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3_Data_Structure_1.ipynb 43 kB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# 数据结构 - 1"
  8. ]
  9. },
  10. {
  11. "cell_type": "markdown",
  12. "metadata": {},
  13. "source": [
  14. "数据结构是计算机存储、组织数据的方式,简单来说是指相互之间存在一种或多种特定关系的数据元素的集合。\n",
  15. "\n",
  16. "Python中的数据结构设计的非常巧妙,使用起来非常方便,几乎绝大多数的数据结构都可以通过`list`, `tuple`, `dict`, `string`, `set`等表示,因此用户几乎不需要自己定义数据结构,仅仅使用Python内置的数据结构即可实现非常复杂的算法和操作。"
  17. ]
  18. },
  19. {
  20. "cell_type": "markdown",
  21. "metadata": {},
  22. "source": [
  23. "## 1. 列表"
  24. ]
  25. },
  26. {
  27. "cell_type": "markdown",
  28. "metadata": {},
  29. "source": [
  30. "列表是最常用的数据结构,可以把它看作用方括号括起来的数据序列,数据之间用逗号分隔。这些数据都可以通过调用其索引值来访问。\n",
  31. "\n",
  32. "`list`的声明只需将变量等同于`[ ]`或`list`即可。"
  33. ]
  34. },
  35. {
  36. "cell_type": "code",
  37. "execution_count": 2,
  38. "metadata": {
  39. "collapsed": true
  40. },
  41. "outputs": [],
  42. "source": [
  43. "a = []"
  44. ]
  45. },
  46. {
  47. "cell_type": "code",
  48. "execution_count": 3,
  49. "metadata": {},
  50. "outputs": [
  51. {
  52. "name": "stdout",
  53. "output_type": "stream",
  54. "text": [
  55. "<class 'list'>\n"
  56. ]
  57. }
  58. ],
  59. "source": [
  60. "print(type(a))"
  61. ]
  62. },
  63. {
  64. "cell_type": "markdown",
  65. "metadata": {},
  66. "source": [
  67. "可以直接将数据序列分配给列表x,如下所示。"
  68. ]
  69. },
  70. {
  71. "cell_type": "code",
  72. "execution_count": 4,
  73. "metadata": {},
  74. "outputs": [
  75. {
  76. "name": "stdout",
  77. "output_type": "stream",
  78. "text": [
  79. "['apple', 'orange', 'peach']\n"
  80. ]
  81. }
  82. ],
  83. "source": [
  84. "x = ['apple', 'orange', 'peach']\n",
  85. "print(x)"
  86. ]
  87. },
  88. {
  89. "cell_type": "markdown",
  90. "metadata": {},
  91. "source": [
  92. "### 1.1 索引"
  93. ]
  94. },
  95. {
  96. "cell_type": "markdown",
  97. "metadata": {},
  98. "source": [
  99. "**MOTE: 在Python中,索引从`0`开始。**\n",
  100. "\n",
  101. "因此,现在包含两个元素的列表`x`的apple索引值为`0`,orange索引值为`1`。"
  102. ]
  103. },
  104. {
  105. "cell_type": "code",
  106. "execution_count": 5,
  107. "metadata": {},
  108. "outputs": [
  109. {
  110. "data": {
  111. "text/plain": [
  112. "'apple'"
  113. ]
  114. },
  115. "execution_count": 5,
  116. "metadata": {},
  117. "output_type": "execute_result"
  118. }
  119. ],
  120. "source": [
  121. "x[0]"
  122. ]
  123. },
  124. {
  125. "cell_type": "markdown",
  126. "metadata": {},
  127. "source": [
  128. "索引也可以按照相反的顺序进行,如果想先访问最后一个元素,索引从`-1`开始。因此,索引`-1`对应是`peach`,索引`-2`对应的是`orange`。"
  129. ]
  130. },
  131. {
  132. "cell_type": "code",
  133. "execution_count": 11,
  134. "metadata": {},
  135. "outputs": [
  136. {
  137. "data": {
  138. "text/plain": [
  139. "'peach'"
  140. ]
  141. },
  142. "execution_count": 11,
  143. "metadata": {},
  144. "output_type": "execute_result"
  145. }
  146. ],
  147. "source": [
  148. "x[-1]"
  149. ]
  150. },
  151. {
  152. "cell_type": "markdown",
  153. "metadata": {},
  154. "source": [
  155. "正如你可能猜到的一样,x[0] = x[-2], x[1] = x[-1]。这个概念可以扩展到更多包含元素的列表。"
  156. ]
  157. },
  158. {
  159. "cell_type": "code",
  160. "execution_count": 13,
  161. "metadata": {
  162. "collapsed": true
  163. },
  164. "outputs": [],
  165. "source": [
  166. "y = ['carrot','potato']"
  167. ]
  168. },
  169. {
  170. "cell_type": "markdown",
  171. "metadata": {},
  172. "source": [
  173. "在这里我们已经声明过两个列表`x`和`y`,每一个包含自己的数据。现在,这两个列表可以再一次被放入另一个列表`z`中,这个列表被称为`嵌套列表`。\n",
  174. "\n",
  175. "NOTE:**这是和很多其他计算机语言不同的地方,不要求列表的元素是相同类型,因此编程的时候会非常方便,这也是为什么Python对人类比较友好的原因。**"
  176. ]
  177. },
  178. {
  179. "cell_type": "code",
  180. "execution_count": 14,
  181. "metadata": {},
  182. "outputs": [
  183. {
  184. "name": "stdout",
  185. "output_type": "stream",
  186. "text": [
  187. "[['apple', 'orange', 'peach'], ['carrot', 'potato'], 'Test']\n"
  188. ]
  189. }
  190. ],
  191. "source": [
  192. "z = [x,y, 'Test']\n",
  193. "print(z)"
  194. ]
  195. },
  196. {
  197. "cell_type": "code",
  198. "execution_count": 16,
  199. "metadata": {},
  200. "outputs": [
  201. {
  202. "data": {
  203. "text/plain": [
  204. "'orange'"
  205. ]
  206. },
  207. "execution_count": 16,
  208. "metadata": {},
  209. "output_type": "execute_result"
  210. }
  211. ],
  212. "source": [
  213. "z[0][1]"
  214. ]
  215. },
  216. {
  217. "cell_type": "markdown",
  218. "metadata": {},
  219. "source": [
  220. "如何获得嵌套列表中的某个元素?以访问上述嵌套列表中数据'apple'为例:\n",
  221. "* 首先在索引为0处,有一个列表`['apple','orange']` 而在索引为1处有另外一个列表`['carrot','potato']` 。\n",
  222. "* 因此z[0] 应该给我们第一个包含'apple'的列表。"
  223. ]
  224. },
  225. {
  226. "cell_type": "code",
  227. "execution_count": 17,
  228. "metadata": {},
  229. "outputs": [
  230. {
  231. "name": "stdout",
  232. "output_type": "stream",
  233. "text": [
  234. "['apple', 'orange', 'peach']\n"
  235. ]
  236. }
  237. ],
  238. "source": [
  239. "z1 = z[0]\n",
  240. "print(z1)"
  241. ]
  242. },
  243. {
  244. "cell_type": "markdown",
  245. "metadata": {},
  246. "source": [
  247. "现在观察z1并不是一个嵌套列表,因此为了获得'apple',z1的索引应该为0。"
  248. ]
  249. },
  250. {
  251. "cell_type": "code",
  252. "execution_count": 18,
  253. "metadata": {},
  254. "outputs": [
  255. {
  256. "data": {
  257. "text/plain": [
  258. "'apple'"
  259. ]
  260. },
  261. "execution_count": 18,
  262. "metadata": {},
  263. "output_type": "execute_result"
  264. }
  265. ],
  266. "source": [
  267. "z1[0]"
  268. ]
  269. },
  270. {
  271. "cell_type": "markdown",
  272. "metadata": {},
  273. "source": [
  274. "在python中,你可以通过每次并排写索引值来访问“apple”,而不是像上面那样做。"
  275. ]
  276. },
  277. {
  278. "cell_type": "code",
  279. "execution_count": 19,
