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3_Data_Structure_1.ipynb 42 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": 5,
  103. "metadata": {},
  104. "outputs": [
  105. {
  106. "data": {
  107. "text/plain": [
  108. "'apple'"
  109. ]
  110. },
  111. "execution_count": 5,
  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": 6,
  130. "metadata": {},
  131. "outputs": [
  132. {
  133. "data": {
  134. "text/plain": [
  135. "'peach'"
  136. ]
  137. },
  138. "execution_count": 6,
  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": 7,
  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": 8,
  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": 9,
  191. "metadata": {},
  192. "outputs": [
  193. {
  194. "data": {
  195. "text/plain": [
  196. "'orange'"
  197. ]
  198. },
  199. "execution_count": 9,
  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": 10,
  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": 11,
  245. "metadata": {},
  246. "outputs": [
  247. {
  248. "data": {
  249. "text/plain": [
  250. "'apple'"
  251. ]
  252. },
  253. "execution_count": 11,
  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": 12,
  272. "metadata": {},
  273. "outputs": [
  274. {
  275. "data": {
  276. "text/plain": [
  277. "'apple'"
  278. ]
  279. },
  280. "execution_count": 12,
  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": 13,
  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": 14,
  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": 15,
  365. "metadata": {},
  366. "outputs": [
  367. {
  368. "data": {
  369. "text/plain": [
  370. "[2, 3, 6]"
  371. ]
  372. },
  373. "execution_count": 15,
  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": 16,
  399. "metadata": {},
  400. "outputs": [
  401. {
  402. "data": {
  403. "text/plain": [
  404. "10"
  405. ]
  406. },
  407. "execution_count": 16,
  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": 17,
  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": 17,
  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": 18,
  454. "metadata": {},
  455. "outputs": [
  456. {
  457. "data": {
  458. "text/plain": [
  459. "9"
  460. ]
  461. },
  462. "execution_count": 18,
  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": 19,
  481. "metadata": {},
  482. "outputs": [
  483. {
  484. "data": {
  485. "text/plain": [
  486. "[1, 2, 3, 5, 4, 7]"
  487. ]
  488. },
  489. "execution_count": 19,
  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": 20,
  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": 21,
  524. "metadata": {},
  525. "outputs": [
  526. {
  527. "data": {
  528. "text/plain": [
  529. "False"
  530. ]
  531. },
  532. "execution_count": 21,
  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": 22,
  544. "metadata": {},
  545. "outputs": [
  546. {
  547. "data": {
  548. "text/plain": [
  549. "True"
  550. ]
  551. },
  552. "execution_count": 22,
  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": 23,
  564. "metadata": {},
  565. "outputs": [
  566. {
  567. "data": {
  568. "text/plain": [
  569. "False"
  570. ]
  571. },
  572. "execution_count": 23,
  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": 24,
  591. "metadata": {},
  592. "outputs": [],
  593. "source": [
  594. "mlist = ['bzaa','ds','nc','az','z','klm']"
  595. ]
  596. },
  597. {
  598. "cell_type": "code",
  599. "execution_count": 25,
  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": 26,
  626. "metadata": {},
  627. "outputs": [],
  628. "source": [
  629. "nlist = ['1','94','93','1000']"
  630. ]
  631. },
  632. {
  633. "cell_type": "code",
  634. "execution_count": 27,
  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": 29,
  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": 30,
  699. "metadata": {},
  700. "outputs": [
  701. {
  702. "data": {
  703. "text/plain": [
  704. "['h', 'e', 'l', 'l', 'o']"
  705. ]
  706. },
  707. "execution_count": 30,
