Pandas 2.2 中文官方教程和指南（七）（2）https://developer.aliyun.com/article/1509748
选择
注意
虽然用于选择和设置的标准 Python/NumPy 表达式直观且对交互式工作很方便，但对于生产代码，我们建议使用优化的 pandas 数据访问方法，DataFrame.at()，DataFrame.iat()，DataFrame.loc()和DataFrame.iloc()。
查看索引文档索引和选择数据和 MultiIndex /高级索引。
获取项（[]）
对于DataFrame，传递单个标签选择列并产生等同于df.A的Series：
In [24]: df["A"] Out[24]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64
对于DataFrame，传递切片:选择匹配的行：
In [25]: df[0:3] Out[25]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [26]: df["20130102":"20130104"] Out[26]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
按标签选择
查看更多关于按标签选择的信息，使用DataFrame.loc()或DataFrame.at()。
选择匹配标签的行：
In [27]: df.loc[dates[0]] Out[27]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64
选择所有行（:）与选择列标签：
In [28]: df.loc[:, ["A", "B"]] Out[28]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648
对于标签切片，两个端点都是包含的：
In [29]: df.loc["20130102":"20130104", ["A", "B"]] Out[29]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771
选择单个行和列标签返回一个标量：
In [30]: df.loc[dates[0], "A"] Out[30]: 0.4691122999071863
获取快速访问标量（等同于先前的方法）：
In [31]: df.at[dates[0], "A"] Out[31]: 0.4691122999071863
按位置选择
查看更多关于按位置选择的信息，使用DataFrame.iloc()或DataFrame.iat()。
通过传递整数的位置选择：
In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64
整数切片类似于 NumPy/Python：
In [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020
整数位置位置列表：
In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232
明确切片行：
In [35]: df.iloc[1:3, :] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
明确切片列：
In [36]: df.iloc[:, 1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427
明确获取值：
In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858
获取快速访问标量（等同于先前的方法）：
In [38]: df.iat[1, 1] Out[38]: -0.17321464905330858
布尔索引
选择df.A大于0的行。
In [39]: df[df["A"] > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
从满足布尔条件的DataFrame中选择值：
In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988
使用isin()方法进行过滤：
In [41]: df2 = df.copy() In [42]: df2["E"] = ["one", "one", "two", "three", "four", "three"] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2["E"].isin(["two", "four"])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
设置
设置新列会自动根据索引对齐数据：
In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df["F"] = s1
按标签设置值：
In [48]: df.at[dates[0], "A"] = 0
按位置设置值：
In [49]: df.iat[0, 1] = 0
通过分配 NumPy 数组进行设置：
In [50]: df.loc[:, "D"] = np.array([5] * len(df))
先前设置操作的结果：
In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5.0 NaN 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 2013-01-05 -0.424972 0.567020 0.276232 5.0 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5.0 5.0
具有设置的where操作：
In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5.0 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5.0 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5.0 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5.0 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5.0 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5.0 -5.0
获取项（[]）
对于DataFrame，传递单个标签选择列并产生等同于df.A的Series：
In [24]: df["A"] Out[24]: 2013-01-01 0.469112 2013-01-02 1.212112 2013-01-03 -0.861849 2013-01-04 0.721555 2013-01-05 -0.424972 2013-01-06 -0.673690 Freq: D, Name: A, dtype: float64
