Pandas TimeGrouper issue - Typeerror on "time" index

Question

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".

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'

Edit: 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

Would you mind sharing the output of `df.info()` and `df.head()`? — Stefan, Apr 01 '16 at 14:28
`Index: 150 entries` suggests your `index` columns needs to be converted to `datetime` using `pd.to_datetime()` first. `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')` before setting to index. — Stefan, Apr 01 '16 at 14:59
@Stefan Thanks a lot. The following seems to have done the trick (changed the format string slightly): `df[0] = pd.to_datetime(df[0], format='%m/%d/%y %H:%M:%S')` `df.info()` now returns `DatetimeIndex: 150 entries`. Thanks for pointing that out. — thobru, Apr 01 '16 at 15:33

score 1 · Accepted Answer · answered Apr 01 '16 at 15:35

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

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')

before setting to index.

Pandas TimeGrouper issue - Typeerror on "time" index

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