python可视化神器

一.些简单图形的绘制
二.进阶操作
三.图片的保存
四.图片一些属性的配置
优缺点

python可视化神器altair

今天介绍一个python库altair，它的语法与r的ggplot有点类似

对中文的兼容性也很好，以一幅简单的散点图举例：

安装说明:

pip install altair
pip install vega-datasets#注意这里是"-"不是"_"，我们要使用到其中的数据

import altair as alt
from vega_datasets import data
cars = data.cars()
cars

alt.Chart(cars).mark_point().encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
    shape='Origin'
).interactive()

输出以下图形，点击旁边的三个点，还能将其保存为各种形式的图片。

可以发现它的语法也是及其简单：

cars是我们所需要的数据，他是一个数据框(dataframe的形式)
make-point 就是散点图
x=‘Horsepower’ ， y='Miles_per_Gallon’分别对应我们的x轴和y轴数据
color=‘Origin’ 根据产地来映射颜色，这与ggplot的语法很相似
shape=‘Origin’，这里就是根据产地来映射点的形状
interactive() 生成交互式图片，效果如下

请添加图片描述

一.些简单图形的绘制

(一).柱状图

语法很简单

import altair as alt
import pandas as pd

source = pd.DataFrame({
    
    
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

alt.Chart(source).mark_bar().encode(
    x='a',
    y='b',
    color="a"
)

1. 然后我们还可以设置高亮柱状图的某一根柱子，其他柱子设置为一样的颜色：

import altair as alt
import pandas as pd

source = pd.DataFrame({
    
    
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

alt.Chart(source).mark_bar().encode(
    x='a:O',
    y='b:Q',
    color=alt.condition(
        alt.datum.a=="A",#这里设置条件，如果a的值是"A"，需要改动的只有a这个地方和"A"这个地方，后者是前者满足的条件
        alt.value("red"),#如果满足上面的条件颜色就变成红色
        alt.value("yellow")#如果不满足就变成黄色
    )
).properties(width=600,height=400)#这里的height和width分别设置图片的大小和高度

2. 翻转图片，同时添加图片标注，在图上加上数据

呃呃呃，其实翻转图片，就是x和y轴数据互换

import altair as alt
import pandas as pd

source = pd.DataFrame({
    
    
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

bars=   alt.Chart(source).mark_bar().encode(
    x='b:Q',
    y='a:O',
    color="a")
text = bars.mark_text(
    align='right',#在这里选择一个['left', 'center', 'right']
    baseline='middle',
    dx=10  # Nudges text to right so it doesn't appear on top of the bar
).encode(
    text='a'#这里是添加数据
)
bars+text

3.在图形上添加线条

import altair as alt
import pandas as pd

source = pd.DataFrame({
    
    
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})

bars=   alt.Chart(source).mark_bar().encode(
    x='a',
    y='b',
    color="a")

rule = alt.Chart(source).mark_rule(color='red').encode(
    y='mean(b)',
)
(bars+rule).properties(width=600,height=400)

4. 组合图，柱状图+折线图

首先我们需要固定好x轴

import altair as alt
from vega_datasets import data
import pandas as pd

source = pd.DataFrame({
    
    
    'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
    'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})
base = alt.Chart(source).encode(x='a:O')

bar = base.mark_bar().encode(y='b:Q')

line =  base.mark_line(color='red').encode(
    y='b:Q'
)

(bar + line).properties(width=600)

(二).热力图

import altair as alt
import numpy as np
import pandas as pd

# Compute x^2 + y^2 across a 2D grid
x, y = np.meshgrid(range(-5, 5), range(-5, 5))
z = x ** 2 + y ** 2

# Convert this grid to columnar data expected by Altair
source = pd.DataFrame({
    
    'x': x.ravel(),
                     'y': y.ravel(),
                     'z': z.ravel()})

alt.Chart(source).mark_rect().encode(
    x='x:O',
    y='y:O',
    color='z:Q'
)

(三).直方图

统计不同范围的数字出现的次数

这里还是以我们一开始cars数据举例说明：

import altair as alt
from vega_datasets import data
cars = data.cars()
cars
alt.Chart(cars).mark_bar().encode(
    alt.X("Displacement", bin=True),
    y='count()',
    color="Origin"
)

(四).线图

可以用来画函数曲线，比如:
$\displaystyle y=\frac{\sin x}{5}$

import altair as alt
import numpy as np
import pandas as pd

x = np.arange(100)
source = pd.DataFrame({
    
    
  'x': x,
  'f(x)': np.sin(x / 5)
})

alt.Chart(source).mark_line().encode(
    x='x',
    y='f(x)'
)

