python numpy/scipy curve fitting
librerias importantes
import pandas_datareader as pdr
import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
scipy.optimize.curve_fit tries to fit a function f that you must know to a set of points.
This is a simple 3 degree polynomial fit using numpy.polyfit and poly1d, the first performs a least squares polynomial fit and the second calculates the new points:
import numpy as np
import matplotlib.pyplot as plt
points = np.array([(1, 1), (2, 4), (3, 1), (9, 3)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
z = np.polyfit(x, y, 3)
f = np.poly1d(z)
# calculate new x's and y's
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)
plt.plot(x,y,'o', x_new, y_new)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
import numpy as np
import matplotlib.pyplot as plt
points = np.array([
(197,3116.2),
(198,3103.0),
(199,3156.1),
(200,3078.1),
(201,3008.7),
(202,2954.9),
(203,2960.5),
(204,3129.0),
(205,2999.9),
(206,3019.8),
(207,3095.1),
(208,3174.1),
(209,3144.9),
(210,3148.7),
(211,3221.3),
(212,3125.0),
(213,3199.2),
(214,3100.0),
(215,3195.7),
(216,3190.6),
(217,3286.6),
(218,3442.9),
(219,3443.6),
(220,3363.7),
(221,3338.6),
(222,3272.7),
(223,3207.2),
(224,3217.0),
(225,3184.9),
(226,3176.4),
(227,3204.4),
(228,3207.0),
(229,3286.3),
(230,3162.8),
(231,3211.0),
(232,3036.1),
(233,3004.5),
(234,3048.4),
(235,3241.2),
(236,3322.0),
(237,3311.4),
(238,3143.7),
(239,3035.0),
(240,3137.4),
(241,3110.3),
(242,3128.8),
(243,3131.1),
(244,3135.7),
(245,3105.5),
(246,3117.0),
(247,3099.4),
(248,3098.4),
(249,3118.1),
(250,3185.1),
(251,3195.3),
(252,3168.0),
(253,3220.1),
(254,3203.5),
(255,3186.7),
(256,3162.6),
(257,3158.0),
(258,3177.3),
(259,3104.2),
(260,3101.5),
(261,3116.4),
(262,3157.0),
(263,3165.1),
(264,3241.0),
(265,3236.1),
(266,3201.6),
(267,3206.2),
(268,3206.5),
(269,3185.3),
(270,3172.7),
(271,3284.0),
(272,3322.0),
(273,3285.9),
(274,3256.9),
(275,3186.6),
(276,3218.5)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
z = np.polyfit(x, y, 3)
f = np.poly1d(z)
# calculate new x's and y's
x_new = np.linspace(x[0], x[-1], 50)
y_new = f(x_new)
plt.plot(x,y,'o', x_new, y_new)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
*********************************************
C:\pythonPRACTICA>python fit2.py
z=
[ 0.08703704 -0.81349206 1.69312169 -0.03968254]
np.poly1d(z)=
3 2
0.08704 x - 0.8135 x + 1.693 x - 0.03968
p(0.5) =
0.6143849206349206
p(3.5)) =
-0.34732142857142845
p(10) =
22.579365079364862
p30 = np.poly1d(np.polyfit(x, y, 30)) =
30 29 28 27
-7.16e-22 x - 3.532e-21 x - 1.736e-20 x - 8.496e-20 x
26 25 24 23
- 4.132e-19 x - 1.994e-18 x - 9.516e-18 x - 4.476e-17 x
22 21 20 19
- 2.063e-16 x - 9.223e-16 x - 3.934e-15 x - 1.548e-14 x
18 17 16 15
- 5.15e-14 x - 9.843e-14 x + 4.796e-13 x + 8.283e-12 x
14 13 12 11
+ 7.662e-11 x + 5.899e-10 x + 4.133e-09 x + 2.714e-08 x
10 9 8 7
+ 1.687e-07 x + 9.879e-07 x + 5.346e-06 x + 2.539e-05 x
6 5 4 3 2
