python 数据分析 Numpy(Numerical Python Basic)
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# 导入numpy 模块
1 import numpy as np 10 a = np.random.random((2,4)) 11 a 12 Out[5]: 13 array([[0.20974732, 0.73822026, 0.82760722, 0.050551 ], 14 [0.77337155, 0.06521922, 0.55524187, 0.59209907]])
# 求矩阵所有数据的和，最小值，最大值 22 np.sum(a) 23 Out[7]: 3.812057513268513 24 np.min(a) 25 Out[8]: 0.05055099733013646 26 np.max(a) 27 Out[9]: 0.8276072194278252 28 print("a=",a) 29 a= [[0.20974732 0.73822026 0.82760722 0.050551 ] 30 [0.77337155 0.06521922 0.55524187 0.59209907]]
# axis=0 代表列， axis=1代表行
31 print("min",np.min(a)) 32 min 0.05055099733013646
#求每列当中的最小值 33 print("lmin:",np.min(a,axis=0)) 34 lmin: [0.20974732 0.06521922 0.55524187 0.050551 ] 35 print("lmin:",np.min(a,axis=1)) 36 lmin: [0.050551 0.06521922] 37 print("sum:",np.sum(a,axis=1)) 38 sum: [1.8261258 1.98593171]
# reshape 数据， 3行4列 39 A = np.arange(2,14).reshape(3,4) 40 A 41 Out[16]: 42 array([[ 2, 3, 4, 5], 43 [ 6, 7, 8, 9], 44 [10, 11, 12, 13]])
# ndarray中最小值，最大值的序号 45 print(np.argmin(A)) 46 0 47 print(np.argmax(A)) 48 11 49 print(np.mean(A)) 50 7.5 51 print(np.average(A)) 52 7.5 53 print(A.mean()) 54 7.5
# cumsum 迭代相加 69 A 70 Out[24]: 71 array([[ 2, 3, 4, 5], 72 [ 6, 7, 8, 9], 73 [10, 11, 12, 13]]) 81 print(A.cumsum()) 82 [ 2 5 9 14 20 27 35 44 54 65 77 90] 83 A 84 Out[27]: 85 array([[ 2, 3, 4, 5], 86 [ 6, 7, 8, 9], 87 [10, 11, 12, 13]])
# clip(a, a_min, a_max) 将ndarray中的数据进行判断，小于a_min的值都赋值为a_min, 大于a_max的都赋值a_max，在这之间的值不变。 88 print(np.clip(A,5,8)) 89 [[5 5 5 5] 90 [6 7 8 8] 91 [8 8 8 8]]
# 判断ndarray阶数，几维向量
99 A.ndim 100 Out[30]: 2 101 A 102 Out[31]: 103 array([[ 2, 3, 4, 5], 104 [ 6, 7, 8, 9], 105 [10, 11, 12, 13]]) 106 A.ndim 107 Out[32]: 2 108 a 109 Out[33]: 110 array([[0.20974732, 0.73822026, 0.82760722, 0.050551 ], 111 [0.77337155, 0.06521922, 0.55524187, 0.59209907]]) 112 a.ndim 113 Out[34]: 2 114 A 115 Out[35]: 116 array([[ 2, 3, 4, 5], 117 [ 6, 7, 8, 9], 118 [10, 11, 12, 13]])
1 names = np.array([\'Bob\', \'Joe\', \'Will\', \'Bob\', \'Will\', \'Joe\', \'Joe\']) 2 names 3 Out[37]: array([\'Bob\', \'Joe\', \'Will\', \'Bob\', \'Will\', \'Joe\', \'Joe\'], dtype=\'<U4\')
# 中间出了一些错误调用random的时候需要俩括号,(7,4)作为一个tuple变量传入random 33 data = np.random.random((7,4)) 34 data 35 Out[43]: 36 array([[0.89497078, 0.61138776, 0.69472434, 0.27105599], 37 [0.23114404, 0.1423609 , 0.06016109, 0.56939826], 38 [0.84711124, 0.00776355, 0.24954255, 0.96157959], 39 [0.34937375, 0.6013533 , 0.66481223, 0.18210067], 40 [0.82706912, 0.64240956, 0.95575726, 0.40232292], 41 [0.57225917, 0.0958916 , 0.969577 , 0.47824937], 42 [0.52181664, 0.59962513, 0.19175081, 0.92442871]])
# 注意random 和 randn的区别，numpy.random.randn(d0, d1, …, dn)是从标准正态分布中返回一个或多个样本值，numpy.random.rand(d0, d1, …, dn)的随机样本位于[0, 1)中。 