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3_Data_Structure_1.ipynb 37 kB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# 数据结构 - 1"
  8. ]
  9. },
  10. {
  11. "cell_type": "markdown",
  12. "metadata": {},
  13. "source": [
  14. "数据结构是计算机存储、组织数据的方式,简单来说是指相互之间存在一种或多种特定关系的数据元素的集合。\n",
  15. "\n",
  16. "Python中的数据结构设计的非常巧妙,使用起来非常方便,几乎绝大多数的数据结构都可以通过`list`, `tuple`, `dict`, `string`, `set`等表示,因此用户几乎不需要自己定义数据结构,仅仅使用Python内置的数据结构即可实现非常复杂的算法和操作。"
  17. ]
  18. },
  19. {
  20. "cell_type": "markdown",
  21. "metadata": {},
  22. "source": [
  23. "## 1. 列表"
  24. ]
  25. },
  26. {
  27. "cell_type": "markdown",
  28. "metadata": {},
  29. "source": [
  30. "列表是最常用的数据结构。可以把它看作用方括号括起来的数据序列,数据之间用逗号分隔。这些数据都可以通过调用其索引值来访问。\n",
  31. "\n",
  32. "`list`的声明只需将变量等同于`[ ]`或`list`即可。"
  33. ]
  34. },
  35. {
  36. "cell_type": "code",
  37. "execution_count": 1,
  38. "metadata": {},
  39. "outputs": [],
  40. "source": [
  41. "a = []"
  42. ]
  43. },
  44. {
  45. "cell_type": "code",
  46. "execution_count": 2,
  47. "metadata": {},
  48. "outputs": [
  49. {
  50. "name": "stdout",
  51. "output_type": "stream",
  52. "text": [
  53. "<class 'list'>\n"
  54. ]
  55. }
  56. ],
  57. "source": [
  58. "print(type(a))"
  59. ]
  60. },
  61. {
  62. "cell_type": "markdown",
  63. "metadata": {},
  64. "source": [
  65. "可以直接将数据序列分配给列表x,如下所示。"
  66. ]
  67. },
  68. {
  69. "cell_type": "code",
  70. "execution_count": 3,
  71. "metadata": {},
  72. "outputs": [
  73. {
  74. "name": "stdout",
  75. "output_type": "stream",
  76. "text": [
  77. "['apple', 'orange', 'peach']\n"
  78. ]
  79. }
  80. ],
  81. "source": [
  82. "x = ['apple', 'orange', 'peach']\n",
  83. "print(x)"
  84. ]
  85. },
  86. {
  87. "cell_type": "markdown",
  88. "metadata": {},
  89. "source": [
  90. "### 1.1 索引"
  91. ]
  92. },
  93. {
  94. "cell_type": "markdown",
  95. "metadata": {},
  96. "source": [
  97. "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`。"
  98. ]
  99. },
  100. {
  101. "cell_type": "code",
  102. "execution_count": 4,
  103. "metadata": {},
  104. "outputs": [
  105. {
  106. "data": {
  107. "text/plain": [
  108. "'apple'"
  109. ]
  110. },
  111. "execution_count": 4,
  112. "metadata": {},
  113. "output_type": "execute_result"
  114. }
  115. ],
  116. "source": [
  117. "x[0]"
  118. ]
  119. },
  120. {
  121. "cell_type": "markdown",
  122. "metadata": {},
  123. "source": [
  124. "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。"
  125. ]
  126. },
  127. {
  128. "cell_type": "code",
  129. "execution_count": 5,
  130. "metadata": {},
  131. "outputs": [
  132. {
  133. "data": {
  134. "text/plain": [
  135. "'peach'"
  136. ]
  137. },
  138. "execution_count": 5,
  139. "metadata": {},
  140. "output_type": "execute_result"
  141. }
  142. ],
  143. "source": [
  144. "x[-1]"
  145. ]
  146. },
  147. {
  148. "cell_type": "markdown",
  149. "metadata": {},
  150. "source": [
  151. "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。"
  152. ]
  153. },
  154. {
  155. "cell_type": "code",
  156. "execution_count": 6,
  157. "metadata": {},
  158. "outputs": [],
  159. "source": [
  160. "y = ['carrot','potato']"
  161. ]
  162. },
  163. {
  164. "cell_type": "markdown",
  165. "metadata": {},
  166. "source": [
  167. "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**"
  168. ]
  169. },
  170. {
  171. "cell_type": "code",
  172. "execution_count": 7,
  173. "metadata": {},
  174. "outputs": [
  175. {
  176. "name": "stdout",
  177. "output_type": "stream",
  178. "text": [
  179. "[['apple', 'orange', 'peach'], ['carrot', 'potato'], 'Test']\n"
  180. ]
  181. }
  182. ],
  183. "source": [
  184. "z = [x,y, 'Test']\n",
  185. "print(z)"
  186. ]
  187. },
  188. {
  189. "cell_type": "code",
  190. "execution_count": 8,
  191. "metadata": {},
  192. "outputs": [
  193. {
  194. "data": {
  195. "text/plain": [
  196. "'orange'"
  197. ]
  198. },
  199. "execution_count": 8,
  200. "metadata": {},
  201. "output_type": "execute_result"
  202. }
  203. ],
  204. "source": [
  205. "z[0][1]"
  206. ]
  207. },
  208. {
  209. "cell_type": "markdown",
  210. "metadata": {},
  211. "source": [
  212. "如何获得嵌套列表中的某个元素?让我们在上述嵌套列表中获得数据'apple'为例。\n",
  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']` 。\n",
  214. "* 因此z[0] 应该给我们第一个包含'apple'的列表。"
  215. ]
  216. },
  217. {
  218. "cell_type": "code",
  219. "execution_count": 9,
  220. "metadata": {},
  221. "outputs": [
  222. {
  223. "name": "stdout",
  224. "output_type": "stream",
  225. "text": [
  226. "['apple', 'orange', 'peach']\n"
  227. ]
  228. }
  229. ],
  230. "source": [
  231. "z1 = z[0]\n",
  232. "print(z1)"
  233. ]
  234. },
  235. {
  236. "cell_type": "markdown",
  237. "metadata": {},
  238. "source": [
  239. "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。"
  240. ]
  241. },
  242. {
  243. "cell_type": "code",
  244. "execution_count": 10,
  245. "metadata": {},
  246. "outputs": [
  247. {
  248. "data": {
  249. "text/plain": [
  250. "'apple'"