+ 8.972e-05 x + 2.249e-05 x - 0.00352 x - 0.03957 x - 0.2124 x + 1.055 x - 1.405e-15
p30(4) =
-0.7999999999999977
p30(5) =
-0.9999999999999879
p30(4.5) =
-0.11804856672740378
+++++++++++++++++++++++++++++++++++
OUT
INPUT
import numpy as np
import matplotlib.pyplot as plt
import warnings
x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
z = np.polyfit(x, y, 3)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 30))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
print("p30(4) =")
print(p30(4))
print("p30(5) =")
print(p30(5))
print("p30(4.5) =")
print(p30(4.5))
xp = np.linspace(-2, 6, 100)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
plt.ylim(-2,2)
plt.show()
exactamente el mismo resultado, mas convieniente para usar csv
import numpy as np
import matplotlib.pyplot as plt
import warnings
points = np.array([(0.0, 0.0), (1.0, 0.8), (2.0, 0.9), (3.0, 0.1),(4.0, -0.8),(5.0, -1.0)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
#z = np.polyfit(x, y, 3)
#f = np.poly1d(z)
###########
#x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
#y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
z = np.polyfit(x, y, 3)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 30))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
print("p30(4) =")
print(p30(4))
print("p30(5) =")
print(p30(5))
print("p30(4.5) =")
print(p30(4.5))
xp = np.linspace(-2, 6, 100)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
plt.ylim(-2,2)
plt.show()
out
import numpy as np
import matplotlib.pyplot as plt
import warnings
points = np.array([
(197,3116.2),
(198,3103.0),
(199,3156.1),
(200,3078.1),
(201,3008.7),
(202,2954.9),
(203,2960.5),
(204,3129.0),
(205,2999.9),
(206,3019.8),
(207,3095.1),
(208,3174.1),
(209,3144.9),
(210,3148.7),
(211,3221.3),
(212,3125.0),
(213,3199.2),
(214,3100.0),
(215,3195.7),
(216,3190.6),
(217,3286.6),
(218,3442.9),
(219,3443.6),
(220,3363.7),
(221,3338.6),
(222,3272.7),
(223,3207.2),
(224,3217.0),
(225,3184.9),
(226,3176.4),
(227,3204.4),
(228,3207.0),
(229,3286.3),
(230,3162.8),
(231,3211.0),
(232,3036.1),
(233,3004.5),
(234,3048.4),
(235,3241.2),
(236,3322.0),
(237,3311.4),
(238,3143.7),
(239,3035.0),
(240,3137.4),
(241,3110.3),
(242,3128.8),
(243,3131.1),
(244,3135.7),
(245,3105.5),
(246,3117.0),
(247,3099.4),
(248,3098.4),
(249,3118.1),
(250,3185.1),
(251,3195.3),
(252,3168.0),
(253,3220.1),
(254,3203.5),
(255,3186.7),
(256,3162.6),
(257,3158.0),
(258,3177.3),
(259,3104.2),
(260,3101.5),
(261,3116.4),
(262,3157.0),
(263,3165.1),
(264,3241.0),
(265,3236.1),
(266,3201.6),
(267,3206.2),
(268,3206.5),
(269,3185.3),
(270,3172.7),
(271,3284.0),
(272,3322.0),
(273,3285.9),
(274,3256.9),
(275,3186.6),
(276,3218.5)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
#z = np.polyfit(x, y, 3)
#f = np.poly1d(z)
###########
#x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
#y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
