A Grouper allows the user to specify a groupby instruction for an object.
This specification will select a column via the key parameter, or if the level parameter is given, a level of the index of the target object.
If level is passed as a keyword to both Grouper and groupby, the values passed to Grouper take precedence.
Currently unused, reserved for future use.
Dictionary of the keyword arguments to pass to Grouper.
Attributes
A TimeGrouper is returned if freq is not None. Otherwise, a Grouper is returned.
Examples
df.groupby(pd.Grouper(key="Animal")) is equivalent to df.groupby('Animal')
>>> df = pd.DataFrame(
... {
... "Animal": ["Falcon", "Parrot", "Falcon", "Falcon", "Parrot"],
... "Speed": [100, 5, 200, 300, 15],
... }
... )
>>> df
Animal Speed
0 Falcon 100
1 Parrot 5
2 Falcon 200
3 Falcon 300
4 Parrot 15
>>> df.groupby(pd.Grouper(key="Animal")).mean()
Speed
Animal
Falcon 200.0
Parrot 10.0
Specify a resample operation on the column âPublish dateâ
>>> df = pd.DataFrame(
... {
... "Publish date": [
... pd.Timestamp("2000-01-02"),
... pd.Timestamp("2000-01-02"),
... pd.Timestamp("2000-01-09"),
... pd.Timestamp("2000-01-16"),
... ],
... "ID": [0, 1, 2, 3],
... "Price": [10, 20, 30, 40],
... }
... )
>>> df
Publish date ID Price
0 2000-01-02 0 10
1 2000-01-02 1 20
2 2000-01-09 2 30
3 2000-01-16 3 40
>>> df.groupby(pd.Grouper(key="Publish date", freq="1W")).mean()
ID Price
Publish date
2000-01-02 0.5 15.0
2000-01-09 2.0 30.0
2000-01-16 3.0 40.0
If you want to adjust the start of the bins based on a fixed timestamp:
>>> start, end = "2000-10-01 23:30:00", "2000-10-02 00:30:00" >>> rng = pd.date_range(start, end, freq="7min") >>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng) >>> ts 2000-10-01 23:30:00 0 2000-10-01 23:37:00 3 2000-10-01 23:44:00 6 2000-10-01 23:51:00 9 2000-10-01 23:58:00 12 2000-10-02 00:05:00 15 2000-10-02 00:12:00 18 2000-10-02 00:19:00 21 2000-10-02 00:26:00 24 Freq: 7min, dtype: int64
>>> ts.groupby(pd.Grouper(freq="17min")).sum() 2000-10-01 23:14:00 0 2000-10-01 23:31:00 9 2000-10-01 23:48:00 21 2000-10-02 00:05:00 54 2000-10-02 00:22:00 24 Freq: 17min, dtype: int64
>>> ts.groupby(pd.Grouper(freq="17min", origin="epoch")).sum() 2000-10-01 23:18:00 0 2000-10-01 23:35:00 18 2000-10-01 23:52:00 27 2000-10-02 00:09:00 39 2000-10-02 00:26:00 24 Freq: 17min, dtype: int64
>>> ts.groupby(pd.Grouper(freq="17min", origin="2000-01-01")).sum() 2000-10-01 23:24:00 3 2000-10-01 23:41:00 15 2000-10-01 23:58:00 45 2000-10-02 00:15:00 45 Freq: 17min, dtype: int64
If you want to adjust the start of the bins with an offset Timedelta, the two following lines are equivalent:
>>> ts.groupby(pd.Grouper(freq="17min", origin="start")).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64
>>> ts.groupby(pd.Grouper(freq="17min", offset="23h30min")).sum() 2000-10-01 23:30:00 9 2000-10-01 23:47:00 21 2000-10-02 00:04:00 54 2000-10-02 00:21:00 24 Freq: 17min, dtype: int64
To replace the use of the deprecated base argument, you can now use offset, in this example it is equivalent to have base=2:
>>> ts.groupby(pd.Grouper(freq="17min", offset="2min")).sum() 2000-10-01 23:16:00 0 2000-10-01 23:33:00 9 2000-10-01 23:50:00 36 2000-10-02 00:07:00 39 2000-10-02 00:24:00 24 Freq: 17min, dtype: int64
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