《Python数据分析基础教程：Numpy学习指南》- 速记 - 第九章

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import numpy as np
import matplotlib.pyplot as plt
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
x = np.linspace(-10, 10, 30)
y = func(x)
plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y(x)')
plt.show()

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import numpy as np
import matplotlib.pyplot as plt
func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
func1 = func.deriv(m=1)
x = np.linspace(-10, 10, 30)
y = func(x)
y1 = func1(x)
plt.plot(x, y, 'ro', x, y1, 'g--')
plt.xlabel('x')
plt.ylabel('y')
plt.show()

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import numpy as np
import matplotlib.pyplot as plt

func = np.poly1d(np.array([1, 2, 3, 4]).astype(float))
x = np.linspace(-10, 10, 30)
y = func(x)
func1 = func.deriv(m=1) #一次导数
y1 = func1(x)
func2 = func.deriv(m=2) #二次导数
y2 = func2(x)
plt.subplot(311)
plt.plot(x, y, 'r-' )
plt.title("Polynomial")
plt.subplot(312)
plt.plot(x, y1, 'b^')
plt.title("First Derivative")
plt.subplot(313)
plt.plot(x, y2, 'go')
plt.title("Second Derivative")
plt.xlabel('x')
plt.ylabel('y')
plt.show()

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from matplotlib.dates import DateFormatter
from matplotlib.dates import DayLocator
from matplotlib.dates import MonthLocator
from matplotlib.finance import quotes_historical_yahoo
from matplotlib.finance import candlestick
import sys
from datetime import date
import matplotlib.pyplot as plt

today = date.today()
start = (today.year - 1, today.month, today.day) #将当前的日期减去1年作为起始日期

#创建定位器（locator），这些来自matplotlib.dates包中的对象可以在x轴上定位月份和日期。
alldays = DayLocator()
months = MonthLocator()

#创建一个日期格式化器（date formatter）以格式化x轴上的日期。该格式化器将创建一个字符串，包含简写的月份和年份。
month_formatter = DateFormatter("%b %Y")

#股票名称
symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]

#从雅虎财经频道下载股价数据。
quotes = quotes_historical_yahoo(symbol, start, today)
fig = plt.figure()
ax = fig.add_subplot(111)

#将x轴上的主定位器设置为月定位器。该定位器负责x轴上较粗的刻度。
ax.xaxis.set_major_locator(months)

#将x轴上的次定位器设置为日定位器。该定位器负责x轴上较细的刻度。
ax.xaxis.set_minor_locator(alldays)

#将x轴上的主格式化器设置为月格式化器。该格式化器负责x轴上较粗刻度的标签。
ax.xaxis.set_major_formatter(month_formatter)

#可以指定K线图的矩形宽度，现在先使用默认值。
candlestick(ax, quotes)

#将x轴上的标签格式化为日期。为了更好地适应x轴的长度，标签将被旋转。
fig.autofmt_xdate()
plt.show()

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from matplotlib.finance import quotes_historical_yahoo
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
today = date.today()
start = (today.year - 1, today.month, today.day)
symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]
quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
close = quotes.T[4] #提取出收盘价数据
plt.hist(close, np.sqrt(len(close)))
plt.show()

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from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import DateFormatter
from matplotlib.dates import DayLocator
from matplotlib.dates import MonthLocator
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
today = date.today()
start = (today.year - 1, today.month, today.day)

symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]

quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
dates = quotes.T[0]
volume = quotes.T[5]
alldays = DayLocator()
months = MonthLocator()
month_formatter = DateFormatter("%b %Y")

fig = plt.figure()
ax = fig.add_subplot(111)
plt.semilogy(dates, volume)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(month_formatter)
fig.autofmt_xdate()
plt.show()

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from matplotlib.finance import quotes_historical_yahoo
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
today = date.today()
start = (today.year - 1, today.month, today.day)

symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]

quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
close = quotes.T[4]
volume = quotes.T[5]

#计算股票收益率和成交量的变化值
ret = np.diff(close)/close[:-1] #out[n] = a[n+1] - a[n] 
volchange = np.diff(volume)/volume[:-1]

fig = plt.figure()
ax = fig.add_subplot(111)
#数据点的颜色与股票收益率相关联，数据点的大小与成交量的变化相关联 c,color s,size
ax.scatter(ret, volchange, c=ret * 100, s=volchange * 100, alpha=0.5)
ax.set_title('Close and volume returns')
ax.grid(True)
plt.show()

