机器学习---数据科学包-第4天
2024-06-10 04:55:09
1 Numpy简介
1
# 1 通过python的基础数据对象转化
2
import numpy as np
3
x = [1, 2, 3, 4]
4
x = np.array(x)
5
x
Out[1]:
array([1, 2, 3, 4])
In [2]:1
type(x)
Out[2]:
numpy.ndarray
In [3]:1
# 2 通过numpy内生的函数直接生成
2
x = np.arange(0, 11)
3
x
Out[3]:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
In [4]:1
# 3 从硬盘读取数据
2
# 参数依次是：文件名；分隔符；跳过哪几行；选取哪几列；
3
x = np.loadtxt('000001.csv', delimiter = ',', skiprows = 1, usecols = (1, 4, 6), unpack = False)
4
x
Out[4]:
array([[3.25863000e+03, 3.35052000e+03, 5.31352384e+10],[3.33080000e+03, 3.35145000e+03, 5.01661696e+10],[3.32665000e+03, 3.37395000e+03, 3.91918880e+10],[3.37196000e+03, 3.29346000e+03, 3.71131168e+10],[3.27697000e+03, 3.28541000e+03, 4.10240864e+10],[3.25821000e+03, 3.22932000e+03, 3.22064672e+10],[3.22354000e+03, 3.23530000e+03, 2.30725760e+10],[3.24234000e+03, 3.22244000e+03, 2.40190752e+10],[3.22407000e+03, 3.33646000e+03, 2.82546240e+10],[3.34360000e+03, 3.37650000e+03, 3.39876768e+10],[3.18973000e+03, 3.11635000e+03, 4.01098784e+10],[3.11456000e+03, 3.17305000e+03, 3.57080800e+10],[3.18909000e+03, 3.32361000e+03, 4.10956032e+10],[3.32732000e+03, 3.34334000e+03, 3.53382976e+10],[3.35710000e+03, 3.35176000e+03, 3.66249248e+10],[3.34726000e+03, 3.38318000e+03, 3.17540992e+10],[3.38985000e+03, 3.35296000e+03, 3.74517536e+10],[3.32572000e+03, 3.30574000e+03, 3.01927104e+10],[3.25900000e+03, 3.26230000e+03, 2.74658624e+10],[3.27375000e+03, 3.21036000e+03, 2.58312544e+10],[3.14814000e+03, 3.12830000e+03, 2.50861632e+10],[3.15609000e+03, 3.20491000e+03, 2.48192160e+10],[3.21282000e+03, 3.17413000e+03, 2.49098080e+10],[3.25121000e+03, 3.13653000e+03, 3.06139296e+10],[3.12009000e+03, 3.07591000e+03, 2.46749664e+10],[3.06351000e+03, 3.09512000e+03, 2.06108384e+10],[3.09049000e+03, 3.14159000e+03, 1.93817136e+10],[3.14576000e+03, 3.15770000e+03, 1.72840096e+10],[3.15796000e+03, 3.17342000e+03, 1.94592304e+10],[3.18681000e+03, 3.20383000e+03, 2.61290432e+10],[3.20614000e+03, 3.22236000e+03, 2.23797424e+10],[3.23088000e+03, 3.24691000e+03, 2.28332624e+10],[3.25648000e+03, 3.22884000e+03, 2.33348096e+10],[3.22215000e+03, 3.29836000e+03, 3.01263872e+10],[3.29683000e+03, 3.31030000e+03, 2.99163712e+10],[3.33272000e+03, 3.33628000e+03, 3.46445664e+10],[3.31770000e+03, 3.26305000e+03, 3.82044608e+10],[3.26418000e+03, 3.27953000e+03, 2.93639520e+10],[3.26409000e+03, 3.24848000e+03, 3.20663584e+10],[3.24804000e+03, 3.24119000e+03, 2.82915776e+10],[3.22431000e+03, 3.30241000e+03, 3.21495424e+10],[3.28909000e+03, 3.28607000e+03, 2.85817568e+10],[3.28959000e+03, 3.29090000e+03, 2.82985536e+10],[3.31481000e+03, 3.34932000e+03, 3.57295104e+10],[3.35949000e+03, 3.37291000e+03, 3.28410144e+10],[3.39116000e+03, 3.44930000e+03, 3.99132416e+10],[3.46960000e+03, 3.50285000e+03, 5.20939520e+10],[3.51050000e+03, 3.57730000e+03, 5.45217152e+10],[3.57602000e+03, 3.58227000e+03, 5.37346624e+10],[3.58708000e+03, 3.61732000e+03, 5.16661664e+10],[3.64010000e+03, 3.68773000e+03, 5.36062848e+10],[3.69257000e+03, 3.69141000e+03, 6.39554688e+10],[3.68095000e+03, 3.66073000e+03, 5.21886336e+10],[3.64194000e+03, 3.68210000e+03, 4.88647200e+10],[3.68613000e+03, 3.69110000e+03, 4.08945152e+10],[3.71061000e+03, 3.78657000e+03, 5.64702336e+10],[3.82299000e+03, 3.74790000e+03, 5.61676032e+10],[3.74834000e+03, 3.81029000e+03, 4.47458304e+10],[3.82769000e+03, 3.82578000e+03, 4.79299680e+10],[3.80338000e+03, 3.86393000e+03, 4.73033312e+10],[3.89942000e+03, 3.96138000e+03, 5.70447552e+10],[3.97653000e+03, 3.99481000e+03, 6.18085440e+10],[4.00613000e+03, 3.95753000e+03, 5.85176832e+10],[3.94749000e+03, 4.03431000e+03, 4.84283616e+10],[4.07272000e+03, 4.12171000e+03, 5.89814208e+10],[4.12578000e+03, 4.13556000e+03, 6.10683520e+10],[4.13565000e+03, 4.08416000e+03, 