如何從Pandas 資料框列擷取元組
問題:
問題:在das在資料框中,包含元組的列是很常見的。然而,使用這些元組可能很麻煩。為了方便分析,通常需要將這些欄位拆分為包含各個元組元素的多個欄位。
解決方案:將一列元組轉換為單獨的列,按照以下步驟操作:
<code class="python">column_list = column.tolist()</code>
<code class="python">new_df = pd.DataFrame(column_list, index=dataframe.index)</code>使用tolist🎜>
使用tolist () 方法將列轉換為元組列表:
<code class="python">dataframe[['column_a', 'column_b']] = new_df[['0', '1']]</code>
建立一個新的元組列表中的資料框:
將新資料框作為新欄位指派給原始資料框:
<code class="python">>>> d1 y norm test y norm train len(y_train) len(y_test) \ 0 64.904368 116.151232 1645 549 1 70.852681 112.639876 1645 549 SVR RBF \ 0 (35.652207342877873, 22.95533537448393) 1 (39.563683797747622, 27.382483096332511) LCV \ 0 (19.365430594452338, 13.880062435173587) 1 (19.099614489458364, 14.018867136617146) RIDGE CV \ 0 (4.2907610988480362, 12.416745648065584) 1 (4.18864306788194, 12.980833914392477) RF \ 0 (9.9484841581029428, 16.46902345373697) 1 (10.139848213735391, 16.282141345406522) GB \ 0 (0.012816232716538605, 15.950164822266007) 1 (0.012814519804493328, 15.305745202851712) ET DATA 0 (0.00034337162272515505, 16.284800366214057) j2m 1 (0.00024811554516431878, 15.556506191784194) j2m >>></code>
<code class="python">df[['LCV-a', 'LCV-b']] = pd.DataFrame(df['LCV'].tolist(), index=df.index)</code>範例:
<code class="python">>>> df y norm test y norm train len(y_train) len(y_test) \ 0 64.904368 116.151232 1645 549 1 70.852681 112.639876 1645 549 SVR RBF \ 0 (35.652207342877873, 22.95533537448393) 1 (39.563683797747622, 27.382483096332511) LCV-a LCV-b \ 0 19.365430594452338 13.880062435173587 1 19.099614489458364 14.018867136617146 RIDGE CV \ 0 (4.2907610988480362, 12.416745648065584) 1 (4.18864306788194, 12.980833914392477) RF \ 0 (9.9484841581029428, 16.46902345373697) 1 (10.139848213735391, 16.282141345406522) GB \ 0 (0.012816232716538605, 15.950164822266007) 1 (0.012814519804493328, 15.305745202851712) ET DATA 0 (0.00034337162272515505, 16.284800366214057) j2m 1 (0.00024811554516431878, 15.556506191784194) j2m</code>考慮以下資料框: 要將LCV 列拆分為單獨的列LCV-a 和LCV-b,您可以使用以下程式碼: 產生的資料框將是:
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