本文共 11468 字，大约阅读时间需要 38 分钟。
这节课主要讲了numpy，pandas，tushare，talib，matlabplot用法，如果有基础的化不用听了
能看懂如下样例代码就行了
####numpy数据结构####v = [0.5, 0.75, 1.0, 1.5, 2.0] m = [v, v, v] # matrix of numbersmm[1]m[1][0]v1 = [0.5, 1.5]v2 = [1, 2]m = [v1, v2]c = [m, m] # cube of numberscc[1][1][0]v = [0.5, 0.75, 1.0, 1.5, 2.0]m = [v, v, v]mv[0] = 'Python'mfrom copy import deepcopyv = [0.5, 0.75, 1.0, 1.5, 2.0]m = 3 * [deepcopy(v), ]mv[0] = 'Python'mimport numpy as npa = np.array([0, 0.5, 1.0, 1.5, 2.0])type(a)a[:2] a.sum()a.std()a.cumsum()a * 2a ** 2np.sqrt(a)b = np.array([a, a * 2])bb[0]b[0, 2] # third element of first rowb.sum()b.sum(axis=0)b.sum(axis=1)
import numpy as npimport pandas as pdfrom pandas import Series, DataFramedf = pd.DataFrame([10,20,30,40],columns=['number'],index = ['a','b','c','d'])df['floats'] = (1.5,2.5,3.5,4.5)df.append(pd.DataFrame({'number':100,'floats':5.7},index=['z']))df['squares'] = (1,4,9,16)df[['number','squares']].mean()df[['number','squares']].std()a = np.random.standard_normal((9,4))df = pd.DataFrame(a)#print dfdf.columns = [['No1','No2','No3','No4']]#print df##print df['No2'][3]dates = pd.date_range('2015-1-1',periods = 9,freq = 'D')#print datesdf.index = datesprint df #MAimport pandas as pdimport tushare as tsimport talib as ta#从tushare获取数据sh = ts.get_hist_data('sh')sh = sh.sort_index()#print sh['close']#使用TALIB计算20日均线ma = ta.MA(sh['close'].values,20)sh.loc[:,'tama'] = mash[['close','tama']].plot(figsize = (15,5),grid = True)#bollingerimport numpy as npimport pandas as pdimport tushare as tsimport talib as tash = ts.get_hist_data('sh')sh = sh.sort_index()close = sh[['close','volume']]#函数用的price必须为narrayupper,middle,lower = ta.BBANDS( sh['close'].values, timeperiod=20, nbdevup = 2, nbdevdn = 2, matype = 0 )close.loc[:, 'upper'] = upperclose.loc[:,'middle']=middleclose.loc[:,'lower']=lowerclose[['close','middle','upper','lower']].plot(figsize=(15,8),grid = True) # -*- coding: utf-8 -*-import numpy as npimport matplotlib as mplimport matplotlib.pyplot as pltimport warnings; warnings.simplefilter('ignore')# import seaborn as sns; sns.set()get_ipython().magic(u'matplotlib inline')####二维绘图######折线图#一维数据集np.random.seed(1000)y = np.random.standard_normal(20)x = range(len(y))plt.plot(x, y)plt.plot(y)plt.plot(y.cumsum())plt.plot(y.cumsum())plt.grid(True) # adds a gridplt.axis('tight')plt.plot(y.cumsum())plt.grid(True)plt.xlim(-1, 20)plt.ylim(np.min(y.cumsum()) - 1, np.max(y.cumsum()) + 1) plt.figure(figsize=(7, 4)) # the figsize parameter defines the # size of the figure in (width, height)plt.plot(y.cumsum(), 'b', lw=1.5)plt.plot(y.cumsum(), 'ro')plt.grid(True)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A Simple Plot')#二维数据集np.random.seed(2000)y = np.random.standard_normal((20, 2)).cumsum(axis=0)plt.figure(figsize=(7, 4))plt.plot(y, lw=1.5) # plots two linesplt.plot(y, 'ro') # plots two dotted linesplt.grid(True)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A Simple Plot')plt.figure(figsize=(7, 4))plt.plot(y[:, 0], lw=1.5, label='1st')plt.plot(y[:, 1], lw=1.5, label='2nd')plt.plot(y, 'ro')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A Simple Plot')y[:, 0] = y[:, 0] * 100plt.figure(figsize=(7, 4))plt.plot(y[:, 0], lw=1.5, label='1st')plt.plot(y[:, 1], lw=1.5, label='2nd')plt.plot(y, 'ro')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('A Simple Plot')fig, ax1 = plt.subplots()plt.plot(y[:, 0], 'b', lw=1.5, label='1st')plt.plot(y[:, 0], 'ro')plt.grid(True)plt.legend(loc=8)plt.axis('tight')plt.xlabel('index')plt.ylabel('value 1st')plt.title('A Simple Plot')ax2 = ax1.twinx()plt.plot(y[:, 1], 'g', lw=1.5, label='2nd')plt.plot(y[:, 