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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": "markdown",
  582. "metadata": {},
  583. "source": [
  584. "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。"
  585. ]
  586. },
  587. {
  588. "cell_type": "code",
  589. "execution_count": 24,
  590. "metadata": {},
  591. "outputs": [],
  592. "source": [
  593. "mlist = ['bzaa','ds','nc','az','z','klm']"
  594. ]
  595. },
  596. {
  597. "cell_type": "code",
  598. "execution_count": 25,
  599. "metadata": {},
  600. "outputs": [
  601. {
  602. "name": "stdout",
  603. "output_type": "stream",
  604. "text": [
  605. "z\n",
  606. "az\n"
  607. ]
  608. }
  609. ],
  610. "source": [
  611. "print(max(mlist))\n",
  612. "print(min(mlist))"
  613. ]
  614. },
  615. {
  616. "cell_type": "markdown",
  617. "metadata": {},
  618. "source": [
  619. "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?"
  620. ]
  621. },
  622. {
  623. "cell_type": "code",
  624. "execution_count": 26,
  625. "metadata": {},
  626. "outputs": [],
  627. "source": [
  628. "nlist = ['1','94','93','1000']"
  629. ]
  630. },
  631. {
  632. "cell_type": "code",
  633. "execution_count": 27,
  634. "metadata": {},
  635. "outputs": [
  636. {
  637. "name": "stdout",
  638. "output_type": "stream",
  639. "text": [
  640. "94\n",
  641. "1\n"
  642. ]
  643. }
  644. ],
  645. "source": [
  646. "print(max(nlist))\n",
  647. "print(min(nlist))"
  648. ]
  649. },
  650. {
  651. "cell_type": "markdown",
  652. "metadata": {},
  653. "source": [
  654. "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。"
  655. ]
  656. },
  657. {
  658. "cell_type": "markdown",
  659. "metadata": {},
  660. "source": [
  661. "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'。"
  662. ]
  663. },
  664. {
  665. "cell_type": "code",
  666. "execution_count": 28,
  667. "metadata": {},
  668. "outputs": [
  669. {
  670. "name": "stdout",
  671. "output_type": "stream",
  672. "text": [
  673. "Earth\n",
  674. "Jet\n"
  675. ]
  676. }
  677. ],
  678. "source": [
  679. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  680. "print(max(names, key=len))\n",
  681. "print(min(names, key=len))"
  682. ]
  683. },
  684. {
  685. "cell_type": "markdown",
  686. "metadata": {},
  687. "source": [
  688. "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",
  689. "\n",
  690. "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",
  691. "\n",
  692. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  693. ]
  694. },
  695. {
  696. "cell_type": "code",
  697. "execution_count": 29,
  698. "metadata": {},
  699. "outputs": [
  700. {
  701. "data": {
  702. "text/plain": [
  703. "['h', 'e', 'l', 'l', 'o']"
  704. ]
  705. },
  706. "execution_count": 29,
  707. "metadata": {},
  708. "output_type": "execute_result"
  709. }
  710. ],
  711. "source": [
  712. "list('hello')"
  713. ]
  714. },
  715. {
  716. "cell_type": "markdown",
  717. "metadata": {},
  718. "source": [
  719. "**append( )** 被用来在列表的最后添加一个元素。"
  720. ]
  721. },
  722. {
  723. "cell_type": "code",
  724. "execution_count": 30,
  725. "metadata": {},
  726. "outputs": [],
  727. "source": [
  728. "lst = [1,1,4,8,7]"
  729. ]
  730. },
  731. {
  732. "cell_type": "code",
  733. "execution_count": 31,
  734. "metadata": {},
  735. "outputs": [
  736. {
  737. "name": "stdout",
  738. "output_type": "stream",
  739. "text": [
  740. "[1, 1, 4, 8, 7, 1]\n"
  741. ]
  742. }
  743. ],
  744. "source": [
  745. "lst.append(1)\n",
  746. "print(lst)"
  747. ]
  748. },
  749. {
  750. "cell_type": "markdown",
  751. "metadata": {},
  752. "source": [
  753. "**count( )** 用于计算列表中出现的特定元素的数量。"
  754. ]
  755. },
  756. {
  757. "cell_type": "code",
  758. "execution_count": 32,
  759. "metadata": {},
  760. "outputs": [
  761. {
  762. "data": {
  763. "text/plain": [
  764. "3"
  765. ]
  766. },
  767. "execution_count": 32,
  768. "metadata": {},
  769. "output_type": "execute_result"
  770. }
  771. ],
  772. "source": [
  773. "lst.count(1)"
  774. ]
  775. },
  776. {
  777. "cell_type": "markdown",
  778. "metadata": {},
  779. "source": [
  780. "**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。"
  781. ]
  782. },
  783. {
  784. "cell_type": "code",
  785. "execution_count": 33,
  786. "metadata": {},
  787. "outputs": [],
  788. "source": [
  789. "lst1 = [5,4,2,8]"
  790. ]
  791. },
  792. {
  793. "cell_type": "code",
  794. "execution_count": 35,
