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3_Data_Structure_1.ipynb 41 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": 1,
  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": 2,
  101. "metadata": {},
  102. "outputs": [
  103. {
  104. "data": {
  105. "text/plain": [
  106. "'apple'"
  107. ]
  108. },
  109. "execution_count": 2,
  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": 4,
  128. "metadata": {},
  129. "outputs": [
  130. {
  131. "data": {
  132. "text/plain": [
  133. "'peach'"
  134. ]
  135. },
  136. "execution_count": 4,
  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": 5,
  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": 7,
  171. "metadata": {},
  172. "outputs": [
  173. {
  174. "name": "stdout",
  175. "output_type": "stream",
  176. "text": [
  177. "[['apple', 'orange', 'peach'], ['carrot', 'potato']]\n"
  178. ]
  179. }
  180. ],
  181. "source": [
  182. "z = [x,y]\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": 8,
  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": 9,
  244. "metadata": {},
  245. "outputs": [
  246. {
  247. "data": {
  248. "text/plain": [
  249. "'apple'"
  250. ]
  251. },
  252. "execution_count": 9,
  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": 13,
  271. "metadata": {},
  272. "outputs": [
  273. {
  274. "data": {
  275. "text/plain": [
  276. "'apple'"
  277. ]
  278. },
  279. "execution_count": 13,
  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": 13,
  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. ]
  324. }
  325. ],
  326. "source": [
  327. "num = [2,3,2,3,4,5,6,7,8,9]\n",
  328. "print(num[1:4])\n",
  329. "print(num[0:])\n",
  330. "print(num[:])"
  331. ]
  332. },
  333. {
  334. "cell_type": "code",
  335. "execution_count": 14,
  336. "metadata": {},
  337. "outputs": [
  338. {
  339. "name": "stdout",
  340. "output_type": "stream",
  341. "text": [
  342. "[2, 3, 2, 3]\n",
  343. "[4, 5, 6, 7, 8, 9]\n"
  344. ]
  345. }
  346. ],
  347. "source": [
  348. "print(num[0:4])\n",
  349. "print(num[4:])"
  350. ]
  351. },
  352. {
  353. "cell_type": "markdown",
  354. "metadata": {},
  355. "source": [
  356. "您还可以使用固定长度或步长对父列表进行切片。"
  357. ]
  358. },
  359. {
  360. "cell_type": "code",
  361. "execution_count": 15,
  362. "metadata": {},
  363. "outputs": [
  364. {
  365. "data": {
  366. "text/plain": [
  367. "[2, 3, 6]"
  368. ]
  369. },
  370. "execution_count": 15,
  371. "metadata": {},
  372. "output_type": "execute_result"
  373. }
  374. ],
  375. "source": [
  376. "num[:9:3]"
  377. ]
  378. },
  379. {
  380. "cell_type": "markdown",
  381. "metadata": {},
  382. "source": [
  383. "### 1.3 列表的内置函数"
  384. ]
  385. },
  386. {
  387. "cell_type": "markdown",
  388. "metadata": {},
  389. "source": [
  390. "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( )**。"
  391. ]
  392. },
  393. {
  394. "cell_type": "code",
  395. "execution_count": 13,
  396. "metadata": {},
  397. "outputs": [
  398. {
  399. "data": {
  400. "text/plain": [
  401. "10"
  402. ]
  403. },
  404. "execution_count": 13,
  405. "metadata": {},
  406. "output_type": "execute_result"
  407. }
  408. ],
  409. "source": [
  410. "len(num)"
  411. ]
  412. },
  413. {
  414. "cell_type": "markdown",
  415. "metadata": {},
  416. "source": [
  417. "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。"
  418. ]
  419. },
  420. {
  421. "cell_type": "code",
  422. "execution_count": 14,
  423. "metadata": {},
  424. "outputs": [
  425. {
  426. "data": {
  427. "text/plain": [
  428. "0"
  429. ]
  430. },
  431. "execution_count": 14,
  432. "metadata": {},
  433. "output_type": "execute_result"
  434. }
  435. ],
  436. "source": [
  437. "min(num)"
  438. ]
  439. },
  440. {
  441. "cell_type": "code",
  442. "execution_count": 15,
  443. "metadata": {},
  444. "outputs": [
  445. {
  446. "data": {
  447. "text/plain": [
  448. "9"
  449. ]
  450. },
  451. "execution_count": 15,
  452. "metadata": {},
  453. "output_type": "execute_result"
  454. }
  455. ],
  456. "source": [
  457. "max(num)"
  458. ]
  459. },
  460. {
  461. "cell_type": "markdown",
  462. "metadata": {},
  463. "source": [
  464. "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。"
  465. ]
  466. },
