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3_Data_Structure_1.ipynb 40 kB

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  1. {
  2. "cells": [
  3. {
  4. "cell_type": "markdown",
  5. "metadata": {},
  6. "source": [
  7. "# 数据结构"
  8. ]
  9. },
  10. {
  11. "cell_type": "markdown",
  12. "metadata": {},
  13. "source": [
  14. "简单地说,它是特定结构中的数据集合或数据组。"
  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": 4,
  69. "metadata": {},
  70. "outputs": [
  71. {
  72. "name": "stdout",
  73. "output_type": "stream",
  74. "text": [
  75. "['apple', 'orange', 'peach']\n"
  76. ]
  77. }
  78. ],
  79. "source": [
  80. "x = ['apple', 'orange', 'peach']\n",
  81. "print(x)"
  82. ]
  83. },
  84. {
  85. "cell_type": "markdown",
  86. "metadata": {},
  87. "source": [
  88. "### 1.1 索引"
  89. ]
  90. },
  91. {
  92. "cell_type": "markdown",
  93. "metadata": {},
  94. "source": [
  95. "在python中,索引从0开始。因此,现在包含两个元素的列表x的apple索引值为0,orange索引值为1。"
  96. ]
  97. },
  98. {
  99. "cell_type": "code",
  100. "execution_count": 4,
  101. "metadata": {},
  102. "outputs": [
  103. {
  104. "data": {
  105. "text/plain": [
  106. "'apple'"
  107. ]
  108. },
  109. "execution_count": 4,
  110. "metadata": {},
  111. "output_type": "execute_result"
  112. }
  113. ],
  114. "source": [
  115. "x[0]"
  116. ]
  117. },
  118. {
  119. "cell_type": "markdown",
  120. "metadata": {},
  121. "source": [
  122. "索引也可以按照相反的顺序进行。这是最后一个可以被首先访问的元素。这里,索引从-1开始。因此,指数-1将是橙色,指数-2将是苹果。"
  123. ]
  124. },
  125. {
  126. "cell_type": "code",
  127. "execution_count": 5,
  128. "metadata": {},
  129. "outputs": [
  130. {
  131. "data": {
  132. "text/plain": [
  133. "'peach'"
  134. ]
  135. },
  136. "execution_count": 5,
  137. "metadata": {},
  138. "output_type": "execute_result"
  139. }
  140. ],
  141. "source": [
  142. "x[-1]"
  143. ]
  144. },
  145. {
  146. "cell_type": "markdown",
  147. "metadata": {},
  148. "source": [
  149. "正如你可能猜到的一样,x[0] = x[-2], x[1] = x[-1]。这个概念可以扩展到更多包含元素的列表。"
  150. ]
  151. },
  152. {
  153. "cell_type": "code",
  154. "execution_count": 2,
  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。"
  166. ]
  167. },
  168. {
  169. "cell_type": "code",
  170. "execution_count": 5,
  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": 10,
  314. "metadata": {},
  315. "outputs": [
  316. {
  317. "name": "stdout",
  318. "output_type": "stream",
  319. "text": [
  320. "[0, 1, 2, 3]\n",
  321. "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n",
  322. "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
  323. ]
  324. }
  325. ],
  326. "source": [
  327. "num = [0,1,2,3,4,5,6,7,8,9]\n",
  328. "print(num[0:4])\n",
  329. "print(num[0:])\n",
  330. "print(num[:])"
  331. ]
  332. },
  333. {
  334. "cell_type": "code",
  335. "execution_count": 11,
  336. "metadata": {},
  337. "outputs": [
  338. {
  339. "name": "stdout",
  340. "output_type": "stream",
  341. "text": [
  342. "[0, 1, 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": 12,
  362. "metadata": {},
  363. "outputs": [
  364. {
  365. "data": {
  366. "text/plain": [
  367. "[0, 3, 6]"
  368. ]
  369. },
  370. "execution_count": 12,
  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 构造列表函数"
  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": 17,
  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( )** 是可以应用的。**max( )** 会返回一个ASCII码最高的元素而最低的元素会在使用**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": 21,
  600. "metadata": {},
  601. "outputs": [],
  602. "source": [
  603. "mlist = ['bzaa','ds','nc','az','z','klm']"
  604. ]
  605. },
  606. {
  607. "cell_type": "code",
  608. "execution_count": 22,
  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": 23,
  635. "metadata": {},
  636. "outputs": [],
  637. "source": [
  638. "nlist = ['1','94','93','1000']"
  639. ]
  640. },
  641. {
  642. "cell_type": "code",
  643. "execution_count": 24,
  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": 25,
  677. "metadata": {},
