https://gb.yeadoc.com/tags/%E5%8F%AF%E8%A7%86%E5%8C%96/ Recent content in 可视化 on Clarity Hugo -- gohugo.io zh Copyright © 2008–2018, Steve Francia and the Hugo Authors; all rights reserved. Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/stock-analysis/ Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/stock-analysis/ <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">gupiao</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;谷歌&#39;</span><span class="p">:</span><span class="s1">&#39;GOOG&#39;</span><span class="p">,</span><span class="s1">&#39;亚马逊&#39;</span><span class="p">:</span><span class="s1">&#39;AMZN&#39;</span><span class="p">,</span><span class="s1">&#39;Facebook&#39;</span><span class="p">:</span><span class="s1">&#39;FB&#39;</span><span class="p">,</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"> <span class="s1">&#39;苹果&#39;</span><span class="p">:</span><span class="s1">&#39;AAPL&#39;</span><span class="p">,</span><span class="s1">&#39;阿里巴巴&#39;</span><span class="p">:</span><span class="s1">&#39;BABA&#39;</span><span class="p">,</span><span class="s1">&#39;腾讯&#39;</span><span class="p">:</span><span class="s1">&#39;0700.hk&#39;</span><span class="p">}</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">from</span> <span class="nn">pandas_datareader</span> <span class="kn">import</span> <span class="n">data</span> </span></span></code></pre></div><h1 id="阿里巴巴">阿里巴巴</h1> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">start_date</span> <span class="o">=</span> <span class="s1">&#39;2017-01-01&#39;</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="n">end_date</span> <span class="o">=</span> <span class="s1">&#39;2018-01-01&#39;</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="n">ali</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">get_data_yahoo</span><span class="p">(</span><span class="n">gupiao</span><span class="p">[</span><span class="s2">&#34;阿里巴巴&#34;</span><span class="p">],</span><span class="n">start_date</span><span class="p">,</span><span class="n">end_date</span><span class="p">)</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">ali</span><span class="o">.</span><span class="n">head</span><span class="p">()</span> </span></span></code></pre></div><pre><code> High Low Open Close Volume Adj Close </code></pre> <p>Date <br> 2017-01-03 89.000000 88.080002 89.000000 88.599998 8789400 88.599998 2017-01-04 90.889999 88.580002 88.985001 90.510002 11490200 90.510002 2017-01-05 94.809998 91.639999 91.910004 94.370003 16821500 94.370003 2017-01-06 94.500000 93.000000 94.400002 93.889999 7639800 93.889999 2017-01-09 95.650002 93.309998 94.160004 94.720001 10792900 94.720001</p> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="k">def</span> <span class="nf">change</span><span class="p">(</span><span class="n">column</span><span class="p">):</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"> <span class="n">prichange</span><span class="o">=</span><span class="p">(</span><span class="n">column</span><span class="p">[</span><span class="n">column</span><span class="o">.</span><span class="n">size</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">column</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="n">column</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"> <span class="k">if</span> <span class="p">(</span><span class="n">prichange</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span> </span></span><span class="line"><span class="ln">4</span><span class="cl"> <span class="nb">print</span><span class="p">(</span><span class="s2">&#34;股票累计上涨：&#34;</span><span class="p">,</span><span class="n">prichange</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span><span class="s2">&#34;%&#34;</span><span class="p">)</span> </span></span><span class="line"><span class="ln">5</span><span class="cl"> <span class="k">elif</span> <span class="p">(</span><span class="n">prichange</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span> </span></span><span class="line"><span class="ln">6</span><span class="cl"> <span class="nb">print</span><span class="p">(</span><span class="s2">&#34;股票基本不变：&#34;</span><span class="p">,</span><span class="n">prichange</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span><span class="s2">&#34;%&#34;</span><span class="p">)</span> </span></span><span class="line"><span class="ln">7</span><span class="cl"> <span class="k">else</span> <span class="p">:</span> </span></span><span class="line"><span class="ln">8</span><span class="cl"> <span class="nb">print</span><span class="p">(</span><span class="s2">&#34;股票累计下跌：&#34;</span><span class="p">,</span><span class="n">prichange</span><span class="o">*</span><span class="mi">100</span><span class="p">,</span><span class="s2">&#34;%&#34;</span><span class="p">)</span> </span></span><span class="line"><span class="ln">9</span><span class="cl"> <span class="k">return</span> <span class="n">prichange</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">alichange</span><span class="o">=</span><span class="n">change</span><span class="p">(</span><span class="n">ali</span><span class="p">[</span><span class="s2">&#34;Low&#34;</span><span class="p">])</span> </span></span></code></pre></div><p>股票累计上涨： 94.36874817125808 %</p> https://gb.yeadoc.com/bike-sharing-analysis/ Sat, 11 Apr 2026 19:27:54 +0000 https://gb.yeadoc.com/bike-sharing-analysis/ <h1 id="共享单车项目分析">共享单车项目分析</h1> <p>作者：马建龙</p> <h1 id="目录">目录</h1> <ol> <li>提出问题</li> <li>理解数据</li> </ol> <ul> <li>采集数据</li> <li>导入数据</li> <li>查看数据集信息</li> </ul> <ol start="3"> <li>数据清洗</li> </ol> <ul> <li>数据预处理</li> </ul> <ol start="4"> <li>数据分析</li> <li>数据可视化</li> <li>方案实施</li> </ol> <ul> <li>报告撰写</li> </ul> <h2 id="1提出问题">1、提出问题</h2> <p>根据kaggle项目：共享单车的数据进行描述性统计分析，查看不同因素对共享单车使用数量的影响。</p> <h2 id="2理解数据">2、理解数据</h2> <h3 id="21-采集数据">2.1 采集数据</h3> <p>从kaggle网站下载，https://www.kaggle.com/c/bike-sharing-demand</p> <h3 id="21-导入数据">2.1 导入数据</h3> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> </span></span><span class="line"><span class="ln">2</span><span class="cl"><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span> </span></span><span class="line"><span class="ln">3</span><span class="cl"><span class="kn">import</span> <span class="nn">datetime</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="o">%</span><span class="n">matplotlib</span> <span class="n">inline</span> </span></span></code></pre></div><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"><span class="line"><span class="ln">1</span><span class="cl"><span class="n">bike</span><span class="o">=</span><span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s2">&#34;C:/Users/my/data/train(bike).csv&#34;</span><span class="p">)</span> </span></span></code></pre></div><h3 id="23-查看数据信息">2.3 查看数据信息</h3> <p>datetime（日期） - hourly date + timestamp</p> <p>season（季节） - 1 = spring, 2 = summer, 3 = fall, 4 = winter</p> <p>holiday（是否假日） - whether the day is considered a holiday</p> <p>workingday（是否工作日） - whether the day is neither a weekend nor holiday</p>