  467. {
  468. "cell_type": "code",
  469. "execution_count": 16,
  470. "metadata": {},
  471. "outputs": [
  472. {
  473. "data": {
  474. "text/plain": [
  475. "[1, 2, 3, 5, 4, 7]"
  476. ]
  477. },
  478. "execution_count": 16,
  479. "metadata": {},
  480. "output_type": "execute_result"
  481. }
  482. ],
  483. "source": [
  484. "[1,2,3] + [5,4,7]"
  485. ]
  486. },
  487. {
  488. "cell_type": "markdown",
  489. "metadata": {},
  490. "source": [
  491. "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。"
  492. ]
  493. },
  494. {
  495. "cell_type": "code",
  496. "execution_count": 18,
  497. "metadata": {},
  498. "outputs": [],
  499. "source": [
  500. "names = ['Earth','Air','Fire','Water']"
  501. ]
  502. },
  503. {
  504. "cell_type": "markdown",
  505. "metadata": {},
  506. "source": [
  507. "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\""
  508. ]
  509. },
  510. {
  511. "cell_type": "code",
  512. "execution_count": 18,
  513. "metadata": {},
  514. "outputs": [
  515. {
  516. "data": {
  517. "text/plain": [
  518. "False"
  519. ]
  520. },
  521. "execution_count": 18,
  522. "metadata": {},
  523. "output_type": "execute_result"
  524. }
  525. ],
  526. "source": [
  527. "'Fir' in names"
  528. ]
  529. },
  530. {
  531. "cell_type": "code",
  532. "execution_count": 19,
  533. "metadata": {},
  534. "outputs": [
  535. {
  536. "data": {
  537. "text/plain": [
  538. "True"
  539. ]
  540. },
  541. "execution_count": 19,
  542. "metadata": {},
  543. "output_type": "execute_result"
  544. }
  545. ],
  546. "source": [
  547. "'Fire' in names"
  548. ]
  549. },
  550. {
  551. "cell_type": "code",
  552. "execution_count": 20,
  553. "metadata": {},
  554. "outputs": [
  555. {
  556. "data": {
  557. "text/plain": [
  558. "False"
  559. ]
  560. },
  561. "execution_count": 20,
  562. "metadata": {},
  563. "output_type": "execute_result"
  564. }
  565. ],
  566. "source": [
  567. "'fire' in names"
  568. ]
  569. },
  570. {
  571. "cell_type": "code",
  572. "execution_count": 25,
  573. "metadata": {},
  574. "outputs": [
  575. {
  576. "data": {
  577. "text/plain": [
  578. "False"
  579. ]
  580. },
  581. "execution_count": 25,
  582. "metadata": {},
  583. "output_type": "execute_result"
  584. }
  585. ],
  586. "source": [
  587. "'Rajath' in names"
  588. ]
  589. },
  590. {
  591. "cell_type": "markdown",
  592. "metadata": {},
  593. "source": [
  594. "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。"
  595. ]
  596. },
  597. {
  598. "cell_type": "code",
  599. "execution_count": 19,
  600. "metadata": {},
  601. "outputs": [],
  602. "source": [
  603. "mlist = ['bzaa','ds','nc','az','z','klm']"
  604. ]
  605. },
  606. {
  607. "cell_type": "code",
  608. "execution_count": 20,
  609. "metadata": {},
  610. "outputs": [
  611. {
  612. "name": "stdout",
  613. "output_type": "stream",
  614. "text": [
  615. "z\n",
  616. "az\n"
  617. ]
  618. }
  619. ],
  620. "source": [
  621. "print(max(mlist))\n",
  622. "print(min(mlist))"
  623. ]
  624. },
  625. {
  626. "cell_type": "markdown",
  627. "metadata": {},
  628. "source": [
  629. "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?"
  630. ]
  631. },
  632. {
  633. "cell_type": "code",
  634. "execution_count": 21,
  635. "metadata": {},
  636. "outputs": [],
  637. "source": [
  638. "nlist = ['1','94','93','1000']"
  639. ]
  640. },
  641. {
  642. "cell_type": "code",
  643. "execution_count": 22,
  644. "metadata": {},
  645. "outputs": [
  646. {
  647. "name": "stdout",
  648. "output_type": "stream",
  649. "text": [
  650. "94\n",
  651. "1\n"
  652. ]
  653. }
  654. ],
  655. "source": [
  656. "print(max(nlist))\n",
  657. "print(min(nlist))"
  658. ]
  659. },
  660. {
  661. "cell_type": "markdown",
  662. "metadata": {},
  663. "source": [
  664. "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。"
  665. ]
  666. },
  667. {
  668. "cell_type": "markdown",
  669. "metadata": {},
  670. "source": [
  671. "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'。"
  672. ]
  673. },
  674. {
  675. "cell_type": "code",
  676. "execution_count": 23,
  677. "metadata": {},
  678. "outputs": [
  679. {
  680. "name": "stdout",
  681. "output_type": "stream",