  280. "metadata": {},
  281. "outputs": [
  282. {
  283. "data": {
  284. "text/plain": [
  285. "'apple'"
  286. ]
  287. },
  288. "execution_count": 19,
  289. "metadata": {},
  290. "output_type": "execute_result"
  291. }
  292. ],
  293. "source": [
  294. "z[0][0]"
  295. ]
  296. },
  297. {
  298. "cell_type": "markdown",
  299. "metadata": {},
  300. "source": [
  301. "如果列表中有一个列表,那么您可以通过执行 z[ ][ ][ ] 来访问最里面的值。"
  302. ]
  303. },
  304. {
  305. "cell_type": "markdown",
  306. "metadata": {},
  307. "source": [
  308. "### 1.2 切片"
  309. ]
  310. },
  311. {
  312. "cell_type": "markdown",
  313. "metadata": {},
  314. "source": [
  315. "索引只限于访问单个元素,而切片则是访问列表内的一系列数据。换句话说,`切片`返回的是一个列表。\n",
  316. "\n",
  317. "切片是通过定义切片列表中需要的父列表中的第一个元素和最后一个元素的索引值来完成的。它被写成`parentlist[a: b]`,其中`a`,`b`是父列表的索引值。如果`a`或`b`未定义,则认为该索引值是`a`未定义时的第一个值,以及`b`未定义时的最后一个值。"
  318. ]
  319. },
  320. {
  321. "cell_type": "code",
  322. "execution_count": 23,
  323. "metadata": {},
  324. "outputs": [
  325. {
  326. "name": "stdout",
  327. "output_type": "stream",
  328. "text": [
  329. "[2, 3, 4]\n",
  330. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  331. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  332. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  333. ]
  334. }
  335. ],
  336. "source": [
  337. "num = [1,2,3,4,5,6,7,8,9]\n",
  338. "print(num[1:4])\n",
  339. "print(num[0:])\n",
  340. "print(num[:])\n",
  341. "print(num)"
  342. ]
  343. },
  344. {
  345. "cell_type": "code",
  346. "execution_count": 24,
  347. "metadata": {},
  348. "outputs": [
  349. {
  350. "name": "stdout",
  351. "output_type": "stream",
  352. "text": [
  353. "[1, 2, 3, 4]\n",
  354. "[5, 6, 7, 8, 9]\n"
  355. ]
  356. }
  357. ],
  358. "source": [
  359. "print(num[0:4])\n",
  360. "print(num[4:])"
  361. ]
  362. },
  363. {
  364. "cell_type": "markdown",
  365. "metadata": {},
  366. "source": [
  367. "您还可以使用固定长度或步长对父列表进行切片。"
  368. ]
  369. },
  370. {
  371. "cell_type": "code",
  372. "execution_count": 26,
  373. "metadata": {},
  374. "outputs": [
  375. {
  376. "data": {
  377. "text/plain": [
  378. "[1, 4, 7]"
  379. ]
  380. },
  381. "execution_count": 26,
  382. "metadata": {},
  383. "output_type": "execute_result"
  384. }
  385. ],
  386. "source": [
  387. "num[0:9:3]"
  388. ]
  389. },
  390. {
  391. "cell_type": "markdown",
  392. "metadata": {},
  393. "source": [
  394. "### 1.3 列表的内置函数"
  395. ]
  396. },
  397. {
  398. "cell_type": "markdown",
  399. "metadata": {},
  400. "source": [
  401. "为了找到列表的长度或者列表中元素的数量,我们可以使用**len( )**。"
  402. ]
  403. },
  404. {
  405. "cell_type": "code",
  406. "execution_count": 27,
  407. "metadata": {},
  408. "outputs": [
  409. {
  410. "data": {
  411. "text/plain": [
  412. "9"
  413. ]
  414. },
  415. "execution_count": 27,
  416. "metadata": {},
  417. "output_type": "execute_result"
  418. }
  419. ],
  420. "source": [
  421. "len(num)"
  422. ]
  423. },
  424. {
  425. "cell_type": "markdown",
  426. "metadata": {},
  427. "source": [
  428. "如果列表包含所有的整数元素,那么 **min( )** 和 **max( )** 给出列表中的最大值和最小值。"
  429. ]
  430. },
  431. {
  432. "cell_type": "code",
  433. "execution_count": 28,
  434. "metadata": {},
  435. "outputs": [
  436. {
  437. "name": "stdout",
  438. "output_type": "stream",
  439. "text": [
  440. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  441. ]
  442. },
  443. {
  444. "data": {
  445. "text/plain": [
  446. "1"
  447. ]
  448. },
  449. "execution_count": 28,
  450. "metadata": {},
  451. "output_type": "execute_result"
  452. }
  453. ],
  454. "source": [
  455. "print(num)\n",
  456. "min(num)"
  457. ]
  458. },
  459. {
  460. "cell_type": "code",
  461. "execution_count": 29,
  462. "metadata": {},
  463. "outputs": [
  464. {
  465. "data": {
  466. "text/plain": [
  467. "9"
  468. ]
  469. },
  470. "execution_count": 29,
  471. "metadata": {},
  472. "output_type": "execute_result"
  473. }
  474. ],
  475. "source": [
  476. "max(num)"
  477. ]
  478. },
  479. {
  480. "cell_type": "markdown",
  481. "metadata": {},
  482. "source": [
  483. "列表可以通过添加\"`+`\"来连接。生成的列表将包含添加的列表的所有元素。结果列表将不是嵌套列表。"
  484. ]
  485. },
  486. {
  487. "cell_type": "code",
  488. "execution_count": 30,
  489. "metadata": {},
  490. "outputs": [
  491. {
  492. "data": {
  493. "text/plain": [
  494. "[1, 2, 3, 5, 4, 7]"
  495. ]
  496. },
  497. "execution_count": 30,
  498. "metadata": {},
  499. "output_type": "execute_result"
  500. }
  501. ],
  502. "source": [
  503. "[1,2,3] + [5,4,7]"
  504. ]
  505. },
  506. {
  507. "cell_type": "markdown",
  508. "metadata": {},
  509. "source": [
  510. "可能会出现这样的需求,需要检查预定义列表中是否存在特定的元素。考虑下面的列表。"
  511. ]
  512. },
  513. {
  514. "cell_type": "code",
  515. "execution_count": 31,
  516. "metadata": {
  517. "collapsed": true
  518. },
  519. "outputs": [],
  520. "source": [
  521. "names = ['Earth','Air','Fire','Water']"
  522. ]
  523. },
  524. {
  525. "cell_type": "markdown",
  526. "metadata": {},
  527. "source": [
  528. "检查“Fire”和“Rajath”是否出现在列表名称中。传统的方法是使用for循环遍历列表并使用if条件。但在python中,你可以使用\" a在b中\"的概念,如果a在b中出现,它会返回\"True\"如果不是,它会返回\"False\""
  529. ]
  530. },
  531. {
  532. "cell_type": "code",
  533. "execution_count": 32,
  534. "metadata": {},
  535. "outputs": [
  536. {
  537. "data": {
  538. "text/plain": [
  539. "False"
  540. ]
  541. },
  542. "execution_count": 32,
  543. "metadata": {},
  544. "output_type": "execute_result"
  545. }
  546. ],
  547. "source": [
  548. "'Fir' in names"
  549. ]
  550. },
  551. {
  552. "cell_type": "code",
  553. "execution_count": 33,
  554. "metadata": {},
  555. "outputs": [
  556. {
  557. "data": {
  558. "text/plain": [
  559. "True"