6.13005824e+10],[4.05592000e+03, 4.19482000e+03, 5.51242944e+10],[4.25472000e+03, 4.28730000e+03, 7.01706176e+10],[4.30135000e+03, 4.21708000e+03, 8.57132800e+10],[4.21219000e+03, 4.29362000e+03, 6.34470656e+10],[4.30460000e+03, 4.39849000e+03, 6.80305088e+10],[4.41448000e+03, 4.41451000e+03, 6.67344640e+10],[4.35595000e+03, 4.39369000e+03, 6.28555008e+10],[4.44193000e+03, 4.52740000e+03, 6.71088512e+10],[4.52763000e+03, 4.47621000e+03, 7.67676416e+10],[4.44612000e+03, 4.47662000e+03, 5.19834208e+10],[4.48301000e+03, 4.44165000e+03, 5.26727968e+10],[4.44134000e+03, 4.48046000e+03, 4.94173408e+10],[4.47985000e+03, 4.29871000e+03, 5.72858624e+10],[4.31164000e+03, 4.22927000e+03, 4.81732992e+10],[4.19790000e+03, 4.11221000e+03, 3.94566656e+10],[4.15298000e+03, 4.20592000e+03, 3.97428096e+10],[4.23127000e+03, 4.33358000e+03, 4.88750528e+10],[4.34237000e+03, 4.40122000e+03, 5.21866400e+10],[4.40238000e+03, 4.37576000e+03, 5.10490464e+10],[4.37282000e+03, 4.37831000e+03, 4.49077920e+10],[4.36682000e+03, 4.30869000e+03, 4.39706208e+10],[4.27790000e+03, 4.28349000e+03, 3.80057440e+10],[4.28578000e+03, 4.41755000e+03, 4.36735232e+10],[4.43498000e+03, 4.44629000e+03, 5.14106208e+10],[4.45644000e+03, 4.52942000e+03, 4.64996512e+10],[4.58498000e+03, 4.65760000e+03, 6.55591296e+10],[4.66008000e+03, 4.81380000e+03, 6.82461376e+10],[4.85485000e+03, 4.91090000e+03, 7.04892800e+10],[4.93285000e+03, 4.94171000e+03, 6.81165376e+10],[4.94374000e+03, 4.62027000e+03, 7.82964608e+10],[4.60346000e+03, 4.61174000e+03, 6.11262400e+10],[4.63310000e+03, 4.82874000e+03, 5.93389056e+10],[4.84470000e+03, 4.91053000e+03, 6.23748096e+10],[4.92438000e+03, 4.90998000e+03, 6.11453824e+10],[4.91294000e+03, 4.94710000e+03, 6.74952384e+10],[5.01609000e+03, 5.02310000e+03, 7.72240832e+10],[5.04569000e+03, 5.13188000e+03, 8.55035072e+10],[5.14598000e+03, 5.11353000e+03, 7.29893824e+10],[5.04920000e+03, 5.10604000e+03, 5.96969024e+10],[5.10144000e+03, 5.12159000e+03, 5.63990528e+10],[5.14334000e+03, 5.16635000e+03, 6.25627840e+10],[5.17442000e+03, 5.06299000e+03, 6.37803968e+10],[5.00441000e+03, 4.88743000e+03, 5.50801408e+10],[4.89055000e+03, 4.96790000e+03, 5.37101184e+10],[4.94252000e+03, 4.78536000e+03, 5.07440896e+10],[4.68993000e+03, 4.47836000e+03, 4.52689632e+10],[4.47161000e+03, 4.57649000e+03, 4.73526144e+10],[4.60458000e+03, 4.69015000e+03, 5.43003712e+10],[4.71176000e+03, 4.52778000e+03, 5.72797504e+10],[4.39993000e+03, 4.19287000e+03, 5.65217856e+10],[4.28977000e+03, 4.05303000e+03, 6.73786368e+10],[4.00675000e+03, 4.27722000e+03, 7.09176640e+10],[4.21415000e+03, 4.05370000e+03, 5.98769408e+10],[4.05862000e+03, 3.91277000e+03, 5.86015616e+10],[3.79371000e+03, 3.68692000e+03, 5.48163136e+10],[3.97521000e+03, 3.77591000e+03, 8.31139264e+10],[3.65478000e+03, 3.72712000e+03, 6.98818688e+10],[3.46740000e+03, 3.50719000e+03, 6.80356928e+10],[3.43245000e+03, 3.70933000e+03, 6.56914624e+10],[3.70746000e+03, 3.87780000e+03, 5.86364224e+10],[3.91899000e+03, 3.97039000e+03, 6.43489024e+10],[3.95837000e+03, 3.92449000e+03, 6.70558784e+10],[3.87497000e+03, 3.80570000e+03, 6.01301312e+10],[3.75850000e+03, 3.82318000e+03, 4.92256192e+10],[3.83142000e+03, 3.95735000e+03, 4.81726272e+10],[3.94842000e+03, 3.99211000e+03, 5.39106688e+10],[3.93990000e+03, 4.01767000e+03, 5.04288032e+10],[3.99643000e+03, 4.02604000e+03, 5.20732224e+10],[4.02227000e+03, 4.12392000e+03, 5.63585984e+10],[4.12475000e+03, 4.07091000e+03, 6.27424832e+10],[3.98557000e+03, 3.72556000e+03, 5.56003264e+10],[3.57314000e+03, 3.66300000e+03, 5.63330048e+10],[3.68982000e+03, 3.78917000e+03, 4.34352096e+10],[3.77379000e+03, 3.70577000e+03, 4.57943232e+10],[3.65567000e+03, 3.66373000e+03, 3.50955744e+10],[3.61499000e+03, 3.62291000e+03, 3.63968736e+10],[3.62186000e+03, 3.75654000e+03, 