  682. "text": [
  683. "Earth\n",
  684. "Jet\n"
  685. ]
  686. }
  687. ],
  688. "source": [
  689. "names = ['Earth','Jet', 'Air','Fire','Water']\n",
  690. "print(max(names, key=len))\n",
  691. "print(min(names, key=len))"
  692. ]
  693. },
  694. {
  695. "cell_type": "markdown",
  696. "metadata": {},
  697. "source": [
  698. "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",
  699. "\n",
  700. "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",
  701. "\n",
  702. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  703. ]
  704. },
  705. {
  706. "cell_type": "code",
  707. "execution_count": 24,
  708. "metadata": {},
  709. "outputs": [
  710. {
  711. "data": {
  712. "text/plain": [
  713. "['h', 'e', 'l', 'l', 'o']"
  714. ]
  715. },
  716. "execution_count": 24,
  717. "metadata": {},
  718. "output_type": "execute_result"
  719. }
  720. ],
  721. "source": [
  722. "list('hello')"
  723. ]
  724. },
  725. {
  726. "cell_type": "markdown",
  727. "metadata": {},
  728. "source": [
  729. "**append( )** 被用来在列表的最后添加一个元素。"
  730. ]
  731. },
  732. {
  733. "cell_type": "code",
  734. "execution_count": 44,
  735. "metadata": {},
  736. "outputs": [],
  737. "source": [
  738. "lst = [1,1,4,8,7]"
  739. ]
  740. },
  741. {
  742. "cell_type": "code",
  743. "execution_count": 45,
  744. "metadata": {},
  745. "outputs": [
  746. {
  747. "name": "stdout",
  748. "output_type": "stream",
  749. "text": [
  750. "[1, 1, 4, 8, 7, 1]\n"
  751. ]
  752. }
  753. ],
  754. "source": [
  755. "lst.append(1)\n",
  756. "print(lst)"
  757. ]
  758. },
  759. {
  760. "cell_type": "markdown",
  761. "metadata": {},
  762. "source": [
  763. "**count( )** 用于计算列表中出现的特定元素的数量。"
  764. ]
  765. },
  766. {
  767. "cell_type": "code",
  768. "execution_count": 46,
  769. "metadata": {},
  770. "outputs": [
  771. {
  772. "data": {
  773. "text/plain": [
  774. "3"
  775. ]
  776. },
  777. "execution_count": 46,
  778. "metadata": {},
  779. "output_type": "execute_result"
  780. }
  781. ],
  782. "source": [
  783. "lst.count(1)"
  784. ]
  785. },
  786. {
  787. "cell_type": "markdown",
  788. "metadata": {},
  789. "source": [
  790. "**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。"
  791. ]
  792. },
  793. {
  794. "cell_type": "code",
  795. "execution_count": 47,
  796. "metadata": {},
  797. "outputs": [],
  798. "source": [
  799. "lst1 = [5,4,2,8]"
  800. ]
  801. },
  802. {
  803. "cell_type": "code",
  804. "execution_count": 48,
  805. "metadata": {},
  806. "outputs": [
  807. {
  808. "name": "stdout",
  809. "output_type": "stream",
  810. "text": [
  811. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8]]\n"
  812. ]
  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": 49,
  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]\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": 50,
  855. "metadata": {},
  856. "outputs": [
  857. {
  858. "data": {
  859. "text/plain": [
  860. "0"
  861. ]
  862. },
  863. "execution_count": 50,
  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": 51,
  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[0m Traceback (most recent call last)",
  884. "\u001b[0;32m<ipython-input-51-344da5bcf759>\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": 52,
  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]\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": 53,
  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]\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": 54,
  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]"
  959. ]
  960. },
  961. "execution_count": 54,
  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": 55,
  981. "metadata": {},
  982. "outputs": [
  983. {
  984. "data": {
  985. "text/plain": [
  986. "4"
  987. ]
  988. },
  989. "execution_count": 55,
  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": 57,
  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]\n"
  1008. ]
  1009. },
  1010. {
  1011. "data": {
  1012. "text/plain": [
  1013. "4"
  1014. ]
  1015. },
  1016. "execution_count": 57,
  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": 58,
  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, 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": 60,
