Pandas: resample timeseries with groupby

Question

Given the below pandas DataFrame:

In [115]: times = pd.to_datetime(pd.Series(['2014-08-25 21:00:00','2014-08-25 21:04:00',
                                            '2014-08-25 22:07:00','2014-08-25 22:09:00']))
          locations = ['HK', 'LDN', 'LDN', 'LDN']
          event = ['foo', 'bar', 'baz', 'qux']
          df = pd.DataFrame({'Location': locations,
                             'Event': event}, index=times)
          df
Out[115]:
                               Event Location
          2014-08-25 21:00:00  foo   HK
          2014-08-25 21:04:00  bar   LDN
          2014-08-25 22:07:00  baz   LDN
          2014-08-25 22:09:00  qux   LDN

I would like resample the data to aggregate it hourly by count while grouping by location to produce a data frame that looks like this:

Out[115]:
                               HK    LDN
          2014-08-25 21:00:00  1     1
          2014-08-25 22:00:00  0     2

I've tried various combinations of resample() and groupby() but with no luck. How would I go about this?

Answer 1

In my original post, I suggested using pd.TimeGrouper. Nowadays, use pd.Grouper instead of pd.TimeGrouper. The syntax is largely the same, but TimeGrouper is now deprecated in favor of pd.Grouper.

Moreover, while pd.TimeGrouper could only group by DatetimeIndex, pd.Grouper can group by datetime columns which you can specify through the key parameter.

---

You could use a pd.Grouper to group the DatetimeIndex'ed DataFrame by hour:

grouper = df.groupby([pd.Grouper(freq='1H'), 'Location'])

use count to count the number of events in each group:

grouper['Event'].count()
#                      Location
# 2014-08-25 21:00:00  HK          1
#                      LDN         1
# 2014-08-25 22:00:00  LDN         2
# Name: Event, dtype: int64

use unstack to move the Location index level to a column level:

grouper['Event'].count().unstack()
# Out[49]: 
# Location             HK  LDN
# 2014-08-25 21:00:00   1    1
# 2014-08-25 22:00:00 NaN    2

and then use fillna to change the NaNs into zeros.

---

Putting it all together,

grouper = df.groupby([pd.Grouper(freq='1H'), 'Location'])
result = grouper['Event'].count().unstack('Location').fillna(0)

yields

Location             HK  LDN
2014-08-25 21:00:00   1    1
2014-08-25 22:00:00   0    2

Answer 2

Pandas 0.21 answer: TimeGrouper is getting deprecated

There are two options for doing this. They actually can give different results based on your data. The first option groups by Location and within Location groups by hour. The second option groups by Location and hour at the same time.

Option 1: Use groupby + resample

grouped = df.groupby('Location').resample('H')['Event'].count()

Option 2: Group both the location and DatetimeIndex together with groupby(pd.Grouper)

grouped = df.groupby(['Location', pd.Grouper(freq='H')])['Event'].count()

They both will result in the following:

Location                     
HK        2014-08-25 21:00:00    1
LDN       2014-08-25 21:00:00    1
          2014-08-25 22:00:00    2
Name: Event, dtype: int64

And then reshape:

grouped.unstack('Location', fill_value=0)

Will output

Location             HK  LDN
2014-08-25 21:00:00   1    1
2014-08-25 22:00:00   0    2

Answer 3

Multiple Column Group By

untubu is spot on with his answer but I wanted to add in what you could do if you had a third column, say Cost and wanted to aggregate it like above. It was through combining unutbu's answer and this one that I found out how to do this and thought I would share for future users.

Create a DataFrame with Cost column:

In[1]:
import pandas as pd
import numpy as np
times = pd.to_datetime([
    "2014-08-25 21:00:00", "2014-08-25 21:04:00",
    "2014-08-25 22:07:00", "2014-08-25 22:09:00"
])
df = pd.DataFrame({
    "Location": ["HK", "LDN", "LDN", "LDN"],
    "Event":    ["foo", "bar", "baz", "qux"],
    "Cost":     [20, 24, 34, 52]
}, index = times)
df

Out[1]:
                     Location  Event  Cost
2014-08-25 21:00:00        HK    foo    20
2014-08-25 21:04:00       LDN    bar    24
2014-08-25 22:07:00       LDN    baz    34
2014-08-25 22:09:00       LDN    qux    52

Now we group by using the agg function to specify each column's aggregation method, e.g. count, mean, sum, etc.

In[2]:
grp = df.groupby([pd.Grouper(freq = "1H"), "Location"]) \
      .agg({"Event": np.size, "Cost": np.mean})
grp

Out[2]:
                               Event  Cost
                     Location
2014-08-25 21:00:00  HK            1    20
                     LDN           1    24
2014-08-25 22:00:00  LDN           2    43

Then the final unstack with fill NaN with zeros and display as int because it's nice.

In[3]: 
grp.unstack().fillna(0).astype(int)

Out[3]:
                    Event     Cost
Location               HK LDN   HK LDN
2014-08-25 21:00:00     1   1   20  24
2014-08-25 22:00:00     0   2    0  43

Answer 4

If you want to keep all the columns

df = (df.groupby("Location")
      .resample("H", on="date")
      .last()
      .reset_index(drop=True))

Answer 5

This can be done without using resample or Grouper as follows:

df.groupby([df.index.floor("1H"), "Location"]).count()

Answer 6

pd.resample >>> pd.groupby() seems to be the quite faster by many times

df = 

PJMW_MW
Datetime    
2002-04-01 01:00:00     4374.0
2002-04-01 02:00:00     4306.0
2002-04-01 03:00:00     4322.0
2002-04-01 04:00:00     4359.0
2002-04-01 05:00:00     4436.0
...     ...
2017-12-31 19:00:00     8205.0
2017-12-31 20:00:00     8053.0
2017-12-31 21:00:00     8012.0
2017-12-31 22:00:00     7864.0
2017-12-31 23:00:00     7710.0

138066 rows × 1 columns

%timeit df.resample(rule='24H', kind='interval').mean()
3.45 ms ± 41.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

%timeit df.groupby(df.index.strftime('%Y-%m-%d')).mean()
169 ms ± 1.09 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)