gudvinr - 1 month ago 12x

Python Question

Now I'm kinda confused about grouping stuff using pandas.

I have set of data (over 60k rows) with 3 columns:

`2015/12/18 11:12:49 +0300 d1 b1`

2015/12/18 11:12:50 +0300 d2 b2

2015/12/18 11:13:08 +0300 d1 b3

2015/12/18 11:13:36 +0300 d2 b4

2015/12/18 11:13:43 +0300 d2 b5

2015/12/18 11:14:21 +0300 d2 c0

2015/12/18 11:14:42 +0300 d2 c1

2015/12/18 11:15:13 +0300 d1 c2

2015/12/18 11:15:19 +0300 d3 c3

And I need to get count of rows grouped by time periods (let's say 0-4, 4-8, 8-12 etc. by 4 hours) and weekdays and then get a single set for periods within a week.

I can get sum for every hour in a week (time is the name of 1st column):

`dind = pd.DatetimeIndex(df.time)`

gr = df.groupby([dind.weekday, dind.hour])

gr.size()

But I can't figure out how to group by chunks and then merge resulting

`MultiIndex`

I hope it was clear description of the problem.

Answer

The first part of you question, how to group by 4 hour chunks is easy and is addressed in both options below. `df.index.hour // 4`

The second part was vague as there are several ways to interpret "merge into a single column". I provided you two alternatives.

*Option 1*

```
gpd = df.groupby([df.index.weekday, df.index.hour // 4]).size()
gpd.index = gpd.index.to_series()
gpd
(4, 2) 9
dtype: int64
```

*Option 2*

```
gpd = df.groupby([df.index.weekday, df.index.hour // 4]).size()
gpd.index = ['{}_{}'.format(*i) for i in gpd.index]
gpd
4_2 9
dtype: int64
```

Source (Stackoverflow)

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