python-3.x - 值错误 : index must be monotonic-6ren

python-3.x - 值错误 : index must be monotonic

转载作者：行者123 更新时间：2023-12-03 21:14:47

dfdata.Speed.rolling('60T', closed='right').sum()

我试图在这一列上应用滚动总和，并整理出整个数据，但仍然遇到相同的错误。谁能帮我解决一下？？数据中索引列的第一个日期和时间列，第二个是普通列。这就是为什么它看起来有点重复。

DateTime            DateTime            Speed       distance    IDs totalHours          
2011-01-01 00:19:00 2011-01-01 00:19:00 0.041916    0.000710    19  0.016944
2011-01-01 00:20:00 2011-01-01 00:20:00 0.033719    0.000562    19  0.016667
2011-01-01 00:20:59 2011-01-01 00:20:59 0.153553    0.002517    19  0.016389
2011-01-01 00:21:59 2011-01-01 00:21:59 0.142272    0.002371    19  0.016667
2011-01-01 00:23:00 2011-01-01 00:23:00 0.033166    0.000562    19  0.016944
2011-01-01 00:24:00 2011-01-01 00:24:00 0.037843    0.000631    19  0.016667
2011-01-01 00:26:00 2011-01-01 00:26:00 0.050262    0.001675    19  0.033333
2011-01-01 00:27:00 2011-01-01 00:27:00 0.032249    0.000537    19  0.016667
2011-01-01 00:27:59 2011-01-01 00:27:59 0.180206    0.002953    19  0.016389
2011-01-01 00:29:00 2011-01-01 00:29:00 0.133477    0.002262    19  0.016944
2011-01-01 00:30:00 2011-01-01 00:30:00 0.128053    0.002134    19  0.016667
2011-01-01 00:30:59 2011-01-01 00:30:59 0.041964    0.000688    19  0.016389
2011-01-01 00:32:00 2011-01-01 00:32:00 0.072529    0.001229    19  0.016944
2011-01-01 00:33:00 2011-01-01 00:33:00 0.052437    0.000874    19  0.016667
2011-01-01 00:33:59 2011-01-01 00:33:59 0.033903    0.000556    19  0.016389
2011-01-01 00:35:00 2011-01-01 00:35:00 0.060076    0.001018    19  0.016944
2011-01-01 00:36:00 2011-01-01 00:36:00 0.121709    0.002028    19  0.016667
2011-01-01 00:36:59 2011-01-01 00:36:59 0.090517    0.001483    19  0.016389
2011-01-01 00:37:59 2011-01-01 00:37:59 0.088304    0.001472    19  0.016667
2011-01-01 00:39:00 2011-01-01 00:39:00 0.100654    0.001706    19  0.016944
2011-01-01 00:40:00 2011-01-01 00:40:00 0.034839    0.000581    19  0.016667
2011-01-01 00:40:59 2011-01-01 00:40:59 0.164753    0.002700    19  0.016389
2011-01-01 00:42:00 2011-01-01 00:42:00 0.214163    0.003629    19  0.016944
2011-01-01 00:43:00 2011-01-01 00:43:00 0.283706    0.004728    19  0.016667
2011-01-01 00:45:00 2011-01-01 00:45:00 0.055676    0.001856    19  0.033333
2011-01-01 00:46:00 2011-01-01 00:46:00 0.138059    0.002301    19  0.016667
2011-01-01 00:46:59 2011-01-01 00:46:59 0.339829    0.005569    19  0.016389
2011-01-01 00:48:00 2011-01-01 00:48:00 0.169921    0.002879    19  0.016944
2011-01-01 00:49:00 2011-01-01 00:49:00 0.072382    0.001206    19  0.016667
2011-01-01 00:49:59 2011-01-01 00:49:59 0.029009    0.000475    19  0.016389

这是样本数据。

这是我得到的错误。

--------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-59-3224ac27b0b8> in <module>()
      1 # dfdata.Speed.rolling('60T', closed='right').sum()
----> 2 dfdata.Speed.rolling('60T', closed='right').sum()

~/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py in rolling(self, window, min_periods, freq, center, win_type, on, axis, closed)
   6193                                    min_periods=min_periods, freq=freq,
   6194                                    center=center, win_type=win_type,
-> 6195                                    on=on, axis=axis, closed=closed)
   6196 
   6197         cls.rolling = rolling