3.62901664e+10],[3.74565000e+03, 3.69457000e+03, 3.66422976e+10],[3.62550000e+03, 3.66154000e+03, 2.74074656e+10],[3.69261000e+03, 3.74420000e+03, 3.40757184e+10],[3.78603000e+03, 3.92842000e+03, 4.97304320e+10],[3.92881000e+03, 3.92791000e+03, 5.38923456e+10],[3.88123000e+03, 3.88632000e+03, 4.42688288e+10],[3.86991000e+03, 3.95456000e+03, 4.30073312e+10],[3.97641000e+03, 3.96534000e+03, 4.67988224e+10],[3.94784000e+03, 3.99367000e+03, 4.60432064e+10],[3.99913000e+03, 3.74816000e+03, 5.43770816e+10],[3.64680000e+03, 3.79411000e+03, 4.75396224e+10],[3.75457000e+03, 3.66429000e+03, 3.90063072e+10],[3.60996000e+03, 3.50774000e+03, 3.69920480e+10],[3.37348000e+03, 3.20991000e+03, 3.34671808e+10],[3.00413000e+03, 2.96497000e+03, 3.52325120e+10],[2.98079000e+03, 2.92729000e+03, 4.66699648e+10],[2.97803000e+03, 3.08359000e+03, 4.00308384e+10],[3.12526000e+03, 3.23235000e+03, 4.43136928e+10],[3.20356000e+03, 3.20599000e+03, 3.97431392e+10],[3.15783000e+03, 3.16662000e+03, 4.32432480e+10],[3.02768000e+03, 3.16017000e+03, 4.38170144e+10],[3.14938000e+03, 3.08042000e+03, 2.96468128e+10],[3.05444000e+03, 3.17045000e+03, 2.55415472e+10],[3.18255000e+03, 3.24309000e+03, 3.75327968e+10],[3.19055000e+03, 3.19789000e+03, 2.73261760e+10],[3.18948000e+03, 3.20023000e+03, 2.24557824e+10],[3.22117000e+03, 3.11480000e+03, 3.46631168e+10],[3.04380000e+03, 3.00517000e+03, 2.49194448e+10],[2.99804000e+03, 3.15226000e+03, 2.77524512e+10],[3.13198000e+03, 3.08606000e+03, 3.17602880e+10],[3.10028000e+03, 3.09792000e+03, 2.09175392e+10],[3.07209000e+03, 3.15654000e+03, 2.39897360e+10],[3.16132000e+03, 3.18562000e+03, 2.74786144e+10],[3.13772000e+03, 3.11589000e+03, 2.36322672e+10],[3.12649000e+03, 3.14269000e+03, 2.12887728e+10],[3.13085000e+03, 3.09235000e+03, 2.36263872e+10],[3.08557000e+03, 3.10076000e+03, 1.56727536e+10],[3.05522000e+03, 3.03814000e+03, 1.63222672e+10],[3.05284000e+03, 3.05278000e+03, 1.46642448e+10],[3.15607000e+03, 3.14336000e+03, 2.34276048e+10],[3.14664000e+03, 3.18315000e+03, 2.34851440e+10],[3.19354000e+03, 3.28766000e+03, 3.86294720e+10],[3.26216000e+03, 3.29323000e+03, 2.97153120e+10],[3.28002000e+03, 3.26244000e+03, 2.95077728e+10],[3.25503000e+03, 3.33807000e+03, 3.16283840e+10],[3.35830000e+03, 3.39135000e+03, 3.95460576e+10],[3.40163000e+03, 3.38670000e+03, 3.78112192e+10],[3.37755000e+03, 3.42533000e+03, 3.18973760e+10],[3.42856000e+03, 3.32068000e+03, 4.58455424e+10],[3.29229000e+03, 3.36874000e+03, 3.23739328e+10],[3.37755000e+03, 3.41243000e+03, 3.47372864e+10],[3.44865000e+03, 3.42958000e+03, 3.65560864e+10],[3.40914000e+03, 3.43434000e+03, 3.28172768e+10],[3.41701000e+03, 3.37520000e+03, 2.93523296e+10],[3.38777000e+03, 3.38732000e+03, 2.35676016e+10],[3.38028000e+03, 3.38256000e+03, 2.43595120e+10],[3.33758000e+03, 3.32508000e+03, 2.30951136e+10],[3.33032000e+03, 3.31670000e+03, 1.92897440e+10],[3.32562000e+03, 3.45964000e+03, 3.39078752e+10],[3.45922000e+03, 3.52282000e+03, 5.53254976e+10],[3.51444000e+03, 3.59003000e+03, 4.29167040e+10],[3.58850000e+03, 3.64688000e+03, 5.03016704e+10],[3.61740000e+03, 3.64049000e+03, 4.29746592e+10],[3.63500000e+03, 3.65025000e+03, 3.60972672e+10],[3.65682000e+03, 3.63290000e+03, 3.61717600e+10],[3.60076000e+03, 3.58084000e+03, 3.45870944e+10],[3.52246000e+03, 3.60696000e+03, 2.76187040e+10],[3.62998000e+03, 3.60480000e+03, 3.83575456e+10],[3.60506000e+03, 3.56847000e+03, 2.97580736e+10],[3.57378000e+03, 3.61706000e+03, 2.47915584e+10],[3.62079000e+03, 3.63050000e+03, 3.10801984e+10],[3.63087000e+03, 3.61032000e+03, 3.15997472e+10],[3.60289000e+03, 3.61611000e+03, 2.48810512e+10],[3.61407000e+03, 3.64793000e+03, 2.73024864e+10],[3.65957000e+03, 3.63555000e+03, 3.06761600e+10],[3.61654000e+03, 3.43630000e+03, 3.54287520e+10],[3.43386000e+03, 