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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.

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



  • Workers: 1
  • Cores: 4
  • Memory: 2.00 GB

Artifical dataset

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

import dask
df = dask.datasets.timeseries()
Dask DataFrame Structure:
id name x y
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.

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).

Alice       0.003630
Bob         0.000397
Charlie    -0.001581
Dan         0.000776
Edith       0.000334
Frank       0.000784
George      0.001297
Hannah     -0.000115
Ingrid      0.002062
Jerry      -0.000366
Kevin       0.000254
Laura       0.003402
Michael    -0.001583
Norbert     0.001751
Oliver      0.001049
Patricia   -0.001195
Quinn      -0.005934
Ray        -0.001216
Sarah      -0.001349
Tim        -0.000535
Ursula      0.002290
Victor     -0.001332
Wendy       0.000956
Xavier      0.000566
Yvonne      0.000791
Zelda       0.003276
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

%time _ = df.groupby('id').x.mean().compute()
CPU times: user 205 ms, sys: 12.6 ms, total: 217 ms
Wall time: 215 ms
%time _ = df.groupby('name').x.mean().compute()
CPU times: user 473 ms, sys: 9.4 ms, total: 482 ms
Wall time: 432 ms
%time df.groupby('name').agg({'x': ['mean', 'std'], 'y': ['mean', 'count']}).compute().head()
CPU times: user 446 ms, sys: 3.68 ms, total: 449 ms
Wall time: 437 ms
x y
mean std mean count
Alice 0.003630 0.577881 -0.001514 100200
Bob 0.000397 0.576624 -0.000977 99383
Charlie -0.001581 0.577539 0.000510 99682
Dan 0.000776 0.576871 0.000579 99972
Edith 0.000334 0.577893 -0.002549 99818

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

# 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'})
# 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'})

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.

from sklearn.linear_model import LinearRegression

def train(partition):
    est = LinearRegression()[['x', 'id']].values, partition.y.values)
    return est
%time df.groupby('name').apply(train, meta=object).compute().sort_index()
CPU times: user 4.03 s, sys: 528 ms, total: 4.55 s
Wall time: 4.47 s
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