如何按每天的升序和降序对日期和时间进行排序，并为排序后的日期创建指定列？

Date (UTC) X Y 0 2020-01-01 00:00:00 2.4497 30.8736 1 2020-01-01 01:00:00 3.4148 30.1853 2 2020-01-01 02:00:00 2.6045 32.4904 3 2020-01-01 03:00:00 1.7704 32.5102 4 2020-01-01 04:00:00 -7.1172 25.1757 ... ... ... ... 5784 2020-08-22 18:23:59 -15.0077 -11.4135 5785 2020-08-22 19:23:59 -13.9187 -12.5453 5786 2020-08-22 20:23:59 -13.5544 -14.0992 5787 2020-08-22 21:23:59 -10.0891 -17.9784 5788 2020-08-22 22:23:59 -6.8927 -16.5153

Date (UTC) X Y Date_Asc Time_Asc X_Asc \ 0 2020-01-01 00:00:00 2.4497 30.8736 2020-01-01 00:00:00 2.4497 1 2020-01-01 01:00:00 3.4148 30.1853 2020-01-01 01:00:00 3.4148 2 2020-01-01 02:00:00 2.6045 32.4904 2020-01-01 02:00:00 2.6045 3 2020-01-01 03:00:00 1.7704 32.5102 2020-01-01 03:00:00 1.7704 4 2020-01-01 04:00:00 -7.1172 25.1757 2020-01-01 04:00:00 -7.1172 5 2020-01-01 05:00:00 -15.4088 24.4337 2020-01-01 05:00:00 -15.4088 6 2020-01-01 06:00:00 -27.3290 29.6146 2020-01-01 06:00:00 -27.3290 7 2020-01-01 07:00:00 -35.1151 34.9471 2020-01-01 07:00:00 -35.1151 8 2020-01-01 08:00:00 -42.1931 40.1325 2020-01-01 08:00:00 -42.1931 9 2020-01-01 09:00:00 -38.2346 38.4806 2020-01-01 09:00:00 -38.2346 10 2020-01-01 10:00:00 -34.8297 33.7403 2020-01-01 10:00:00 -34.8297 11 2020-01-01 11:00:00 -34.1078 33.0918 2020-01-01 11:00:00 -34.1078 12 2020-01-01 12:00:00 -35.3642 30.9796 2020-01-01 12:00:00 -35.3642 13 2020-01-01 13:00:00 -33.9252 30.8584 2020-01-01 13:00:00 -33.9252 14 2020-01-01 14:00:00 -32.2875 32.0968 2020-01-01 14:00:00 -32.2875 15 2020-01-01 15:00:00 -28.5886 30.5157 2020-01-01 15:00:00 -28.5886 16 2020-01-01 16:00:00 -26.2631 28.5850 2020-01-01 16:00:00 -26.2631 17 2020-01-01 17:00:00 -24.9291 27.7629 2020-01-01 17:00:00 -24.9291 18 2020-01-01 18:00:00 -20.9242 28.1081 2020-01-01 18:00:00 -20.9242 19 2020-01-01 19:00:00 -19.3358 28.4239 2020-01-01 19:00:00 -19.3358 20 2020-01-01 20:00:00 -18.8498 27.5946 2020-01-01 20:00:00 -18.8498 21 2020-01-01 21:00:00 -17.6154 27.5777 2020-01-01 21:00:00 -17.6154 22 2020-01-01 22:00:00 -17.5111 27.6045 2020-01-01 22:00:00 -17.5111 23 2020-01-01 23:00:00 -17.8218 30.6621 2020-01-01 23:00:00 -17.8218 24 2020-01-02 00:00:00 -18.2261 32.9834 2020-01-02 00:00:00 -18.2261 25 2020-01-02 01:00:00 -19.1256 33.9799 2020-01-02 01:00:00 -19.1256 26 2020-01-02 02:00:00 -22.5682 33.0480 2020-01-02 02:00:00 -22.5682 27 2020-01-02 03:00:00 -28.0774 34.2475 2020-01-02 03:00:00 -28.0774 28 2020-01-02 04:00:00 -36.0161 28.1081 2020-01-02 04:00:00 -36.0161 29 2020-01-02 05:00:00 -39.8972 32.2631 2020-01-02 05:00:00 -39.8972 30 2020-01-02 06:00:00 -37.7294 33.6043 2020-01-02 06:00:00 -37.7294 31 2020-01-02 07:00:00 -29.6897 31.3064 2020-01-02 07:00:00 -29.6897 32 2020-01-02 08:00:00 -18.4394 26.5454 2020-01-02 08:00:00 -18.4394 33 2020-01-02 09:00:00 -14.2478 29.2039 2020-01-02 09:00:00 -14.2478 34 2020-01-02 10:00:00 -11.6615 26.4184 2020-01-02 10:00:00 -11.6615 35 2020-01-02 11:00:00 -8.2162 26.8972 2020-01-02 11:00:00 -8.2162 36 2020-01-02 12:00:00 -4.4618 25.9781 2020-01-02 12:00:00 -4.4618 37 2020-01-02 13:00:00 -2.4028 26.8930 2020-01-02 13:00:00 -2.4028 38 2020-01-02 14:00:00 -1.1351 27.6698 2020-01-02 14:00:00 -1.1351 39 2020-01-02 15:00:00 2.3592 27.3872 2020-01-02 15:00:00 2.3592 40 2020-01-02 16:00:00 4.5456 29.5461 2020-01-02 16:00:00 4.5456 41 2020-01-02 17:00:00 6.6295 31.3422 2020-01-02 17:00:00 6.6295 42 2020-01-02 18:00:00 7.6197 32.0937 2020-01-02 18:00:00 7.6197 43 2020-01-02 19:00:00 8.6444 31.9142 2020-01-02 19:00:00 