1. numpy科学计算基础库
1.1 numpy读取数据
CSV:Comma-Separated Value,逗号分隔值文件
显示:表格状态
源文件:换行和逗号分隔行列的格式化文本,每一行的数据表示一条记录。
由于csv便于展示,读取和写入,所以很多地方也是用csv的格式存储和传输中小型的数据,为了方便教学,我们会经常操作csv格式的文件,但是操作数据库中的数据也是很容易的实现的。
np.loadtxt(fname,dtype=np.float,delimiter=None,skiprows=0,usecols=None,unpack=False)
np.loadtxt(US_video_data_ numbers_path,delimiter=\",\",dtype=int,unpack=1)
注意其中添加delimiter和dtype以及unpack的效果
delimiter:指定边界符号是什么,不指定会导致每行数据为一个整体的字符串而报错
dtype:默认情况下对于较大的数据会将其变为科学计数的方式
那么unpack的效果呢?
upack:默认是Flase(0),默认情况下,有多少条数据,就会有多少行
为True(1)的情况下,每一列的数据会组成一行,原始数据有多少列,加载出来的数据就会有多少行,相当于转置的效果
1.2 numpy中的转置
转置是一种变换,对于numpy中的数组来说,就是在对角线方向交换数据,目的也是为了更方便的去处理数据。
# coding=utf-8 import numpy as np t = np.array([[0,1,2,3,4,5],[6,7,8,9,10,11],[12,13,1,15,16,17]]) print(t) print(t.swapaxes(1,0)) print(t.transpose()) print(t.T)
运行结果:
1.3 numpy索引和切片
对于刚刚加载出来的数据,我如果只想选择其中的某一列(行)我们应该怎么做呢?
# coding=utf-8 import numpy as np a = np.array([[0,1,2,3],[4,5,6,7],[8,9,10,11]]) #取一行 print(a[1]) #取多行 print(a[1:3]) #取一列 print(a[:,2]) #取多列 print(a[:,2:4])
运行结果:
1.4 numpy中数值的修改
# coding=utf-8 import numpy as np t = np.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]]) print(t) print(t[:,2:4]) t[:,2:4] = 0 print(t)
运行结果:
1.5 numpy中布尔索引
# coding=utf-8 import numpy as np t = np.arange(24).reshape((4,6)) print(t) print(t<10) t[t<10] = 0 print(t)
运行结果:
1.6 numpy中三元运算符
# coding=utf-8 import numpy as np t = np.arange(24).reshape((4,6)) print(t) print(np.where(t<10,0,10))
运行结果:
1.7 numpy中的nan和inf
nan(NAN,Nan):not a number表示不是一个数字
什么时候numpy中会出现nan:
当我们读取本地的文件为float的时候,如果有缺失,就会出现nan
当做了一个不合适的计算的时候(比如无穷大(inf)减去无穷大)
inf(-inf,inf):infinity,inf表示正无穷,-inf表示负无穷
什么时候回出现inf包括(-inf,+inf)
比如一个数字除以0,(python中直接会报错,numpy中是一个inf或者-inf)
# coding=utf-8 import numpy as np #两个nan是不相等的 print(np.nan == np.nan) print(np.nan != np.nan) #以上的特性,判断数组中nan的个数 t = np.array([1.,2.,np.nan]) print(np.count_nonzero(t!=t)) #由于np.nan != np.nan为True,那么如何判断一个数字是否为nan呢? #通过np.isnan(a)来判断,返回bool类型,比如希望把nan替换为0 t[np.isnan(t)]=0 print(t) #nan和任何值计算都为nan
运行结果:
# coding=utf-8 import numpy as np def fill_ndarray(t1): for i in range(t1.shape[1]): # 遍历每一列 temp_col = t1[:, i] # 当前的一列 nan_num = np.count_nonzero(temp_col != temp_col) if nan_num != 0: # 不为0,说明当前这一列中有nan temp_not_nan_col = temp_col[temp_col == temp_col] # 当前一列不为nan的array # 选中当前为nan的位置,把值赋值为不为nan的均值 temp_col[np.isnan(temp_col)] = temp_not_nan_col.mean() return t1 if __name__ == '__main__': t1 = np.arange(24).reshape((4, 6)).astype("float") t1[1, 2:] = np.nan print(t1) t1 = fill_ndarray(t1) print(t1)
运行结果:
1.8 numpy中常用统计函数
求和:t.sum(axis=None)
均值:t.mean(a,axis=None) 受离群点的影响较大
中值:np.median(t,axis=None)
最大值:t.max(axis=None)
最小值:t.min(axis=None)
极值:np.ptp(t,axis=None) 即最大值和最小值只差
标准差:t.std(axis=None)
标准差是一组数据平均值分散程度的一种度量。一个较大的标准差,代表大部分数值和其平均值之间差异较大;一个较小的标准差,代表这些数值较接*均值
反映出数据的波动稳定情况,越大表示波动越大,约不稳定。
缺失值填充均值:
# coding=utf-8 import numpy as np def fill_ndarray(t1): for i in range(t1.shape[1]): #遍历每一列 temp_col = t1[:,i] #当前的一列 nan_num = np.count_nonzero(temp_col!=temp_col) if nan_num !=0: #不为0,说明当前这一列中有nan temp_not_nan_col = temp_col[temp_col==temp_col] #当前一列不为nan的array # 选中当前为nan的位置,把值赋值为不为nan的均值 temp_col[np.isnan(temp_col)] = temp_not_nan_col.mean() return t1 if __name__ == '__main__': t1 = np.arange(24).reshape((4, 6)).astype("float") t1[1, 2:] = np.nan print(t1) t1 = fill_ndarray(t1) print(t1)
运行结果:
1.9 numpy生成随机数
1.10 实例运用
假设我们现在有一个英国和美国各自youtube1000多个视频的点击,喜欢,不喜欢,评论数量(["views","likes","dislikes","comment_total"])的csv,运用刚刚所学习的只是,我们尝试来对其进行操作。
准备数据:
7426393,78240,13548,705 494203,2651,1309,0 142819,13119,151,1141 1580028,65729,1529,3598 40592,5019,57,490 317696,9449,135,464 479291,23935,638,1941 10532409,384841,7547,23849 65453,2761,33,223 751743,42272,358,3250 1121715,65988,591,51025 199102,14916,324,1098 1024398,61352,967,4097 483360,16251,245,1588 601419,19564,658,2510 185541,12172,114,827 814262,66290,3557,5546 169898,5470,362,490 1691734,39633,1775,5191 917746,25455,731,1073 40884,1126,15,131 125196,5388,46,418 162818,9523,416,1079 1936216,74528,1059,6552 22668,62,0,27 1701667,53795,8517,36290 35536,2094,40,415 189389,15787,104,2479 369541,7658,469,1887 4659935,17509,2542,2157 153427,8915,91,1080 109101,4209,109,462 4615562,104889,2180,11355 379393,14093,323,1338 274358,9213,476,838 218536,10659,432,1424 2524251,17100,676,5311 463425,26446,8020,12806 3263,108,2,7 182142,4041,820,3267 82601,5311,67,1662 136677,490,206,1841 214126,9278,139,349 807265,26518,522,5996 141937,9886,130,1224 58169,1906,73,198 315091,19533,204,3172 503585,41072,362,3399 498126,6298,112,789 330727,18020,112,1017 324532,6060,72,252 141356,9465,55,956 162018,2566,72,0 703750,2921,2196,1042 246389,15307,247,1842 331993,14276,626,1927 265337,3986,197,252 1560006,190937,793,19925 452338,6684,298,587 121447,1302,102,512 119670,3821,73,377 12393,761,6,45 230701,11767,184,1519 1023353,22255,264,3209 228731,9743,292,1119 127097,1068,61,260 157166,19869,77,2027 1617120,83410,1014,7114 61781,2543,58,267 1152639,45401,4310,5708 273962,3105,129,594 3576396,155585,6168,6702 412117,16287,748,2254 39245,2376,103,479 27794,1690,34,174 185984,6155,129,320 688485,6543,509,350 50058,3299,14,241 76714,5830,47,337 355492,4811,363,1478 506525,13349,463,1492 496166,8235,450,1098 352276,13098,508,1297 1769256,8048,2123,3359 659654,3964,712,278 1006825,57541,570,3333 787961,17140,1054,5521 236805,4631,67,201 43179,196,14,183 94048,5982,67,699 991790,47532,626,2006 5042942,183473,16578,19443 1683617,15347,690,887 77753,805,82,636 1598,0,0,0 266862,5496,74,300 13721029,468496,6596,20989 47756,393,157,144 1307367,19733,984,3029 538100,15693,234,1962 76410,2193,17,156 166386,6301,67,502 14268,80,5,9 59288,839,102,179 1811958,133815,5380,23181 163175,3337,111,447 5951,63,8,73 387841,12097,458,1771 2384126,50006,1918,6584 699671,49246,995,4863 36297533,1431489,28044,100632 413246,17086,338,1587 2594,100,2,17 1244984,29748,2639,4112 104920,268,60,6 67151,264,18,33 406279,20499,516,6361 131005,859,867,532 25343,167,17,42 952919,17703,3207,0 