  795. "metadata": {},
  796. "outputs": [
  797. {
  798. "name": "stdout",
  799. "output_type": "stream",
  800. "text": [
  801. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8]]\n"
  802. ]
  803. },
  804. {
  805. "data": {
  806. "text/plain": [
  807. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]"
  808. ]
  809. },
  810. "execution_count": 35,
  811. "metadata": {},
  812. "output_type": "execute_result"
  813. }
  814. ],
  815. "source": [
  816. "lst.append(lst1)\n",
  817. "print(lst)"
  818. ]
  819. },
  820. {
  821. "cell_type": "markdown",
  822. "metadata": {},
  823. "source": [
  824. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  825. ]
  826. },
  827. {
  828. "cell_type": "code",
  829. "execution_count": 36,
  830. "metadata": {},
  831. "outputs": [
  832. {
  833. "name": "stdout",
  834. "output_type": "stream",
  835. "text": [
  836. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  837. ]
  838. }
  839. ],
  840. "source": [
  841. "lst.extend(lst1)\n",
  842. "print(lst)"
  843. ]
  844. },
  845. {
  846. "cell_type": "markdown",
  847. "metadata": {},
  848. "source": [
  849. "**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。"
  850. ]
  851. },
  852. {
  853. "cell_type": "code",
  854. "execution_count": 37,
  855. "metadata": {},
  856. "outputs": [
  857. {
  858. "data": {
  859. "text/plain": [
  860. "0"
  861. ]
  862. },
  863. "execution_count": 37,
  864. "metadata": {},
  865. "output_type": "execute_result"
  866. }
  867. ],
  868. "source": [
  869. "lst.index(1)"
  870. ]
  871. },
  872. {
  873. "cell_type": "code",
  874. "execution_count": 38,
  875. "metadata": {},
  876. "outputs": [
  877. {
  878. "ename": "ValueError",
  879. "evalue": "999 is not in list",
  880. "output_type": "error",
  881. "traceback": [
  882. "\u001b[0;31m------------------------------------------\u001b[0m",
  883. "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
  884. "\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",
  885. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  886. ]
  887. }
  888. ],
  889. "source": [
  890. "lst.index(999)"
  891. ]
  892. },
  893. {
  894. "cell_type": "markdown",
  895. "metadata": {},
  896. "source": [
  897. "**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。"
  898. ]
  899. },
  900. {
  901. "cell_type": "code",
  902. "execution_count": 39,
  903. "metadata": {},
  904. "outputs": [
  905. {
  906. "name": "stdout",
  907. "output_type": "stream",
  908. "text": [
  909. "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  910. ]
  911. }
  912. ],
  913. "source": [
  914. "lst.insert(5, 'name')\n",
  915. "print(lst)"
  916. ]
  917. },
  918. {
  919. "cell_type": "markdown",
  920. "metadata": {},
  921. "source": [
  922. "**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。"
  923. ]
  924. },
  925. {
  926. "cell_type": "code",
  927. "execution_count": 40,
  928. "metadata": {},
  929. "outputs": [
  930. {
  931. "name": "stdout",
  932. "output_type": "stream",
  933. "text": [
  934. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2, 8]\n"
  935. ]
  936. }
  937. ],
  938. "source": [
  939. "lst[5] = 'Python'\n",
  940. "print(lst)"
  941. ]
  942. },
  943. {
  944. "cell_type": "markdown",
  945. "metadata": {},
  946. "source": [
  947. "**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。"
  948. ]
  949. },
  950. {
  951. "cell_type": "code",
  952. "execution_count": 41,
  953. "metadata": {},
  954. "outputs": [
  955. {
  956. "data": {
  957. "text/plain": [
  958. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2]"
  959. ]
  960. },
  961. "execution_count": 41,
  962. "metadata": {},
  963. "output_type": "execute_result"
  964. }
  965. ],
  966. "source": [
  967. "lst.pop()\n",
  968. "lst"
  969. ]
  970. },
  971. {
  972. "cell_type": "markdown",
  973. "metadata": {},
  974. "source": [
  975. "可以指定索引值来弹出与该索引值对应的元素。"
  976. ]
  977. },
  978. {
  979. "cell_type": "code",
  980. "execution_count": 42,
  981. "metadata": {},
  982. "outputs": [
  983. {
  984. "data": {
  985. "text/plain": [
  986. "4"
  987. ]
  988. },
  989. "execution_count": 42,
  990. "metadata": {},
  991. "output_type": "execute_result"
  992. }
  993. ],
  994. "source": [
  995. "lst.pop(2)"
  996. ]
  997. },
  998. {
  999. "cell_type": "code",
  1000. "execution_count": 43,
  1001. "metadata": {},
  1002. "outputs": [
  1003. {
  1004. "name": "stdout",
