<h1>問(wèn)：如何創(chuàng)建一個(gè) 1 維數(shù)組？</h1><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-d07a9325f26c3b1c.jpeg" img-data="{"format":"jpeg","size":54879,"width":640,"height":360,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><p><span style="font-size:0.882em"><span>創(chuàng)建一維數(shù)組是數(shù)據(jù)處理和科學(xué)計(jì)算中的基本操作。在Python中，我們可以采用多種不同的方法來(lái)實(shí)現(xiàn)這一點(diǎn)。以下是五種主流的方法，以及它們各自的優(yōu)缺點(diǎn)分析：</span></span></p><h1>1. 使用基礎(chǔ)Python：列表（List）</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：Python原生支持，不需要任何額外的庫(kù)。列表是動(dòng)態(tài)數(shù)組，可以容易地增加、刪除或更改元素。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：性能上不如專門(mén)的數(shù)組處理庫(kù)，如NumPy，尤其是在大數(shù)據(jù)集上操作時(shí)。不支持高級(jí)的數(shù)值計(jì)算功能。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-e67ff17ccd1d6f4e.jpeg" img-data="{"format":"jpeg","size":23282,"width":640,"height":423,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>2. 使用NumPy：np.array()</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：NumPy是科學(xué)計(jì)算的標(biāo)準(zhǔn)庫(kù)，提供了優(yōu)化的數(shù)組操作和廣泛的數(shù)學(xué)函數(shù)庫(kù)。支持向量化操作，性能遠(yuǎn)超純Python實(shí)現(xiàn)。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：需要安裝外部庫(kù)。對(duì)于非數(shù)值計(jì)算任務(wù)，NumPy的功能可能有些過(guò)剩。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-5a2ec6aa53f35856.jpeg" img-data="{"format":"jpeg","size":10968,"width":640,"height":209,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>3. 使用NumPy：np.arange()</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：可以快速生成一個(gè)數(shù)值范圍內(nèi)的數(shù)組，用法類似于Python的range()，但直接返回NumPy數(shù)組。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：同樣需要安裝NumPy庫(kù)，且只能創(chuàng)建數(shù)值連續(xù)的數(shù)組，不適用于創(chuàng)建自定義序列。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-a83d1ad21056feff.jpeg" img-data="{"format":"jpeg","size":19059,"width":640,"height":313,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>題外話：Python的list和numpy的數(shù)組有什么區(qū)別？</h1><ol><li><span style="font-size:0.882em"><strong><span>Python列表</span></strong><span>：提供基本的序列操作，如追加（append）、擴(kuò)展（extend）、插入（insert）等。</span></span></li><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：提供大量的數(shù)學(xué)和科學(xué)計(jì)算方法，如矩陣運(yùn)算、統(tǒng)計(jì)分析、傅立葉變換等。</span></span></li></ol><p><span style="font-size:0.882em"><strong><span>內(nèi)存占用</span></strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>Python列表</span></strong><span>：因?yàn)榱斜硎菍?duì)象的集合，每個(gè)對(duì)象都有自己的類型信息、引用計(jì)數(shù)和其他信息，所以列表比NumPy數(shù)組占用更多內(nèi)存。</span></span></li><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：由于類型相同且緊湊存儲(chǔ)，通常占用更少的內(nèi)存。</span></span></li></ol><p><span style="font-size:0.882em"><strong><span>用途</span></strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>Python列表</span></strong><span>：適用于通用編程，特別是當(dāng)你需要一個(gè)可以包含不同類型元素的動(dòng)態(tài)數(shù)組時(shí)。</span></span></li><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：適用于需要進(jìn)行大量數(shù)值計(jì)算的場(chǎng)景，特別是在數(shù)據(jù)分析、機(jī)器學(xué)習(xí)、科學(xué)計(jì)算等領(lǐng)域。</span></span></li></ol><h1>4. 使用NumPy：np.linspace()</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：可以創(chuàng)建在指定的區(qū)間內(nèi)均勻分布的數(shù)值數(shù)組，適合數(shù)值分析和圖形表示。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：需要NumPy庫(kù)，且功能專一，主要用于生成線性間隔的數(shù)值點(diǎn)。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-628a823b55013ded.jpeg" img-data="{"format":"jpeg","size":15651,"width":640,"height":215,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>5. 使用Pandas：pd.Series()</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：Pandas是處理表格數(shù)據(jù)的強(qiáng)大工具，Series對(duì)象不僅可以存儲(chǔ)數(shù)值，還可以有自己的標(biāo)簽（index），適合于時(shí)間序列等應(yīng)用。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：Pandas庫(kù)比NumPy更為龐大，對(duì)于簡(jiǎn)單的一維數(shù)組來(lái)說(shuō)，其功能可能過(guò)于復(fù)雜。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-967235a6726ab787.jpeg" img-data="{"format":"jpeg","size":13156,"width":640,"height":400,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>題外話：Numpy的數(shù)組和Pandas的Series有什么區(qū)別？