~/anaconda3/lib/python3.6/site-packages/pandas/core/window.py in rolling(obj, win_type, **kwds)
   2050         return Window(obj, win_type=win_type, **kwds)
   2051 
-> 2052     return Rolling(obj, **kwds)
   2053 
   2054 

~/anaconda3/lib/python3.6/site-packages/pandas/core/window.py in __init__(self, obj, window, min_periods, freq, center, win_type, axis, on, closed, **kwargs)
     84         self.win_freq = None
     85         self.axis = obj._get_axis_number(axis) if axis is not None else None
---> 86         self.validate()
     87 
     88     @property

~/anaconda3/lib/python3.6/site-packages/pandas/core/window.py in validate(self)
   1085                                          timedelta))):
   1086 
-> 1087             self._validate_monotonic()
   1088             freq = self._validate_freq()
   1089 

~/anaconda3/lib/python3.6/site-packages/pandas/core/window.py in _validate_monotonic(self)
   1117             formatted = self.on or 'index'
   1118             raise ValueError("{0} must be "
-> 1119                              "monotonic".format(formatted))
   1120 
   1121     def _validate_freq(self):

ValueError: index must be monotonic

最佳答案

我刚刚复制了您的示例，创建了一个新的 CSV (data.csv) 文件来尝试。最后我创建了新的 DataFrame (df) 并执行了您的语句，它起作用了。如果您遇到任何问题，请查看并发表评论。

注:请检查您的 pandas版本，我的是 0.23.4 (使用 Python 3.6.5)和 this是文档的链接。

data.csv

Datetime,Speed,distance,IDs,totalHours
2011-01-01 00:19:00,0.041916,0.000710,19,0.016944
2011-01-01 00:20:00,0.033719,0.000562,19,0.016667
2011-01-01 00:20:59,0.153553,0.002517,19,0.016389
2011-01-01 00:21:59,0.142272,0.002371,19,0.016667
2011-01-01 00:23:00,0.033166,0.000562,19,0.016944
2011-01-01 00:24:00,0.037843,0.000631,19,0.016667
2011-01-01 00:26:00,0.050262,0.001675,19,0.033333
2011-01-01 00:27:00,0.032249,0.000537,19,0.016667
2011-01-01 00:27:59,0.180206,0.002953,19,0.016389
2011-01-01 00:29:00,0.133477,0.002262,19,0.016944
2011-01-01 00:30:00,0.128053,0.002134,19,0.016667
2011-01-01 00:30:59,0.041964,0.000688,19,0.016389
2011-01-01 00:32:00,0.072529,0.001229,19,0.016944
2011-01-01 00:33:00,0.052437,0.000874,19,0.016667
2011-01-01 00:33:59,0.033903,0.000556,19,0.016389
2011-01-01 00:35:00,0.060076,0.001018,19,0.016944
2011-01-01 00:36:00,0.121709,0.002028,19,0.016667
2011-01-01 00:36:59,0.090517,0.001483,19,0.016389
2011-01-01 00:37:59,0.088304,0.001472,19,0.016667
2011-01-01 00:39:00,0.100654,0.001706,19,0.016944
2011-01-01 00:40:00,0.034839,0.000581,19,0.016667
2011-01-01 00:40:59,0.164753,0.002700,19,0.016389
2011-01-01 00:42:00,0.214163,0.003629,19,0.016944
2011-01-01 00:43:00,0.283706,0.004728,19,0.016667
2011-01-01 00:45:00,0.055676,0.001856,19,0.033333
2011-01-01 00:46:00,0.138059,0.002301,19,0.016667
2011-01-01 00:46:59,0.339829,0.005569,19,0.016389
2011-01-01 00:48:00,0.169921,0.002879,19,0.016944
2011-01-01 00:49:00,0.072382,0.001206,19,0.016667
2011-01-01 00:49:59,0.029009,0.000475,19,0.016389