  1005. "output_type": "stream",
  1006. "text": [
  1007. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 4, 2]\n"
  1008. ]
  1009. },
  1010. {
  1011. "data": {
  1012. "text/plain": [
  1013. "4"
  1014. ]
  1015. },
  1016. "execution_count": 43,
  1017. "metadata": {},
  1018. "output_type": "execute_result"
  1019. }
  1020. ],
  1021. "source": [
  1022. "print(lst)\n",
  1023. "lst.pop(-2)"
  1024. ]
  1025. },
  1026. {
  1027. "cell_type": "code",
  1028. "execution_count": 44,
  1029. "metadata": {},
  1030. "outputs": [
  1031. {
  1032. "name": "stdout",
  1033. "output_type": "stream",
  1034. "text": [
  1035. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], [5, 4, 2, 8], 5, 2]\n"
  1036. ]
  1037. }
  1038. ],
  1039. "source": [
  1040. "print(lst)"
  1041. ]
  1042. },
  1043. {
  1044. "cell_type": "markdown",
  1045. "metadata": {},
  1046. "source": [
  1047. "**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。"
  1048. ]
  1049. },
  1050. {
  1051. "cell_type": "code",
  1052. "execution_count": 46,
  1053. "metadata": {},
  1054. "outputs": [
  1055. {
  1056. "ename": "ValueError",
  1057. "evalue": "list.remove(x): x not in list",
  1058. "output_type": "error",
  1059. "traceback": [
  1060. "\u001b[0;31m------------------------------------------\u001b[0m",
  1061. "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
  1062. "\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",
  1063. "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
  1064. ]
  1065. }
  1066. ],
  1067. "source": [
  1068. "lst.remove('Python')\n",
  1069. "print(lst)"
  1070. ]
  1071. },
  1072. {
  1073. "cell_type": "markdown",
  1074. "metadata": {},
  1075. "source": [
  1076. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1077. ]
  1078. },
  1079. {
  1080. "cell_type": "code",
  1081. "execution_count": 52,
  1082. "metadata": {},
  1083. "outputs": [
  1084. {
  1085. "name": "stdout",
  1086. "output_type": "stream",
  1087. "text": [
  1088. "[1, [5, 4, 2, 8], 5, 2]\n",
  1089. "[1, 5, 2]\n"
  1090. ]
  1091. }
  1092. ],
  1093. "source": [
  1094. "print(lst)\n",
  1095. "del lst[1]\n",
  1096. "print(lst)"
  1097. ]
  1098. },
  1099. {
  1100. "cell_type": "markdown",
  1101. "metadata": {},
  1102. "source": [
  1103. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1104. ]
  1105. },
  1106. {
  1107. "cell_type": "code",
  1108. "execution_count": 53,
  1109. "metadata": {},
  1110. "outputs": [
  1111. {
  1112. "name": "stdout",
  1113. "output_type": "stream",
  1114. "text": [
  1115. "[2, 5, 1]\n"
  1116. ]
  1117. }
  1118. ],
  1119. "source": [
  1120. "lst.reverse()\n",
  1121. "print(lst)"
  1122. ]
  1123. },
  1124. {
  1125. "cell_type": "markdown",
  1126. "metadata": {},
  1127. "source": [
  1128. "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",
  1129. "\n",
  1130. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1131. ]
  1132. },
  1133. {
  1134. "cell_type": "code",
  1135. "execution_count": 54,
  1136. "metadata": {},
  1137. "outputs": [
  1138. {
  1139. "name": "stdout",
  1140. "output_type": "stream",
  1141. "text": [
  1142. "[1, 4, 8, 8, 10]\n"
  1143. ]
  1144. }
  1145. ],
  1146. "source": [
  1147. "lst = [1, 4, 8, 8, 10]\n",
  1148. "lst.sort()\n",
  1149. "print(lst)"
  1150. ]
  1151. },
  1152. {
  1153. "cell_type": "markdown",
  1154. "metadata": {},
  1155. "source": [
  1156. "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。"
  1157. ]
  1158. },
  1159. {
  1160. "cell_type": "code",
  1161. "execution_count": 56,
  1162. "metadata": {},
  1163. "outputs": [
  1164. {
  1165. "ename": "TypeError",
  1166. "evalue": "must use keyword argument for key function",
  1167. "output_type": "error",
  1168. "traceback": [
  1169. "\u001b[0;31m------------------------------------------\u001b[0m",
  1170. "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
  1171. "\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",
  1172. "\u001b[0;31mTypeError\u001b[0m: must use keyword argument for key function"
  1173. ]
  1174. }
  1175. ],
  1176. "source": [
  1177. "lst.sort(reverse=True)\n",
  1178. "print(lst)"
  1179. ]
  1180. },
  1181. {
  1182. "cell_type": "markdown",
  1183. "metadata": {},
  1184. "source": [
  1185. "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。"
  1186. ]
  1187. },
  1188. {
  1189. "cell_type": "code",
  1190. "execution_count": 57,
  1191. "metadata": {},
  1192. "outputs": [
