数据可视化matplotlib的应用（四）
创建子plot：
import random
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
def create_plots():
xs = []
ys = []
for i in range(10):
x = i
y = random.randrange(10)
xs.append(x)
ys.append(y)
return xs, ys
ax1 = fig.add_subplot(221) #通过fig添加子plot（221）解释：第一个参数是行，第二个参数是列，
ax2 = fig.add_subplot(222) #第三个参数起点。
ax3 = fig.add_subplot(212)
x, y =create_plots()
ax1.plot(x,y)
x, y =create_plots()
ax2.plot(x,y)
x, y =create_plots()
ax3.plot(x,y)
plt.show()
通过plt.subplot2grid添加子：
import random
import matplotlib.pyplot as plt
from matplotlib import style
style.use('fivethirtyeight')
fig = plt.figure()
def create_plots():
xs = []
ys = []
for i in range(10):
x = i
y = random.randrange(10)
xs.append(x)
ys.append(y)
return xs, ys
ax1 = plt.subplot2grid((6,1),(0,0),rowspan=1, colspan=1) #第一个参数(6,1)表示行和列，第二个参数(0,0)坐标
ax2 = plt.subplot2grid((6,1),(1,0),rowspan=4, colspan=1) #起始值，第三个参数rowspan表示占的行数，第四
ax3 = plt.subplot2grid((6,1),(5,0),rowspan=1, colspan=1) #个参数colspan表示列占。
x, y =create_plots()
ax1.plot(x,y)
x, y =create_plots()
ax2.plot(x,y)
x, y =create_plots()
ax3.plot(x,y)
plt.show()
3.再次应用子plot
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
from matplotlib import style
import numpy as np
import urllib
import datetime as dt
style.use('fivethirtyeight')
print(plt.style.available)
print(plt.__file__)
def bytespdate2num(fmt, encoding='utf-8'):
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
s = b.decode(encoding)
return strconverter(s)
return bytesconverter
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((6,1), (0,0),rowspan=1,colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6,1), (1,0),rowspan=4,colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6,1), (5,0),rowspan=1,colspan=1)
stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1m/csv'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source:
split_line = line.split(',')
if len(split_line) == 6:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y%m%d')})
x = 0
y = len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x+=1
candlestick_ohlc(ax2, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.grid(True)
bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)
ax2.annotate(str(closep[-1]), (date[-1], closep[-1]),
xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)
## # Annotation example with arrow
## ax1.annotate('Bad News!',(date[11],highp[11]),
## xytext=(0.8, 0.9), textcoords='axes fraction',
## arrowprops = dict(facecolor='grey',color='grey'))
##
##
## # Font dict example
## font_dict = {'family':'serif',
## 'color':'darkred',
## 'size':15}
## # Hard coded text
## ax1.text(date[10], closep[1],'Text Example', fontdict=font_dict)
#plt.legend()
plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
plt.show()
graph_data('EBAY')
更多的数据指示：
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
from matplotlib import style
import numpy as np
import urllib
import datetime as dt
style.use('fivethirtyeight')
print(plt.style.available)
print(plt.__file__)
MA1 = 10 #计算moving_average时，设置的参数
MA2 = 30 #MA就是moving_average的缩写
def moving_average(values, window):
weights = np.repeat(1.0, window)/window #设置参数repeat重复表示参数
smas = np.convolve(values, weights, 'valid') #参数按照权重卷积运算
return smas
def high_minus_low(highs, lows): #计算highs和lows的差值
return highs-lows
def bytespdate2num(fmt, encoding='utf-8'):
strconverter = mdates.strpdate2num(fmt)
def bytesconverter(b):
s = b.decode(encoding)
return strconverter(s)
return bytesconverter
def graph_data(stock):
fig = plt.figure()
ax1 = plt.subplot2grid((6,1), (0,0), rowspan=1, colspan=1)
plt.title(stock)
ax2 = plt.subplot2grid((6,1), (1,0), rowspan=4, colspan=1)
plt.xlabel('Date')
plt.ylabel('Price')
ax3 = plt.subplot2grid((6,1), (5,0), rowspan=1, colspan=1)
stock_price_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=1y/csv'
source_code = urllib.request.urlopen(stock_price_url).read().decode()
stock_data = []
split_source = source_code.split('\n')
for line in split_source:
split_line = line.split(',')
if len(split_line) == 6:
if 'values' not in line and 'labels' not in line:
stock_data.append(line)
date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data,
delimiter=',',
unpack=True,
converters={0: bytespdate2num('%Y%m%d')})
x = 0
y = len(date)
ohlc = []
while x < y:
append_me = date[x], openp[x], highp[x], lowp[x], closep[x], volume[x]
ohlc.append(append_me)
x+=1
ma1 = moving_average(closep,MA1)
ma2 = moving_average(closep,MA2)
start = len(date[MA2-1:])
h_l = list(map(high_minus_low, highp, lowp)) #计算出差值列表
ax1.plot_date(date,h_l,'-') #画出差值的函数，在第一个子plot里面
candlestick_ohlc(ax2, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax2.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax2.grid(True)
bbox_props = dict(boxstyle='round',fc='w', ec='k',lw=1)
ax2.annotate(str(closep[-1]), (date[-1], closep[-1]),
xytext = (date[-1]+4, closep[-1]), bbox=bbox_props)
ax3.plot(date[-start:], ma1[-start:]) #第三个图画出变化的图
ax3.plot(date[-start:], ma2[-start:])
plt.subplots_adjust(left=0.11, bottom=0.24, right=0.90, top=0.90, wspace=0.2, hspace=0)
plt.show()
graph_data('EBAY')