Statements executed on Python's interactive terminal

>>> import pandas as pd
>>>
>>> df = pd.read_csv("data.csv")
>>> df
               Datetime     Speed  distance  IDs  totalHours
0   2011-01-01 00:19:00  0.041916  0.000710   19    0.016944
1   2011-01-01 00:20:00  0.033719  0.000562   19    0.016667
2   2011-01-01 00:20:59  0.153553  0.002517   19    0.016389
3   2011-01-01 00:21:59  0.142272  0.002371   19    0.016667
4   2011-01-01 00:23:00  0.033166  0.000562   19    0.016944
5   2011-01-01 00:24:00  0.037843  0.000631   19    0.016667
6   2011-01-01 00:26:00  0.050262  0.001675   19    0.033333
7   2011-01-01 00:27:00  0.032249  0.000537   19    0.016667
8   2011-01-01 00:27:59  0.180206  0.002953   19    0.016389
9   2011-01-01 00:29:00  0.133477  0.002262   19    0.016944
10  2011-01-01 00:30:00  0.128053  0.002134   19    0.016667
11  2011-01-01 00:30:59  0.041964  0.000688   19    0.016389
12  2011-01-01 00:32:00  0.072529  0.001229   19    0.016944
13  2011-01-01 00:33:00  0.052437  0.000874   19    0.016667
14  2011-01-01 00:33:59  0.033903  0.000556   19    0.016389
15  2011-01-01 00:35:00  0.060076  0.001018   19    0.016944
16  2011-01-01 00:36:00  0.121709  0.002028   19    0.016667
17  2011-01-01 00:36:59  0.090517  0.001483   19    0.016389
18  2011-01-01 00:37:59  0.088304  0.001472   19    0.016667
19  2011-01-01 00:39:00  0.100654  0.001706   19    0.016944
20  2011-01-01 00:40:00  0.034839  0.000581   19    0.016667
21  2011-01-01 00:40:59  0.164753  0.002700   19    0.016389
22  2011-01-01 00:42:00  0.214163  0.003629   19    0.016944
23  2011-01-01 00:43:00  0.283706  0.004728   19    0.016667
24  2011-01-01 00:45:00  0.055676  0.001856   19    0.033333
25  2011-01-01 00:46:00  0.138059  0.002301   19    0.016667
26  2011-01-01 00:46:59  0.339829  0.005569   19    0.016389
27  2011-01-01 00:48:00  0.169921  0.002879   19    0.016944
28  2011-01-01 00:49:00  0.072382  0.001206   19    0.016667
29  2011-01-01 00:49:59  0.029009  0.000475   19    0.016389
>>>
>>> df.index = pd.to_datetime(df.Datetime)
>>> df
                                Datetime     Speed  distance  IDs  totalHours
Datetime
2011-01-01 00:19:00  2011-01-01 00:19:00  0.041916  0.000710   19    0.016944
2011-01-01 00:20:00  2011-01-01 00:20:00  0.033719  0.000562   19    0.016667
2011-01-01 00:20:59  2011-01-01 00:20:59  0.153553  0.002517   19    0.016389
2011-01-01 00:21:59  2011-01-01 00:21:59  0.142272  0.002371   19    0.016667
2011-01-01 00:23:00  2011-01-01 00:23:00  0.033166  0.000562   19    0.016944
2011-01-01 00:24:00  2011-01-01 00:24:00  0.037843  0.000631   19    0.016667
2011-01-01 00:26:00  2011-01-01 00:26:00  0.050262  0.001675   19    0.033333
2011-01-01 00:27:00  2011-01-01 00:27:00  0.032249  0.000537   19    0.016667