  1053. "metadata": {},
  1054. "outputs": [
  1055. {
  1056. "name": "stdout",
  1057. "output_type": "stream",
  1058. "text": [
  1059. "[1, 1, 8, 7, 1, [5, 4, 2, 8], 5, 2]\n"
  1060. ]
  1061. }
  1062. ],
  1063. "source": [
  1064. "lst.remove('Python')\n",
  1065. "print(lst)"
  1066. ]
  1067. },
  1068. {
  1069. "cell_type": "markdown",
  1070. "metadata": {},
  1071. "source": [
  1072. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1073. ]
  1074. },
  1075. {
  1076. "cell_type": "code",
  1077. "execution_count": 61,
  1078. "metadata": {},
  1079. "outputs": [
  1080. {
  1081. "name": "stdout",
  1082. "output_type": "stream",
  1083. "text": [
  1084. "[1, 1, 8, 7, 1, [5, 4, 2, 8], 5, 2]\n",
  1085. "[1, 8, 7, 1, [5, 4, 2, 8], 5, 2]\n"
  1086. ]
  1087. }
  1088. ],
  1089. "source": [
  1090. "print(lst)\n",
  1091. "del lst[1]\n",
  1092. "print(lst)"
  1093. ]
  1094. },
  1095. {
  1096. "cell_type": "markdown",
  1097. "metadata": {},
  1098. "source": [
  1099. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1100. ]
  1101. },
  1102. {
  1103. "cell_type": "code",
  1104. "execution_count": 62,
  1105. "metadata": {},
  1106. "outputs": [
  1107. {
  1108. "name": "stdout",
  1109. "output_type": "stream",
  1110. "text": [
  1111. "[2, 5, [5, 4, 2, 8], 1, 7, 8, 1]\n"
  1112. ]
  1113. }
  1114. ],
  1115. "source": [
  1116. "lst.reverse()\n",
  1117. "print(lst)"
  1118. ]
  1119. },
  1120. {
  1121. "cell_type": "markdown",
  1122. "metadata": {},
  1123. "source": [
  1124. "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",
  1125. "\n",
  1126. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1127. ]
  1128. },
  1129. {
  1130. "cell_type": "code",
  1131. "execution_count": 63,
  1132. "metadata": {},
  1133. "outputs": [
  1134. {
  1135. "name": "stdout",
  1136. "output_type": "stream",
  1137. "text": [
  1138. "[1, 4, 8, 8, 10]\n"
  1139. ]
  1140. }
  1141. ],
  1142. "source": [
  1143. "lst = [1, 4, 8, 8, 10]\n",
  1144. "lst.sort()\n",
  1145. "print(lst)"
  1146. ]
  1147. },
  1148. {
  1149. "cell_type": "markdown",
  1150. "metadata": {},
  1151. "source": [
  1152. "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。"
  1153. ]
  1154. },
  1155. {
  1156. "cell_type": "code",
  1157. "execution_count": 64,
  1158. "metadata": {},
  1159. "outputs": [
  1160. {
  1161. "name": "stdout",
  1162. "output_type": "stream",
  1163. "text": [
  1164. "[10, 8, 8, 4, 1]\n"
  1165. ]
  1166. }
  1167. ],
  1168. "source": [
  1169. "lst.sort(reverse=True)\n",
  1170. "print(lst)"
  1171. ]
  1172. },
  1173. {
  1174. "cell_type": "markdown",
  1175. "metadata": {},
  1176. "source": [
  1177. "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。"
  1178. ]
  1179. },
  1180. {
  1181. "cell_type": "code",
  1182. "execution_count": 65,
  1183. "metadata": {},
  1184. "outputs": [
  1185. {
  1186. "name": "stdout",
  1187. "output_type": "stream",
  1188. "text": [
  1189. "['apple', 'orange', 'peach']\n",
  1190. "['peach', 'orange', 'apple']\n"
  1191. ]
  1192. }
  1193. ],
  1194. "source": [
  1195. "names = ['apple', 'orange', 'peach']\n",
  1196. "names.sort()\n",
  1197. "print(names)\n",
  1198. "names.sort(reverse=True)\n",
  1199. "print(names)"
  1200. ]
  1201. },
  1202. {
  1203. "cell_type": "markdown",
  1204. "metadata": {},
  1205. "source": [
  1206. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1207. ]
  1208. },
  1209. {
  1210. "cell_type": "code",
  1211. "execution_count": 46,
  1212. "metadata": {},
  1213. "outputs": [
  1214. {
  1215. "name": "stdout",
  1216. "output_type": "stream",
  1217. "text": [
  1218. "['peach', 'apple', 'orange']\n",
  1219. "['orange', 'peach', 'apple']\n"
  1220. ]
  1221. }
  1222. ],
  1223. "source": [
  1224. "names.sort(key=len)\n",
  1225. "print(names)\n",
  1226. "names.sort(key=len,reverse=True)\n",
  1227. "print(names)"
  1228. ]
  1229. },
  1230. {
  1231. "cell_type": "markdown",
  1232. "metadata": {},
  1233. "source": [
  1234. "### 1.4 复制一个列表"
  1235. ]
  1236. },
  1237. {
  1238. "cell_type": "markdown",
  1239. "metadata": {},
  1240. "source": [
  1241. "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:"
  1242. ]
  1243. },
  1244. {
  1245. "cell_type": "code",
  1246. "execution_count": 66,
  1247. "metadata": {},
  1248. "outputs": [],
  1249. "source": [
  1250. "lista= [2,1,4,3]"
  1251. ]
  1252. },
  1253. {
  1254. "cell_type": "code",
  1255. "execution_count": 67,
  1256. "metadata": {},
  1257. "outputs": [
  1258. {
  1259. "name": "stdout",
  1260. "output_type": "stream",
  1261. "text": [
  1262. "[2, 1, 4, 3]\n"
  1263. ]
  1264. }
  1265. ],
  1266. "source": [
  1267. "listb = lista # 对象赋值\n",
  1268. "print(listb)"
  1269. ]
  1270. },
  1271. {
  1272. "cell_type": "markdown",
  1273. "metadata": {},
  1274. "source": [
  1275. "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。"
  1276. ]
  1277. },
  1278. {
  1279. "cell_type": "code",
  1280. "execution_count": 68,
  1281. "metadata": {},
  1282. "outputs": [
  1283. {
  1284. "name": "stdout",
  1285. "output_type": "stream",
  1286. "text": [
  1287. "[2, 1, 4]\n",
  1288. "[2, 1, 4, 9]\n"
  1289. ]
  1290. }
  1291. ],
  1292. "source": [
  1293. "lista.pop()\n",
  1294. "print(lista)\n",
  1295. "lista.append(9)\n",
  1296. "print(lista)"
  1297. ]
  1298. },
  1299. {
  1300. "cell_type": "code",
  1301. "execution_count": 69,
  1302. "metadata": {},
  1303. "outputs": [
  1304. {
  1305. "name": "stdout",
  1306. "output_type": "stream",
  1307. "text": [
  1308. "[2, 1, 4, 9]\n"
  1309. ]
  1310. }
  1311. ],
  1312. "source": [
  1313. "print(listb)"
  1314. ]
  1315. },
  1316. {
  1317. "cell_type": "markdown",
  1318. "metadata": {},
  1319. "source": [
  1320. "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",
  1321. "\n",
  1322. "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。"
  1323. ]
  1324. },
  1325. {
  1326. "cell_type": "code",
  1327. "execution_count": 71,
  1328. "metadata": {},
  1329. "outputs": [],
  1330. "source": [
  1331. "lista = [2,1,4,3]"
  1332. ]
  1333. },
  1334. {
  1335. "cell_type": "code",
  1336. "execution_count": 72,
  1337. "metadata": {},
  1338. "outputs": [
  1339. {
  1340. "name": "stdout",
  1341. "output_type": "stream",
  1342. "text": [
  1343. "[2, 1, 4, 3]\n"
  1344. ]
  1345. }
  1346. ],
  1347. "source": [
  1348. "listb = lista[:]\n",
  1349. "print(listb)"
  1350. ]
  1351. },
  1352. {
  1353. "cell_type": "code",
  1354. "execution_count": 73,
  1355. "metadata": {},
  1356. "outputs": [
  1357. {
  1358. "name": "stdout",
  1359. "output_type": "stream",
  1360. "text": [
  1361. "[2, 1, 4]\n",
  1362. "[2, 1, 4, 9]\n"
  1363. ]
  1364. }
  1365. ],
  1366. "source": [
  1367. "lista.pop()\n",
  1368. "print(lista)\n",
  1369. "lista.append(9)\n",
  1370. "print(lista)"
  1371. ]
  1372. },
  1373. {
  1374. "cell_type": "code",
  1375. "execution_count": 74,
  1376. "metadata": {},
  1377. "outputs": [
  1378. {
  1379. "name": "stdout",
  1380. "output_type": "stream",
  1381. "text": [
  1382. "[2, 1, 4, 3]\n"
  1383. ]
  1384. }
  1385. ],
  1386. "source": [
  1387. "print(listb)"
  1388. ]
  1389. },
  1390. {
  1391. "cell_type": "markdown",
  1392. "metadata": {},
  1393. "source": [
  1394. "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?"
  1395. ]
  1396. },
  1397. {
  1398. "cell_type": "markdown",
  1399. "metadata": {},
  1400. "source": [
  1401. "## 2. 元组"
  1402. ]
  1403. },
  1404. {
  1405. "cell_type": "markdown",
  1406. "metadata": {},
  1407. "source": [
  1408. "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。"
  1409. ]
  1410. },
  1411. {
  1412. "cell_type": "code",
  1413. "execution_count": 51,
  1414. "metadata": {},
  1415. "outputs": [
  1416. {
  1417. "name": "stdout",
  1418. "output_type": "stream",
  1419. "text": [
  1420. "(3, 1)\n",
  1421. "<class 'tuple'>\n"
  1422. ]
  1423. }
  1424. ],
  1425. "source": [
  1426. "xyz = divmod(10,3)\n",
  1427. "print(xyz)\n",
  1428. "print(type(xyz))"
  1429. ]
  1430. },
  1431. {
  1432. "cell_type": "markdown",
  1433. "metadata": {},
  1434. "source": [
  1435. "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。"
  1436. ]
  1437. },
  1438. {
  1439. "cell_type": "markdown",
  1440. "metadata": {},
  1441. "source": [
  1442. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1443. ]
  1444. },
  1445. {
  1446. "cell_type": "code",
  1447. "execution_count": 75,
  1448. "metadata": {},
  1449. "outputs": [],
  1450. "source": [
  1451. "tup = ()\n",
  1452. "tup2 = tuple()"
  1453. ]
  1454. },
  1455. {
  1456. "cell_type": "markdown",
  1457. "metadata": {},
  1458. "source": [
  1459. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1460. ]
  1461. },
  1462. {
  1463. "cell_type": "code",
  1464. "execution_count": 76,
  1465. "metadata": {},
  1466. "outputs": [
  1467. {
  1468. "data": {
  1469. "text/plain": [
  1470. "(27,)"
  1471. ]
  1472. },
  1473. "execution_count": 76,
  1474. "metadata": {},
  1475. "output_type": "execute_result"
  1476. }
  1477. ],
  1478. "source": [
  1479. "27,"
  1480. ]
  1481. },
  1482. {
  1483. "cell_type": "markdown",
  1484. "metadata": {},
  1485. "source": [
  1486. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1487. ]
  1488. },
  1489. {
  1490. "cell_type": "code",
  1491. "execution_count": 79,
  1492. "metadata": {},
  1493. "outputs": [
  1494. {
  1495. "data": {
  1496. "text/plain": [
  1497. "(27, 27)"
  1498. ]
  1499. },
  1500. "execution_count": 79,
  1501. "metadata": {},
  1502. "output_type": "execute_result"
  1503. }
  1504. ],
  1505. "source": [
  1506. "2*(27,)"
  1507. ]
  1508. },
  1509. {
  1510. "cell_type": "markdown",
  1511. "metadata": {},
  1512. "source": [
  1513. "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。"
  1514. ]
  1515. },
  1516. {
  1517. "cell_type": "code",
  1518. "execution_count": 80,
  1519. "metadata": {
  1520. "scrolled": true
  1521. },
  1522. "outputs": [
  1523. {
  1524. "name": "stdout",
  1525. "output_type": "stream",
  1526. "text": [
  1527. "(1, 2, 3)\n",
  1528. "('H', 'e', 'l', 'l', 'o')\n"
  1529. ]
  1530. }
  1531. ],
  1532. "source": [
  1533. "tup3 = tuple([1,2,3])\n",
  1534. "print(tup3)\n",
  1535. "tup4 = tuple('Hello')\n",
  1536. "print(tup4)"
  1537. ]
  1538. },
  1539. {
  1540. "cell_type": "markdown",
  1541. "metadata": {},
  1542. "source": [
  1543. "它遵循与列表相同的索引和切片。"
  1544. ]
  1545. },
  1546. {
  1547. "cell_type": "code",
  1548. "execution_count": 81,
  1549. "metadata": {},
  1550. "outputs": [
  1551. {
  1552. "name": "stdout",
  1553. "output_type": "stream",
  1554. "text": [
  1555. "2\n",
  1556. "('H', 'e', 'l')\n"
  1557. ]
  1558. }
  1559. ],
  1560. "source": [
  1561. "print(tup3[1])\n",
  1562. "tup5 = tup4[:3]\n",
  1563. "print(tup5)"
  1564. ]
  1565. },
  1566. {
  1567. "cell_type": "markdown",
  1568. "metadata": {},
  1569. "source": [
  1570. "### 2.1 将一个元组映射到另一个元组"
  1571. ]
  1572. },
  1573. {
  1574. "cell_type": "code",
  1575. "execution_count": 82,
  1576. "metadata": {},
  1577. "outputs": [],
  1578. "source": [
  1579. "(a,b,c)= ('alpha','beta','gamma')"
  1580. ]
  1581. },
  1582. {
  1583. "cell_type": "code",
  1584. "execution_count": 46,
  1585. "metadata": {},
  1586. "outputs": [
  1587. {
  1588. "name": "stdout",
  1589. "output_type": "stream",
  1590. "text": [
  1591. "alpha beta gamma\n"
  1592. ]
  1593. }
  1594. ],
  1595. "source": [
  1596. "print(a,b,c)"
  1597. ]
  1598. },
  1599. {
  1600. "cell_type": "code",
  1601. "execution_count": 58,
  1602. "metadata": {},
  1603. "outputs": [
  1604. {
  1605. "name": "stdout",
  1606. "output_type": "stream",
  1607. "text": [
  1608. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1609. ]
  1610. }
  1611. ],
  1612. "source": [
  1613. "d = tuple('RajathKumarMP')\n",
  1614. "print(d)"
  1615. ]
  1616. },
  1617. {
  1618. "cell_type": "markdown",
  1619. "metadata": {},
  1620. "source": [
  1621. "### 2.2 元组内置函数"
  1622. ]
  1623. },
  1624. {
  1625. "cell_type": "markdown",
  1626. "metadata": {},
  1627. "source": [
  1628. "**count()** 函数计算元组中存在的指定元素的数量。"
  1629. ]
  1630. },
  1631. {
  1632. "cell_type": "code",
  1633. "execution_count": 59,
  1634. "metadata": {},
  1635. "outputs": [
  1636. {
  1637. "data": {
  1638. "text/plain": [
  1639. "3"
  1640. ]
  1641. },
  1642. "execution_count": 59,
  1643. "metadata": {},
  1644. "output_type": "execute_result"
  1645. }
  1646. ],
  1647. "source": [
  1648. "d.count('a')"
  1649. ]
  1650. },
  1651. {
  1652. "cell_type": "markdown",
  1653. "metadata": {},
  1654. "source": [
  1655. "**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"
  1656. ]
  1657. },
  1658. {
  1659. "cell_type": "code",
  1660. "execution_count": 60,
