数据可视化利器pyechart和matplotlib比较

python中用作数据可视化的工具有多种，其中matplotlib最为基础。故在工具选择上，图形美观之外，操作方便即上乘。

本文着重说明常见图表用基础版matplotlib和改良版pyecharts作图间的差异

一、maplotlib

基本用法如下：

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
import matplotlib.pyplot as plt
import matplotlib.finance as mpf
%matplotlib inline

fig = plt.figure(figsize=(10,8))  #建立一个大小为10*8的画板
ax1 = fig.add_subplot(321)  #在画板上添加3*3个画布，位置是第1个
ax2 = fig.add_subplot(3,2,2)
ax3 = fig.add_subplot(3,2,3)
ax4 = fig.add_subplot(3,2,4)
ax5 = fig.add_subplot(3,2,5)
ax6 = fig.add_subplot(3,2,6)

data1 = np.random.randn(20)
x = np.arange(20)

ax1.plot(data1)  #作折线图
ax2.scatter(data1,data1,color='r')  #作散点图
ax3.hist(data1,bins=10,alpha=0.3)  #作直方图
ax4.bar(x,data1)  #作柱形图
ax5.pie(np.random.randint(1,15,5),explode=[0,0,0.2,0,0])  #作饼形图
ax6.plot(x,data1,color='green')  #组合图
ax6.bar(x,data1,color='k')

fig,axes = plt.subplots(3,3,figsize=(20,16))
data2 = DataFrame(np.random.randn(10,5),columns = ['A','B','C','D','E'],index = np.arange(0,100,10))

data2.plot(kind='line',ax=axes[0][0])  #作折线图
data2.plot(kind = 'scatter',x = data2.index[0] ,y = data2.index[0],ax=axes[0][1])  #作散点图
data2.plot(kind='hist',ax=axes[0][2],legend=False)  #作直方图
data2.plot(kind='bar',ax=axes[1][0])  #作柱形图
data2.plot(kind='area',ax=axes[1][1],stacked = False) #面积图

train_data = np.array(data2)  
mpf.candlestick_ohlc(axes[1][2],train_data.tolist(),width=1.5,colorup='r',colordown='g')  #K线图

axim = axes[2][0].imshow(data2.values,interpolation='nearest')  #热力图
plt.colorbar(axim)

plt.legend(loc = 'best')
plt.show()

可能遇到的问题：

1.axes[ ][ ]前面的代表行,后面代表列

2.散点图表示因变量随自变量而变化的大致趋势，x,y大小写有区分

3.面积图若不添加stacked = False会报错，提示每列必须得全正或全负

4.K线图至少得5列数据

更多操作：

plt.xlabel('横坐标名称')
plt.title('标题')
plt.xlim([0,20])  #添加x轴范围
ax.set_xyick([0,250,500,750,1000])  #设置x轴标签
ax.text(x,y,'text',family = '',fontsize = '')  #添加文本
plt.setp(ax.get_xticklables(),viseble = False)  #隐藏x轴标签，多图使用
plt.subplot_adjust(left = None,right = None,top = None,wspace = None,hspace = None)  #调整子图间距
plt.axhline(y = 0,linewidth = 1,color = 'green')   #添加分割线
plt.grid(True)   #添加网格
plt.savefig('filepath')  #保存图片

二、pyecharts

基本用法如下：

import pandas as pd
import numpy as np
from pyecharts import Page

#pandas类型的数据处理
index = pd.date_range('2/1/2017',periods = 6,freq = 'M')
df1 = pd.DataFrame(np.random.randn(6),index = index)
df2 = pd.DataFrame(np.random.randn(6),index = index)
df1_value = [i[0] for i in df1.values]
df2_value = [i[0] for i in df2.values]
#print type(df1_value) 列表

#pandas类型转list类型
data = ts.get_hist_data('',start='2017-01-01')
new_data = data.ix[:,['open','close','high','low']]
train_data = np.array(new_data)
train_index = np.array(data.index)

#元组列表
d = [(11,19),(12,21),(13,32),(10,20),(10,20),(10,33)]
dat = dict(d)
d1 = dat.keys()
d2 = dat.values()
#print type(d1)  列表

#列表数据
attr = ['衬衫','羊毛衫','雪纺衫','裤子','高跟鞋','袜子']
v1 = [11,12,13,10,10,10]
v2 = [19,21,32,20,20,33]

from pyecharts import Bar

page = Page()   #实例化，同一网页按顺序展示多图
bar = Bar('bar示例图','ryana')
bar.add('test',index,df1_value,mark_point = ['average'])
bar.add('test',attr,v1,mark_line = ['max'])
page.add(bar)

from pyecharts import Line

line = Line('折线图示例')
#line.add('test',attr,v1,mark_point = 'average')
line.add('test',attr,v2,is_fill = True,area_opacity = 0.3) #面积图
page.add(line)

from pyecharts import Pie

pie = Pie('饼图示例')
#pie.add('test',attr,v1,is_label_show = True)
pie.add('test',attr,v2,radius = [40,75],is_label_show = True) #饼环图
page.add(pie)

"""
#组合图line+pie
from pyecharts import Grid

grid = Grid()
grid.add(line, grid_right="10%")
grid.add(pie, grid_left="10%")
grid.render()
"""

from pyecharts import Scatter

scatter = Scatter('散点图示例')
scatter.add('test',v1,v2)
page.add(scatter)

from pyecharts import HeatMap
import random

x_axis = ['1a','2a','3a','4a','5a','6a','7a','8a','9a']
y_axis = ['Sat','Fri','Thu','Wed','Tue','Mon','Sun']

dataH = [[i,j,random.randint(0,50)] for i in range(9) for j in range(7)]
heatmap = HeatMap('热力图示例')
heatmap.add('test',x_axis,y_axis,dataH,is_visualmap = True,visual_text_color = "#000",visual_orient='horizontal')
page.add(heatmap)

from pyecharts import Kline

dataK = [[2320.26,2320.33,2287.4,3513.4],[2340.26,2310.33,2187.4,2513.4],[2329.26,2420.33,2257.4,2573.4],
[2321.26,2370.33,2187.4,3013.4],[2380.26,2220.33,2487.4,3113.4],[2350.26,2520.33,2087.4,2813.4],
[2300.26,2390.33,2187.4,2413.4]]

kline = Kline('K线图示例')
kline.add('test',['2017/8/{}'.format(i+1) for i in range(7)],dataK)
page.add(kline)

from pyecharts import WordCloud

dataW = {'Python':55,'HBASE':49,'JAVA':72,'MongoDB':88,'SAS':39,'Spark':63}

wordcloud = WordCloud('词云图例',width = 1200,height = 720)
wordcloud.add('test',dataW.keys(),dataW.values(),word_size_range = [20,80])
page.add(wordcloud)

page.render(r'E:\echart.html')

参考：

pyechart : https://github.com/chenjiandongx/pyecharts/blob/master/docs/zh-cn/documentation.md

maplotlib : http://blog.csdn.net/xiaodongxiexie/article/details/53123371