2011-01-01 00:27:59  2011-01-01 00:27:59  0.180206  0.002953   19    0.016389
2011-01-01 00:29:00  2011-01-01 00:29:00  0.133477  0.002262   19    0.016944
2011-01-01 00:30:00  2011-01-01 00:30:00  0.128053  0.002134   19    0.016667
2011-01-01 00:30:59  2011-01-01 00:30:59  0.041964  0.000688   19    0.016389
2011-01-01 00:32:00  2011-01-01 00:32:00  0.072529  0.001229   19    0.016944
2011-01-01 00:33:00  2011-01-01 00:33:00  0.052437  0.000874   19    0.016667
2011-01-01 00:33:59  2011-01-01 00:33:59  0.033903  0.000556   19    0.016389
2011-01-01 00:35:00  2011-01-01 00:35:00  0.060076  0.001018   19    0.016944
2011-01-01 00:36:00  2011-01-01 00:36:00  0.121709  0.002028   19    0.016667
2011-01-01 00:36:59  2011-01-01 00:36:59  0.090517  0.001483   19    0.016389
2011-01-01 00:37:59  2011-01-01 00:37:59  0.088304  0.001472   19    0.016667
2011-01-01 00:39:00  2011-01-01 00:39:00  0.100654  0.001706   19    0.016944
2011-01-01 00:40:00  2011-01-01 00:40:00  0.034839  0.000581   19    0.016667
2011-01-01 00:40:59  2011-01-01 00:40:59  0.164753  0.002700   19    0.016389
2011-01-01 00:42:00  2011-01-01 00:42:00  0.214163  0.003629   19    0.016944
2011-01-01 00:43:00  2011-01-01 00:43:00  0.283706  0.004728   19    0.016667
2011-01-01 00:45:00  2011-01-01 00:45:00  0.055676  0.001856   19    0.033333
2011-01-01 00:46:00  2011-01-01 00:46:00  0.138059  0.002301   19    0.016667
2011-01-01 00:46:59  2011-01-01 00:46:59  0.339829  0.005569   19    0.016389
2011-01-01 00:48:00  2011-01-01 00:48:00  0.169921  0.002879   19    0.016944
2011-01-01 00:49:00  2011-01-01 00:49:00  0.072382  0.001206   19    0.016667
2011-01-01 00:49:59  2011-01-01 00:49:59  0.029009  0.000475   19    0.016389
>>>
>>>
>>> df.Speed.rolling('60T', closed='right').sum()
Datetime
2011-01-01 00:19:00    0.041916
2011-01-01 00:20:00    0.075635
2011-01-01 00:20:59    0.229188
2011-01-01 00:21:59    0.371460
2011-01-01 00:23:00    0.404626
2011-01-01 00:24:00    0.442469
2011-01-01 00:26:00    0.492731
2011-01-01 00:27:00    0.524980
2011-01-01 00:27:59    0.705186
2011-01-01 00:29:00    0.838663
2011-01-01 00:30:00    0.966716
2011-01-01 00:30:59    1.008680
2011-01-01 00:32:00    1.081209
2011-01-01 00:33:00    1.133646
2011-01-01 00:33:59    1.167549
2011-01-01 00:35:00    1.227625
2011-01-01 00:36:00    1.349334
2011-01-01 00:36:59    1.439851
2011-01-01 00:37:59    1.528155
2011-01-01 00:39:00    1.628809
2011-01-01 00:40:00    1.663648
2011-01-01 00:40:59    1.828401
2011-01-01 00:42:00    2.042564
2011-01-01 00:43:00    2.326270
2011-01-01 00:45:00    2.381946
2011-01-01 00:46:00    2.520005
2011-01-01 00:46:59    2.859834
2011-01-01 00:48:00    3.029755
2011-01-01 00:49:00    3.102137
2011-01-01 00:49:59    3.131146
Name: Speed, dtype: float64
>>>