  1193. {
  1194. "name": "stdout",
  1195. "output_type": "stream",
  1196. "text": [
  1197. "['apple', 'orange', 'peach']\n",
  1198. "['peach', 'orange', 'apple']\n"
  1199. ]
  1200. }
  1201. ],
  1202. "source": [
  1203. "names = ['apple', 'orange', 'peach']\n",
  1204. "names.sort()\n",
  1205. "print(names)\n",
  1206. "names.sort(reverse=True)\n",
  1207. "print(names)"
  1208. ]
  1209. },
  1210. {
  1211. "cell_type": "markdown",
  1212. "metadata": {},
  1213. "source": [
  1214. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1215. ]
  1216. },
  1217. {
  1218. "cell_type": "code",
  1219. "execution_count": 58,
  1220. "metadata": {},
  1221. "outputs": [
  1222. {
  1223. "name": "stdout",
  1224. "output_type": "stream",
  1225. "text": [
  1226. "['peach', 'apple', 'orange']\n",
  1227. "['orange', 'peach', 'apple']\n"
  1228. ]
  1229. }
  1230. ],
  1231. "source": [
  1232. "names.sort(key=len)\n",
  1233. "print(names)\n",
  1234. "names.sort(key=len,reverse=True)\n",
  1235. "print(names)"
  1236. ]
  1237. },
  1238. {
  1239. "cell_type": "markdown",
  1240. "metadata": {},
  1241. "source": [
  1242. "### 1.4 复制一个列表"
  1243. ]
  1244. },
  1245. {
  1246. "cell_type": "markdown",
  1247. "metadata": {},
  1248. "source": [
  1249. "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:"
  1250. ]
  1251. },
  1252. {
  1253. "cell_type": "code",
  1254. "execution_count": 59,
  1255. "metadata": {},
  1256. "outputs": [],
  1257. "source": [
  1258. "lista= [2,1,4,3]"
  1259. ]
  1260. },
  1261. {
  1262. "cell_type": "code",
  1263. "execution_count": 60,
  1264. "metadata": {},
  1265. "outputs": [
  1266. {
  1267. "name": "stdout",
  1268. "output_type": "stream",
  1269. "text": [
  1270. "[2, 1, 4, 3]\n"
  1271. ]
  1272. }
  1273. ],
  1274. "source": [
  1275. "listb = lista # 对象赋值\n",
  1276. "print(listb)"
  1277. ]
  1278. },
  1279. {
  1280. "cell_type": "markdown",
  1281. "metadata": {},
  1282. "source": [
  1283. "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。"
  1284. ]
  1285. },
  1286. {
  1287. "cell_type": "code",
  1288. "execution_count": 61,
  1289. "metadata": {},
  1290. "outputs": [
  1291. {
  1292. "name": "stdout",
  1293. "output_type": "stream",
  1294. "text": [
  1295. "[2, 1, 4]\n",
  1296. "[2, 1, 4, 9]\n"
  1297. ]
  1298. }
  1299. ],
  1300. "source": [
  1301. "lista.pop()\n",
  1302. "print(lista)\n",
  1303. "lista.append(9)\n",
  1304. "print(lista)"
  1305. ]
  1306. },
  1307. {
  1308. "cell_type": "code",
  1309. "execution_count": 62,
  1310. "metadata": {},
  1311. "outputs": [
  1312. {
  1313. "name": "stdout",
  1314. "output_type": "stream",
  1315. "text": [
  1316. "[2, 1, 4, 9]\n"
  1317. ]
  1318. }
  1319. ],
  1320. "source": [
  1321. "print(listb)"
  1322. ]
  1323. },
  1324. {
  1325. "cell_type": "markdown",
  1326. "metadata": {},
  1327. "source": [
  1328. "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",
  1329. "\n",
  1330. "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。"
  1331. ]
  1332. },
  1333. {
  1334. "cell_type": "code",
  1335. "execution_count": 63,
  1336. "metadata": {},
  1337. "outputs": [],
  1338. "source": [
  1339. "lista = [2,1,4,3]"
  1340. ]
  1341. },
  1342. {
  1343. "cell_type": "code",
  1344. "execution_count": 64,
  1345. "metadata": {},
  1346. "outputs": [
  1347. {
  1348. "name": "stdout",
  1349. "output_type": "stream",
  1350. "text": [
  1351. "[2, 1, 4, 3]\n"
  1352. ]
  1353. }
  1354. ],
  1355. "source": [
  1356. "listb = lista[:]\n",
  1357. "print(listb)"
  1358. ]
  1359. },
  1360. {
  1361. "cell_type": "code",
  1362. "execution_count": 65,
  1363. "metadata": {},
  1364. "outputs": [
  1365. {
  1366. "name": "stdout",
  1367. "output_type": "stream",
  1368. "text": [
  1369. "[2, 1, 4]\n",
  1370. "[2, 1, 4, 9]\n"
  1371. ]
  1372. }
  1373. ],
  1374. "source": [
  1375. "lista.pop()\n",
  1376. "print(lista)\n",
  1377. "lista.append(9)\n",
  1378. "print(lista)"
  1379. ]
  1380. },
  1381. {
  1382. "cell_type": "code",
  1383. "execution_count": 66,
  1384. "metadata": {},
  1385. "outputs": [
  1386. {
  1387. "name": "stdout",
  1388. "output_type": "stream",
  1389. "text": [
  1390. "[2, 1, 4, 3]\n"
  1391. ]
  1392. }
  1393. ],
  1394. "source": [
  1395. "print(listb)"
  1396. ]
  1397. },
  1398. {
  1399. "cell_type": "markdown",
  1400. "metadata": {},
  1401. "source": [
  1402. "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?"