  251. ]
  252. },
  253. "execution_count": 10,
  254. "metadata": {},
  255. "output_type": "execute_result"
  256. }
  257. ],
  258. "source": [
  259. "z1[0]"
  260. ]
  261. },
  262. {
  263. "cell_type": "markdown",
  264. "metadata": {},
  265. "source": [
  266. "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。"
  267. ]
  268. },
  269. {
  270. "cell_type": "code",
  271. "execution_count": 11,
  272. "metadata": {},
  273. "outputs": [
  274. {
  275. "data": {
  276. "text/plain": [
  277. "'apple'"
  278. ]
  279. },
  280. "execution_count": 11,
  281. "metadata": {},
  282. "output_type": "execute_result"
  283. }
  284. ],
  285. "source": [
  286. "z[0][0]"
  287. ]
  288. },
  289. {
  290. "cell_type": "markdown",
  291. "metadata": {},
  292. "source": [
  293. "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。"
  294. ]
  295. },
  296. {
  297. "cell_type": "markdown",
  298. "metadata": {},
  299. "source": [
  300. "### 1.2 切片"
  301. ]
  302. },
  303. {
  304. "cell_type": "markdown",
  305. "metadata": {},
  306. "source": [
  307. "索引只限于访问单个元素,而切片则是访问列表内的一系列数据。换句话说,`切片`返回的是一个列表。\n",
  308. "\n",
  309. "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。"
  310. ]
  311. },
  312. {
  313. "cell_type": "code",
  314. "execution_count": 12,
  315. "metadata": {},
  316. "outputs": [
  317. {
  318. "name": "stdout",
  319. "output_type": "stream",
  320. "text": [
  321. "[3, 2, 3]\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. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  325. ]
  326. }
  327. ],
  328. "source": [
  329. "num = [2,3,2,3,4,5,6,7,8,9]\n",
  330. "print(num[1:4])\n",
  331. "print(num[0:])\n",
  332. "print(num[:])\n",
  333. "print(num)"
  334. ]
  335. },
  336. {
  337. "cell_type": "code",
  338. "execution_count": 13,
  339. "metadata": {},
  340. "outputs": [
  341. {
  342. "name": "stdout",
  343. "output_type": "stream",
  344. "text": [
  345. "[2, 3, 2, 3]\n",
  346. "[4, 5, 6, 7, 8, 9]\n"
  347. ]
  348. }
  349. ],
  350. "source": [
  351. "print(num[0:4])\n",
  352. "print(num[4:])"
  353. ]
  354. },
  355. {
  356. "cell_type": "markdown",
  357. "metadata": {},
  358. "source": [
  359. "您还可以使用固定长度或步长对父列表进行切片。"
  360. ]
  361. },
  362. {
  363. "cell_type": "code",
  364. "execution_count": 14,
  365. "metadata": {},
  366. "outputs": [
  367. {
  368. "data": {
  369. "text/plain": [
  370. "[2, 3, 6]"
  371. ]
  372. },
  373. "execution_count": 14,
  374. "metadata": {},
  375. "output_type": "execute_result"
  376. }
  377. ],
  378. "source": [
  379. "num[:9:3]"
  380. ]
  381. },
  382. {
  383. "cell_type": "markdown",
  384. "metadata": {},
  385. "source": [
  386. "### 1.3 列表的内置函数"
  387. ]
  388. },
  389. {
  390. "cell_type": "markdown",
  391. "metadata": {},
  392. "source": [
  393. "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( )**。"
  394. ]
  395. },
  396. {
  397. "cell_type": "code",
  398. "execution_count": 15,
  399. "metadata": {},
  400. "outputs": [
  401. {
  402. "data": {
  403. "text/plain": [
  404. "10"
  405. ]
  406. },
  407. "execution_count": 15,
  408. "metadata": {},
  409. "output_type": "execute_result"
  410. }
  411. ],
  412. "source": [
  413. "len(num)"
  414. ]
  415. },
  416. {
  417. "cell_type": "markdown",
  418. "metadata": {},
  419. "source": [
  420. "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。"
  421. ]
  422. },
  423. {
  424. "cell_type": "code",
  425. "execution_count": 16,
  426. "metadata": {},
  427. "outputs": [
  428. {
  429. "name": "stdout",
  430. "output_type": "stream",
  431. "text": [
  432. "[2, 3, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  433. ]
  434. },
  435. {
  436. "data": {
  437. "text/plain": [
  438. "2"
  439. ]
  440. },
  441. "execution_count": 16,
  442. "metadata": {},
  443. "output_type": "execute_result"
  444. }
  445. ],
  446. "source": [
  447. "print(num)\n",
  448. "min(num)"
  449. ]
  450. },
  451. {
  452. "cell_type": "code",
  453. "execution_count": 17,
  454. "metadata": {},
  455. "outputs": [
  456. {
  457. "data": {
  458. "text/plain": [
  459. "9"
  460. ]
  461. },
  462. "execution_count": 17,
  463. "metadata": {},
  464. "output_type": "execute_result"
  465. }
  466. ],
  467. "source": [
  468. "max(num)"
  469. ]
  470. },
  471. {
  472. "cell_type": "markdown",
  473. "metadata": {},
  474. "source": [
  475. "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。"
  476. ]
  477. },
  478. {
  479. "cell_type": "code",
  480. "execution_count": 18,
  481. "metadata": {},
  482. "outputs": [
  483. {
  484. "data": {
  485. "text/plain": [
  486. "[1, 2, 3, 5, 4, 7]"
  487. ]
  488. },
  489. "execution_count": 18,
  490. "metadata": {},
  491. "output_type": "execute_result"
  492. }
  493. ],
  494. "source": [
  495. "[1,2,3] + [5,4,7]"
  496. ]
  497. },
  498. {
  499. "cell_type": "markdown",
  500. "metadata": {},
  501. "source": [
  502. "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。"
  503. ]
  504. },
  505. {
