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<div id="toc"><ul><li><a class="toc-href" href="#" title="資料科學家的 pandas 實戰手冊：掌握 40 個實用數據技巧">資料科學家的 pandas 實戰手冊：掌握 40 個實用數據技巧</a><ul><li><a class="toc-href" href="#建立-DataFrame" title="建立 DataFrame">建立 DataFrame</a><ul><li><a class="toc-href" href="#用-Python-dict-建立-DataFrame" title="用 Python dict 建立 DataFrame">用 Python dict 建立 DataFrame</a></li><li><a class="toc-href" href="#使用-pd.util.testing-隨機建立-DataFrame" title="使用 pd.util.testing 隨機建立 DataFrame">使用 pd.util.testing 隨機建立 DataFrame</a></li><li><a class="toc-href" href="#將剪貼簿內容轉換成-DataFrame" title="將剪貼簿內容轉換成 DataFrame">將剪貼簿內容轉換成 DataFrame</a></li><li><a class="toc-href" href="#讀取線上-CSV-檔" title="讀取線上 CSV 檔">讀取線上 CSV 檔</a></li><li><a class="toc-href" href="#優化記憶體使用量" title="優化記憶體使用量">優化記憶體使用量</a></li><li><a class="toc-href" href="#讀入並合併多個-CSV-檔案成單一-DataFrame" title="讀入並合併多個 CSV 檔案成單一 DataFrame">讀入並合併多個 CSV 檔案成單一 DataFrame</a></li></ul></li><li><a class="toc-href" href="#客製化-DataFrame-顯示設定_1" title="客製化 DataFrame 顯示設定">客製化 DataFrame 顯示設定</a><ul><li><a class="toc-href" href="#完整顯示所有欄位" title="完整顯示所有欄位">完整顯示所有欄位</a></li><li><a class="toc-href" href="#減少顯示的欄位長度" title="減少顯示的欄位長度">減少顯示的欄位長度</a></li><li><a class="toc-href" href="#改變浮點數顯示位數" title="改變浮點數顯示位數">改變浮點數顯示位數</a></li><li><a class="toc-href" href="#為特定-DataFrame-加點樣式" title="為特定 DataFrame 加點樣式">為特定 DataFrame 加點樣式</a></li></ul></li><li><a class="toc-href" href="#數據清理-&-整理_1" title="數據清理 & 整理">數據清理 & 整理</a><ul><li><a class="toc-href" href="#處理空值" title="處理空值">處理空值</a></li><li><a class="toc-href" href="#捨棄不需要的行列" title="捨棄不需要的行列">捨棄不需要的行列</a></li><li><a class="toc-href" href="#重置並捨棄索引" title="重置並捨棄索引">重置並捨棄索引</a></li><li><a class="toc-href" href="#將字串切割成多個欄位" title="將字串切割成多個欄位">將字串切割成多個欄位</a></li><li><a class="toc-href" href="#將-list-分成多個欄位" title="將 list 分成多個欄位">將 list 分成多個欄位</a></li></ul></li><li><a class="toc-href" href="#取得想要關注的數據_1" title="取得想要關注的數據">取得想要關注的數據</a><ul><li><a class="toc-href" href="#基本數據切割" title="基本數據切割">基本數據切割</a></li><li><a class="toc-href" href="#反向選取行列" title="反向選取行列">反向選取行列</a></li><li><a class="toc-href" href="#條件選取數據" title="條件選取數據">條件選取數據</a></li><li><a class="toc-href" href="#選擇任一欄有空值的樣本" title="選擇任一欄有空值的樣本">選擇任一欄有空值的樣本</a></li><li><a class="toc-href" href="#選取或排除特定類型欄位" title="選取或排除特定類型欄位">選取或排除特定類型欄位</a></li><li><a class="toc-href" href="#選取所有出現在-list-內的樣本" title="選取所有出現在 list 內的樣本">選取所有出現在 list 內的樣本</a></li><li><a class="toc-href" href="#選取某欄位為-top-k-值的樣本" title="選取某欄位為 top-k 值的樣本">選取某欄位為 top-k 值的樣本</a></li><li><a class="toc-href" href="#找出符合特定字串的樣本" title="找出符合特定字串的樣本">找出符合特定字串的樣本</a></li><li><a class="toc-href" href="#使用正規表示式選取數據" title="使用正規表示式選取數據">使用正規表示式選取數據</a></li><li><a class="toc-href" href="#選取從某時間點開始的區間樣本" title="選取從某時間點開始的區間樣本">選取從某時間點開始的區間樣本</a></li></ul></li><li><a class="toc-href" href="#基本數據處理與轉換_1" title="基本數據處理與轉換">基本數據處理與轉換</a><ul><li><a class="toc-href" href="#對某一軸套用相同運算" title="對某一軸套用相同運算">對某一軸套用相同運算</a></li><li><a class="toc-href" href="#對每一個樣本做自定義運算" title="對每一個樣本做自定義運算">對每一個樣本做自定義運算</a></li><li><a class="toc-href" href="#將連續數值轉換成分類數據" title="將連續數值轉換成分類數據">將連續數值轉換成分類數據</a></li><li><a class="toc-href" href="#將-DataFrame-隨機切成兩個子集" title="將 DataFrame 隨機切成兩個子集">將 DataFrame 隨機切成兩個子集</a></li><li><a class="toc-href" href="#用-SQL-的方式合併兩個-DataFrames" title="用 SQL 