Matplotlib Simple Drawing

0, Matplotlib--Introduction. Note

Official website:http://matplotlib.org/

1, Matplotlib--draw the polynomial function. Note

#-*-coding:utf-8-*-#绘制多项式函数import numpy as Npimport matplotlib.pyplot as plt# coefficients of the natural number sequence as a polynomial, using the POLYLD function to create the polynomial Func = NP.POLY1D (Np.array ([1,2,3,4]). Astype (float)) print func# creates an x-axis value using NumPy linspace functions, producing 30 evenly spaced values between 10 and 10 x= Np.linspace ( -10,10,30) #计算我们在第一步中创建的多项式的值y =func (x) #调用plot函数, this does not immediately show the function image Plt.plot, x, y # Use the Xlabel function to add the x-axis label Plt.xlabel (' X ') #使用ylabel函数添加y轴的标签plt. Ylabel (' Y (x) ') #调用show函数显示函数图像plt. Show ()

2, Matplotlib--draw the polynomial function and its derivative function. Note

#-*-coding:utf-8-*-#绘制多项式函数及其导函数 # Draw a polynomial function and a guide function using two different styles of curves, and only call the plot function import NumPy as Npimport Matplotlib.pyplot As plt# plots a polynomial, and the first order function func=np.poly1d (Np.array ([1,2,3,4]) obtained using the derive function and the parameter M 1. Astype (float)) Func1=func.deriv (m=1 ) X=np.linspace ( -10,10,30) y=func (x) y1=func1 (x) # Plt.plot (x, y) #多项式函数plt. Plot (x, y, ' ro ', x,y1, ' g--')  # Polynomial functions and first-order derivative functions    red is the derivative, Green is the first-order derivative Plt.xlabel (' x ') Plt.ylabel (' Y ') plt.show ()

3, Matplotlib--draw the polynomial function and its derivative function, first Order guide and second derivative. Note

#-*-Coding:utf-8-*-#绘制多项式函数及其导函数, first derivative and second order import NumPy as Npimport matplotlib.pyplot as plt# create polynomial and its derivative 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) y1=func1 (x) func2= Func.deriv (m=2) y2=func2 (x) #使用subplot函数创建第一个子图. # The first parameter of the function is: the number of rows of the child graph # The second parameter of the function is: number of columns of the sub-graph # The third parameter of the function is: Ordinal plt.subplot (311) plt.plot (x, Y, ' R '), starting from 1 ("Ploynomial") # Use the subplot function to create a second sub-graph, using the blue triangle to draw Plt.subplot (312) plt.plot (x,y1, ' b^ ') plt.title ("first derivative") #使用subplot函数绘制第三个子图 , use green circle to draw Plt.subplot (313) Plt.plot (x,y2, ' Go ') plt.title ("Secord derivaive") Plt.xlabel (' x ') Plt.ylabel (' Y ') plt.show ()

4, Matplotlib--draw the stock price throughout the year. Note

#-*-Coding:utf-8-*-#绘制全年的股票价格from matplotlib.dates import dateformatterfrom matplotlib.dates import Daylocatorfrom Matplotlib.dates Import monthlocatorfrom matplotlib.finance import Quotes_historical_yahoofrom matplotlib.finance Import candlestickimport sysfrom datetime import Dateimport Matplotlib.pyplot as Plttoday=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 # Timer Locator, you can locate the month and date on the x-axis alldays=daylocator () months= Monthlocator () #创建一个日期格式化data formatter to format the date on the x-axis. The formatting will create a string containing the abbreviated month and year Month_formatter=dateformatter ("%b%Y") symbol = ' DISH ' #Dish Network Company, DISH stock 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) Print quotes# Create a Matplotlib figure object, which is the top-level container for the drawing component Fig=plt.figure () #增加一个子图ax =fig.add_subplot (111) #将X轴上的主定位器设置为日定位器. The positioner is responsible for the coarse scale ax.xaxis.set_major_locator (months) #将X轴上的次定位器设置为日定位器 on the x-axis. The locator is responsible for the finer scale ax.xaxis.set_minor_locator (alldays) #将X轴上的主格式化器设置为月格式化器 on the x-axis, which is responsible for the label on the x-axis on the thicker scale ax.xaxis.set_major_ Formatter (MoNth_formatter) #使用获取肚饿股价数据绘制K线图. We can specify the width of the candlestick chart candlestick (ax, quotes) #将X轴上的标签格式化为日期. To better fit the x-axis length, the label will be rotated Fig.autofmt_xdate () plt.show ()