  560. ]
  561. },
  562. "execution_count": 33,
  563. "metadata": {},
  564. "output_type": "execute_result"
  565. }
  566. ],
  567. "source": [
  568. "'Fire' in names"
  569. ]
  570. },
  571. {
  572. "cell_type": "code",
  573. "execution_count": 34,
  574. "metadata": {},
  575. "outputs": [
  576. {
  577. "data": {
  578. "text/plain": [
  579. "False"
  580. ]
  581. },
  582. "execution_count": 34,
  583. "metadata": {},
  584. "output_type": "execute_result"
  585. }
  586. ],
  587. "source": [
  588. "'fire' in names"
  589. ]
  590. },
  591. {
  592. "cell_type": "markdown",
  593. "metadata": {},
  594. "source": [
  595. "在一个有字符串作为元素的列表中,**max( )** 和 **min( )** 可以使用。**max( )** 会返回一个ASCII码最大的元素而最小的元素会在使用**min( )** 返回。注意,每次只考虑每个元素的第一个索引,如果它们的值相同,则考虑第二个索引,依此类推。"
  596. ]
  597. },
  598. {
  599. "cell_type": "code",
  600. "execution_count": 35,
  601. "metadata": {
  602. "collapsed": true
  603. },
  604. "outputs": [],
  605. "source": [
  606. "mlist = ['bzaa','ds','nc','az','z','klm']"
  607. ]
  608. },
  609. {
  610. "cell_type": "code",
  611. "execution_count": 36,
  612. "metadata": {},
  613. "outputs": [
  614. {
  615. "name": "stdout",
  616. "output_type": "stream",
  617. "text": [
  618. "z\n",
  619. "az\n"
  620. ]
  621. }
  622. ],
  623. "source": [
  624. "print(max(mlist))\n",
  625. "print(min(mlist))"
  626. ]
  627. },
  628. {
  629. "cell_type": "markdown",
  630. "metadata": {},
  631. "source": [
  632. "这里考虑每个元素的第一个索引,因此z有最高的ASCII值,因此它被返回,最小的ASCII值是a。但是如果数字被声明为字符串呢?"
  633. ]
  634. },
  635. {
  636. "cell_type": "code",
  637. "execution_count": 37,
  638. "metadata": {
  639. "collapsed": true
  640. },
  641. "outputs": [],
  642. "source": [
  643. "nlist = ['1','94','93','1000']"
  644. ]
  645. },
  646. {
  647. "cell_type": "code",
  648. "execution_count": 27,
  649. "metadata": {},
  650. "outputs": [
  651. {
  652. "name": "stdout",
  653. "output_type": "stream",
  654. "text": [
  655. "94\n",
  656. "1\n"
  657. ]
  658. }
  659. ],
  660. "source": [
  661. "print(max(nlist))\n",
  662. "print(min(nlist))"
  663. ]
  664. },
  665. {
  666. "cell_type": "markdown",
  667. "metadata": {},
  668. "source": [
  669. "即使数字是在字符串中声明的,也会考虑每个元素的第一个索引,并相应地返回最大值和最小值。"
  670. ]
  671. },
  672. {
  673. "cell_type": "markdown",
  674. "metadata": {},
  675. "source": [
  676. "但是如果你想找到给予字符串长度的 **max( )** 字符串元素,那么我们要在 **max( )** 和 **min( )** 中声明参数'key=len'。"
  677. ]
  678. },
  679. {
  680. "cell_type": "code",
  681. "execution_count": 38,
  682. "metadata": {},
  683. "outputs": [
  684. {
  685. "name": "stdout",
  686. "output_type": "stream",
  687. "text": [
  688. "Earth\n",
  689. "Jet\n"
  690. ]
  691. }
  692. ],
  693. "source": [
  694. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  695. "print(max(names, key=len))\n",
  696. "print(min(names, key=len))"
  697. ]
  698. },
  699. {
  700. "cell_type": "markdown",
  701. "metadata": {},
  702. "source": [
  703. "但是即使'Water'的长度为5。**max()** 或 **min()** 函数返回第一个元素当两个或者多个元素具有相同的长度。\n",
  704. "\n",
  705. "可以使用任何其他内建函数或lambda函数(后面将讨论)来代替len。\n",
  706. "\n",
  707. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  708. ]
  709. },
  710. {
  711. "cell_type": "code",
  712. "execution_count": 39,
  713. "metadata": {},
  714. "outputs": [
  715. {
  716. "data": {
  717. "text/plain": [
  718. "['h', 'e', 'l', 'l', 'o']"
  719. ]
  720. },
  721. "execution_count": 39,
  722. "metadata": {},
  723. "output_type": "execute_result"
  724. }
  725. ],
  726. "source": [
  727. "list('hello')"
  728. ]
  729. },
  730. {
  731. "cell_type": "markdown",
  732. "metadata": {},
  733. "source": [
  734. "**append( )** 被用来在列表的最后添加一个元素。"
  735. ]
  736. },
  737. {
  738. "cell_type": "code",
  739. "execution_count": 40,
  740. "metadata": {
  741. "collapsed": true
  742. },
  743. "outputs": [],
  744. "source": [
  745. "lst = [1,1,4,8,7]"
  746. ]
  747. },
  748. {
  749. "cell_type": "code",
  750. "execution_count": 43,
  751. "metadata": {},
  752. "outputs": [
  753. {
  754. "name": "stdout",
  755. "output_type": "stream",
  756. "text": [
  757. "[1, 1, 4, 8, 7, 1, 1, 1]\n"
  758. ]
  759. }
  760. ],
  761. "source": [
  762. "lst.append(1)\n",
  763. "print(lst)"
  764. ]
  765. },
  766. {
  767. "cell_type": "markdown",
  768. "metadata": {},
  769. "source": [
  770. "**count( )** 用于计算列表中出现的特定元素的数量。"
  771. ]
  772. },
  773. {
  774. "cell_type": "code",
  775. "execution_count": 50,
  776. "metadata": {},
  777. "outputs": [
  778. {
  779. "data": {
  780. "text/plain": [
  781. "0"
  782. ]
  783. },
  784. "execution_count": 50,
  785. "metadata": {},
  786. "output_type": "execute_result"
  787. }
  788. ],
  789. "source": [
  790. "lst.count(999)"
  791. ]
  792. },
  793. {
  794. "cell_type": "markdown",
  795. "metadata": {},
  796. "source": [
  797. "**append( )** 函数也可以被用来在末尾添加一整个列表。观察可以发现最终得到的列表是嵌套列表。"
  798. ]
  799. },
  800. {
  801. "cell_type": "code",
  802. "execution_count": 45,
  803. "metadata": {
  804. "collapsed": true
  805. },
  806. "outputs": [],
  807. "source": [
  808. "lst1 = [5,4,2,8]"
  809. ]
  810. },
  811. {
  812. "cell_type": "code",
  813. "execution_count": 46,
  814. "metadata": {},
  815. "outputs": [
  816. {
  817. "name": "stdout",
  818. "output_type": "stream",
  819. "text": [
  820. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8]]\n"
  821. ]
  822. }
  823. ],
  824. "source": [
  825. "lst.append(lst1)\n",
  826. "print(lst)"
  827. ]
  828. },
  829. {
  830. "cell_type": "markdown",
  831. "metadata": {},
  832. "source": [
  833. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  834. ]
  835. },
  836. {
  837. "cell_type": "code",