关于python-3.x - 值错误 : index must be monotonic，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/54139380/

文章推荐： julia - 切片元组时避免类型不稳定

文章推荐： for-loop - 进度条 DOS

文章推荐： coq - 在 bigop 上分配减法

python - Python 中的集群或合并集群以减少组数 (Python)
我正在处理一组标记为 160 个组的 173k 点。我想通过合并最接近的(到 9 或 10 个组)来减少组/集群的数量。我搜索过 sklearn 或类似的库，但没有成功。我猜它只是通过 knn 聚类
python - python 列表的子集基于同一列表的元素组，pythonically
我有一个扁平数字列表，这些数字逻辑上以 3 为一组，其中每个三元组是 (number, __ignored, flag[0 or 1])，例如: [7,56,1, 8,0,0, 2,0,0, 6,1,
python - 激活 Python 虚拟环境并在另一个 Python 脚本中调用 Python 脚本
我正在使用 pipenv 来管理我的包。我想编写一个 python 脚本来调用另一个使用不同虚拟环境(VE)的 python 脚本。如何运行使用 VE1 的 python 脚本 1 并调用另一个 p
python - 在焕然一新的 Python 环境中以编程方式从 Python 内部执行 Python 文件
假设我有一个文件 script.py 位于 path = "foo/bar/script.py"。我正在寻找一种在 Python 中通过函数 execute_script() 从我的主要 Python
python - 从 python 脚本但在 python 脚本之外运行 python 脚本
这听起来像是谜语或笑话，但实际上我还没有找到这个问题的答案。问题到底是什么？我想运行 2 个脚本。在第一个脚本中，我调用另一个脚本，但我希望它们继续并行，而不是在两个单独的线程中。主要是我不希望第
python - 使用不同的 python 从 python 运行 python 脚本
我有一个带有 python 2.5.5 的软件。我想发送一个命令，该命令将在 python 2.7.5 中启动一个脚本，然后继续执行该脚本。我试过用 #!python2.7.5 和http://re
python - 为什么从 Python 命令行调用 Python 时 Python 无法找到并运行我的脚本？
我在 python 命令行(使用 python 2.7)中，并尝试运行 Python 脚本。我的操作系统是 Windows 7。我已将我的目录设置为包含我所有脚本的文件夹，使用: os.chdir("
python - 使用动态版本的 Python 执行嵌入的 Python 代码时出现致命的 Python 错误
剧透:部分解决(见最后)。以下是使用 Python 嵌入的代码示例: #include int main(int argc, char** argv) { Py_SetPythonHome
python - python 中识别 python 数组或列表中最大累积差异的最快方法是什么？
假设我有以下列表，对应于及时的股票价格: prices = [1, 3, 7, 10, 9, 8, 5, 3, 6, 8, 12, 9, 6, 10, 13, 8, 4, 11] 我想确定以下总体上最
python - (Python) 通过单选按钮 python 更新背景
所以我试图在选择某个单选按钮时更改此框架的背景。我的框架位于一个类中，并且单选按钮的功能位于该类之外。 (这样我就可以在所有其他框架上调用它们。) 问题是每当我选择单选按钮时都会出现以下错误: co
python - python 中的字符串与正则表达式比较在 python 中失败
我正在尝试将字符串与 python 中的正则表达式进行比较，如下所示， #!/usr/bin/env python3 import re str1 = "Expecting property name
python - python 如何加载Boost.Python 库？
考虑以下原型(prototype) Boost.Python 模块，该模块从单独的 C++ 头文件中引入类“D”。 /* file: a/b.cpp */ BOOST_PYTHON_MODULE(c)
python - python 检查模块 python 的问题
如何编写一个程序来“识别函数调用的行号？” python 检查模块提供了定位行号的选项，但是， def di(): return inspect.currentframe().f_back.f_l
python - 系统 python 与用户 python
我已经使用 macports 安装了 Python 2.7，并且由于我的 $PATH 变量，这就是我输入 $ python 时得到的变量。然而，virtualenv 默认使用 Python 2.6，除
python - [Python] : Python re. 长字符串行的搜索速度优化
我只想问如何加快 python 上的 re.search 速度。我有一个很长的字符串行，长度为 176861(即带有一些符号的字母数字字符)，我使用此函数测试了该行以进行研究: def getExe
python - 编辑字符串 python 正则表达式 python
list1= [u'%app%%General%%Council%', u'%people%', u'%people%%Regional%%Council%%Mandate%', u'%ppp%%Ge
python - Python 映射中的副作用(Python "do" block )
这个问题在这里已经有了答案: Is it Pythonic to use list comprehensions for just side effects? (7 个答案) 关闭 4 个月前。告
python - 使用其值逻辑组合两个 python 列表 - Python
我想用 Python 将两个列表组合成一个列表，方法如下: a = [1,1,1,2,2,2,3,3,3,3] b= ["Sun", "is", "bright", "June","and" ,"Ju
python - Boost.Python python 链接错误
我正在运行带有最新 Boost 发行版 (1.55.0) 的 Mac OS X 10.8.4 (Darwin 12.4.0)。我正在按照说明 here构建包含在我的发行版中的教程 Boost-Pyth
python - 在 Python 中仅使用内置库制作一个基本的网络抓取工具 - Python
学习 Python，我正在尝试制作一个没有任何第 3 方库的网络抓取工具，这样过程对我来说并没有简化，而且我知道我在做什么。我浏览了一些在线资源，但所有这些都让我对某些事情感到困惑。 html 看起来

行者123

个人简介

我是一名优秀的程序员,十分优秀！

作者热门文章

滴滴打车优惠券免费领取

全站热门文章

首页

博学

6Ren·AI

商城

python-3.x - 值错误 : index must be monotonic