u1and0 · November 26, 2016 14:33
diff --git a/file0.txt b/file0.txt
 n=20
diff --git a/file1.txt b/file1.txt
 a = np.arange(n).reshape(4, -1); a  # 5列の行列
diff --git a/file10.txt b/file10.txt
 plt.contourf(df, cmap='jet')
diff --git a/file11.txt b/file11.txt
 <matplotlib.contour.QuadContourSet at 0x1769a1a12b0>
diff --git a/file12.txt b/file12.txt
 plt.pcolor(df, cmap='jet')
diff --git a/file13.txt b/file13.txt
 <matplotlib.collections.PolyCollection at 0x1769b1e2208>
diff --git a/file14.txt b/file14.txt
 n=100
 x = np.linspace(0, 2*np.pi, n)
diff --git a/file15.txt b/file15.txt
 s = pd.Series(np.sin(x), index=x)
 s.plot()
diff --git a/file16.txt b/file16.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x1769e695780>
diff --git a/file17.txt b/file17.txt
 snoise = s + 0.1 * np.random.randn(n)
 sdf = pd.DataFrame({'sin wave':s, 'noise wave': snoise})
 sdf.plot(color=('r', 'b'))
diff --git a/file18.txt b/file18.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x1769e8586d8>
diff --git a/file19.txt b/file19.txt
 from  scipy import stats as ss
diff --git a/file2.txt b/file2.txt
 array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24],
       [25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
        42, 43, 44, 45, 46, 47, 48, 49],
       [50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
        67, 68, 69, 70, 71, 72, 73, 74],
       [75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
        92, 93, 94, 95, 96, 97, 98, 99]])
diff --git a/file20.txt b/file20.txt
 median = x[int(n/2)]  # xの中央値
 g = pd.Series(ss.norm.pdf(x, loc=median), x)
 g.plot()
diff --git a/file21.txt b/file21.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x1769ffba128>
diff --git a/file22.txt b/file22.txt
 gnoise = g + 0.01 * np.random.randn(n)
 df = pd.DataFrame({'gauss wave':g, 'noise wave': gnoise})
 df.plot(color=('r', 'b'))
diff --git a/file23.txt b/file23.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x1769e970828>
diff --git a/file24.txt b/file24.txt
 median = x[int(n/2)]  # xの中央値
 x1 = x + 10e-3
 l = pd.Series(np.log(x1), x1)
 l.plot()
diff --git a/file25.txt b/file25.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x1769ffba5f8>
diff --git a/file26.txt b/file26.txt
 lnoise = l + 0.1 * np.random.randn(n)
 df = pd.DataFrame({'log wave':l, 'noise wave': lnoise})
 df.plot(color=('r', 'b'))
diff --git a/file27.txt b/file27.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x176a00ec358>
diff --git a/file28.txt b/file28.txt
 n = 1000
 se = pd.Series(np.random.randint(-1, 2, n)).cumsum()
 se.plot()
diff --git a/file29.txt b/file29.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x284f3c62c18>
diff --git a/file3.txt b/file3.txt
 df = pd.DataFrame(a, columns=list('abcde')); df
diff --git a/file30.txt b/file30.txt
 sma100 = se.rolling(100).mean()
 ema100 = se.ewm(span=100).mean()

 df = pd.DataFrame({'Chart': se,  'SMA100': sma100, 'EMA100': ema100})
 df.plot(style = ['--','-','-'])
diff --git a/file31.txt b/file31.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x284f3cadcc0>
diff --git a/file4.txt b/file4.txt
 r = np.random.randn(4, 5); r
diff --git a/file5.txt b/file5.txt
 array([[-0.37840346, -0.84591793,  0.50590263,  0.0544243 ,  0.59361247],
       [-0.2726931 , -1.74415635,  0.0199559 , -0.20695113, -1.19559455],
       [-0.59799566, -0.26810224, -0.18738038,  1.05843686,  0.72317579],
       [ 1.23389386,  1.91293041, -1.33322818,  0.78255026,  2.04737357]])
diff --git a/file6.txt b/file6.txt
 df = pd.DataFrame(r, columns=list('abcde')); df
diff --git a/file7.txt b/file7.txt
 df.plot()
diff --git a/file8.txt b/file8.txt
 <matplotlib.axes._subplots.AxesSubplot at 0x17699af2a58>
diff --git a/file9.txt b/file9.txt
 df = pd.DataFrame(np.random.randn(n,n))
	snoise = s + 0.1 * np.random.randn(n)
	sdf = pd.DataFrame({'sin wave':s, 'noise wave': snoise})
	sdf.plot(color=('r', 'b'))
	array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
	17, 18, 19, 20, 21, 22, 23, 24],
	[25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
	42, 43, 44, 45, 46, 47, 48, 49],
	[50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66,
	67, 68, 69, 70, 71, 72, 73, 74],
	[75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91,
	92, 93, 94, 95, 96, 97, 98, 99]])
	median = x[int(n/2)] # xの中央値
	g = pd.Series(ss.norm.pdf(x, loc=median), x)
	g.plot()
	gnoise = g + 0.01 * np.random.randn(n)
	df = pd.DataFrame({'gauss wave':g, 'noise wave': gnoise})
	df.plot(color=('r', 'b'))
	median = x[int(n/2)] # xの中央値
	x1 = x + 10e-3
	l = pd.Series(np.log(x1), x1)
	l.plot()
	lnoise = l + 0.1 * np.random.randn(n)
	df = pd.DataFrame({'log wave':l, 'noise wave': lnoise})
	df.plot(color=('r', 'b'))
	n = 1000
	se = pd.Series(np.random.randint(-1, 2, n)).cumsum()
	se.plot()
	sma100 = se.rolling(100).mean()
	ema100 = se.ewm(span=100).mean()

	df = pd.DataFrame({'Chart': se, 'SMA100': sma100, 'EMA100': ema100})
	df.plot(style = ['--','-','-'])
	array([[-0.37840346, -0.84591793, 0.50590263, 0.0544243 , 0.59361247],
	[-0.2726931 , -1.74415635, 0.0199559 , -0.20695113, -1.19559455],
	[-0.59799566, -0.26810224, -0.18738038, 1.05843686, 0.72317579],
	[ 1.23389386, 1.91293041, -1.33322818, 0.78255026, 2.04737357]])