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3_Data_Structure_1.ipynb 44 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"
  15. ]
  16. },
  17. {
  18. "cell_type": "markdown",
  19. "metadata": {},
  20. "source": [
  21. "## 1. 列表"
  22. ]
  23. },
  24. {
  25. "cell_type": "markdown",
  26. "metadata": {},
  27. "source": [
  28. "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",
  29. "\n",
  30. "`list`的声明只需将变量等同于`[ ]`或`list`即可。"
  31. ]
  32. },
  33. {
  34. "cell_type": "code",
  35. "execution_count": 1,
  36. "metadata": {},
  37. "outputs": [],
  38. "source": [
  39. "a = []"
  40. ]
  41. },
  42. {
  43. "cell_type": "code",
  44. "execution_count": 2,
  45. "metadata": {},
  46. "outputs": [
  47. {
  48. "name": "stdout",
  49. "output_type": "stream",
  50. "text": [
  51. "<class 'list'>\n"
  52. ]
  53. }
  54. ],
  55. "source": [
  56. "print(type(a))"
  57. ]
  58. },
  59. {
  60. "cell_type": "markdown",
  61. "metadata": {},
  62. "source": [
  63. "可以直接将数据序列分配给列表x,如下所示。"
  64. ]
  65. },
  66. {
  67. "cell_type": "code",
  68. "execution_count": 3,
  69. "metadata": {},
  70. "outputs": [
  71. {
  72. "name": "stdout",
  73. "output_type": "stream",
  74. "text": [
  75. "['apple', 'orange', 'peach']\n"
  76. ]
  77. }
  78. ],
  79. "source": [
  80. "x = ['apple', 'orange', 'peach']\n",
  81. "print(x)"
  82. ]
  83. },
  84. {
  85. "cell_type": "markdown",
  86. "metadata": {},
  87. "source": [
  88. "### 1.1 索引"
  89. ]
  90. },
  91. {
  92. "cell_type": "markdown",
  93. "metadata": {},
  94. "source": [
  95. "在Python中,索引从`0`开始。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`。"
  96. ]
  97. },
  98. {
  99. "cell_type": "code",
  100. "execution_count": 4,
  101. "metadata": {},
  102. "outputs": [
  103. {
  104. "data": {
  105. "text/plain": [
  106. "'apple'"
  107. ]
  108. },
  109. "execution_count": 4,
  110. "metadata": {},
  111. "output_type": "execute_result"
  112. }
  113. ],
  114. "source": [
  115. "x[0]"
  116. ]
  117. },
  118. {
  119. "cell_type": "markdown",
  120. "metadata": {},
  121. "source": [
  122. "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。"
  123. ]
  124. },
  125. {
  126. "cell_type": "code",
  127. "execution_count": 5,
  128. "metadata": {},
  129. "outputs": [
  130. {
  131. "data": {
  132. "text/plain": [
  133. "'peach'"
  134. ]
  135. },
  136. "execution_count": 5,
  137. "metadata": {},
  138. "output_type": "execute_result"
  139. }
  140. ],
  141. "source": [
  142. "x[-1]"
  143. ]
  144. },
  145. {
  146. "cell_type": "markdown",
  147. "metadata": {},
  148. "source": [
  149. "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。"
  150. ]
  151. },
  152. {
  153. "cell_type": "code",
  154. "execution_count": 7,
  155. "metadata": {},
  156. "outputs": [],
  157. "source": [
  158. "y = ['carrot','potato']"
  159. ]
  160. },
  161. {
  162. "cell_type": "markdown",
  163. "metadata": {},
  164. "source": [
  165. "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**"
  166. ]
  167. },
  168. {
  169. "cell_type": "code",
  170. "execution_count": 12,
  171. "metadata": {},
  172. "outputs": [
  173. {
  174. "name": "stdout",
  175. "output_type": "stream",
  176. "text": [
  177. "[['apple', 'orange', 'peach'], ['carrot', 'potato'], 'Test']\n"
  178. ]
  179. }
  180. ],
  181. "source": [
  182. "z = [x,y, 'Test']\n",
  183. "print(z)"
  184. ]
  185. },
  186. {
  187. "cell_type": "code",
  188. "execution_count": 6,
  189. "metadata": {},
  190. "outputs": [
  191. {
  192. "data": {
  193. "text/plain": [
  194. "'orange'"
  195. ]
  196. },
  197. "execution_count": 6,
  198. "metadata": {},
  199. "output_type": "execute_result"
  200. }
  201. ],
  202. "source": [
  203. "z[0][1]"
  204. ]
  205. },
  206. {
  207. "cell_type": "markdown",
  208. "metadata": {},
  209. "source": [
  210. "如果您不了解在Python中索引是如何工作的,那么在嵌套列表中索引可能会非常令人困惑。所以让我们把它分解一下,然后得出一个结论。\n",
  211. "\n",
  212. "让我们在上述嵌套列表中获得数据'apple'。\n",
  213. "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']` 。因此z[0] 应该给我们第一个包含'apple'的列表。"
  214. ]
  215. },
  216. {
  217. "cell_type": "code",
  218. "execution_count": 9,
  219. "metadata": {},
  220. "outputs": [
  221. {
  222. "name": "stdout",
  223. "output_type": "stream",
  224. "text": [
  225. "['apple', 'orange', 'peach']\n"
  226. ]
  227. }
  228. ],
  229. "source": [
  230. "z1 = z[0]\n",
  231. "print(z1)"
  232. ]
  233. },
  234. {
  235. "cell_type": "markdown",
  236. "metadata": {},
  237. "source": [
  238. "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。"
  239. ]
  240. },
  241. {
  242. "cell_type": "code",
  243. "execution_count": 10,
  244. "metadata": {},
  245. "outputs": [
  246. {
  247. "data": {
  248. "text/plain": [
  249. "'apple'"
  250. ]
  251. },
  252. "execution_count": 10,
  253. "metadata": {},
  254. "output_type": "execute_result"
  255. }
  256. ],
  257. "source": [
  258. "z1[0]"
  259. ]
  260. },
  261. {
  262. "cell_type": "markdown",
  263. "metadata": {},
  264. "source": [
  265. "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。"
  266. ]
  267. },
  268. {
  269. "cell_type": "code",
  270. "execution_count": 11,
  271. "metadata": {},
  272. "outputs": [
  273. {
  274. "data": {
  275. "text/plain": [
  276. "'apple'"
  277. ]
  278. },
  279. "execution_count": 11,
  280. "metadata": {},
  281. "output_type": "execute_result"
  282. }
  283. ],
  284. "source": [
  285. "z[0][0]"
  286. ]
  287. },
  288. {
  289. "cell_type": "markdown",
  290. "metadata": {},
  291. "source": [
  292. "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。"
  293. ]
  294. },
  295. {
  296. "cell_type": "markdown",
  297. "metadata": {},
  298. "source": [
  299. "### 1.2 切片"
  300. ]
  301. },
  302. {
  303. "cell_type": "markdown",
  304. "metadata": {},
  305. "source": [
  306. "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”列表。\n",
  307. "\n",
  308. "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。"
  309. ]
  310. },
  311. {
  312. "cell_type": "code",
  313. "execution_count": 14,
  314. "metadata": {},
  315. "outputs": [
  316. {
  317. "name": "stdout",
  318. "output_type": "stream",
  319. "text": [
  320. "[3, 2, 3]\n",
  321. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  322. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  323. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  324. ]
  325. }
  326. ],
  327. "source": [
  328. "num = [2,3,2,3,4,5,6,7,8,9]\n",
  329. "print(num[1:4])\n",
  330. "print(num[0:])\n",
  331. "print(num[:])\n",
  332. "print(num)"
  333. ]
  334. },
  335. {
  336. "cell_type": "code",
  337. "execution_count": 15,
  338. "metadata": {},
  339. "outputs": [
  340. {
  341. "name": "stdout",
  342. "output_type": "stream",
  343. "text": [
  344. "[2, 3, 2, 3]\n",
  345. "[4, 5, 6, 7, 8, 9]\n"
  346. ]
  347. }
  348. ],
  349. "source": [
  350. "print(num[0:4])\n",
  351. "print(num[4:])"
  352. ]
  353. },
  354. {
  355. "cell_type": "markdown",
  356. "metadata": {},
  357. "source": [
  358. "您还可以使用固定长度或步长对父列表进行切片。"
  359. ]
  360. },
  361. {
  362. "cell_type": "code",
  363. "execution_count": 16,
  364. "metadata": {},
  365. "outputs": [
  366. {
  367. "data": {
  368. "text/plain": [
  369. "[2, 3, 6]"
  370. ]
  371. },
  372. "execution_count": 16,
  373. "metadata": {},
  374. "output_type": "execute_result"
  375. }
  376. ],
  377. "source": [
  378. "num[:9:3]"
  379. ]
  380. },
  381. {
  382. "cell_type": "markdown",
  383. "metadata": {},
  384. "source": [
  385. "### 1.3 列表的内置函数"
  386. ]
  387. },
  388. {
  389. "cell_type": "markdown",
  390. "metadata": {},
  391. "source": [
  392. "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( )**。"
  393. ]
  394. },
  395. {
  396. "cell_type": "code",
  397. "execution_count": 17,
  398. "metadata": {},
  399. "outputs": [
  400. {
  401. "data": {
  402. "text/plain": [
  403. "10"
  404. ]
  405. },
  406. "execution_count": 17,
  407. "metadata": {},
  408. "output_type": "execute_result"
  409. }
  410. ],
  411. "source": [
  412. "len(num)"
  413. ]
  414. },
  415. {
  416. "cell_type": "markdown",
  417. "metadata": {},
  418. "source": [
  419. "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。"
  420. ]
  421. },
  422. {
  423. "cell_type": "code",
  424. "execution_count": 20,
  425. "metadata": {},
  426. "outputs": [
  427. {
  428. "name": "stdout",
  429. "output_type": "stream",
  430. "text": [
  431. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  432. ]
  433. },
  434. {
  435. "data": {
  436. "text/plain": [
  437. "2"
  438. ]
  439. },
  440. "execution_count": 20,
  441. "metadata": {},
  442. "output_type": "execute_result"
  443. }
  444. ],
  445. "source": [
  446. "print(num)\n",
  447. "min(num)"
  448. ]
  449. },
  450. {
  451. "cell_type": "code",
  452. "execution_count": 19,
  453. "metadata": {},
  454. "outputs": [
  455. {
  456. "data": {
  457. "text/plain": [
  458. "9"
  459. ]
  460. },
  461. "execution_count": 19,
  462. "metadata": {},
  463. "output_type": "execute_result"
  464. }
  465. ],
  466. "source": [
  467. "max(num)"
  468. ]
  469. },
  470. {
  471. "cell_type": "markdown",
  472. "metadata": {},
  473. "source": [
  474. "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。"
  475. ]
  476. },
  477. {
  478. "cell_type": "code",
  479. "execution_count": 21,
  480. "metadata": {},
  481. "outputs": [
  482. {
  483. "data": {
  484. "text/plain": [
  485. "[1, 2, 3, 5, 4, 7]"
  486. ]
  487. },
  488. "execution_count": 21,
  489. "metadata": {},
  490. "output_type": "execute_result"
  491. }
  492. ],
  493. "source": [
  494. "[1,2,3] + [5,4,7]"
  495. ]
  496. },
  497. {
  498. "cell_type": "markdown",
  499. "metadata": {},
  500. "source": [
  501. "可能会出现这样的需求,您可能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。"
  502. ]
  503. },
  504. {
  505. "cell_type": "code",
  506. "execution_count": 22,
  507. "metadata": {},
  508. "outputs": [],
  509. "source": [
  510. "names = ['Earth','Air','Fire','Water']"
  511. ]
  512. },
  513. {
  514. "cell_type": "markdown",
  515. "metadata": {},
  516. "source": [
  517. "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\""
  518. ]
  519. },
  520. {
  521. "cell_type": "code",
  522. "execution_count": 23,
  523. "metadata": {},
  524. "outputs": [
  525. {
  526. "data": {
  527. "text/plain": [
  528. "False"
  529. ]
  530. },
  531. "execution_count": 23,
  532. "metadata": {},
  533. "output_type": "execute_result"
  534. }
  535. ],
  536. "source": [
  537. "'Fir' in names"
  538. ]
  539. },
  540. {
  541. "cell_type": "code",
  542. "execution_count": 19,
  543. "metadata": {},
  544. "outputs": [
  545. {
  546. "data": {
  547. "text/plain": [
  548. "True"
  549. ]
  550. },
  551. "execution_count": 19,
  552. "metadata": {},
  553. "output_type": "execute_result"
  554. }
  555. ],
  556. "source": [
  557. "'Fire' in names"
  558. ]
  559. },
  560. {
  561. "cell_type": "code",
  562. "execution_count": 20,
  563. "metadata": {},
  564. "outputs": [
  565. {
  566. "data": {
  567. "text/plain": [
  568. "False"
  569. ]
  570. },
  571. "execution_count": 20,
  572. "metadata": {},
  573. "output_type": "execute_result"
  574. }
  575. ],
  576. "source": [
  577. "'fire' in names"
  578. ]
  579. },
  580. {
  581. "cell_type": "code",
  582. "execution_count": 25,
  583. "metadata": {},
  584. "outputs": [
  585. {
  586. "data": {
  587. "text/plain": [
  588. "False"
  589. ]
  590. },
  591. "execution_count": 25,
  592. "metadata": {},
  593. "output_type": "execute_result"
  594. }
  595. ],
  596. "source": [
  597. "'Rajath' in names"
  598. ]
  599. },
  600. {
  601. "cell_type": "markdown",
  602. "metadata": {},
  603. "source": [
  604. "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。"
  605. ]
  606. },
  607. {
  608. "cell_type": "code",
  609. "execution_count": 24,
  610. "metadata": {},
  611. "outputs": [],
  612. "source": [
  613. "mlist = ['bzaa','ds','nc','az','z','klm']"
  614. ]
  615. },
  616. {
  617. "cell_type": "code",
  618. "execution_count": 25,
  619. "metadata": {},
  620. "outputs": [
  621. {
  622. "name": "stdout",
  623. "output_type": "stream",
  624. "text": [
  625. "z\n",
  626. "az\n"
  627. ]
  628. }
  629. ],
  630. "source": [
  631. "print(max(mlist))\n",
  632. "print(min(mlist))"
  633. ]
  634. },
  635. {
  636. "cell_type": "markdown",
  637. "metadata": {},
  638. "source": [
  639. "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?"
  640. ]
  641. },
  642. {
  643. "cell_type": "code",
  644. "execution_count": 26,
  645. "metadata": {},
  646. "outputs": [],
  647. "source": [
  648. "nlist = ['1','94','93','1000']"
  649. ]
  650. },
  651. {
  652. "cell_type": "code",
  653. "execution_count": 27,
  654. "metadata": {},
  655. "outputs": [
  656. {
  657. "name": "stdout",
  658. "output_type": "stream",
  659. "text": [
  660. "94\n",
  661. "1\n"
  662. ]
  663. }
  664. ],
  665. "source": [
  666. "print(max(nlist))\n",
  667. "print(min(nlist))"
  668. ]
  669. },
  670. {
  671. "cell_type": "markdown",
  672. "metadata": {},
  673. "source": [
  674. "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。"
  675. ]
  676. },
  677. {
  678. "cell_type": "markdown",
  679. "metadata": {},
  680. "source": [
  681. "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'。"
  682. ]
  683. },
  684. {
  685. "cell_type": "code",
  686. "execution_count": 28,
  687. "metadata": {},
  688. "outputs": [
  689. {
  690. "name": "stdout",
  691. "output_type": "stream",
  692. "text": [
  693. "Earth\n",
  694. "Jet\n"
  695. ]
  696. }
  697. ],
  698. "source": [
  699. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  700. "print(max(names, key=len))\n",
  701. "print(min(names, key=len))"
  702. ]
  703. },
  704. {
  705. "cell_type": "markdown",
  706. "metadata": {},
  707. "source": [
  708. "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",
  709. "\n",
  710. "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",
  711. "\n",
  712. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  713. ]
  714. },
  715. {
  716. "cell_type": "code",
  717. "execution_count": 29,
  718. "metadata": {},
  719. "outputs": [
  720. {
  721. "data": {
  722. "text/plain": [
  723. "['h', 'e', 'l', 'l', 'o']"
  724. ]
  725. },
  726. "execution_count": 29,
  727. "metadata": {},
  728. "output_type": "execute_result"
  729. }
  730. ],
  731. "source": [
  732. "list('hello')"
  733. ]
  734. },
  735. {
  736. "cell_type": "markdown",
  737. "metadata": {},
  738. "source": [
  739. "**append( )** 被用来在列表的最后添加一个元素。"
  740. ]
  741. },
  742. {
  743. "cell_type": "code",
  744. "execution_count": 30,
  745. "metadata": {},
  746. "outputs": [],
  747. "source": [
  748. "lst = [1,1,4,8,7]"
  749. ]
  750. },
  751. {
  752. "cell_type": "code",
  753. "execution_count": 31,
  754. "metadata": {},
  755. "outputs": [
  756. {
  757. "name": "stdout",
  758. "output_type": "stream",
  759. "text": [
  760. "[1, 1, 4, 8, 7, 1]\n"
  761. ]
  762. }
  763. ],
  764. "source": [
  765. "lst.append(1)\n",
  766. "print(lst)"
  767. ]
  768. },
  769. {
  770. "cell_type": "markdown",
  771. "metadata": {},
  772. "source": [
  773. "**count( )** 用于计算列表中出现的特定元素的数量。"
  774. ]
  775. },
  776. {
  777. "cell_type": "code",
  778. "execution_count": 32,
  779. "metadata": {},
  780. "outputs": [
  781. {
  782. "data": {
  783. "text/plain": [
  784. "3"
  785. ]
  786. },
  787. "execution_count": 32,
  788. "metadata": {},
  789. "output_type": "execute_result"
  790. }
  791. ],
  792. "source": [
  793. "lst.count(1)"
  794. ]
  795. },
  796. {
  797. "cell_type": "markdown",
  798. "metadata": {},
  799. "source": [
  800. "**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。"
  801. ]
  802. },
  803. {
  804. "cell_type": "code",
  805. "execution_count": 33,
  806. "metadata": {},
  807. "outputs": [],
  808. "source": [
  809. "lst1 = [5,4,2,8]"
  810. ]
  811. },
  812. {
  813. "cell_type": "code",
  814. "execution_count": 35,
  815. "metadata": {},
  816. "outputs": [
  817. {
  818. "name": "stdout",
  819. "output_type": "stream",
  820. "text": [
  821. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8]]\n"
  822. ]
  823. },
  824. {
  825. "data": {
  826. "text/plain": [
  827. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]"
  828. ]
  829. },
  830. "execution_count": 35,
  831. "metadata": {},
  832. "output_type": "execute_result"
  833. }
  834. ],
  835. "source": [
  836. "lst.append(lst1)\n",
  837. "print(lst)"
  838. ]
  839. },
  840. {
  841. "cell_type": "markdown",
  842. "metadata": {},
  843. "source": [
  844. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  845. ]
  846. },
  847. {
  848. "cell_type": "code",
  849. "execution_count": 36,
  850. "metadata": {},
  851. "outputs": [
  852. {
  853. "name": "stdout",
  854. "output_type": "stream",
  855. "text": [
  856. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  857. ]
  858. }
  859. ],
  860. "source": [
  861. "lst.extend(lst1)\n",
  862. "print(lst)"
  863. ]
  864. },
  865. {
  866. "cell_type": "markdown",
  867. "metadata": {},
  868. "source": [
  869. "**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。"
  870. ]
  871. },
  872. {
  873. "cell_type": "code",
  874. "execution_count": 37,
  875. "metadata": {},
  876. "outputs": [
  877. {
  878. "data": {
  879. "text/plain": [
  880. "0"
  881. ]
  882. },
  883. "execution_count": 37,
  884. "metadata": {},
  885. "output_type": "execute_result"
  886. }
  887. ],
  888. "source": [
  889. "lst.index(1)"
  890. ]
  891. },
  892. {
  893. "cell_type": "code",
  894. "execution_count": 38,
  895. "metadata": {},
  896. "outputs": [
  897. {
  898. "ename": "ValueError",
  899. "evalue": "999 is not in list",
  900. "output_type": "error",
  901. "traceback": [
  902. "\u001b[0;31m------------------------------------------\u001b[0m",
  903. "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
  904. "\u001b[0;32m<ipython-input-38-344da5bcf759>\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  905. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  906. ]
  907. }
  908. ],
  909. "source": [
  910. "lst.index(999)"
  911. ]
  912. },
  913. {
  914. "cell_type": "markdown",
  915. "metadata": {},
  916. "source": [
  917. "**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。"
  918. ]
  919. },
  920. {
  921. "cell_type": "code",
  922. "execution_count": 39,
  923. "metadata": {},
  924. "outputs": [
  925. {
  926. "name": "stdout",
  927. "output_type": "stream",
  928. "text": [
  929. "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  930. ]
  931. }
  932. ],
  933. "source": [
  934. "lst.insert(5, 'name')\n",
  935. "print(lst)"
  936. ]
  937. },
  938. {
  939. "cell_type": "markdown",
  940. "metadata": {},
  941. "source": [
  942. "**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。"
  943. ]
  944. },
  945. {
  946. "cell_type": "code",
  947. "execution_count": 40,
  948. "metadata": {},
  949. "outputs": [
  950. {
  951. "name": "stdout",
  952. "output_type": "stream",
  953. "text": [
  954. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  955. ]
  956. }
  957. ],
  958. "source": [
  959. "lst[5] = 'Python'\n",
  960. "print(lst)"
  961. ]
  962. },
  963. {
  964. "cell_type": "markdown",
  965. "metadata": {},
  966. "source": [
  967. "**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。"
  968. ]
  969. },
  970. {
  971. "cell_type": "code",
  972. "execution_count": 41,
  973. "metadata": {},
  974. "outputs": [
  975. {
  976. "data": {
  977. "text/plain": [
  978. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2]"
  979. ]
  980. },
  981. "execution_count": 41,
  982. "metadata": {},
  983. "output_type": "execute_result"
  984. }
  985. ],
  986. "source": [
  987. "lst.pop()\n",
  988. "lst"
  989. ]
  990. },
  991. {
  992. "cell_type": "markdown",
  993. "metadata": {},
  994. "source": [
  995. "可以指定索引值来弹出与该索引值对应的元素。"
  996. ]
  997. },
  998. {
  999. "cell_type": "code",
  1000. "execution_count": 42,
  1001. "metadata": {},
  1002. "outputs": [
  1003. {
  1004. "data": {
  1005. "text/plain": [
  1006. "4"
  1007. ]
  1008. },
  1009. "execution_count": 42,
  1010. "metadata": {},
  1011. "output_type": "execute_result"
  1012. }
  1013. ],
  1014. "source": [
  1015. "lst.pop(2)"
  1016. ]
  1017. },
  1018. {
  1019. "cell_type": "code",
  1020. "execution_count": 43,
  1021. "metadata": {},
  1022. "outputs": [
  1023. {
  1024. "name": "stdout",
  1025. "output_type": "stream",
  1026. "text": [
  1027. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2]\n"
  1028. ]
  1029. },
  1030. {
  1031. "data": {
  1032. "text/plain": [
  1033. "4"
  1034. ]
  1035. },
  1036. "execution_count": 43,
  1037. "metadata": {},
  1038. "output_type": "execute_result"
  1039. }
  1040. ],
  1041. "source": [
  1042. "print(lst)\n",
  1043. "lst.pop(-2)"
  1044. ]
  1045. },
  1046. {
  1047. "cell_type": "code",
  1048. "execution_count": 44,
  1049. "metadata": {},
  1050. "outputs": [
  1051. {
  1052. "name": "stdout",
  1053. "output_type": "stream",
  1054. "text": [
  1055. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 2]\n"
  1056. ]
  1057. }
  1058. ],
  1059. "source": [
  1060. "print(lst)"
  1061. ]
  1062. },
  1063. {
  1064. "cell_type": "markdown",
  1065. "metadata": {},
  1066. "source": [
  1067. "**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。"
  1068. ]
  1069. },
  1070. {
  1071. "cell_type": "code",
  1072. "execution_count": 46,
  1073. "metadata": {},
  1074. "outputs": [
  1075. {
  1076. "ename": "ValueError",
  1077. "evalue": "list.remove(x): x not in list",
  1078. "output_type": "error",
  1079. "traceback": [
  1080. "\u001b[0;31m------------------------------------------\u001b[0m",
  1081. "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
  1082. "\u001b[0;32m<ipython-input-46-77711ca19f77>\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[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
  1083. "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
  1084. ]
  1085. }
  1086. ],
  1087. "source": [
  1088. "lst.remove('Python')\n",
  1089. "print(lst)"
  1090. ]
  1091. },
  1092. {
  1093. "cell_type": "markdown",
  1094. "metadata": {},
  1095. "source": [
  1096. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1097. ]
  1098. },
  1099. {
  1100. "cell_type": "code",
  1101. "execution_count": 52,
  1102. "metadata": {},
  1103. "outputs": [
  1104. {
  1105. "name": "stdout",
  1106. "output_type": "stream",
  1107. "text": [
  1108. "[1, [5, 4, 2, 8], 5, 2]\n",
  1109. "[1, 5, 2]\n"
  1110. ]
  1111. }
  1112. ],
  1113. "source": [
  1114. "print(lst)\n",
  1115. "del lst[1]\n",
  1116. "print(lst)"
  1117. ]
  1118. },
  1119. {
  1120. "cell_type": "markdown",
  1121. "metadata": {},
  1122. "source": [
  1123. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1124. ]
  1125. },
  1126. {
  1127. "cell_type": "code",
  1128. "execution_count": 53,
  1129. "metadata": {},
  1130. "outputs": [
  1131. {
  1132. "name": "stdout",
  1133. "output_type": "stream",
  1134. "text": [
  1135. "[2, 5, 1]\n"
  1136. ]
  1137. }
  1138. ],
  1139. "source": [
  1140. "lst.reverse()\n",
  1141. "print(lst)"
  1142. ]
  1143. },
  1144. {
  1145. "cell_type": "markdown",
  1146. "metadata": {},
  1147. "source": [
  1148. "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",
  1149. "\n",
  1150. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1151. ]
  1152. },
  1153. {
  1154. "cell_type": "code",
  1155. "execution_count": 54,
  1156. "metadata": {},
  1157. "outputs": [
  1158. {
  1159. "name": "stdout",
  1160. "output_type": "stream",
  1161. "text": [
  1162. "[1, 4, 8, 8, 10]\n"
  1163. ]
  1164. }
  1165. ],
  1166. "source": [
  1167. "lst = [1, 4, 8, 8, 10]\n",
  1168. "lst.sort()\n",
  1169. "print(lst)"
  1170. ]
  1171. },
  1172. {
  1173. "cell_type": "markdown",
  1174. "metadata": {},
  1175. "source": [
  1176. "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。"
  1177. ]
  1178. },
  1179. {
  1180. "cell_type": "code",
  1181. "execution_count": 56,
  1182. "metadata": {},
  1183. "outputs": [
  1184. {
  1185. "ename": "TypeError",
  1186. "evalue": "must use keyword argument for key function",
  1187. "output_type": "error",
  1188. "traceback": [
  1189. "\u001b[0;31m------------------------------------------\u001b[0m",
  1190. "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
  1191. "\u001b[0;32m<ipython-input-56-2b0ae825b0c3>\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[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
  1192. "\u001b[0;31mTypeError\u001b[0m: must use keyword argument for key function"
  1193. ]
  1194. }
  1195. ],
  1196. "source": [
  1197. "lst.sort(reverse=True)\n",
  1198. "print(lst)"
  1199. ]
  1200. },
  1201. {
  1202. "cell_type": "markdown",
  1203. "metadata": {},
  1204. "source": [
  1205. "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。"
  1206. ]
  1207. },
  1208. {
  1209. "cell_type": "code",
  1210. "execution_count": 57,
  1211. "metadata": {},
  1212. "outputs": [
  1213. {
  1214. "name": "stdout",
  1215. "output_type": "stream",
  1216. "text": [
  1217. "['apple', 'orange', 'peach']\n",
  1218. "['peach', 'orange', 'apple']\n"
  1219. ]
  1220. }
  1221. ],
  1222. "source": [
  1223. "names = ['apple', 'orange', 'peach']\n",
  1224. "names.sort()\n",
  1225. "print(names)\n",
  1226. "names.sort(reverse=True)\n",
  1227. "print(names)"
  1228. ]
  1229. },
  1230. {
  1231. "cell_type": "markdown",
  1232. "metadata": {},
  1233. "source": [
  1234. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1235. ]
  1236. },
  1237. {
  1238. "cell_type": "code",
  1239. "execution_count": 58,
  1240. "metadata": {},
  1241. "outputs": [
  1242. {
  1243. "name": "stdout",
  1244. "output_type": "stream",
  1245. "text": [
  1246. "['peach', 'apple', 'orange']\n",
  1247. "['orange', 'peach', 'apple']\n"
  1248. ]
  1249. }
  1250. ],
  1251. "source": [
  1252. "names.sort(key=len)\n",
  1253. "print(names)\n",
  1254. "names.sort(key=len,reverse=True)\n",
  1255. "print(names)"
  1256. ]
  1257. },
  1258. {
  1259. "cell_type": "markdown",
  1260. "metadata": {},
  1261. "source": [
  1262. "### 1.4 复制一个列表"
  1263. ]
  1264. },
  1265. {
  1266. "cell_type": "markdown",
  1267. "metadata": {},
  1268. "source": [
  1269. "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:"
  1270. ]
  1271. },
  1272. {
  1273. "cell_type": "code",
  1274. "execution_count": 59,
  1275. "metadata": {},
  1276. "outputs": [],
  1277. "source": [
  1278. "lista= [2,1,4,3]"
  1279. ]
  1280. },
  1281. {
  1282. "cell_type": "code",
  1283. "execution_count": 60,
  1284. "metadata": {},
  1285. "outputs": [
  1286. {
  1287. "name": "stdout",
  1288. "output_type": "stream",
  1289. "text": [
  1290. "[2, 1, 4, 3]\n"
  1291. ]
  1292. }
  1293. ],
  1294. "source": [
  1295. "listb = lista # 对象赋值\n",
  1296. "print(listb)"
  1297. ]
  1298. },
  1299. {
  1300. "cell_type": "markdown",
  1301. "metadata": {},
  1302. "source": [
  1303. "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。"
  1304. ]
  1305. },
  1306. {
  1307. "cell_type": "code",
  1308. "execution_count": 61,
  1309. "metadata": {},
  1310. "outputs": [
  1311. {
  1312. "name": "stdout",
  1313. "output_type": "stream",
  1314. "text": [
  1315. "[2, 1, 4]\n",
  1316. "[2, 1, 4, 9]\n"
  1317. ]
  1318. }
  1319. ],
  1320. "source": [
  1321. "lista.pop()\n",
  1322. "print(lista)\n",
  1323. "lista.append(9)\n",
  1324. "print(lista)"
  1325. ]
  1326. },
  1327. {
  1328. "cell_type": "code",
  1329. "execution_count": 62,
  1330. "metadata": {},
  1331. "outputs": [
  1332. {
  1333. "name": "stdout",
  1334. "output_type": "stream",
  1335. "text": [
  1336. "[2, 1, 4, 9]\n"
  1337. ]
  1338. }
  1339. ],
  1340. "source": [
  1341. "print(listb)"
  1342. ]
  1343. },
  1344. {
  1345. "cell_type": "markdown",
  1346. "metadata": {},
  1347. "source": [
  1348. "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",
  1349. "\n",
  1350. "如果您还记得,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考虑第一个和最后一个元素。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。"
  1351. ]
  1352. },
  1353. {
  1354. "cell_type": "code",
  1355. "execution_count": 63,
  1356. "metadata": {},
  1357. "outputs": [],
  1358. "source": [
  1359. "lista = [2,1,4,3]"
  1360. ]
  1361. },
  1362. {
  1363. "cell_type": "code",
  1364. "execution_count": 64,
  1365. "metadata": {},
  1366. "outputs": [
  1367. {
  1368. "name": "stdout",
  1369. "output_type": "stream",
  1370. "text": [
  1371. "[2, 1, 4, 3]\n"
  1372. ]
  1373. }
  1374. ],
  1375. "source": [
  1376. "listb = lista[:]\n",
  1377. "print(listb)"
  1378. ]
  1379. },
  1380. {
  1381. "cell_type": "code",
  1382. "execution_count": 65,
  1383. "metadata": {},
  1384. "outputs": [
  1385. {
  1386. "name": "stdout",
  1387. "output_type": "stream",
  1388. "text": [
  1389. "[2, 1, 4]\n",
  1390. "[2, 1, 4, 9]\n"
  1391. ]
  1392. }
  1393. ],
  1394. "source": [
  1395. "lista.pop()\n",
  1396. "print(lista)\n",
  1397. "lista.append(9)\n",
  1398. "print(lista)"
  1399. ]
  1400. },
  1401. {
  1402. "cell_type": "code",
  1403. "execution_count": 66,
  1404. "metadata": {},
  1405. "outputs": [
  1406. {
  1407. "name": "stdout",
  1408. "output_type": "stream",
  1409. "text": [
  1410. "[2, 1, 4, 3]\n"
  1411. ]
  1412. }
  1413. ],
  1414. "source": [
  1415. "print(listb)"
  1416. ]
  1417. },
  1418. {
  1419. "cell_type": "markdown",
  1420. "metadata": {},
  1421. "source": [
  1422. "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?"
  1423. ]
  1424. },
  1425. {
  1426. "cell_type": "markdown",
  1427. "metadata": {},
  1428. "source": [
  1429. "## 2. 元组"
  1430. ]
  1431. },
  1432. {
  1433. "cell_type": "markdown",
  1434. "metadata": {},
  1435. "source": [
  1436. "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。"
  1437. ]
  1438. },
  1439. {
  1440. "cell_type": "code",
  1441. "execution_count": 68,
  1442. "metadata": {},
  1443. "outputs": [
  1444. {
  1445. "name": "stdout",
  1446. "output_type": "stream",
  1447. "text": [
  1448. "(3, 1)\n",
  1449. "<class 'tuple'>\n"
  1450. ]
  1451. },
  1452. {
  1453. "ename": "TypeError",
  1454. "evalue": "'tuple' object does not support item assignment",
  1455. "output_type": "error",
  1456. "traceback": [
  1457. "\u001b[0;31m------------------------------------------\u001b[0m",
  1458. "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
  1459. "\u001b[0;32m<ipython-input-68-9f8df9d106cc>\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[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[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[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
  1460. "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
  1461. ]
  1462. }
  1463. ],
  1464. "source": [
  1465. "xyz = divmod(10,3)\n",
  1466. "print(xyz)\n",
  1467. "print(type(xyz))\n",
  1468. "xyz[0]=10"
  1469. ]
  1470. },
  1471. {
  1472. "cell_type": "markdown",
  1473. "metadata": {},
  1474. "source": [
  1475. "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。"
  1476. ]
  1477. },
  1478. {
  1479. "cell_type": "markdown",
  1480. "metadata": {},
  1481. "source": [
  1482. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1483. ]
  1484. },
  1485. {
  1486. "cell_type": "code",
  1487. "execution_count": 69,
  1488. "metadata": {},
  1489. "outputs": [],
  1490. "source": [
  1491. "tup = ()\n",
  1492. "tup2 = tuple()"
  1493. ]
  1494. },
  1495. {
  1496. "cell_type": "markdown",
  1497. "metadata": {},
  1498. "source": [
  1499. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1500. ]
  1501. },
  1502. {
  1503. "cell_type": "code",
  1504. "execution_count": 74,
  1505. "metadata": {},
  1506. "outputs": [
  1507. {
  1508. "data": {
  1509. "text/plain": [
  1510. "(27,)"
  1511. ]
  1512. },
  1513. "execution_count": 74,
  1514. "metadata": {},
  1515. "output_type": "execute_result"
  1516. }
  1517. ],
  1518. "source": [
  1519. "27,"
  1520. ]
  1521. },
  1522. {
  1523. "cell_type": "markdown",
  1524. "metadata": {},
  1525. "source": [
  1526. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1527. ]
  1528. },
  1529. {
  1530. "cell_type": "code",
  1531. "execution_count": 75,
  1532. "metadata": {},
  1533. "outputs": [
  1534. {
  1535. "data": {
  1536. "text/plain": [
  1537. "(27, 27)"
  1538. ]
  1539. },
  1540. "execution_count": 75,
  1541. "metadata": {},
  1542. "output_type": "execute_result"
  1543. }
  1544. ],
  1545. "source": [
  1546. "2*(27,)"
  1547. ]
  1548. },
  1549. {
  1550. "cell_type": "markdown",
  1551. "metadata": {},
  1552. "source": [
  1553. "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。"
  1554. ]
  1555. },
  1556. {
  1557. "cell_type": "code",
  1558. "execution_count": 76,
  1559. "metadata": {
  1560. "scrolled": true
  1561. },
  1562. "outputs": [
  1563. {
  1564. "name": "stdout",
  1565. "output_type": "stream",
  1566. "text": [
  1567. "(1, 2, 3)\n",
  1568. "('H', 'e', 'l', 'l', 'o')\n"
  1569. ]
  1570. }
  1571. ],
  1572. "source": [
  1573. "tup3 = tuple([1,2,3])\n",
  1574. "print(tup3)\n",
  1575. "tup4 = tuple('Hello')\n",
  1576. "print(tup4)"
  1577. ]
  1578. },
  1579. {
  1580. "cell_type": "markdown",
  1581. "metadata": {},
  1582. "source": [
  1583. "它遵循与列表相同的索引和切片。"
  1584. ]
  1585. },
  1586. {
  1587. "cell_type": "code",
  1588. "execution_count": 77,
  1589. "metadata": {},
  1590. "outputs": [
  1591. {
  1592. "name": "stdout",
  1593. "output_type": "stream",
  1594. "text": [
  1595. "2\n",
  1596. "('H', 'e', 'l')\n"
  1597. ]
  1598. }
  1599. ],
  1600. "source": [
  1601. "print(tup3[1])\n",
  1602. "tup5 = tup4[:3]\n",
  1603. "print(tup5)"
  1604. ]
  1605. },
  1606. {
  1607. "cell_type": "markdown",
  1608. "metadata": {},
  1609. "source": [
  1610. "### 2.1 将一个元组映射到另一个元组"
  1611. ]
  1612. },
  1613. {
  1614. "cell_type": "code",
  1615. "execution_count": 80,
  1616. "metadata": {},
  1617. "outputs": [],
  1618. "source": [
  1619. "(a,b,c)= ('alpha','beta','gamma')"
  1620. ]
  1621. },
  1622. {
  1623. "cell_type": "code",
  1624. "execution_count": 46,
  1625. "metadata": {},
  1626. "outputs": [
  1627. {
  1628. "name": "stdout",
  1629. "output_type": "stream",
  1630. "text": [
  1631. "alpha beta gamma\n"
  1632. ]
  1633. }
  1634. ],
  1635. "source": [
  1636. "print(a,b,c)"
  1637. ]
  1638. },
  1639. {
  1640. "cell_type": "code",
  1641. "execution_count": 81,
  1642. "metadata": {},
  1643. "outputs": [
  1644. {
  1645. "name": "stdout",
  1646. "output_type": "stream",
  1647. "text": [
  1648. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1649. ]
  1650. }
  1651. ],
  1652. "source": [
  1653. "d = tuple('RajathKumarMP')\n",
  1654. "print(d)"
  1655. ]
  1656. },
  1657. {
  1658. "cell_type": "markdown",
  1659. "metadata": {},
  1660. "source": [
  1661. "### 2.2 元组内置函数"
  1662. ]
  1663. },
  1664. {
  1665. "cell_type": "markdown",
  1666. "metadata": {},
  1667. "source": [
  1668. "**count()** 函数计算元组中存在的指定元素的数量。"
  1669. ]
  1670. },
  1671. {
  1672. "cell_type": "code",
  1673. "execution_count": 82,
  1674. "metadata": {},
  1675. "outputs": [
  1676. {
  1677. "data": {
  1678. "text/plain": [
  1679. "3"
  1680. ]
  1681. },
  1682. "execution_count": 82,
  1683. "metadata": {},
  1684. "output_type": "execute_result"
  1685. }
  1686. ],
  1687. "source": [
  1688. "d.count('a')"
  1689. ]
  1690. },
  1691. {
  1692. "cell_type": "markdown",
  1693. "metadata": {},
  1694. "source": [
  1695. "**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"
  1696. ]
  1697. },
  1698. {
  1699. "cell_type": "code",
  1700. "execution_count": 83,
  1701. "metadata": {},
  1702. "outputs": [
  1703. {
  1704. "data": {
  1705. "text/plain": [
  1706. "1"
  1707. ]
  1708. },
  1709. "execution_count": 83,
  1710. "metadata": {},
  1711. "output_type": "execute_result"
  1712. }
  1713. ],
  1714. "source": [
  1715. "d.index('a')"
  1716. ]
  1717. },
  1718. {
  1719. "cell_type": "markdown",
  1720. "metadata": {},
  1721. "source": [
  1722. "## 3. 集合"
  1723. ]
  1724. },
  1725. {
  1726. "cell_type": "markdown",
  1727. "metadata": {},
  1728. "source": [
  1729. "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",
  1730. "\n",
  1731. "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"
  1732. ]
  1733. },
  1734. {
  1735. "cell_type": "code",
  1736. "execution_count": 85,
  1737. "metadata": {},
  1738. "outputs": [
  1739. {
  1740. "name": "stdout",
  1741. "output_type": "stream",
  1742. "text": [
  1743. "<class 'set'>\n"
  1744. ]
  1745. }
  1746. ],
  1747. "source": [
  1748. "set1 = set()\n",
  1749. "print(type(set1))"
  1750. ]
  1751. },
  1752. {
  1753. "cell_type": "code",
  1754. "execution_count": 87,
  1755. "metadata": {},
  1756. "outputs": [
  1757. {
  1758. "name": "stdout",
  1759. "output_type": "stream",
  1760. "text": [
  1761. "{1, 2, 3, 4}\n"
  1762. ]
  1763. }
  1764. ],
  1765. "source": [
  1766. "set0 = set([1,2,2,3,3,4])\n",
  1767. "print(set0)"
  1768. ]
  1769. },
  1770. {
  1771. "cell_type": "code",
  1772. "execution_count": 88,
  1773. "metadata": {},
  1774. "outputs": [
  1775. {
  1776. "name": "stdout",
  1777. "output_type": "stream",
  1778. "text": [
  1779. "{1, 2, 3, 4}\n"
  1780. ]
  1781. }
  1782. ],
  1783. "source": [
  1784. "set1 = set((1,2,2,3,3,4))\n",
  1785. "print(set1)"
  1786. ]
  1787. },
  1788. {
  1789. "cell_type": "markdown",
  1790. "metadata": {},
  1791. "source": [
  1792. "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。"
  1793. ]
  1794. },
  1795. {
  1796. "cell_type": "markdown",
  1797. "metadata": {},
  1798. "source": [
  1799. "### 3.1 内置函数"
  1800. ]
  1801. },
  1802. {
  1803. "cell_type": "code",
  1804. "execution_count": 89,
  1805. "metadata": {},
  1806. "outputs": [],
  1807. "source": [
  1808. "set1 = set([1,2,3])"
  1809. ]
  1810. },
  1811. {
  1812. "cell_type": "code",
  1813. "execution_count": 90,
  1814. "metadata": {},
  1815. "outputs": [],
  1816. "source": [
  1817. "set2 = set([2,3,4,5])"
  1818. ]
  1819. },
  1820. {
  1821. "cell_type": "markdown",
  1822. "metadata": {},
  1823. "source": [
  1824. "**union( )** 函数返回一个集合,该集合包含两个集合的所有元素,但是没有重复。"
  1825. ]
  1826. },
  1827. {
  1828. "cell_type": "code",
  1829. "execution_count": 91,
  1830. "metadata": {},
  1831. "outputs": [
  1832. {
  1833. "data": {
  1834. "text/plain": [
  1835. "{1, 2, 3, 4, 5}"
  1836. ]
  1837. },
  1838. "execution_count": 91,
  1839. "metadata": {},
  1840. "output_type": "execute_result"
  1841. }
  1842. ],
  1843. "source": [
  1844. "set1.union(set2)"
  1845. ]
  1846. },
  1847. {
  1848. "cell_type": "markdown",
  1849. "metadata": {},
  1850. "source": [
  1851. "**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。"
  1852. ]
  1853. },
  1854. {
  1855. "cell_type": "code",
  1856. "execution_count": 92,
  1857. "metadata": {},
  1858. "outputs": [
  1859. {
  1860. "name": "stdout",
  1861. "output_type": "stream",
  1862. "text": [
  1863. "{1, 2, 3}\n"
  1864. ]
  1865. },
  1866. {
  1867. "data": {
  1868. "text/plain": [
  1869. "{0, 1, 2, 3}"
  1870. ]
  1871. },
  1872. "execution_count": 92,
  1873. "metadata": {},
  1874. "output_type": "execute_result"
  1875. }
  1876. ],
  1877. "source": [
  1878. "print(set1)\n",
  1879. "set1.add(0)\n",
  1880. "set1"
  1881. ]
  1882. },
  1883. {
  1884. "cell_type": "markdown",
  1885. "metadata": {},
  1886. "source": [
  1887. "**intersection( )** 函数输出一个集合,该集合包含两个集合中的所有元素。"
  1888. ]
  1889. },
  1890. {
  1891. "cell_type": "code",
  1892. "execution_count": 93,
  1893. "metadata": {},
  1894. "outputs": [
  1895. {
  1896. "data": {
  1897. "text/plain": [
  1898. "{2, 3}"
  1899. ]
  1900. },
  1901. "execution_count": 93,
  1902. "metadata": {},
  1903. "output_type": "execute_result"
  1904. }
  1905. ],
  1906. "source": [
  1907. "set1.intersection(set2)"
  1908. ]
  1909. },
  1910. {
  1911. "cell_type": "markdown",
  1912. "metadata": {},
  1913. "source": [
  1914. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1915. ]
  1916. },
  1917. {
  1918. "cell_type": "code",
  1919. "execution_count": 94,
  1920. "metadata": {},
  1921. "outputs": [
  1922. {
  1923. "name": "stdout",
  1924. "output_type": "stream",
  1925. "text": [
  1926. "{0, 1, 2, 3}\n",
  1927. "{2, 3, 4, 5}\n"
  1928. ]
  1929. },
  1930. {
  1931. "data": {
  1932. "text/plain": [
  1933. "{0, 1}"
  1934. ]
  1935. },
  1936. "execution_count": 94,
  1937. "metadata": {},
  1938. "output_type": "execute_result"
  1939. }
  1940. ],
  1941. "source": [
  1942. "print(set1)\n",
  1943. "print(set2)\n",
  1944. "set1.difference(set2)"
  1945. ]
  1946. },
  1947. {
  1948. "cell_type": "markdown",
  1949. "metadata": {},
  1950. "source": [
  1951. "**symmetric_difference( )** 函数输出一个函数,该函数包含一个集合中的元素。"
  1952. ]
  1953. },
  1954. {
  1955. "cell_type": "code",
  1956. "execution_count": 95,
  1957. "metadata": {},
  1958. "outputs": [
  1959. {
  1960. "data": {
  1961. "text/plain": [
  1962. "{0, 1, 4, 5}"
  1963. ]
  1964. },
  1965. "execution_count": 95,
  1966. "metadata": {},
  1967. "output_type": "execute_result"
  1968. }
  1969. ],
  1970. "source": [
  1971. "set2.symmetric_difference(set1)"
  1972. ]
  1973. },
  1974. {
  1975. "cell_type": "markdown",
  1976. "metadata": {},
  1977. "source": [
  1978. "**issubset( ), isdisjoint( ), issuperset( )** 分别用于检查set1/set2是否是set2/set1的子集、不相交或超集。"
  1979. ]
  1980. },
  1981. {
  1982. "cell_type": "code",
  1983. "execution_count": 96,
  1984. "metadata": {},
  1985. "outputs": [
  1986. {
  1987. "data": {
  1988. "text/plain": [
  1989. "False"
  1990. ]
  1991. },
  1992. "execution_count": 96,
  1993. "metadata": {},
  1994. "output_type": "execute_result"
  1995. }
  1996. ],
  1997. "source": [
  1998. "set1.issubset(set2)"
  1999. ]
  2000. },
  2001. {
  2002. "cell_type": "code",
  2003. "execution_count": 97,
  2004. "metadata": {},
  2005. "outputs": [
  2006. {
  2007. "data": {
  2008. "text/plain": [
  2009. "False"
  2010. ]
  2011. },
  2012. "execution_count": 97,
  2013. "metadata": {},
  2014. "output_type": "execute_result"
  2015. }
  2016. ],
  2017. "source": [
  2018. "set2.isdisjoint(set1)"
  2019. ]
  2020. },
  2021. {
  2022. "cell_type": "code",
  2023. "execution_count": 97,
  2024. "metadata": {},
  2025. "outputs": [
  2026. {
  2027. "data": {
  2028. "text/plain": [
  2029. "False"
  2030. ]
  2031. },
  2032. "execution_count": 97,
  2033. "metadata": {},
  2034. "output_type": "execute_result"
  2035. }
  2036. ],
  2037. "source": [
  2038. "set2.issuperset(set1)"
  2039. ]
  2040. },
  2041. {
  2042. "cell_type": "markdown",
  2043. "metadata": {},
  2044. "source": [
  2045. "**pop( )** 是用来移除集合中的任意元素。"
  2046. ]
  2047. },
  2048. {
  2049. "cell_type": "code",
  2050. "execution_count": 115,
  2051. "metadata": {},
  2052. "outputs": [],
  2053. "source": [
  2054. "set1=set([10, 9, 1, 2, 4])"
  2055. ]
  2056. },
  2057. {
  2058. "cell_type": "code",
  2059. "execution_count": 113,
  2060. "metadata": {},
  2061. "outputs": [
  2062. {
  2063. "name": "stdout",
  2064. "output_type": "stream",
  2065. "text": [
  2066. "set()\n"
  2067. ]
  2068. }
  2069. ],
  2070. "source": [
  2071. "set1.pop()\n",
  2072. "print(set1)"
  2073. ]
  2074. },
  2075. {
  2076. "cell_type": "markdown",
  2077. "metadata": {},
  2078. "source": [
  2079. "**remove( )** 函数从集合中删除指定的元素。"
  2080. ]
  2081. },
  2082. {
  2083. "cell_type": "code",
  2084. "execution_count": 116,
  2085. "metadata": {},
  2086. "outputs": [
  2087. {
  2088. "data": {
  2089. "text/plain": [
  2090. "{1, 4, 9, 10}"
  2091. ]
  2092. },
  2093. "execution_count": 116,
  2094. "metadata": {},
  2095. "output_type": "execute_result"
  2096. }
  2097. ],
  2098. "source": [
  2099. "set1.remove(2)\n",
  2100. "set1"
  2101. ]
  2102. },
  2103. {
  2104. "cell_type": "markdown",
  2105. "metadata": {},
  2106. "source": [
  2107. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2108. ]
  2109. },
  2110. {
  2111. "cell_type": "code",
  2112. "execution_count": 117,
  2113. "metadata": {},
  2114. "outputs": [
  2115. {
  2116. "data": {
  2117. "text/plain": [
  2118. "set()"
  2119. ]
  2120. },
  2121. "execution_count": 117,
  2122. "metadata": {},
  2123. "output_type": "execute_result"
  2124. }
  2125. ],
  2126. "source": [
  2127. "set1.clear()\n",
  2128. "set1"
  2129. ]
  2130. }
  2131. ],
  2132. "metadata": {
  2133. "kernelspec": {
  2134. "display_name": "Python 3",
  2135. "language": "python",
  2136. "name": "python3"
  2137. },
  2138. "language_info": {
  2139. "codemirror_mode": {
  2140. "name": "ipython",
  2141. "version": 3
  2142. },
  2143. "file_extension": ".py",
  2144. "mimetype": "text/x-python",
  2145. "name": "python",
  2146. "nbconvert_exporter": "python",
  2147. "pygments_lexer": "ipython3",
  2148. "version": "3.6.9"
  2149. }
  2150. },
  2151. "nbformat": 4,
  2152. "nbformat_minor": 1
  2153. }

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》。