1], 'ro')plt.legend(loc=0)plt.ylabel('value 2nd')plt.figure(figsize=(7, 5))plt.subplot(211)plt.plot(y[:, 0], lw=1.5, label='1st')plt.plot(y[:, 0], 'ro')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.ylabel('value')plt.title('A Simple Plot')plt.subplot(212)plt.plot(y[:, 1], 'g', lw=1.5, label='2nd')plt.plot(y[:, 1], 'ro')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.figure(figsize=(9, 4))plt.subplot(121)plt.plot(y[:, 0], lw=1.5, label='1st')plt.plot(y[:, 0], 'ro')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.ylabel('value')plt.title('1st Data Set')plt.subplot(122)plt.bar(np.arange(len(y)), y[:, 1], width=0.5, color='g', label='2nd')plt.grid(True)plt.legend(loc=0)plt.axis('tight')plt.xlabel('index')plt.title('2nd Data Set')##散点图y = np.random.standard_normal((1000, 2))plt.figure(figsize=(7, 5))plt.plot(y[:, 0], y[:, 1], 'ro')plt.grid(True)plt.xlabel('1st')plt.ylabel('2nd')plt.title('Scatter Plot')plt.figure(figsize=(7, 5))plt.scatter(y[:, 0], y[:, 1], marker='o')plt.grid(True)plt.xlabel('1st')plt.ylabel('2nd')plt.title('Scatter Plot')c = np.random.randint(0, 10, len(y))plt.figure(figsize=(7, 5))plt.scatter(y[:, 0], y[:, 1], c=c, marker='o')plt.colorbar()plt.grid(True)plt.xlabel('1st')plt.ylabel('2nd')plt.title('Scatter Plot')##直方图plt.figure(figsize=(7, 4))plt.hist(y, label=['1st', '2nd'], bins=25)plt.grid(True)plt.legend(loc=0)plt.xlabel('value')plt.ylabel('frequency')plt.title('Histogram')plt.figure(figsize=(7, 4))plt.hist(y, label=['1st', '2nd'], color=['b', 'g'], stacked=True, bins=20)plt.grid(True)plt.legend(loc=0)plt.xlabel('value')plt.ylabel('frequency')plt.title('Histogram')##箱线图fig, ax = plt.subplots(figsize=(7, 4))plt.boxplot(y)plt.grid(True)plt.setp(ax, xticklabels=['1st', '2nd'])plt.xlabel('data set')plt.ylabel('value')plt.title('Boxplot')#面积图from matplotlib.patches import Polygondef func(x): return 0.5 * np.exp(x) + 1a, b = 0.5, 1.5 # integral limitsx = np.linspace(0, 2)y = func(x)fig, ax = plt.subplots(figsize=(7, 5))plt.plot(x, y, 'b', linewidth=2)plt.ylim(ymin=0)Ix = np.linspace(a, b)Iy = func(Ix)verts = [(a, 0)] + list(zip(Ix, Iy)) + [(b, 0)]poly = Polygon(verts, facecolor='0.7', edgecolor='0.5')ax.add_patch(poly)plt.text(0.5 * (a + b), 1, r"$\int_a^b f(x)\mathrm{d}x$", horizontalalignment='center', fontsize=20)plt.figtext(0.9, 0.075, '$x$')plt.figtext(0.075, 0.9, '$f(x)$')ax.set_xticks((a, b))ax.set_xticklabels(('$a$', '$b$'))ax.set_yticks([func(a), func(b)])ax.set_yticklabels(('$f(a)$', '$f(b)$'))plt.grid(True)##蜡烛图import matplotlib.finance as mpfstart = (2014, 5, 1)end = (2014, 6, 30)quotes = mpf.quotes_historical_yahoo_ohlc('^GDAXI', start, end)quotes[:2]fig, ax = plt.subplots(figsize=(8, 5))fig.subplots_adjust(bottom=0.2)mpf.candlestick_ohlc(ax, quotes, width=0.6, colorup='b', colordown='r')plt.grid(True)ax.xaxis_date() # dates on the x axisax.autoscale_view()plt.setp(plt.gca().get_xticklabels(), rotation=30)fig, ax = plt.subplots(figsize=(8, 5))mpf.plot_day_summary_ohlc(ax, quotes, colorup='b', colordown='r')plt.grid(True)ax.xaxis_date()plt.title('DAX Index')plt.ylabel('index level')plt.setp(plt.gca().get_xticklabels(), rotation=30)quotes = np.array(mpf.quotes_historical_yahoo_ochl('YHOO', start, end))fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(8, 6))mpf.candlestick_ohlc(ax1, quotes, width=0.6, colorup='b', colordown='r')ax1.set_title('Yahoo Inc.')ax1.set_ylabel('index level')ax1.grid(True)ax1.xaxis_date()plt.bar(quotes[:, 0] - 0.25, quotes[:, 5], width=0.5)ax2.set_ylabel('volume')ax2.grid(True)ax2.autoscale_view()plt.setp(plt.gca().get_xticklabels(), rotation=30)####3d绘图####strike = np.linspace(50, 