3.44540000e+03, 3.04197888e+10],[3.44244000e+03, 3.45631000e+03, 2.52390752e+10],[3.45028000e+03, 3.53691000e+03, 3.01491488e+10],[3.52573000e+03, 3.58482000e+03, 2.81111232e+10],[3.55815000e+03, 3.52499000e+03, 2.51736416e+10],[3.52981000e+03, 3.53693000e+03, 2.08302576e+10],[3.51865000e+03, 3.47007000e+03, 2.24367312e+10],[3.46258000e+03, 3.47244000e+03, 1.95698848e+10],[3.46981000e+03, 3.45550000e+03, 2.00427520e+10],[3.44160000e+03, 3.43458000e+03, 1.82908880e+10],[3.40351000e+03, 3.52067000e+03, 2.15374624e+10],[3.51813000e+03, 3.51035000e+03, 2.00471344e+10],[3.52209000e+03, 3.51619000e+03, 1.93482304e+10],[3.53363000e+03, 3.58000000e+03, 2.83856480e+10],[3.57494000e+03, 3.57896000e+03, 2.73707904e+10],[3.56858000e+03, 3.64247000e+03, 2.99849280e+10],[3.64599000e+03, 3.65177000e+03, 2.61178752e+10],[3.65328000e+03, 3.63609000e+03, 2.98201792e+10],[3.63131000e+03, 3.61249000e+03, 2.27785216e+10],[3.61405000e+03, 3.62791000e+03, 1.98451120e+10],[3.63577000e+03, 3.53378000e+03, 2.69983264e+10],[3.52840000e+03, 3.51876000e+03, 7.50513110e+09]])
In [6]:1
x.shape
Out[6]:
(242, 3)
In [7]:1
# unpack=True表示把数据分成几个不同的变量存储
2
open, close, volume = np.loadtxt('000001.csv', delimiter = ',', skiprows = 1, usecols = (1, 4, 6), unpack = True)
3
open
Out[7]:
array([3258.63, 3330.8 , 3326.65, 3371.96, 3276.97, 3258.21, 3223.54,3242.34, 3224.07, 3343.6 , 3189.73, 3114.56, 3189.09, 3327.32,3357.1 , 3347.26, 3389.85, 3325.72, 3259. , 3273.75, 3148.14,3156.09, 3212.82, 3251.21, 3120.09, 3063.51, 3090.49, 3145.76,3157.96, 3186.81, 3206.14, 3230.88, 3256.48, 3222.15, 3296.83,3332.72, 3317.7 , 3264.18, 3264.09, 3248.04, 3224.31, 3289.09,3289.59, 3314.81, 3359.49, 3391.16, 3469.6 , 3510.5 , 3576.02,3587.08, 3640.1 , 3692.57, 3680.95, 3641.94, 3686.13, 3710.61,3822.99, 3748.34, 3827.69, 3803.38, 3899.42, 3976.53, 4006.13,3947.49, 4072.72, 4125.78, 4135.65, 4055.92, 4254.72, 4301.35,4212.19, 4304.6 , 4414.48, 4355.95, 4441.93, 4527.63, 4446.12,4483.01, 4441.34, 4479.85, 4311.64, 4197.9 , 4152.98, 4231.27,4342.37, 4402.38, 4372.82, 4366.82, 4277.9 , 4285.78, 4434.98,4456.44, 4584.98, 4660.08, 4854.85, 4932.85, 4943.74, 4603.46,4633.1 , 4844.7 , 4924.38, 4912.94, 5016.09, 5045.69, 5145.98,5049.2 , 5101.44, 5143.34, 5174.42, 5004.41, 4890.55, 4942.52,4689.93, 4471.61, 4604.58, 4711.76, 4399.93, 4289.77, 4006.75,4214.15, 4058.62, 3793.71, 3975.21, 3654.78, 3467.4 , 3432.45,3707.46, 3918.99, 3958.37, 3874.97, 3758.5 , 3831.42, 3948.42,3939.9 , 3996.43, 4022.27, 4124.75, 3985.57, 3573.14, 3689.82,3773.79, 3655.67, 3614.99, 3621.86, 3745.65, 3625.5 , 3692.61,3786.03, 3928.81, 3881.23, 3869.91, 3976.41, 3947.84, 3999.13,3646.8 , 3754.57, 3609.96, 3373.48, 3004.13, 2980.79, 2978.03,3125.26, 3203.56, 3157.83, 3027.68, 3149.38, 3054.44, 3182.55,3190.55, 3189.48, 3221.17, 3043.8 , 2998.04, 3131.98, 3100.28,3072.09, 3161.32, 3137.72, 3126.49, 3130.85, 3085.57, 3055.22,3052.84, 3156.07, 3146.64, 3193.54, 3262.16, 3280.02, 3255.03,3358.3 , 3401.63, 3377.55, 3428.56, 3292.29, 3377.55, 3448.65,3409.14, 3417.01, 3387.77, 3380.28, 3337.58, 3330.32, 3325.62,3459.22, 3514.44, 3588.5 , 3617.4 , 3635. , 3656.82, 3600.76,3522.46, 3629.98, 3605.06, 3573.78, 3620.79, 3630.87, 3602.89,3614.07, 3659.57, 3616.54, 3433.86, 3442.44, 3450.28, 3525.73,3558.15, 3529.81, 3518.65, 3462.58, 3469.81, 3441.6 , 3403.51,3518.13, 3522.09, 3533.63, 3574.94, 3568.58, 3645.99, 3653.28,3631.31, 3614.05, 3635.77, 3528.4 ])
索引
In [8]:1
x = np.arange(11)
2
x
Out[8]:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
In [9]:1
x[::2] # 第一个冒号表示选择所有，第二个冒号表示步长
Out[9]:
array([ 0, 2, 4, 6, 8, 10])
In [10]:1
x[::-1] # -1会倒序输出
Out[10]:
array([10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0])
排序
如果使用np.sort(x)排序，会生成一个新的有序数组，原x不变 如果使用x.sort()排序，会改变x的值，不会生成新数组
In [11]:1
