This maybe useful to someone besides me. I had a dataframe in the following format:

``` 0,2013-07-15 17:15:19,1,8872,291840,92 1,2011-07-19 23:26:24,2,18890,760336,0 2,2011-07-26 22:58:35,2,2902,76746,0 3,2011-07-28 22:27:12,2,103222,954442,0 4,2011-07-29 21:26:27,2,107134,885380,0 5,2011-07-31 04:11:38,2,44228,813568,0 6,2011-08-02 23:17:39,2,109242,1157330,0 7,2011-08-03 22:14:08,2,51736,870914,0 8,2011-08-04 21:41:33,2,18390,652704,0

```

And I wanted to sum the third column by day, wee and month. There are multiple reasons why you can just read in this code with a simple

df = pd.read_csv(file)

And go to town. First we need to change the second column (_id) from a string to a python datetime object to run the analysis:

``` import pandas as pd import numpy as np

df = pd.read_csv(file,sep=’,’) df[“_id”] = pd.to_datetime(df[“_id”])

```

OK, now the _id column is a datetime column, but how to we sum the count column by day,week, and/or month? First, we need to change the pandas default index on the dataframe (int64). You can find out what type of index your dataframe is using by using the following command

print(df.index)

To perform this type of operation, we need a pandas.DateTimeIndex and then we can use pandas.resample, but first lets strip modify the _id column because I do not care about the time, just the dates.

df['date_minus_time'] = df["_id"].apply( lambda df : datetime.datetime(year=df.year, month=df.month, day=df.day)) df.set_index(df["date_minus_time"],inplace=True)

Finally, if you want to group by day, week, month respectively:

``` df[‘count’].resample(‘D’, how=’sum’) df[‘count’].resample(‘W’, how=’sum’) df[‘count’].resample(‘M’, how=’sum’)

```