干货 | 一文带你搞定Python 数据可视化

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干货 | 一文带你搞定Python 数据可视化

技术小能手 2018-10-30 14:04:29 浏览2038
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01前言

在之前的一篇文章《Python 数据可视化利器》中,我写了 Bokeh、pyecharts 的用法,但是有一个挺强大的库 Plotly 没写,主要是我看到它的教程都是在 Jupyter Notebooks 中使用,说来也奇怪,硬是找不到如何本地使用(就是本地输出 HTML 文件),所以不敢写出来。现在已经找到方法了,这里我就在原文的基础上增加了 Plotly 的部分教程。

数据可视化的第三方库挺多的,这里我主要推荐两个,分别是 Bokeh、pyecharts。

02推荐

数据可视化的库有挺多的,这里推荐几个比较常用的:

 ●  Matplotlib
 ●  Plotly
 ●  Seaborn
 ●  Ggplot
 ●  Bokeh
 ●  Pyechart
 ●  Pygal

03Plotly

Plotly 文档地址:

 ●  https://plot.ly/python/#financial-charts

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使用方式:

Plotly 有 online 和 offline 两种方式,这里只介绍 offline 的。

4294db1349301898168a88ab884de1b1a047ef06

这是 Plotly 官方教程的一部分
import plotly.plotly as py
import numpy as np
data = [dict(
visible=False,
line=dict(color='#00CED1', width=6), # 配置线宽和颜色
name='𝜈 = ' + str(step),
x=np.arange(0, 10, 0.01), # x 轴参数
y=np.sin(step * np.arange(0, 10, 0.01))) for step in np.arange(0, 5, 0.1)] # y 轴参数
data[10]['visible'] = True
py.iplot(data, filename='Single Sine Wave')

只要将最后一行中的


py.iplot

替换为下面代码


py.offline.plot

便可以运行。

漏斗图

这个图代码太长了,就不 po 出来了。

0bb2f58ecfef8c92b86a6a48c1b60e8a34061876

Basic Box Plot

30b4a94f95d80959ea5d39e3d9aaabfa86ed30f2


import plotly.plotly
import plotly.graph_objs as go
import numpy as np
y0 = np.random.randn(50)-1
y1 = np.random.randn(50)+1

trace0 = go.Box(
y=y0
)
trace1 = go.Box(
y=y1
)
data = [trace0, trace1]
plotly.offline.plot(data)

Wind Rose Chart

2309bcaa9598d6a750ccc358eae106b1a5fb63e5


import plotly.graph_objs as go

trace1 = go.Barpolar(
r=[77.5, 72.5, 70.0, 45.0, 22.5, 42.5, 40.0, 62.5],
text=['North', 'N-E', 'East', 'S-E', 'South', 'S-W', 'West', 'N-W'],
name='11-14 m/s',
marker=dict(
color='rgb(106,81,163)'
)
)
trace2 = go.Barpolar(
r=[57.49999999999999, 50.0, 45.0, 35.0, 20.0, 22.5, 37.5, 55.00000000000001],
text=['North', 'N-E', 'East', 'S-E', 'South', 'S-W', 'West', 'N-W'], # 鼠标浮动标签文字描述
name='8-11 m/s',
marker=dict(
color='rgb(158,154,200)'
)
)
trace3 = go.Barpolar(
r=[40.0, 30.0, 30.0, 35.0, 7.5, 7.5, 32.5, 40.0],
text=['North', 'N-E', 'East', 'S-E', 'South', 'S-W', 'West', 'N-W'],
name='5-8 m/s',
marker=dict(
color='rgb(203,201,226)'
)
)
trace4 = go.Barpolar(
r=[20.0, 7.5, 15.0, 22.5, 2.5, 2.5, 12.5, 22.5],
text=['North', 'N-E', 'East', 'S-E', 'South', 'S-W', 'West', 'N-W'],
name='< 5 m/s',
marker=dict(
color='rgb(242,240,247)'
)
)
data = [trace1, trace2, trace3, trace4]
layout = go.Layout(
title='Wind Speed Distribution in Laurel, NE',
font=dict(
size=16
),
legend=dict(
font=dict(
size=16
)
),
radialaxis=dict(
ticksuffix='%'
),
orientation=270
)
fig = go.Figure(data=data, layout=layout)
plotly.offline.plot(fig, filename='polar-area-chart')