  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": 31,
  726. "metadata": {},
  727. "outputs": [],
  728. "source": [
  729. "lst = [1,1,4,8,7]"
  730. ]
  731. },
  732. {
  733. "cell_type": "code",
  734. "execution_count": 32,
  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": 35,
  760. "metadata": {},
  761. "outputs": [
  762. {
  763. "data": {
  764. "text/plain": [
  765. "3"
  766. ]
  767. },
  768. "execution_count": 35,
  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": 36,
  787. "metadata": {},
  788. "outputs": [],
  789. "source": [
  790. "lst1 = [5,4,2,8]"
  791. ]
  792. },
  793. {
  794. "cell_type": "code",
  795. "execution_count": 37,
  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": 38,
  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": 39,
  846. "metadata": {},
  847. "outputs": [
  848. {
  849. "data": {
  850. "text/plain": [
  851. "0"
  852. ]
  853. },
  854. "execution_count": 39,
  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": 40,
  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-40-344da5bcf759>\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": 41,
  894. "metadata": {},
  895. "outputs": [
  896. {
  897. "name": "stdout",
  898. "output_type": "stream",
  899. "text": [
  900. "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  901. ]
  902. }
  903. ],
  904. "source": [
  905. "lst.insert(5, 'name')\n",
  906. "print(lst)"
  907. ]
  908. },
  909. {
  910. "cell_type": "code",
  911. "execution_count": 42,
  912. "metadata": {},
  913. "outputs": [],
  914. "source": [
  915. "lst.insert(-1, 10)"
  916. ]
  917. },
  918. {
  919. "cell_type": "code",
  920. "execution_count": 43,
  921. "metadata": {},
  922. "outputs": [
  923. {
  924. "name": "stdout",
  925. "output_type": "stream",
  926. "text": [
  927. "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4, 2, 10, 8]\n"
  928. ]
  929. }
  930. ],
  931. "source": [
  932. "print(lst)"
  933. ]
  934. },
  935. {
  936. "cell_type": "markdown",
  937. "metadata": {},
  938. "source": [
  939. "**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。"
  940. ]
  941. },
  942. {
  943. "cell_type": "code",
  944. "execution_count": 44,
  945. "metadata": {},
  946. "outputs": [
  947. {
  948. "name": "stdout",
  949. "output_type": "stream",
  950. "text": [
  951. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4, 2, 10, 8]\n"
  952. ]
  953. }
  954. ],
  955. "source": [
  956. "lst[5] = 'Python'\n",
  957. "print(lst)"
  958. ]
  959. },
  960. {
  961. "cell_type": "markdown",
  962. "metadata": {},
  963. "source": [
  964. "**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。"
  965. ]
  966. },
  967. {
  968. "cell_type": "code",
  969. "execution_count": 48,
  970. "metadata": {},
  971. "outputs": [
  972. {
  973. "data": {
  974. "text/plain": [
  975. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5]"
  976. ]
  977. },
  978. "execution_count": 48,
  979. "metadata": {},
  980. "output_type": "execute_result"
  981. }
  982. ],
  983. "source": [
  984. "lst.pop()\n",
  985. "lst"
  986. ]
  987. },
  988. {
  989. "cell_type": "markdown",
  990. "metadata": {},
  991. "source": [
  992. "可以指定索引值来弹出与该索引值对应的元素。"
  993. ]
  994. },
  995. {
  996. "cell_type": "code",
  997. "execution_count": 49,
  998. "metadata": {},
  999. "outputs": [
  1000. {
  1001. "name": "stdout",
  1002. "output_type": "stream",
  1003. "text": [
  1004. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], 5]\n"
  1005. ]
  1006. }
  1007. ],
  1008. "source": [
  1009. "lst.pop(2)\n",
  1010. "print(lst)"
  1011. ]
  1012. },
  1013. {
  1014. "cell_type": "code",
  1015. "execution_count": 50,
  1016. "metadata": {},
  1017. "outputs": [
  1018. {
  1019. "name": "stdout",
  1020. "output_type": "stream",
  1021. "text": [
  1022. "[1, 1, 8, 7, 'Python', 1, [5, 4, 2, 8], 5]\n"
  1023. ]
  1024. },
  1025. {
  1026. "data": {
  1027. "text/plain": [
  1028. "[5, 4, 2, 8]"
  1029. ]
  1030. },
  1031. "execution_count": 50,
  1032. "metadata": {},
  1033. "output_type": "execute_result"
  1034. }
  1035. ],
  1036. "source": [
  1037. "print(lst)\n",
  1038. "lst.pop(-2)"
  1039. ]
  1040. },
  1041. {
  1042. "cell_type": "code",
  1043. "execution_count": 51,
  1044. "metadata": {},
  1045. "outputs": [
  1046. {
  1047. "name": "stdout",
  1048. "output_type": "stream",
  1049. "text": [
  1050. "[1, 1, 8, 7, 'Python', 1, 5]\n"
  1051. ]
  1052. }
  1053. ],
  1054. "source": [
  1055. "print(lst)"
  1056. ]
  1057. },
  1058. {
  1059. "cell_type": "markdown",
  1060. "metadata": {},
  1061. "source": [
  1062. "**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。"
  1063. ]
  1064. },
  1065. {
  1066. "cell_type": "code",
  1067. "execution_count": 52,
  1068. "metadata": {},
  1069. "outputs": [
  1070. {
  1071. "name": "stdout",
  1072. "output_type": "stream",
  1073. "text": [
  1074. "[1, 1, 8, 7, 1, 5]\n"
  1075. ]
  1076. }
  1077. ],
  1078. "source": [
  1079. "lst.remove('Python')\n",
  1080. "print(lst)"
  1081. ]
  1082. },
  1083. {
  1084. "cell_type": "markdown",
  1085. "metadata": {},
  1086. "source": [
  1087. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1088. ]
  1089. },
  1090. {
  1091. "cell_type": "code",
  1092. "execution_count": 56,
  1093. "metadata": {},
  1094. "outputs": [
  1095. {
  1096. "name": "stdout",
  1097. "output_type": "stream",
  1098. "text": [
  1099. "[1, 7, 1, 5]\n",
  1100. "[1, 1, 5]\n"
  1101. ]
  1102. }
  1103. ],
  1104. "source": [
  1105. "print(lst)\n",
  1106. "del(lst[1])\n",
  1107. "print(lst)"
  1108. ]
  1109. },
  1110. {
  1111. "cell_type": "markdown",
  1112. "metadata": {},
  1113. "source": [
  1114. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1115. ]
  1116. },
  1117. {
  1118. "cell_type": "code",
  1119. "execution_count": 57,
  1120. "metadata": {},
  1121. "outputs": [
  1122. {
  1123. "name": "stdout",
  1124. "output_type": "stream",
  1125. "text": [
  1126. "[5, 1, 1]\n"
  1127. ]
  1128. }
  1129. ],
  1130. "source": [
  1131. "lst.reverse()\n",
  1132. "print(lst)"
  1133. ]
  1134. },
  1135. {
  1136. "cell_type": "markdown",
  1137. "metadata": {},
  1138. "source": [
  1139. "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",
  1140. "\n",
  1141. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1142. ]
  1143. },
  1144. {
  1145. "cell_type": "code",
  1146. "execution_count": 58,
  1147. "metadata": {},
  1148. "outputs": [
  1149. {
  1150. "name": "stdout",
  1151. "output_type": "stream",
  1152. "text": [
  1153. "[1, 4, 8, 8, 10]\n"
  1154. ]
  1155. }
  1156. ],
  1157. "source": [
  1158. "lst = [1, 4, 8, 8, 10]\n",
  1159. "lst.sort()\n",
  1160. "print(lst)"
  1161. ]
  1162. },
  1163. {
  1164. "cell_type": "markdown",
  1165. "metadata": {},
  1166. "source": [
  1167. "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。"
  1168. ]
  1169. },
  1170. {
  1171. "cell_type": "code",
  1172. "execution_count": 59,
  1173. "metadata": {},
  1174. "outputs": [
  1175. {
  1176. "name": "stdout",
  1177. "output_type": "stream",
  1178. "text": [
  1179. "[10, 8, 8, 4, 1]\n"
  1180. ]
  1181. }
  1182. ],
  1183. "source": [
  1184. "lst.sort(reverse=True)\n",
  1185. "print(lst)"
  1186. ]
  1187. },
  1188. {
  1189. "cell_type": "markdown",
  1190. "metadata": {},
  1191. "source": [
  1192. "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。"
  1193. ]
  1194. },
  1195. {
  1196. "cell_type": "code",
  1197. "execution_count": 62,
  1198. "metadata": {},
  1199. "outputs": [
  1200. {
  1201. "name": "stdout",
  1202. "output_type": "stream",
  1203. "text": [
  1204. "['apple', 'orange', 'peach']\n",
  1205. "['peach', 'orange', 'apple']\n"
  1206. ]
  1207. }
  1208. ],
  1209. "source": [
  1210. "names = ['apple', 'orange', 'peach']\n",
  1211. "names.sort()\n",
  1212. "print(names)\n",
  1213. "names.sort(reverse=True)\n",
  1214. "print(names)"
  1215. ]
  1216. },
  1217. {
  1218. "cell_type": "markdown",
  1219. "metadata": {},
  1220. "source": [
  1221. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1222. ]
  1223. },
  1224. {
  1225. "cell_type": "code",
  1226. "execution_count": 63,
  1227. "metadata": {},
  1228. "outputs": [
  1229. {
  1230. "name": "stdout",
  1231. "output_type": "stream",
  1232. "text": [
  1233. "['peach', 'apple', 'orange']\n",
  1234. "['orange', 'peach', 'apple']\n"
  1235. ]
  1236. }
  1237. ],
  1238. "source": [
  1239. "names.sort(key=len)\n",
  1240. "print(names)\n",
  1241. "names.sort(key=len,reverse=True)\n",
  1242. "print(names)"
  1243. ]
  1244. },
  1245. {
  1246. "cell_type": "markdown",
  1247. "metadata": {},
  1248. "source": [
  1249. "### 1.4 复制一个列表"
  1250. ]
  1251. },
  1252. {
  1253. "cell_type": "markdown",
  1254. "metadata": {},
  1255. "source": [
  1256. "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:"
  1257. ]
  1258. },
  1259. {
  1260. "cell_type": "code",
  1261. "execution_count": 64,
  1262. "metadata": {},
  1263. "outputs": [],
  1264. "source": [
  1265. "lista= [2,1,4,3]"
  1266. ]
  1267. },
  1268. {
  1269. "cell_type": "code",
  1270. "execution_count": 65,
  1271. "metadata": {},
  1272. "outputs": [
  1273. {
  1274. "name": "stdout",
  1275. "output_type": "stream",
  1276. "text": [
  1277. "[2, 1, 4, 3]\n"
  1278. ]
  1279. }
  1280. ],
  1281. "source": [
  1282. "listb = lista # 对象赋值\n",
  1283. "print(listb)"
  1284. ]
  1285. },
  1286. {
  1287. "cell_type": "markdown",
  1288. "metadata": {},
  1289. "source": [
  1290. "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。"
  1291. ]
  1292. },
  1293. {
  1294. "cell_type": "code",
  1295. "execution_count": 66,
  1296. "metadata": {},
  1297. "outputs": [
  1298. {
  1299. "name": "stdout",
  1300. "output_type": "stream",
  1301. "text": [
  1302. "[2, 1, 4]\n",
  1303. "[2, 1, 4, 9]\n"
  1304. ]
  1305. }
  1306. ],
  1307. "source": [
  1308. "lista.pop()\n",
  1309. "print(lista)\n",
  1310. "lista.append(9)\n",
  1311. "print(lista)"
  1312. ]
  1313. },
  1314. {
  1315. "cell_type": "code",
  1316. "execution_count": 67,
  1317. "metadata": {},
  1318. "outputs": [
  1319. {
  1320. "name": "stdout",
  1321. "output_type": "stream",
  1322. "text": [
  1323. "[2, 1, 4, 9]\n"
  1324. ]
  1325. }
  1326. ],
  1327. "source": [
  1328. "print(listb)"
  1329. ]
  1330. },
  1331. {
  1332. "cell_type": "markdown",
  1333. "metadata": {},
  1334. "source": [
  1335. "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",
  1336. "\n",
  1337. "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。"
  1338. ]
  1339. },
  1340. {
  1341. "cell_type": "code",
  1342. "execution_count": 68,
  1343. "metadata": {},
  1344. "outputs": [],
  1345. "source": [
  1346. "lista = [2,1,4,3]"
  1347. ]
  1348. },
  1349. {
  1350. "cell_type": "code",
  1351. "execution_count": 70,
  1352. "metadata": {},
  1353. "outputs": [
  1354. {
  1355. "name": "stdout",
  1356. "output_type": "stream",
  1357. "text": [
  1358. "[2, 1, 4, 3]\n"
  1359. ]
  1360. }
  1361. ],
  1362. "source": [
  1363. "listb = lista[:]\n",
  1364. "print(listb)"
  1365. ]
  1366. },
  1367. {
  1368. "cell_type": "code",
  1369. "execution_count": 71,
  1370. "metadata": {},
  1371. "outputs": [
  1372. {
  1373. "name": "stdout",
  1374. "output_type": "stream",
  1375. "text": [
  1376. "[2, 1, 4]\n",
  1377. "[2, 1, 4, 9]\n"
  1378. ]
  1379. }
  1380. ],
  1381. "source": [
  1382. "lista.pop()\n",
  1383. "print(lista)\n",
  1384. "lista.append(9)\n",
  1385. "print(lista)"
  1386. ]
  1387. },
  1388. {
  1389. "cell_type": "code",
  1390. "execution_count": 72,
  1391. "metadata": {},
  1392. "outputs": [
  1393. {
  1394. "name": "stdout",
  1395. "output_type": "stream",
  1396. "text": [
  1397. "[2, 1, 4, 3]\n"
  1398. ]
  1399. }
  1400. ],
  1401. "source": [
  1402. "print(listb)"
  1403. ]
  1404. },
  1405. {
  1406. "cell_type": "markdown",
  1407. "metadata": {},
  1408. "source": [
  1409. "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?"
  1410. ]
  1411. },
  1412. {
  1413. "cell_type": "markdown",
  1414. "metadata": {},
  1415. "source": [
  1416. "## 2. 元组"
  1417. ]
  1418. },
  1419. {
  1420. "cell_type": "markdown",
  1421. "metadata": {},
  1422. "source": [
  1423. "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。"
  1424. ]
  1425. },
  1426. {
  1427. "cell_type": "code",
  1428. "execution_count": 73,
  1429. "metadata": {},
  1430. "outputs": [
  1431. {
  1432. "name": "stdout",
  1433. "output_type": "stream",
  1434. "text": [
  1435. "(3, 1)\n",
  1436. "<class 'tuple'>\n"
  1437. ]
  1438. },
  1439. {
  1440. "ename": "TypeError",
  1441. "evalue": "'tuple' object does not support item assignment",
  1442. "output_type": "error",
  1443. "traceback": [
  1444. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  1445. "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
  1446. "\u001b[0;32m<ipython-input-73-9f8df9d106cc>\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",
  1447. "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
  1448. ]
  1449. }
  1450. ],
  1451. "source": [
  1452. "xyz = divmod(10,3)\n",
  1453. "print(xyz)\n",
  1454. "print(type(xyz))\n",
  1455. "xyz[0]=10"
  1456. ]
  1457. },
  1458. {
  1459. "cell_type": "markdown",
  1460. "metadata": {},
  1461. "source": [
  1462. "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。"
  1463. ]
  1464. },
  1465. {
  1466. "cell_type": "markdown",
  1467. "metadata": {},
  1468. "source": [
  1469. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1470. ]
  1471. },
  1472. {
  1473. "cell_type": "code",
  1474. "execution_count": 75,
  1475. "metadata": {},
  1476. "outputs": [],
  1477. "source": [
  1478. "tup = ()\n",
  1479. "tup2 = tuple()"
  1480. ]
  1481. },
  1482. {
  1483. "cell_type": "markdown",
  1484. "metadata": {},
  1485. "source": [
  1486. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1487. ]
  1488. },
  1489. {
  1490. "cell_type": "code",
  1491. "execution_count": 76,
  1492. "metadata": {},
  1493. "outputs": [
  1494. {
  1495. "data": {
  1496. "text/plain": [
  1497. "(27,)"
  1498. ]
  1499. },
  1500. "execution_count": 76,
  1501. "metadata": {},
  1502. "output_type": "execute_result"
  1503. }
  1504. ],
  1505. "source": [
  1506. "27,"
  1507. ]
  1508. },
  1509. {
  1510. "cell_type": "markdown",
  1511. "metadata": {},
  1512. "source": [
  1513. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1514. ]
  1515. },
  1516. {
  1517. "cell_type": "code",
  1518. "execution_count": 77,
  1519. "metadata": {},
  1520. "outputs": [
  1521. {
  1522. "data": {
  1523. "text/plain": [
  1524. "(27, 27)"
  1525. ]
  1526. },
  1527. "execution_count": 77,
  1528. "metadata": {},
  1529. "output_type": "execute_result"
  1530. }
  1531. ],
  1532. "source": [
  1533. "2*(27,)"
  1534. ]
  1535. },
  1536. {
  1537. "cell_type": "markdown",
  1538. "metadata": {},
  1539. "source": [
  1540. "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。"
  1541. ]
  1542. },
  1543. {
  1544. "cell_type": "code",
  1545. "execution_count": 78,
  1546. "metadata": {
  1547. "scrolled": true
  1548. },
  1549. "outputs": [
  1550. {
  1551. "name": "stdout",
  1552. "output_type": "stream",
  1553. "text": [
  1554. "(1, 2, 3)\n",
  1555. "('H', 'e', 'l', 'l', 'o')\n"
  1556. ]
  1557. }
  1558. ],
  1559. "source": [
  1560. "tup3 = tuple([1,2,3])\n",