</h1><p><span style="font-size:0.882em"><span>Pandas的Series和NumPy的數(shù)組（numpy.ndarray）是Python數(shù)據(jù)分析中常用的兩種數(shù)據(jù)結(jié)構(gòu)，它們都能夠存儲(chǔ)數(shù)據(jù)序列，但設(shè)計(jì)理念、功能特性及用途存在明顯差異。以下是它們之間的一些主要區(qū)別：</span></span></p><p><span style="font-size:0.882em"><strong>數(shù)據(jù)類型和結(jié)構(gòu)</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：通常存儲(chǔ)單一數(shù)據(jù)類型的元素。它是一個(gè)多維數(shù)組，提供快速的向量化數(shù)值計(jì)算功能。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：可以看作是帶有標(biāo)簽的一維數(shù)組。Series可以存儲(chǔ)不同類型的數(shù)據(jù)（整數(shù)、字符串、浮點(diǎn)數(shù)等），每個(gè)元素都有一個(gè)唯一的標(biāo)簽（索引）。</span></span></li></ol><p><span style="font-size:0.882em"><strong>性能</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：在進(jìn)行大規(guī)模數(shù)值計(jì)算時(shí)表現(xiàn)出極高的效率，特別是在數(shù)組操作和數(shù)學(xué)函數(shù)應(yīng)用方面。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：雖然在性能上優(yōu)于Python原生列表，但在處理大規(guī)模數(shù)據(jù)時(shí)通常比NumPy數(shù)組慢。不過(guò)，Series提供的高級(jí)索引功能可以方便地進(jìn)行數(shù)據(jù)查詢和處理。</span></span></li></ol><p><span style="font-size:0.882em"><strong>功能和用途</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：適合進(jìn)行科學(xué)計(jì)算和數(shù)值分析，如線性代數(shù)運(yùn)算、統(tǒng)計(jì)分析等。NumPy庫(kù)提供了豐富的數(shù)值計(jì)算方法。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：設(shè)計(jì)用于處理結(jié)構(gòu)化數(shù)據(jù)，支持復(fù)雜的數(shù)據(jù)操作，如數(shù)據(jù)對(duì)齊、缺失數(shù)據(jù)處理、時(shí)間序列功能等。Series非常適合數(shù)據(jù)清洗、數(shù)據(jù)探索及統(tǒng)計(jì)分析等任務(wù)。</span></span></li></ol><p><span style="font-size:0.882em"><strong>索引</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：使用基于位置的默認(rèn)整數(shù)索引，也可以執(zhí)行布爾索引等高級(jí)索引操作。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：每個(gè)元素都有一個(gè)標(biāo)簽（索引），這些索引可以是整數(shù)也可以是字符串（或其他Python對(duì)象），使得數(shù)據(jù)操作更加直觀和靈活。</span></span></li></ol><p><span style="font-size:0.882em"><strong>內(nèi)存占用</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：由于存儲(chǔ)的是同質(zhì)數(shù)據(jù)類型，通常占用內(nèi)存較小。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：由于存儲(chǔ)了額外的索引信息，以及支持不同數(shù)據(jù)類型，因此可能占用更多的內(nèi)存。</span></span></li></ol><p><span style="font-size:0.882em"><strong>用途差異</strong></span></p><ol><li><span style="font-size:0.882em"><strong><span>NumPy數(shù)組</span></strong><span>：更適合進(jìn)行科學(xué)計(jì)算和技術(shù)計(jì)算，尤其是需要高性能計(jì)算的場(chǎng)景。</span></span></li><li><span style="font-size:0.882em"><strong><span>Pandas Series</span></strong><span>：由于其靈活的數(shù)據(jù)處理能力，更適合數(shù)據(jù)分析、數(shù)據(jù)探索和統(tǒng)計(jì)分析等任務(wù)，特別是在處理實(shí)際問(wèn)題中的結(jié)構(gòu)化數(shù)據(jù)時(shí)。</span></span></li></ol><h1>6.使用列表推導(dǎo)式</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：列表推導(dǎo)式是Python的語(yǔ)法糖，允許以一種直觀、清晰的方式創(chuàng)建列表，非常適合從其他列表或可迭代對(duì)象生成新列表，特別是當(dāng)你需要對(duì)每個(gè)元素應(yīng)用某種操作時(shí)。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：對(duì)于非常大的數(shù)據(jù)集，列表推導(dǎo)式可能會(huì)消耗大量?jī)?nèi)存，并且對(duì)性能有負(fù)面影響。此外，復(fù)雜的列表推導(dǎo)式可能難以閱讀和維護(hù)。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-1846ca3dc062bcc3.jpeg" img-data="{"format":"jpeg","size":13081,"width":640,"height":201,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><h1>7.使用運(yùn)算符</h1><p><span style="font-size:0.882em"><strong><span>優(yōu)點(diǎn)</span></strong><span>：非常適合快速初始化一個(gè)具有固定數(shù)值或?qū)ο蟮牧斜怼＿@種方法簡(jiǎn)潔明了，可以迅速得到一個(gè)大小固定的數(shù)組。</span></span></p><p><span style="font-size:0.882em"><strong><span>缺點(diǎn)</span></strong><span>：僅限于創(chuàng)建包含重復(fù)元素的列表。如果你需要初始化一個(gè)由唯一、動(dòng)態(tài)計(jì)算出的元素組成的數(shù)組，這種方法就不合適了。此外，使用運(yùn)算符復(fù)制的是對(duì)象的引用，而不是對(duì)象本身，這在使用可變對(duì)象時(shí)可能會(huì)導(dǎo)致不期望的行為。</span></span></p><div class="image-package"><img src="https://upload-images.jianshu.io/upload_images/16495147-1d172e6b2fbd8ae0.jpeg" img-data="{"format":"jpeg","size":22867,"width":640,"height":336,"space":"srgb","channels":3,"depth":"uchar","density":72,"chromaSubsampling":"4:2:0","isProgressive":false,"hasProfile":false,"hasAlpha":false}" class="uploaded-img" style="min-height:200px;min-width:200px;" width="auto" height="auto"/>
</div><blockquote><p>以上便是常用的創(chuàng)建一維數(shù)組的七種方式，每一種方式都有一個(gè)專門(mén)的適用場(chǎng)景，這也是它們存在的意義，希望這篇小小的匯總能夠幫助到各路大神！</p></blockquote>