8.6444 44 2020-01-02 20:00:00 8.1178 31.5972 2020-01-02 20:00:00 8.1178 45 2020-01-02 21:00:00 7.0879 29.9666 2020-01-02 21:00:00 7.0879 46 2020-01-02 22:00:00 5.9424 29.3420 2020-01-02 22:00:00 5.9424 47 2020-01-02 23:00:00 4.1502 31.9366 2020-01-02 23:00:00 4.1502 Y_Asc Date_Desc Time_Desc X_Dsc Y_Dsc X_Difference \ 0 30.8736 2020-01-01 23:00:00 -17.8218 30.6621 -20.2715 1 30.1853 2020-01-01 22:00:00 -17.5111 27.6045 -20.9259 2 32.4904 2020-01-01 21:00:00 -17.6154 27.5777 -20.2199 3 32.5102 2020-01-01 20:00:00 -18.8498 27.5946 -20.6202 4 25.1757 2020-01-01 19:00:00 -19.3358 28.4239 -12.2186 5 24.4337 2020-01-01 18:00:00 -20.9242 28.1081 -5.5154 6 29.6146 2020-01-01 17:00:00 -24.9291 27.7629 2.3999 7 34.9471 2020-01-01 16:00:00 -26.2631 28.5850 8.8520 8 40.1325 2020-01-01 15:00:00 -28.5886 30.5157 13.6045 9 38.4806 2020-01-01 14:00:00 -32.2875 32.0968 5.9471 10 33.7403 2020-01-01 13:00:00 -33.9252 30.8584 0.9045 11 33.0918 2020-01-01 12:00:00 -35.3642 30.9796 -1.2564 12 30.9796 2020-01-01 11:00:00 -34.1078 33.0918 1.2564 13 30.8584 2020-01-01 10:00:00 -34.8297 33.7403 -0.9045 14 32.0968 2020-01-01 09:00:00 -38.2346 38.4806 -5.9471 15 30.5157 2020-01-01 08:00:00 -42.1931 40.1325 -13.6045 16 28.5850 2020-01-01 07:00:00 -35.1151 34.9471 -8.8520 17 27.7629 2020-01-01 06:00:00 -27.3290 29.6146 -2.3999 18 28.1081 2020-01-01 05:00:00 -15.4088 24.4337 5.5154 19 28.4239 2020-01-01 04:00:00 -7.1172 25.1757 12.2186 20 27.5946 2020-01-01 03:00:00 1.7704 32.5102 20.6202 21 27.5777 2020-01-01 02:00:00 2.6045 32.4904 20.2199 22 27.6045 2020-01-01 01:00:00 3.4148 30.1853 20.9259 23 30.6621 2020-01-01 00:00:00 2.4497 30.8736 20.2715 24 32.9834 2020-01-02 23:00:00 4.1502 31.9366 22.3763 25 33.9799 2020-01-02 22:00:00 5.9424 29.3420 25.0680 26 33.0480 2020-01-02 21:00:00 7.0879 29.9666 29.6561 27 34.2475 2020-01-02 20:00:00 8.1178 31.5972 36.1952 28 28.1081 2020-01-02 19:00:00 8.6444 31.9142 44.6605 29 32.2631 2020-01-02 18:00:00 7.6197 32.0937 47.5169 30 33.6043 2020-01-02 17:00:00 6.6295 31.3422 44.3589 31 31.3064 2020-01-02 16:00:00 4.5456 29.5461 34.2353 32 26.5454 2020-01-02 15:00:00 2.3592 27.3872 20.7986 33 29.2039 2020-01-02 14:00:00 -1.1351 27.6698 13.1127 34 26.4184 2020-01-02 13:00:00 -2.4028 26.8930 9.2587 35 26.8972 2020-01-02 12:00:00 -4.4618 25.9781 3.7544 36 25.9781 2020-01-02 11:00:00 -8.2162 26.8972 -3.7544 37 26.8930 2020-01-02 10:00:00 -11.6615 26.4184 -9.2587 38 27.6698 2020-01-02 09:00:00 -14.2478 29.2039 -13.1127 39 27.3872 2020-01-02 08:00:00 -18.4394 26.5454 -20.7986 40 29.5461 2020-01-02 07:00:00 -29.6897 31.3064 -34.2353 41 31.3422 2020-01-02 06:00:00 -37.7294 33.6043 -44.3589 42 32.0937 2020-01-02 05:00:00 -39.8972 32.2631 -47.5169 43 31.9142 2020-01-02 04:00:00 -36.0161 28.1081 -44.6605 44 31.5972 2020-01-02 03:00:00 -28.0774 34.2475 -36.1952 45 29.9666 2020-01-02 02:00:00 -22.5682 33.0480 -29.6561 46 29.3420 2020-01-02 01:00:00 -19.1256 33.9799 -25.0680 47 31.9366 2020-01-02 00:00:00 -18.2261 32.9834 -22.3763 Y_Difference 0 -0.2115 1 -2.5808 2 -4.9127 3 -4.9156 4 3.2482 5 3.6744 6 -1.8517 7 -6.3621 8 -9.6168 9 -6.3838 10 -2.8819 11 -2.1122 12 2.1122 13 2.8819 14 6.3838 15 9.6168 16 6.3621 17 1.8517 18 -3.6744 19 -3.2482 20 4.9156 21 4.9127 22 2.5808 23 0.2115 24 -1.0468 25 -4.6379 26 -3.0814 27 -2.6503 28 3.8061 29 -0.1694 30 -2.2621 31 -1.7603 32 0.8418 33 -1.5341 34 0.4746 35 -0.9191 36 0.9191 37 -0.4746 38 1.5341 39 -0.8418 40 1.7603 41 2.2621 42 0.1694 43 -3.8061 44 2.6503 45 3.0814 46 4.6379 47 1.0468