  1561. "print(tup3)\n",
  1562. "tup4 = tuple('Hello')\n",
  1563. "print(tup4)"
  1564. ]
  1565. },
  1566. {
  1567. "cell_type": "markdown",
  1568. "metadata": {},
  1569. "source": [
  1570. "它遵循与列表相同的索引和切片。"
  1571. ]
  1572. },
  1573. {
  1574. "cell_type": "code",
  1575. "execution_count": 41,
  1576. "metadata": {},
  1577. "outputs": [
  1578. {
  1579. "name": "stdout",
  1580. "output_type": "stream",
  1581. "text": [
  1582. "2\n",
  1583. "('H', 'e', 'l')\n"
  1584. ]
  1585. }
  1586. ],
  1587. "source": [
  1588. "print(tup3[1])\n",
  1589. "tup5 = tup4[:3]\n",
  1590. "print(tup5)"
  1591. ]
  1592. },
  1593. {
  1594. "cell_type": "markdown",
  1595. "metadata": {},
  1596. "source": [
  1597. "### 2.1 将一个元组映射到另一个元组"
  1598. ]
  1599. },
  1600. {
  1601. "cell_type": "code",
  1602. "execution_count": 79,
  1603. "metadata": {},
  1604. "outputs": [],
  1605. "source": [
  1606. "(a,b,c)= ('alpha','beta','gamma')"
  1607. ]
  1608. },
  1609. {
  1610. "cell_type": "code",
  1611. "execution_count": 80,
  1612. "metadata": {},
  1613. "outputs": [
  1614. {
  1615. "name": "stdout",
  1616. "output_type": "stream",
  1617. "text": [
  1618. "alpha beta gamma\n"
  1619. ]
  1620. }
  1621. ],
  1622. "source": [
  1623. "print(a,b,c)"
  1624. ]
  1625. },
  1626. {
  1627. "cell_type": "code",
  1628. "execution_count": 81,
  1629. "metadata": {},
  1630. "outputs": [],
  1631. "source": [
  1632. "(c, b, a) = (a, b, c)"
  1633. ]
  1634. },
  1635. {
  1636. "cell_type": "code",
  1637. "execution_count": 82,
  1638. "metadata": {},
  1639. "outputs": [
  1640. {
  1641. "name": "stdout",
  1642. "output_type": "stream",
  1643. "text": [
  1644. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1645. ]
  1646. }
  1647. ],
  1648. "source": [
  1649. "d = tuple('RajathKumarMP')\n",
  1650. "print(d)"
  1651. ]
  1652. },
  1653. {
  1654. "cell_type": "markdown",
  1655. "metadata": {},
  1656. "source": [
  1657. "### 2.2 元组内置函数"
  1658. ]
  1659. },
  1660. {
  1661. "cell_type": "markdown",
  1662. "metadata": {},
  1663. "source": [
  1664. "**count()** 函数计算元组中存在的指定元素的数量。"
  1665. ]
  1666. },
  1667. {
  1668. "cell_type": "code",
  1669. "execution_count": 83,
  1670. "metadata": {},
  1671. "outputs": [
  1672. {
  1673. "data": {
  1674. "text/plain": [
  1675. "3"
  1676. ]
  1677. },
  1678. "execution_count": 83,
  1679. "metadata": {},
  1680. "output_type": "execute_result"
  1681. }
  1682. ],
  1683. "source": [
  1684. "d.count('a')"
  1685. ]
  1686. },
  1687. {
  1688. "cell_type": "markdown",
  1689. "metadata": {},
  1690. "source": [
  1691. "**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"
  1692. ]
  1693. },
  1694. {
  1695. "cell_type": "code",
  1696. "execution_count": 84,
  1697. "metadata": {},
  1698. "outputs": [
  1699. {
  1700. "data": {
  1701. "text/plain": [
  1702. "1"
  1703. ]
  1704. },
  1705. "execution_count": 84,
  1706. "metadata": {},
  1707. "output_type": "execute_result"
  1708. }
  1709. ],
  1710. "source": [
  1711. "d.index('a')"
  1712. ]
  1713. },
  1714. {
  1715. "cell_type": "markdown",
  1716. "metadata": {},
  1717. "source": [
  1718. "## 3. 集合"
  1719. ]
  1720. },
  1721. {
  1722. "cell_type": "markdown",
  1723. "metadata": {},
  1724. "source": [
  1725. "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",
  1726. "\n",
  1727. "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"