  506. "cell_type": "code",
  507. "execution_count": 19,
  508. "metadata": {},
  509. "outputs": [],
  510. "source": [
  511. "names = ['Earth','Air','Fire','Water']"
  512. ]
  513. },
  514. {
  515. "cell_type": "markdown",
  516. "metadata": {},
  517. "source": [
  518. "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\""
  519. ]
  520. },
  521. {
  522. "cell_type": "code",
  523. "execution_count": 20,
  524. "metadata": {},
  525. "outputs": [
  526. {
  527. "data": {
  528. "text/plain": [
  529. "False"
  530. ]
  531. },
  532. "execution_count": 20,
  533. "metadata": {},
  534. "output_type": "execute_result"
  535. }
  536. ],
  537. "source": [
  538. "'Fir' in names"
  539. ]
  540. },
  541. {
  542. "cell_type": "code",
  543. "execution_count": 21,
  544. "metadata": {},
  545. "outputs": [
  546. {
  547. "data": {
  548. "text/plain": [
  549. "True"
  550. ]
  551. },
  552. "execution_count": 21,
  553. "metadata": {},
  554. "output_type": "execute_result"
  555. }
  556. ],
  557. "source": [
  558. "'Fire' in names"
  559. ]
  560. },
  561. {
  562. "cell_type": "code",
  563. "execution_count": 22,
  564. "metadata": {},
  565. "outputs": [
  566. {
  567. "data": {
  568. "text/plain": [
  569. "False"
  570. ]
  571. },
  572. "execution_count": 22,
  573. "metadata": {},
  574. "output_type": "execute_result"
  575. }
  576. ],
  577. "source": [
  578. "'fire' in names"
  579. ]
  580. },
  581. {
  582. "cell_type": "markdown",
  583. "metadata": {},
  584. "source": [
  585. "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。"
  586. ]
  587. },
  588. {
  589. "cell_type": "code",
  590. "execution_count": 23,
  591. "metadata": {},
  592. "outputs": [],
  593. "source": [
  594. "mlist = ['bzaa','ds','nc','az','z','klm']"
  595. ]
  596. },
  597. {
  598. "cell_type": "code",
  599. "execution_count": 24,
  600. "metadata": {},
  601. "outputs": [
  602. {
  603. "name": "stdout",
  604. "output_type": "stream",
  605. "text": [
  606. "z\n",
  607. "az\n"
  608. ]
  609. }
  610. ],
  611. "source": [
  612. "print(max(mlist))\n",
  613. "print(min(mlist))"
  614. ]
  615. },
  616. {
  617. "cell_type": "markdown",
  618. "metadata": {},
  619. "source": [
  620. "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?"
  621. ]
  622. },
  623. {
  624. "cell_type": "code",
  625. "execution_count": 25,
  626. "metadata": {},
  627. "outputs": [],
  628. "source": [
  629. "nlist = ['1','94','93','1000']"
  630. ]
  631. },
  632. {
  633. "cell_type": "code",
  634. "execution_count": 26,
  635. "metadata": {},
  636. "outputs": [
  637. {
  638. "name": "stdout",
  639. "output_type": "stream",
  640. "text": [
  641. "94\n",
  642. "1\n"
  643. ]
  644. }
  645. ],
  646. "source": [
  647. "print(max(nlist))\n",
  648. "print(min(nlist))"
  649. ]
  650. },
  651. {
  652. "cell_type": "markdown",
  653. "metadata": {},
  654. "source": [
  655. "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。"
  656. ]
  657. },
  658. {
  659. "cell_type": "markdown",
  660. "metadata": {},
  661. "source": [
  662. "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'。"
  663. ]
  664. },
  665. {
  666. "cell_type": "code",
  667. "execution_count": 27,
  668. "metadata": {},
  669. "outputs": [
  670. {
  671. "name": "stdout",
  672. "output_type": "stream",
  673. "text": [
  674. "Earth\n",
  675. "Jet\n"
  676. ]
  677. }
  678. ],
  679. "source": [
  680. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  681. "print(max(names, key=len))\n",
  682. "print(min(names, key=len))"
  683. ]
  684. },
  685. {
  686. "cell_type": "markdown",
  687. "metadata": {},
  688. "source": [
  689. "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",
  690. "\n",
  691. "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",
  692. "\n",
  693. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  694. ]
  695. },
  696. {
  697. "cell_type": "code",
  698. "execution_count": 28,
  699. "metadata": {},
  700. "outputs": [
  701. {
  702. "data": {
  703. "text/plain": [
  704. "['h', 'e', 'l', 'l', 'o']"
  705. ]
  706. },
  707. "execution_count": 28,
  708. "metadata": {},
  709. "output_type": "execute_result"
  710. }
  711. ],
  712. "source": [
  713. "list('hello')"
  714. ]
  715. },
  716. {
  717. "cell_type": "markdown",
  718. "metadata": {},
  719. "source": [
  720. "**append( )** 被用来在列表的最后添加一个元素。"
  721. ]
  722. },
  723. {
  724. "cell_type": "code",
  725. "execution_count": 29,
  726. "metadata": {},
  727. "outputs": [],
  728. "source": [
  729. "lst = [1,1,4,8,7]"
  730. ]
  731. },
  732. {
  733. "cell_type": "code",
  734. "execution_count": 30,
  735. "metadata": {},
  736. "outputs": [
  737. {
  738. "name": "stdout",
  739. "output_type": "stream",
  740. "text": [
  741. "[1, 1, 4, 8, 7, 1]\n"
  742. ]
  743. }
  744. ],
  745. "source": [
  746. "lst.append(1)\n",
  747. "print(lst)"
  748. ]
  749. },
  750. {
  751. "cell_type": "markdown",
  752. "metadata": {},
  753. "source": [
  754. "**count( )** 用于计算列表中出现的特定元素的数量。"