43 data = np.random.randn(7,4) 44 data 45 Out[45]: 46 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 47 [ 0.91135901, -0.90451748, -1.12549659, 1.69668984], 48 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 49 [-0.56646272, 0.87848794, -1.29842767, 0.65293394], 50 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648], 51 [-0.53991665, 0.3098865 , 2.18921935, 0.83933456], 52 [-1.21083646, -0.30640711, 0.36142124, 0.9664484 ]]) 58 names = np.array([\'Bob\', \'Joe\', \'Will\', \'Bob\', \'Will\', \'Joe\', \'Joe\'])
# 通过判断重新生成array 59 names == \'Bob\' 60 Out[50]: array([ True, False, False, True, False, False, False]) 61 data 62 Out[51]: 63 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 64 [ 0.91135901, -0.90451748, -1.12549659, 1.69668984], 65 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 66 [-0.56646272, 0.87848794, -1.29842767, 0.65293394], 67 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648], 68 [-0.53991665, 0.3098865 , 2.18921935, 0.83933456], 69 [-1.21083646, -0.30640711, 0.36142124, 0.9664484 ]])
#高级用法，根据names判断生成的array再进行一次迭代选择， 和切片还有区别 70 data[names == \'Bob\'] 71 Out[52]: 72 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 73 [-0.56646272, 0.87848794, -1.29842767, 0.65293394]])
# 切片的选择 74 data[names == \'Bob\', 2:] 75 Out[53]: 76 array([[-1.03263407, 0.06053961], 77 [-1.29842767, 0.65293394]]) 78 data[names == \'Bob\', 3] 79 Out[54]: array([0.06053961, 0.65293394]) 80 names 81 Out[55]: array([\'Bob\', \'Joe\', \'Will\', \'Bob\', \'Will\', \'Joe\', \'Joe\'], dtype=\'<U4\') 82 names == \'Joe\' 83 Out[56]: array([False, True, False, False, False, True, True]) 84 names != \'Bob\' 85 Out[57]: array([False, True, True, False, True, True, True])
#这一例程没成功，待调查。。。 86 data[-(names == \'Bob\')] 87 Traceback (most recent call last): 88 File "E:\\Software\\Software\\Anaconda2.5.01\\envs\\intro_dl\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2963, in run_code 89 exec(code_obj, self.user_global_ns, self.user_ns) 90 File "<ipython-input-58-1242c1c7d3ed>", line 1, in <module> 91 data[-(names == \'Bob\')] 92 TypeError: The numpy boolean negative, the `-` operator, is not supported, use the `~` operator or the logical_not function instead. 93 data 94 Out[59]: 95 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 96 [ 0.91135901, -0.90451748, -1.12549659, 1.69668984], 97 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 98 [-0.56646272, 0.87848794, -1.29842767, 0.65293394], 99 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648], 100 [-0.53991665, 0.3098865 , 2.18921935, 0.83933456], 101 [-1.21083646, -0.30640711, 0.36142124, 0.9664484 ]]) 102 data[-(names == \'Bob\')] 103 Traceback (most recent call last): 104 File "E:\\Software\\Software\\Anaconda2.5.01\\envs\\intro_dl\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2963, in run_code 105 exec(code_obj, self.user_global_ns, self.user_ns) 106 File "<ipython-input-60-1242c1c7d3ed>", line 1, in <module> 107 data[-(names == \'Bob\')] 108 TypeError: The numpy boolean negative, the `-` operator, is not supported, use the `~` operator or the logical_not function instead. 