  1728. ]
  1729. },
  1730. {
  1731. "cell_type": "code",
  1732. "execution_count": 85,
  1733. "metadata": {},
  1734. "outputs": [
  1735. {
  1736. "name": "stdout",
  1737. "output_type": "stream",
  1738. "text": [
  1739. "<class 'set'>\n"
  1740. ]
  1741. }
  1742. ],
  1743. "source": [
  1744. "set1 = set()\n",
  1745. "print(type(set1))"
  1746. ]
  1747. },
  1748. {
  1749. "cell_type": "code",
  1750. "execution_count": 86,
  1751. "metadata": {},
  1752. "outputs": [
  1753. {
  1754. "name": "stdout",
  1755. "output_type": "stream",
  1756. "text": [
  1757. "{1, 2, 3, 4}\n"
  1758. ]
  1759. }
  1760. ],
  1761. "source": [
  1762. "set0 = set([1,2,2,3,3,4])\n",
  1763. "print(set0)"
  1764. ]
  1765. },
  1766. {
  1767. "cell_type": "code",
  1768. "execution_count": 87,
  1769. "metadata": {},
  1770. "outputs": [
  1771. {
  1772. "name": "stdout",
  1773. "output_type": "stream",
  1774. "text": [
  1775. "{1, 2, 3, 4}\n"
  1776. ]
  1777. }
  1778. ],
  1779. "source": [
  1780. "set1 = set((1,2,2,3,3,4))\n",
  1781. "print(set1)"
  1782. ]
  1783. },
  1784. {
  1785. "cell_type": "markdown",
  1786. "metadata": {},
  1787. "source": [
  1788. "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。"
  1789. ]
  1790. },
  1791. {
  1792. "cell_type": "markdown",
  1793. "metadata": {},
  1794. "source": [
  1795. "### 3.1 内置函数"
  1796. ]
  1797. },
  1798. {
  1799. "cell_type": "code",
  1800. "execution_count": 101,
  1801. "metadata": {},
  1802. "outputs": [],
  1803. "source": [
  1804. "set1 = set([1,2,3])"
  1805. ]
  1806. },
  1807. {
  1808. "cell_type": "code",
  1809. "execution_count": 102,
  1810. "metadata": {},
  1811. "outputs": [],
  1812. "source": [
  1813. "set2 = set([2,3,4,5])"
  1814. ]
  1815. },
  1816. {
  1817. "cell_type": "markdown",
  1818. "metadata": {},
  1819. "source": [
  1820. "**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。"
  1821. ]
  1822. },
  1823. {
  1824. "cell_type": "code",
  1825. "execution_count": 90,
  1826. "metadata": {},
  1827. "outputs": [
  1828. {
  1829. "data": {
  1830. "text/plain": [
  1831. "{1, 2, 3, 4, 5}"
  1832. ]
  1833. },
  1834. "execution_count": 90,
  1835. "metadata": {},
  1836. "output_type": "execute_result"
  1837. }
  1838. ],
  1839. "source": [
  1840. "set1.union(set2)"
  1841. ]
  1842. },
  1843. {
  1844. "cell_type": "markdown",
  1845. "metadata": {},
  1846. "source": [
  1847. "**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。"
  1848. ]
  1849. },
  1850. {
  1851. "cell_type": "code",
  1852. "execution_count": 94,
  1853. "metadata": {},
  1854. "outputs": [
  1855. {
  1856. "name": "stdout",
  1857. "output_type": "stream",
  1858. "text": [
  1859. "{0, 1, 2, 3}\n"
  1860. ]
  1861. },
  1862. {
  1863. "data": {
  1864. "text/plain": [
  1865. "{0, 1, 2, 3}"
  1866. ]
  1867. },
  1868. "execution_count": 94,
  1869. "metadata": {},
  1870. "output_type": "execute_result"
  1871. }
  1872. ],
  1873. "source": [
  1874. "print(set1)\n",
  1875. "set1.add(0)\n",
  1876. "set1"
  1877. ]
  1878. },
  1879. {
  1880. "cell_type": "markdown",
  1881. "metadata": {},
  1882. "source": [
  1883. "**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。"
  1884. ]
  1885. },
  1886. {
  1887. "cell_type": "code",
  1888. "execution_count": 95,
  1889. "metadata": {},
  1890. "outputs": [
  1891. {
  1892. "data": {