  1403. ]
  1404. },
  1405. {
  1406. "cell_type": "markdown",
  1407. "metadata": {},
  1408. "source": [
  1409. "## 2. 元组"
  1410. ]
  1411. },
  1412. {
  1413. "cell_type": "markdown",
  1414. "metadata": {},
  1415. "source": [
  1416. "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。"
  1417. ]
  1418. },
  1419. {
  1420. "cell_type": "code",
  1421. "execution_count": 68,
  1422. "metadata": {},
  1423. "outputs": [
  1424. {
  1425. "name": "stdout",
  1426. "output_type": "stream",
  1427. "text": [
  1428. "(3, 1)\n",
  1429. "<class 'tuple'>\n"
  1430. ]
  1431. },
  1432. {
  1433. "ename": "TypeError",
  1434. "evalue": "'tuple' object does not support item assignment",
  1435. "output_type": "error",
  1436. "traceback": [
  1437. "\u001b[0;31m------------------------------------------\u001b[0m",
  1438. "\u001b[0;31mTypeError\u001b[0mTraceback (most recent call last)",
  1439. "\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",
  1440. "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
  1441. ]
  1442. }
  1443. ],
  1444. "source": [
  1445. "xyz = divmod(10,3)\n",
  1446. "print(xyz)\n",
  1447. "print(type(xyz))\n",
  1448. "xyz[0]=10"
  1449. ]
  1450. },
  1451. {
  1452. "cell_type": "markdown",
  1453. "metadata": {},
  1454. "source": [
  1455. "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。"
  1456. ]
  1457. },
  1458. {
  1459. "cell_type": "markdown",
  1460. "metadata": {},
  1461. "source": [
  1462. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1463. ]
  1464. },
  1465. {
  1466. "cell_type": "code",
  1467. "execution_count": 69,
  1468. "metadata": {},
  1469. "outputs": [],
  1470. "source": [
  1471. "tup = ()\n",
  1472. "tup2 = tuple()"
  1473. ]
  1474. },
  1475. {
  1476. "cell_type": "markdown",
  1477. "metadata": {},
  1478. "source": [
  1479. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1480. ]
  1481. },
  1482. {
  1483. "cell_type": "code",
  1484. "execution_count": 74,
  1485. "metadata": {},
  1486. "outputs": [
  1487. {
  1488. "data": {
  1489. "text/plain": [
  1490. "(27,)"
  1491. ]
  1492. },
  1493. "execution_count": 74,
  1494. "metadata": {},
  1495. "output_type": "execute_result"
  1496. }
  1497. ],
  1498. "source": [
  1499. "27,"
  1500. ]
  1501. },
  1502. {
  1503. "cell_type": "markdown",
  1504. "metadata": {},
  1505. "source": [
  1506. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1507. ]
  1508. },
  1509. {
  1510. "cell_type": "code",
  1511. "execution_count": 75,
  1512. "metadata": {},
  1513. "outputs": [
  1514. {
  1515. "data": {
  1516. "text/plain": [
  1517. "(27, 27)"
  1518. ]
  1519. },
  1520. "execution_count": 75,
  1521. "metadata": {},
  1522. "output_type": "execute_result"
  1523. }
  1524. ],
  1525. "source": [
  1526. "2*(27,)"
  1527. ]
  1528. },
  1529. {
  1530. "cell_type": "markdown",
  1531. "metadata": {},
  1532. "source": [
  1533. "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。"
  1534. ]
  1535. },
  1536. {
  1537. "cell_type": "code",
  1538. "execution_count": 76,
  1539. "metadata": {
  1540. "scrolled": true
  1541. },
  1542. "outputs": [
  1543. {
  1544. "name": "stdout",
  1545. "output_type": "stream",
  1546. "text": [
  1547. "(1, 2, 3)\n",
  1548. "('H', 'e', 'l', 'l', 'o')\n"
  1549. ]
  1550. }
  1551. ],
  1552. "source": [
  1553. "tup3 = tuple([1,2,3])\n",
  1554. "print(tup3)\n",
  1555. "tup4 = tuple('Hello')\n",
  1556. "print(tup4)"
  1557. ]
  1558. },
  1559. {
  1560. "cell_type": "markdown",
  1561. "metadata": {},
  1562. "source": [
  1563. "它遵循与列表相同的索引和切片。"
  1564. ]
  1565. },
  1566. {
  1567. "cell_type": "code",
  1568. "execution_count": 77,
  1569. "metadata": {},
  1570. "outputs": [
  1571. {
  1572. "name": "stdout",