  838. "execution_count": 47,
  839. "metadata": {},
  840. "outputs": [
  841. {
  842. "name": "stdout",
  843. "output_type": "stream",
  844. "text": [
  845. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  846. ]
  847. }
  848. ],
  849. "source": [
  850. "lst.extend(lst1)\n",
  851. "print(lst)"
  852. ]
  853. },
  854. {
  855. "cell_type": "markdown",
  856. "metadata": {},
  857. "source": [
  858. "**index( )** 被用来找到一个特殊元素的索引值。注意如果有许多个元素具有相同的值那么元素第一个索引值会被返回。"
  859. ]
  860. },
  861. {
  862. "cell_type": "code",
  863. "execution_count": 48,
  864. "metadata": {},
  865. "outputs": [
  866. {
  867. "data": {
  868. "text/plain": [
  869. "0"
  870. ]
  871. },
  872. "execution_count": 48,
  873. "metadata": {},
  874. "output_type": "execute_result"
  875. }
  876. ],
  877. "source": [
  878. "lst.index(1)"
  879. ]
  880. },
  881. {
  882. "cell_type": "code",
  883. "execution_count": 51,
  884. "metadata": {},
  885. "outputs": [
  886. {
  887. "ename": "ValueError",
  888. "evalue": "999 is not in list",
  889. "output_type": "error",
  890. "traceback": [
  891. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  892. "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
  893. "\u001b[0;32m<ipython-input-51-67053dd5b65b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  894. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  895. ]
  896. }
  897. ],
  898. "source": [
  899. "lst.index(999)"
  900. ]
  901. },
  902. {
  903. "cell_type": "markdown",
  904. "metadata": {},
  905. "source": [
  906. "**insert(x,y)** 用于在指定的索引值x处插入元素y。**append( )** 函数使得它只能插在最后。"
  907. ]
  908. },
  909. {
  910. "cell_type": "code",
  911. "execution_count": 52,
  912. "metadata": {},
  913. "outputs": [
  914. {
  915. "name": "stdout",
  916. "output_type": "stream",
  917. "text": [
  918. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  919. ]
  920. }
  921. ],
  922. "source": [
  923. "lst.insert(5, 'name')\n",
  924. "print(lst)"
  925. ]
  926. },
  927. {
  928. "cell_type": "code",
  929. "execution_count": 53,
  930. "metadata": {
  931. "collapsed": true
  932. },
  933. "outputs": [],
  934. "source": [
  935. "lst.insert(-1, 10)"
  936. ]
  937. },
  938. {
  939. "cell_type": "code",
  940. "execution_count": 54,
  941. "metadata": {},
  942. "outputs": [
  943. {
  944. "name": "stdout",
  945. "output_type": "stream",
  946. "text": [
  947. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8]\n"
  948. ]
  949. }
  950. ],
  951. "source": [
  952. "print(lst)"
  953. ]
  954. },
  955. {
  956. "cell_type": "code",
  957. "execution_count": 55,
  958. "metadata": {},
  959. "outputs": [
  960. {
  961. "data": {
  962. "text/plain": [
  963. "15"
  964. ]
  965. },
  966. "execution_count": 55,
  967. "metadata": {},
  968. "output_type": "execute_result"
  969. }
  970. ],
  971. "source": [
  972. "len(lst)"
  973. ]
  974. },
  975. {
  976. "cell_type": "code",
  977. "execution_count": 56,
  978. "metadata": {
  979. "collapsed": true
  980. },
  981. "outputs": [],
  982. "source": [
  983. "lst.insert(15, 20)"
  984. ]
  985. },
  986. {
  987. "cell_type": "code",
  988. "execution_count": 57,
  989. "metadata": {},
  990. "outputs": [
  991. {
  992. "name": "stdout",
  993. "output_type": "stream",
  994. "text": [
  995. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8, 20]\n"
  996. ]
  997. }
  998. ],
  999. "source": [
  1000. "print(lst)"
  1001. ]
  1002. },
  1003. {
  1004. "cell_type": "markdown",
  1005. "metadata": {},
  1006. "source": [
  1007. "**insert(x,y)** 插入但不替换元素。如果希望用另一个元素替换该元素,只需将值赋给该特定索引。"
  1008. ]
  1009. },
  1010. {
  1011. "cell_type": "code",
  1012. "execution_count": 58,
  1013. "metadata": {},
  1014. "outputs": [
  1015. {
  1016. "name": "stdout",
  1017. "output_type": "stream",
  1018. "text": [
  1019. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8, 20]\n"
  1020. ]
  1021. }
  1022. ],
  1023. "source": [
  1024. "lst[5] = 'Python'\n",
  1025. "print(lst)"
  1026. ]
  1027. },
  1028. {
  1029. "cell_type": "markdown",
  1030. "metadata": {},
  1031. "source": [
  1032. "**pop( )** 函数返回列表中的最后一个元素。这类似于堆栈的操作。因此,说列表可以作为堆栈使用是正确的。"
  1033. ]
  1034. },
  1035. {
  1036. "cell_type": "code",
  1037. "execution_count": 61,
  1038. "metadata": {},
  1039. "outputs": [
  1040. {
  1041. "data": {
  1042. "text/plain": [
  1043. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2]"
  1044. ]
  1045. },
  1046. "execution_count": 61,
  1047. "metadata": {},
  1048. "output_type": "execute_result"
  1049. }
  1050. ],
  1051. "source": [
  1052. "lst.pop()\n",
  1053. "lst"
  1054. ]
  1055. },
  1056. {
  1057. "cell_type": "markdown",
  1058. "metadata": {},
  1059. "source": [
  1060. "可以指定索引值来弹出与该索引值对应的元素。"
  1061. ]
  1062. },
  1063. {
  1064. "cell_type": "code",
  1065. "execution_count": 49,
  1066. "metadata": {},
  1067. "outputs": [
  1068. {
  1069. "name": "stdout",
  1070. "output_type": "stream",
  1071. "text": [
  1072. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], 5]\n"
  1073. ]
  1074. }
  1075. ],
  1076. "source": [
  1077. "lst.pop(2)\n",
  1078. "print(lst)"
  1079. ]
  1080. },
  1081. {
  1082. "cell_type": "code",
  1083. "execution_count": 62,
  1084. "metadata": {},
  1085. "outputs": [
  1086. {
  1087. "name": "stdout",
  1088. "output_type": "stream",
  1089. "text": [
  1090. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2]\n"
  1091. ]
  1092. },
  1093. {
  1094. "data": {
  1095. "text/plain": [
  1096. "4"
  1097. ]
  1098. },
  1099. "execution_count": 62,
  1100. "metadata": {},
  1101. "output_type": "execute_result"
  1102. }
  1103. ],
  1104. "source": [
  1105. "print(lst)\n",