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from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import DateFormatter
from matplotlib.dates import DayLocator
from matplotlib.dates import MonthLocator
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
today = date.today()
start = (today.year - 1, today.month, today.day)
symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]
quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
dates = quotes.T[0]
close = quotes.T[4]
alldays = DayLocator()
months = MonthLocator()
month_formatter = DateFormatter("%b %Y")
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates, close)

#对收盘价下方的区域进行着色，依据低于或高于平均收盘价使用不同的颜色填充。
plt.fill_between(dates, close.min(), close, where=close>close.mean(), facecolor="green",alpha=0.4)
plt.fill_between(dates, close.min(), close, where=close<close.mean(),facecolor="red", alpha=0.4)

ax.xaxis.set_major_locator(months)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(month_formatter)
ax.grid(True)
fig.autofmt_xdate()
plt.show()

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from matplotlib.finance import quotes_historical_yahoo
from matplotlib.dates import DateFormatter
from matplotlib.dates import DayLocator
from matplotlib.dates import MonthLocator
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np

today = date.today()
start = (today.year - 1, today.month, today.day)

symbol = 'DISH'
if len(sys.argv) == 2:
    symbol = sys.argv[1]

quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
dates = quotes.T[0]
close = quotes.T[4]

fig = plt.figure()
ax = fig.add_subplot(111)

#计算并绘制指数移动平均线(第3章),分别使用9、12和15作为周期数计算和绘制指数移动平均线。添加了一个图例，并使用注释将其中两条曲线的交点标注了出来。
其中两条曲线的交点标注了出来
其中两条曲线的交点标注了出来。示
方法。分别使用9、12和15作为周期数计算和绘制指数移动平均线。
emas = []
for i in range(9, 18, 3):
    weights = np.exp(np.linspace(-1., 0., i))
    weights / = weights.sum()
    ema = np.convolve(weights, close)[i-1:-i+1]
    idx = (i - 6)/3
    ax.plot(dates[i-1:], ema, lw=idx, label="EMA(%s)" % (i))
    data = np.column_stack((dates[i-1:], ema))
    emas.append(np.rec.fromrecords(data, names=["dates", "ema"]))

#找到两条指数移动平均曲线的交点。
first = emas[0]["ema"].flatten()
second = emas[1]["ema"].flatten()
bools = np.abs(first[-len(second):] - second)/second < 0.0001
xpoints = np.compress(bools, emas[1])

#将找到的交点用注释和箭头标注出来，并确保注释文本在交点的不远处。
for xpoint in xpoints:
    ax.annotate('x', xy=xpoint, textcoords='offset points',xytext=(-50, 30),arrowprops=dict(arrowstyle="->"))

#添加一个图例并由Matplotlib自动确定其摆放位置。
leg = ax.legend(loc='best', fancybox=True)

#设置alpha通道值，将图例透明化。
leg.get_frame().set_alpha(0.5)

alldays = DayLocator()
months = MonthLocator()
month_formatter = DateFormatter("%b %Y")
ax.plot(dates, close, lw=1.0, label="Close")
ax.xaxis.set_major_locator(months)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(month_formatter)
ax.grid(True)
fig.autofmt_xdate()
plt.show()

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from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm

fig = plt.figure()
#使用3d关键字来指定图像的三维投影。
ax = fig.add_subplot(111, projection='3d')

#使用meshgrid函数创建一个二维的坐标网格。
u = np.linspace(-1, 1, 100)
x, y = np.meshgrid(u, u)
z = x ** 2 + y ** 2 #
ax.plot_surface(x, y, z, rstride=4, cstride=4, cmap=cm.YlGnBu_r) #cmap colormap
plt.show()

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import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
fig = plt.figure()
ax = fig.add_subplot(111)
u = np.linspace(-1, 1, 100)
x, y = np.meshgrid(u, u)
z = x ** 2 + y ** 2
ax.contourf(x, y, z)
plt.show()

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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation

fig = plt.figure()
ax = fig.add_subplot(111)
N = 10
x = np.random.rand(N)
y = np.random.rand(N)
z = np.random.rand(N)

#用不同颜色的圆形、小圆点和三角形来绘制三个数据集中的数据点。
circles, triangles, dots = ax.plot(x, 'ro', y, 'g^', z, 'b.')
ax.set_ylim(0, 1)
plt.axis('off')

#定期调用以更新屏幕上的内容。将随机更新两个数据集中的y坐标值。
def update(data):
    circles.set_ydata(data[0])
    triangles.set_ydata(data[1])
    return circles, triangles

#使用NumPy生成随机数。  
def generated:
    while True: yield np.random.rand(2, N)

anim = animation.FuncAnimation(fig, update, generate, interval=150)
plt.show()