  1573. "output_type": "stream",
  1574. "text": [
  1575. "2\n",
  1576. "('H', 'e', 'l')\n"
  1577. ]
  1578. }
  1579. ],
  1580. "source": [
  1581. "print(tup3[1])\n",
  1582. "tup5 = tup4[:3]\n",
  1583. "print(tup5)"
  1584. ]
  1585. },
  1586. {
  1587. "cell_type": "markdown",
  1588. "metadata": {},
  1589. "source": [
  1590. "### 2.1 将一个元组映射到另一个元组"
  1591. ]
  1592. },
  1593. {
  1594. "cell_type": "code",
  1595. "execution_count": 80,
  1596. "metadata": {},
  1597. "outputs": [],
  1598. "source": [
  1599. "(a,b,c)= ('alpha','beta','gamma')"
  1600. ]
  1601. },
  1602. {
  1603. "cell_type": "code",
  1604. "execution_count": 46,
  1605. "metadata": {},
  1606. "outputs": [
  1607. {
  1608. "name": "stdout",
  1609. "output_type": "stream",
  1610. "text": [
  1611. "alpha beta gamma\n"
  1612. ]
  1613. }
  1614. ],
  1615. "source": [
  1616. "print(a,b,c)"
  1617. ]
  1618. },
  1619. {
  1620. "cell_type": "code",
  1621. "execution_count": 81,
  1622. "metadata": {},
  1623. "outputs": [
  1624. {
  1625. "name": "stdout",
  1626. "output_type": "stream",
  1627. "text": [
  1628. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1629. ]
  1630. }
  1631. ],
  1632. "source": [
  1633. "d = tuple('RajathKumarMP')\n",
  1634. "print(d)"
  1635. ]
  1636. },
  1637. {
  1638. "cell_type": "markdown",
  1639. "metadata": {},
  1640. "source": [
  1641. "### 2.2 元组内置函数"
  1642. ]
  1643. },
  1644. {
  1645. "cell_type": "markdown",
  1646. "metadata": {},
  1647. "source": [
  1648. "**count()** 函数计算元组中存在的指定元素的数量。"
  1649. ]
  1650. },
  1651. {
  1652. "cell_type": "code",
  1653. "execution_count": 82,
  1654. "metadata": {},
  1655. "outputs": [
  1656. {
  1657. "data": {
  1658. "text/plain": [
  1659. "3"
  1660. ]
  1661. },
  1662. "execution_count": 82,
  1663. "metadata": {},
  1664. "output_type": "execute_result"
  1665. }
  1666. ],
  1667. "source": [
  1668. "d.count('a')"
  1669. ]
  1670. },
  1671. {
  1672. "cell_type": "markdown",
  1673. "metadata": {},
  1674. "source": [
  1675. "**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"
  1676. ]
  1677. },
  1678. {
  1679. "cell_type": "code",
  1680. "execution_count": 83,
  1681. "metadata": {},
  1682. "outputs": [
  1683. {
  1684. "data": {
  1685. "text/plain": [
  1686. "1"
  1687. ]
  1688. },
  1689. "execution_count": 83,
  1690. "metadata": {},
  1691. "output_type": "execute_result"
  1692. }
  1693. ],
  1694. "source": [
  1695. "d.index('a')"
  1696. ]
  1697. },
  1698. {
  1699. "cell_type": "markdown",
  1700. "metadata": {},
  1701. "source": [
  1702. "## 3. 集合"
  1703. ]
  1704. },
  1705. {
  1706. "cell_type": "markdown",
  1707. "metadata": {},
  1708. "source": [
  1709. "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",
  1710. "\n",
  1711. "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"
  1712. ]
  1713. },
  1714. {
  1715. "cell_type": "code",
  1716. "execution_count": 85,
  1717. "metadata": {},
  1718. "outputs": [
  1719. {
  1720. "name": "stdout",
  1721. "output_type": "stream",
  1722. "text": [
  1723. "<class 'set'>\n"
  1724. ]
  1725. }
  1726. ],
  1727. "source": [
  1728. "set1 = set()\n",
  1729. "print(type(set1))"
  1730. ]
  1731. },
  1732. {
  1733. "cell_type": "code",
  1734. "execution_count": 87,
  1735. "metadata": {},
  1736. "outputs": [
  1737. {
  1738. "name": "stdout",
  1739. "output_type": "stream",
  1740. "text": [
  1741. "{1, 2, 3, 4}\n"
  1742. ]
  1743. }
  1744. ],
  1745. "source": [
  1746. "set0 = set([1,2,2,3,3,4])\n",
  1747. "print(set0)"
  1748. ]
  1749. },
  1750. {
  1751. "cell_type": "code",
  1752. "execution_count": 88,
  1753. "metadata": {},
  1754. "outputs": [
  1755. {
  1756. "name": "stdout",
