Python列表list的split()用法详解
Python列表list的split() 方法:
split()函数
语法:str.split(str=" ",num=string.count(str))[n]
参数说明:
str: 表示为分隔符,默认为空格,但是不能为空( ”)。若字符串中没有分隔符,则把整个字符串作为列表的一个元素 num:表示分割次数。如果存在参数num,则仅分隔成 num+1 个子字符串,并且每一个子字符串可以赋给新的变量 [n]: 表示选取第n个分片
注意:当使用空格作为分隔符时,对于中间为空的项会自动忽略
str="hello boy<[www.doiido.com]>byebye" print(str.split("[")[1].split("]")[0]) www.doiido.com print(str.split("[")[1].split("]")[0].split(".")) ['www', 'doiido', 'com']
一个简单的Example:
目的是在混杂有文本和数字的txt文件中读取数字,作图。(其实是在用sklearn做神经网络训练过程的一个输出)
import pandas as pd import numpy as np data=pd.read_table('learning.txt',header=None) data = data.drop([i for i in range(1,254,2)]) data=np.array(data[0]).tolist() x=[] y=[] x_=[] y_=[] for i in data: a=i.split(',',1)[0] b=i.split(',',1)[1] x_.append(a) y_.append(b) for i in y_: a=i.split(' ',-1)[-1] y.append(a) for i in x_: a=i.split(' ',-1)[-1] x.append(a) x=[int(i) for i in x] y=[float(i) for i in y] import matplotlib.pyplot as plt plt.figure(figsize=(16,9)) ax=plt.gca() plt.plot(x, y) ax.tick_params(labelcolor='k', labelsize='20', width=3) plt.legend(labels=['learning curve'],loc=0,prop={'size': 20}) ax.set_xlabel('Iterations', size='20') ax.set_ylabel('Loss', size='20') plt.show()
learning.txt 文件内容:
Iteration 1, loss = 1.07707919
Validation score: -0.759675
Iteration 2, loss = 0.86785333
Validation score: -0.382800
Iteration 3, loss = 0.65217935
Validation score: -0.036673
Iteration 4, loss = 0.47041240
Validation score: 0.245380
Iteration 5, loss = 0.33352409
Validation score: 0.448944
Iteration 6, loss = 0.24185446
Validation score: 0.586900
Iteration 7, loss = 0.18681933
Validation score: 0.670878
Iteration 8, loss = 0.15894719
Validation score: 0.712551
Iteration 9, loss = 0.14840941
Validation score: 0.730509
Iteration 10, loss = 0.14607523
Validation score: 0.740826
Iteration 11, loss = 0.14493495
Validation score: 0.751460
Iteration 12, loss = 0.14051347
Validation score: 0.765123
Iteration 13, loss = 0.13127751
Validation score: 0.783078
Iteration 14, loss = 0.11785379
Validation score: 0.803541
Iteration 15, loss = 0.10211765
Validation score: 0.823697
Iteration 16, loss = 0.08606523
Validation score: 0.842771
Iteration 17, loss = 0.07125665
Validation score: 0.859080
Iteration 18, loss = 0.05864895
Validation score: 0.871903
Iteration 19, loss = 0.04862303
Validation score: 0.881497
Iteration 20, loss = 0.04109700
Validation score: 0.888700
Iteration 21, loss = 0.03573981
Validation score: 0.894126
Iteration 22, loss = 0.03191231
Validation score: 0.898618
Iteration 23, loss = 0.02912329
Validation score: 0.902683
Iteration 24, loss = 0.02703483
Validation score: 0.906685
Iteration 25, loss = 0.02530589
Validation score: 0.910762
Iteration 26, loss = 0.02365941
Validation score: 0.914878
Iteration 27, loss = 0.02202639
Validation score: 0.919035
Iteration 28, loss = 0.02040115
Validation score: 0.923112
Iteration 29, loss = 0.01878876
Validation score: 0.927080
Iteration 30, loss = 0.01724594
Validation score: 0.930809
Iteration 31, loss = 0.01582957
Validation score: 0.934184
Iteration 32, loss = 0.01462510
Validation score: 0.936995
Iteration 33, loss = 0.01359889
Validation score: 0.939518
Iteration 34, loss = 0.01274997
Validation score: 0.941784
Iteration 35, loss = 0.01205514
Validation score: 0.943816
Iteration 36, loss = 0.01148151
Validation score: 0.945663
Iteration 37, loss = 0.01098952
Validation score: 0.947369
Iteration 38, loss = 0.01054425
Validation score: 0.948967
Iteration 39, loss = 0.01012376
Validation score: 0.950490
Iteration 40, loss = 0.00970200
Validation score: 0.951954
Iteration 41, loss = 0.00926981
Validation score: 0.953357
Iteration 42, loss = 0.00882367
Validation score: 0.954697
Iteration 43, loss = 0.00837260
Validation score: 0.955966
Iteration 44, loss = 0.00792918
Validation score: 0.957140
Iteration 45, loss = 0.00750475
Validation score: 0.958220
Iteration 46, loss = 0.00710750
Validation score: 0.959206
Iteration 47, loss = 0.00674201
Validation score: 0.960103
Iteration 48, loss = 0.00641247
Validation score: 0.960921
Iteration 49, loss = 0.00611715
Validation score: 0.961682
Iteration 50, loss = 0.00585395
Validation score: 0.962376
Iteration 51, loss = 0.00561830
Validation score: 0.963028
Iteration 52, loss = 0.00541014
Validation score: 0.963642
Iteration 53, loss = 0.00522108
Validation score: 0.964233
Iteration 54, loss = 0.00504609
Validation score: 0.964804