z = np.polyfit(x, y, 3)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 30))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
print("p30(4) =")
print(p30(4))
print("p30(5) =")
print(p30(5))
print("p30(4.5) =")
print(p30(4.5))
#xp = np.linspace(-2, 6, 100)
xp = np.linspace(x[0], x[-1], 50)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
#plt.ylim(-2,2)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
*******************************
great , aproximacion 5
rows 197 a 276
z=
[ 4.48573513e-03 -3.20336361e+00 7.59164990e+02 -5.65254060e+04]
np.poly1d(z)=
3 2
0.004486 x - 3.203 x + 759.2 x - 5.653e+04
p(0.5) =
-56146.62379395407
p(3.5)) =
-53907.377422800346
p(10) =
-49249.60673658942
p30 = np.poly1d(np.polyfit(x, y, 30)) =
30 29 28 27
-1.59e-61 x + 1.569e-58 x - 3.283e-56 x - 7.982e-54 x
26 25 24 23
+ 9.918e-52 x + 6.817e-49 x + 9.43e-47 x - 2.052e-44 x
22 21 20 19
- 1.23e-41 x - 2.307e-39 x + 1.338e-37 x + 2.061e-34 x
18 17 16 15
+ 5.818e-32 x + 4.532e-30 x - 2.683e-27 x - 1.219e-24 x
14 13 12 11
- 2.067e-22 x + 2.177e-20 x + 2.204e-17 x + 5.23e-15 x
10 9 8 7 6
- 5.978e-14 x - 4.019e-10 x - 1.023e-07 x + 4.885e-06 x + 0.008613 x
5 4 3 2
+ 1.133 x - 508.1 x - 1.088e+05 x + 4.835e+07 x - 5.629e+09 x + 2.237e+11
p30(4) =
201965505129.64587
p30(5) =
196765049008.72754
p30(4.5) =
199353571921.81915
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import numpy as np
import matplotlib.pyplot as plt
import warnings
points = np.array([(1,1781.6),
(2,1770.0),
(3,1760.7),
(4,1740.5),
(5,1751.6),
(6,1749.5),
(7,1739.2),
(8,1748.7),
(9,1760.3),
(10,1760.9),
(11,1769.2),
(12,1790.7),
(13,1784.0),
(14,1792.3),
(15,1786.5),
(16,1793.0),
(17,1789.2),
(18,1868.8),
(19,1869.8),
(20,1846.9),
(21,1847.8),
(22,1898.0),
(23,1875.0),
(24,1902.9),
(25,1906.9),
(26,1892.0),
(27,1901.1),
(28,1883.2),
(29,1891.3),
(30,1869.4),
(31,1862.0),
(32,1877.9),
(33,1864.7),
(34,1892.0),
(35,1887.5),
(36,1884.6),
(37,1861.6),
(38,1828.3),
(39,1853.3),
(40,1858.0),
(41,1870.7),
(42,2008.7),
(43,2004.2),
(44,2049.7),
(45,2039.9),
(46,2050.2),
(47,2079.3),
(48,2133.9),
(49,2150.8),
(50,2160.0),
(51,2149.9),
(52,2134.9),
(53,2155.7),
(54,2170.2),
(55,2153.1),
(56,2096.0),
(57,2009.3),
(58,1972.7),
(59,1979.6),
(60,1884.3),
(61,1883.8),
(62,1953.9),
(63,1909.0),
(64,1975.8),
(65,1924.0),
(66,1901.1),
(67,1800.6),
(68,1891.8),
(69,1820.9),
(70,1676.6),
(71,1785.0),
(72,1689.2),
(73,1807.8),
(74,1830.0),
(75,1880.9),
(76,1846.1),
(77,1902.8),
(78,1940.1),
(79,1885.8),
(80,1955.5),
(81,1900.1),
(82,1963.9),
(83,1949.7),
(84,1907.7),
(85,1918.8),
(86,1906.6),
(87,1997.6),
(88,2011.6),
(89,2043.0),
(90,2042.8),
(91,2168.9),
(92,2283.3),
(93,2307.7),
(94,2408.2),
(95,2375.0),
(96,2393.6),
(97,2328.1),
(98,2363.5),
(99,2399.4),
(100,2410.2),
(101,2376.0),
(102,2314.1),
(103,2372.7),
(104,2474.0),
(105,2286.0),
(106,2316.0),
(107,2317.8),
(108,2351.3),
(109,2367.6),
(110,2379.6),
(111,2409.0),
(112,2356.9),
(113,2367.9),
(114,2388.9),
(115,2409.8),
(116,2426.3),
(117,2449.3),