(五).带有鼠标提示的散点图

就是当你点击某个位置的时候，会给你相应的信息，比如说它的坐标

比如我在下面的代码中设置了tooltip，当我点击某个点时就会显示出相应的名称，归属地，马力

import altair as alt
from vega_datasets import data

source = data.cars()

alt.Chart(source).mark_circle(size=60).encode(
    x='Horsepower',
    y='Miles_per_Gallon',
    color='Origin',
    tooltip=['Name', 'Origin', 'Horsepower', 'Miles_per_Gallon']
).interactive()

请添加图片描述

(六).堆积面积图

比如下面的代码,这里的x就是不同的年份，y就是使用不同原料的净发电量

import altair as alt
from vega_datasets import data

source = data.iowa_electricity()
source
alt.Chart(source).mark_area().encode(
    x="year:T",
    y="net_generation:Q",
    color="source:N"
)

(七).扇形图

import pandas as pd
import altair as alt

source = pd.DataFrame({
    
    "category": [1, 2, 3, 4, 5, 6], "value": [4, 6, 10, 3, 7, 8]})

alt.Chart(source).mark_arc(innerRadius=50).encode(
    theta=alt.Theta(field="value", type="quantitative"),
    color=alt.Color(field="category", type="nominal"),
)

二.进阶操作

1. 折线图

1.制作一个带有95%置信区间带的折线图。

## 带有置信区间
import altair as alt
from vega_datasets import data

source = data.cars()

line = alt.Chart(source).mark_line().encode(
    x='Year',
    y='mean(Miles_per_Gallon)'
)

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

band + line

2.折线图标记

#折线图标记
import altair as alt
import numpy as np
import pandas as pd

x = np.arange(100)
source = pd.DataFrame({
    
    
  'x': x,
  'f(x)': np.sin(x / 5)
})

alt.Chart(source).mark_line(
    point=alt.OverlayMarkDef(color="red")
).encode(
    x='x',
    y='f(x)'
)

3.在不同的位置设置折线图线条的粗细

#线条粗细随之变化
import altair as alt
from vega_datasets import data

source = data.wheat()

alt.Chart(source).mark_trail().encode(
    x='year:T',
    y='wheat:Q',
    size='wheat:Q'
)

2.标准的面积堆积图

区别就是他会堆满整个图片

import altair as alt
from vega_datasets import data

source = data.iowa_electricity()

alt.Chart(source).mark_area().encode(
    x="year:T",
    y=alt.Y("net_generation:Q", stack="normalize"),
    color="source:N"
)

3. 带有缺口的扇形图

import numpy as np
import altair as alt

alt.Chart().mark_arc(color="gold").encode(
    theta=alt.datum((5 / 8) * np.pi, scale=None),
    theta2=alt.datum((19 / 8) * np.pi),
    radius=alt.datum(100, scale=None),
)

1.饼图

import pandas as pd
import altair as alt

source = pd.DataFrame({
    
    "category": [1, 2, 3, 4, 5, 6], "value": [4, 6, 10, 3, 7, 8]})

alt.Chart(source).mark_arc().encode(
    theta=alt.Theta(field="value", type="quantitative"),
    color=alt.Color(field="category", type="nominal"),
)

2.辐射状的饼图

import pandas as pd
import altair as alt

source = pd.DataFrame({
    
    "values": [12, 23, 47, 6, 52, 19]})

base = alt.Chart(source).encode(
    theta=alt.Theta("values:Q", stack=True),
    radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
    color="values:N",
)

c1 = base.mark_arc(innerRadius=20, stroke="#fff")

c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")

c1 + c2

4.散点图进阶

1.带有误差棒的散点图

import altair as alt
import pandas as pd
import numpy as np

# generate some data points with uncertainties
np.random.seed(0)
x = [1, 2, 3, 4, 5]
y = np.random.normal(10, 0.5, size=len(x))
yerr = 0.2

# set up data frame
source = pd.DataFrame({
    
    "x": x, "y": y, "yerr": yerr})

# the base chart
base = alt.Chart(source).transform_calculate(
    ymin="datum.y-datum.yerr",
    ymax="datum.y+datum.yerr"
)

# generate the points
points = base.mark_point(
    filled=True,
    size=50,
    color='black'
).encode(
    x=alt.X('x', scale=alt.Scale(domain=(0, 6))),
    y=alt.Y('y', scale=alt.Scale(zero=False))
)