288535,4772,159,397 97368,335,11,56 614608,49441,1986,3970 463276,19286,393,2304 299228,16290,155,901 241329,12247,203,1013 3970592,154729,1972,7476 61565,1039,14,471 738448,8415,199,949 1884820,55235,1132,3279 1834397,45754,1406,10764 1101502,4117,993,2358 108797,3677,107,732 626419,20553,1033,2138 3026138,76495,2880,6414 2217,14,1,7 104056,16458,32,1263 1851397,24773,2844,2242 3994782,291371,11625,24235 204310,19566,57,1727 737567,10261,802,1232 713480,24229,673,2204 2450950,176104,1017,6257 57819,682,29,147 599742,29851,299,453 1655312,23960,455,2777 220053,6740,853,1455 657439,24309,390,1783 836133,19314,1206,0 59384,209,12,19 74896,2606,93,240 384624,21301,353,1559 79826,3740,52,375 189372,7070,112,288 319276,4939,3627,0 185604,8689,202,492 538884,4610,1276,3651 659862,43887,593,342 338544,16458,217,2527 445180,5189,313,502 288843,8899,55,544 900916,75199,254,7395 60191,972,76,312 149811,5737,87,1439 291531,7401,251,585 87063,2203,18,351 1055582,39081,842,4809 803941,27542,553,2512 85630,521,35,99 1868689,31842,1395,2798 56949,1129,19,111 467118,1474,385,310 828608,2935,599,2113 181598,3370,41,199 364765,9537,1270,1629 64273,3756,32,843 1368378,38304,581,2762 62161,1709,20,125 181645,2399,374,1300 129516,5166,221,339 503997,15015,1147,1319 118932,5296,51,255 854299,26749,548,2233 105687,4819,35,354 2214471,85209,4165,18085 4690194,505334,13072,83992 10139,113,0,18 81262,6822,120,431 1997363,69424,5278,16930 70042,5632,33,307 104581,3763,106,254 54723,2317,51,197 255648,5970,112,178 17521230,437405,25272,72329 167243,8387,147,453 126475,937,40,46 311056,4939,318,471 740296,16644,789,1424 149777,6768,92,793 278555,12005,165,330 1796205,26393,464,2278 5457215,349832,17471,52478 1109739,11238,1809,3626 7920718,81587,14433,1460 2083895,128378,1136,8733 193416,7410,212,472 1398012,35862,619,6241 3654631,30830,6099,16709 1568532,174682,2162,19186 153181,9241,134,907 504620,29196,457,2233 1341911,33110,1035,4709 197058,14771,224,1257 380211,11550,1284,1778 27927,169,3,85 1637505,76339,936,6052 743189,28799,609,1980 1805172,72614,1714,4056 1461311,35310,1062,6077 1152533,25561,1142,4237 1668188,88156,5892,7369 1091878,49488,537,3881 558740,4842,259,1083 11475270,398493,8158,24861 585991,26814,778,2165 257201,10096,227,1362 81675,3085,42,308 46864,1893,47,159 482329,11797,178,575 705475,34493,549,3180 9749,153,4,10 772128,21776,813,2839 8940,72,10,49 482637,28975,336,2423 57168,1305,17,176 111776,4848,68,311 292538,16890,322,1834 746904,12073,755,1562 304490,10867,171,385 207071,2021,1977,0 87061,220,43,115 579530,17842,277,1852 214481,12908,125,937 1247121,68785,1092,4582 205103,10944,471,1206 86229,1870,97,759 53745,483,57,206 413020,18223,404,1671 743630,48909,471,3746 209260,6056,420,573 393968,10053,596,1191 115702,4367,111,479 2511096,90132,1319,7936 429138,18013,343,3900 947706,25968,741,1139 1759154,54336,8854,37011 187511,4086,842,3372 1180879,47863,1640,4295 250268,11351,457,1270 4959620,18534,2722,2450 