5, Matplotlib--Draw the stock distribution histogram. Note

#-*-Coding:utf-8-*-#绘制股票分布直方图 # Histogram can visualize the distribution of the data # Draw dish the distribution histogram of the stock price from matplotlib.finance import Quotes_historical_ Yahooimport sysfrom datetime import dateimport matplotlib.pyplot as Pltimport numpy as np# download data for the past year 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) #上一步得到的股价数据存储在python列表中, converts it into an numpy array and extracts the closing price data Quotes=np.array (quotes) Close =quotes. T[4] #指定合理数量的柱形, plot the distribution histogram plt.hist (Close,np.sqrt (Len (Close)) Plt.show ()

6, Matplotlib--logarithmic axis to draw stock volume. Note

#-*-Coding:utf-8-*-#对数坐标轴 draw stock turnover from matplotlib.dates import dateformatterfrom matplotlib.dates import Daylocatorfro M matplotlib.dates import monthlocatorfrom matplotlib.finance import quotes_historical_yahooimport sysfrom datetime Import Dateimport Matplotlib.pyplot as Pltimport NumPy as Nptoday=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symbol = ' DISH ' #Dish Network company, DISH stock price If Len (sys.argv) = = 2:symbol=sys.argv[1] #从雅虎财经频道下载股 Price Data Quotes=quotes_historical_yahoo (symbol,start,today) print Quotesquotes=np.array (quotes) dates=quotes. T[0]volume=quotes. T[5]alldays=daylocator () Months=monthlocator () month_formatter=dateformatter ("%b%Y") #创建一个Matplotlib的figure对象, This is the top-level container for the drawing component Fig=plt.figure () #增加一个子图ax =fig.add_subplot (111) #使用对数坐标plt. Semilogy (Dates,volume) #将X轴上的主定位器设置为日定位器. The positioner is responsible for the coarse scale ax.xaxis.set_major_locator (months) #将X轴上的次定位器设置为日定位器 on the x-axis. The locator is responsible for the finer scale ax.xaxis.set_minor_locator (alldays) #将X轴上的主格式化器设置为月格式化器 on the x-axis, which is responsible for the label on the x-axis on the thicker scale ax.xaxis.set_major_ Formatter (Month_formatter) #将X轴上的标签格式化为日期.  To better fit the x-axis length, the label will be rotated Fig.autofmt_xdate () plt.show ()

7, Matplotlib--scatter plot chart plot dish stock return rate and volume of scatter plot. Note

#-*-Coding:utf-8-*-#散点图    Draw a scatter chart of dish stock yield and volume from matplotlib.finance import Quotes_historical_yahooimport Sysfrom datetime import Dateimport matplotlib.pyplot as Pltimport NumPy as Nptoday=date.today () start= (Today.year-1, Today.month,today.day) 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)    #没有网, this code does not run print quotes# the quotes data obtained is stored in the Python list, Format it as a numpy array and extract the closing price and volume data 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]volchange=np.diff (volume)/volume[:-1]# Create a Matplotlib Figure Object Fig=plt.figure () #在图像中绘制一个子图ax =fig.add_subplot (111) #创建散点图 and correlate the color of the data point with the stock yield, The size of the data point is associated with the change in volume Ax.scatter (ret,volchange,c=ret*100,s=volchange*100,alpha=0.5) #设置图像的标题, and the addition of a grid line Ax.set_title (' Close and Volume returns ') Ax.grid (True) plt.show ()

8, Matplotlib--according to the conditions of coloring. Note

#-*-Coding:utf-8-*-#根据条件进行着色 The #fill_between function fills the area of the image with the specified color # The stock graph is shaded and the close price below the mean and above mean is populated with a different color from Matplotlib.finance Import quotes_historical_yahoofrom matplotlib.dates import Dateformatterfrom matplotlib.dates Import daylocatorfrom matplotlib.dates import monthlocatorimport sysfrom datetime import Dateimport Matplotlib.pyplot as    Pltimport NumPy as Nptoday=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") #创建一个Matplotlib的figure对象fig = Plt.figure () #在图像中添加一个子图ax =fig.add_subplot (111) #绘制收盘价数据ax. Plot (dates,close) #对收盘价下方的数据进行着色, Below or above average close prices are filled with different colors 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) # Set the locator and format the x-axis as a dayPeriod 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 ()