150, 24)ttm = np.linspace(0.5, 2.5, 24)strike, ttm = np.meshgrid(strike, ttm)strike[:2]iv = (strike - 100) ** 2 / (100 * strike) / ttmfrom mpl_toolkits.mplot3d import Axes3Dfig = plt.figure(figsize=(9, 6))ax = fig.gca(projection='3d')surf = ax.plot_surface(strike, ttm, iv, rstride=2, cstride=2, cmap=plt.cm.coolwarm, linewidth=0.5, antialiased=True)ax.set_xlabel('strike')ax.set_ylabel('time-to-maturity')ax.set_zlabel('implied volatility')fig.colorbar(surf, shrink=0.5, aspect=5)fig = plt.figure(figsize=(8, 5))ax = fig.add_subplot(111, projection='3d')ax.view_init(30, 60)ax.scatter(strike, ttm, iv, zdir='z', s=25, c='b', marker='^')ax.set_xlabel('strike')ax.set_ylabel('time-to-maturity')ax.set_zlabel('implied volatility')#####figure与subplot#figure对象fig = plt.figure()ax1 = fig.add_subplot(2, 2, 1)ax2 = fig.add_subplot(2, 2, 2)ax3 = fig.add_subplot(2, 2, 3)plt.show()from numpy.random import randnplt.plot(randn(50).cumsum(), 'k--')fig.show()_ = ax1.hist(randn(100), bins=20, color='k', alpha=0.3)ax2.scatter(np.arange(30), np.arange(30) + 3 * randn(30))plt.close('all')fig, axes = plt.subplots(2, 3)axes#调整subplot周围的间距plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)fig, axes = plt.subplots(2, 2, sharex=True, sharey=True)for i in range(2): for j in range(2): axes[i, j].hist(randn(500), bins=50, color='k', alpha=0.5)plt.subplots_adjust(wspace=0, hspace=0)#####matplotlib基本设置#颜色、标记和线型plt.figure()plt.plot(x,y,linestyle='--',color='g')plt.plot(randn(30).cumsum(), 'ko--')plt.plot(randn(30).cumsum(),color='k',linestyle='dashed',marker='o')plt.close('all')data = randn(30).cumsum()plt.plot(data, 'k--', label='Default')plt.plot(data, 'k-', drawstyle='steps-post', label='steps-post')plt.legend(loc='best')#设置标题、轴标签、刻度以及刻度标签fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)ax.plot(randn(1000).cumsum())ticks = ax.set_xticks([0, 250, 500, 750, 1000])labels = ax.set_xticklabels(['one', 'two', 'three', 'four', 'five'], rotation=30, fontsize='small')ax.set_title('My first matplotlib plot')ax.set_xlabel('Stages')#添加图例fig = plt.figure(); ax = fig.add_subplot(1, 1, 1)ax.plot(randn(1000).cumsum(), 'k', label='one')ax.plot(randn(1000).cumsum(), 'k--', label='two')ax.plot(randn(1000).cumsum(), 'k.', label='three')ax.legend(loc='best')#注释以及在subplot上绘图from datetime import datetimeimport pandas as pdfig = plt.figure()ax = fig.add_subplot(1, 1, 1)data = pd.read_csv('d:/data/spx.csv', index_col=0, parse_dates=True)spx = data['SPX']spx.plot(ax=ax, style='k-')crisis_data = [ (datetime(2007, 10, 11), 'Peak of bull market'), (datetime(2008, 3, 12), 'Bear Stearns Fails'), (datetime(2008, 9, 15), 'Lehman Bankruptcy')]for date, label in crisis_data: ax.annotate(label, xy=(date, spx.asof(date) + 50), xytext=(date, spx.asof(date) + 200), arrowprops=dict(facecolor='black'), horizontalalignment='left', verticalalignment='top')ax.set_xlim(['1/1/2007', '1/1/2011'])ax.set_ylim([600, 1800])ax.set_title('Important dates in 2008-2009 financial crisis')fig = plt.figure()ax = fig.add_subplot(1, 1, 1)rect = plt.Rectangle((0.2, 0.75), 0.4, 0.15, color='k', alpha=0.3)circ = plt.Circle((0.7, 0.2), 0.15, color='b', alpha=0.3)pgon = plt.Polygon([[0.15, 0.15], [0.35, 0.4], [0.2, 0.6]], color='g', alpha=0.5)ax.add_patch(rect)ax.add_patch(circ)ax.add_patch(pgon)#图表的保存figfig.savefig('figpath.svg')fig.savefig('figpath.png', dpi=400, bbox_inches='tight')from io import BytesIObuffer = BytesIO()plt.savefig(buffer)plot_data = buffer.getvalue()#matplotlib配置plt.rc('figure', figsize=(10, 10))font_options={'family':'monospace', 'weight':'bold','size':'small'}plt.rc('font',**font_options) 转载地址：http://qeyws.baihongyu.com/