  678. "outputs": [
  679. {
  680. "name": "stdout",
  681. "output_type": "stream",
  682. "text": [
  683. "Earth\n",
  684. "Air\n"
  685. ]
  686. }
  687. ],
  688. "source": [
  689. "print(max(names, key=len))\n",
  690. "print(min(names, key=len))"
  691. ]
  692. },
  693. {
  694. "cell_type": "markdown",
  695. "metadata": {},
  696. "source": [
  697. "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",
  698. "\n",
  699. "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",
  700. "\n",
  701. "通过使用**list()** 函数,一个字符串可以被转化成列表。"
  702. ]
  703. },
  704. {
  705. "cell_type": "code",
  706. "execution_count": 26,
  707. "metadata": {},
  708. "outputs": [
  709. {
  710. "data": {
  711. "text/plain": [
  712. "['h', 'e', 'l', 'l', 'o']"
  713. ]
  714. },
  715. "execution_count": 26,
  716. "metadata": {},
  717. "output_type": "execute_result"
  718. }
  719. ],
  720. "source": [
  721. "list('hello')"
  722. ]
  723. },
  724. {
  725. "cell_type": "markdown",
  726. "metadata": {},
  727. "source": [
  728. "**append( )** 被用来在列表的最后添加一个元素。"
  729. ]
  730. },
  731. {
  732. "cell_type": "code",
  733. "execution_count": 27,
  734. "metadata": {},
  735. "outputs": [],
  736. "source": [
  737. "lst = [1,1,4,8,7]"
  738. ]
  739. },
  740. {
  741. "cell_type": "code",
  742. "execution_count": 28,
  743. "metadata": {},
  744. "outputs": [
  745. {
  746. "name": "stdout",
  747. "output_type": "stream",
  748. "text": [
  749. "[1, 1, 4, 8, 7, 1]\n"
  750. ]
  751. }
  752. ],
  753. "source": [
  754. "lst.append(1)\n",
  755. "print(lst)"
  756. ]
  757. },
  758. {
  759. "cell_type": "markdown",
  760. "metadata": {},
  761. "source": [
  762. "**count( )** 用于计算列表中出现的特定元素的数量。"
  763. ]
  764. },
  765. {
  766. "cell_type": "code",
  767. "execution_count": 29,
  768. "metadata": {},
  769. "outputs": [
  770. {
  771. "data": {
  772. "text/plain": [
  773. "3"
  774. ]
  775. },
  776. "execution_count": 29,
  777. "metadata": {},
  778. "output_type": "execute_result"
  779. }
  780. ],
  781. "source": [
  782. "lst.count(1)"
  783. ]
  784. },
  785. {
  786. "cell_type": "markdown",
  787. "metadata": {},
  788. "source": [
  789. "**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。"
  790. ]
  791. },
  792. {
  793. "cell_type": "code",
  794. "execution_count": 30,
  795. "metadata": {},
  796. "outputs": [],
  797. "source": [
  798. "lst1 = [5,4,2,8]"
  799. ]
  800. },
  801. {
  802. "cell_type": "code",
  803. "execution_count": 31,
  804. "metadata": {},
  805. "outputs": [
  806. {
  807. "name": "stdout",
  808. "output_type": "stream",
  809. "text": [
  810. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8]]\n"
  811. ]
  812. }
  813. ],
  814. "source": [
  815. "lst.append(lst1)\n",
  816. "print(lst)"
  817. ]
  818. },
  819. {
  820. "cell_type": "markdown",
  821. "metadata": {},
  822. "source": [
  823. "但是如果嵌套列表不是需要的,那么可以使用**extend()** 函数。"
  824. ]
  825. },
  826. {
  827. "cell_type": "code",
  828. "execution_count": 32,
  829. "metadata": {},
  830. "outputs": [
  831. {
  832. "name": "stdout",
  833. "output_type": "stream",
  834. "text": [
  835. "[1, 1, 4, 8, 7, 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  836. ]
  837. }
  838. ],
  839. "source": [
  840. "lst.extend(lst1)\n",
  841. "print(lst)"
  842. ]
  843. },
  844. {
  845. "cell_type": "markdown",
  846. "metadata": {},
  847. "source": [
  848. "**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。"
  849. ]
  850. },
  851. {
  852. "cell_type": "code",
  853. "execution_count": 33,
  854. "metadata": {},
  855. "outputs": [
  856. {
  857. "data": {
  858. "text/plain": [
  859. "0"
  860. ]
  861. },
  862. "execution_count": 33,
  863. "metadata": {},
  864. "output_type": "execute_result"
  865. }
  866. ],
  867. "source": [
  868. "lst.index(1)"
  869. ]
  870. },
  871. {
  872. "cell_type": "code",
  873. "execution_count": 34,
  874. "metadata": {},
  875. "outputs": [
  876. {
  877. "ename": "ValueError",