109 data 110 Out[61]: 111 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 112 [ 0.91135901, -0.90451748, -1.12549659, 1.69668984], 113 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 114 [-0.56646272, 0.87848794, -1.29842767, 0.65293394], 115 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648], 116 [-0.53991665, 0.3098865 , 2.18921935, 0.83933456], 117 [-1.21083646, -0.30640711, 0.36142124, 0.9664484 ]]) 118 -data 119 Out[62]: 120 array([[ 0.41118699, 0.55989348, 1.03263407, -0.06053961], 121 [-0.91135901, 0.90451748, 1.12549659, -1.69668984], 122 [-0.54079498, -1.23213331, -0.86787185, -2.33957776], 123 [ 0.56646272, -0.87848794, 1.29842767, -0.65293394], 124 [-0.96861489, -1.5155331 , -0.328894 , -0.25768648], 125 [ 0.53991665, -0.3098865 , -2.18921935, -0.83933456], 126 [ 1.21083646, 0.30640711, -0.36142124, -0.9664484 ]]) 127 -data[names == \'Bob\'] 128 Out[63]: 129 array([[ 0.41118699, 0.55989348, 1.03263407, -0.06053961], 130 [ 0.56646272, -0.87848794, 1.29842767, -0.65293394]]) 131 data[names != \'Bob\'] 132 Out[64]: 133 array([[ 0.91135901, -0.90451748, -1.12549659, 1.69668984], 134 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 135 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648], 136 [-0.53991665, 0.3098865 , 2.18921935, 0.83933456], 137 [-1.21083646, -0.30640711, 0.36142124, 0.9664484 ]]) 138 data[-names != \'Bob\'] 139 Traceback (most recent call last): 140 File "E:\\Software\\Software\\Anaconda2.5.01\\envs\\intro_dl\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2963, in run_code 141 exec(code_obj, self.user_global_ns, self.user_ns) 142 File "<ipython-input-65-5976a92eae9b>", line 1, in <module> 143 data[-names != \'Bob\'] 144 TypeError: ufunc \'negative\' did not contain a loop with signature matching types dtype(\'<U4\') dtype(\'<U4\')
#组合选择 145 msk = (names == \'Bob\') | (names == \'Will\') 146 msk 147 Out[67]: array([ True, False, True, True, True, False, False]) 148 data[msk] 149 Out[68]: 150 array([[-0.41118699, -0.55989348, -1.03263407, 0.06053961], 151 [ 0.54079498, 1.23213331, 0.86787185, 2.33957776], 152 [-0.56646272, 0.87848794, -1.29842767, 0.65293394], 153 [ 0.96861489, 1.5155331 , 0.328894 , 0.25768648]])
#根据判断冲洗赋值 154 data[data < 0] = 0 155 data 156 Out[70]: 157 array([[0. , 0. , 0. , 0.06053961], 158 [0.91135901, 0. , 0. , 1.69668984], 159 [0.54079498, 1.23213331, 0.86787185, 2.33957776], 160 [0. , 0.87848794, 0. , 0.65293394], 161 [0.96861489, 1.5155331 , 0.328894 , 0.25768648], 162 [0. , 0.3098865 , 2.18921935, 0.83933456], 163 [0. , 0. , 0.36142124, 0.9664484 ]]) 164 arr = np.empty((8,4)) 165 arr 166 Out[72]: 167 array([[6.23042070e-307, 4.22795269e-307, 2.04722549e-306, 168 6.23054972e-307], 169 [1.78019761e-306, 9.34608432e-307, 7.56599807e-307, 170 8.90104239e-307], 171 [1.16820282e-307, 6.23037317e-307, 1.69121639e-306, 172 1.78020848e-306], 173 [8.90094053e-307, 1.11261027e-306, 