  755. ]
  756. },
  757. {
  758. "cell_type": "code",
  759. "execution_count": 31,
  760. "metadata": {},
  761. "outputs": [
  762. {
  763. "data": {
  764. "text/plain": [
  765. "3"
  766. ]
  767. },
  768. "execution_count": 31,
  769. "metadata": {},
  770. "output_type": "execute_result"
  771. }
  772. ],
  773. "source": [
  774. "lst.count(1)"
  775. ]
  776. },
  777. {
  778. "cell_type": "markdown",
  779. "metadata": {},
  780. "source": [
  781. "**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。"
  782. ]
  783. },
  784. {
  785. "cell_type": "code",
  786. "execution_count": 32,
  787. "metadata": {},
  788. "outputs": [],
  789. "source": [
  790. "lst1 = [5,4,2,8]"
  791. ]
  792. },
  793. {
  794. "cell_type": "code",
  795. "execution_count": 33,
  796. "metadata": {},
  797. "outputs": [
  798. {
  799. "name": "stdout",
  800. "output_type": "stream",
  801. "text": [
  802. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8]]\n"
  803. ]
  804. }
  805. ],
  806. "source": [
  807. "lst.append(lst1)\n",
  808. "print(lst)"
  809. ]
  810. },
  811. {
  812. "cell_type": "markdown",
  813. "metadata": {},
  814. "source": [
  815. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  816. ]
  817. },
  818. {
  819. "cell_type": "code",
  820. "execution_count": 34,
  821. "metadata": {},
  822. "outputs": [
  823. {
  824. "name": "stdout",
  825. "output_type": "stream",
  826. "text": [
  827. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  828. ]
  829. }
  830. ],
  831. "source": [
  832. "lst.extend(lst1)\n",
  833. "print(lst)"
  834. ]
  835. },
  836. {
  837. "cell_type": "markdown",
  838. "metadata": {},
  839. "source": [
  840. "**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。"
  841. ]
  842. },
  843. {
  844. "cell_type": "code",
  845. "execution_count": 35,
  846. "metadata": {},
  847. "outputs": [
  848. {
  849. "data": {
  850. "text/plain": [
  851. "0"
  852. ]
  853. },
  854. "execution_count": 35,
  855. "metadata": {},
  856. "output_type": "execute_result"
  857. }
  858. ],
  859. "source": [
  860. "lst.index(1)"
  861. ]
  862. },
  863. {
  864. "cell_type": "code",
  865. "execution_count": 36,
  866. "metadata": {},
  867. "outputs": [
  868. {
  869. "ename": "ValueError",
  870. "evalue": "999 is not in list",
  871. "output_type": "error",
  872. "traceback": [
  873. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  874. "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
  875. "\u001b[0;32m<ipython-input-36-344da5bcf759>\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",
  876. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  877. ]
  878. }
  879. ],
  880. "source": [
  881. "lst.index(999)"
  882. ]
  883. },
  884. {
  885. "cell_type": "markdown",
  886. "metadata": {},
  887. "source": [
  888. "**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。"
  889. ]
  890. },
  891. {
  892. "cell_type": "code",
  893. "execution_count": null,
  894. "metadata": {},
  895. "outputs": [],
  896. "source": [
  897. "lst.insert(5, 'name')\n",
  898. "print(lst)"
  899. ]
  900. },
  901. {
  902. "cell_type": "markdown",
  903. "metadata": {},
  904. "source": [
  905. "**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。"
  906. ]
  907. },
  908. {
  909. "cell_type": "code",
  910. "execution_count": null,
  911. "metadata": {},
  912. "outputs": [],
  913. "source": [
  914. "lst[5] = 'Python'\n",
  915. "print(lst)"
  916. ]
  917. },
  918. {
  919. "cell_type": "markdown",
  920. "metadata": {},
  921. "source": [
  922. "**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。"
  923. ]
  924. },
  925. {
  926. "cell_type": "code",
  927. "execution_count": null,
  928. "metadata": {},
  929. "outputs": [],
  930. "source": [
  931. "lst.pop()\n",
  932. "lst"
  933. ]
  934. },
  935. {
  936. "cell_type": "markdown",
  937. "metadata": {},
  938. "source": [
  939. "可以指定索引值来弹出与该索引值对应的元素。"
  940. ]
  941. },
  942. {
  943. "cell_type": "code",
  944. "execution_count": null,
  945. "metadata": {},
  946. "outputs": [],
  947. "source": [
  948. "lst.pop(2)"
  949. ]
  950. },
  951. {
  952. "cell_type": "code",
  953. "execution_count": null,
  954. "metadata": {},
  955. "outputs": [],
  956. "source": [
  957. "print(lst)\n",
  958. "lst.pop(-2)"
  959. ]
  960. },
  961. {
  962. "cell_type": "code",
  963. "execution_count": null,
  964. "metadata": {},
  965. "outputs": [],
  966. "source": [
  967. "print(lst)"
  968. ]
  969. },
  970. {
  971. "cell_type": "markdown",
  972. "metadata": {},
  973. "source": [
  974. "**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。"
  975. ]
  976. },
  977. {
  978. "cell_type": "code",
  979. "execution_count": null,
  980. "metadata": {},
  981. "outputs": [],
  982. "source": [
  983. "lst.remove('Python')\n",
  984. "print(lst)"
  985. ]
  986. },
  987. {
  988. "cell_type": "markdown",
  989. "metadata": {},
  990. "source": [
  991. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  992. ]