的方式合併兩個 DataFrames">用 SQL 的方式合併兩個 DataFrames</a></li><li><a class="toc-href" href="#存取並操作每一個樣本" title="存取並操作每一個樣本">存取並操作每一個樣本</a></li></ul></li><li><a class="toc-href" href="#簡單匯總-&-分析數據_1" title="簡單匯總 & 分析數據">簡單匯總 & 分析數據</a><ul><li><a class="toc-href" href="#取出某欄位-top-k-的值" title="取出某欄位 top k 的值">取出某欄位 top k 的值</a></li><li><a class="toc-href" href="#一行描述數值欄位" title="一行描述數值欄位">一行描述數值欄位</a></li><li><a class="toc-href" href="#找出欄位裡所有出現過的值" title="找出欄位裡所有出現過的值">找出欄位裡所有出現過的值</a></li><li><a class="toc-href" href="#分組匯總結果" title="分組匯總結果">分組匯總結果</a></li><li><a class="toc-href" href="#結合原始數據與匯總結果" title="結合原始數據與匯總結果">結合原始數據與匯總結果</a></li><li><a class="toc-href" href="#對時間數據做匯總" title="對時間數據做匯總">對時間數據做匯總</a></li><li><a class="toc-href" href="#簡易繪圖並修改預設樣式" title="簡易繪圖並修改預設樣式">簡易繪圖並修改預設樣式</a></li></ul></li><li><a class="toc-href" href="#與-pandas-相得益彰的實用工具_1" title="與 pandas 相得益彰的實用工具">與 pandas 相得益彰的實用工具</a><ul><li><a class="toc-href" href="#tqdm：了解你的數據處理進度" title="tqdm：了解你的數據處理進度">tqdm：了解你的數據處理進度</a></li><li><a class="toc-href" href="#swifter：加速你的數據處理" title="swifter：加速你的數據處理">swifter：加速你的數據處理</a></li><li><a class="toc-href" href="#qgrid：即時排序、篩選及編輯你的-DataFrame" title="qgrid：即時排序、篩選及編輯你的 DataFrame">qgrid：即時排序、篩選及編輯你的 DataFrame</a></li><li><a class="toc-href" href="#pandas-profiling：你的一鍵-EDA-神器" title="pandas-profiling：你的一鍵 EDA 神器">pandas-profiling：你的一鍵 EDA 神器</a></li></ul></li><li><a class="toc-href" href="#結語：實際運用所學_1" title="結語：實際運用所學">結語：實際運用所學</a></li></ul></li></ul></div>
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資料科學家的 pandas 實戰手冊：掌握 40 個實用數據技巧
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故學然後知不足，教然後知困。知不足，然後能自反也；知困，然後能自強也，故曰：教學相長也。
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<span style="float:right;margin-right: 1.5rem">─ 《禮記．學記》</span>
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<p><a href="https://pandas.pydata.org/">pandas</a> 是 <a href="https://www.python.org/">Python</a> 的一個資料分析函式庫，提供如 <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html">DataFrame</a> 等十分容易操作的資料結構，是近年做數據分析時不可或需的工具之一。</p>
<p>雖然已經有滿坑滿谷的教學文章、影片或是線上課程，正是因為 pandas 學習資源之多，導致初學者常常不知如何踏出第一步。在這篇文章裡頭，我以自身作為資料科學家（<strong>D</strong>ata <strong>S</strong>cientist, DS）的工作經驗，將接近 40 個實用的 pandas 技巧由淺入深地分成 6 大類別：</p>
<ol>
<li><a href="#建立-DataFrame">建立 DataFrame</a></li>
<li><a href="#客製化-DataFrame-顯示設定_1">客製化 DataFrame 顯示設定</a></li>
<li><a href="#數據清理-&-整理_1">數據清理 & 整理</a></li>
<li><a href="#取得想要關注的數據_1">取得想要關注的數據</a></li>
<li><a href="#基本數據處理與轉換_1">基本數據處理與轉換</a></li>
<li><a href="#簡單匯總-&-分析數據_1">簡單匯總 & 分析數據</a></li>
</ol>
<p>透過有系統地呈現這些 pandas 技巧，我希望能讓更多想要利用 Python 做資料分析或是想成為 DS 的你，能用最有效率的方式掌握核心 pandas 能力；同時也希望你能將自己認為實用但本文沒有提到的技巧與我分享，達到開頭引文所說的<strong>教學相長</strong>：）</p>
<p>如果你是使用寬螢幕瀏覽本文，隨時可以點擊左側傳送門瀏覽各章內容：</p>
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<p>除了 pandas 的操作技巧以外，我在最後一節：<a href="#與-pandas-相得益彰的實用工具_1">與 pandas 相得益彰的實用工具</a>裡也介紹了幾個實用函式庫。就算你是 pandas 老手，或許也能從中得到些收穫。當然也非常歡迎與我分享其他厲害工具，我會更新到文章裡頭讓更多人知道。</p>
<p>前言已盡，讓我們開始這趟 pandas 旅程吧！當然，首先你得 <code>import pandas</code>：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">pd</span><span class="o">.</span><span class="n">__version__</span>
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<pre>'0.24.2'</pre>
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<h2 id="建立-DataFrame">建立 DataFrame<a class="anchor-link" href="#建立-DataFrame">¶</a></h2>
