def knn(ipaddress="localhost", port="9999"):
'''
用D3.JS展示Knn算法的运行结果
:return:
'''
testNum, errorRate, errorCount, classifierData, realData = knn1.displayData(
'data/edx_knn.csv')
x = np.linspace(0, testNum, testNum)
df = pandas.DataFrame({
'x': x,
'y': classifierData[:testNum],
'z': realData[:testNum],
})
print "testNummber = %d \n" % testNum, "error rate : %f \n" % (
errorCount / float(testNum)), "error count：%d \n" % errorCount
webbrowser.open_new_tab("http://%s:%s/%s.html" %
(ipaddress, port, "disply_knn"))
with d3py.PandasFigure(df,
'disply_knn',
width=20000,
height=200,
port=int(port)) as fig:
fig += d3py.geoms.Line('x', 'y', stroke='BlueViolet')
fig += d3py.geoms.Line('x', 'z', stroke='DeepPink')
fig += d3py.xAxis('x', label="test number")
fig += d3py.yAxis('y', label="test label")
fig.show()
def getD3(self, xlabel="item", ylabel="count"):
df = pd.DataFrame({xlabel: [], ylabel: []})
p = d3py.PandasFigure(df)
p += d3py.Bar(x=xlabel, y=ylabel)
# p += d3py.Line(x = xlabel, y=ylabel)
p += d3py.xAxis(x=xlabel)
p += d3py.yAxis(y=ylabel)
p.update()
p.js.merge(p.js_geoms)
d3 = {}
d3['js'] = p.js
d3['css'] = "%s\n%s" % (p.css, p.css_geoms)
return d3
import d3py
import pandas as pd
import random
x = ['apples', 'oranges', 'grapes', 'bananas', 'plums', 'blackberries']
y = [10, 17, 43, 23, 31, 18]
df = pd.DataFrame({'x': x, 'y': y})
#Create Pandas figure
fig = d3py.PandasFigure(df, 'd3py_area', port=8000, columns=['x', 'y'])
#Add Vega Area plot
fig += d3py.vega.Bar()
#Show figure
fig.show()
import numpy as np
import d3py
import pandas
N = 500
T = 5 * np.pi
x = np.linspace(-T, T, N)
y = np.sin(x)
y0 = np.cos(x)
df = pandas.DataFrame({
'x': x,
'y': y,
'y0': y0,
})
with d3py.PandasFigure(df, 'd3py_area', width=500, height=250) as fig:
fig += d3py.geoms.Area('x', 'y', 'y0')
fig += d3py.geoms.xAxis('x')
fig += d3py.geoms.yAxis('y')
fig.show()
import numpy as np
import d3py
import pandas
T = 5 * np.pi
x = np.linspace(-T, T, 100)
a = 0.05
y = np.exp(-a * x) * np.sin(x)
z = np.exp(-a * x) * np.sin(0.5 * x)
df = pandas.DataFrame({
'x': x,
'y': y,
'z': z,
})
with d3py.PandasFigure(df, 'd3py_line', width=600, height=200) as fig:
fig += d3py.geoms.Line('x', 'y', stroke='BlueViolet')
fig += d3py.geoms.Line('x', 'z', stroke='DeepPink')
fig += d3py.xAxis('x')
fig += d3py.yAxis('y')
fig.show()
import numpy as np
import pandas
import d3py
n = 400
df = pandas.DataFrame({'d1': np.arange(0, n), 'd2': np.random.normal(0, 1, n)})
with d3py.PandasFigure(df,
"example scatter plot using d3py",
width=400,
height=400) as fig:
fig += d3py.Point("d1", "d2", fill="DodgerBlue")
fig += d3py.xAxis('d1', label="Random")
fig += d3py.yAxis('d2', label="Also random")
fig.show()
import pandas
import d3py
import logging
logging.basicConfig(level=logging.DEBUG)
df = pandas.DataFrame({
"count": [1, 4, 7, 3, 2, 9],
"apple_type": ["a", "b", "c", "d", "e", "f"],
})
# use 'with' if you are writing a script and want to serve this up forever
with d3py.PandasFigure(df) as p:
p += d3py.Bar(x="apple_type", y="count", fill="MediumAquamarine")
p += d3py.xAxis(x="apple_type")
p.show()
# if you are writing in a terminal, use without 'with' to keep everything nice
# and interactive
"""
p = d3py.PandasFigure(df)
p += d3py.Bar(x = "apple_type", y = "count", fill = "MediumAquamarine")
p += d3py.xAxis(x = "apple_type")
p.show()
"""
def test_data_to_json(self):
p = d3py.PandasFigure(self.df)
p._data_to_json()
# coding = utf-8 ''' @author = super_fazai @File : d3py_demo.py @Time : 2017/8/14 13:16 @connect : [email protected] ''' import d3py import pandas import numpy as np # some test data T = 100 # this is a data frame with three columns (we only use 2) df = pandas.DataFrame({ "time": range(T), "pressure": np.random.rand(T), "temp": np.random.rand(T) }) ## build up a figure, ggplot2 style # instantiate the figure object fig = d3py.PandasFigure(df, name="basic_example", width=300, height=300) # add some red points fig += d3py.geoms.Point(x="pressure", y="temp", fill="red") # writes 3 files, starts up a server, then draws some beautiful points in Chrome fig.show()
