Grouping Pandas DataFrame by date

Question

I have a Pandas DataFrame that includes a `date` column. Elements of that column are of type `pandas.tslib.Timestamp`. I'd like to group the dataframe by date, but exclude timestamp information that is more granular that date (ie. grouping by date, where all `Feb 23, 2011` are grouped). I know how to express this in SQL, but am quite new to Pandas. This question does something very similar, but I don't understand the code and it uses `datetime` objects. From the documentation, I don't even understand how to retrieve the date from a Pandas Timestamp object. I could convert to `datetime` object, but that seems very roundabout. As requested, the output of `df.head()`: ``` date show network timed session_id 0 2011-12-03 02:48:52 Monk TV38 670 00003DA9-01D2-E7A9-4177-203BE6A9E2BA 1 2011-12-03 03:00:09 WBZ News TV38 205 00003DA9-01D2-E7A9-4177-203BE6A9E2BA 2 2011-12-03 03:04:04 Dateline NBC NBC 30 00003DA9-01D2-E7A9-4177-203BE6A9E2BA 3 2011-12-03 03:04:35 20/20 ABC 25 00003DA9-01D2-E7A9-417…

Accepted Answer

You can use the `normalize` DatetimeIndex method (which takes it to midnight that day):

```
In [11]: df['date']
Out[11]: 
0   2011-12-03 02:48:52
1   2011-12-03 03:00:09
2   2011-12-03 03:04:04
3   2011-12-03 03:04:35
4   2011-12-03 03:04:56
Name: date, dtype: datetime64[ns]

In [12]: pd.DatetimeIndex(df['date']).normalize()
Out[12]: 
<class 'pandas.tseries.index.DatetimeIndex'>
[2011-12-03 00:00:00, ..., 2011-12-03 00:00:00]
Length: 5, Freq: None, Timezone: None
```

And you can groupby this:

```
g = df.groupby(pd.DatetimeIndex(df['date']).normalize())
```

In 0.15 you'll have access to the dt attribute, so can write this as:

```
g = df.groupby(df['date'].dt.normalize())
```

Grouping Pandas DataFrame by date

Solution

Problem