  878. "evalue": "999 is not in list",
  879. "output_type": "error",
  880. "traceback": [
  881. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  882. "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
  883. "\u001b[0;32m<ipython-input-34-344da5bcf759>\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[0m\n\u001b[0m",
  884. "\u001b[0;31mValueError\u001b[0m: 999 is not in list"
  885. ]
  886. }
  887. ],
  888. "source": [
  889. "lst.index(999)"
  890. ]
  891. },
  892. {
  893. "cell_type": "markdown",
  894. "metadata": {},
  895. "source": [
  896. "**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。"
  897. ]
  898. },
  899. {
  900. "cell_type": "code",
  901. "execution_count": 1,
  902. "metadata": {},
  903. "outputs": [
  904. {
  905. "ename": "NameError",
  906. "evalue": "name 'lst' is not defined",
  907. "output_type": "error",
  908. "traceback": [
  909. "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
  910. "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
  911. "\u001b[0;32m<ipython-input-1-c109f115d7f9>\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[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'name'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
  912. "\u001b[0;31mNameError\u001b[0m: name 'lst' is not defined"
  913. ]
  914. }
  915. ],
  916. "source": [
  917. "lst.insert(5, 'name')\n",
  918. "print(lst)"
  919. ]
  920. },
  921. {
  922. "cell_type": "markdown",
  923. "metadata": {},
  924. "source": [
  925. "**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。"
  926. ]
  927. },
  928. {
  929. "cell_type": "code",
  930. "execution_count": 18,
  931. "metadata": {},
  932. "outputs": [
  933. {
  934. "name": "stdout",
  935. "output_type": "stream",
  936. "text": [
  937. "[1, 1, 4, 8, 7, 'Python', 1, [5, 4, 2, 8], 5, 4, 2, 8]\n"
  938. ]
  939. }
  940. ],
  941. "source": [
  942. "lst[5] = 'Python'\n",
  943. "print(lst)"
  944. ]
  945. },
  946. {
  947. "cell_type": "markdown",
  948. "metadata": {},
  949. "source": [
  950. "**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。"
  951. ]
  952. },
  953. {
  954. "cell_type": "code",
  955. "execution_count": 37,
  956. "metadata": {},
  957. "outputs": [
  958. {
  959. "data": {
  960. "text/plain": [
  961. "[1, 1, 4, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]"
  962. ]
  963. },
  964. "execution_count": 37,
  965. "metadata": {},
  966. "output_type": "execute_result"
  967. }
  968. ],
  969. "source": [
  970. "lst.pop()\n",
  971. "lst"
  972. ]
  973. },
  974. {
  975. "cell_type": "markdown",
  976. "metadata": {},
  977. "source": [
  978. "可以指定索引值来弹出与该索引值对应的元素。"
  979. ]
  980. },
  981. {
  982. "cell_type": "code",
  983. "execution_count": 38,
  984. "metadata": {},
  985. "outputs": [
  986. {
  987. "data": {
  988. "text/plain": [
  989. "4"
  990. ]
  991. },
  992. "execution_count": 38,
  993. "metadata": {},
  994. "output_type": "execute_result"
  995. }
  996. ],
  997. "source": [
  998. "lst.pop(2)"
  999. ]
  1000. },
  1001. {
  1002. "cell_type": "code",
  1003. "execution_count": 39,
  1004. "metadata": {},
  1005. "outputs": [
  1006. {
  1007. "name": "stdout",
  1008. "output_type": "stream",
  1009. "text": [
  1010. "[1, 1, 8, 7, 'name', 1, [5, 4, 2, 8], 5, 4]\n"
  1011. ]
  1012. }
  1013. ],
  1014. "source": [
  1015. "print(lst)"
  1016. ]
  1017. },
  1018. {
  1019. "cell_type": "markdown",
  1020. "metadata": {},
  1021. "source": [
  1022. "**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。"
  1023. ]
  1024. },
  1025. {
  1026. "cell_type": "code",
  1027. "execution_count": 41,
  1028. "metadata": {},
  1029. "outputs": [
  1030. {
  1031. "name": "stdout",
  1032. "output_type": "stream",
  1033. "text": [
  1034. "[1, 1, 8, 7, 1, [5, 4, 2, 8], 5, 4]\n"
  1035. ]
  1036. }
  1037. ],
  1038. "source": [
  1039. "lst.remove('name')\n",
  1040. "print(lst)"
  1041. ]
  1042. },
  1043. {
  1044. "cell_type": "markdown",
  1045. "metadata": {},
  1046. "source": [
