Python数据可视化 | 10、可交互式可视化

Bokeh

• Boken: an interactive visualization library for modern web browsers

  • 基于Web浏览器呈现的交互式可视化
  • 多种语言支持Python, Scala, Julia,和R.
  • Json作为BokehJS的输入
  • HTML作为BokehJS的输出
  • OFFICIAL:
    • https://docs.bokeh.org
    • https://github.com/bokeh/bokeh
  • Tutorial: http://chdoig.github.io/scipy2015-blaze-bokeh/
    

• Gallery: https://docs.bokeh.org/en/latest/docs/gallery.html

import bokeh
print(bokeh.__version__)  # 1.4.0

- Line / Circle

from bokeh.plotting import figure, output_file, show

# prepare some data
x = [0.1, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0]
y0 = [i**2 for i in x]
y1 = [10**i for i in x]
y2 = [10**(i**2) for i in x]

# output to static HTML file
output_file("log_lines.html")

# create a new plot
p = figure(
   tools="pan,box_zoom,reset,save",
   y_axis_type="log", y_range=[0.001, 10**11], title="log axis example",
   x_axis_label='sections', y_axis_label='particles'
)

# add some renderers
p.line(x, x, legend_label="y=x")
p.circle(x, x, legend_label="y=x", fill_color="white", size=8)
p.line(x, y0, legend_label="y=x^2", line_width=3)
p.line(x, y1, legend_label="y=10^x", line_color="red")
p.circle(x, y1, legend_label="y=10^x", fill_color="red", line_color="red", size=6)
p.line(x, y2, legend_label="y=10^x^2", line_color="orange", line_dash="4 4")

# show the results
show(p)

from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure
from bokeh.sampledata.commits import data
from bokeh.transform import jitter

output_file("categorical_scatter_jitter.html")

DAYS = ['Sun', 'Sat', 'Fri', 'Thu', 'Wed', 'Tue', 'Mon']

source = ColumnDataSource(data)

p = figure(plot_width=800, plot_height=300, y_range=DAYS, x_axis_type='datetime',
           title="Commits by Time of Day (US/Central) 2012-2016")

p.circle(x='time', y=jitter('day', width=0.6, range=p.y_range),  source=source, alpha=0.3)

p.xaxis.formatter.days = ['%Hh']
p.x_range.range_padding = 0
p.ygrid.grid_line_color = None

show(p)

• 六角箱图 Hexbin

import numpy as np

from bokeh.io import output_file, show
from bokeh.models import HoverTool
from bokeh.plotting import figure

n = 500
x = 2 + 2*np.random.standard_normal(n)
y = 2 + 2*np.random.standard_normal(n)

p = figure(title="Hexbin for 500 points", match_aspect=True,
           tools="wheel_zoom,reset", background_fill_color='#440154')
p.grid.visible = False

r, bins = p.hexbin(x, y, size=0.5, hover_color="pink", hover_alpha=0.8)

p.circle(x, y, color="white", size=1)

p.add_tools(HoverTool(
    tooltips=[("count", "@c"), ("(q,r)", "(@q, @r)")],
    mode="mouse", point_policy="follow_mouse", renderers=[r]
))

output_file("hexbin.html")

show(p)

• Element (元素周期表)

from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.sampledata.periodic_table import elements
from bokeh.transform import dodge, factor_cmap

output_file("periodic.html")

periods = ["I", "II", "III", "IV", "V", "VI", "VII"]
groups = [str(x) for x in range(1, 19)]

df = elements.copy()
df["atomic mass"] = df["atomic mass"].astype(str)
df["group"] = df["group"].astype(str)
df["period"] = [periods[x-1] for x in df.period]
df = df[df.group != "-"]
df = df[df.symbol != "Lr"]
df = df[df.symbol != "Lu"]

cmap = {
    "alkali metal"         : "#a6cee3",
    "alkaline earth metal" : "#1f78b4",
    "metal"                : "#d93b43",
    "halogen"              : "#999d9a",
    "metalloid"            : "#e08d49",
    "noble gas"            : "#eaeaea",
    "nonmetal"             : "#f1d4Af",
    "transition metal"     : "#599d7A",
}

TOOLTIPS = [
    ("Name", "@name"),
    ("Atomic number", "@{atomic number}"),
    ("Atomic mass", "@{atomic mass}"),
    ("Type", "@metal"),
    ("CPK color", "$color[hex, swatch]:CPK"),
    ("Electronic configuration", "@{electronic configuration}"),
]

p = figure(title="Periodic Table (omitting LA and AC Series)", plot_width=1000, plot_height=450,
           x_range=groups, y_range=list(reversed(periods)),
           tools="hover", toolbar_location=None, tooltips=TOOLTIPS)

r = p.rect("group", "period", 0.95, 0.95, source=df, fill_alpha=0.6, legend_field="metal",
           color=factor_cmap('metal', palette=list(cmap.values()), factors=list(cmap.keys())))

text_props = {"source": df, "text_align": "left", "text_baseline": "middle"}

x = dodge("group", -0.4, range=p.x_range)

p.text(x=x, y="period", text="symbol", text_font_style="bold", **text_props)

p.text(x=x, y=dodge("period", 0.3, range=p.y_range), text="atomic number",
       text_font_size="8pt", **text_props)

p.text(x=x, y=dodge("period", -0.35, range=p.y_range), text="name",
       text_font_size="5pt", **text_props)

p.text(x=x, y=dodge("period", -0.2, range=p.y_range), text="atomic mass",
       text_font_size="5pt", **text_props)

p.text(x=["3", "3"], y=["VI", "VII"], text=["LA", "AC"], text_align="center", text_baseline="middle")

p.outline_line_color = None
p.grid.grid_line_color = None
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.major_label_standoff = 0
p.legend.orientation = "horizontal"
p.legend.location ="top_center"
p.hover.renderers = [r] # only hover element boxes

show(p)

Plotly

• Plotly: Modern Analytic Apps for the Enterprise
  • 强大的在线数据分析和可视化工具
  • 多种语言支持python、javascript、matlab、R等许多API
  • 基于D3.js框架
  • OFFICIAL:
    • https://plot.ly
    • https://github.com/plotly
  • Tutorial: https://plot.ly/python/
    
• https://plot.ly/python/

import plotly
import plotly.offline as py
py.init_notebook_mode(connected=False)

print(plotly.__version__) # 4.4.1

- A simple example

import plotly
import plotly.offline as py
py.init_notebook_mode(connected=False)
import plotly.graph_objs as go

# x and y given as array_like objects
import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()

• Hist

import plotly.graph_objects as go
import plotly.offline as py
trace0 = go.Bar(
    x = ['Jan','Feb','Mar','Apr', 'May','Jun',
         'Jul','Aug','Sep','Oct','Nov','Dec'],
    y = [20,14,25,16,18,22,19,15,12,16,14,17],
    name = 'Primary Product',
    marker=dict(
        color = 'rgb(49,130,189)'
    )
)
trace1 = go.Bar(
    x = ['Jan','Feb','Mar','Apr', 'May','Jun',
         'Jul','Aug','Sep','Oct','Nov','Dec'],
    y = [19,14,22,14,16,19,15,14,10,12,12,16],
    name = 'Secondary Product',
    marker=dict(
        color = 'rgb(204,204,204)'
    )
)
data = [trace0,trace1]
py.iplot(data)

交互数据可视化演示视频