  1757. "output_type": "stream",
  1758. "text": [
  1759. "{1, 2, 3, 4}\n"
  1760. ]
  1761. }
  1762. ],
  1763. "source": [
  1764. "set1 = set((1,2,2,3,3,4))\n",
  1765. "print(set1)"
  1766. ]
  1767. },
  1768. {
  1769. "cell_type": "markdown",
  1770. "metadata": {},
  1771. "source": [
  1772. "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。"
  1773. ]
  1774. },
  1775. {
  1776. "cell_type": "markdown",
  1777. "metadata": {},
  1778. "source": [
  1779. "### 3.1 内置函数"
  1780. ]
  1781. },
  1782. {
  1783. "cell_type": "code",
  1784. "execution_count": 89,
  1785. "metadata": {},
  1786. "outputs": [],
  1787. "source": [
  1788. "set1 = set([1,2,3])"
  1789. ]
  1790. },
  1791. {
  1792. "cell_type": "code",
  1793. "execution_count": 90,
  1794. "metadata": {},
  1795. "outputs": [],
  1796. "source": [
  1797. "set2 = set([2,3,4,5])"
  1798. ]
  1799. },
  1800. {
  1801. "cell_type": "markdown",
  1802. "metadata": {},
  1803. "source": [
  1804. "**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。"
  1805. ]
  1806. },
  1807. {
  1808. "cell_type": "code",
  1809. "execution_count": 91,
  1810. "metadata": {},
  1811. "outputs": [
  1812. {
  1813. "data": {
  1814. "text/plain": [
  1815. "{1, 2, 3, 4, 5}"
  1816. ]
  1817. },
  1818. "execution_count": 91,
  1819. "metadata": {},
  1820. "output_type": "execute_result"
  1821. }
  1822. ],
  1823. "source": [
  1824. "set1.union(set2)"
  1825. ]
  1826. },
  1827. {
  1828. "cell_type": "markdown",
  1829. "metadata": {},
  1830. "source": [
  1831. "**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。"
  1832. ]
  1833. },
  1834. {
  1835. "cell_type": "code",
  1836. "execution_count": 92,
  1837. "metadata": {},
  1838. "outputs": [
  1839. {
  1840. "name": "stdout",
  1841. "output_type": "stream",
  1842. "text": [
  1843. "{1, 2, 3}\n"
  1844. ]
  1845. },
  1846. {
  1847. "data": {
  1848. "text/plain": [
  1849. "{0, 1, 2, 3}"
  1850. ]
  1851. },
  1852. "execution_count": 92,
  1853. "metadata": {},
  1854. "output_type": "execute_result"
  1855. }
  1856. ],
  1857. "source": [
  1858. "print(set1)\n",
  1859. "set1.add(0)\n",
  1860. "set1"
  1861. ]
  1862. },
  1863. {
  1864. "cell_type": "markdown",
  1865. "metadata": {},
  1866. "source": [
  1867. "**intersection( )** 函数输出一个集合,该集合包含两个集合中的所有元素。"
  1868. ]
  1869. },
  1870. {
  1871. "cell_type": "code",
  1872. "execution_count": 93,
  1873. "metadata": {},
  1874. "outputs": [
  1875. {
  1876. "data": {
  1877. "text/plain": [
  1878. "{2, 3}"
  1879. ]
  1880. },
  1881. "execution_count": 93,
  1882. "metadata": {},
  1883. "output_type": "execute_result"
  1884. }
  1885. ],
  1886. "source": [
  1887. "set1.intersection(set2)"
  1888. ]
  1889. },
  1890. {
  1891. "cell_type": "markdown",
  1892. "metadata": {},
  1893. "source": [
  1894. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1895. ]
  1896. },
  1897. {
  1898. "cell_type": "code",
  1899. "execution_count": 94,
  1900. "metadata": {},
  1901. "outputs": [
  1902. {
  1903. "name": "stdout",
  1904. "output_type": "stream",
  1905. "text": [
  1906. "{0, 1, 2, 3}\n",
  1907. "{2, 3, 4, 5}\n"
  1908. ]
  1909. },
  1910. {
  1911. "data": {
  1912. "text/plain": [
  1913. "{0, 1}"
  1914. ]
  1915. },
  1916. "execution_count": 94,
  1917. "metadata": {},
  1918. "output_type": "execute_result"
  1919. }
  1920. ],
  1921. "source": [
  1922. "print(set1)\n",
  1923. "print(set2)\n",
  1924. "set1.difference(set2)"
  1925. ]
  1926. },
  1927. {
  1928. "cell_type": "markdown",
  1929. "metadata": {},
  1930. "source": [
  1931. "**symmetric_difference( )** 函数输出一个函数,该函数包含一个集合中的元素。"
  1932. ]
  1933. },
  1934. {
  1935. "cell_type": "code",
  1936. "execution_count": 95,
  1937. "metadata": {},
