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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=False, memory_limit='2GB')
client
/usr/share/miniconda3/envs/dask-examples/lib/python3.8/site-packages/distributed/node.py:151: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 32835 instead
  warnings.warn(
[1]:

Client

Cluster

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

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       0.001369
Bob        -0.000963
Charlie    -0.000431
Dan        -0.000321
Edith       0.001725
Frank      -0.001475
George      0.000663
Hannah     -0.002060
Ingrid      0.000095
Jerry      -0.001983
Kevin      -0.001235
Laura       0.001072
Michael     0.001660
Norbert    -0.002399
Oliver      0.001177
Patricia   -0.002669
Quinn      -0.003512
Ray         0.001388
Sarah      -0.000515
Tim         0.001579
Ursula      0.001319
Victor     -0.000022
Wendy       0.003195
Xavier     -0.000561
Yvonne      0.003613
Zelda      -0.001126
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 185 ms, sys: 9.62 ms, total: 194 ms
Wall time: 202 ms
[6]:
%time _ = df.groupby('name').x.mean().compute()
CPU times: user 509 ms, sys: 16.8 ms, total: 526 ms
Wall time: 476 ms
[7]:
%time df.groupby('name').agg({'x': ['mean', 'std'], 'y': ['mean', 'count']}).compute().head()
CPU times: user 400 ms, sys: 20.1 ms, total: 420 ms
Wall time: 417 ms
[7]:
x y
mean std mean count
name
Alice 0.001369 0.577258 -0.002401 99022
Bob -0.000963 0.577068 -0.003548 99741
Charlie -0.000431 0.577216 0.000713 99205
Dan -0.000321 0.576703 0.003713 99727
Edith 0.001725 0.577625 -0.000145 99864

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):
    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()
CPU times: user 3.62 s, sys: 401 ms, total: 4.02 s
Wall time: 3.8 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