# generate the error bars
errorbars = base.mark_errorbar().encode(
    x="x",
    y="ymin:Q",
    y2="ymax:Q"
)

points + errorbars

2. 散点图加标签

#散点图加标签
import altair as alt
import pandas as pd

source = pd.DataFrame({
    
    
    'x': [1, 3, 5, 7, 9],
    'y': [1, 3, 5, 7, 9],
    'label': ['我', '是', '你', '爸', '爸']
})

points = alt.Chart(source).mark_point().encode(
    x='x:Q',
    y='y:Q'
)

text = points.mark_text(
    align='left',
    baseline='middle',
    dx=7
).encode(
    text='label'
)

points + text

5. 世界地图

import altair as alt
from vega_datasets import data

# 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
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)

三.图片的保存

你可以将其保存为svg,png,html,pdf，json等格式

import altair as alt
from vega_datasets import data

chart = alt.Chart(data.cars.url).mark_point().encode(
    x='Horsepower:Q',
    y='Miles_per_Gallon:Q',
    color='Origin:N'
)

chart.save('chart.json')

chart.save('chart.html')

chart.save('chart.png')
chart.save('chart.svg')
chart.save('chart.pdf')

同时设置保存图片的大小

chart.save('chart.png', scale_factor=2.0)

四.图片一些属性的配置

比如说给图片添加标题：

#世界地图
import altair as alt
from vega_datasets import data

# 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
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,title="世界地图").configure_view(stroke=None)