162622,9173,94,951 496246,25067,236,3634 410405,14670,344,1407 4830992,107671,2276,11743 62029,1985,76,216 792162,3186,2354,929 481646,26919,8151,13162 41152,2278,44,470 71912,2013,17,165 892442,27573,554,6161 2817039,18480,723,5724 55602,2433,84,453 141012,498,208,1951 85814,5415,68,1723 2055,62,24,0 169383,2615,73,0 376653,21238,220,3538 144652,5837,46,455 179765,2642,25,466 352810,4634,238,331 158779,10142,60,1003 541685,6617,117,901 348472,18536,115,1070 352123,6769,81,283 372944,15547,646,2081 205695,16389,111,2554 152653,1454,116,661 45841,2548,115,504 461896,6740,299,572 125476,3931,75,382 25787,968,36,294 1603599,193810,804,20195 91179,3302,153,1285 2072539,90345,4722,15645 564123,33312,488,4152 29598,84,2,8 252320,12479,202,1635 150257,10184,137,1107 264903,11033,304,1231 168560,20643,81,2100 130228,1083,62,271 1681417,85016,1038,7196 274309,16266,266,1965 63686,2599,58,275 1228122,47166,4719,6048 89695,509,22,205 33577,1850,35,207 15300,860,6,52 145607,7767,86,766 274503,3106,129,613 34939,1803,14,318 1043993,58481,587,3393 197356,6356,142,357 54815,4499,129,640 46272,210,14,188 3635565,156317,6214,6770 694324,6563,509,369 50742,3328,15,249 78823,5922,48,345 289823,5729,82,310 516399,13503,467,1529 1774499,8057,2128,3396 230912,3601,89,1320 667703,3996,722,280 808962,17616,1060,5678 78873,807,84,647 15076956,485775,6885,20825 1622,0,0,0 1703223,15421,693,905 5292941,187245,17327,19816 170284,6342,67,503 362037,4853,365,1497 1013541,47911,633,2015 47999,396,157,150 354557,13162,508,1306 1361619,20381,1020,3113 583429,16338,246,2142 246424,4720,68,203 84550,2289,17,143 30048,1586,50,237 114203,270,62,8 111620,379,11,62 1849540,135168,5430,23408 180554,3458,113,459 6014,63,8,73 396083,12221,463,1772 723980,50162,1000,4923 38678096,1458937,28784,102238 3006,101,2,21 426248,17361,345,1613 80647,475,23,106 414089,20838,521,6595 67706,265,18,33 628024,49972,2011,4049 131734,860,871,536 25469,167,17,43 1256061,29934,2662,4152 472426,19479,394,2327 195079,3410,102,355 753656,10411,822,1235 775743,8659,208,969 243245,12305,204,1022 4230773,294499,11811,24462 4045505,156186,1996,7562 958504,17762,3218,0 1854629,46052,1412,10828 1102459,4117,993,2395 1984548,25975,2916,2345 111083,3738,109,739 3251331,79631,2984,6576 69958,1145,15,507 2315,14,2,7 105701,16596,32,1268 303575,16427,156,918 212719,19876,58,1732 823000,25570,758,2288 2473585,177014,1018,6290 324360,4975,3652,0 637361,20701,1048,2151 60016,696,29,147 197531,7176,117,291 220483,6752,853,1454 865377,19381,1208,0 671475,44185,596,341 60283,217,12,20 75664,2619,93,239 89514,538,37,103 80953,3767,53,376 547462,4645,1279,3682 337686,10121,65,618 188572,8765,204,493 342639,16582,218,2550 681308,24556,399,1815 455309,5288,321,516 605425,29993,301,470 1976278,32733,1435,2866 914602,75684,256,7438 72491,1806,26,133 60628,976,76,315 392014,21510,354,1567 3619251,47568,993,4017 612932,26114,210,1387 754452,74303,947,10379 1920241,20272,4006,7615 145342,1820,53,273 