x = np.random.randint(1, 100, 10)
2
x
Out[11]:
array([46, 54, 24, 22, 42, 13, 91, 10, 16, 14])
In [12]:1
y = np.sort(x)
2
y
Out[12]:
array([10, 13, 14, 16, 22, 24, 42, 46, 54, 91])
In [13]:1
x
Out[13]:
array([46, 54, 24, 22, 42, 13, 91, 10, 16, 14])
In [14]:1
z = x.sort()
2
z # 什么都没有输出说明没有生成新的数组
In [15]:1
x
Out[15]:
array([10, 13, 14, 16, 22, 24, 42, 46, 54, 91])
In [ ]:1
2 散点图
1
import numpy as np
2
import matplotlib.pyplot as plt
In [3]:1
height = [160, 175, 170, 180, 185, 165]
2
weight = [80, 120, 122, 140, 145, 90]
3
plt.scatter(height, weight)
4
plt.show()不相关
In [4]:1
n = 1000
2
x = np.random.randn(n)
3
y = np.random.randn(n)
4
plt.scatter(x, y)
5
plt.show()正相关
In [5]:1
n = 1000
2
x = np.random.randn(n)
3
y = x + np.random.randn(n) * 0.5
4
plt.scatter(x, y)
5
plt.show()负相关
In [6]:1
n = 1000
2
x = np.random.randn(n)
3
y = -x + np.random.randn(n) * 0.5
4
plt.scatter(x, y)
5
plt.show()实例
判断股票每天开盘价与收盘价之间的差值是否与下一天有关联
In [7]:1
open, close = np.loadtxt('000001.csv', delimiter = ',', skiprows = 1, usecols = (1, 4), unpack = True)
2
change = close - open
3
change
Out[7]:
array([ 9.1890e+01, 2.0650e+01, 4.7300e+01, -7.8500e+01, 8.4400e+00,-2.8890e+01, 1.1760e+01, -1.9900e+01, 1.1239e+02, 3.2900e+01,-7.3380e+01, 5.8490e+01, 1.3452e+02, 1.6020e+01, -5.3400e+00,3.5920e+01, -3.6890e+01, -1.9980e+01, 3.3000e+00, -6.3390e+01,-1.9840e+01, 4.8820e+01, -3.8690e+01, -1.1468e+02, -4.4180e+01,3.1610e+01, 5.1100e+01, 1.1940e+01, 1.5460e+01, 1.7020e+01,1.6220e+01, 1.6030e+01, -2.7640e+01, 7.6210e+01, 1.3470e+01,3.5600e+00, -5.4650e+01, 1.5350e+01, -1.5610e+01, -6.8500e+00,7.8100e+01, -3.0200e+00, 1.3100e+00, 3.4510e+01, 1.3420e+01,5.8140e+01, 3.3250e+01, 6.6800e+01, 6.2500e+00, 3.0240e+01,4.7630e+01, -1.1600e+00, -2.0220e+01, 4.0160e+01, 4.9700e+00,7.5960e+01, -7.5090e+01, 6.1950e+01, -1.9100e+00, 6.0550e+01,6.1960e+01, 1.8280e+01, -4.8600e+01, 8.6820e+01, 4.8990e+01,9.7800e+00, -5.1490e+01, 1.3890e+02, 3.2580e+01, -8.4270e+01,8.1430e+01, 9.3890e+01, 3.0000e-02, 3.7740e+01, 8.5470e+01,-5.1420e+01, 3.0500e+01, -4.1360e+01, 3.9120e+01, -1.8114e+02,-8.2370e+01, -8.5690e+01, 5.2940e+01, 1.0231e+02, 5.8850e+01,-2.6620e+01, 5.4900e+00, -5.8130e+01, 5.5900e+00, 1.3177e+02,1.1310e+01, 7.2980e+01, 7.2620e+01, 1.5372e+02, 5.6050e+01,8.8600e+00, -3.2347e+02, 8.2800e+00, 1.9564e+02, 6.5830e+01,-1.4400e+01, 3.4160e+01, 7.0100e+00, 8.6190e+01, -3.2450e+01,5.6840e+01, 2.0150e+01, 2.3010e+01, -1.1143e+02, -1.1698e+02,7.7350e+01, -1.5716e+02, -2.1157e+02, 1.0488e+02, 8.5570e+01,-1.8398e+02, -2.0706e+02, -2.3674e+02, 2.7047e+02, -1.6045e+02,-1.4585e+02, -1.0679e+02, -1.9930e+02, 7.2340e+01, 3.9790e+01,2.7688e+02, 1.7034e+02, 5.1400e+01, -3.3880e+01, -6.9270e+01,6.4680e+01, 1.2593e+02, 4.3690e+01, 7.7770e+01, 2.9610e+01,1.0165e+02, -5.3840e+01, -2.6001e+02, 8.9860e+01, 9.9350e+01,-6.8020e+01, 8.0600e+00, 7.9200e+00, 1.3468e+02, -5.1080e+01,3.6040e+01, 5.1590e+01, 1.4239e+02, -9.0000e-01, 5.0900e+00,8.4650e+01, -1.1070e+01, 4.5830e+01, -2.5097e+02, 1.4731e+02,-9.0280e+01, -1.0222e+02, -1.6357e+02, -3.9160e+01, -5.3500e+01,1.0556e+02, 1.0709e+02, 2.4300e+00, 8.7900e+00, 1.3249e+02,-6.8960e+01, 1.1601e+02, 6.0540e+01, 7.3400e+00, 1.0750e+01,-1.0637e+02, -3.8630e+01, 1.5422e+02, -4.5920e+01, -2.3600e+00,8.4450e+01, 2.4300e+01, -2.1830e+01, 1.6200e+01, -3.8500e+01,1.5190e+01, -1.7080e+01, -6.0000e-02, -1.2710e+01, 3.6510e+01,9.4120e+01, 3.1070e+01, -1.7580e+01, 8.3040e+01, 3.3050e+01,-1.4930e+01, 4.7780e+01, -1.0788e+02, 7.6450e+01, 3.4880e+01,-1.9070e+01, 2.5200e+01, -4.1810e+01, -4.5000e-01, 2.2800e+00,-1.2500e+01, -1.3620e+01, 1.3402e+02, 6.3600e+01, 7.5590e+01,5.8380e+01, 2.3090e+01, 1.5250e+01, -2.3920e+01, -1.9920e+01,8.4500e+01, -2.5180e+01, -3.6590e+01, 4.3280e+01, 9.7100e+00,-2.0550e+01, 1.3220e+01, 3.3860e+01, -2.4020e+01, -1.8024e+02,1.1540e+01, 1.3870e+01, 8.6630e+01, 5.9090e+01, -3.3160e+01,7.1200e+00, -4.8580e+01, 9.8600e+00, -1.4310e+01, -7.0200e+00,1.1716e+02, -7.7800e+00, -5.9000e+00, 4.6370e+01, 4.0200e+00,7.3890e+01, 5.7800e+00, -1.7190e+01, -1.8820e+01, 1.3860e+01,-1.0199e+02, -9.6400e+00])
In [8]:1
change.shape
Out[8]:
(242,)
In [9]:1
today = change[:-1]
2
yestoday = change[1:]
3
plt.scatter(today, yestoday)
4
plt.show()
5
# 由结果可看出，相邻两天之间的差值并无关系散点图的外观调整
In [10]:1
today = change[:-1]
2
yestoday = change[1:]
3
# 参数含义依次是：横坐标；纵坐标；点的面积；点的颜色；点的形状；点的透明度
4
plt.scatter(today, yestoday, s = 100, c = 'r', marker = '<', alpha = 0.5)
5
plt.show()In [ ]:1
3 折线图
1
import numpy as np
2
import matplotlib.pyplot as plt
3
import matplotlib.dates as mdates
In [2]:1
x = np.linspace(-10, 10, 100)
2
y = x ** 2
3
plt.plot(x, y)
4
plt.show()In [4]:1
date, open, close= np.loadtxt('000001.csv', delimiter = ',', converters =
2{0:mdates.bytespdate2num('%m/%d/%Y')}, skiprows = 1, usecols = (0, 1, 4), unpack = True)
3
date
A:\anaconda\anaconda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:1: MatplotlibDeprecationWarning:
The bytespdate2num class was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use time.strptime or dateutil.parser.parse or datestr2num instead."""Entry point for launching an IPython kernel.
A:\anaconda\anaconda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:1: MatplotlibDeprecationWarning:
The strpdate2num class was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use time.strptime or dateutil.parser.parse or datestr2num instead."""Entry point for launching an IPython kernel.
Out[4]:
array([735603., 735604., 735605., 735606., 735607., 735610., 735611.,735612., 735613., 735614., 735617., 735618., 735619., 735620.,735621., 735624., 735625., 735626., 735627., 735628., 735631.,735632., 735633., 735634., 735635., 735638., 735639., 735640.,735641., 735642., 735645., 735646., 735654., 735655., 735656.,735659., 735660., 735661., 735662., 735663., 735666., 735667.,735668., 735669., 735670., 735673., 735674., 735675., 735676.,735677., 735680., 735681., 735682., 735683., 735684., 735687.,735688., 735689., 735690., 735691., 735695., 735696., 735697.,735698., 735701., 735702., 735703., 735704., 735705., 735708.,735709., 735710., 735711., 735712., 735715., 735716., 735717.,735718., 735722., 735723., 735724., 735725., 735726., 735729.,735730., 735731., 735732., 735733., 735736., 735737., 735738.,735739., 735740., 735743., 735744., 735745., 735746., 735747.,735750., 735751., 735752., 735753., 735754., 735757., 735758.,735759., 735760., 735761., 735764., 735765., 735766., 735767.,735768., 735772., 735773., 735774., 735775., 735778., 735779.,735780., 735781., 735782., 735785., 735786., 735787., 735788.,735789., 735792., 735793., 735794., 735795., 735796., 735799.,735800., 735801., 735802., 735803., 735806., 735807., 735808.,735809., 735810., 735813., 735814., 735815., 735816., 735817.,735820., 735821., 735822., 735823., 735824., 735827., 735828.,735829., 735830., 735831., 735834., 735835., 735836., 735837.,735838., 735841., 735842., 735843., 735848., 735849., 735850.,735851., 735852., 735855., 735856., 735857., 735858., 735859.,735862., 735863., 735864., 735865., 735866., 735869., 735870.,735871., 735879., 735880., 735883., 735884., 735885., 735886.,735887., 735890., 735891., 735892., 735893., 735894., 735897.,735898., 735899., 735900., 735901., 735904., 735905., 735906.,735907., 735908., 735911., 735912., 735913., 735914., 735915.,735918., 735919., 735920., 735921., 735922., 735925., 735926.,735927., 735928., 735929., 735932., 735933., 735934., 735935.,735936., 735939., 735940., 735941., 735942., 735943., 735946.,735947., 735948., 735949., 735950., 735953., 735954., 735955.,735956., 735957., 735960., 735961.])
