此列表的生成方式来自我创建的 Excel 文档，其中表 1 包含通道编号，表 2 包含通道介于低值和高值之间的瓦数值。试图保留一个列表，并准确定位与 Excel 文档相关的位置。我能找到的唯一方法是从 for 语句中获取并将其作为行号附加到下面的结果中。这恰好是来自通道数的瓦特。我是 python 新手，刚在这里发帖，如果我搞砸了代码以正确发布，请耐心等待。我记得上次我搞砸了很多事情。
[0, 1225, 1, 160, 1, 160, 1, 160, 1, 160, 1, 350, 1, 350, 1, 350, 1, 350, 2, 870, 2, 870, 3, 582, 3, 582, 3, 582, 3, 582, 3, 582, 4, 161, 4, 161, 4, 161, 4, 161, 4, 161, 5, 1225, 6, 870, 6, 870, 7, 1225, 8, 582, 8, 582, 8, 582, 8, 582, 9, 160, 9, 160, 9, 160, 10, 161, 10, 161, 10, 161, 10, 161, 10, 161, 11, 1225, 12, 870, 12, 870, 13, 582, 13, 582, 13, 582, 13, 582, 13, 582, 14, 160, 14, 160, 14, 160, 15, 161, 15, 161, 15, 161, 15, 161, 15, 161, 16, 1225, 17, 1225, 18, 1225, 18, 1225, 19, 160, 19, 160, 19, 160, 19, 350, 19, 350, 19, 350, 19, 350, 20, 160, 20, 870, 20, 870, 21, 582, 21, 582, 21, 582, 21, 582, 21, 582, 22, 161, 22, 161, 22, 161, 22, 161, 22, 161, 24, 870, 24, 1225, 25, 870, 25, 1225, 26, 582, 26, 582, 26, 582, 26, 582, 27, 160, 27, 160, 27, 160, 28, 161, 28, 161, 28, 161, 28, 161, 28, 161, 30, 870, 30, 870, 31, 582, 31, 582, 31, 582, 31, 582, 31, 582, 32, 160, 32, 160, 32, 160, 33, 161, 33, 161, 33, 161, 33, 161, 33, 161, 34, 1225, 34, 1225, 37, 900, 37, 900, 38, 900, 38, 900, 39, 820, 41, 820, 41, 900, 42, 900, 42, 900, 44, 820, 44, 900, 47, 900, 47, 900]
您会注意到这只是一个列表。如果你有办法让我用调查结果创建一本字典，我很想听听。我使用 Pandas 进行比较，得到了上面的列表。我的下一步正如您所注意到的，在 3 位数字之前是一个反映 Excel 文件中同一行的数字。如您所见，1 有 160 和 350 个值。我一直在想出一个循环来迭代列表，也就是说，如果值为 1，则 TotalWatts += 160 += 350 等等。等等。在每一行都会附加另一个列表，即该行的总瓦数。我不确定 pandas 是否是最佳选择，或者是否还有其他更好的选择。第一行的答案是totalLineWatts.append(totalWatts)，它将给我一个列表，我将用它来计算excel中该行的安培数，总数为2040。我可以看到我也将做另一个在总瓦特之前附加数字，因为缺少一些行，原因是该行在 Excel 文档中没有通道。因此，如果有人可以向我展示一个如何完成这一步的示例，我将非常感激。
import pandas as pd
data_list = [0, 1225, 1, 160, 1, 160, 1, 160, 1, 160, 1, 350, 1, 350, 1, 350, 1, 350, 2, 870, 2, 870, 3, 582, 3, 582, 3, 582, 3, 582, 3, 582, 4, 161, 4, 161, 4, 161, 4, 161, 4, 161, 5, 1225, 6, 870, 6, 870, 7, 1225, 8, 582, 8, 582, 8, 582, 8, 582, 9, 160, 9, 160, 9, 160, 10, 161, 10, 161, 10, 161, 10, 161, 10, 161, 11, 1225, 12, 870, 12, 870, 13, 582, 13, 582, 13, 582, 13, 582, 13, 582, 14, 160, 14, 160, 14, 160, 15, 161, 15, 161, 15, 161, 15, 161, 15, 161, 16, 1225, 17, 1225, 18, 1225, 18, 1225, 19, 160, 19, 160, 19, 160, 19, 350, 19, 350, 19, 350, 19, 350, 20, 160, 20, 870, 20, 870, 21, 582, 21, 582, 21, 582, 21, 582, 21, 582, 22, 161, 22, 161, 22, 161, 22, 161, 22, 161, 24, 870, 24, 1225, 25, 870, 25, 1225, 26, 582, 26, 582, 26, 582, 26, 582, 27, 160, 27, 160, 27, 160, 28, 161, 28, 161, 28, 161, 28, 161, 28, 161, 30, 870, 30, 870, 31, 582, 31, 582, 31, 582, 31, 582, 31, 582, 32, 160, 32, 160, 32, 160, 33, 161, 33, 161, 33, 161, 33, 161, 33, 161, 34, 1225, 34, 1225, 37, 900, 37, 900, 38, 900, 38, 900, 39, 820, 41, 820, 41, 900, 42, 900, 42, 900, 44, 820, 44, 900, 47, 900, 47, 900]
df = pd.DataFrame([data_list[i:i+2] for i in range(0, len(data_list), 2)], columns=['chan', 'watt'])
# print(df)
df_chan = df.groupby("chan").sum()
print(df_chan)
输出
watt
chan
0 1225
1 2040
2 1740
3 2910
... ...
42 1800
44 1720
47 1800