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<p>pandas 裡有非常多種可以初始化一個 <a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html">DataFrame</a> 的技巧。以下列出一些我覺得實用的初始化方式。</p>
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<h3 id="用-Python-dict-建立-DataFrame">用 Python dict 建立 DataFrame<a class="anchor-link" href="#用-Python-dict-建立-DataFrame">¶</a></h3>
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<p>使用 Python 的 <code>dict</code> 來初始化 DataFrame 十分直覺。基本上 <code>dict</code> 裡頭的每一個鍵值（key）都對應到一個欄位名稱，而其值（value）則是一個 iterable，代表該欄位裡頭所有的數值。</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">dic</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"col 1"</span><span class="p">:</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span>
<span class="s2">"col 2"</span><span class="p">:</span> <span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">,</span> <span class="mi">30</span><span class="p">],</span>
<span class="s2">"col 3"</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="s1">'xyz'</span><span class="p">),</span>
<span class="s2">"col 4"</span><span class="p">:</span> <span class="p">[</span><span class="s1">'a'</span><span class="p">,</span> <span class="s1">'b'</span><span class="p">,</span> <span class="s1">'c'</span><span class="p">],</span>
<span class="s2">"col 5"</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
<span class="p">}</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">dic</span><span class="p">)</span>
<span class="n">df</span>
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<p>在需要管理多個 DataFrames 時你會想要用更有意義的名字來代表它們，但在資料科學領域裡只要看到 <code>df</code>，每個人都會預期它是一個 <strong>D</strong>ata<strong>F</strong>rame，不論是 Python 或是 R 語言的使用者。</p>
<p>很多時候你也會需要改變 DataFrame 裡的欄位名稱：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">rename_dic</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"col 1"</span><span class="p">:</span> <span class="s2">"x"</span><span class="p">,</span> <span class="s2">"col 2"</span><span class="p">:</span> <span class="s2">"10x"</span><span class="p">}</span>
<span class="n">df</span><span class="o">.</span><span class="n">rename</span><span class="p">(</span><span class="n">rename_dic</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<th>col 5</th>
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</thead>
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<th>0</th>
<td>1</td>
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<td>x</td>
<td>a</td>
<td>0</td>
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<td>y</td>
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<td>1</td>
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<td>3</td>
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<td>z</td>
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<td>2</td>
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<p>這邊也很直覺，就是給一個將舊欄位名對應到新欄位名的 Python <code>dict</code>。值得注意的是參數 <code>axis=1</code>：在 pandas 裡大部分函式預設處理的軸為列（row）：以 <code>axis=0</code> 表示；而將 <code>axis</code> 設置為 <code>1</code> 則代表你想以行（column）為單位套用該函式。</p>
<p>你也可以用 <code>df.columns</code> 的方式改欄位名稱：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'x(new)'</span><span class="p">,</span> <span class="s1">'10x(new)'</span><span class="p">]</span> <span class="o">+</span> <span class="nb">list</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="mi">2</span><span class="p">:])</span>