5853747,361416,22019,54666 960528,42543,773,3130 2587155,144158,1480,9933 1936912,42199,835,7351 168197,7101,210,241 8260919,84514,15178,2049 2888266,222480,3287,23533 161886,10258,338,1274 126204,5106,133,530 4257099,33616,6881,18323 846828,9316,650,1943 317578,2054,227,684 43486,397,15,170 667161,48052,1015,2956 73989,2933,234,617 195172,11516,399,1166 181052,6496,101,375 327108,10093,354,609 68893,5739,27,337 52740,2604,58,303 85461,4119,76,392 516978,14114,1718,2228 1493954,35531,1135,5316 11519,1504,27,282 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1150895,8900,3174,1798 385096,12006,326,4061 4112187,26609,8982,0 1703268,67601,1123,3373 122191,7384,593,3143 314005,15492,736,1318 1161121,67930,522,7469 108829,320,2,19 248787,12683,566,2096 446594,13138,424,1379 192937,10880,192,1376 63069,2725,53,162 395563,7615,184,498 18293,294,7,28 2218837,29355,4868,5214 117199,4674,225,539 222861,21781,141,2412 4699392,388954,7586,19412 191166,730,125,75 1689730,17709,1171,2035 429424,28624,291,2208 168337,2453,36,245 49418,2561,98,346 326073,13654,222,1716 2206513,91418,1002,17577 3864917,322474,1450,27422 188774,19494,76,807 303550,6567,145,560 652319,26543,755,1370 173696,15463,148,742 439139,1277,679,1041 319412,5750,272,289 3932117,150979,1861,7990 80333,106,2,9 2860774,78841,690,5333 2822860,175347,1710,16202 793296,23330,1761,2695 53740,2078,31,146 2125368,57482,419,2083 2517566,124876,2318,12000 837476,39223,841,4158 2172912,90925,2464,3963 1091561,75196,1139,7456 6668,77,1,17 425684,7817,1893,989 1561336,87097,1065,10525 677750,63206,247,3616 785534,29247,624,2218 979551,64289,465,3450 51110,378,60,433 215640,2105,47,169 396776,18156,716,1487 219221,4532,152,553 91572,448,221,1024 469986,17749,676,2303 50664,331,11,17 291348,15749,233,1330 72124,1018,13,93 2379689,24008,4727,7665 483496,1369,1645,1115 4672,234,0,0 142463,4231,148,279 2162240,41032,1384,4737 515000,34727,195,4722US_video_data_numbers.csv
案例1:
import numpy as np from matplotlib import pyplot as plt us_file_path = "./US_video_data_numbers.csv" uk_file_path = "./GB_video_data_numbers.csv" # t1 = np.loadtxt(us_file_path,delimiter=",",dtype="int",unpack=True) t_us = np.loadtxt(us_file_path,delimiter=",",dtype="int") #取评论的数据 t_us_comments = t_us[:,-1] #选择比5000小的数据 t_us_comments = t_us_comments[t_us_comments<=5000] print(t_us_comments.max(),t_us_comments.min()) d = 50 bin_nums = (t_us_comments.max()-t_us_comments.min())//d #绘图 plt.figure(figsize=(20,8),dpi=80) plt.hist(t_us_comments,bin_nums) plt.show()
运行结果:
案例2:
import numpy as np from matplotlib import pyplot as plt us_file_path = "./US_video_data_numbers.csv" uk_file_path = "./GB_video_data_numbers.csv" # t1 = np.loadtxt(us_file_path,delimiter=",",dtype="int",unpack=True) t_uk = np.loadtxt(uk_file_path,delimiter=",",dtype="int") #选择喜欢书比50万小的数据 t_uk = t_uk[t_uk[:,1]<=500000] t_uk_comment = t_uk[:,-1] t_uk_like = t_uk[:,1] plt.figure(figsize=(20,8),dpi=80) plt.scatter(t_uk_like,t_uk_comment) plt.show()
运行结果: