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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