Property	Type	Description
arc	`RectConfig`	Arc-specific Config
area	`AreaConfig`	Area-Specific Config
aria	`boolean`	A boolean flag indicating if ARIA default attributes should be included for marks and guides (SVG output only). If false, the `"aria-hidden"` attribute will be set for all guides, removing them from the ARIA accessibility tree and Vega-Lite will not generate default descriptions for marks.Default value: `true`.
autosize	anyOf(`AutosizeType`, `AutoSizeParams`)	How the visualization size should be determined. If a string, should be one of `"pad"`, `"fit"` or `"none"`. Object values can additionally specify parameters for content sizing and automatic resizing.Default value: `pad`
axis	`AxisConfig`	Axis configuration, which determines default properties for all `x` and `y` axes. For a full list of axis configuration options, please see the corresponding section of the axis documentation.
axisBand	`AxisConfig`	Config for axes with “band” scales.
axisBottom	`AxisConfig`	Config for x-axis along the bottom edge of the chart.
axisDiscrete	`AxisConfig`	Config for axes with “point” or “band” scales.
axisLeft	`AxisConfig`	Config for y-axis along the left edge of the chart.
axisPoint	`AxisConfig`	Config for axes with “point” scales.
axisQuantitative	`AxisConfig`	Config for quantitative axes.
axisRight	`AxisConfig`	Config for y-axis along the right edge of the chart.
axisTemporal	`AxisConfig`	Config for temporal axes.
axisTop	`AxisConfig`	Config for x-axis along the top edge of the chart.
axisX	`AxisConfig`	X-axis specific config.
axisXBand	`AxisConfig`	Config for x-axes with “band” scales.
axisXDiscrete	`AxisConfig`	Config for x-axes with “point” or “band” scales.
axisXPoint	`AxisConfig`	Config for x-axes with “point” scales.
axisXQuantitative	`AxisConfig`	Config for x-quantitative axes.
axisXTemporal	`AxisConfig`	Config for x-temporal axes.
axisY	`AxisConfig`	Y-axis specific config.
axisYBand	`AxisConfig`	Config for y-axes with “band” scales.
axisYDiscrete	`AxisConfig`	Config for y-axes with “point” or “band” scales.
axisYPoint	`AxisConfig`	Config for y-axes with “point” scales.
axisYQuantitative	`AxisConfig`	Config for y-quantitative axes.
axisYTemporal	`AxisConfig`	Config for y-temporal axes.
background	anyOf(`Color`, `ExprRef`)	CSS color property to use as the background of the entire view.Default value: `"white"`
bar	`BarConfig`	Bar-Specific Config
boxplot	`BoxPlotConfig`	Box Config
circle	`MarkConfig`	Circle-Specific Config
concat	`CompositionConfig`	Default configuration for all concatenation and repeat view composition operators (`concat`, `hconcat`, `vconcat`, and `repeat`)
countTitle	`string`	Default axis and legend title for count fields.Default value: `'Count of Records`.
customFormatTypes	`boolean`	Allow the `formatType` property for text marks and guides to accept a custom formatter function registered as a Vega expression.
errorband	`ErrorBandConfig`	ErrorBand Config
errorbar	`ErrorBarConfig`	ErrorBar Config
facet	`CompositionConfig`	Default configuration for the `facet` view composition operator
fieldTitle	[‘verbal’, ‘functional’, ‘plain’]	Defines how Vega-Lite generates title for fields. There are three possible styles: - `"verbal"` (Default) - displays function in a verbal style (e.g., “Sum of field”, “Year-month of date”, “field (binned)”). - `"function"` - displays function using parentheses and capitalized texts (e.g., “SUM(field)”, “YEARMONTH(date)”, “BIN(field)”). - `"plain"` - displays only the field name without functions (e.g., “field”, “date”, “field”).
font	`string`	Default font for all text marks, titles, and labels.
geoshape	`MarkConfig`	Geoshape-Specific Config
header	`HeaderConfig`	Header configuration, which determines default properties for all headers.For a full list of header configuration options, please see the corresponding section of in the header documentation.
headerColumn	`HeaderConfig`	Header configuration, which determines default properties for column headers.For a full list of header configuration options, please see the corresponding section of in the header documentation.
headerFacet	`HeaderConfig`	Header configuration, which determines default properties for non-row/column facet headers.For a full list of header configuration options, please see the corresponding section of in the header documentation.
headerRow	`HeaderConfig`	Header configuration, which determines default properties for row headers.For a full list of header configuration options, please see the corresponding section of in the header documentation.
image	`RectConfig`	Image-specific Config
legend	`LegendConfig`	Legend configuration, which determines default properties for all legends. For a full list of legend configuration options, please see the corresponding section of in the legend documentation.
line	`LineConfig`	Line-Specific Config
lineBreak	anyOf(`string`, `ExprRef`)	A delimiter, such as a newline character, upon which to break text strings into multiple lines. This property provides a global default for text marks, which is overridden by mark or style config settings, and by the lineBreak mark encoding channel. If signal-valued, either string or regular expression (regexp) values are valid.
mark	`MarkConfig`	Mark Config
numberFormat	`string`	D3 Number format for guide labels and text marks. For example `"s"` for SI units. Use D3’s number format pattern.
padding	anyOf(`Padding`, `ExprRef`)	The default visualization padding, in pixels, from the edge of the visualization canvas to the data rectangle. If a number, specifies padding for all sides. If an object, the value should have the format `{"left": 5, "top": 5, "right": 5, "bottom": 5}` to specify padding for each side of the visualization.Default value: `5`
params	array(`Parameter`)	Dynamic variables that parameterize a visualization.
point	`MarkConfig`	Point-Specific Config
projection	`ProjectionConfig`	Projection configuration, which determines default properties for all projections. For a full list of projection configuration options, please see the corresponding section of the projection documentation.
range	`RangeConfig`	An object hash that defines default range arrays or schemes for using with scales. For a full list of scale range configuration options, please see the corresponding section of the scale documentation.
rect	`RectConfig`	Rect-Specific Config
rule	`MarkConfig`	Rule-Specific Config
scale	`ScaleConfig`	Scale configuration determines default properties for all scales. For a full list of scale configuration options, please see the corresponding section of the scale documentation.
selection	`SelectionConfig`	An object hash for defining default properties for each type of selections.
square	`MarkConfig`	Square-Specific Config
style	`StyleConfigIndex`	An object hash that defines key-value mappings to determine default properties for marks with a given style. The keys represent styles names; the values have to be valid mark configuration objects.
text	`MarkConfig`	Text-Specific Config
tick	`TickConfig`	Tick-Specific Config
timeFormat	`string`	Default time format for raw time values (without time units) in text marks, legend labels and header labels.Default value: `"%b %d, %Y"` Note: Axes automatically determine the format for each label automatically so this config does not affect axes.
title	`TitleConfig`	Title configuration, which determines default properties for all titles. For a full list of title configuration options, please see the corresponding section of the title documentation.
trail	`LineConfig`	Trail-Specific Config
view	`ViewConfig`	Default properties for single view plots.

优缺点

优点：语法简单，对中文的兼容性好，与r语言的ggplot很类似。

缺点：生成图片不能直接复制，需要保存到本地，这一点不如matplotlib

有兴趣的研究的话：点击此链接

展示一下部分图片

参考：更多内容请点我：https://altair-viz.github.io/gallery/index.html

＞python可视化神器altair