  1106. "lst.pop(-2)"
  1107. ]
  1108. },
  1109. {
  1110. "cell_type": "code",
  1111. "execution_count": 63,
  1112. "metadata": {},
  1113. "outputs": [
  1114. {
  1115. "name": "stdout",
  1116. "output_type": "stream",
  1117. "text": [
  1118. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1119. ]
  1120. }
  1121. ],
  1122. "source": [
  1123. "print(lst)"
  1124. ]
  1125. },
  1126. {
  1127. "cell_type": "markdown",
  1128. "metadata": {},
  1129. "source": [
  1130. "**pop( )** 用于根据可分配给变量的元素的索引值来删除元素。还可以通过使用**remove()** 函数指定元素本身来删除元素。"
  1131. ]
  1132. },
  1133. {
  1134. "cell_type": "code",
  1135. "execution_count": 64,
  1136. "metadata": {},
  1137. "outputs": [
  1138. {
  1139. "name": "stdout",
  1140. "output_type": "stream",
  1141. "text": [
  1142. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1143. ]
  1144. }
  1145. ],
  1146. "source": [
  1147. "lst.remove('Python')\n",
  1148. "print(lst)"
  1149. ]
  1150. },
  1151. {
  1152. "cell_type": "markdown",
  1153. "metadata": {},
  1154. "source": [
  1155. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1156. ]
  1157. },
  1158. {
  1159. "cell_type": "code",
  1160. "execution_count": 66,
  1161. "metadata": {
  1162. "scrolled": true
  1163. },
  1164. "outputs": [
  1165. {
  1166. "name": "stdout",
  1167. "output_type": "stream",
  1168. "text": [
  1169. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n",
  1170. "[1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1171. ]
  1172. }
  1173. ],
  1174. "source": [
  1175. "print(lst)\n",
  1176. "del(lst[1])\n",
  1177. "print(lst)"
  1178. ]
  1179. },
  1180. {
  1181. "cell_type": "code",
  1182. "execution_count": 67,
  1183. "metadata": {},
  1184. "outputs": [
  1185. {
  1186. "name": "stdout",
  1187. "output_type": "stream",
  1188. "text": [
  1189. "[1, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1190. ]
  1191. }
  1192. ],
  1193. "source": [
  1194. "del(lst[1:4])\n",
  1195. "print(lst)"
  1196. ]
  1197. },
  1198. {
  1199. "cell_type": "markdown",
  1200. "metadata": {},
  1201. "source": [
  1202. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1203. ]
  1204. },
  1205. {
  1206. "cell_type": "code",
  1207. "execution_count": 68,
  1208. "metadata": {},
  1209. "outputs": [
  1210. {
  1211. "name": "stdout",
  1212. "output_type": "stream",
  1213. "text": [
  1214. "[2, 5, [5, 4, 2, 8], 1, 1, 1, 1]\n"
  1215. ]
  1216. }
  1217. ],
  1218. "source": [
  1219. "lst.reverse()\n",
  1220. "print(lst)"
  1221. ]
  1222. },
  1223. {
  1224. "cell_type": "markdown",
  1225. "metadata": {},
  1226. "source": [
  1227. "注意嵌套列表 [5,4,2,8] 被视为父列表lst的单个元素。因此在嵌套列表里的元素是不可以被翻转的。\n",
  1228. "\n",
  1229. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1230. ]
  1231. },
  1232. {
  1233. "cell_type": "code",
  1234. "execution_count": 70,
  1235. "metadata": {},
  1236. "outputs": [
  1237. {
  1238. "name": "stdout",
  1239. "output_type": "stream",
  1240. "text": [
  1241. "[1, 4, 7, 8, 8, 10]\n"
  1242. ]
  1243. }
  1244. ],
  1245. "source": [
  1246. "lst = [8, 7, 1, 4, 8, 10]\n",
  1247. "lst.sort()\n",
  1248. "print(lst)"
  1249. ]
  1250. },
  1251. {
  1252. "cell_type": "markdown",
  1253. "metadata": {},
  1254. "source": [
  1255. "对于降序,因为默认情况下反向条件为False。因此,将其更改为True将按降序排列元素。"
  1256. ]
  1257. },
  1258. {
  1259. "cell_type": "code",
  1260. "execution_count": 59,
  1261. "metadata": {},
  1262. "outputs": [
  1263. {
  1264. "name": "stdout",
  1265. "output_type": "stream",
  1266. "text": [
  1267. "[10, 8, 8, 4, 1]\n"
  1268. ]
  1269. }
  1270. ],
  1271. "source": [
  1272. "lst.sort(reverse=True)\n",
  1273. "print(lst)"
  1274. ]
  1275. },
  1276. {
  1277. "cell_type": "markdown",
  1278. "metadata": {},
  1279. "source": [
  1280. "相似地对于包含字符串元素的列表, **sort( )** 会根据他们的ASCII值以升序的方式排列而通过确定reverse=True可以让他们以降序的方式排列。"
  1281. ]
  1282. },
  1283. {
  1284. "cell_type": "code",
  1285. "execution_count": 74,
  1286. "metadata": {},
  1287. "outputs": [
  1288. {
  1289. "name": "stdout",
  1290. "output_type": "stream",
  1291. "text": [
  1292. "['apple', 'orange', 'peach']\n",
  1293. "['peach', 'orange', 'apple']\n"
  1294. ]
  1295. }
  1296. ],
  1297. "source": [
  1298. "names = ['apple', 'orange', 'peach']\n",
  1299. "names.sort()\n",
  1300. "print(names)\n",
  1301. "names.sort(reverse=True)\n",
  1302. "print(names)"
  1303. ]
  1304. },
  1305. {
  1306. "cell_type": "markdown",
  1307. "metadata": {},
  1308. "source": [
  1309. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1310. ]
  1311. },
  1312. {
  1313. "cell_type": "code",
  1314. "execution_count": 72,
  1315. "metadata": {},
  1316. "outputs": [
  1317. {
  1318. "name": "stdout",
  1319. "output_type": "stream",
  1320. "text": [
  1321. "['peach', 'apple', 'orange']\n",
  1322. "['orange', 'peach', 'apple']\n"
  1323. ]
  1324. }
  1325. ],
  1326. "source": [
  1327. "names.sort(key=len)\n",
  1328. "print(names)\n",
  1329. "names.sort(key=len,reverse=True)\n",
  1330. "print(names)"
  1331. ]
  1332. },
  1333. {
  1334. "cell_type": "markdown",
  1335. "metadata": {},
  1336. "source": [
  1337. "### 1.4 复制一个列表"
  1338. ]
  1339. },
  1340. {
  1341. "cell_type": "markdown",
  1342. "metadata": {},
  1343. "source": [
  1344. "大多数新的python程序员都会犯这个错误,即**对象的赋值和拷贝的差异**。考虑以下的例子:"
  1345. ]
  1346. },
  1347. {
  1348. "cell_type": "code",
  1349. "execution_count": 75,
  1350. "metadata": {
  1351. "collapsed": true
  1352. },
  1353. "outputs": [],
  1354. "source": [
  1355. "lista= [2,1,4,3]"
  1356. ]
  1357. },
  1358. {
  1359. "cell_type": "code",
  1360. "execution_count": 76,
  1361. "metadata": {},
  1362. "outputs": [
  1363. {
  1364. "name": "stdout",