  1047. "可以替代 **remove** 但是使用索引值的函数是 **del**。"
  1048. ]
  1049. },
  1050. {
  1051. "cell_type": "code",
  1052. "execution_count": 24,
  1053. "metadata": {},
  1054. "outputs": [
  1055. {
  1056. "name": "stdout",
  1057. "output_type": "stream",
  1058. "text": [
  1059. "[1, 4, 7, 1, [5, 4, 2, 8], 5, 4, 2]\n",
  1060. "[1, 7, 1, [5, 4, 2, 8], 5, 4, 2]\n"
  1061. ]
  1062. }
  1063. ],
  1064. "source": [
  1065. "print(lst)\n",
  1066. "del lst[1]\n",
  1067. "print(lst)"
  1068. ]
  1069. },
  1070. {
  1071. "cell_type": "markdown",
  1072. "metadata": {},
  1073. "source": [
  1074. "可以使用**reverse()** 函数反转列表中出现的所有元素。"
  1075. ]
  1076. },
  1077. {
  1078. "cell_type": "code",
  1079. "execution_count": 42,
  1080. "metadata": {},
  1081. "outputs": [
  1082. {
  1083. "name": "stdout",
  1084. "output_type": "stream",
  1085. "text": [
  1086. "[4, 5, [5, 4, 2, 8], 1, 7, 8, 1, 1]\n"
  1087. ]
  1088. }
  1089. ],
  1090. "source": [
  1091. "lst.reverse()\n",
  1092. "print(lst)"
  1093. ]
  1094. },
  1095. {
  1096. "cell_type": "markdown",
  1097. "metadata": {},
  1098. "source": [
  1099. "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",
  1100. "\n",
  1101. "Python提供了内置函数 **sort( )** 去按升序排列元素。"
  1102. ]
  1103. },
  1104. {
  1105. "cell_type": "code",
  1106. "execution_count": 43,
  1107. "metadata": {},
  1108. "outputs": [
  1109. {
  1110. "name": "stdout",
  1111. "output_type": "stream",
  1112. "text": [
  1113. "[1, 4, 8, 8, 10]\n"
  1114. ]
  1115. }
  1116. ],
  1117. "source": [
  1118. "lst = [1, 4, 8, 8, 10]\n",
  1119. "lst.sort()\n",
  1120. "print(lst)"
  1121. ]
  1122. },
  1123. {
  1124. "cell_type": "markdown",
  1125. "metadata": {},
  1126. "source": [
  1127. "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。"
  1128. ]
  1129. },
  1130. {
  1131. "cell_type": "code",
  1132. "execution_count": 44,
  1133. "metadata": {},
  1134. "outputs": [
  1135. {
  1136. "name": "stdout",
  1137. "output_type": "stream",
  1138. "text": [
  1139. "[10, 8, 8, 4, 1]\n"
  1140. ]
  1141. }
  1142. ],
  1143. "source": [
  1144. "lst.sort(reverse=True)\n",
  1145. "print(lst)"
  1146. ]
  1147. },
  1148. {
  1149. "cell_type": "markdown",
  1150. "metadata": {},
  1151. "source": [
  1152. "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。"
  1153. ]
  1154. },
  1155. {
  1156. "cell_type": "code",
  1157. "execution_count": 45,
  1158. "metadata": {},
  1159. "outputs": [
  1160. {
  1161. "name": "stdout",
  1162. "output_type": "stream",
  1163. "text": [
  1164. "['apple', 'orange', 'peach']\n",
  1165. "['peach', 'orange', 'apple']\n"
  1166. ]
  1167. }
  1168. ],
  1169. "source": [
  1170. "names = ['apple', 'orange', 'peach']\n",
  1171. "names.sort()\n",
  1172. "print(names)\n",
  1173. "names.sort(reverse=True)\n",
  1174. "print(names)"
  1175. ]
  1176. },
  1177. {
  1178. "cell_type": "markdown",
  1179. "metadata": {},
  1180. "source": [
  1181. "如果要根据长度排序我们应该像图示的一样确定key=len。"
  1182. ]
  1183. },
  1184. {
  1185. "cell_type": "code",
  1186. "execution_count": 46,
  1187. "metadata": {},
  1188. "outputs": [
  1189. {
  1190. "name": "stdout",
  1191. "output_type": "stream",
  1192. "text": [
  1193. "['peach', 'apple', 'orange']\n",
  1194. "['orange', 'peach', 'apple']\n"
  1195. ]
  1196. }
  1197. ],
  1198. "source": [
  1199. "names.sort(key=len)\n",
  1200. "print(names)\n",
  1201. "names.sort(key=len,reverse=True)\n",
  1202. "print(names)"
  1203. ]
  1204. },
  1205. {
  1206. "cell_type": "markdown",
  1207. "metadata": {},
  1208. "source": [
  1209. "### 1.4 复制一个列表"
  1210. ]
  1211. },
  1212. {
  1213. "cell_type": "markdown",
  1214. "metadata": {},
  1215. "source": [
  1216. "大多数新的python程序员都会犯这个错误。考虑以下,"
  1217. ]
  1218. },
  1219. {
  1220. "cell_type": "code",
  1221. "execution_count": 47,
  1222. "metadata": {},
  1223. "outputs": [],
  1224. "source": [
  1225. "lista= [2,1,4,3]"
  1226. ]
  1227. },
  1228. {
  1229. "cell_type": "code",
  1230. "execution_count": 48,
