks = data.stocks()
print("data.stocks():\n",source_stocks)
'''
data.stocks():
     symbol       date   price
0     MSFT 2000-01-01   39.81
1     MSFT 2000-02-01   36.35
... 
557   AAPL 2010-01-01  192.06
558   AAPL 2010-02-01  204.62
'''
mark_area_D=alt.Chart(source_stocks).transform_filter(
    alt.datum.symbol != 'GOOG'
).mark_area().encode(
    x='date:T',
    y='price:Q',
    color='symbol:N',
    row=alt.Row('symbol:N', sort=['MSFT', 'AAPL', 'IBM', 'AMZN'])
).properties(height=50, width=400)
mark_area_D.save("mark_area_D.html")


print("-------------- mark_point+mark_tick--------------")
# Configure the options common to all layers
brush = alt.selection(type='interval')
base = alt.Chart(cars).add_selection(brush)

# Configure the points
points = base.mark_point().encode(
    x=alt.X('Miles_per_Gallon', title=''),
    y=alt.Y('Horsepower', title=''),
    color=alt.condition(brush, 'Origin', alt.value('grey'))
)

# Configure the ticks
tick_axis = alt.Axis(labels=False, domain=False, ticks=False)

x_ticks = base.mark_tick().encode(
    alt.X('Miles_per_Gallon', axis=tick_axis),
    alt.Y('Origin', title='', axis=tick_axis),
    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
)

y_ticks = base.mark_tick().encode(
    alt.X('Origin', title='', axis=tick_axis),
    alt.Y('Horsepower', axis=tick_axis),
    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
)

# Build the chart
cars_tick=y_ticks | (points & x_ticks)
cars_tick.save("cars_tick.html")

print("-------------- mark_circle--------------")
mark_circle_car=alt.Chart(cars).mark_circle().encode(
    alt.X(alt.repeat("column"), type='quantitative'),
    alt.Y(alt.repeat("row"), type='quantitative'),
    color='Origin:N'
).properties(
    width=150,
    height=150
).repeat(
    row=['Horsepower', 'Acceleration', 'Miles_per_Gallon'],
    column=['Miles_per_Gallon', 'Acceleration', 'Horsepower']
).interactive()
mark_circle_car.save("mark_circle_car.html")

np.random.seed(1)
source_loess = pd.DataFrame({
    'x': np.arange(100),
    'A': np.random.randn(100).cumsum(),
    'B': np.random.randn(100).cumsum(),
    'C': np.random.randn(100).cumsum(),
})

print("-------------- 趋势线--------------")
base1 = alt.Chart(source_loess).mark_circle(opacity=0.5).transform_fold(
    fold=['C'],
    as_=['category', 'y'],
).encode(
    alt.X('x:Q'),
    alt.Y('y:Q'),
    alt.Color('category:N')
)
base2 = alt.Chart(source_loess).mark_circle(opacity=0.5).transform_fold(
    fold=['A', 'B'],
    as_=['category2', 'y2'],
).encode(
    alt.X('x:Q'),
    alt.Y('y2:Q'),
    alt.Color('category2:N')
)
mark_circle_loess=base1 + base2+ base2.transform_loess('x', 'y2', groupby=['category2']).mark_line(size=4)
mark_circle_loess.save("mark_circle_loess.html")

print("-------------- MAPS--------------")
# Data generators for the background
sphere = alt.sphere()
graticule = alt.graticule()

# Source of land data
source = alt.topo_feature(data.world_110m.url, 'countries')

# Layering and configuring the components
maps_world=alt.layer(
    alt.Chart(sphere).mark_geoshape(fill='lightblue'),
    alt.Chart(graticule).mark_geoshape(stroke='white', strokeWidth=0.5),
    alt.Chart(source).mark_geoshape(fill='ForestGreen', stroke='black')
).project(
    'naturalEarth1'
).properties(width=600, height=400).configure_view(stroke=None)
maps_world.save("maps_world.html")