  1365. "output_type": "stream",
  1366. "text": [
  1367. "[2, 1, 4, 3]\n"
  1368. ]
  1369. }
  1370. ],
  1371. "source": [
  1372. "listb = lista # 对象赋值\n",
  1373. "print(listb)"
  1374. ]
  1375. },
  1376. {
  1377. "cell_type": "markdown",
  1378. "metadata": {},
  1379. "source": [
  1380. "这里,我们声明了一个列表,lista = [2,1,4,3]。通过赋值将该列表复制到listb,并复制该列表。现在我们对lista执行一些随机操作。"
  1381. ]
  1382. },
  1383. {
  1384. "cell_type": "code",
  1385. "execution_count": 77,
  1386. "metadata": {},
  1387. "outputs": [
  1388. {
  1389. "name": "stdout",
  1390. "output_type": "stream",
  1391. "text": [
  1392. "[2, 1, 4]\n",
  1393. "[2, 1, 4, 9]\n"
  1394. ]
  1395. }
  1396. ],
  1397. "source": [
  1398. "lista.pop()\n",
  1399. "print(lista)\n",
  1400. "lista.append(9)\n",
  1401. "print(lista)"
  1402. ]
  1403. },
  1404. {
  1405. "cell_type": "code",
  1406. "execution_count": 78,
  1407. "metadata": {},
  1408. "outputs": [
  1409. {
  1410. "name": "stdout",
  1411. "output_type": "stream",
  1412. "text": [
  1413. "[2, 1, 4, 9]\n"
  1414. ]
  1415. }
  1416. ],
  1417. "source": [
  1418. "print(listb)"
  1419. ]
  1420. },
  1421. {
  1422. "cell_type": "markdown",
  1423. "metadata": {},
  1424. "source": [
  1425. "虽然没有对listb执行任何操作,但它也发生了变化。这是因为您将lista、listb指向相同的内存空间。那么如何解决这个问题呢?\n",
  1426. "\n",
  1427. "在切片中我们已经看到parentlist[a:b]从父列表返回一个起始索引a和结束索引b的列表,如果a和b没有被提及,那么默认情况下它会包含第一个到最后一个元素。我们在这里使用相同的概念。通过这样做,我们将lista的数据作为变量分配给listb。"
  1428. ]
  1429. },
  1430. {
  1431. "cell_type": "code",
  1432. "execution_count": 79,
  1433. "metadata": {
  1434. "collapsed": true
  1435. },
  1436. "outputs": [],
  1437. "source": [
  1438. "lista = [2,1,4,3]"
  1439. ]
  1440. },
  1441. {
  1442. "cell_type": "code",
  1443. "execution_count": 80,
  1444. "metadata": {},
  1445. "outputs": [
  1446. {
  1447. "name": "stdout",
  1448. "output_type": "stream",
  1449. "text": [
  1450. "[2, 1, 4, 3]\n"
  1451. ]
  1452. }
  1453. ],
  1454. "source": [
  1455. "listb = lista[:]\n",
  1456. "print(listb)"
  1457. ]
  1458. },
  1459. {
  1460. "cell_type": "code",
  1461. "execution_count": 81,
  1462. "metadata": {},
  1463. "outputs": [
  1464. {
  1465. "name": "stdout",
  1466. "output_type": "stream",
  1467. "text": [
  1468. "[2, 1, 4]\n",
  1469. "[2, 1, 4, 9]\n"
  1470. ]
  1471. }
  1472. ],
  1473. "source": [
  1474. "lista.pop()\n",
  1475. "print(lista)\n",
  1476. "lista.append(9)\n",
  1477. "print(lista)"
  1478. ]
  1479. },
  1480. {
  1481. "cell_type": "code",
  1482. "execution_count": 82,
  1483. "metadata": {},
  1484. "outputs": [
  1485. {
  1486. "name": "stdout",
  1487. "output_type": "stream",
  1488. "text": [
  1489. "[2, 1, 4, 3]\n"
  1490. ]
  1491. }
  1492. ],
  1493. "source": [
  1494. "print(listb)"
  1495. ]
  1496. },
  1497. {
  1498. "cell_type": "markdown",
  1499. "metadata": {},
  1500. "source": [
  1501. "还有其他什么方法能够拷贝一个对象到一个新的变量名字?"
  1502. ]
  1503. },
  1504. {
  1505. "cell_type": "markdown",
  1506. "metadata": {},
  1507. "source": [
  1508. "## 2. 元组"
  1509. ]
  1510. },
  1511. {
  1512. "cell_type": "markdown",
  1513. "metadata": {},
  1514. "source": [
  1515. "元组与列表相似,但唯一大的区别是列表中的元素可以更改,而**元组中的元素不能更改**。为了更好地理解,请回忆**divmod()** 函数。"
  1516. ]
  1517. },
  1518. {
  1519. "cell_type": "code",
  1520. "execution_count": 84,
  1521. "metadata": {},
  1522. "outputs": [
  1523. {
  1524. "name": "stdout",
  1525. "output_type": "stream",
  1526. "text": [
  1527. "(3, 1)\n",
  1528. "<class 'tuple'>\n"
  1529. ]
  1530. },
  1531. {
  1532. "ename": "TypeError",
  1533. "evalue": "'tuple' object does not support item assignment",
  1534. "output_type": "error",
  1535. "traceback": [
  1536. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  1537. "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
  1538. "\u001b[0;32m<ipython-input-84-aeef14ba6d28>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxyz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxyz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mxyz\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  1539. "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
  1540. ]
  1541. }
  1542. ],
  1543. "source": [
  1544. "xyz = divmod(10,3)\n",
  1545. "print(xyz)\n",
  1546. "print(type(xyz))\n",
  1547. "xyz[0]=10"
  1548. ]
  1549. },
  1550. {
  1551. "cell_type": "markdown",
  1552. "metadata": {},
  1553. "source": [
  1554. "这里的商必须是3余数必须是1。当10除以3时,这些值不能改变。因此,divmod以元组的形式返回这些值。"
  1555. ]
  1556. },
  1557. {
  1558. "cell_type": "markdown",
  1559. "metadata": {},
  1560. "source": [
  1561. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1562. ]
  1563. },
  1564. {
  1565. "cell_type": "code",
  1566. "execution_count": 85,
  1567. "metadata": {
  1568. "collapsed": true
  1569. },
  1570. "outputs": [],
  1571. "source": [
  1572. "tup = ()\n",
  1573. "tup2 = tuple()"
  1574. ]
  1575. },
  1576. {
  1577. "cell_type": "markdown",
  1578. "metadata": {},
  1579. "source": [
  1580. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1581. ]
  1582. },
  1583. {
  1584. "cell_type": "code",
  1585. "execution_count": 76,
  1586. "metadata": {},
  1587. "outputs": [
  1588. {
  1589. "data": {
  1590. "text/plain": [
  1591. "(27,)"
  1592. ]
  1593. },
  1594. "execution_count": 76,
  1595. "metadata": {},
  1596. "output_type": "execute_result"
  1597. }
  1598. ],
  1599. "source": [
  1600. "27,"
  1601. ]
  1602. },
  1603. {
  1604. "cell_type": "markdown",
  1605. "metadata": {},
  1606. "source": [
  1607. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1608. ]
  1609. },
  1610. {
  1611. "cell_type": "code",
  1612. "execution_count": 77,
  1613. "metadata": {},
  1614. "outputs": [