  1231. "metadata": {},
  1232. "outputs": [
  1233. {
  1234. "name": "stdout",
  1235. "output_type": "stream",
  1236. "text": [
  1237. "[2, 1, 4, 3]\n"
  1238. ]
  1239. }
  1240. ],
  1241. "source": [
  1242. "listb = lista\n",
  1243. "print(listb)"
  1244. ]
  1245. },
  1246. {
  1247. "cell_type": "markdown",
  1248. "metadata": {},
  1249. "source": [
  1250. "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。"
  1251. ]
  1252. },
  1253. {
  1254. "cell_type": "code",
  1255. "execution_count": 49,
  1256. "metadata": {},
  1257. "outputs": [
  1258. {
  1259. "name": "stdout",
  1260. "output_type": "stream",
  1261. "text": [
  1262. "[2, 1, 4]\n",
  1263. "[2, 1, 4, 9]\n"
  1264. ]
  1265. }
  1266. ],
  1267. "source": [
  1268. "lista.pop()\n",
  1269. "print(lista)\n",
  1270. "lista.append(9)\n",
  1271. "print(lista)"
  1272. ]
  1273. },
  1274. {
  1275. "cell_type": "code",
  1276. "execution_count": 50,
  1277. "metadata": {},
  1278. "outputs": [
  1279. {
  1280. "name": "stdout",
  1281. "output_type": "stream",
  1282. "text": [
  1283. "[2, 1, 4, 9]\n"
  1284. ]
  1285. }
  1286. ],
  1287. "source": [
  1288. "print(listb)"
  1289. ]
  1290. },
  1291. {
  1292. "cell_type": "markdown",
  1293. "metadata": {},
  1294. "source": [
  1295. "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的内存空间分配给了listb。那么如何解决这个问题呢?\n",
  1296. "\n",
  1297. "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。"
  1298. ]
  1299. },
  1300. {
  1301. "cell_type": "code",
  1302. "execution_count": 35,
  1303. "metadata": {},
  1304. "outputs": [],
  1305. "source": [
  1306. "lista = [2,1,4,3]"
  1307. ]
  1308. },
  1309. {
  1310. "cell_type": "code",
  1311. "execution_count": 36,
  1312. "metadata": {},
  1313. "outputs": [
  1314. {
  1315. "name": "stdout",
  1316. "output_type": "stream",
  1317. "text": [
  1318. "[2, 1, 4, 3]\n"
  1319. ]
  1320. }
  1321. ],
  1322. "source": [
  1323. "listb = lista[:]\n",
  1324. "print(listb)"
  1325. ]
  1326. },
  1327. {
  1328. "cell_type": "code",
  1329. "execution_count": 37,
  1330. "metadata": {},
  1331. "outputs": [
  1332. {
  1333. "name": "stdout",
  1334. "output_type": "stream",
  1335. "text": [
  1336. "[2, 1, 4]\n",
  1337. "[2, 1, 4, 9]\n"
  1338. ]
  1339. }
  1340. ],
  1341. "source": [
  1342. "lista.pop()\n",
  1343. "print(lista)\n",
  1344. "lista.append(9)\n",
  1345. "print(lista)"
  1346. ]
  1347. },
  1348. {
  1349. "cell_type": "code",
  1350. "execution_count": 38,
  1351. "metadata": {},
  1352. "outputs": [
  1353. {
  1354. "name": "stdout",
  1355. "output_type": "stream",
  1356. "text": [
  1357. "[2, 1, 4, 3]\n"
  1358. ]
  1359. }
  1360. ],
  1361. "source": [
  1362. "print(listb)"
  1363. ]
  1364. },
  1365. {
  1366. "cell_type": "markdown",
  1367. "metadata": {},
  1368. "source": [
  1369. "## 2. 元组"
  1370. ]
  1371. },
  1372. {
  1373. "cell_type": "markdown",
  1374. "metadata": {},
  1375. "source": [
  1376. "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。把元组想象成对特定的东西为True,而对其他任何值都不为True。为了更好地理解,请回忆**divmod()** 函数。"
  1377. ]
  1378. },
  1379. {
  1380. "cell_type": "code",
  1381. "execution_count": 51,
  1382. "metadata": {},
  1383. "outputs": [
  1384. {
  1385. "name": "stdout",
  1386. "output_type": "stream",
  1387. "text": [
  1388. "(3, 1)\n",
  1389. "<class 'tuple'>\n"
  1390. ]
  1391. }
  1392. ],
  1393. "source": [
  1394. "xyz = divmod(10,3)\n",
  1395. "print(xyz)\n",
  1396. "print(type(xyz))"
  1397. ]
  1398. },
  1399. {
  1400. "cell_type": "markdown",
  1401. "metadata": {},
  1402. "source": [
  1403. "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。"
  1404. ]
  1405. },
  1406. {
  1407. "cell_type": "markdown",
  1408. "metadata": {},
  1409. "source": [
  1410. "要定义元组,将一个变量分配给paranthesis()或tuple()。"
  1411. ]
  1412. },
  1413. {
  1414. "cell_type": "code",
  1415. "execution_count": 52,
  1416. "metadata": {},
  1417. "outputs": [],
  1418. "source": [
  1419. "tup = ()\n",
  1420. "tup2 = tuple()"
  1421. ]