9, Matplotlib-use legends and annotations. Note

#-*-Coding:utf-8-*-#使用图例和注释 # Creates a transparent legend with the legend function and automatically determines its placement by matplotlib. You can also use the annotate function to annotate the image accurately, #并有很多可选的注释和箭头风格from matplotlib.finance import Quotes_historical_yahoofrom Matplotlib.dates Import dateformatterfrom matplotlib.dates import daylocatorfrom matplotlib.dates Import Monthlocatorimport sysfrom datetime import dateimport matplotlib.pyplot as Pltimport NumPy as Nptoday=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)/3ax.plot (dates[i-1:],ema,lw= Idx,label= "EMA (%s)"% (i)) Date=np.column_stack ((Dates[i-1:],ema)) Emas.append (Np.rec.fromrecords (date,names=[) Datas "," EMA "])) #找到两条指数移动平均曲线的交点first =emas[0][" EMA "].flaTten () secord=emas[1]["EMA"].flatten () Bools=np.abs (First[-len (Secord):]-secord)/secord<0.0001xpoints= Np.compress (Bools,emas[1]) #将找到的交点用注释和箭头标注出来 and make sure that the annotation text is not far from the intersection 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, transparency of the legend Leg.get_frame (). Set_alpha (0.5) alldays=daylocator () months= Monthlocator () month_formatter=dateformatter ("%b%Y") #绘制收盘价数据ax. Plot (dates,close,lw=1.0,label= "close") # The data below the closing price is shaded, below or above the average closing price using a different color fill 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) #设置定位器并将X轴格式化为日期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 ()

10, Matplotlib--drawing in 3 dimensional space. Note

#-*-Coding:utf-8-*-#在3维空间中绘图from mpl_toolkits.mplot3d import  axes3dimport matplotlib.pyplot as Pltimport NumPy as Npfrom matplotlib Import cmfig=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 ') # Use the Meshgrid function to create a two-dimensional coordinate grid u=np.linspace ( -1,1,100) X,y=np.meshgrid (u,u) z=x**2+y**2# Specify the row and column stride, and the color table used to draw the surface ax.plot_ Surface (x,y,z,rstride=4,cstride=4,cmap=cm. Ylgnbu_r) Plt.show ()

11, Matplotlib--draw a high-level map. Note

#-*-Coding:utf-8-*-#绘制等高图 the Contour 3D drawing in #matplotlib has two styles: filled and non-populated. #我们可以使用contour函数创建一般的等高线图, for contour plots of color fills, you can use Contourfimport Matplotlib.pyplot as Pltimport numpy as Npfrom matplotlib Import cmfig=plt.figure () Ax=fig.add_subplot (111) u=np.linspace ( -1,1,100) X,y=np.meshgrid (u,u) z=x**2+y**2# Draw Contour Map Ax.contourf (x, Y, z) plt.show ()

12, Matplotlib--making animation. Note

#-*-Coding:utf-8-*-#制作动画 # draw 3 randomly generated datasets to display the import NumPy as Npimport Matplotlib.pyplot as pltimport with circles, dots, and triangles, respectively Matplotlib.animation as Animationfig=plt.figure () Ax=fig.add_subplot (111) N=10x=np.random.rand (N) Y=np.random.rand ( N) z=np.random.rand (n) #用不同颜色的圆形, dots, and triangles draw data points from three datasets Circles,triangles,dots=ax.plot (x, ' ro ', y, ' g^ ', Z, ' B. ') ax.set_ Ylim (0,1) plt.axis (' off ') #这个函数将被定期调用以更新屏幕的内容def update (data):    circles.set_ydata (data[0])    triangles.set_ Ydata (Data[1]) return circles,triangles# use NumPy to generate a random number def generate (): While True:yield Np.random.rand (2,n) anim= Animation. Funcanimation (fig,update,generate,interval=150) plt.show ()

Matplotlib Simple Drawing