<span class="n">df</span>
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<th>col 5</th>
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<th>0</th>
<td>1</td>
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<td>x</td>
<td>a</td>
<td>0</td>
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<th>1</th>
<td>2</td>
<td>20</td>
<td>y</td>
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<td>1</td>
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<td>3</td>
<td>30</td>
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<td>c</td>
<td>2</td>
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<h3 id="使用-pd.util.testing-隨機建立-DataFrame">使用 pd.util.testing 隨機建立 DataFrame<a class="anchor-link" href="#使用-pd.util.testing-隨機建立-DataFrame">¶</a></h3>
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<p>當你想要隨意初始化一個 DataFrame 並測試 pandas 功能時，<code>pd.util.testing</code> 就顯得十分好用：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">makeDataFrame</span><span class="p">()</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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<th>B</th>
<th>C</th>
<th>D</th>
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<th>xPlIXbQqGU</th>
<td>1.086581</td>
<td>-0.002484</td>
<td>-0.335693</td>
<td>0.226988</td>
</tr>
<tr>
<th>IAFe6K8mpA</th>
<td>-0.547556</td>
<td>-0.290935</td>
<td>-0.014313</td>
<td>-0.301007</td>
</tr>
<tr>
<th>OoATGY0k2M</th>
<td>1.017636</td>
<td>0.568835</td>
<td>-0.272382</td>
<td>0.659657</td>
</tr>
<tr>
<th>uRN2yGacDw</th>
<td>-0.662390</td>
<td>1.929820</td>
<td>-1.206670</td>
<td>0.250626</td>
</tr>
<tr>
<th>ElphZli9nK</th>
<td>-0.697235</td>
<td>0.942415</td>
<td>-0.894887</td>
<td>0.701790</td>
</tr>
<tr>
<th>oiEoCPCXK8</th>
<td>-1.049284</td>
<td>-1.019107</td>
<td>-0.640271</td>
<td>-0.613056</td>
</tr>
<tr>
<th>NUrQFrYQw1</th>
<td>0.759355</td>
<td>0.717367</td>
<td>-0.449368</td>
<td>1.889321</td>
</tr>
<tr>
<th>oC9iiEBneW</th>
<td>0.665412</td>
<td>-0.391204</td>
<td>-0.974010</td>
<td>0.248326</td>
</tr>
<tr>
<th>4hD6Eea7yF</th>
<td>-0.862819</td>
<td>2.092149</td>
<td>0.976645</td>
<td>-0.388735</td>
</tr>
<tr>
<th>3QD5mMfstw</th>
<td>-0.312762</td>
<td>-0.110278</td>
<td>1.162421</td>
<td>-0.335144</td>
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<p><code>head</code> 函式預設用來顯示 DataFrame 中前 5 筆數據。要顯示後面數據則可以使用 <code>tail</code> 函式。</p>
<p>你也可以用 <code>makeMixedDataFrame</code> 建立一個有各種資料型態的 DataFrame 方便測試：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">pd</span><span class="o">.</span><span class="n">util</span><span class="o">.</span><span class="n">testing</span><span class="o">.</span><span class="n">makeMixedDataFrame</span><span class="p">()</span>
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<th>0</th>
<td>0.0</td>
<td>0.0</td>
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<th>1</th>
<td>1.0</td>
<td>1.0</td>
<td>foo2</td>
<td>2009-01-02</td>
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<th>2</th>
<td>2.0</td>
<td>0.0</td>
<td>foo3</td>
<td>2009-01-05</td>
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<th>3</th>
<td>3.0</td>
<td>1.0</td>
<td>foo4</td>
<td>2009-01-06</td>
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<th>4</th>