  1422. },
  1423. {
  1424. "cell_type": "markdown",
  1425. "metadata": {},
  1426. "source": [
  1427. "如果想直接声明元组,可以在数据的末尾使用逗号。"
  1428. ]
  1429. },
  1430. {
  1431. "cell_type": "code",
  1432. "execution_count": 53,
  1433. "metadata": {},
  1434. "outputs": [
  1435. {
  1436. "data": {
  1437. "text/plain": [
  1438. "(27,)"
  1439. ]
  1440. },
  1441. "execution_count": 53,
  1442. "metadata": {},
  1443. "output_type": "execute_result"
  1444. }
  1445. ],
  1446. "source": [
  1447. "27,"
  1448. ]
  1449. },
  1450. {
  1451. "cell_type": "markdown",
  1452. "metadata": {},
  1453. "source": [
  1454. "27乘以2得到54,但是乘以一个元组,数据重复两次。"
  1455. ]
  1456. },
  1457. {
  1458. "cell_type": "code",
  1459. "execution_count": 54,
  1460. "metadata": {},
  1461. "outputs": [
  1462. {
  1463. "data": {
  1464. "text/plain": [
  1465. "(27, 27)"
  1466. ]
  1467. },
  1468. "execution_count": 54,
  1469. "metadata": {},
  1470. "output_type": "execute_result"
  1471. }
  1472. ],
  1473. "source": [
  1474. "2*(27,)"
  1475. ]
  1476. },
  1477. {
  1478. "cell_type": "markdown",
  1479. "metadata": {},
  1480. "source": [
  1481. "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。"
  1482. ]
  1483. },
  1484. {
  1485. "cell_type": "code",
  1486. "execution_count": 55,
  1487. "metadata": {
  1488. "scrolled": true
  1489. },
  1490. "outputs": [
  1491. {
  1492. "name": "stdout",
  1493. "output_type": "stream",
  1494. "text": [
  1495. "(1, 2, 3)\n",
  1496. "('H', 'e', 'l', 'l', 'o')\n"
  1497. ]
  1498. }
  1499. ],
  1500. "source": [
  1501. "tup3 = tuple([1,2,3])\n",
  1502. "print(tup3)\n",
  1503. "tup4 = tuple('Hello')\n",
  1504. "print(tup4)"
  1505. ]
  1506. },
  1507. {
  1508. "cell_type": "markdown",
  1509. "metadata": {},
  1510. "source": [
  1511. "它遵循与列表相同的索引和切片。"
  1512. ]
  1513. },
  1514. {
  1515. "cell_type": "code",
  1516. "execution_count": 56,
  1517. "metadata": {},
  1518. "outputs": [
  1519. {
  1520. "name": "stdout",
  1521. "output_type": "stream",
  1522. "text": [
  1523. "2\n",
  1524. "('H', 'e', 'l')\n"
  1525. ]
  1526. }
  1527. ],
  1528. "source": [
  1529. "print(tup3[1])\n",
  1530. "tup5 = tup4[:3]\n",
  1531. "print(tup5)"
  1532. ]
  1533. },
  1534. {
  1535. "cell_type": "markdown",
  1536. "metadata": {},
  1537. "source": [
  1538. "### 2.1 将一个元组映射到另一个元组"
  1539. ]
  1540. },
  1541. {
  1542. "cell_type": "code",
  1543. "execution_count": 57,
  1544. "metadata": {},
  1545. "outputs": [],
  1546. "source": [
  1547. "(a,b,c)= ('alpha','beta','gamma')"
  1548. ]
  1549. },
  1550. {
  1551. "cell_type": "code",
  1552. "execution_count": 46,
  1553. "metadata": {},
  1554. "outputs": [
  1555. {
  1556. "name": "stdout",
  1557. "output_type": "stream",
  1558. "text": [
  1559. "alpha beta gamma\n"
  1560. ]
  1561. }
  1562. ],
  1563. "source": [
  1564. "print(a,b,c)"
  1565. ]
  1566. },
  1567. {
  1568. "cell_type": "code",
  1569. "execution_count": 58,
  1570. "metadata": {},
  1571. "outputs": [
  1572. {
  1573. "name": "stdout",
  1574. "output_type": "stream",
  1575. "text": [
  1576. "('R', 'a', 'j', 'a', 't', 'h', 'K', 'u', 'm', 'a', 'r', 'M', 'P')\n"
  1577. ]
  1578. }
  1579. ],
  1580. "source": [
  1581. "d = tuple('RajathKumarMP')\n",
  1582. "print(d)"
  1583. ]
  1584. },
  1585. {
  1586. "cell_type": "markdown",
  1587. "metadata": {},
  1588. "source": [
  1589. "### 2.2 内置元组函数"
  1590. ]
  1591. },
  1592. {
  1593. "cell_type": "markdown",
  1594. "metadata": {},
  1595. "source": [
  1596. "**count()** 函数计算元组中存在的指定元素的数量。"
  1597. ]
  1598. },
  1599. {
  1600. "cell_type": "code",
  1601. "execution_count": 59,
  1602. "metadata": {},
  1603. "outputs": [
  1604. {
  1605. "data": {
  1606. "text/plain": [
  1607. "3"
  1608. ]
  1609. },
  1610. "execution_count": 59,
  1611. "metadata": {},
  1612. "output_type": "execute_result"
  1613. }
  1614. ],
  1615. "source": [
  1616. "d.count('a')"
  1617. ]
  1618. },
  1619. {
  1620. "cell_type": "markdown",
  1621. "metadata": {},