df = pd.read_csv(i, parse_dates=[" Date (UTC)"]) ascending_data = pd.DataFrame() ascending_data = df.sort_values(by=[" Date (UTC)"]) ascending_data['Date_Asc'] = [d.date() for d in ascending_data[' Date (UTC)']] ascending_data['Time_Asc'] = [d.time() for d in ascending_data[' Date (UTC)']] ascending_data['X_Asc'] = df['X'] ascending_data['Y_Asc'] = df['Y'] print(ascending_data) descending_data = pd.DataFrame() descending_data = df.sort_values(by=[" Date (UTC)"], ascending=False) descending_data['Date_Desc'] = [d.date() for d in descending_data[' Date (UTC)']] descending_data['Time_Desc'] = [d.time() for d in descending_data[' Date (UTC)']] descending_data['X_Desc'] = df['X'] descending_data['Y_Desc'] = df['Y'] descending_data = descending_data.reset_index(drop=True) print(descending_data) result = pd.concat([ascending_data, descending_data], axis=1) result['X_Diff'] = result['X_Desc'] - result['X_Asc'] result['Y_Diff'] = result['Y_Desc'] - result['Y_Asc'] print(result)

1条回答

网友

1楼 · 发布于 2024-05-17 00:15:11

您可以从原始日期列创建包含日期和时间的新列：

df['Date'] = df['Date (UTC)'].dt.day
df['Time'] = df['Date (UTC)'].dt.time

但是，我不建议按排序顺序命名列，因为假设有两列：Date Asc和Date Desc-这没有任何意义，因为数据不能同时按升序和降序排序，除非它是一个单值序列，但即使这样，两列也将包含相同的数据。因此，您应该调用Date和Time列，如果需要对整个数据帧进行永久性排序，请使用：

df.sort_values(['Date', 'Time'], ascending=True, inplace=True)

如果需要计算按不同顺序排序的数据坐标之间的差异，可以通过以下方式进行计算：

# the difference between the DESCENDING X values and ASCENDING X values per row is reflected in the X_Difference column (X_Dsc-X_Asc)

df['X_Dsc-X_Asc'] = df.sort_values(['Date', 'Time'], ascending=False).X.sub(df.sort_values(['Date', 'Time'], ascending=True).X)


# the difference between the DESCENDING Y values and ASCENDING Y values per row is reflected in the Y_Difference column (Y_Dsc-Y_Asc)

df['Y_Dsc-Y_Asc'] = df.sort_values(['Date', 'Time'], ascending=False).Y.sub(df.sort_values(['Date', 'Time'], ascending=True).Y)

因此，正如您所看到的，列中数据的顺序无关紧要，因为您可以按任何顺序强制转换数据帧以执行列或行操作，并将结果保存在新列中，但您必须确保新列中数据的顺序与数据帧的顺序相匹配，例如，如果数据帧未排序，但新列已排序，则会出现行不匹配

相关问题更多 >

编程相关推荐

热门问题

热门文章