<td>4.0</td>
<td>0.0</td>
<td>foo5</td>
<td>2009-01-07</td>
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<p>其他函式如 <code>makeMissingDataframe</code> 及 <code>makeTimeDataFrame</code> 在後面的章節都還會看到。</p>
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<h3 id="將剪貼簿內容轉換成-DataFrame">將剪貼簿內容轉換成 DataFrame<a class="anchor-link" href="#將剪貼簿內容轉換成-DataFrame">¶</a></h3>
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<p>你可以從 Excel、Google Sheet 或是網頁上複製表格並將其轉成 DataFrame。</p>
<p>簡單 2 步驟：</p>
<ul>
<li>複製其他來源的表格</li>
<li>執行 <code>pd.read_clipboard</code></li>
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<video autoplay="" loop="" muted="" playsinline="" poster="https://leemeng.tw/images/pandas/pandas_clipboard.jpg" style="mix-blend-mode: initial;">
<source src="https://leemeng.tw/images/pandas/pandas_clipboard.mp4" type="video/mp4"/>
您的瀏覽器不支援影片標籤，請留言通知我：S
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<p>這個技巧在你想要快速將一些數據轉成 DataFrame 時非常方便。當然，你得考量重現性（reproducibility）。</p>
<p>為了讓未來的自己以及他人可以重現你當下的結果，必要時記得另存新檔以供後人使用：</p>
<div class="highlight"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="s2">"some_data.csv"</span><span class="p">)</span>
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<h3 id="讀取線上-CSV-檔">讀取線上 CSV 檔<a class="anchor-link" href="#讀取線上-CSV-檔">¶</a></h3>
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<p>不限於本地檔案，只要有正確的 URL 以及網路連線就可以將網路上的任意 CSV 檔案轉成 DataFrame。</p>
<p>比方說你可以將 Kaggle 著名的<a href="https://www.kaggle.com/c/titanic">鐵達尼號競賽</a>的 CSV 檔案從網路上下載下來並轉成 DataFrame：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">df</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="s1">'http://bit.ly/kaggletrain'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>Pclass</th>
<th>Name</th>
<th>Sex</th>
<th>Age</th>
<th>SibSp</th>
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<td>0</td>
<td>3</td>
<td>Braund, Mr. Owen Harris</td>
<td>male</td>
<td>22.0</td>
<td>1</td>
<td>0</td>
<td>A/5 21171</td>
<td>7.2500</td>
<td>NaN</td>
<td>S</td>
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<th>1</th>
<td>2</td>
<td>1</td>
<td>1</td>
<td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
<td>female</td>
<td>38.0</td>
<td>1</td>
<td>0</td>
<td>PC 17599</td>
<td>71.2833</td>
<td>C85</td>
<td>C</td>
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<th>2</th>
<td>3</td>
<td>1</td>
<td>3</td>
<td>Heikkinen, Miss. Laina</td>
<td>female</td>
<td>26.0</td>
<td>0</td>
<td>0</td>
<td>STON/O2. 3101282</td>
<td>7.9250</td>
<td>NaN</td>
<td>S</td>
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<th>3</th>
<td>4</td>
<td>1</td>
<td>1</td>
<td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
<td>female</td>
<td>35.0</td>
<td>1</td>
<td>0</td>
<td>113803</td>
<td>53.1000</td>
<td>C123</td>
<td>S</td>
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<th>4</th>
<td>5</td>
<td>0</td>
<td>3</td>
<td>Allen, Mr. William Henry</td>
<td>male</td>
<td>35.0</td>
<td>0</td>
<td>0</td>
<td>373450</td>
<td>8.0500</td>
<td>NaN</td>
<td>S</td>
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<p>以下則是另個使用 pandas 爬取網路上數據並作分析的真實案例。</p>