  1622. "source": [
  1623. "**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"
  1624. ]
  1625. },
  1626. {
  1627. "cell_type": "code",
  1628. "execution_count": 60,
  1629. "metadata": {},
  1630. "outputs": [
  1631. {
  1632. "data": {
  1633. "text/plain": [
  1634. "1"
  1635. ]
  1636. },
  1637. "execution_count": 60,
  1638. "metadata": {},
  1639. "output_type": "execute_result"
  1640. }
  1641. ],
  1642. "source": [
  1643. "d.index('a')"
  1644. ]
  1645. },
  1646. {
  1647. "cell_type": "markdown",
  1648. "metadata": {},
  1649. "source": [
  1650. "## 3. 集合"
  1651. ]
  1652. },
  1653. {
  1654. "cell_type": "markdown",
  1655. "metadata": {},
  1656. "source": [
  1657. "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",
  1658. "\n",
  1659. "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"
  1660. ]
  1661. },
  1662. {
  1663. "cell_type": "code",
  1664. "execution_count": 61,
  1665. "metadata": {},
  1666. "outputs": [
  1667. {
  1668. "name": "stdout",
  1669. "output_type": "stream",
  1670. "text": [
  1671. "<class 'set'>\n"
  1672. ]
  1673. }
  1674. ],
  1675. "source": [
  1676. "set1 = set()\n",
  1677. "print(type(set1))"
  1678. ]
  1679. },
  1680. {
  1681. "cell_type": "code",
  1682. "execution_count": 62,
  1683. "metadata": {},
  1684. "outputs": [
  1685. {
  1686. "name": "stdout",
  1687. "output_type": "stream",
  1688. "text": [
  1689. "{1, 2, 3, 4}\n"
  1690. ]
  1691. }
  1692. ],
  1693. "source": [
  1694. "set0 = set([1,2,2,3,3,4])\n",
  1695. "print(set0)"
  1696. ]
  1697. },
  1698. {
  1699. "cell_type": "markdown",
  1700. "metadata": {},
  1701. "source": [
  1702. "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。"
  1703. ]
  1704. },
  1705. {
  1706. "cell_type": "markdown",
  1707. "metadata": {},
  1708. "source": [
  1709. "### 3.1 内置函数"
  1710. ]
  1711. },
  1712. {
  1713. "cell_type": "code",
  1714. "execution_count": 63,
  1715. "metadata": {},
  1716. "outputs": [],
  1717. "source": [
  1718. "set1 = set([1,2,3])"
  1719. ]
  1720. },
  1721. {
  1722. "cell_type": "code",
  1723. "execution_count": 64,
  1724. "metadata": {},
  1725. "outputs": [],
  1726. "source": [
  1727. "set2 = set([2,3,4,5])"
  1728. ]
  1729. },
  1730. {
  1731. "cell_type": "markdown",
  1732. "metadata": {},
  1733. "source": [
  1734. "**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。"
  1735. ]
  1736. },
  1737. {
  1738. "cell_type": "code",
  1739. "execution_count": 65,
  1740. "metadata": {},
  1741. "outputs": [
  1742. {
  1743. "data": {
  1744. "text/plain": [
  1745. "{1, 2, 3, 4, 5}"
  1746. ]
  1747. },
  1748. "execution_count": 65,
  1749. "metadata": {},
  1750. "output_type": "execute_result"
  1751. }
  1752. ],
  1753. "source": [
  1754. "set1.union(set2)"
  1755. ]
  1756. },
  1757. {
  1758. "cell_type": "markdown",
  1759. "metadata": {},
  1760. "source": [
  1761. "**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。"
  1762. ]
  1763. },
  1764. {
  1765. "cell_type": "code",
  1766. "execution_count": 66,
  1767. "metadata": {},
  1768. "outputs": [
  1769. {
  1770. "name": "stdout",
  1771. "output_type": "stream",
  1772. "text": [
  1773. "{1, 2, 3}\n"
  1774. ]
  1775. },
  1776. {
  1777. "data": {
  1778. "text/plain": [
  1779. "{0, 1, 2, 3}"
  1780. ]
  1781. },
  1782. "execution_count": 66,
  1783. "metadata": {},
  1784. "output_type": "execute_result"
  1785. }
  1786. ],
  1787. "source": [
  1788. "print(set1)\n",
  1789. "set1.add(0)\n",
  1790. "set1"
  1791. ]
  1792. },
  1793. {
  1794. "cell_type": "markdown",
  1795. "metadata": {},
  1796. "source": [
  1797. "**intersection( )** 函数输出一个集合,该集合包含两个集合中的所有元素。"
  1798. ]
  1799. },
  1800. {
  1801. "cell_type": "code",
  1802. "execution_count": 67,
  1803. "metadata": {},
  1804. "outputs": [
  1805. {
  1806. "data": {
  1807. "text/plain": [
  1808. "{2, 3}"
  1809. ]
  1810. },
  1811. "execution_count": 67,
  1812. "metadata": {},
  1813. "output_type": "execute_result"
  1814. }