  1661. "metadata": {},
  1662. "outputs": [
  1663. {
  1664. "data": {
  1665. "text/plain": [
  1666. "1"
  1667. ]
  1668. },
  1669. "execution_count": 60,
  1670. "metadata": {},
  1671. "output_type": "execute_result"
  1672. }
  1673. ],
  1674. "source": [
  1675. "d.index('a')"
  1676. ]
  1677. },
  1678. {
  1679. "cell_type": "markdown",
  1680. "metadata": {},
  1681. "source": [
  1682. "## 3. 集合"
  1683. ]
  1684. },
  1685. {
  1686. "cell_type": "markdown",
  1687. "metadata": {},
  1688. "source": [
  1689. "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",
  1690. "\n",
  1691. "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"
  1692. ]
  1693. },
  1694. {
  1695. "cell_type": "code",
  1696. "execution_count": 83,
  1697. "metadata": {},
  1698. "outputs": [
  1699. {
  1700. "name": "stdout",
  1701. "output_type": "stream",
  1702. "text": [
  1703. "<class 'set'>\n"
  1704. ]
  1705. }
  1706. ],
  1707. "source": [
  1708. "set1 = set()\n",
  1709. "print(type(set1))"
  1710. ]
  1711. },
  1712. {
  1713. "cell_type": "code",
  1714. "execution_count": 84,
  1715. "metadata": {},
  1716. "outputs": [
  1717. {
  1718. "name": "stdout",
  1719. "output_type": "stream",
  1720. "text": [
  1721. "{1, 2, 3, 4}\n"
  1722. ]
  1723. }
  1724. ],
  1725. "source": [
  1726. "set0 = set([1,2,2,3,3,4])\n",
  1727. "print(set0)"
  1728. ]
  1729. },
  1730. {
  1731. "cell_type": "code",
  1732. "execution_count": 86,
  1733. "metadata": {},
  1734. "outputs": [
  1735. {
  1736. "name": "stdout",
  1737. "output_type": "stream",
  1738. "text": [
  1739. "{1, 2, 3, 4}\n"
  1740. ]
  1741. }
  1742. ],
  1743. "source": [
  1744. "set1 = set((1,2,2,3,3,4))\n",
  1745. "print(set1)"
  1746. ]
  1747. },
  1748. {
  1749. "cell_type": "markdown",
  1750. "metadata": {},
  1751. "source": [
  1752. "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。"
  1753. ]
  1754. },
  1755. {
  1756. "cell_type": "markdown",
  1757. "metadata": {},
  1758. "source": [
  1759. "### 3.1 内置函数"
  1760. ]
  1761. },
  1762. {
  1763. "cell_type": "code",
  1764. "execution_count": 87,
  1765. "metadata": {},
  1766. "outputs": [],
  1767. "source": [
  1768. "set1 = set([1,2,3])"
  1769. ]
  1770. },
  1771. {
  1772. "cell_type": "code",
  1773. "execution_count": 88,
  1774. "metadata": {},
  1775. "outputs": [],
  1776. "source": [
  1777. "set2 = set([2,3,4,5])"
  1778. ]
  1779. },
  1780. {
  1781. "cell_type": "markdown",
  1782. "metadata": {},
  1783. "source": [
  1784. "**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。"
  1785. ]
  1786. },
  1787. {
  1788. "cell_type": "code",
  1789. "execution_count": 89,
  1790. "metadata": {},
  1791. "outputs": [
  1792. {
  1793. "data": {
  1794. "text/plain": [
  1795. "{1, 2, 3, 4, 5}"
  1796. ]
  1797. },
  1798. "execution_count": 89,
  1799. "metadata": {},
  1800. "output_type": "execute_result"
  1801. }
  1802. ],
  1803. "source": [
  1804. "set1.union(set2)"
  1805. ]
  1806. },
  1807. {
  1808. "cell_type": "markdown",
  1809. "metadata": {},
  1810. "source": [
  1811. "**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。"
  1812. ]
  1813. },
  1814. {
  1815. "cell_type": "code",
  1816. "execution_count": 90,
  1817. "metadata": {},
  1818. "outputs": [
  1819. {
  1820. "name": "stdout",
  1821. "output_type": "stream",
  1822. "text": [
  1823. "{1, 2, 3}\n"
  1824. ]
  1825. },
  1826. {
  1827. "data": {
  1828. "text/plain": [
  1829. "{0, 1, 2, 3}"
  1830. ]
  1831. },
  1832. "execution_count": 90,
  1833. "metadata": {},
  1834. "output_type": "execute_result"
  1835. }
  1836. ],
  1837. "source": [
  1838. "print(set1)\n",
  1839. "set1.add(0)\n",
  1840. "set1"
  1841. ]
  1842. },
  1843. {
  1844. "cell_type": "markdown",
  1845. "metadata": {},
  1846. "source": [
  1847. "**intersection( )** 函数输出一个集合,该集合包含两个集合中的所有元素。"
  1848. ]
  1849. },
  1850. {
  1851. "cell_type": "code",
  1852. "execution_count": 91,
  1853. "metadata": {},
  1854. "outputs": [
  1855. {
  1856. "data": {
  1857. "text/plain": [
  1858. "{2, 3}"
  1859. ]
  1860. },
  1861. "execution_count": 91,
  1862. "metadata": {},
  1863. "output_type": "execute_result"
  1864. }
  1865. ],
  1866. "source": [
  1867. "set1.intersection(set2)"
  1868. ]