Basic Ternary Plot with Markers

篇幅有点长,这里就不 po 代码了。

34c4cd44fad86e9a009ba5c722b161e70d721f76

04Bokeh

这里展示一下常用的图表和比较抢眼的图表,详细的文档可查看。

条形图

这配色看着还挺舒服的,比 pyecharts 条形图的配色好看一点。

2c49abfc1ef768d44d8b610ed37da574bc51cb98


from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.palettes import Spectral6
from bokeh.plotting import figure
output_file("colormapped_bars.html")# 配置输出文件名
fruits = ['Apples', '魅族', 'OPPO', 'VIVO', '小米', '华为'] # 数据
counts = [5, 3, 4, 2, 4, 6] # 数据
source = ColumnDataSource(data=dict(fruits=fruits, counts=counts, color=Spectral6))
p = figure(x_range=fruits, y_range=(0,9), plot_height=250, title="Fruit Counts",
toolbar_location=None, tools="")# 条形图配置项
p.vbar(x='fruits', top='counts', width=0.9, color='color', legend="fruits", source=source)
p.xgrid.grid_line_color = None # 配置网格线颜色
p.legend.orientation = "horizontal" # 图表方向为水平方向
p.legend.location = "top_center"
show(p) # 展示图表

年度条形图

可以对比不同时间点的量。

ce5caddf815b74eeccc1981f4fc73bf5d09de13d


from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, FactorRange
from bokeh.plotting import figure
output_file("bars.html") # 输出文件名
fruits = ['Apple', '魅族', 'OPPO', 'VIVO', '小米', '华为'] # 参数
years = ['2015', '2016', '2017'] # 参数
data = {'fruits': fruits,
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 3, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]}
x = [(fruit, year) for fruit in fruits for year in years]
counts = sum(zip(data['2015'], data['2016'], data['2017']), ())
source = ColumnDataSource(data=dict(x=x, counts=counts))
p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year",
toolbar_location=None, tools="")
p.vbar(x='x', top='counts', width=0.9, source=source)
p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None
show(p)

饼图

2a9d04dd5f8c256f4c85f85c88d68c10c17fc89a


from collections import Counter
from math import pi
import pandas as pd
from bokeh.io import output_file, show
from bokeh.palettes import Category20c
from bokeh.plotting import figure
from bokeh.transform import cumsum
output_file("pie.html")
x = Counter({
'中国': 157,
'美国': 93,
'日本': 89,
'巴西': 63,
'德国': 44,
'印度': 42,
'意大利': 40,
'澳大利亚': 35,
'法国': 31,
'西班牙': 29
})
data = pd.DataFrame.from_dict(dict(x), orient='index').reset_index().rename(index=str, columns={0:'value', 'index':'country'})
data['angle'] = data['value']/sum(x.values()) * 2*pi
data['color'] = Category20c[len(x)]
p = figure(plot_height=350, title="Pie Chart", toolbar_location=None,
tools="hover", tooltips="@country: @value")
p.wedge(x=0, y=1, radius=0.4,
start_angle=cumsum('angle', include_zero=True), end_angle=cumsum('angle'),
line_color="white", fill_color='color', legend='country', source=data)
p.axis.axis_label=None
p.axis.visible=False
p.grid.grid_line_color = None
show(p)