1.11261502e-306, 174 1.42410839e-306], 175 [7.56597770e-307, 6.23059726e-307, 1.42419530e-306, 176 1.37961302e-306], 177 [1.29060531e-306, 1.11261570e-306, 7.56602523e-307, 178 9.34609790e-307], 179 [8.34451504e-308, 1.22383391e-307, 1.33511562e-306, 180 8.90103560e-307], 181 [1.42410974e-306, 1.00132228e-307, 1.33511969e-306, 182 2.18568966e-312]]) 183 for i in range(8): 184 a[i] = i 185 186 Traceback (most recent call last): 187 File "E:\\Software\\Software\\Anaconda2.5.01\\envs\\intro_dl\\lib\\site-packages\\IPython\\core\\interactiveshell.py", line 2963, in run_code 188 exec(code_obj, self.user_global_ns, self.user_ns) 189 File "<ipython-input-73-077106ef35e3>", line 2, in <module> 190 a[i] = i 191 IndexError: index 2 is out of bounds for axis 0 with size 2
# arr[i]是行的地址，给行地址指针赋值相当于改写了整块内存的值， 整行赋值， 不知道理解的对不对 192 for i in range(8): 193 arr[i] = i 194 195 arr 196 Out[75]: 197 array([[0., 0., 0., 0.], 198 [1., 1., 1., 1.], 199 [2., 2., 2., 2.], 200 [3., 3., 3., 3.], 201 [4., 4., 4., 4.], 202 [5., 5., 5., 5.], 203 [6., 6., 6., 6.], 204 [7., 7., 7., 7.]]) 205 arr[0] = 9 206 arr 207 Out[77]: 208 array([[9., 9., 9., 9.], 209 [1., 1., 1., 1.], 210 [2., 2., 2., 2.], 211 [3., 3., 3., 3.], 212 [4., 4., 4., 4.], 213 [5., 5., 5., 5.], 214 [6., 6., 6., 6.], 215 [7., 7., 7., 7.]]) 216 arr[[4,3,0,6]] 217 Out[78]: 218 array([[4., 4., 4., 4.], 219 [3., 3., 3., 3.], 220 [9., 9., 9., 9.], 221 [6., 6., 6., 6.]]) 222 arr = np.arange(15).reshape((3,5)) 223 arr 224 Out[80]: 225 array([[ 0, 1, 2, 3, 4], 226 [ 5, 6, 7, 8, 9], 227 [10, 11, 12, 13, 14]])
# .T转置矩阵 228 arr.T 229 Out[81]: 230 array([[ 0, 5, 10], 231 [ 1, 6, 11], 232 [ 2, 7, 12], 233 [ 3, 8, 13], 234 [ 4, 9, 14]])
#np.dot 矩阵相乘 235 np.dot(arr.T, arr) 236 Out[82]: 237 array([[125, 140, 155, 170, 185], 238 [140, 158, 176, 194, 212], 239 [155, 176, 197, 218, 239], 240 [170, 194, 218, 242, 266], 241 [185, 212, 239, 266, 293]]) 242 arr 243 Out[83]: 244 array([[ 0, 1, 2, 3, 4], 245 [ 5, 6, 7, 8, 9], 246 [10, 11, 12, 13, 14]]) 247 arr.T 248 Out[84]: 249 array([[ 0, 5, 10], 250 [ 1, 6, 11], 251 [ 2, 7, 12], 252 [ 3, 8, 13], 253 [ 4, 9, 14]]) 254 np.dot(arr.T, arr) 255 Out[85]: 256 array([[125, 140, 155, 170, 185], 257 [140, 158, 176, 194, 212], 258 [155, 176, 197, 218, 239], 259 [170, 194, 218, 242, 266], 260 [185, 212, 239, 266, 293]]) 261 np.dot(arr, arr.T) 262 Out[86]: 263 array([[ 30, 80, 130], 264 [ 80, 255, 430], 265 [130, 430, 730]]) 266 arr = np.arange(10)
#矩阵开方 267 np.sqrt(arr) 268 Out[88]: 269 array([0. , 1. , 1.41421356, 1.73205081, 2. , 270 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ]) 271 np.exp(arr) 272 Out[89]: 273 array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 274 5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 275 2.98095799e+03, 8.10308393e+03]) 276 x = np.random.randn(8) 277 y = np.random.randn(8) 278 x 279 Out[92]: 280 array([-0.80864713, -1.10307828, 0.39407346, -1.51956716, -0.69376606, 281 -0.5599136 , 0.37168709, -0.3947183 ]) 282 y 283 Out[93]: 284 array([ 1.49291073, -0.30018043, -0.1632179 , -0.53365993, 2.48673945, 285 -0.72669644, -0.18439522, 2.03956463])