  993. },
  994. {
  995. "cell_type": "code",
  996. "execution_count": null,
  997. "metadata": {},
  998. "outputs": [],
  999. "source": [
  1000. "print(lst)\n",
  1001. "del lst[1]\n",
  1002. "print(lst)"
  1003. ]
  1004. },
  1005. {
  1006. "cell_type": "markdown",
  1007. "metadata": {},
  1008. "source": [
  1009. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1010. ]
  1011. },
  1012. {
  1013. "cell_type": "code",
  1014. "execution_count": null,
  1015. "metadata": {},
  1016. "outputs": [],
  1017. "source": [
  1018. "lst.reverse()\n",
  1019. "print(lst)"
  1020. ]
  1021. },
  1022. {
  1023. "cell_type": "markdown",
  1024. "metadata": {},
  1025. "source": [
  1026. "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",
  1027. "\n",
  1028. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1029. ]
  1030. },
  1031. {
  1032. "cell_type": "code",
  1033. "execution_count": null,
  1034. "metadata": {},
  1035. "outputs": [],
  1036. "source": [
  1037. "lst = [1, 4, 8, 8, 10]\n",
  1038. "lst.sort()\n",
  1039. "print(lst)"
  1040. ]
  1041. },
  1042. {
  1043. "cell_type": "markdown",
  1044. "metadata": {},
  1045. "source": [
  1046. "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。"
  1047. ]
  1048. },
  1049. {
  1050. "cell_type": "code",
  1051. "execution_count": null,
  1052. "metadata": {},
  1053. "outputs": [],
  1054. "source": [
  1055. "lst.sort(reverse=True)\n",
  1056. "print(lst)"
  1057. ]
  1058. },
  1059. {
  1060. "cell_type": "markdown",
  1061. "metadata": {},
  1062. "source": [
  1063. "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。"
  1064. ]
  1065. },
  1066. {
  1067. "cell_type": "code",
  1068. "execution_count": null,
  1069. "metadata": {},
  1070. "outputs": [],
  1071. "source": [
  1072. "names = ['apple', 'orange', 'peach']\n",
  1073. "names.sort()\n",
  1074. "print(names)\n",
  1075. "names.sort(reverse=True)\n",
  1076. "print(names)"
  1077. ]
  1078. },
  1079. {
  1080. "cell_type": "markdown",
  1081. "metadata": {},
  1082. "source": [
  1083. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1084. ]
  1085. },
  1086. {
  1087. "cell_type": "code",
  1088. "execution_count": null,
  1089. "metadata": {},
  1090. "outputs": [],
  1091. "source": [
  1092. "names.sort(key=len)\n",
  1093. "print(names)\n",
  1094. "names.sort(key=len,reverse=True)\n",
  1095. "print(names)"
  1096. ]
  1097. },
  1098. {
  1099. "cell_type": "markdown",
  1100. "metadata": {},
  1101. "source": [
  1102. "### 1.4 复制一个列表"
  1103. ]
  1104. },
  1105. {
  1106. "cell_type": "markdown",
  1107. "metadata": {},
  1108. "source": [
  1109. "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:"
  1110. ]
  1111. },
  1112. {
  1113. "cell_type": "code",
  1114. "execution_count": null,
  1115. "metadata": {},
  1116. "outputs": [],
  1117. "source": [
  1118. "lista= [2,1,4,3]"
  1119. ]
  1120. },
  1121. {
  1122. "cell_type": "code",
  1123. "execution_count": null,
  1124. "metadata": {},
  1125. "outputs": [],
  1126. "source": [
  1127. "listb = lista # 对象赋值\n",
  1128. "print(listb)"
  1129. ]
  1130. },
  1131. {
  1132. "cell_type": "markdown",
  1133. "metadata": {},
  1134. "source": [
  1135. "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。"
  1136. ]
  1137. },
  1138. {
  1139. "cell_type": "code",
  1140. "execution_count": null,
  1141. "metadata": {},
  1142. "outputs": [],
  1143. "source": [
  1144. "lista.pop()\n",
  1145. "print(lista)\n",
  1146. "lista.append(9)\n",
  1147. "print(lista)"
  1148. ]
  1149. },
  1150. {
  1151. "cell_type": "code",
  1152. "execution_count": null,
  1153. "metadata": {},
  1154. "outputs": [],
  1155. "source": [
  1156. "print(listb)"
  1157. ]
  1158. },
  1159. {
  1160. "cell_type": "markdown",
  1161. "metadata": {},
  1162. "source": [
  1163. "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",
  1164. "\n",
  1165. "在切片中我们已经看到parentlist[a:b]从父列表返回一个起始索引a和结束索引b的列表,如果a和b没有被提及,那么默认情况下它会包含第一个到最后一个元素。我们在这里使用相同的概念。通过这样做,我们将lista的数据作为变量分配给listb。"
  1166. ]
  1167. },
  1168. {
  1169. "cell_type": "code",
  1170. "execution_count": null,
  1171. "metadata": {},
  1172. "outputs": [],
  1173. "source": [
  1174. "lista = [2,1,4,3]"
  1175. ]
  1176. },
  1177. {
  1178. "cell_type": "code",
  1179. "execution_count": null,
  1180. "metadata": {},
  1181. "outputs": [],
  1182. "source": [
  1183. "listb = lista[:]\n",
  1184. "print(listb)"
  1185. ]
  1186. },
  1187. {
  1188. "cell_type": "code",
  1189. "execution_count": null,
  1190. "metadata": {},
  1191. "outputs": [],
  1192. "source": [
  1193. "lista.pop()\n",
  1194. "print(lista)\n",
  1195. "lista.append(9)\n",
  1196. "print(lista)"
  1197. ]
  1198. },
  1199. {
  1200. "cell_type": "code",
  1201. "execution_count": null,
  1202. "metadata": {},
  1203. "outputs": [],
  1204. "source": [
  1205. "print(listb)"
  1206. ]
  1207. },
  1208. {
  1209. "cell_type": "markdown",
  1210. "metadata": {},
  1211. "source": [
  1212. "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?"
