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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。这是最后一个可以被首先访问的元素。这里,索引从`-1`开始。因此,索引`-1`对应是橙色,索引`-2`对应的是苹果。"
  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也具有自己的数据的列表`z`中。列表中的这个列表被称为`嵌套列表`,这就是数组的声明方式,我们将在后面看到。**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。**"
  174. ]
  175. },
  176. {
  177. "cell_type": "code",
  178. "execution_count": 14,
  179. "metadata": {},
  180. "outputs": [
  181. {
  182. "name": "stdout",
  183. "output_type": "stream",
  184. "text": [
  185. "[['apple', 'orange', 'peach'], ['carrot', 'potato'], 'Test']\n"
  186. ]
  187. }
  188. ],
  189. "source": [
  190. "z = [x,y, 'Test']\n",
  191. "print(z)"
  192. ]
  193. },
  194. {
  195. "cell_type": "code",
  196. "execution_count": 16,
  197. "metadata": {},
  198. "outputs": [
  199. {
  200. "data": {
  201. "text/plain": [
  202. "'orange'"
  203. ]
  204. },
  205. "execution_count": 16,
  206. "metadata": {},
  207. "output_type": "execute_result"
  208. }
  209. ],
  210. "source": [
  211. "z[0][1]"
  212. ]
  213. },
  214. {
  215. "cell_type": "markdown",
  216. "metadata": {},
  217. "source": [
  218. "如何获得嵌套列表中的某个元素?让我们在上述嵌套列表中获得数据'apple'为例。\n",
  219. "* 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",
  220. "* 因此z[0] 应该给我们第一个包含'apple'的列表。"
  221. ]
  222. },
  223. {
  224. "cell_type": "code",
  225. "execution_count": 17,
  226. "metadata": {},
  227. "outputs": [
  228. {
  229. "name": "stdout",
  230. "output_type": "stream",
  231. "text": [
  232. "['apple', 'orange', 'peach']\n"
  233. ]
  234. }
  235. ],
  236. "source": [
  237. "z1 = z[0]\n",
  238. "print(z1)"
  239. ]
  240. },
  241. {
  242. "cell_type": "markdown",
  243. "metadata": {},
  244. "source": [
  245. "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。"
  246. ]
  247. },
  248. {
  249. "cell_type": "code",
  250. "execution_count": 18,
  251. "metadata": {},
  252. "outputs": [
  253. {
  254. "data": {
  255. "text/plain": [
  256. "'apple'"
  257. ]
  258. },
  259. "execution_count": 18,
  260. "metadata": {},
  261. "output_type": "execute_result"
  262. }
  263. ],
  264. "source": [
  265. "z1[0]"
  266. ]
  267. },
  268. {
  269. "cell_type": "markdown",
  270. "metadata": {},
  271. "source": [
  272. "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。"
  273. ]
  274. },
  275. {
  276. "cell_type": "code",
  277. "execution_count": 19,
  278. "metadata": {},
  279. "outputs": [
  280. {
  281. "data": {
  282. "text/plain": [
  283. "'apple'"
  284. ]
  285. },
  286. "execution_count": 19,
  287. "metadata": {},
  288. "output_type": "execute_result"
  289. }
  290. ],
  291. "source": [
  292. "z[0][0]"
  293. ]
  294. },
  295. {
  296. "cell_type": "markdown",
  297. "metadata": {},
  298. "source": [
  299. "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。"
  300. ]
  301. },
  302. {
  303. "cell_type": "markdown",
  304. "metadata": {},
  305. "source": [
  306. "### 1.2 切片"
  307. ]
  308. },
  309. {
  310. "cell_type": "markdown",
  311. "metadata": {},
  312. "source": [
  313. "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",
  314. "\n",
  315. "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。"
  316. ]
  317. },
  318. {
  319. "cell_type": "code",
  320. "execution_count": 23,
  321. "metadata": {},
  322. "outputs": [
  323. {
  324. "name": "stdout",
  325. "output_type": "stream",
  326. "text": [
  327. "[2, 3, 4]\n",
  328. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  329. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  330. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  331. ]
  332. }
  333. ],
  334. "source": [
  335. "num = [1,2,3,4,5,6,7,8,9]\n",
  336. "print(num[1:4])\n",
  337. "print(num[0:])\n",
  338. "print(num[:])\n",
  339. "print(num)"
  340. ]
  341. },
  342. {
  343. "cell_type": "code",
  344. "execution_count": 24,
  345. "metadata": {},
  346. "outputs": [
  347. {
  348. "name": "stdout",
  349. "output_type": "stream",
  350. "text": [
  351. "[1, 2, 3, 4]\n",
  352. "[5, 6, 7, 8, 9]\n"
  353. ]
  354. }
  355. ],
  356. "source": [
  357. "print(num[0:4])\n",
  358. "print(num[4:])"
  359. ]
  360. },
  361. {
  362. "cell_type": "markdown",
  363. "metadata": {},
  364. "source": [
  365. "您还可以使用固定长度或步长对父列表进行切片。"
  366. ]
  367. },
  368. {
  369. "cell_type": "code",
  370. "execution_count": 26,
  371. "metadata": {},
  372. "outputs": [
  373. {
  374. "data": {
  375. "text/plain": [
  376. "[1, 4, 7]"
  377. ]
  378. },
  379. "execution_count": 26,
  380. "metadata": {},
  381. "output_type": "execute_result"
  382. }
  383. ],
  384. "source": [
  385. "num[0:9:3]"
  386. ]
  387. },
  388. {
  389. "cell_type": "markdown",
  390. "metadata": {},
  391. "source": [
  392. "### 1.3 列表的内置函数"
  393. ]
  394. },
  395. {
  396. "cell_type": "markdown",
  397. "metadata": {},
  398. "source": [
  399. "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( )**。"
  400. ]
  401. },
  402. {
  403. "cell_type": "code",
  404. "execution_count": 27,
  405. "metadata": {},
  406. "outputs": [
  407. {
  408. "data": {
  409. "text/plain": [
  410. "9"
  411. ]
  412. },
  413. "execution_count": 27,
  414. "metadata": {},
  415. "output_type": "execute_result"
  416. }
  417. ],
  418. "source": [
  419. "len(num)"
  420. ]
  421. },
  422. {
  423. "cell_type": "markdown",
  424. "metadata": {},
  425. "source": [
  426. "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。"
  427. ]
  428. },
  429. {
  430. "cell_type": "code",
  431. "execution_count": 28,
  432. "metadata": {},
  433. "outputs": [
  434. {
  435. "name": "stdout",
  436. "output_type": "stream",
  437. "text": [
  438. "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  439. ]
  440. },
  441. {
  442. "data": {
  443. "text/plain": [
  444. "1"
  445. ]
  446. },
  447. "execution_count": 28,
  448. "metadata": {},
  449. "output_type": "execute_result"
  450. }
  451. ],
  452. "source": [
  453. "print(num)\n",
  454. "min(num)"
  455. ]
  456. },
  457. {
  458. "cell_type": "code",
  459. "execution_count": 29,
  460. "metadata": {},
  461. "outputs": [
  462. {
  463. "data": {
  464. "text/plain": [
  465. "9"
  466. ]
  467. },
  468. "execution_count": 29,
  469. "metadata": {},
  470. "output_type": "execute_result"
  471. }
  472. ],
  473. "source": [
  474. "max(num)"
  475. ]
  476. },
  477. {
  478. "cell_type": "markdown",
  479. "metadata": {},
  480. "source": [
  481. "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。"
  482. ]
  483. },
  484. {
  485. "cell_type": "code",
  486. "execution_count": 30,
  487. "metadata": {},
  488. "outputs": [
  489. {
  490. "data": {
  491. "text/plain": [
  492. "[1, 2, 3, 5, 4, 7]"
  493. ]
  494. },
  495. "execution_count": 30,
  496. "metadata": {},
  497. "output_type": "execute_result"
  498. }
  499. ],
  500. "source": [
  501. "[1,2,3] + [5,4,7]"
  502. ]
  503. },
  504. {
  505. "cell_type": "markdown",
  506. "metadata": {},
  507. "source": [
  508. "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。"
  509. ]
  510. },
  511. {
  512. "cell_type": "code",
  513. "execution_count": 31,
  514. "metadata": {
  515. "collapsed": true
  516. },
  517. "outputs": [],
  518. "source": [
  519. "names = ['Earth','Air','Fire','Water']"
  520. ]
  521. },
  522. {
  523. "cell_type": "markdown",
  524. "metadata": {},
  525. "source": [
  526. "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\""
  527. ]
  528. },
  529. {
  530. "cell_type": "code",
  531. "execution_count": 32,
  532. "metadata": {},
  533. "outputs": [
  534. {
  535. "data": {
  536. "text/plain": [
  537. "False"
  538. ]
  539. },
  540. "execution_count": 32,
  541. "metadata": {},
  542. "output_type": "execute_result"
  543. }
  544. ],
  545. "source": [
  546. "'Fir' in names"
  547. ]
  548. },
  549. {
  550. "cell_type": "code",
  551. "execution_count": 33,
  552. "metadata": {},
  553. "outputs": [
  554. {
  555. "data": {
  556. "text/plain": [
  557. "True"
  558. ]
  559. },
  560. "execution_count": 33,
  561. "metadata": {},
  562. "output_type": "execute_result"
  563. }
  564. ],
  565. "source": [
  566. "'Fire' in names"
  567. ]
  568. },
  569. {
  570. "cell_type": "code",
  571. "execution_count": 34,
  572. "metadata": {},
  573. "outputs": [
  574. {
  575. "data": {
  576. "text/plain": [
  577. "False"
  578. ]
  579. },
  580. "execution_count": 34,
  581. "metadata": {},
  582. "output_type": "execute_result"
  583. }
  584. ],
  585. "source": [
  586. "'fire' in names"
  587. ]
  588. },
  589. {
  590. "cell_type": "markdown",
  591. "metadata": {},
  592. "source": [
  593. "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。"
  594. ]
  595. },
  596. {
  597. "cell_type": "code",
  598. "execution_count": 35,
  599. "metadata": {
  600. "collapsed": true
  601. },
  602. "outputs": [],
  603. "source": [
  604. "mlist = ['bzaa','ds','nc','az','z','klm']"
  605. ]
  606. },
  607. {
  608. "cell_type": "code",
  609. "execution_count": 36,
  610. "metadata": {},
  611. "outputs": [
  612. {
  613. "name": "stdout",
  614. "output_type": "stream",
  615. "text": [
  616. "z\n",
  617. "az\n"
  618. ]
  619. }
  620. ],
  621. "source": [
  622. "print(max(mlist))\n",
  623. "print(min(mlist))"
  624. ]
  625. },
  626. {
  627. "cell_type": "markdown",
  628. "metadata": {},
  629. "source": [
  630. "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?"
  631. ]
  632. },
  633. {
  634. "cell_type": "code",
  635. "execution_count": 37,
  636. "metadata": {
  637. "collapsed": true
  638. },
  639. "outputs": [],
  640. "source": [
  641. "nlist = ['1','94','93','1000']"
  642. ]
  643. },
  644. {
  645. "cell_type": "code",
  646. "execution_count": 27,
  647. "metadata": {},
  648. "outputs": [
  649. {
  650. "name": "stdout",
  651. "output_type": "stream",
  652. "text": [
  653. "94\n",
  654. "1\n"
  655. ]
  656. }
  657. ],
  658. "source": [
  659. "print(max(nlist))\n",
  660. "print(min(nlist))"
  661. ]
  662. },
  663. {
  664. "cell_type": "markdown",
  665. "metadata": {},
  666. "source": [
  667. "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。"
  668. ]
  669. },
  670. {
  671. "cell_type": "markdown",
  672. "metadata": {},
  673. "source": [
  674. "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'。"
  675. ]
  676. },
  677. {
  678. "cell_type": "code",
  679. "execution_count": 38,
  680. "metadata": {},
  681. "outputs": [
  682. {
  683. "name": "stdout",
  684. "output_type": "stream",
  685. "text": [
  686. "Earth\n",
  687. "Jet\n"
  688. ]
  689. }
  690. ],
  691. "source": [
  692. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  693. "print(max(names, key=len))\n",
  694. "print(min(names, key=len))"
  695. ]
  696. },
  697. {
  698. "cell_type": "markdown",
  699. "metadata": {},
  700. "source": [
  701. "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",
  702. "\n",
  703. "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",
  704. "\n",
  705. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  706. ]
  707. },
  708. {
  709. "cell_type": "code",
  710. "execution_count": 39,
  711. "metadata": {},
  712. "outputs": [
  713. {
  714. "data": {
  715. "text/plain": [
  716. "['h', 'e', 'l', 'l', 'o']"
  717. ]
  718. },
  719. "execution_count": 39,
  720. "metadata": {},
  721. "output_type": "execute_result"
  722. }
  723. ],
  724. "source": [
  725. "list('hello')"
  726. ]
  727. },
  728. {
  729. "cell_type": "markdown",
  730. "metadata": {},
  731. "source": [
  732. "**append( )** 被用来在列表的最后添加一个元素。"
  733. ]
  734. },
  735. {
  736. "cell_type": "code",
  737. "execution_count": 40,
  738. "metadata": {
  739. "collapsed": true
  740. },
  741. "outputs": [],
  742. "source": [
  743. "lst = [1,1,4,8,7]"
  744. ]
  745. },
  746. {
  747. "cell_type": "code",
  748. "execution_count": 43,
  749. "metadata": {},
  750. "outputs": [
  751. {
  752. "name": "stdout",
  753. "output_type": "stream",
  754. "text": [
  755. "[1, 1, 4, 8, 7, 1, 1, 1]\n"
  756. ]
  757. }
  758. ],
  759. "source": [
  760. "lst.append(1)\n",
  761. "print(lst)"
  762. ]
  763. },
  764. {
  765. "cell_type": "markdown",
  766. "metadata": {},
  767. "source": [
  768. "**count( )** 用于计算列表中出现的特定元素的数量。"
  769. ]
  770. },
  771. {
  772. "cell_type": "code",
  773. "execution_count": 50,
  774. "metadata": {},
  775. "outputs": [
  776. {
  777. "data": {
  778. "text/plain": [
  779. "0"
  780. ]
  781. },
  782. "execution_count": 50,
  783. "metadata": {},
  784. "output_type": "execute_result"
  785. }
  786. ],
  787. "source": [
  788. "lst.count(999)"
  789. ]
  790. },
  791. {
  792. "cell_type": "markdown",
  793. "metadata": {},
  794. "source": [
  795. "**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。"
  796. ]
  797. },
  798. {
  799. "cell_type": "code",
  800. "execution_count": 45,
  801. "metadata": {
  802. "collapsed": true
  803. },
  804. "outputs": [],
  805. "source": [
  806. "lst1 = [5,4,2,8]"
  807. ]
  808. },
  809. {
  810. "cell_type": "code",
  811. "execution_count": 46,
  812. "metadata": {},
  813. "outputs": [
  814. {
  815. "name": "stdout",
  816. "output_type": "stream",
  817. "text": [
  818. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8]]\n"
  819. ]
  820. }
  821. ],
  822. "source": [
  823. "lst.append(lst1)\n",
  824. "print(lst)"
  825. ]
  826. },
  827. {
  828. "cell_type": "markdown",
  829. "metadata": {},
  830. "source": [
  831. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  832. ]
  833. },
  834. {
  835. "cell_type": "code",
  836. "execution_count": 47,
  837. "metadata": {},
  838. "outputs": [
  839. {
  840. "name": "stdout",
  841. "output_type": "stream",
  842. "text": [
  843. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  844. ]
  845. }
  846. ],
  847. "source": [
  848. "lst.extend(lst1)\n",
  849. "print(lst)"
  850. ]
  851. },
  852. {
  853. "cell_type": "markdown",
  854. "metadata": {},
  855. "source": [
  856. "**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。"
  857. ]
  858. },
  859. {
  860. "cell_type": "code",
  861. "execution_count": 48,
  862. "metadata": {},
  863. "outputs": [
  864. {
  865. "data": {
  866. "text/plain": [
  867. "0"
  868. ]
  869. },
  870. "execution_count": 48,
  871. "metadata": {},
  872. "output_type": "execute_result"
  873. }
  874. ],
  875. "source": [
  876. "lst.index(1)"
  877. ]
  878. },
  879. {
  880. "cell_type": "code",
  881. "execution_count": 51,
  882. "metadata": {},
  883. "outputs": [
  884. {
  885. "ename": "ValueError",
  886. "evalue": "999 is not in list",
  887. "output_type": "error",
  888. "traceback": [
  889. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  890. "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
  891. "\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",
  892. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  893. ]
  894. }
  895. ],
  896. "source": [
  897. "lst.index(999)"
  898. ]
  899. },
  900. {
  901. "cell_type": "markdown",
  902. "metadata": {},
  903. "source": [
  904. "**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。"
  905. ]
  906. },
  907. {
  908. "cell_type": "code",
  909. "execution_count": 52,
  910. "metadata": {},
  911. "outputs": [
  912. {
  913. "name": "stdout",
  914. "output_type": "stream",
  915. "text": [
  916. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  917. ]
  918. }
  919. ],
  920. "source": [
  921. "lst.insert(5, 'name')\n",
  922. "print(lst)"
  923. ]
  924. },
  925. {
  926. "cell_type": "code",
  927. "execution_count": 53,
  928. "metadata": {
  929. "collapsed": true
  930. },
  931. "outputs": [],
  932. "source": [
  933. "lst.insert(-1, 10)"
  934. ]
  935. },
  936. {
  937. "cell_type": "code",
  938. "execution_count": 54,
  939. "metadata": {},
  940. "outputs": [
  941. {
  942. "name": "stdout",
  943. "output_type": "stream",
  944. "text": [
  945. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8]\n"
  946. ]
  947. }
  948. ],
  949. "source": [
  950. "print(lst)"
  951. ]
  952. },
  953. {
  954. "cell_type": "code",
  955. "execution_count": 55,
  956. "metadata": {},
  957. "outputs": [
  958. {
  959. "data": {
  960. "text/plain": [
  961. "15"
  962. ]
  963. },
  964. "execution_count": 55,
  965. "metadata": {},
  966. "output_type": "execute_result"
  967. }
  968. ],
  969. "source": [
  970. "len(lst)"
  971. ]
  972. },
  973. {
  974. "cell_type": "code",
  975. "execution_count": 56,
  976. "metadata": {
  977. "collapsed": true
  978. },
  979. "outputs": [],
  980. "source": [
  981. "lst.insert(15, 20)"
  982. ]
  983. },
  984. {
  985. "cell_type": "code",
  986. "execution_count": 57,
  987. "metadata": {},
  988. "outputs": [
  989. {
  990. "name": "stdout",
  991. "output_type": "stream",
  992. "text": [
  993. "[1, 1, 4, 8, 7, 'name', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8, 20]\n"
  994. ]
  995. }
  996. ],
  997. "source": [
  998. "print(lst)"
  999. ]
  1000. },
  1001. {
  1002. "cell_type": "markdown",
  1003. "metadata": {},
  1004. "source": [
  1005. "**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。"
  1006. ]
  1007. },
  1008. {
  1009. "cell_type": "code",
  1010. "execution_count": 58,
  1011. "metadata": {},
  1012. "outputs": [
  1013. {
  1014. "name": "stdout",
  1015. "output_type": "stream",
  1016. "text": [
  1017. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2, 10, 8, 20]\n"
  1018. ]
  1019. }
  1020. ],
  1021. "source": [
  1022. "lst[5] = 'Python'\n",
  1023. "print(lst)"
  1024. ]
  1025. },
  1026. {
  1027. "cell_type": "markdown",
  1028. "metadata": {},
  1029. "source": [
  1030. "**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。"
  1031. ]
  1032. },
  1033. {
  1034. "cell_type": "code",
  1035. "execution_count": 61,
  1036. "metadata": {},
  1037. "outputs": [
  1038. {
  1039. "data": {
  1040. "text/plain": [
  1041. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2]"
  1042. ]
  1043. },
  1044. "execution_count": 61,
  1045. "metadata": {},
  1046. "output_type": "execute_result"
  1047. }
  1048. ],
  1049. "source": [
  1050. "lst.pop()\n",
  1051. "lst"
  1052. ]
  1053. },
  1054. {
  1055. "cell_type": "markdown",
  1056. "metadata": {},
  1057. "source": [
  1058. "可以指定索引值来弹出与该索引值对应的元素。"
  1059. ]
  1060. },
  1061. {
  1062. "cell_type": "code",
  1063. "execution_count": 49,
  1064. "metadata": {},
  1065. "outputs": [
  1066. {
  1067. "name": "stdout",
  1068. "output_type": "stream",
  1069. "text": [
  1070. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], 5]\n"
  1071. ]
  1072. }
  1073. ],
  1074. "source": [
  1075. "lst.pop(2)\n",
  1076. "print(lst)"
  1077. ]
  1078. },
  1079. {
  1080. "cell_type": "code",
  1081. "execution_count": 62,
  1082. "metadata": {},
  1083. "outputs": [
  1084. {
  1085. "name": "stdout",
  1086. "output_type": "stream",
  1087. "text": [
  1088. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 4, 2]\n"
  1089. ]
  1090. },
  1091. {
  1092. "data": {
  1093. "text/plain": [
  1094. "4"
  1095. ]
  1096. },
  1097. "execution_count": 62,
  1098. "metadata": {},
  1099. "output_type": "execute_result"
  1100. }
  1101. ],
  1102. "source": [
  1103. "print(lst)\n",
  1104. "lst.pop(-2)"
  1105. ]
  1106. },
  1107. {
  1108. "cell_type": "code",
  1109. "execution_count": 63,
  1110. "metadata": {},
  1111. "outputs": [
  1112. {
  1113. "name": "stdout",
  1114. "output_type": "stream",
  1115. "text": [
  1116. "[1, 1, 4, 8, 7, 'Python', 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1117. ]
  1118. }
  1119. ],
  1120. "source": [
  1121. "print(lst)"
  1122. ]
  1123. },
  1124. {
  1125. "cell_type": "markdown",
  1126. "metadata": {},
  1127. "source": [
  1128. "**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。"
  1129. ]
  1130. },
  1131. {
  1132. "cell_type": "code",
  1133. "execution_count": 64,
  1134. "metadata": {},
  1135. "outputs": [
  1136. {
  1137. "name": "stdout",
  1138. "output_type": "stream",
  1139. "text": [
  1140. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1141. ]
  1142. }
  1143. ],
  1144. "source": [
  1145. "lst.remove('Python')\n",
  1146. "print(lst)"
  1147. ]
  1148. },
  1149. {
  1150. "cell_type": "markdown",
  1151. "metadata": {},
  1152. "source": [
  1153. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1154. ]
  1155. },
  1156. {
  1157. "cell_type": "code",
  1158. "execution_count": 66,
  1159. "metadata": {
  1160. "scrolled": true
  1161. },
  1162. "outputs": [
  1163. {
  1164. "name": "stdout",
  1165. "output_type": "stream",
  1166. "text": [
  1167. "[1, 1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n",
  1168. "[1, 4, 8, 7, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1169. ]
  1170. }
  1171. ],
  1172. "source": [
  1173. "print(lst)\n",
  1174. "del(lst[1])\n",
  1175. "print(lst)"
  1176. ]
  1177. },
  1178. {
  1179. "cell_type": "code",
  1180. "execution_count": 67,
  1181. "metadata": {},
  1182. "outputs": [
  1183. {
  1184. "name": "stdout",
  1185. "output_type": "stream",
  1186. "text": [
  1187. "[1, 1, 1, 1, [5, 4, 2, 8], 5, 2]\n"
  1188. ]
  1189. }
  1190. ],
  1191. "source": [
  1192. "del(lst[1:4])\n",
  1193. "print(lst)"
  1194. ]
  1195. },
  1196. {
  1197. "cell_type": "markdown",
  1198. "metadata": {},
  1199. "source": [
  1200. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1201. ]
  1202. },
  1203. {
  1204. "cell_type": "code",
  1205. "execution_count": 68,
  1206. "metadata": {},
  1207. "outputs": [
  1208. {
  1209. "name": "stdout",
  1210. "output_type": "stream",
  1211. "text": [
  1212. "[2, 5, [5, 4, 2, 8], 1, 1, 1, 1]\n"
  1213. ]
  1214. }
  1215. ],
  1216. "source": [
  1217. "lst.reverse()\n",
  1218. "print(lst)"
  1219. ]
  1220. },
  1221. {
  1222. "cell_type": "markdown",
  1223. "metadata": {},
  1224. "source": [
  1225. "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",
  1226. "\n",
  1227. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1228. ]
  1229. },
  1230. {
  1231. "cell_type": "code",
  1232. "execution_count": 70,
  1233. "metadata": {},
  1234. "outputs": [
  1235. {
  1236. "name": "stdout",
  1237. "output_type": "stream",
  1238. "text": [
  1239. "[1, 4, 7, 8, 8, 10]\n"
  1240. ]
  1241. }
  1242. ],
  1243. "source": [
  1244. "lst = [8, 7, 1, 4, 8, 10]\n",
  1245. "lst.sort()\n",
  1246. "print(lst)"
  1247. ]
  1248. },
  1249. {
  1250. "cell_type": "markdown",
  1251. "metadata": {},
  1252. "source": [
  1253. "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。"
  1254. ]
  1255. },
  1256. {
  1257. "cell_type": "code",
  1258. "execution_count": 59,
  1259. "metadata": {},
  1260. "outputs": [
  1261. {
  1262. "name": "stdout",
  1263. "output_type": "stream",
  1264. "text": [
  1265. "[10, 8, 8, 4, 1]\n"
  1266. ]
  1267. }
  1268. ],
  1269. "source": [
  1270. "lst.sort(reverse=True)\n",
  1271. "print(lst)"
  1272. ]
  1273. },
  1274. {
  1275. "cell_type": "markdown",
  1276. "metadata": {},
  1277. "source": [
  1278. "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。"
  1279. ]
  1280. },
  1281. {
  1282. "cell_type": "code",
  1283. "execution_count": 74,
  1284. "metadata": {},
  1285. "outputs": [
  1286. {
  1287. "name": "stdout",
  1288. "output_type": "stream",
  1289. "text": [
  1290. "['apple', 'orange', 'peach']\n",
  1291. "['peach', 'orange', 'apple']\n"
  1292. ]
  1293. }
  1294. ],
  1295. "source": [
  1296. "names = ['apple', 'orange', 'peach']\n",
  1297. "names.sort()\n",
  1298. "print(names)\n",
  1299. "names.sort(reverse=True)\n",
  1300. "print(names)"
  1301. ]
  1302. },
  1303. {
  1304. "cell_type": "markdown",
  1305. "metadata": {},
  1306. "source": [
  1307. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1308. ]
  1309. },
  1310. {
  1311. "cell_type": "code",
  1312. "execution_count": 72,
  1313. "metadata": {},
  1314. "outputs": [
  1315. {
  1316. "name": "stdout",
  1317. "output_type": "stream",
  1318. "text": [
  1319. "['peach', 'apple', 'orange']\n",
  1320. "['orange', 'peach', 'apple']\n"
  1321. ]
  1322. }
  1323. ],
  1324. "source": [
  1325. "names.sort(key=len)\n",
  1326. "print(names)\n",
  1327. "names.sort(key=len,reverse=True)\n",
  1328. "print(names)"
  1329. ]
  1330. },
  1331. {
  1332. "cell_type": "markdown",
  1333. "metadata": {},
  1334. "source": [
  1335. "### 1.4 复制一个列表"
  1336. ]
  1337. },
  1338. {
  1339. "cell_type": "markdown",
  1340. "metadata": {},
  1341. "source": [
  1342. "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:"
  1343. ]
  1344. },
  1345. {
  1346. "cell_type": "code",
  1347. "execution_count": 75,
  1348. "metadata": {
  1349. "collapsed": true
  1350. },
  1351. "outputs": [],
  1352. "source": [
  1353. "lista= [2,1,4,3]"
  1354. ]
  1355. },
  1356. {
  1357. "cell_type": "code",
  1358. "execution_count": 76,
  1359. "metadata": {},
  1360. "outputs": [
  1361. {
  1362. "name": "stdout",
  1363. "output_type": "stream",
  1364. "text": [
  1365. "[2, 1, 4, 3]\n"
  1366. ]
  1367. }
  1368. ],
  1369. "source": [
  1370. "listb = lista # 对象赋值\n",
  1371. "print(listb)"
  1372. ]
  1373. },
  1374. {
  1375. "cell_type": "markdown",
  1376. "metadata": {},
  1377. "source": [
  1378. "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。"
  1379. ]
  1380. },
  1381. {
  1382. "cell_type": "code",
  1383. "execution_count": 77,
  1384. "metadata": {},
  1385. "outputs": [
  1386. {
  1387. "name": "stdout",
  1388. "output_type": "stream",
  1389. "text": [
  1390. "[2, 1, 4]\n",
  1391. "[2, 1, 4, 9]\n"
  1392. ]
  1393. }
  1394. ],
  1395. "source": [
  1396. "lista.pop()\n",
  1397. "print(lista)\n",
  1398. "lista.append(9)\n",
  1399. "print(lista)"
  1400. ]
  1401. },
  1402. {
  1403. "cell_type": "code",
  1404. "execution_count": 78,
  1405. "metadata": {},
  1406. "outputs": [
  1407. {
  1408. "name": "stdout",
  1409. "output_type": "stream",
  1410. "text": [
  1411. "[2, 1, 4, 9]\n"
  1412. ]
  1413. }
  1414. ],
  1415. "source": [
  1416. "print(listb)"
  1417. ]
  1418. },
  1419. {
  1420. "cell_type": "markdown",
  1421. "metadata": {},
  1422. "source": [
  1423. "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",
  1424. "\n",
  1425. "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。"
  1426. ]
  1427. },
  1428. {
  1429. "cell_type": "code",
  1430. "execution_count": 79,
  1431. "metadata": {
  1432. "collapsed": true
  1433. },
  1434. "outputs": [],
  1435. "source": [
  1436. "lista = [2,1,4,3]"
  1437. ]
  1438. },
  1439. {
  1440. "cell_type": "code",
  1441. "execution_count": 80,
  1442. "metadata": {},
  1443. "outputs": [
  1444. {
  1445. "name": "stdout",
  1446. "output_type": "stream",
  1447. "text": [
  1448. "[2, 1, 4, 3]\n"
  1449. ]
  1450. }
  1451. ],
  1452. "source": [
  1453. "listb = lista[:]\n",
  1454. "print(listb)"
  1455. ]
  1456. },
  1457. {
  1458. "cell_type": "code",
  1459. "execution_count": 81,
  1460. "metadata": {},
  1461. "outputs": [
  1462. {
  1463. "name": "stdout",
  1464. "output_type": "stream",
  1465. "text": [
  1466. "[2, 1, 4]\n",
  1467. "[2, 1, 4, 9]\n"
  1468. ]
  1469. }
  1470. ],
  1471. "source": [
  1472. "lista.pop()\n",
  1473. "print(lista)\n",
  1474. "lista.append(9)\n",
  1475. "print(lista)"
  1476. ]
  1477. },
  1478. {
  1479. "cell_type": "code",
  1480. "execution_count": 82,
  1481. "metadata": {},
  1482. "outputs": [
  1483. {
  1484. "name": "stdout",
  1485. "output_type": "stream",
  1486. "text": [
  1487. "[2, 1, 4, 3]\n"
  1488. ]
  1489. }
  1490. ],
  1491. "source": [
  1492. "print(listb)"
  1493. ]
  1494. },
  1495. {
  1496. "cell_type": "markdown",
  1497. "metadata": {},
  1498. "source": [
  1499. "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?"
  1500. ]
  1501. },
  1502. {
  1503. "cell_type": "markdown",
  1504. "metadata": {},
  1505. "source": [
  1506. "## 2. 元组"
  1507. ]
  1508. },
  1509. {
  1510. "cell_type": "markdown",
  1511. "metadata": {},
  1512. "source": [
  1513. "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。"
  1514. ]
  1515. },
  1516. {
  1517. "cell_type": "code",
  1518. "execution_count": 84,
  1519. "metadata": {},
  1520. "outputs": [
  1521. {
  1522. "name": "stdout",
  1523. "output_type": "stream",
  1524. "text": [
  1525. "(3, 1)\n",
  1526. "<class 'tuple'>\n"
  1527. ]
  1528. },
  1529. {
  1530. "ename": "TypeError",
  1531. "evalue": "'tuple' object does not support item assignment",
  1532. "output_type": "error",
  1533. "traceback": [
  1534. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  1535. "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
  1536. "\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",
  1537. "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
  1538. ]
  1539. }
  1540. ],
  1541. "source": [
  1542. "xyz = divmod(10,3)\n",
  1543. "print(xyz)\n",
  1544. "print(type(xyz))\n",
  1545. "xyz[0]=10"
  1546. ]
  1547. },
  1548. {
  1549. "cell_type": "markdown",
  1550. "metadata": {},
  1551. "source": [
  1552. "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。"
  1553. ]
  1554. },
  1555. {
  1556. "cell_type": "markdown",
  1557. "metadata": {},
  1558. "source": [
  1559. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1560. ]
  1561. },
  1562. {
  1563. "cell_type": "code",
  1564. "execution_count": 85,
  1565. "metadata": {
  1566. "collapsed": true
  1567. },
  1568. "outputs": [],
  1569. "source": [
  1570. "tup = ()\n",
  1571. "tup2 = tuple()"
  1572. ]
  1573. },
  1574. {
  1575. "cell_type": "markdown",
  1576. "metadata": {},
  1577. "source": [
  1578. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1579. ]
  1580. },
  1581. {
  1582. "cell_type": "code",
  1583. "execution_count": 76,
  1584. "metadata": {},
  1585. "outputs": [
  1586. {
  1587. "data": {
  1588. "text/plain": [
  1589. "(27,)"
  1590. ]
  1591. },
  1592. "execution_count": 76,
  1593. "metadata": {},
  1594. "output_type": "execute_result"
  1595. }
  1596. ],
  1597. "source": [
  1598. "27,"
  1599. ]
  1600. },
  1601. {
  1602. "cell_type": "markdown",
  1603. "metadata": {},
  1604. "source": [
  1605. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1606. ]
  1607. },
  1608. {
  1609. "cell_type": "code",
  1610. "execution_count": 77,
  1611. "metadata": {},
  1612. "outputs": [
  1613. {
  1614. "data": {
  1615. "text/plain": [
  1616. "(27, 27)"
  1617. ]
  1618. },
  1619. "execution_count": 77,
  1620. "metadata": {},
  1621. "output_type": "execute_result"
  1622. }
  1623. ],
  1624. "source": [
  1625. "2*(27,)"
  1626. ]
  1627. },
  1628. {
  1629. "cell_type": "markdown",
  1630. "metadata": {},
  1631. "source": [
  1632. "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。"
  1633. ]
  1634. },
  1635. {
  1636. "cell_type": "code",
  1637. "execution_count": 78,
  1638. "metadata": {
  1639. "scrolled": true
  1640. },
  1641. "outputs": [
  1642. {
  1643. "name": "stdout",
  1644. "output_type": "stream",
  1645. "text": [
  1646. "(1, 2, 3)\n",
  1647. "('H', 'e', 'l', 'l', 'o')\n"
  1648. ]
  1649. }
  1650. ],
  1651. "source": [
  1652. "tup3 = tuple([1,2,3])\n",
  1653. "print(tup3)\n",
  1654. "tup4 = tuple('Hello')\n",
  1655. "print(tup4)"
  1656. ]
  1657. },
  1658. {
  1659. "cell_type": "markdown",
  1660. "metadata": {},
  1661. "source": [
  1662. "它遵循与列表相同的索引和切片。"
  1663. ]
  1664. },
  1665. {
  1666. "cell_type": "code",
  1667. "execution_count": 41,
  1668. "metadata": {},
  1669. "outputs": [
  1670. {
  1671. "name": "stdout",
  1672. "output_type": "stream",
  1673. "text": [
  1674. "2\n",
  1675. "('H', 'e', 'l')\n"
  1676. ]
  1677. }
  1678. ],
  1679. "source": [
  1680. "print(tup3[1])\n",
  1681. "tup5 = tup4[:3]\n",
  1682. "print(tup5)"
  1683. ]
  1684. },
  1685. {
  1686. "cell_type": "markdown",
  1687. "metadata": {},
  1688. "source": [
  1689. "### 2.1 将一个元组映射到另一个元组"
  1690. ]
  1691. },
  1692. {
  1693. "cell_type": "code",
  1694. "execution_count": 90,
  1695. "metadata": {
  1696. "collapsed": true
  1697. },
  1698. "outputs": [],
  1699. "source": [
  1700. "(a,b,c)= ('alpha','beta','gamma')"
  1701. ]
  1702. },
  1703. {
  1704. "cell_type": "code",
  1705. "execution_count": 91,
  1706. "metadata": {},
  1707. "outputs": [
  1708. {
  1709. "name": "stdout",
  1710. "output_type": "stream",
  1711. "text": [
  1712. "alpha beta gamma\n"
  1713. ]
  1714. }
  1715. ],
  1716. "source": [
  1717. "print(a,b,c)"
  1718. ]
  1719. },
  1720. {
  1721. "cell_type": "code",
  1722. "execution_count": 92,
  1723. "metadata": {
  1724. "collapsed": true
  1725. },
  1726. "outputs": [],
  1727. "source": [
  1728. "(c, b, a) = (a, b, c)"
  1729. ]
  1730. },
  1731. {
  1732. "cell_type": "code",
  1733. "execution_count": 93,
  1734. "metadata": {},
  1735. "outputs": [
  1736. {
  1737. "name": "stdout",
  1738. "output_type": "stream",
  1739. "text": [
  1740. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1741. ]
  1742. }
  1743. ],
  1744. "source": [
  1745. "d = tuple('RajathKumarMP')\n",
  1746. "print(d)"
  1747. ]
  1748. },
  1749. {
  1750. "cell_type": "markdown",
  1751. "metadata": {},
  1752. "source": [
  1753. "### 2.2 元组内置函数"
  1754. ]
  1755. },
  1756. {
  1757. "cell_type": "markdown",
  1758. "metadata": {},
  1759. "source": [
  1760. "**count()** 函数计算元组中存在的指定元素的数量。"
  1761. ]
  1762. },
  1763. {
  1764. "cell_type": "code",
  1765. "execution_count": 94,
  1766. "metadata": {},
  1767. "outputs": [
  1768. {
  1769. "data": {
  1770. "text/plain": [
  1771. "3"
  1772. ]
  1773. },
  1774. "execution_count": 94,
  1775. "metadata": {},
  1776. "output_type": "execute_result"
  1777. }
  1778. ],
  1779. "source": [
  1780. "d.count('a')"
  1781. ]
  1782. },
  1783. {
  1784. "cell_type": "markdown",
  1785. "metadata": {},
  1786. "source": [
  1787. "**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"
  1788. ]
  1789. },
  1790. {
  1791. "cell_type": "code",
  1792. "execution_count": 95,
  1793. "metadata": {},
  1794. "outputs": [
  1795. {
  1796. "data": {
  1797. "text/plain": [
  1798. "1"
  1799. ]
  1800. },
  1801. "execution_count": 95,
  1802. "metadata": {},
  1803. "output_type": "execute_result"
  1804. }
  1805. ],
  1806. "source": [
  1807. "d.index('a')"
  1808. ]
  1809. },
  1810. {
  1811. "cell_type": "markdown",
  1812. "metadata": {},
  1813. "source": [
  1814. "## 3. 集合"
  1815. ]
  1816. },
  1817. {
  1818. "cell_type": "markdown",
  1819. "metadata": {},
  1820. "source": [
  1821. "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",
  1822. "\n",
  1823. "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"
  1824. ]
  1825. },
  1826. {
  1827. "cell_type": "code",
  1828. "execution_count": 85,
  1829. "metadata": {},
  1830. "outputs": [
  1831. {
  1832. "name": "stdout",
  1833. "output_type": "stream",
  1834. "text": [
  1835. "<class 'set'>\n"
  1836. ]
  1837. }
  1838. ],
  1839. "source": [
  1840. "set1 = set()\n",
  1841. "print(type(set1))"
  1842. ]
  1843. },
  1844. {
  1845. "cell_type": "code",
  1846. "execution_count": 86,
  1847. "metadata": {},
  1848. "outputs": [
  1849. {
  1850. "name": "stdout",
  1851. "output_type": "stream",
  1852. "text": [
  1853. "{1, 2, 3, 4}\n"
  1854. ]
  1855. }
  1856. ],
  1857. "source": [
  1858. "set0 = set([1,2,2,3,3,4])\n",
  1859. "print(set0)"
  1860. ]
  1861. },
  1862. {
  1863. "cell_type": "code",
  1864. "execution_count": 87,
  1865. "metadata": {},
  1866. "outputs": [
  1867. {
  1868. "name": "stdout",
  1869. "output_type": "stream",
  1870. "text": [
  1871. "{1, 2, 3, 4}\n"
  1872. ]
  1873. }
  1874. ],
  1875. "source": [
  1876. "set1 = set((1,2,2,3,3,4))\n",
  1877. "print(set1)"
  1878. ]
  1879. },
  1880. {
  1881. "cell_type": "markdown",
  1882. "metadata": {},
  1883. "source": [
  1884. "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。"
  1885. ]
  1886. },
  1887. {
  1888. "cell_type": "markdown",
  1889. "metadata": {},
  1890. "source": [
  1891. "### 3.1 内置函数"
  1892. ]
  1893. },
  1894. {
  1895. "cell_type": "code",
  1896. "execution_count": 101,
  1897. "metadata": {
  1898. "collapsed": true
  1899. },
  1900. "outputs": [],
  1901. "source": [
  1902. "set1 = set([1,2,3])"
  1903. ]
  1904. },
  1905. {
  1906. "cell_type": "code",
  1907. "execution_count": 102,
  1908. "metadata": {
  1909. "collapsed": true
  1910. },
  1911. "outputs": [],
  1912. "source": [
  1913. "set2 = set([2,3,4,5])"
  1914. ]
  1915. },
  1916. {
  1917. "cell_type": "markdown",
  1918. "metadata": {},
  1919. "source": [
  1920. "**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。"
  1921. ]
  1922. },
  1923. {
  1924. "cell_type": "code",
  1925. "execution_count": 90,
  1926. "metadata": {},
  1927. "outputs": [
  1928. {
  1929. "data": {
  1930. "text/plain": [
  1931. "{1, 2, 3, 4, 5}"
  1932. ]
  1933. },
  1934. "execution_count": 90,
  1935. "metadata": {},
  1936. "output_type": "execute_result"
  1937. }
  1938. ],
  1939. "source": [
  1940. "set1.union(set2)"
  1941. ]
  1942. },
  1943. {
  1944. "cell_type": "markdown",
  1945. "metadata": {},
  1946. "source": [
  1947. "**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。"
  1948. ]
  1949. },
  1950. {
  1951. "cell_type": "code",
  1952. "execution_count": 94,
  1953. "metadata": {},
  1954. "outputs": [
  1955. {
  1956. "name": "stdout",
  1957. "output_type": "stream",
  1958. "text": [
  1959. "{0, 1, 2, 3}\n"
  1960. ]
  1961. },
  1962. {
  1963. "data": {
  1964. "text/plain": [
  1965. "{0, 1, 2, 3}"
  1966. ]
  1967. },
  1968. "execution_count": 94,
  1969. "metadata": {},
  1970. "output_type": "execute_result"
  1971. }
  1972. ],
  1973. "source": [
  1974. "print(set1)\n",
  1975. "set1.add(0)\n",
  1976. "set1"
  1977. ]
  1978. },
  1979. {
  1980. "cell_type": "markdown",
  1981. "metadata": {},
  1982. "source": [
  1983. "**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。"
  1984. ]
  1985. },
  1986. {
  1987. "cell_type": "code",
  1988. "execution_count": 95,
  1989. "metadata": {},
  1990. "outputs": [
  1991. {
  1992. "data": {
  1993. "text/plain": [
  1994. "{2, 3}"
  1995. ]
  1996. },
  1997. "execution_count": 95,
  1998. "metadata": {},
  1999. "output_type": "execute_result"
  2000. }
  2001. ],
  2002. "source": [
  2003. "set1.intersection(set2)"
  2004. ]
  2005. },
  2006. {
  2007. "cell_type": "markdown",
  2008. "metadata": {},
  2009. "source": [
  2010. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  2011. ]
  2012. },
  2013. {
  2014. "cell_type": "code",
  2015. "execution_count": 96,
  2016. "metadata": {},
  2017. "outputs": [
  2018. {
  2019. "name": "stdout",
  2020. "output_type": "stream",
  2021. "text": [
  2022. "{0, 1, 2, 3}\n",
  2023. "{2, 3, 4, 5}\n"
  2024. ]
  2025. },
  2026. {
  2027. "data": {
  2028. "text/plain": [
  2029. "{0, 1}"
  2030. ]
  2031. },
  2032. "execution_count": 96,
  2033. "metadata": {},
  2034. "output_type": "execute_result"
  2035. }
  2036. ],
  2037. "source": [
  2038. "print(set1)\n",
  2039. "print(set2)\n",
  2040. "set1.difference(set2)"
  2041. ]
  2042. },
  2043. {
  2044. "cell_type": "markdown",
  2045. "metadata": {},
  2046. "source": [
  2047. "**pop( )** 是用来移除集合中的任意元素。"
  2048. ]
  2049. },
  2050. {
  2051. "cell_type": "code",
  2052. "execution_count": 97,
  2053. "metadata": {
  2054. "collapsed": true
  2055. },
  2056. "outputs": [],
  2057. "source": [
  2058. "set1=set([10, 9, 1, 2, 4])"
  2059. ]
  2060. },
  2061. {
  2062. "cell_type": "code",
  2063. "execution_count": 99,
  2064. "metadata": {},
  2065. "outputs": [
  2066. {
  2067. "name": "stdout",
  2068. "output_type": "stream",
  2069. "text": [
  2070. "{4, 9, 10}\n"
  2071. ]
  2072. }
  2073. ],
  2074. "source": [
  2075. "set1.pop()\n",
  2076. "print(set1)"
  2077. ]
  2078. },
  2079. {
  2080. "cell_type": "markdown",
  2081. "metadata": {},
  2082. "source": [
  2083. "**remove( )** 函数从集合中删除指定的元素。"
  2084. ]
  2085. },
  2086. {
  2087. "cell_type": "code",
  2088. "execution_count": 103,
  2089. "metadata": {},
  2090. "outputs": [
  2091. {
  2092. "data": {
  2093. "text/plain": [
  2094. "{1, 3}"
  2095. ]
  2096. },
  2097. "execution_count": 103,
  2098. "metadata": {},
  2099. "output_type": "execute_result"
  2100. }
  2101. ],
  2102. "source": [
  2103. "set1.remove(2)\n",
  2104. "set1"
  2105. ]
  2106. },
  2107. {
  2108. "cell_type": "markdown",
  2109. "metadata": {},
  2110. "source": [
  2111. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2112. ]
  2113. },
  2114. {
  2115. "cell_type": "code",
  2116. "execution_count": 104,
  2117. "metadata": {},
  2118. "outputs": [
  2119. {
  2120. "data": {
  2121. "text/plain": [
  2122. "set()"
  2123. ]
  2124. },
  2125. "execution_count": 104,
  2126. "metadata": {},
  2127. "output_type": "execute_result"
  2128. }
  2129. ],
  2130. "source": [
  2131. "set1.clear()\n",
  2132. "set1"
  2133. ]
  2134. }
  2135. ],
  2136. "metadata": {
  2137. "kernelspec": {
  2138. "display_name": "Python 3",
  2139. "language": "python",
  2140. "name": "python3"
  2141. },
  2142. "language_info": {
  2143. "codemirror_mode": {
  2144. "name": "ipython",
  2145. "version": 3
  2146. },
  2147. "file_extension": ".py",
  2148. "mimetype": "text/x-python",
  2149. "name": "python",
  2150. "nbconvert_exporter": "python",
  2151. "pygments_lexer": "ipython3",
  2152. "version": "3.7.9"
  2153. }
  2154. },
  2155. "nbformat": 4,
  2156. "nbformat_minor": 1
  2157. }

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》。