#俩矩阵相比较 293 np.maximum(x, y) 294 Out[95]: 295 array([ 1.49291073, -0.30018043, 0.39407346, -0.53365993, 2.48673945, 296 -0.5599136 , 0.37168709, 2.03956463]) 297 arr = np.random.randn(7)
#矩阵相乘 298 arr*5 299 Out[97]: 300 array([-9.09778567, -1.2577255 , 2.85527111, -1.10915396, -3.61125732, 301 4.83669313, 0.49764244]) 302 arr 303 Out[98]: 304 array([-1.81955713, -0.2515451 , 0.57105422, -0.22183079, -0.72225146, 305 0.96733863, 0.09952849]) 306 arr = np.random.randn(7) * 5 307 arr 308 Out[100]: 309 array([ 2.02351861, 6.79384776, -5.29035855, 4.15965833, 7.93557854, 310 -1.93563595, 1.45949827])
#np.modf是个神奇的函数， 分别显示生辰改动连个不同矩阵 311 np.modf(arr) 312 Out[101]: 313 (array([ 0.02351861, 0.79384776, -0.29035855, 0.15965833, 0.93557854, 314 -0.93563595, 0.45949827]), array([ 2., 6., -5., 4., 7., -1., 1.]))
322 points = np.arange(-5, 5, 0.01) 323 xs, ys = np.meshgrid(points, points) 324 xs 325 Out[105]: 326 array([[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], 327 [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], 328 [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], 329 ..., 330 [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], 331 [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99], 332 [-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99]]) 333 ys 334 Out[106]: 335 array([[-5. , -5. , -5. , ..., -5. , -5. , -5. ], 336 [-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99], 337 [-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98], 338 ..., 339 [ 4.97, 4.97, 4.97, ..., 4.97, 4.97, 4.97], 340 [ 4.98, 4.98, 4.98, ..., 4.98, 4.98, 4.98], 341 [ 4.99, 4.99, 4.99, ..., 4.99, 4.99, 4.99]]) 342 import matplotlib.pyplot as plt 343 Backend TkAgg is interactive backend. Turning interactive mode on. 344 z = np.sqrt(xs**2 + ys**2) 345 z 346 Out[109]: 347 array([[7.07106781, 7.06400028, 7.05693985, ..., 7.04988652, 7.05693985, 348 7.06400028], 349 [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815, 350 7.05692568], 351 [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354, 352 7.04985815], 353 ..., 354 [7.04988652, 7.04279774, 7.03571603, ..., 7.0286414 , 7.03571603, 355 7.04279774], 356 [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354, 357 7.04985815], 358 [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815, 359 7.05692568]])
#plt 的两个图像是进行叠加显示的。 360 plt.imshow(z, cmap=plt.cm.gray); plt.colorbar() 361 Out[110]: <matplotlib.colorbar.Colorbar at 0x8a3ccc0> 362 plt.title("Image plot of $\\sqrt{x^2 + y^2}$ for a grid of values") 363 Out[111]: Text(0.5,1,\'Image plot of $\\\\sqrt{x^2 + y^2}$ for a grid of values\') 364 plt.imshow(z, cmap=plt.cm.gray); plt.colorbar() 365 Out[112]: <matplotlib.colorbar.Colorbar at 0xa894668>
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