条形图

99498b354ffc8a41f4810205d041ebe32ae8a331

年度水果进出口
from bokeh.io import output_file, show
from bokeh.models import ColumnDataSource
from bokeh.palettes import GnBu3, OrRd3
from bokeh.plotting import figure
output_file("stacked_split.html")
fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ["2015", "2016", "2017"]
exports = {'fruits': fruits,
'2015': [2, 1, 4, 3, 2, 4],
'2016': [5, 3, 4, 2, 4, 6],
'2017': [3, 2, 4, 4, 5, 3]}
imports = {'fruits': fruits,
'2015': [-1, 0, -1, -3, -2, -1],
'2016': [-2, -1, -3, -1, -2, -2],
'2017': [-1, -2, -1, 0, -2, -2]}
p = figure(y_range=fruits, plot_height=250, x_range=(-16, 16), title="Fruit import/export, by year",
toolbar_location=None)
p.hbar_stack(years, y='fruits', height=0.9, color=GnBu3, source=ColumnDataSource(exports),
legend=["%s exports" % x for x in years])
p.hbar_stack(years, y='fruits', height=0.9, color=OrRd3, source=ColumnDataSource(imports),
legend=["%s imports" % x for x in years])
p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.legend.location = "top_left"
p.axis.minor_tick_line_color = None
p.outline_line_color = None
show(p)

散点图

6644c9a067e9f2fb5264c65a9ec60e076a549413


from bokeh.plotting import figure, output_file, show
output_file("line.html")
p = figure(plot_width=400, plot_height=400)
p.circle([1, 2, 3, 4, 5], [6, 7, 2, 4, 5], size=20, color="navy", alpha=0.5)
show(p)

六边形图

这两天,马蜂窝刚被发现数据造假,这不,与马蜂窝应应景。

b803f6d7f149989141f523bfae9f7bd562037764


import numpy as np
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.util.hex import axial_to_cartesian
output_file("hex_coords.html")
q = np.array([0, 0, 0, -1, -1, 1, 1])
r = np.array([0, -1, 1, 0, 1, -1, 0])
p = figure(plot_width=400, plot_height=400, toolbar_location=None) #
p.grid.visible = False # 配置网格是否可见
p.hex_tile(q, r, size=1, fill_color=["firebrick"] * 3 + ["navy"] * 4,
line_color="white", alpha=0.5)
x, y = axial_to_cartesian(q, r, 1, "pointytop")
p.text(x, y, text=["(%d, %d)" % (q, r) for (q, r) in zip(q, r)],
text_baseline="middle", text_align="center")
show(p)

环比条形图

这个实现挺厉害的,看了一眼就吸引了我。我在代码中都做了一些注释,希望对你理解有帮助。注:圆心为正中央,即直角坐标系中标签为(0,0)的地方。

8398d0007e33fd143426e279ef87467dbbdf7619


from collections import OrderedDict
from math import log, sqrt
import numpy as np
import pandas as pd
from six.moves import cStringIO as StringIO
from bokeh.plotting import figure, show, output_file

antibiotics = """
bacteria, penicillin, streptomycin, neomycin, gram
结核分枝杆菌, 800, 5, 2, negative
沙门氏菌, 10, 0.8, 0.09, negative
变形杆菌, 3, 0.1, 0.1, negative
肺炎克雷伯氏菌, 850, 1.2, 1, negative
布鲁氏菌, 1, 2, 0.02, negative
铜绿假单胞菌, 850, 2, 0.4, negative
大肠杆菌, 100, 0.4, 0.1, negative
产气杆菌, 870, 1, 1.6, negative
白色葡萄球菌, 0.007, 0.1, 0.001, positive
溶血性链球菌, 0.001, 14, 10, positive
草绿色链球菌, 0.005, 10, 40, positive
肺炎双球菌, 0.005, 11, 10, positive
"""

drug_color = OrderedDict([# 配置中间标签名称与颜色
("盘尼西林", "#0d3362"),
("链霉素", "#c64737"),
("新霉素", "black"),
])
gram_color = {
"positive": "#aeaeb8",
"negative": "#e69584",
}
# 读取数据
df = pd.read_csv(StringIO(antibiotics),
skiprows=1,
skipinitialspace=True,
engine='python')
width = 800
height = 800
inner_radius = 90
outer_radius = 300 - 10