  1869. },
  1870. {
  1871. "cell_type": "markdown",
  1872. "metadata": {},
  1873. "source": [
  1874. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1875. ]
  1876. },
  1877. {
  1878. "cell_type": "code",
  1879. "execution_count": 93,
  1880. "metadata": {},
  1881. "outputs": [
  1882. {
  1883. "name": "stdout",
  1884. "output_type": "stream",
  1885. "text": [
  1886. "{0, 1, 2, 3}\n",
  1887. "{2, 3, 4, 5}\n"
  1888. ]
  1889. },
  1890. {
  1891. "data": {
  1892. "text/plain": [
  1893. "{0, 1}"
  1894. ]
  1895. },
  1896. "execution_count": 93,
  1897. "metadata": {},
  1898. "output_type": "execute_result"
  1899. }
  1900. ],
  1901. "source": [
  1902. "print(set1)\n",
  1903. "print(set2)\n",
  1904. "set1.difference(set2)"
  1905. ]
  1906. },
  1907. {
  1908. "cell_type": "markdown",
  1909. "metadata": {},
  1910. "source": [
  1911. "**symmetric_difference( )** 函数输出一个函数,该函数包含一个集合中的元素。"
  1912. ]
  1913. },
  1914. {
  1915. "cell_type": "code",
  1916. "execution_count": 94,
  1917. "metadata": {},
  1918. "outputs": [
  1919. {
  1920. "data": {
  1921. "text/plain": [
  1922. "{0, 1, 4, 5}"
  1923. ]
  1924. },
  1925. "execution_count": 94,
  1926. "metadata": {},
  1927. "output_type": "execute_result"
  1928. }
  1929. ],
  1930. "source": [
  1931. "set2.symmetric_difference(set1)"
  1932. ]
  1933. },
  1934. {
  1935. "cell_type": "markdown",
  1936. "metadata": {},
  1937. "source": [
  1938. "**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。"
  1939. ]
  1940. },
  1941. {
  1942. "cell_type": "code",
  1943. "execution_count": 95,
  1944. "metadata": {},
  1945. "outputs": [
  1946. {
  1947. "data": {
  1948. "text/plain": [
  1949. "False"
  1950. ]
  1951. },
  1952. "execution_count": 95,
  1953. "metadata": {},
  1954. "output_type": "execute_result"
  1955. }
  1956. ],
  1957. "source": [
  1958. "set1.issubset(set2)"
  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. "set2.isdisjoint(set1)"
  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.issuperset(set1)"
  1999. ]
  2000. },
  2001. {
  2002. "cell_type": "markdown",
  2003. "metadata": {},
  2004. "source": [
  2005. "**pop( )** 是用来移除集合中的任意元素。"
  2006. ]
  2007. },
  2008. {
  2009. "cell_type": "code",
  2010. "execution_count": 98,
  2011. "metadata": {},
  2012. "outputs": [
  2013. {
  2014. "name": "stdout",
  2015. "output_type": "stream",
  2016. "text": [
  2017. "{1, 2, 3}\n"
  2018. ]
  2019. }
  2020. ],
  2021. "source": [
  2022. "set1.pop()\n",
  2023. "print(set1)"
  2024. ]
  2025. },
  2026. {
  2027. "cell_type": "markdown",
  2028. "metadata": {},
  2029. "source": [
  2030. "**remove( )** 函数从集合中删除指定的元素。"
  2031. ]
  2032. },
  2033. {
  2034. "cell_type": "code",
  2035. "execution_count": 99,
  2036. "metadata": {},
  2037. "outputs": [
  2038. {
  2039. "data": {
  2040. "text/plain": [
  2041. "{1, 3}"
  2042. ]
  2043. },
  2044. "execution_count": 99,
  2045. "metadata": {},
  2046. "output_type": "execute_result"
  2047. }
  2048. ],
  2049. "source": [
  2050. "set1.remove(2)\n",
  2051. "set1"
  2052. ]
  2053. },
  2054. {
  2055. "cell_type": "markdown",
  2056. "metadata": {},
  2057. "source": [
  2058. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2059. ]
  2060. },
  2061. {
  2062. "cell_type": "code",
  2063. "execution_count": 100,
  2064. "metadata": {},
  2065. "outputs": [
  2066. {
  2067. "data": {
  2068. "text/plain": [
  2069. "set()"
  2070. ]
  2071. },
  2072. "execution_count": 100,
  2073. "metadata": {},
  2074. "output_type": "execute_result"
  2075. }
  2076. ],
  2077. "source": [
  2078. "set1.clear()\n",
  2079. "set1"
  2080. ]
  2081. }
  2082. ],
  2083. "metadata": {
  2084. "kernelspec": {
  2085. "display_name": "Python 3",
  2086. "language": "python",
  2087. "name": "python3"
  2088. },
  2089. "language_info": {
  2090. "codemirror_mode": {
  2091. "name": "ipython",
  2092. "version": 3
  2093. },
  2094. "file_extension": ".py",
  2095. "mimetype": "text/x-python",
  2096. "name": "python",
  2097. "nbconvert_exporter": "python",
  2098. "pygments_lexer": "ipython3",
  2099. "version": "3.6.9"
  2100. }
  2101. },
  2102. "nbformat": 4,
  2103. "nbformat_minor": 1
  2104. }

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》。