  1815. ],
  1816. "source": [
  1817. "set1.intersection(set2)"
  1818. ]
  1819. },
  1820. {
  1821. "cell_type": "markdown",
  1822. "metadata": {},
  1823. "source": [
  1824. "**difference( )** 函数输出一个集合,其中包含在set1中而不在set2中的元素。"
  1825. ]
  1826. },
  1827. {
  1828. "cell_type": "code",
  1829. "execution_count": 68,
  1830. "metadata": {},
  1831. "outputs": [
  1832. {
  1833. "data": {
  1834. "text/plain": [
  1835. "{0, 1}"
  1836. ]
  1837. },
  1838. "execution_count": 68,
  1839. "metadata": {},
  1840. "output_type": "execute_result"
  1841. }
  1842. ],
  1843. "source": [
  1844. "set1.difference(set2)"
  1845. ]
  1846. },
  1847. {
  1848. "cell_type": "markdown",
  1849. "metadata": {},
  1850. "source": [
  1851. "**symmetric_difference( )** 函数输出一个函数,该函数包含一个集合中的元素。"
  1852. ]
  1853. },
  1854. {
  1855. "cell_type": "code",
  1856. "execution_count": 59,
  1857. "metadata": {},
  1858. "outputs": [
  1859. {
  1860. "data": {
  1861. "text/plain": [
  1862. "{0, 1, 4, 5}"
  1863. ]
  1864. },
  1865. "execution_count": 59,
  1866. "metadata": {},
  1867. "output_type": "execute_result"
  1868. }
  1869. ],
  1870. "source": [
  1871. "set2.symmetric_difference(set1)"
  1872. ]
  1873. },
  1874. {
  1875. "cell_type": "markdown",
  1876. "metadata": {},
  1877. "source": [
  1878. "**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。"
  1879. ]
  1880. },
  1881. {
  1882. "cell_type": "code",
  1883. "execution_count": 60,
  1884. "metadata": {},
  1885. "outputs": [
  1886. {
  1887. "data": {
  1888. "text/plain": [
  1889. "False"
  1890. ]
  1891. },
  1892. "execution_count": 60,
  1893. "metadata": {},
  1894. "output_type": "execute_result"
  1895. }
  1896. ],
  1897. "source": [
  1898. "set1.issubset(set2)"
  1899. ]
  1900. },
  1901. {
  1902. "cell_type": "code",
  1903. "execution_count": null,
  1904. "metadata": {},
  1905. "outputs": [],
  1906. "source": [
  1907. "set2.isdisjoint(set1)"
  1908. ]
  1909. },
  1910. {
  1911. "cell_type": "code",
  1912. "execution_count": null,
  1913. "metadata": {},
  1914. "outputs": [],
  1915. "source": [
  1916. "set2.issuperset(set1)"
  1917. ]
  1918. },
  1919. {
  1920. "cell_type": "markdown",
  1921. "metadata": {},
  1922. "source": [
  1923. "**pop( )** 是用来移除集合中的任意元素。"
  1924. ]
  1925. },
  1926. {
  1927. "cell_type": "code",
  1928. "execution_count": null,
  1929. "metadata": {},
  1930. "outputs": [],
  1931. "source": [
  1932. "set1.pop()\n",
  1933. "print(set1)"
  1934. ]
  1935. },
  1936. {
  1937. "cell_type": "markdown",
  1938. "metadata": {},
  1939. "source": [
  1940. "**remove( )** 函数从集合中删除指定的元素。"
  1941. ]
  1942. },
  1943. {
  1944. "cell_type": "code",
  1945. "execution_count": null,
  1946. "metadata": {},
  1947. "outputs": [],
  1948. "source": [
  1949. "set1.remove(2)\n",
  1950. "set1"
  1951. ]
  1952. },
  1953. {
  1954. "cell_type": "markdown",
  1955. "metadata": {},
  1956. "source": [
  1957. "**clear( )** 用于清除所有元素并将其设置为空集。"
  1958. ]
  1959. },
  1960. {
  1961. "cell_type": "code",
  1962. "execution_count": 61,
  1963. "metadata": {},
  1964. "outputs": [
  1965. {
  1966. "data": {
  1967. "text/plain": [
  1968. "set()"
  1969. ]
  1970. },
  1971. "execution_count": 61,
  1972. "metadata": {},
  1973. "output_type": "execute_result"
  1974. }
  1975. ],
  1976. "source": [
  1977. "set1.clear()\n",
  1978. "set1"
  1979. ]
  1980. }
  1981. ],
  1982. "metadata": {
  1983. "kernelspec": {
  1984. "display_name": "Python 3",
  1985. "language": "python",
  1986. "name": "python3"
  1987. },
  1988. "language_info": {
  1989. "codemirror_mode": {
  1990. "name": "ipython",
  1991. "version": 3
  1992. },
  1993. "file_extension": ".py",
  1994. "mimetype": "text/x-python",
  1995. "name": "python",
  1996. "nbconvert_exporter": "python",
  1997. "pygments_lexer": "ipython3",
  1998. "version": "3.6.8"
  1999. }
  2000. },
  2001. "nbformat": 4,
  2002. "nbformat_minor": 1
  2003. }

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》。