minr = sqrt(log(.001 * 1E4))
maxr = sqrt(log(1000 * 1E4))
a = (outer_radius - inner_radius) / (minr - maxr)
b = inner_radius - a * maxr


def rad(mic):
return a * np.sqrt(np.log(mic * 1E4)) + b
big_angle = 2.0 * np.pi / (len(df) + 1)
small_angle = big_angle / 7
# 整体配置
p = figure(plot_width=width, plot_height=height, title="",
x_axis_type=None, y_axis_type=None,
x_range=(-420, 420), y_range=(-420, 420),
min_border=0, outline_line_color="black",
background_fill_color="#f0e1d2")
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# annular wedges
angles = np.pi / 2 - big_angle / 2 - df.index.to_series() * big_angle #计算角度
colors = [gram_color[gram] for gram in df.gram] # 配置颜色
p.annular_wedge(
0, 0, inner_radius, outer_radius, -big_angle + angles, angles, color=colors,
)

# small wedges
p.annular_wedge(0, 0, inner_radius, rad(df.penicillin),
-big_angle + angles + 5 * small_angle, -big_angle + angles + 6 * small_angle,
color=drug_color['盘尼西林'])
p.annular_wedge(0, 0, inner_radius, rad(df.streptomycin),
-big_angle + angles + 3 * small_angle, -big_angle + angles + 4 * small_angle,
color=drug_color['链霉素'])
p.annular_wedge(0, 0, inner_radius, rad(df.neomycin),
-big_angle + angles + 1 * small_angle, -big_angle + angles + 2 * small_angle,
color=drug_color['新霉素'])
# 绘制大圆和标签
labels = np.power(10.0, np.arange(-3, 4))
radii = a * np.sqrt(np.log(labels * 1E4)) + b
p.circle(0, 0, radius=radii, fill_color=None, line_color="white")
p.text(0, radii[:-1], [str(r) for r in labels[:-1]],
text_font_size="8pt", text_align="center", text_baseline="middle")
# 半径
p.annular_wedge(0, 0, inner_radius - 10, outer_radius + 10,
-big_angle + angles, -big_angle + angles, color="black")
# 细菌标签
xr = radii[0] * np.cos(np.array(-big_angle / 2 + angles))
yr = radii[0] * np.sin(np.array(-big_angle / 2 + angles))
label_angle = np.array(-big_angle / 2 + angles)
label_angle[label_angle < -np.pi / 2] += np.pi # easier to read labels on the left side
# 绘制各个细菌的名字
p.text(xr, yr, df.bacteria, angle=label_angle,
text_font_size="9pt", text_align="center", text_baseline="middle")
# 绘制圆形,其中数字分别为 x 轴与 y 轴标签
p.circle([-40, -40], [-370, -390], color=list(gram_color.values()), radius=5)
# 绘制文字
p.text([-30, -30], [-370, -390], text=["Gram-" + gr for gr in gram_color.keys()],
text_font_size="7pt", text_align="left", text_baseline="middle")
# 绘制矩形,中间标签部分。其中 -40,-40,-40 为三个矩形的 x 轴坐标。18,0,-18 为三个矩形的 y 轴坐标
p.rect([-40, -40, -40], [18, 0, -18], width=30, height=13,
color=list(drug_color.values()))
# 配置中间标签文字、文字大小、文字对齐方式
p.text([-15, -15, -15], [18, 0, -18], text=list(drug_color),
text_font_size="9pt", text_align="left", text_baseline="middle")
output_file("burtin.html", title="burtin.py example")
show(p)

元素周期表

元素周期表,这个实现好牛逼啊,距离初三刚开始学化学已经很遥远了,想当年我还是化学课代表呢!由于基本用不到化学了,这里就不实现了。

356ca8c3caf445bdcc92bc0aac0f2facfab78d1f

真实状态

05Pyecharts

pyecharts 也是一个比较常用的数据可视化库,用得也是比较多的了,是百度 Echarts 库的 Python 支持。这里也展示一下常用的图表。

文档地址为:

 ●  http://pyecharts.org/#/zh-cn/prepare?id=%E5%AE%89%E8%A3%85-pyecharts

条形图

c2a948e4311af61af171a7c41f3edd473d342187


from pyecharts import Bar
bar = Bar("我的第一个图表", "这里是副标题")
bar.add("服装", ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"], [5, 20, 36, 10, 75, 90])
# bar.print_echarts_options() # 该行只为了打印配置项,方便调试时使用
bar.render() # 生成本地 HTML 文件

散点图

8eaa8307230343db329fc55b3c1cf971b65ecc55


from pyecharts import Polar
import random
data_1 = [(10, random.randint(1, 100)) for i in range(300)]
data_2 = [(11, random.randint(1, 100)) for i in range(300)]
polar = Polar("极坐标系-散点图示例", width=1200, height=600)
polar.add("", data_1, type='scatter')
polar.add("", data_2, type='scatter')
polar.render()

饼图

782300554139ab2db9f133df52a2579dce66bb62


import random
from pyecharts import Pie
attr = ['A', 'B', 'C', 'D', 'E', 'F']
pie = Pie("饼图示例", width=1000, height=600)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[25, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[25, 50],
rosetype="area",
)
pie.add(
"",
attr,
[random.randint(0, 100) for _ in range(6)],
radius=[50, 55],
center=[65, 50],
is_random=True,
)
pie.add(
"",
attr,
[random.randint(20, 100) for _ in range(6)],
radius=[0, 45],
center=[65, 50],
rosetype="radius",
)
pie.render()

词云

这个是我在前面的文章中用到的图片实例,这里就不 po 具体数据了。

842c467a9dc2e33203730dc5e2330d63c8c213cc


from pyecharts import WordCloud
name = ['Sam S Club'] # 词条
value = [10000] # 权重
wordcloud = WordCloud(width=1300, height=620)
wordcloud.add("", name, value, word_size_range=[20, 100])
wordcloud.render()

树图

这个是我在前面的文章中用到的图片实例,这里就不 po 具体数据了。

64f180818a00ab253b359fde216b39511044508e


from pyecharts import TreeMap
data = [ # 键值对数据结构
{
value: 1212, # 数值
# 子节点
children: [
{
# 子节点数值
value: 2323,
# 子节点名
name: 'description of this node',
children: [...],
},
{
value: 4545,
name: 'description of this node',
children: [
{
value: 5656,
name: 'description of this node',
children: [...]
},
...
]
}
]
},
...
]
treemap = TreeMap(title, width=1200, height=600) # 设置标题与宽高
treemap.add("深圳", data, is_label_show=True, label_pos='inside', label_text_size=19)
treemap.render()

地图

a8b699655ff0e6bc09c669848784b6d7e29fad8b


from pyecharts import Map

value = [155, 10, 66, 78, 33, 80, 190, 53, 49.6]
attr = [
"福建", "山东", "北京", "上海", "甘肃", "新疆", "河南", "广西", "西藏"
]
map = Map("Map 结合 VisualMap 示例", width=1200, height=600)
map.add(
"",
attr,
value,
maptype="china",
is_visualmap=True,
visual_text_color="#000",
)
map.render()

3D 散点图

from pyecharts import Scatter3D
import random
data = [
[random.randint(0, 100),
random.randint(0, 100),
random.randint(0, 100)
] for _ in range(80)
]
range_color
= [
'#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
'#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
scatter3D = Scatter3D("3D 散点图示例", width=1200, height=600) # 配置宽高
scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color) # 设置颜色等
scatter3D.render() # 渲染

06后记

大概介绍就是这样了,三个库的功能都挺强大的,Bokeh 的中文资料会少一点,如果阅读英文有点难度,还是建议使用 pyecharts 就好。总体也不是很难,按照文档来修改数据都能够直接上手使用。主要是多练习。


原文发布时间为:2018-10-29

本文作者:zone

本文来自云栖社区合作伙伴“CDA数据分析师”,了解相关信息可以关注“CDA数据分析师”。

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