<p>我在之前的<a href="https://leemeng.tw/chartify-a-simple-yet-powerful-python-data-visualization-tool-which-boost-your-productivity-as-a-data-scientist.html"> Chartify 教學文</a>中從臺北市的資料開放平台爬取<a href="https://data.taipei/#/dataset/detail?id=2f238b4f-1b27-4085-93e9-d684ef0e2735"> A1 及 A2 類交通事故數據</a>後做了簡單匯總：</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># 你可以用類似的方式爬取任何網路上的公開數據集</span>
<span class="n">base_url</span> <span class="o">=</span> <span class="s2">"https://data.taipei/api/getDatasetInfo/downloadResource?id=</span><span class="si">{}</span><span class="s2">&rid=</span><span class="si">{}</span><span class="s2">"</span>
<span class="n">_id</span> <span class="o">=</span> <span class="s2">"2f238b4f-1b27-4085-93e9-d684ef0e2735"</span>
<span class="n">rid</span> <span class="o">=</span> <span class="s2">"ea731a84-e4a1-4523-b981-b733beddbc1f"</span>
<span class="n">csv_url</span> <span class="o">=</span> <span class="n">base_url</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">_id</span><span class="p">,</span> <span class="n">rid</span><span class="p">)</span>
<span class="n">df_raw</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="n">csv_url</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">'big5'</span><span class="p">)</span>
<span class="c1"># 複製一份做處理</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df_raw</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
<span class="c1"># 計算不同區不同性別的死亡、受傷人數</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'區序'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'區序'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">''</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">i</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">x</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="o">.</span><span class="n">isdigit</span><span class="p">()]))</span>
<span class="n">df</span> <span class="o">=</span> <span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s1">'性別'</span><span class="p">]</span><span class="o">.</span><span class="n">isin</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])]</span>
<span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'區序'</span><span class="p">,</span> <span class="s1">'性別'</span><span class="p">])[[</span><span class="s1">'死亡人數'</span><span class="p">,</span> <span class="s1">'受傷人數'</span><span class="p">]]</span>
<span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="o">.</span><span class="n">reset_index</span><span class="p">()</span>
<span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s1">'受傷人數'</span><span class="p">))</span>
<span class="n">df</span><span class="p">[</span><span class="s1">'性別'</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="s1">'性別'</span><span class="p">]</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s1">'男性'</span> <span class="k">if</span> <span class="n">x</span> <span class="o">==</span> <span class="mi">1</span> <span class="k">else</span> <span class="s1">'女性'</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">reset_index</span><span class="p">()</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'index'</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># 顯示結果</span>
<span class="n">display</span><span class="p">(</span><span class="n">df_raw</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
<span class="n">display</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">())</span>
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