  1615. {
  1616. "data": {
  1617. "text/plain": [
  1618. "(27, 27)"
  1619. ]
  1620. },
  1621. "execution_count": 77,
  1622. "metadata": {},
  1623. "output_type": "execute_result"
  1624. }
  1625. ],
  1626. "source": [
  1627. "2*(27,)"
  1628. ]
  1629. },
  1630. {
  1631. "cell_type": "markdown",
  1632. "metadata": {},
  1633. "source": [
  1634. "在声明元组时可以分配值。它接受一个列表作为输入并将其转换为元组,或者接受一个字符串并将其转换为元组。"
  1635. ]
  1636. },
  1637. {
  1638. "cell_type": "code",
  1639. "execution_count": 78,
  1640. "metadata": {
  1641. "scrolled": true
  1642. },
  1643. "outputs": [
  1644. {
  1645. "name": "stdout",
  1646. "output_type": "stream",
  1647. "text": [
  1648. "(1, 2, 3)\n",
  1649. "('H', 'e', 'l', 'l', 'o')\n"
  1650. ]
  1651. }
  1652. ],
  1653. "source": [
  1654. "tup3 = tuple([1,2,3])\n",
  1655. "print(tup3)\n",
  1656. "tup4 = tuple('Hello')\n",
  1657. "print(tup4)"
  1658. ]
  1659. },
  1660. {
  1661. "cell_type": "markdown",
  1662. "metadata": {},
  1663. "source": [
  1664. "它遵循与列表相同的索引和切片。"
  1665. ]
  1666. },
  1667. {
  1668. "cell_type": "code",
  1669. "execution_count": 41,
  1670. "metadata": {},
  1671. "outputs": [
  1672. {
  1673. "name": "stdout",
  1674. "output_type": "stream",
  1675. "text": [
  1676. "2\n",
  1677. "('H', 'e', 'l')\n"
  1678. ]
  1679. }
  1680. ],
  1681. "source": [
  1682. "print(tup3[1])\n",
  1683. "tup5 = tup4[:3]\n",
  1684. "print(tup5)"
  1685. ]
  1686. },
  1687. {
  1688. "cell_type": "markdown",
  1689. "metadata": {},
  1690. "source": [
  1691. "### 2.1 将一个元组映射到另一个元组"
  1692. ]
  1693. },
  1694. {
  1695. "cell_type": "code",
  1696. "execution_count": 90,
  1697. "metadata": {
  1698. "collapsed": true
  1699. },
  1700. "outputs": [],
  1701. "source": [
  1702. "(a,b,c)= ('alpha','beta','gamma')"
  1703. ]
  1704. },
  1705. {
  1706. "cell_type": "code",
  1707. "execution_count": 91,
  1708. "metadata": {},
  1709. "outputs": [
  1710. {
  1711. "name": "stdout",
  1712. "output_type": "stream",
  1713. "text": [
  1714. "alpha beta gamma\n"
  1715. ]
  1716. }
  1717. ],
  1718. "source": [
  1719. "print(a,b,c)"
  1720. ]
  1721. },
  1722. {
  1723. "cell_type": "code",
  1724. "execution_count": 92,
  1725. "metadata": {
  1726. "collapsed": true
  1727. },
  1728. "outputs": [],
  1729. "source": [
  1730. "(c, b, a) = (a, b, c)"
  1731. ]
  1732. },
  1733. {
  1734. "cell_type": "code",
  1735. "execution_count": 93,
  1736. "metadata": {},
  1737. "outputs": [
  1738. {
  1739. "name": "stdout",
  1740. "output_type": "stream",
  1741. "text": [
  1742. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1743. ]
  1744. }
  1745. ],
  1746. "source": [
  1747. "d = tuple('RajathKumarMP')\n",
  1748. "print(d)"
  1749. ]
  1750. },
  1751. {
  1752. "cell_type": "markdown",
  1753. "metadata": {},
  1754. "source": [
  1755. "### 2.2 元组内置函数"
  1756. ]
  1757. },
  1758. {
  1759. "cell_type": "markdown",
  1760. "metadata": {},
  1761. "source": [
  1762. "**count()** 函数计算元组中存在的指定元素的数量。"
  1763. ]
  1764. },
  1765. {
  1766. "cell_type": "code",
  1767. "execution_count": 94,
  1768. "metadata": {},
  1769. "outputs": [
  1770. {
  1771. "data": {
  1772. "text/plain": [
  1773. "3"
  1774. ]
  1775. },
  1776. "execution_count": 94,
  1777. "metadata": {},
  1778. "output_type": "execute_result"
  1779. }
  1780. ],
  1781. "source": [
  1782. "d.count('a')"
  1783. ]
  1784. },
  1785. {
  1786. "cell_type": "markdown",
  1787. "metadata": {},
  1788. "source": [
  1789. "**index()** 函数返回指定元素的索引。如果元素大于1,则返回该指定元素的第一个元素的索引"
  1790. ]
  1791. },
  1792. {
  1793. "cell_type": "code",
  1794. "execution_count": 95,
  1795. "metadata": {},
  1796. "outputs": [
  1797. {
  1798. "data": {
  1799. "text/plain": [
  1800. "1"
  1801. ]
  1802. },
  1803. "execution_count": 95,
  1804. "metadata": {},
  1805. "output_type": "execute_result"
  1806. }
  1807. ],
  1808. "source": [
  1809. "d.index('a')"
  1810. ]
  1811. },
  1812. {
  1813. "cell_type": "markdown",
  1814. "metadata": {},
  1815. "source": [
  1816. "## 3. 集合"
  1817. ]
  1818. },
  1819. {
  1820. "cell_type": "markdown",
  1821. "metadata": {},
  1822. "source": [
  1823. "集合主要用于消除序列/列表中的重复数字。它还用于执行一些标准的集合操作。\n",
  1824. "\n",
  1825. "set被声明为set(),它将初始化一个空集。set([sequence])也可以被执行来声明一个包含元素的集"
  1826. ]
  1827. },
  1828. {
  1829. "cell_type": "code",
  1830. "execution_count": 1,
  1831. "metadata": {},
  1832. "outputs": [
  1833. {
  1834. "name": "stdout",
  1835. "output_type": "stream",
  1836. "text": [
  1837. "<class 'set'>\n"
  1838. ]
  1839. }
  1840. ],
  1841. "source": [
  1842. "set1 = set()\n",
  1843. "print(type(set1))"
  1844. ]
  1845. },
  1846. {
  1847. "cell_type": "code",
  1848. "execution_count": 2,
  1849. "metadata": {},
  1850. "outputs": [
  1851. {
  1852. "name": "stdout",
  1853. "output_type": "stream",
  1854. "text": [
  1855. "{1, 2, 3, 4}\n"
  1856. ]
  1857. }
  1858. ],
  1859. "source": [
  1860. "set0 = set([1,2,2,3,3,4])\n",
  1861. "print(set0)"
  1862. ]
  1863. },
  1864. {
  1865. "cell_type": "code",
  1866. "execution_count": 3,
  1867. "metadata": {},
  1868. "outputs": [
  1869. {
  1870. "name": "stdout",
  1871. "output_type": "stream",
  1872. "text": [
  1873. "{1, 2, 3, 4}\n"
  1874. ]
  1875. }
  1876. ],
  1877. "source": [
  1878. "set1 = set((1,2,2,3,3,4))\n",
  1879. "print(set1)"
  1880. ]
  1881. },
  1882. {
  1883. "cell_type": "markdown",
  1884. "metadata": {},
  1885. "source": [
  1886. "重复两次的元素2,3只会出现一次。因此在一个集合中,每个元素都是不同的。"
  1887. ]
  1888. },
  1889. {
  1890. "cell_type": "markdown",
  1891. "metadata": {},
  1892. "source": [
  1893. "### 3.1 内置函数"
  1894. ]
  1895. },
  1896. {
  1897. "cell_type": "code",