对于DataFrame，通过传递切片:选择匹配的行：
In [25]: df[0:3] Out[25]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 In [26]: df["20130102":"20130104"] Out[26]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
按标签选择
请参阅按标签选择 使用DataFrame.loc() 或 DataFrame.at()。
选择匹配标签的行：
In [27]: df.loc[dates[0]] Out[27]: A 0.469112 B -0.282863 C -1.509059 D -1.135632 Name: 2013-01-01 00:00:00, dtype: float64
选择所有行（:）与选择列标签：
In [28]: df.loc[:, ["A", "B"]] Out[28]: A B 2013-01-01 0.469112 -0.282863 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020 2013-01-06 -0.673690 0.113648
对于标签切片，两个端点都包括：
In [29]: df.loc["20130102":"20130104", ["A", "B"]] Out[29]: A B 2013-01-02 1.212112 -0.173215 2013-01-03 -0.861849 -2.104569 2013-01-04 0.721555 -0.706771
选择单个行和列标签返回一个标量：
In [30]: df.loc[dates[0], "A"] Out[30]: 0.4691122999071863
为了快速访问标量（等效于先前的方法）：
In [31]: df.at[dates[0], "A"] Out[31]: 0.4691122999071863
按位置选择
请参阅按位置选择 使用DataFrame.iloc() 或 DataFrame.iat()。
通过传递整数的位置选择：
In [32]: df.iloc[3] Out[32]: A 0.721555 B -0.706771 C -1.039575 D 0.271860 Name: 2013-01-04 00:00:00, dtype: float64
整数切片类似于 NumPy/Python：
In [33]: df.iloc[3:5, 0:2] Out[33]: A B 2013-01-04 0.721555 -0.706771 2013-01-05 -0.424972 0.567020
整数位置位置列表：
In [34]: df.iloc[[1, 2, 4], [0, 2]] Out[34]: A C 2013-01-02 1.212112 0.119209 2013-01-03 -0.861849 -0.494929 2013-01-05 -0.424972 0.276232
明确切片行：
In [35]: df.iloc[1:3, :] Out[35]: A B C D 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804
明确切片列：
In [36]: df.iloc[:, 1:3] Out[36]: B C 2013-01-01 -0.282863 -1.509059 2013-01-02 -0.173215 0.119209 2013-01-03 -2.104569 -0.494929 2013-01-04 -0.706771 -1.039575 2013-01-05 0.567020 0.276232 2013-01-06 0.113648 -1.478427
明确获取一个值：
In [37]: df.iloc[1, 1] Out[37]: -0.17321464905330858
为了快速访问标量（等效于先前的方法）：
In [38]: df.iat[1, 1] Out[38]: -0.17321464905330858
布尔索引
选择df.A大于0的行。
In [39]: df[df["A"] > 0] Out[39]: A B C D 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 2013-01-04 0.721555 -0.706771 -1.039575 0.271860
从DataFrame 中选择满足布尔条件的值：
In [40]: df[df > 0] Out[40]: A B C D 2013-01-01 0.469112 NaN NaN NaN 2013-01-02 1.212112 NaN 0.119209 NaN 2013-01-03 NaN NaN NaN 1.071804 2013-01-04 0.721555 NaN NaN 0.271860 2013-01-05 NaN 0.567020 0.276232 NaN 2013-01-06 NaN 0.113648 NaN 0.524988
使用isin() 方法进行过滤：
In [41]: df2 = df.copy() In [42]: df2["E"] = ["one", "one", "two", "three", "four", "three"] In [43]: df2 Out[43]: A B C D E 2013-01-01 0.469112 -0.282863 -1.509059 -1.135632 one 2013-01-02 1.212112 -0.173215 0.119209 -1.044236 one 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-04 0.721555 -0.706771 -1.039575 0.271860 three 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four 2013-01-06 -0.673690 0.113648 -1.478427 0.524988 three In [44]: df2[df2["E"].isin(["two", "four"])] Out[44]: A B C D E 2013-01-03 -0.861849 -2.104569 -0.494929 1.071804 two 2013-01-05 -0.424972 0.567020 0.276232 -1.087401 four
设置
设置新列会自动根据索引对齐数据：
In [45]: s1 = pd.Series([1, 2, 3, 4, 5, 6], index=pd.date_range("20130102", periods=6)) In [46]: s1 Out[46]: 2013-01-02 1 2013-01-03 2 2013-01-04 3 2013-01-05 4 2013-01-06 5 2013-01-07 6 Freq: D, dtype: int64 In [47]: df["F"] = s1