  1893. "text/plain": [
  1894. "{2, 3}"
  1895. ]
  1896. },
  1897. "execution_count": 95,
  1898. "metadata": {},
  1899. "output_type": "execute_result"
  1900. }
  1901. ],
  1902. "source": [
  1903. "set1.intersection(set2)"
  1904. ]
  1905. },
  1906. {
  1907. "cell_type": "markdown",
  1908. "metadata": {},
  1909. "source": [
  1910. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1911. ]
  1912. },
  1913. {
  1914. "cell_type": "code",
  1915. "execution_count": 96,
  1916. "metadata": {},
  1917. "outputs": [
  1918. {
  1919. "name": "stdout",
  1920. "output_type": "stream",
  1921. "text": [
  1922. "{0, 1, 2, 3}\n",
  1923. "{2, 3, 4, 5}\n"
  1924. ]
  1925. },
  1926. {
  1927. "data": {
  1928. "text/plain": [
  1929. "{0, 1}"
  1930. ]
  1931. },
  1932. "execution_count": 96,
  1933. "metadata": {},
  1934. "output_type": "execute_result"
  1935. }
  1936. ],
  1937. "source": [
  1938. "print(set1)\n",
  1939. "print(set2)\n",
  1940. "set1.difference(set2)"
  1941. ]
  1942. },
  1943. {
  1944. "cell_type": "markdown",
  1945. "metadata": {},
  1946. "source": [
  1947. "**pop( )** 是用来移除集合中的任意元素。"
  1948. ]
  1949. },
  1950. {
  1951. "cell_type": "code",
  1952. "execution_count": 97,
  1953. "metadata": {},
  1954. "outputs": [],
  1955. "source": [
  1956. "set1=set([10, 9, 1, 2, 4])"
  1957. ]
  1958. },
  1959. {
  1960. "cell_type": "code",
  1961. "execution_count": 99,
  1962. "metadata": {},
  1963. "outputs": [
  1964. {
  1965. "name": "stdout",
  1966. "output_type": "stream",
  1967. "text": [
  1968. "{4, 9, 10}\n"
  1969. ]
  1970. }
  1971. ],
  1972. "source": [
  1973. "set1.pop()\n",
  1974. "print(set1)"
  1975. ]
  1976. },
  1977. {
  1978. "cell_type": "markdown",
  1979. "metadata": {},
  1980. "source": [
  1981. "**remove( )** 函数从集合中删除指定的元素。"
  1982. ]
  1983. },
  1984. {
  1985. "cell_type": "code",
  1986. "execution_count": 103,
  1987. "metadata": {},
  1988. "outputs": [
  1989. {
  1990. "data": {
  1991. "text/plain": [
  1992. "{1, 3}"
  1993. ]
  1994. },
  1995. "execution_count": 103,
  1996. "metadata": {},
  1997. "output_type": "execute_result"
  1998. }
  1999. ],
  2000. "source": [
  2001. "set1.remove(2)\n",
  2002. "set1"
  2003. ]
  2004. },
  2005. {
  2006. "cell_type": "markdown",
  2007. "metadata": {},
  2008. "source": [
  2009. "**clear( )** 用于清除所有元素并将其设置为空集。"
  2010. ]
  2011. },
  2012. {
  2013. "cell_type": "code",
  2014. "execution_count": 104,
  2015. "metadata": {},
  2016. "outputs": [
  2017. {
  2018. "data": {
  2019. "text/plain": [
  2020. "set()"
  2021. ]
  2022. },
  2023. "execution_count": 104,
  2024. "metadata": {},
  2025. "output_type": "execute_result"
  2026. }
  2027. ],
  2028. "source": [
  2029. "set1.clear()\n",
  2030. "set1"
  2031. ]
  2032. }
  2033. ],
  2034. "metadata": {
  2035. "kernelspec": {
  2036. "display_name": "Python 3",
  2037. "language": "python",
  2038. "name": "python3"
  2039. },
  2040. "language_info": {
  2041. "codemirror_mode": {
  2042. "name": "ipython",
  2043. "version": 3
  2044. },
  2045. "file_extension": ".py",
  2046. "mimetype": "text/x-python",
  2047. "name": "python",
  2048. "nbconvert_exporter": "python",
  2049. "pygments_lexer": "ipython3",
  2050. "version": "3.7.9"
  2051. }
  2052. },
  2053. "nbformat": 4,
  2054. "nbformat_minor": 1
  2055. }

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》。