  1213. ]
  1214. },
  1215. {
  1216. "cell_type": "markdown",
  1217. "metadata": {},
  1218. "source": [
  1219. "## 2. 元组"
  1220. ]
  1221. },
  1222. {
  1223. "cell_type": "markdown",
  1224. "metadata": {},
  1225. "source": [
  1226. "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。"
  1227. ]
  1228. },
  1229. {
  1230. "cell_type": "code",
  1231. "execution_count": null,
  1232. "metadata": {},
  1233. "outputs": [],
  1234. "source": [
  1235. "xyz = divmod(10,3)\n",
  1236. "print(xyz)\n",
  1237. "print(type(xyz))\n",
  1238. "xyz[0]=10"
  1239. ]
  1240. },
  1241. {
  1242. "cell_type": "markdown",
  1243. "metadata": {},
  1244. "source": [
  1245. "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。"
  1246. ]
  1247. },
  1248. {
  1249. "cell_type": "markdown",
  1250. "metadata": {},
  1251. "source": [
  1252. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1253. ]
  1254. },
  1255. {
  1256. "cell_type": "code",
  1257. "execution_count": 37,
  1258. "metadata": {},
  1259. "outputs": [],
  1260. "source": [
  1261. "tup = ()\n",
  1262. "tup2 = tuple()"
  1263. ]
  1264. },
  1265. {
  1266. "cell_type": "markdown",
  1267. "metadata": {},
  1268. "source": [
  1269. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1270. ]
  1271. },
  1272. {
  1273. "cell_type": "code",
  1274. "execution_count": 38,
  1275. "metadata": {},
  1276. "outputs": [
  1277. {
  1278. "data": {
  1279. "text/plain": [
  1280. "(27,)"
  1281. ]
  1282. },
  1283. "execution_count": 38,
  1284. "metadata": {},
  1285. "output_type": "execute_result"
  1286. }
  1287. ],
  1288. "source": [
  1289. "27,"
  1290. ]
  1291. },
  1292. {
  1293. "cell_type": "markdown",
  1294. "metadata": {},
  1295. "source": [
  1296. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1297. ]
  1298. },
  1299. {
  1300. "cell_type": "code",
  1301. "execution_count": 39,
  1302. "metadata": {},
  1303. "outputs": [
  1304. {
  1305. "data": {
  1306. "text/plain": [
  1307. "(27, 27)"
  1308. ]
  1309. },
  1310. "execution_count": 39,
  1311. "metadata": {},
  1312. "output_type": "execute_result"
  1313. }
  1314. ],
  1315. "source": [
  1316. "2*(27,)"
  1317. ]
  1318. },
  1319. {
  1320. "cell_type": "markdown",
  1321. "metadata": {},
  1322. "source": [
  1323. "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。"
  1324. ]
  1325. },
  1326. {
  1327. "cell_type": "code",
  1328. "execution_count": 40,
  1329. "metadata": {
  1330. "scrolled": true
  1331. },
  1332. "outputs": [
  1333. {
  1334. "name": "stdout",
  1335. "output_type": "stream",
  1336. "text": [
  1337. "(1, 2, 3)\n",
  1338. "('H', 'e', 'l', 'l', 'o')\n"
  1339. ]
  1340. }
  1341. ],
  1342. "source": [
  1343. "tup3 = tuple([1,2,3])\n",
  1344. "print(tup3)\n",
  1345. "tup4 = tuple('Hello')\n",
  1346. "print(tup4)"
  1347. ]
  1348. },
  1349. {
  1350. "cell_type": "markdown",
  1351. "metadata": {},
  1352. "source": [
  1353. "它遵循与列表相同的索引和切片。"
  1354. ]
  1355. },
  1356. {
  1357. "cell_type": "code",
  1358. "execution_count": 41,
  1359. "metadata": {},
  1360. "outputs": [
  1361. {
  1362. "name": "stdout",
  1363. "output_type": "stream",
  1364. "text": [
  1365. "2\n",
  1366. "('H', 'e', 'l')\n"
  1367. ]
  1368. }
  1369. ],
  1370. "source": [
  1371. "print(tup3[1])\n",
  1372. "tup5 = tup4[:3]\n",
  1373. "print(tup5)"
  1374. ]
  1375. },
  1376. {
  1377. "cell_type": "markdown",
  1378. "metadata": {},
  1379. "source": [
  1380. "### 2.1 将一个元组映射到另一个元组"
  1381. ]
  1382. },
  1383. {
  1384. "cell_type": "code",
  1385. "execution_count": 42,
  1386. "metadata": {},
  1387. "outputs": [],
  1388. "source": [
  1389. "(a,b,c)= ('alpha','beta','gamma')"
  1390. ]
  1391. },
  1392. {
  1393. "cell_type": "code",
  1394. "execution_count": 43,
  1395. "metadata": {},
  1396. "outputs": [
  1397. {
  1398. "name": "stdout",
  1399. "output_type": "stream",
  1400. "text": [
  1401. "alpha beta gamma\n"
  1402. ]
  1403. }
  1404. ],
  1405. "source": [
  1406. "print(a,b,c)"
  1407. ]
  1408. },
  1409. {
  1410. "cell_type": "code",
  1411. "execution_count": 44,
  1412. "metadata": {},
  1413. "outputs": [],
  1414. "source": [
  1415. "(c, b, a) = (a, b, c)"
  1416. ]
  1417. },
  1418. {
  1419. "cell_type": "code",
  1420. "execution_count": 45,