按标签设置值：
In [48]: df.at[dates[0], "A"] = 0
按位置设置值：
In [49]: df.iat[0, 1] = 0
通过分配 NumPy 数组进行设置：
In [50]: df.loc[:, "D"] = np.array([5] * len(df))
先前设置操作的结果：
In [51]: df Out[51]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 5.0 NaN 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 2013-01-05 -0.424972 0.567020 0.276232 5.0 4.0 2013-01-06 -0.673690 0.113648 -1.478427 5.0 5.0
使用设置的where操作：
In [52]: df2 = df.copy() In [53]: df2[df2 > 0] = -df2 In [54]: df2 Out[54]: A B C D F 2013-01-01 0.000000 0.000000 -1.509059 -5.0 NaN 2013-01-02 -1.212112 -0.173215 -0.119209 -5.0 -1.0 2013-01-03 -0.861849 -2.104569 -0.494929 -5.0 -2.0 2013-01-04 -0.721555 -0.706771 -1.039575 -5.0 -3.0 2013-01-05 -0.424972 -0.567020 -0.276232 -5.0 -4.0 2013-01-06 -0.673690 -0.113648 -1.478427 -5.0 -5.0
缺失数据
对于 NumPy 数据类型，np.nan表示缺失数据。默认情况下不包括在计算中。请参阅缺失数据部分。
重新索引允许您更改/添加/删除指定轴上的索引。这将返回数据的副本：
In [55]: df1 = df.reindex(index=dates[0:4], columns=list(df.columns) + ["E"]) In [56]: df1.loc[dates[0] : dates[1], "E"] = 1 In [57]: df1 Out[57]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5.0 NaN 1.0 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 NaN 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 NaN
DataFrame.dropna() 删除任何具有缺失数据的行：
In [58]: df1.dropna(how="any") Out[58]: A B C D F E 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0
DataFrame.fillna() 用于填充缺失数据：
In [59]: df1.fillna(value=5) Out[59]: A B C D F E 2013-01-01 0.000000 0.000000 -1.509059 5.0 5.0 1.0 2013-01-02 1.212112 -0.173215 0.119209 5.0 1.0 1.0 2013-01-03 -0.861849 -2.104569 -0.494929 5.0 2.0 5.0 2013-01-04 0.721555 -0.706771 -1.039575 5.0 3.0 5.0
isna() 获取布尔掩码，其中值为nan：
In [60]: pd.isna(df1) Out[60]: A B C D F E 2013-01-01 False False False False True False 2013-01-02 False False False False False False 2013-01-03 False False False False False True 2013-01-04 False False False False False True
操作
请参阅二进制运算基础部分。
统计
通常的操作排除缺失数据。
计算每列的平均值：
In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64
计算每行的平均值：
In [62]: df.mean(axis=1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64
与具有不同索引或列的另一个Series或DataFrame进行操作将使结果与索引或列标签的并集对齐。此外，pandas 会沿指定维度自动广播，并用np.nan填充未对齐的标签。
In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis="index") Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN
用户定义的函数
DataFrame.agg()和DataFrame.transform()应用用户定义的函数，分别减少或广播其结果。
In [66]: df.agg(lambda x: np.mean(x) * 5.6) Out[66]: A -0.025054 B -2.150294 C -3.851445 D 28.000000 F 16.800000 dtype: float64 In [67]: df.transform(lambda x: x * 101.2) Out[67]: A B C D F 2013-01-01 0.000000 0.000000 -152.716721 506.0 NaN 2013-01-02 122.665737 -17.529322 12.063922 506.0 101.2 2013-01-03 -87.219115 -212.982405 -50.086843 506.0 202.4 2013-01-04 73.021382 -71.525239 -105.204988 506.0 303.6 2013-01-05 -43.007200 57.382459 27.954680 506.0 404.8 2013-01-06 -68.177398 11.501219 -149.616767 506.0 506.0
值计数
查看更多内容，请参阅直方图和离散化。
In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 2 2 6 2 1 1 Name: count, dtype: int64
字符串方法
Series配备了一组字符串处理方法，位于str属性中，使得在数组的每个元素上操作变得容易，如下面的代码片段所示。查看更多内容，请参阅矢量化字符串方法。
In [71]: s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"]) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
统计
通常情况下，操作排除缺失数据。
计算每列的平均值：
In [61]: df.mean() Out[61]: A -0.004474 B -0.383981 C -0.687758 D 5.000000 F 3.000000 dtype: float64
计算每行的平均值：
In [62]: df.mean(axis=1) Out[62]: 2013-01-01 0.872735 2013-01-02 1.431621 2013-01-03 0.707731 2013-01-04 1.395042 2013-01-05 1.883656 2013-01-06 1.592306 Freq: D, dtype: float64
与具有不同索引或列的另一个Series或DataFrame进行操作将使结果与索引或列标签的并集对齐。此外，pandas 会沿指定维度自动广播，并用np.nan填充未对齐的标签。
In [63]: s = pd.Series([1, 3, 5, np.nan, 6, 8], index=dates).shift(2) In [64]: s Out[64]: 2013-01-01 NaN 2013-01-02 NaN 2013-01-03 1.0 2013-01-04 3.0 2013-01-05 5.0 2013-01-06 NaN Freq: D, dtype: float64 In [65]: df.sub(s, axis="index") Out[65]: A B C D F 2013-01-01 NaN NaN NaN NaN NaN 2013-01-02 NaN NaN NaN NaN NaN 2013-01-03 -1.861849 -3.104569 -1.494929 4.0 1.0 2013-01-04 -2.278445 -3.706771 -4.039575 2.0 0.0 2013-01-05 -5.424972 -4.432980 -4.723768 0.0 -1.0 2013-01-06 NaN NaN NaN NaN NaN
用户定义的函数
DataFrame.agg()和DataFrame.transform()应用用户定义的函数，分别减少或广播其结果。
In [66]: df.agg(lambda x: np.mean(x) * 5.6) Out[66]: A -0.025054 B -2.150294 C -3.851445 D 28.000000 F 16.800000 dtype: float64 In [67]: df.transform(lambda x: x * 101.2) Out[67]: A B C D F 2013-01-01 0.000000 0.000000 -152.716721 506.0 NaN 2013-01-02 122.665737 -17.529322 12.063922 506.0 101.2 2013-01-03 -87.219115 -212.982405 -50.086843 506.0 202.4 2013-01-04 73.021382 -71.525239 -105.204988 506.0 303.6 2013-01-05 -43.007200 57.382459 27.954680 506.0 404.8 2013-01-06 -68.177398 11.501219 -149.616767 506.0 506.0
值计数
查看更多内容，请参阅直方图和离散化。
In [68]: s = pd.Series(np.random.randint(0, 7, size=10)) In [69]: s Out[69]: 0 4 1 2 2 1 3 2 4 6 5 4 6 4 7 6 8 4 9 4 dtype: int64 In [70]: s.value_counts() Out[70]: 4 5 2 2 6 2 1 1 Name: count, dtype: int64
字符串方法
Series配备了一组字符串处理方法，位于str属性中，使得在数组的每个元素上操作变得容易，如下面的代码片段所示。查看更多内容，请参阅矢量化字符串方法。
In [71]: s = pd.Series(["A", "B", "C", "Aaba", "Baca", np.nan, "CABA", "dog", "cat"]) In [72]: s.str.lower() Out[72]: 0 a 1 b 2 c 3 aaba 4 baca 5 NaN 6 caba 7 dog 8 cat dtype: object
合并
连接
pandas 提供了各种便捷的功能，用于轻松组合不同种类的Series和DataFrame对象，针对索引的各种集合逻辑以及关系代数功能在连接/合并类型操作的情况下。
请参见合并部分。
使用concat()将 pandas 对象沿行连接在一起：
In [73]: df = pd.DataFrame(np.random.randn(10, 4)) In [74]: df Out[74]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495 # break it into pieces In [75]: pieces = [df[:3], df[3:7], df[7:]] In [76]: pd.concat(pieces) Out[76]: 0 1 2 3 0 -0.548702 1.467327 -1.015962 -0.483075 1 1.637550 -1.217659 -0.291519 -1.745505 2 -0.263952 0.991460 -0.919069 0.266046 3 -0.709661 1.669052 1.037882 -1.705775 4 -0.919854 -0.042379 1.247642 -0.009920 5 0.290213 0.495767 0.362949 1.548106 6 -1.131345 -0.089329 0.337863 -0.945867 7 -0.932132 1.956030 0.017587 -0.016692 8 -0.575247 0.254161 -1.143704 0.215897 9 1.193555 -0.077118 -0.408530 -0.862495
注意
向DataFrame添加列相对较快。但是，添加行需要复制，可能会很昂贵。我们建议将预先构建的记录列表传递给DataFrame构造函数，而不是通过迭代附加记录来构建DataFrame。
Pandas 2.2 中文官方教程和指南（七）（4）https://developer.aliyun.com/article/1509750