(118,2497.9),
(119,2446.7),
(120,2436.9),
(121,2421.9),
(122,2410.4),
(123,2401.1),
(124,2442.4),
(125,2471.0),
(126,2472.4),
(127,2478.4),
(128,2460.6),
(129,2483.0),
(130,2524.1),
(131,2600.9),
(132,2647.4),
(133,2558.0),
(134,2545.0),
(135,2572.7),
(136,2615.3),
(137,2641.0),
(138,2654.0),
(139,2675.0),
(140,2713.8),
(141,2764.4),
(142,2734.4),
(143,2754.6),
(144,2692.9),
(145,2680.4),
(146,2758.8),
(147,2878.7),
(148,2890.3),
(149,3057.0),
(150,3000.1),
(151,3081.1),
(152,3182.6),
(153,3200.0),
(154,3104.0),
(155,3084.0),
(156,3008.9),
(157,2999.9),
(158,2962.0),
(159,3196.8),
(160,3138.3),
(161,3099.9),
(162,2986.6),
(163,3008.9),
(164,3055.2),
(165,3000.3),
(166,3033.5),
(167,3051.9),
(168,3164.7),
(169,3111.9),
(170,3138.8),
(171,3205.0),
(172,3225.0),
(173,3167.5),
(174,3148.2),
(175,3080.7),
(176,3162.2),
(177,3161.0),
(178,3148.0),
(179,3182.4),
(180,3312.5),
(181,3260.5),
(182,3297.4),
(183,3284.7),
(184,3307.5),
(185,3346.5),
(186,3441.9),
(187,3400.0),
(188,3401.8),
(189,3451.0),
(190,3499.1),
(191,3531.4),
(192,3368.0),
(193,3294.6),
(194,3149.8),
(195,3268.6),
(196,3175.1),
(197,3116.2),
(198,3103.0),
(199,3156.1),
(200,3078.1),
(201,3008.7),
(202,2954.9),
(203,2960.5),
(204,3129.0),
(205,2999.9),
(206,3019.8),
(207,3095.1),
(208,3174.1),
(209,3144.9),
(210,3148.7),
(211,3221.3),
(212,3125.0),
(213,3199.2),
(214,3100.0),
(215,3195.7),
(216,3190.6),
(217,3286.6),
(218,3442.9),
(219,3443.6),
(220,3363.7),
(221,3338.6),
(222,3272.7),
(223,3207.2),
(224,3217.0),
(225,3184.9),
(226,3176.4),
(227,3204.4),
(228,3207.0),
(229,3286.3),
(230,3162.8),
(231,3211.0),
(232,3036.1),
(233,3004.5),
(234,3048.4),
(235,3241.2),
(236,3322.0),
(237,3311.4),
(238,3143.7),
(239,3035.0),
(240,3137.4),
(241,3110.3),
(242,3128.8),
(243,3131.1),
(244,3135.7),
(245,3105.5),
(246,3117.0),
(247,3099.4),
(248,3098.4),
(249,3118.1),
(250,3185.1),
(251,3195.3),
(252,3168.0),
(253,3220.1),
(254,3203.5),
(255,3186.7),
(256,3162.6),
(257,3158.0),
(258,3177.3),
(259,3104.2),
(260,3101.5),
(261,3116.4),
(262,3157.0),
(263,3165.1),
(264,3241.0),
(265,3236.1),
(266,3201.6),
(267,3206.2),
(268,3206.5),
(269,3185.3),
(270,3172.7),
(271,3284.0),
(272,3322.0),
(273,3285.9),
(274,3256.9),
(275,3186.6),
(276,3218.5)])
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
#z = np.polyfit(x, y, 3)
#f = np.poly1d(z)
###########
#x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
#y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
z = np.polyfit(x, y, 3)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 30))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
print("p30(4) =")
print(p30(4))
print("p30(5) =")
print(p30(5))
print("p30(4.5) =")
print(p30(4.5))
#xp = np.linspace(-2, 6, 100)
xp = np.linspace(x[0], x[-1], 50)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
#plt.ylim(-2,2)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
#x_new = np.linspace(x[0], x[-1], 50)
#y_new = f(x_new)
#plt.plot(x,y,'o', x_new, y_new)