In [5]:1
plt.plot(date, open)
2
plt.show()
3
# 会发现横坐标不是日期。因为python自动把日期转化成浮点数保存起来In [6]:1
plt.plot_date(date, open)
2
plt.show()
3
# 但是这是变成了散点图In [7]:1
plt.plot_date(date, open, '-')
2
plt.show()In [8]:1
# 参数：线型；颜色；点的形状
2
plt.plot_date(date, open, linestyle = '-', color = 'r', marker = '<')
3
plt.show()In [9]:1
plt.plot_date(date, open, linestyle = '-', color = 'r', marker = '<')
2
plt.plot_date(date, close, linestyle = '--', color = 'b', marker = 'o')
3
plt.show()In [ ]:1
4 条形图
1
import numpy as np
2
import matplotlib.pyplot as plt
In [14]:1
index = np.arange(5)
2
y1 = [10, 25, 20, 30, 35]
3
pl = plt.bar(x = index, height = y)
4
plt.show()In [15]:1
# 调整外观
2
pl = plt.bar(x = index, height = y, color = 'red', width = 0.5)
3
plt.show()如何画水平方向的条形图
In [16]:1
# 方法一
2
pl = plt.bar(x = 0, bottom = index, width = y, height = 0.5, color = 'red', orientation = 'horizontal')
3
plt.show()In [19]:1
# 方法二
2
pl = plt.barh(left = 0, y = index, width = y1, height = 0.5, color = 'red')
3
plt.show()并列式的条形图
In [24]:1
index = np.arange(4)
2
sales_bj = [20, 30, 35, 27]
3
sales_sh = [19, 29, 32, 35]
4
bar_width = 0.3
5
plt.bar(x = index, height = sales_bj, width = bar_width, color = 'red')
6
plt.bar(x = index + bar_width, height = sales_sh, width = bar_width, color = 'blue')
7
plt.show()层叠式的条形图
In [25]:1
plt.bar(x = index, height = sales_bj, width = bar_width, color = 'red')
2
plt.bar(x = index, height = sales_sh, width = bar_width, bottom = sales_bj, color = 'blue')
3
plt.show()In [ ]:1
5 直方图
1
import numpy as np
2
import matplotlib.pyplot as plt
In [4]:1
mu = 100
2
sigma = 20
3
x = mu + sigma * np.random.randn(2000)
4
# 参数含义：直方的个数；颜色；是否标准化（True表示用区间里的个数除以总个数，即频率）
5
plt.hist(x, bins = 10, color = 'red', density = True)
6
plt.show()In [5]:1
# 更改直方图外观
2
plt.hist(x, bins = 30, color = 'green', density = False)
3
plt.show()2-D直方图
In [7]:1
x = np.random.randn(2000) + 2
2
y = np.random.randn(2000) + 3
3
plt.hist2d(x, y, bins = 50)
4
plt.show()In [ ]:1
6 饼状图
1
import matplotlib.pyplot as plt
In [4]:1
labels = 'A', 'B', 'C', 'D'
2
fracs = [20, 30, 25, 40]
3
# plt.axes(aspect = 1) 作用是让x轴与y轴比例一比一。也就是画一个正圆。有没有这句话无所谓
4
plt.pie(x = fracs, labels = labels)
5
plt.show()In [5]:1
# 显示饼的每一块所占的比例
2
plt.pie(x = fracs, labels = labels, autopct = '%.0f%%')
3
plt.show()In [8]:1
# 如何突出的显示某几块
2
explode = [0, 0.1, 0, 0]
3
plt.pie(x = fracs, labels = labels, autopct = '%.0f%%', explode = explode)
4
plt.show()In [10]:1
# 加上阴影
2
explode = [0, 0.1, 0, 0]
3
plt.pie(x = fracs, labels = labels, autopct = '%.1f%%', explode = explode, shadow = True)
4
plt.show()In [ ]:1
7 面向对象 VS Matlab Style
pylab方式作图
In [1]:1
from pylab import * # 从pylab中导入了所有函数，所以可以直接使用
In [10]:1
x = arange(0, 10)
2
y = randn(len(x))
3
title('pylab')
4
plot(x, y)
5
show()pyplot作图
In [8]:1
import matplotlib.pyplot as plt
2
import numpy as np
In [11]:1
x = np.arange(0, 10, 1)
2
y = np.random.randn(len(x))
3
plt.plot(x, y)
4
plt.title('pyplot')
5
plt.show()In [ ]:1
8 子图-subplot
面向对象的方式创建子图
In [1]:1
import matplotlib.pyplot as plt
2
import numpy as np
In [7]:1
x = np.arange(0, 100)
2
fig = plt.figure()
3
ax1 = fig.add_subplot(221)
4
ax1.plot(x, x)
5
ax2 = fig.add_subplot(222)
6
ax2.plot(x, -x)
7
ax3 = fig.add_subplot(223)
8
ax3.plot(x, x*x)
9
ax4 = fig.add_subplot(224)
10
ax4.plot(x, np.log(x))
A:\anaconda\anaconda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:10: RuntimeWarning: divide by zero encountered in log# Remove the CWD from sys.path while we load stuff.
