python 数据可视化有哪几个常用的库 ?

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='? = ' + 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')

py.iplot

py.offline.plot

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)

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')

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

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)

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)                                                                                                          

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)

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)

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)

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)

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

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()

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()

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()

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()

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()

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