pandas TimeGrouper问题-“时间"上出现Typeerror指数 [英] Pandas TimeGrouper issue - Typeerror on "time" index

问题描述

我正在尝试从收件箱中提取时间戳，以便生成有关Pandas的一些统计信息.我的代码最多可以抓取1000封电子邮件，并将时间戳存储在列表中.然后，我将该列表传递给pd.DataFrame，这给了我一个带有时间"类型列的数据框.

I am trying to extract the timestamps from my inbox in order to generate some statistics with Pandas. My code grabs up to 1000 emails, and stores the timestamps in a list. I then pass the list to pd.DataFrame, which gives me a dataframe with a column of type "time".

我想使用groupby和TimeGrouper来按工作日，一天中的时间等方式绘制电子邮件数量，因此我将timestamp列设置为索引，但出现TypeError:仅对DatetimeIndex有效， TimedeltaIndex或PeriodIndex，但获得了"Index"的实例".我尝试使用to_datetime，但是会生成另一个TypeError:类型为"time"的对象没有len().据我所知，df [0]已经是一个日期时间对象，那么为什么在尝试使用TimeGrouper时会抛出错误?

I want to use groupby and TimeGrouper in order to plot the number of emails by weekday, time of day, etc., so I set my timestamp column as the index, but I get a TypeError: "Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'". I have tried using to_datetime, but that generates another TypeError: object of type 'time' has no len(). From what I can tell, df[0] is already a datetime object, so why does it throw an error when trying to use TimeGrouper?

import win32com.client
import pandas as pd
import numpy as np

outlook = win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI")
inbox = outlook.GetDefaultFolder(6)                              
messages = inbox.Items
message = messages.GetLast()
timesReceived = [message.SentOn]

for i in range(1000): 
    try:
        message = messages.GetPrevious()
        timesReceived.append(message.SentOn)
    except(AttributeError):
        break 

df = pd.DataFrame(timesReceived);
df.set_index(df[0],inplace=True)
grouped = df.groupby(pd.TimeGrouper('M'))


TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'

添加df.info()和df.head()

Adding df.info() and df.head()

df.info()
<class 'pandas.core.frame.DataFrame'>
Index: 150 entries, 04/01/16 09:37:07 to 02/11/16 17:40:56
Data columns (total 1 columns):
0    150 non-null object
dtypes: object(1)
memory usage: 2.3+ KB

df.head()
    0
0   
04/01/16 09:37:07   04/01/16 09:37:07
04/01/16 04:34:30   04/01/16 04:34:30
04/01/16 03:02:14   04/01/16 03:02:14
04/01/16 02:15:12   04/01/16 02:15:12
04/01/16 00:16:27   04/01/16 00:16:27

推荐答案

Index: 150 entries建议将您的index列首先需要使用pd.to_datetime()转换为datetime.

Index: 150 entries suggests your index column needs to be converted to datetime using pd.to_datetime() first.

df[0]可能类似于datetime，但需要类型转换，请尝试

df[0] may look like datetime but needs type conversion, try

df[0] = pd.to_datetime(df[0], format='%m/%d/%Y %H:%M:%S') 

设置为索引之前.

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