#plt.xlim([x[0]-1, x[-1] + 1 ])
#plt.show()
#points = np.array([(1, 1), (2, 4), (3, 1), (9, 3)])
# get x and y vectors
#x = points[:,0]
#y = points[:,1]
# calculate polynomial
#z = np.polyfit(x, y, 3)
#f = np.poly1d(z)
# calculate new x's and y's
#x_new = np.linspace(x[0], x[-1], 50)
#y_new = f(x_new)
#plt.plot(x,y,'o', x_new, y_new)
#plt.xlim([x[0]-1, x[-1] + 1 ])
#plt.show()
+++++++++++++++++++++
C:\pythonPRACTICA>python amazonpoli7.py
z=
[-3.34784417e-04 1.24298536e-01 -4.62834888e+00 1.85664534e+03]
np.poly1d(z)=
3 2
-0.0003348 x + 0.1243 x - 4.628 x + 1857
p(0.5) =
1854.3621959174566
p(3.5)) =
1841.9544196631816
p(10) =
1822.4569179179982
p30 = np.poly1d(np.polyfit(x, y, 30)) =
30 29 28 27
-1.444e-61 x + 2.055e-58 x - 9.645e-56 x + 7.731e-54 x
26 25 24 23
+ 5.449e-51 x - 3.02e-49 x - 3.909e-46 x - 2.582e-44 x
22 21 20 19
+ 2.382e-41 x + 5.378e-39 x - 8.957e-37 x - 5.213e-34 x
18 17 16 15
- 6.871e-33 x + 3.711e-29 x + 4.205e-27 x - 2.399e-24 x
14 13 12 11
- 3.789e-22 x + 1.779e-19 x + 1.235e-17 x - 1.416e-14 x
10 9 8 7
+ 2.913e-12 x - 2.911e-10 x + 1.242e-08 x + 3.669e-07 x
6 5 4 3 2
- 7.651e-05 x + 0.004296 x - 0.1236 x + 1.868 x - 12.89 x + 29.4 x + 1755
p30(4) =
1758.3889495399844
p30(5) =
1748.325647242414
p30(4.5) =
1753.1670544079063
++++++++++
4,40,60
+++++++++++++++++++
C:\pythonPRACTICA>python amazonpoli8.py
z=
[ 9.58312992e-07 -8.65689815e-04 2.18952069e-01 -1.04794573e+01
1.93900151e+03]
np.poly1d(z)=
4 3 2
9.583e-07 x - 0.0008657 x + 0.219 x - 10.48 x + 1939
p(0.5) =
1933.8164076661278
p(3.5)) =
1904.9685961899124
p(10) =
1855.2460339175325
p30 = np.poly1d(np.polyfit(x, y, 30)) =
40 39 38 37
3.043e-85 x - 3.241e-82 x + 8.306e-80 x + 1.39e-77 x
36 35 34 33
- 3.728e-75 x - 1.478e-72 x - 6.023e-71 x + 8.61e-68 x
32 31 30 29
+ 2.673e-65 x + 1.346e-63 x - 1.525e-60 x - 5.484e-58 x
28 27 26 25
- 5.322e-56 x + 2.431e-53 x + 1.126e-50 x + 1.539e-48 x
24 23 22 21
- 3.938e-46 x - 2.285e-43 x - 3.316e-41 x + 8.673e-39 x
20 19 18 17
+ 4.7e-36 x + 4.168e-34 x - 2.77e-31 x - 8.317e-29 x
16 15 14 13
+ 9.127e-27 x + 7.605e-24 x - 2.138e-22 x - 6.13e-19 x
12 11 10 9
+ 5.358e-17 x + 4.235e-14 x - 1.541e-11 x + 2.68e-09 x
8 7 6 5 4
- 2.928e-07 x + 2.156e-05 x - 0.001087 x + 0.03706 x - 0.8215 x
3 2
+ 11.06 x - 80.33 x + 256.5 x + 1529
p30(4) =
1800.793366248045
p30(5) =
1772.5089961620001
p30(4.5) =
1787.4067003460548
++++++++++++++++++++++++++++++++
# get x and y vectors
x = points[:,0]
y = points[:,1]
# calculate polynomial
#z = np.polyfit(x, y, 3)
#f = np.poly1d(z)
###########
#x = np.array([0.0, 1.0, 2.0, 3.0, 4.0, 5.0])
#y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
z = np.polyfit(x, y, 10)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 100))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
print("p30(4) =")
print(p30(4))
print("p30(5) =")
print(p30(5))
print("p30(4.5) =")
print(p30(4.5))
#xp = np.linspace(-2, 6, 100)
xp = np.linspace(x[0], x[-1], 70)
_ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
#plt.ylim(-2,2)
plt.xlim([x[0]-1, x[-1] + 1 ])
plt.show()
#################
Y AL 21
SE VE
z = np.polyfit(x, y, 20)
print("z=")
print(z)
p = np.poly1d(z)
print("np.poly1d(z)=")
print(p)
print("p(0.5) =")
print(p(0.5))
print("p(3.5)) =")
print(p(3.5))
print("p(10) =")
print(p(10))
warnings.simplefilter('ignore', np.RankWarning)
p30 = np.poly1d(np.polyfit(x, y, 200))
print("p30 = np.poly1d(np.polyfit(x, y, 30)) =")
print(p30)
para valores 20, 200, o 200, 1000
levanta el error
C:\pythonPRACTICA>python amazonpoli10.py
sys:1: RankWarning: Polyfit may be poorly conditioned
z=
[-1.76756035e-37 3.58223469e-34 -3.06497957e-31 1.36758359e-28
-2.81535807e-26 -1.66530926e-24 2.32319154e-21 -3.94403175e-19
-7.46606724e-17 5.07326718e-14 -1.25076105e-11 1.90383040e-09
-1.97577325e-07 1.43460146e-05 -7.25954736e-04 2.49873712e-02
-5.59107152e-01 7.54733906e+00 -5.41191275e+01 1.66574675e+02
1.62003872e+03]
np.poly1d(z)=
20 19 18 17
-1.768e-37 x + 3.582e-34 x - 3.065e-31 x + 1.368e-28 x
16 15 14 13
- 2.815e-26 x - 1.665e-24 x + 2.323e-21 x - 3.944e-19 x
12 11 10 9
- 7.466e-17 x + 5.073e-14 x - 1.251e-11 x + 1.904e-09 x
8 7 6 5 4
- 1.976e-07 x + 1.435e-05 x - 0.000726 x + 0.02499 x - 0.5591 x
3 2
+ 7.547 x - 54.12 x + 166.6 x + 1620
p(0.5) =
1690.7055204099
p(3.5)) =
1791.6592544942705
p(10) =
1728.4088095616235
C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\lib\polynomial.py:627: RuntimeWarning: overflow encountered in multiply
scale = NX.sqrt((lhs*lhs).sum(axis=0))
C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\core\_methods.py:47: RuntimeWarning: overflow encountered in reduce
return umr_sum(a, axis, dtype, out, keepdims, initial, where)
C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\lib\polynomial.py:628: RuntimeWarning: invalid value encountered in true_divide
lhs /= scale
** On entry to DLASCLS parameter number 4 had an illegal value
** On entry to DLASCLS parameter number 4 had an illegal value
Traceback (most recent call last):
File "C:\pythonPRACTICA\amazonpoli10.py", line 310, in <module>
p30 = np.poly1d(np.polyfit(x, y, 200))
File "<__array_function__ internals>", line 5, in polyfit
File "C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\lib\polynomial.py", line 629, in polyfit
c, resids, rank, s = lstsq(lhs, rhs, rcond)
File "<__array_function__ internals>", line 5, in lstsq
File "C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\linalg\linalg.py", line 2306, in lstsq
x, resids, rank, s = gufunc(a, b, rcond, signature=signature, extobj=extobj)
File "C:\Users\roberto.martinez\AppData\Local\Programs\Python\Python39\lib\site-packages\numpy\linalg\linalg.py", line 100, in _raise_linalgerror_lstsq
raise LinAlgError("SVD did not converge in Linear Least Squares")
numpy.linalg.LinAlgError: SVD did not converge in Linear Least Squares
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
FIRST STEP
con librerias