Out[7]:
[<matplotlib.lines.Line2D at 0x2d9f592b00>]交互式方式创建子图
In [11]:1
plt.subplot(221)
2
plt.plot(x, x)
3
plt.subplot(222)
4
plt.plot(x, -x)
5
plt.subplot(223)
6
plt.plot(x, x*x)
7
plt.subplot(224)
8
plt.plot(x, np.log(x))
A:\anaconda\anaconda\envs\tensorflow\lib\site-packages\ipykernel_launcher.py:8: RuntimeWarning: divide by zero encountered in logOut[11]:
[<matplotlib.lines.Line2D at 0x2da2752c18>]In [ ]:1
9 多图-figure
1
import matplotlib.pyplot as plt
2
import numpy as np
In [4]:1
fig1 = plt.figure()
2
ax1 = fig1.add_subplot(111)
3
ax1.plot([1, 2, 3], [3, 2, 1])
4
fig2 = plt.figure()
5
ax2 = fig2.add_subplot(111)
6
ax2.plot([1, 2, 3], [1, 2, 3])
7
plt.show()In [ ]:1
10 网格
两种方式绘制网格
1.用plt交互式方式绘制网格
In [1]:1
import matplotlib.pyplot as plt
2
import numpy as np
In [2]:1
y = np.arange(1, 5)
2
y
Out[2]:
array([1, 2, 3, 4])
In [5]:1
plt.plot(y, y * 2)
2
plt.grid(True)In [6]:1
# 更改网格外观
2
plt.plot(y, y * 2)
3
plt.grid(True, color = 'red', linewidth = 2, linestyle = '--')面向对象方式创建网格
In [7]:1
x = np.arange(0, 10, 1)
2
fig = plt.figure()
3
ax = fig.add_subplot(111)
4
plt.plot(x, x * 2)
5
ax.grid(color = 'g')
6
plt.show()In [ ]:1
11 图例
plt方式绘制图例
In [1]:1
import matplotlib.pyplot as plt
2
import numpy as np
In [2]:1
x = np.arange(1, 11, 1)
2
x
Out[2]:
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
In [4]:1
plt.plot(x, x * 2, label = 'Normal')
2
plt.plot(x, x * 3, label = 'Fast')
3
plt.plot(x, x * 4, label = 'Faster')
4
plt.legend()
5
plt.show()图例的两个参数
1.位置参数
In [16]:1
plt.plot(x, x * 2, label = 'Normal')
2
plt.plot(x, x * 3, label = 'Fast')
3
plt.plot(x, x * 4, label = 'Faster')
4
plt.legend(loc = 0) # 参数由0到10。0表示自适应找到最佳位置
5
plt.show()2.控制图例的形状
In [17]:1
plt.plot(x, x * 2, label = 'Normal')
2
plt.plot(x, x * 3, label = 'Fast')
3
plt.plot(x, x * 4, label = 'Faster')
4
plt.legend(loc = 0, ncol = 3) # 3表示有3列
5
plt.show()也可以把label写进legend里面
In [18]:1
plt.plot(x, x * 2)
2
plt.plot(x, x * 3)
3
plt.plot(x, x * 4)
4
plt.legend(['Normal', 'Fast', 'Faster'], loc = 0, ncol = 3)
5
plt.show()用面向对象的方式绘制图例
In [21]:1
fig = plt.figure()
2
ax = fig.add_subplot(111)
3
l, = plt.plot(x, x)
4
# 第一种方式
5
ax.legend(['ax legend'])
6
plt.show()In [22]:1
fig = plt.figure()
2
ax = fig.add_subplot(111)
3
l, = plt.plot(x, x)
4
# 第二种方式
5
l.set_label('label via method')
6
ax.legend()
7
plt.show()In [23]:1
fig = plt.figure()
2
ax = fig.add_subplot(111)
3
# 第三种方式
4
l, = plt.plot(x, x, label = 'inline label')
5
ax.legend()
6
plt.show()In [ ]:1
12 坐标轴范围
1
import matplotlib.pyplot as plt
2
import numpy as np
In [2]:1
x = np.arange(-10, 11, 1)
2
x
Out[2]:
array([-10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 0, 1, 2,3, 4, 5, 6, 7, 8, 9, 10])
In [5]:1
plt.plot(x, x * x)
2
plt.axis() # 显示坐标轴范围
Out[5]:
(-11.0, 11.0, -5.0, 105.0)In [6]:1
plt.plot(x, x * x)
2
plt.axis([-5, 5, 20, 60]) # 指定坐标轴范围
Out[6]:
[-5, 5, 20, 60]In [8]:1
plt.plot(x, x * x)
2
plt.xlim() # 显示x轴的范围
Out[8]:
(-11.0, 11.0)In [10]:1
plt.plot(x, x * x)
2
# 同plt.xlim(xmin = -5, xmax = 5)
3
plt.xlim([-5, 5]) # 指定x轴的范围
Out[10]:
(-5, 5)In [12]:1
plt.plot(x, x * x)
2
plt.xlim([-5, 5])
3
plt.ylim([0, 60]) # 指定y轴的范围
Out[12]:
(0, 60)In [13]:1
plt.plot(x, x * x)
2
plt.xlim(xmin = -3) # 只指定一边
Out[13]:
(-3, 11.0)In [ ]:1
13 坐标轴刻度
1
import matplotlib.pyplot as plt
2
import numpy as np
In [4]:1
x = np.arange(1, 11, 1)
2
ax = plt.plot(x, x)
3
plt.show()In [5]:1
x = np.arange(1, 11, 1)
2
plt.plot(x, x)
3
ax = plt.gca() # 获取当前图形的坐标轴
4
ax.locator_params(nbins = 20) # 表示坐标轴划分成多少个格子
5
plt.show()In [6]:1
x = np.arange(1, 11, 1)
2
plt.plot(x, x)
3
ax = plt.gca() # 获取当前图形的坐标轴
4
ax.locator_params('x', nbins = 20) # 只调整一个轴
5
plt.show()当坐标轴是日期时
In [7]:1
import matplotlib.pyplot as plt
2
import matplotlib as mpl
3
import numpy as np
4
import datetime
In [8]:1
fig = plt.figure()
<Figure size 432x288 with 0 Axes>
In [9]:1
start = datetime.datetime(2015,1,1)
2
stop = datetime.datetime(2016,1,1)
3
delta = datetime.timedelta(days = 1)
In [10]:1
dates = mpl.dates.drange(start, stop, delta)
2
y = np.random.rand(len(dates))
In [15]:1
ax = plt.gca()
2
ax.plot_date(dates, y, linestyle = '-', marker = '')
3
plt.show()In [ ]:1
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