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DataFrames: Groupby

This notebook uses the Pandas groupby-aggregate and groupby-apply on scalable Dask dataframes. It will discuss both common use and best practices.

Start Dask Client for Dashboard

Starting the Dask Client is optional. It will provide a dashboard which is useful to gain insight on the computation.

The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning.

[1]:
from dask.distributed import Client
client = Client(n_workers=1, threads_per_worker=4, processes=True, memory_limit='2GB')
client
[1]:

Client

Client-e79d2c22-d51f-11ec-a157-000d3aeabb7a

Connection method: Cluster object Cluster type: distributed.LocalCluster
Dashboard: http://127.0.0.1:8787/status

Cluster Info

Artifical dataset

We create an artificial timeseries dataset to help us work with groupby operations

[2]:
import dask
df = dask.datasets.timeseries()
df
[2]:
Dask DataFrame Structure:
id name x y
npartitions=30
2000-01-01 int64 object float64 float64
2000-01-02 ... ... ... ...
... ... ... ... ...
2000-01-30 ... ... ... ...
2000-01-31 ... ... ... ...
Dask Name: make-timeseries, 30 tasks

This dataset is small enough to fit in the cluster’s memory, so we persist it now.

You would skip this step if your dataset becomes too large to fit into memory.

[3]:
df = df.persist()

Groupby Aggregations

Dask dataframes implement a commonly used subset of the Pandas groupby API (see Pandas Groupby Documentation.

We start with groupby aggregations. These are generally fairly efficient, assuming that the number of groups is small (less than a million).

[4]:
df.groupby('name').x.mean().compute()
[4]:
name
Alice      -0.000655
Bob        -0.000906
Charlie     0.000400
Dan        -0.001782
Edith       0.000759
Frank      -0.000923
George     -0.003571
Hannah     -0.001808
Ingrid      0.000726
Jerry       0.001358
Kevin       0.001362
Laura       0.001990
Michael    -0.002004
Norbert     0.001938
Oliver      0.000115
Patricia   -0.000777
Quinn       0.002719
Ray         0.001743
Sarah      -0.003325
Tim         0.000904
Ursula     -0.000798
Victor     -0.002677
Wendy      -0.001223
Xavier     -0.000110
Yvonne     -0.003973
Zelda       0.001645
Name: x, dtype: float64

Performance will depend on the aggregation you do (mean vs std), the key on which you group (name vs id), and the number of total groups

[5]:
%time _ = df.groupby('id').x.mean().compute()
CPU times: user 63.4 ms, sys: 5 ms, total: 68.4 ms
Wall time: 227 ms
[6]:
%time _ = df.groupby('name').x.mean().compute()
CPU times: user 73.3 ms, sys: 3.97 ms, total: 77.2 ms
Wall time: 401 ms
[7]:
%time df.groupby('name').agg({'x': ['mean', 'std'], 'y': ['mean', 'count']}).compute().head()
CPU times: user 67.2 ms, sys: 0 ns, total: 67.2 ms
Wall time: 346 ms
[7]:
x y
mean std mean count
name
Alice -0.000655 0.576521 0.000879 99507
Bob -0.000906 0.576488 0.003358 99855
Charlie 0.000400 0.577034 0.000062 99939
Dan -0.001782 0.576851 -0.002540 99368
Edith 0.000759 0.577506 0.002194 99660

This is the same as with Pandas. Generally speaking, Dask.dataframe groupby-aggregations are roughly same performance as Pandas groupby-aggregations, just more scalable.

You can read more about Pandas’ common aggregations in the Pandas documentation.

Custom Aggregations

Dask dataframe Aggregate is available for custom aggregations (See Dask dataframe Aggregate Documentation)

Many groups

By default groupby-aggregations (like groupby-mean or groupby-sum) return the result as a single-partition Dask dataframe. Their results are usually quite small, so this is usually a good choice.

However, sometimes people want to do groupby aggregations on many groups (millions or more). In these cases the full result may not fit into a single Pandas dataframe output, and you may need to split your output into multiple partitions. You can control this with the split_out= parameter

[8]:
# Computational graph of a single output aggregation (for a small number of groups, like 1000)
df.groupby('name').x.mean().visualize(node_attr={'penwidth': '6'})
[8]:
../_images/dataframes_02-groupby_16_0.png
[9]:
# Computational graph of an aggregation to four outputs (for a larger number of groups, like 1000000)
df.groupby('id').x.mean(split_out=4).visualize(node_attr={'penwidth': '6'})
[9]:
../_images/dataframes_02-groupby_17_0.png

Groupby Apply

Groupby-aggregations are generally quite fast because they can be broken down easily into well known operations. The data doesn’t have to move around too much and we can just pass around small intermediate values across the network.

For some operations however the function to be applied requires all data from a given group (like every record of someone named “Alice”). This will force a great deal of communication and be more expensive, but is still possible with the Groupby-apply method. This should be avoided if a groupby-aggregation works.

In the following example we train a simple Scikit-Learn machine learning model on every person’s name.

[10]:
from sklearn.linear_model import LinearRegression

def train(partition):
    if partition.empty:
        return
    est = LinearRegression()
    est.fit(partition[['x', 'id']].values, partition.y.values)
    return est
[11]:
%time df.groupby('name').apply(train, meta=object).compute().sort_index()
/usr/share/miniconda3/envs/dask-examples/lib/python3.9/site-packages/dask/dataframe/core.py:6254: FutureWarning: Meta is not valid, `map_partitions` expects output to be a pandas object. Try passing a pandas object as meta or a dict or tuple representing the (name, dtype) of the columns. In the future the meta you passed will not work.
  warnings.warn(
CPU times: user 529 ms, sys: 45.5 ms, total: 574 ms
Wall time: 4.08 s
[11]:
name
Alice       LinearRegression()
Bob         LinearRegression()
Charlie     LinearRegression()
Dan         LinearRegression()
Edith       LinearRegression()
Frank       LinearRegression()
George      LinearRegression()
Hannah      LinearRegression()
Ingrid      LinearRegression()
Jerry       LinearRegression()
Kevin       LinearRegression()
Laura       LinearRegression()
Michael     LinearRegression()
Norbert     LinearRegression()
Oliver      LinearRegression()
Patricia    LinearRegression()
Quinn       LinearRegression()
Ray         LinearRegression()
Sarah       LinearRegression()
Tim         LinearRegression()
Ursula      LinearRegression()
Victor      LinearRegression()
Wendy       LinearRegression()
Xavier      LinearRegression()
Yvonne      LinearRegression()
Zelda       LinearRegression()
dtype: object
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