amzn = pdr.get_data_yahoo('AMZN',
start=datetime.datetime(2019, 12, 1),
end=datetime.datetime(2021, 1, 7))
#Import numpy as np import numpy as np
# Assign `Adj close to "daily_close
daily_close = amzn[['Adj Close']]
# Daily returns
daily_pct_change = daily_close.pct_change()
# Replace NA values with a
daily_pct_change.fillna(0, inplace=True)
# Inspect daily returns
print(daily_pct_change)
# Daily log returns
daily_log_returns = np.log(daily_close.pct_change() +1)
# Print daily log returns
print(daily_log_returns)
# Resample "amzn" to business months, take last observation as value
monthly = amzn.resample('BM').apply(lambda x: x[-1])
# Calculate the monthly percentage change
monthly.pct_change()
# Resample amzn" to quarters, take the mean as value per quarter
quarter = amzn.resample("4M").mean()
# Calculate the quarterly percentage change
quarter.pct_change()
# Import matplotlib import matplotlib.pyplot as plt
# Plot the distribution of daily_pct_c
daily_pct_change.hist(bins=50)
# Show the plot
plt.show()
# Pull up summary statistics
print("daily_pct_change.describe()")
print(daily_pct_change.describe())
# Calculate the cumulative daily returns
cum_daily_return = (1 + daily_pct_change).cumprod()
# Print 'cum_daily_return
print(cum_daily_return)
# Import matplotlib import matplotlib.pyplot as plt
# Plot the cumulative daily returns
cum_daily_return.plot(figsize=(12,8))
# Show the plot I
plt.show()
# Resample the cumulative daily return to cumulative monthly return ?
cum_monthly_return = cum_daily_return.resample("M").mean()
# Print the 'cum_monthly_return
print(cum_monthly_return)
cum_monthly_return.plot(figsize=(12,8))
# Show the plot I
plt.show()
python definitve1.py
Adj Close
Date
2019-12-02 0.000000
2019-12-03 -0.006533
2019-12-04 -0.005237
2019-12-05 -0.011478
2019-12-06 0.006389
... ...
2020-12-31 -0.008801
2021-01-04 -0.021585
2021-01-05 0.010004
2021-01-06 -0.024897
2021-01-07 0.019867
[278 rows x 1 columns]
Adj Close
Date
2019-12-02 NaN
2019-12-03 -0.006555
2019-12-04 -0.005251
2019-12-05 -0.011545
2019-12-06 0.006369
... ...
2020-12-31 -0.008840
2021-01-04 -0.021821
2021-01-05 0.009955
2021-01-06 -0.025212
2021-01-07 0.019672
[278 rows x 1 columns]
daily_pct_change.describe()
Adj Close
count 278.000000
mean 0.002383
std 0.023477
min -0.079221
25% -0.009576
50% 0.001987
75% 0.014116
max 0.079295
Adj Close
Date
2019-12-02 1.000000
2019-12-03 0.993467
2019-12-04 0.988263
2019-12-05 0.976920
2019-12-06 0.983161
... ...
2020-12-31 1.828093
2021-01-04 1.788634
2021-01-05 1.806528
2021-01-06 1.761551
2021-01-07 1.796548
[278 rows x 1 columns]
Adj Close
Date
2019-12-31 1.002342
2020-01-31 1.057610
2020-02-29 1.159730
2020-03-31 1.050915
2020-04-30 1.250957
2020-05-31 1.343839
2020-06-30 1.466965
2020-07-31 1.714107
2020-08-31 1.823785
2020-09-30 1.774107
2020-10-31 1.813314
2020-11-30 1.763476
2020-12-31 1.794875
2021-01-31 1.788315
VECTORS, INITIAL CLASS
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