  1938. "outputs": [
  1939. {
  1940. "data": {
  1941. "text/plain": [
  1942. "{0, 1, 4, 5}"
  1943. ]
  1944. },
  1945. "execution_count": 95,
  1946. "metadata": {},
  1947. "output_type": "execute_result"
  1948. }
  1949. ],
  1950. "source": [
  1951. "set2.symmetric_difference(set1)"
  1952. ]
  1953. },
  1954. {
  1955. "cell_type": "markdown",
  1956. "metadata": {},
  1957. "source": [
  1958. "**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。"
  1959. ]
  1960. },
  1961. {
  1962. "cell_type": "code",
  1963. "execution_count": 96,
  1964. "metadata": {},
  1965. "outputs": [
  1966. {
  1967. "data": {
  1968. "text/plain": [
  1969. "False"
  1970. ]
  1971. },
  1972. "execution_count": 96,
  1973. "metadata": {},
  1974. "output_type": "execute_result"
  1975. }
  1976. ],
  1977. "source": [
  1978. "set1.issubset(set2)"
  1979. ]
  1980. },
  1981. {
  1982. "cell_type": "code",
  1983. "execution_count": 97,
  1984. "metadata": {},
  1985. "outputs": [
  1986. {
  1987. "data": {
  1988. "text/plain": [
  1989. "False"
  1990. ]
  1991. },
  1992. "execution_count": 97,
  1993. "metadata": {},
  1994. "output_type": "execute_result"
  1995. }
  1996. ],
  1997. "source": [
  1998. "set2.isdisjoint(set1)"
  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.issuperset(set1)"
  2019. ]
  2020. },
  2021. {
  2022. "cell_type": "markdown",
  2023. "metadata": {},
  2024. "source": [
  2025. "**pop( )** 是用来移除集合中的任意元素。"
  2026. ]
  2027. },
  2028. {
  2029. "cell_type": "code",
  2030. "execution_count": 115,
  2031. "metadata": {},
  2032. "outputs": [],
  2033. "source": [
  2034. "set1=set([10, 9, 1, 2, 4])"
  2035. ]
  2036. },
  2037. {
  2038. "cell_type": "code",
  2039. "execution_count": 113,
  2040. "metadata": {},
  2041. "outputs": [
  2042. {
  2043. "name": "stdout",
  2044. "output_type": "stream",
  2045. "text": [
  2046. "set()\n"
  2047. ]
  2048. }
  2049. ],
  2050. "source": [
  2051. "set1.pop()\n",
  2052. "print(set1)"
  2053. ]
  2054. },
  2055. {
  2056. "cell_type": "markdown",
  2057. "metadata": {},
  2058. "source": [
  2059. "**remove( )** 函数从集合中删除指定的元素。"
  2060. ]
  2061. },
  2062. {
  2063. "cell_type": "code",
  2064. "execution_count": 116,
  2065. "metadata": {},
  2066. "outputs": [
  2067. {
  2068. "data": {
  2069. "text/plain": [
  2070. "{1, 4, 9, 10}"
  2071. ]
  2072. },
  2073. "execution_count": 116,
  2074. "metadata": {},
  2075. "output_type": "execute_result"
  2076. }
  2077. ],
  2078. "source": [
  2079. "set1.remove(2)\n",
  2080. "set1"
  2081. ]
  2082. },
  2083. {
  2084. "cell_type": "markdown",
  2085. "metadata": {},
  2086. "source": [
  2087. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2088. ]
  2089. },
  2090. {
  2091. "cell_type": "code",
  2092. "execution_count": 117,
  2093. "metadata": {},
  2094. "outputs": [
  2095. {
  2096. "data": {
  2097. "text/plain": [
  2098. "set()"
  2099. ]
  2100. },
  2101. "execution_count": 117,
  2102. "metadata": {},
  2103. "output_type": "execute_result"
  2104. }
  2105. ],
  2106. "source": [
  2107. "set1.clear()\n",
  2108. "set1"
  2109. ]
  2110. }
  2111. ],
  2112. "metadata": {
  2113. "kernelspec": {
  2114. "display_name": "Python 3",
  2115. "language": "python",
  2116. "name": "python3"
  2117. },
  2118. "language_info": {
  2119. "codemirror_mode": {
  2120. "name": "ipython",
  2121. "version": 3
  2122. },
  2123. "file_extension": ".py",
  2124. "mimetype": "text/x-python",
  2125. "name": "python",
  2126. "nbconvert_exporter": "python",
  2127. "pygments_lexer": "ipython3",
  2128. "version": "3.7.9"
  2129. }
  2130. },
  2131. "nbformat": 4,
  2132. "nbformat_minor": 1
  2133. }

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》。