  1421. "metadata": {},
  1422. "outputs": [
  1423. {
  1424. "name": "stdout",
  1425. "output_type": "stream",
  1426. "text": [
  1427. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1428. ]
  1429. }
  1430. ],
  1431. "source": [
  1432. "d = tuple('RajathKumarMP')\n",
  1433. "print(d)"
  1434. ]
  1435. },
  1436. {
  1437. "cell_type": "markdown",
  1438. "metadata": {},
  1439. "source": [
  1440. "### 2.2 元组内置函数"
  1441. ]
  1442. },
  1443. {
  1444. "cell_type": "markdown",
  1445. "metadata": {},
  1446. "source": [
  1447. "**count()** 函数计算元组中存在的指定元素的数量。"
  1448. ]
  1449. },
  1450. {
  1451. "cell_type": "code",
  1452. "execution_count": 46,
  1453. "metadata": {},
  1454. "outputs": [
  1455. {
  1456. "data": {
  1457. "text/plain": [
  1458. "3"
  1459. ]
  1460. },
  1461. "execution_count": 46,
  1462. "metadata": {},
  1463. "output_type": "execute_result"
  1464. }
  1465. ],
  1466. "source": [
  1467. "d.count('a')"
  1468. ]
  1469. },
  1470. {
  1471. "cell_type": "markdown",
  1472. "metadata": {},
  1473. "source": [
  1474. "**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"
  1475. ]
  1476. },
  1477. {
  1478. "cell_type": "code",
  1479. "execution_count": 47,
  1480. "metadata": {},
  1481. "outputs": [
  1482. {
  1483. "data": {
  1484. "text/plain": [
  1485. "1"
  1486. ]
  1487. },
  1488. "execution_count": 47,
  1489. "metadata": {},
  1490. "output_type": "execute_result"
  1491. }
  1492. ],
  1493. "source": [
  1494. "d.index('a')"
  1495. ]
  1496. },
  1497. {
  1498. "cell_type": "markdown",
  1499. "metadata": {},
  1500. "source": [
  1501. "## 3. 集合"
  1502. ]
  1503. },
  1504. {
  1505. "cell_type": "markdown",
  1506. "metadata": {},
  1507. "source": [
  1508. "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",
  1509. "\n",
  1510. "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"
  1511. ]
  1512. },
  1513. {
  1514. "cell_type": "code",
  1515. "execution_count": 48,
  1516. "metadata": {},
  1517. "outputs": [
  1518. {
  1519. "name": "stdout",
  1520. "output_type": "stream",
  1521. "text": [
  1522. "<class 'set'>\n"
  1523. ]
  1524. }
  1525. ],
  1526. "source": [
  1527. "set1 = set()\n",
  1528. "print(type(set1))"
  1529. ]
  1530. },
  1531. {
  1532. "cell_type": "code",
  1533. "execution_count": 49,
  1534. "metadata": {},
  1535. "outputs": [
  1536. {
  1537. "name": "stdout",
  1538. "output_type": "stream",
  1539. "text": [
  1540. "{1, 2, 3, 4}\n"
  1541. ]
  1542. }
  1543. ],
  1544. "source": [
  1545. "set0 = set([1,2,2,3,3,4])\n",
  1546. "print(set0)"
  1547. ]
  1548. },
  1549. {
  1550. "cell_type": "code",
  1551. "execution_count": 50,
  1552. "metadata": {},
  1553. "outputs": [
  1554. {
  1555. "name": "stdout",
  1556. "output_type": "stream",
  1557. "text": [
  1558. "{1, 2, 3, 4}\n"
  1559. ]
  1560. }
  1561. ],
  1562. "source": [
  1563. "set1 = set((1,2,2,3,3,4))\n",
  1564. "print(set1)"
  1565. ]
  1566. },
  1567. {
  1568. "cell_type": "markdown",
  1569. "metadata": {},
  1570. "source": [
  1571. "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。"
  1572. ]
  1573. },
  1574. {
  1575. "cell_type": "markdown",
  1576. "metadata": {},
  1577. "source": [
  1578. "### 3.1 内置函数"
  1579. ]
  1580. },
  1581. {
  1582. "cell_type": "code",
  1583. "execution_count": 51,
  1584. "metadata": {},
  1585. "outputs": [],
  1586. "source": [
  1587. "set1 = set([1,2,3])"
  1588. ]
  1589. },
  1590. {
  1591. "cell_type": "code",
  1592. "execution_count": 52,
  1593. "metadata": {},
  1594. "outputs": [],
  1595. "source": [
  1596. "set2 = set([2,3,4,5])"
  1597. ]
  1598. },
  1599. {
  1600. "cell_type": "markdown",
  1601. "metadata": {},
  1602. "source": [
  1603. "**union( )** 函数返回一个并集合,该集合包含两个集合的所有元素,但是没有重复。"
  1604. ]
  1605. },
  1606. {
  1607. "cell_type": "code",
  1608. "execution_count": 53,
  1609. "metadata": {},
  1610. "outputs": [
  1611. {
  1612. "data": {
  1613. "text/plain": [
  1614. "{1, 2, 3, 4, 5}"
  1615. ]
  1616. },
  1617. "execution_count": 53,
  1618. "metadata": {},
  1619. "output_type": "execute_result"
  1620. }
  1621. ],
  1622. "source": [
  1623. "set1.union(set2)"
  1624. ]
  1625. },
  1626. {
  1627. "cell_type": "markdown",
  1628. "metadata": {},
  1629. "source": [
  1630. "**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。"