  1898. "execution_count": 4,
  1899. "metadata": {
  1900. "collapsed": true
  1901. },
  1902. "outputs": [],
  1903. "source": [
  1904. "set1 = set([1,2,3])"
  1905. ]
  1906. },
  1907. {
  1908. "cell_type": "code",
  1909. "execution_count": 5,
  1910. "metadata": {
  1911. "collapsed": true
  1912. },
  1913. "outputs": [],
  1914. "source": [
  1915. "set2 = set([2,3,4,5])"
  1916. ]
  1917. },
  1918. {
  1919. "cell_type": "markdown",
  1920. "metadata": {},
  1921. "source": [
  1922. "**union( )** 函数返回一个并集合,该集合包含两个集合的所有元素,但是没有重复。"
  1923. ]
  1924. },
  1925. {
  1926. "cell_type": "code",
  1927. "execution_count": 6,
  1928. "metadata": {},
  1929. "outputs": [
  1930. {
  1931. "data": {
  1932. "text/plain": [
  1933. "{1, 2, 3, 4, 5}"
  1934. ]
  1935. },
  1936. "execution_count": 6,
  1937. "metadata": {},
  1938. "output_type": "execute_result"
  1939. }
  1940. ],
  1941. "source": [
  1942. "set1.union(set2)"
  1943. ]
  1944. },
  1945. {
  1946. "cell_type": "markdown",
  1947. "metadata": {},
  1948. "source": [
  1949. "**add()** 将向集合中添加一个特定的元素。注意,新添加的元素的索引是任意的,可以放在末尾不需要的任何位置。"
  1950. ]
  1951. },
  1952. {
  1953. "cell_type": "code",
  1954. "execution_count": 9,
  1955. "metadata": {},
  1956. "outputs": [
  1957. {
  1958. "name": "stdout",
  1959. "output_type": "stream",
  1960. "text": [
  1961. "{0, 1, 2, 3, 10}\n"
  1962. ]
  1963. },
  1964. {
  1965. "data": {
  1966. "text/plain": [
  1967. "{0, 1, 2, 3, 5, 10}"
  1968. ]
  1969. },
  1970. "execution_count": 9,
  1971. "metadata": {},
  1972. "output_type": "execute_result"
  1973. }
  1974. ],
  1975. "source": [
  1976. "print(set1)\n",
  1977. "set1.add(5)\n",
  1978. "set1"
  1979. ]
  1980. },
  1981. {
  1982. "cell_type": "markdown",
  1983. "metadata": {},
  1984. "source": [
  1985. "**intersection( )** 函数输出一个交集合,该集合包含两个集合中的所有元素。"
  1986. ]
  1987. },
  1988. {
  1989. "cell_type": "code",
  1990. "execution_count": 10,
  1991. "metadata": {},
  1992. "outputs": [
  1993. {
  1994. "data": {
  1995. "text/plain": [
  1996. "{2, 3, 5}"
  1997. ]
  1998. },
  1999. "execution_count": 10,
  2000. "metadata": {},
  2001. "output_type": "execute_result"
  2002. }
  2003. ],
  2004. "source": [
  2005. "set1.intersection(set2)"
  2006. ]
  2007. },
  2008. {
  2009. "cell_type": "markdown",
  2010. "metadata": {},
  2011. "source": [
  2012. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  2013. ]
  2014. },
  2015. {
  2016. "cell_type": "code",
  2017. "execution_count": 11,
  2018. "metadata": {},
  2019. "outputs": [
  2020. {
  2021. "name": "stdout",
  2022. "output_type": "stream",
  2023. "text": [
  2024. "{0, 1, 2, 3, 5, 10}\n",
  2025. "{2, 3, 4, 5}\n"
  2026. ]
  2027. },
  2028. {
  2029. "data": {
  2030. "text/plain": [
  2031. "{0, 1, 10}"
  2032. ]
  2033. },
  2034. "execution_count": 11,
  2035. "metadata": {},
  2036. "output_type": "execute_result"
  2037. }
  2038. ],
  2039. "source": [
  2040. "print(set1)\n",
  2041. "print(set2)\n",
  2042. "set1.difference(set2)"
  2043. ]
  2044. },
  2045. {
  2046. "cell_type": "markdown",
  2047. "metadata": {},
  2048. "source": [
  2049. "**pop( )** 是用来移除集合中的任意元素。"
  2050. ]
  2051. },
  2052. {
  2053. "cell_type": "code",
  2054. "execution_count": 19,
  2055. "metadata": {
  2056. "collapsed": true
  2057. },
  2058. "outputs": [],
  2059. "source": [
  2060. "set1=set([10, 9, 1, 2, 4])"
  2061. ]
  2062. },
  2063. {
  2064. "cell_type": "code",
  2065. "execution_count": 21,
  2066. "metadata": {},
  2067. "outputs": [
  2068. {
  2069. "name": "stdout",
  2070. "output_type": "stream",
  2071. "text": [
  2072. "{10, 2, 4, 1}\n",
  2073. "{2, 4, 1}\n"
  2074. ]
  2075. }
  2076. ],
  2077. "source": [
  2078. "print(set1)\n",
  2079. "set1.pop()\n",
  2080. "print(set1)"
  2081. ]
  2082. },
  2083. {
  2084. "cell_type": "markdown",
  2085. "metadata": {},
  2086. "source": [
  2087. "**remove( )** 函数从集合中删除指定的元素。"
  2088. ]
  2089. },
  2090. {
  2091. "cell_type": "code",
  2092. "execution_count": 22,
  2093. "metadata": {},
  2094. "outputs": [
  2095. {
  2096. "data": {
  2097. "text/plain": [
  2098. "{1, 4}"
  2099. ]
  2100. },
  2101. "execution_count": 22,
  2102. "metadata": {},
  2103. "output_type": "execute_result"
  2104. }
  2105. ],
  2106. "source": [
  2107. "set1.remove(2)\n",
  2108. "set1"
  2109. ]
  2110. },
  2111. {
  2112. "cell_type": "markdown",
  2113. "metadata": {},
  2114. "source": [
  2115. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2116. ]
  2117. },
  2118. {
  2119. "cell_type": "code",
  2120. "execution_count": 23,
  2121. "metadata": {},
  2122. "outputs": [
  2123. {
  2124. "data": {
  2125. "text/plain": [
  2126. "set()"
  2127. ]
  2128. },
  2129. "execution_count": 23,
  2130. "metadata": {},
  2131. "output_type": "execute_result"
  2132. }
  2133. ],
  2134. "source": [
  2135. "set1.clear()\n",
  2136. "set1"
  2137. ]
  2138. }
  2139. ],
  2140. "metadata": {
  2141. "kernelspec": {
  2142. "display_name": "Python 3",
  2143. "language": "python",
  2144. "name": "python3"
  2145. },
  2146. "language_info": {
  2147. "codemirror_mode": {
  2148. "name": "ipython",
  2149. "version": 3
  2150. },
  2151. "file_extension": ".py",
  2152. "mimetype": "text/x-python",
  2153. "name": "python",
  2154. "nbconvert_exporter": "python",
  2155. "pygments_lexer": "ipython3",
  2156. "version": "3.8.5"
  2157. }
  2158. },
  2159. "nbformat": 4,
  2160. "nbformat_minor": 1
  2161. }

机器学习越来越多应用到飞行器、机器人等领域,其目的是利用计算机实现类似人类的智能,从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术,通过具体的应用案例激发学生对该学科的兴趣,鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础,机器学习模型,无监督学习、监督学习、深度学习基础知识与实现,并学习如何利用机器学习解决实际问题,从而全面提升自我的《综合能力》。