Iteration 55, loss = 0.00488272
Validation score: 0.965360
Iteration 56, loss = 0.00472770
Validation score: 0.965903
Iteration 57, loss = 0.00458014
Validation score: 0.966434
Iteration 58, loss = 0.00443943
Validation score: 0.966953
Iteration 59, loss = 0.00430580
Validation score: 0.967458
Iteration 60, loss = 0.00417995
Validation score: 0.967952
Iteration 61, loss = 0.00406131
Validation score: 0.968431
Iteration 62, loss = 0.00394928
Validation score: 0.968897
Iteration 63, loss = 0.00384553
Validation score: 0.969347
Iteration 64, loss = 0.00374897
Validation score: 0.969783
Iteration 65, loss = 0.00365821
Validation score: 0.970205
Iteration 66, loss = 0.00357151
Validation score: 0.970614
Iteration 67, loss = 0.00348867
Validation score: 0.971013
Iteration 68, loss = 0.00340915
Validation score: 0.971392
Iteration 69, loss = 0.00333351
Validation score: 0.971756
Iteration 70, loss = 0.00326102
Validation score: 0.972108
Iteration 71, loss = 0.00319100
Validation score: 0.972449
Iteration 72, loss = 0.00312323
Validation score: 0.972780
Iteration 73, loss = 0.00305912
Validation score: 0.973102
Iteration 74, loss = 0.00299757
Validation score: 0.973415
Iteration 75, loss = 0.00293900
Validation score: 0.973718
Iteration 76, loss = 0.00288266
Validation score: 0.974011
Iteration 77, loss = 0.00282841
Validation score: 0.974294
Iteration 78, loss = 0.00277636
Validation score: 0.974566
Iteration 79, loss = 0.00272618
Validation score: 0.974828
Iteration 80, loss = 0.00267802
Validation score: 0.975081
Iteration 81, loss = 0.00263166
Validation score: 0.975324
Iteration 82, loss = 0.00258695
Validation score: 0.975559
Iteration 83, loss = 0.00254460
Validation score: 0.975786
Iteration 84, loss = 0.00250360
Validation score: 0.976006
Iteration 85, loss = 0.00246383
Validation score: 0.976219
Iteration 86, loss = 0.00242525
Validation score: 0.976425
Iteration 87, loss = 0.00238777
Validation score: 0.976625
Iteration 88, loss = 0.00235142
Validation score: 0.976817
Iteration 89, loss = 0.00231608
Validation score: 0.977002
Iteration 90, loss = 0.00228174
Validation score: 0.977181
Iteration 91, loss = 0.00224860
Validation score: 0.977353
Iteration 92, loss = 0.00221649
Validation score: 0.977518
Iteration 93, loss = 0.00218531
Validation score: 0.977678
Iteration 94, loss = 0.00215517
Validation score: 0.977830
Iteration 95, loss = 0.00212549
Validation score: 0.977976
Iteration 96, loss = 0.00209643
Validation score: 0.978116
Iteration 97, loss = 0.00206800
Validation score: 0.978250
Iteration 98, loss = 0.00204020
Validation score: 0.978380
Iteration 99, loss = 0.00201333
Validation score: 0.978504
Iteration 100, loss = 0.00198714
Validation score: 0.978626
Iteration 101, loss = 0.00196159
Validation score: 0.978745
Iteration 102, loss = 0.00193675
Validation score: 0.978858
Iteration 103, loss = 0.00191242
Validation score: 0.978968
Iteration 104, loss = 0.00188890
Validation score: 0.979076
Iteration 105, loss = 0.00186591
Validation score: 0.979181
Iteration 106, loss = 0.00184362
Validation score: 0.979284
Iteration 107, loss = 0.00182207
Validation score: 0.979382
Iteration 108, loss = 0.00180092
Validation score: 0.979478
Iteration 109, loss = 0.00178045
Validation score: 0.979572
Iteration 110, loss = 0.00176040
Validation score: 0.979653
Iteration 111, loss = 0.00174355
Validation score: 0.979727
Iteration 112, loss = 0.00172821
Validation score: 0.979794
Iteration 113, loss = 0.00171420
Validation score: 0.979854
Iteration 114, loss = 0.00170190
Validation score: 0.979908
Iteration 115, loss = 0.00169088
Validation score: 0.979957
Iteration 116, loss = 0.00168098
Validation score: 0.980001
Iteration 117, loss = 0.00167224
Validation score: 0.980040
Iteration 118, loss = 0.00166443
Validation score: 0.980075
Iteration 119, loss = 0.00165744
Validation score: 0.980107
Iteration 120, loss = 0.00165120
Validation score: 0.980136
Iteration 121, loss = 0.00164562
Validation score: 0.980161
Iteration 122, loss = 0.00164062
Validation score: 0.980184
Iteration 123, loss = 0.00163614
Validation score: 0.980205
Iteration 124, loss = 0.00163212
Validation score: 0.980224
Iteration 125, loss = 0.00162852
Validation score: 0.980240
Iteration 126, loss = 0.00162528
Validation score: 0.980255
Iteration 127, loss = 0.00162238
Validation score: 0.980269
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