  1631. ]
  1632. },
  1633. {
  1634. "cell_type": "code",
  1635. "execution_count": 54,
  1636. "metadata": {},
  1637. "outputs": [
  1638. {
  1639. "name": "stdout",
  1640. "output_type": "stream",
  1641. "text": [
  1642. "{1, 2, 3}\n"
  1643. ]
  1644. },
  1645. {
  1646. "data": {
  1647. "text/plain": [
  1648. "{0, 1, 2, 3}"
  1649. ]
  1650. },
  1651. "execution_count": 54,
  1652. "metadata": {},
  1653. "output_type": "execute_result"
  1654. }
  1655. ],
  1656. "source": [
  1657. "print(set1)\n",
  1658. "set1.add(0)\n",
  1659. "set1"
  1660. ]
  1661. },
  1662. {
  1663. "cell_type": "markdown",
  1664. "metadata": {},
  1665. "source": [
  1666. "**intersection( )** 函数输出一个交集合,该集合包含两个集合中的所有元素。"
  1667. ]
  1668. },
  1669. {
  1670. "cell_type": "code",
  1671. "execution_count": 55,
  1672. "metadata": {},
  1673. "outputs": [
  1674. {
  1675. "data": {
  1676. "text/plain": [
  1677. "{2, 3}"
  1678. ]
  1679. },
  1680. "execution_count": 55,
  1681. "metadata": {},
  1682. "output_type": "execute_result"
  1683. }
  1684. ],
  1685. "source": [
  1686. "set1.intersection(set2)"
  1687. ]
  1688. },
  1689. {
  1690. "cell_type": "markdown",
  1691. "metadata": {},
  1692. "source": [
  1693. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1694. ]
  1695. },
  1696. {
  1697. "cell_type": "code",
  1698. "execution_count": 56,
  1699. "metadata": {},
  1700. "outputs": [
  1701. {
  1702. "name": "stdout",
  1703. "output_type": "stream",
  1704. "text": [
  1705. "{0, 1, 2, 3}\n",
  1706. "{2, 3, 4, 5}\n"
  1707. ]
  1708. },
  1709. {
  1710. "data": {
  1711. "text/plain": [
  1712. "{0, 1}"
  1713. ]
  1714. },
  1715. "execution_count": 56,
  1716. "metadata": {},
  1717. "output_type": "execute_result"
  1718. }
  1719. ],
  1720. "source": [
  1721. "print(set1)\n",
  1722. "print(set2)\n",
  1723. "set1.difference(set2)"
  1724. ]
  1725. },
  1726. {
  1727. "cell_type": "markdown",
  1728. "metadata": {},
  1729. "source": [
  1730. "**pop( )** 是用来移除集合中的任意元素。"
  1731. ]
  1732. },
  1733. {
  1734. "cell_type": "code",
  1735. "execution_count": 57,
  1736. "metadata": {},
  1737. "outputs": [],
  1738. "source": [
  1739. "set1=set([10, 9, 1, 2, 4])"
  1740. ]
  1741. },
  1742. {
  1743. "cell_type": "code",
  1744. "execution_count": 58,
  1745. "metadata": {},
  1746. "outputs": [
  1747. {
  1748. "name": "stdout",
  1749. "output_type": "stream",
  1750. "text": [
  1751. "{2, 4, 9, 10}\n"
  1752. ]
  1753. }
  1754. ],
  1755. "source": [
  1756. "set1.pop()\n",
  1757. "print(set1)"
  1758. ]
  1759. },
  1760. {
  1761. "cell_type": "markdown",
  1762. "metadata": {},
  1763. "source": [
  1764. "**remove( )** 函数从集合中删除指定的元素。"
  1765. ]
  1766. },
  1767. {
  1768. "cell_type": "code",
  1769. "execution_count": 59,
  1770. "metadata": {},
  1771. "outputs": [
  1772. {
  1773. "data": {
  1774. "text/plain": [
  1775. "{4, 9, 10}"
  1776. ]
  1777. },
  1778. "execution_count": 59,
  1779. "metadata": {},
  1780. "output_type": "execute_result"
  1781. }
  1782. ],
  1783. "source": [
  1784. "set1.remove(2)\n",
  1785. "set1"
  1786. ]
  1787. },
  1788. {
  1789. "cell_type": "markdown",
  1790. "metadata": {},
  1791. "source": [
  1792. "**clear( )** 用于清除所有元素并将其设置为空集。"
  1793. ]
  1794. },
  1795. {
  1796. "cell_type": "code",
  1797. "execution_count": 60,
  1798. "metadata": {},
  1799. "outputs": [
  1800. {
  1801. "data": {
  1802. "text/plain": [
  1803. "set()"
  1804. ]
  1805. },
  1806. "execution_count": 60,
  1807. "metadata": {},
  1808. "output_type": "execute_result"
  1809. }
  1810. ],
  1811. "source": [
  1812. "set1.clear()\n",
  1813. "set1"
  1814. ]
  1815. }
  1816. ],
  1817. "metadata": {
  1818. "kernelspec": {
  1819. "display_name": "Python 3",
  1820. "language": "python",
  1821. "name": "python3"
  1822. },
  1823. "language_info": {
  1824. "codemirror_mode": {
  1825. "name": "ipython",
  1826. "version": 3
  1827. },
  1828. "file_extension": ".py",
  1829. "mimetype": "text/x-python",
  1830. "name": "python",
  1831. "nbconvert_exporter": "python",
  1832. "pygments_lexer": "ipython3",
  1833. "version": "3.7.9"
  1834. }
  1835. },
  1836. "nbformat": 4,
  1837. "nbformat_minor": 1
  1838. }

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》。