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DataFrames: Groupby¶
This notebook uses the Pandas groupbyaggregate and groupbyapply 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
[1]:
Client

Cluster

Artifical dataset¶
We create an artificial timeseries dataset to help us work with groupby operations
[2]:
import dask
df = dask.datasets.timeseries()
df
[2]:
id  name  x  y  

npartitions=30  
20000101  int64  object  float64  float64 
20000102  ...  ...  ...  ... 
...  ...  ...  ...  ... 
20000130  ...  ...  ...  ... 
20000131  ...  ...  ...  ... 
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 0.002188
Bob 0.001278
Charlie 0.001959
Dan 0.001525
Edith 0.001222
Frank 0.000880
George 0.000345
Hannah 0.001197
Ingrid 0.004614
Jerry 0.000726
Kevin 0.001386
Laura 0.000536
Michael 0.006278
Norbert 0.001555
Oliver 0.000892
Patricia 0.003093
Quinn 0.001393
Ray 0.000811
Sarah 0.003450
Tim 0.001742
Ursula 0.000490
Victor 0.000230
Wendy 0.001067
Xavier 0.001554
Yvonne 0.000148
Zelda 0.001218
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 371 ms, sys: 35 ms, total: 406 ms
Wall time: 308 ms
[6]:
%time _ = df.groupby('name').x.mean().compute()
CPU times: user 734 ms, sys: 33.8 ms, total: 768 ms
Wall time: 545 ms
[7]:
%time df.groupby('name').agg({'x': ['mean', 'std'], 'y': ['mean', 'count']}).compute().head()
CPU times: user 1.37 s, sys: 93.3 ms, total: 1.46 s
Wall time: 1.15 s
[7]:
x  y  

mean  std  mean  count  
name  
Alice  0.002188  0.576594  0.004093  99913 
Bob  0.001278  0.577099  0.001147  99745 
Charlie  0.001959  0.577654  0.001334  99617 
Dan  0.001525  0.577309  0.003029  99732 
Edith  0.001222  0.575809  0.001738  99265 
This is the same as with Pandas. Generally speaking, Dask.dataframe groupbyaggregations are roughly same performance as Pandas groupbyaggregations, just more scalable.
You can read more about Pandas’ common aggregations in the Pandas documentation.
Many groups¶
By default groupbyaggregations (like groupbymean or groupbysum) return the result as a singlepartition 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]:
[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]:
Groupby Apply¶
Groupbyaggregations 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 Groupbyapply method. This should be avoided if a groupbyaggregation works.
In the following example we train a simple ScikitLearn 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 5.99 s, sys: 691 ms, total: 6.69 s
Wall time: 5.49 s
[11]:
name
Alice LinearRegression(copy_X=True, fit_intercept=Tr...
Bob LinearRegression(copy_X=True, fit_intercept=Tr...
Charlie LinearRegression(copy_X=True, fit_intercept=Tr...
Dan LinearRegression(copy_X=True, fit_intercept=Tr...
Edith LinearRegression(copy_X=True, fit_intercept=Tr...
Frank LinearRegression(copy_X=True, fit_intercept=Tr...
George LinearRegression(copy_X=True, fit_intercept=Tr...
Hannah LinearRegression(copy_X=True, fit_intercept=Tr...
Ingrid LinearRegression(copy_X=True, fit_intercept=Tr...
Jerry LinearRegression(copy_X=True, fit_intercept=Tr...
Kevin LinearRegression(copy_X=True, fit_intercept=Tr...
Laura LinearRegression(copy_X=True, fit_intercept=Tr...
Michael LinearRegression(copy_X=True, fit_intercept=Tr...
Norbert LinearRegression(copy_X=True, fit_intercept=Tr...
Oliver LinearRegression(copy_X=True, fit_intercept=Tr...
Patricia LinearRegression(copy_X=True, fit_intercept=Tr...
Quinn LinearRegression(copy_X=True, fit_intercept=Tr...
Ray LinearRegression(copy_X=True, fit_intercept=Tr...
Sarah LinearRegression(copy_X=True, fit_intercept=Tr...
Tim LinearRegression(copy_X=True, fit_intercept=Tr...
Ursula LinearRegression(copy_X=True, fit_intercept=Tr...
Victor LinearRegression(copy_X=True, fit_intercept=Tr...
Wendy LinearRegression(copy_X=True, fit_intercept=Tr...
Xavier LinearRegression(copy_X=True, fit_intercept=Tr...
Yvonne LinearRegression(copy_X=True, fit_intercept=Tr...
Zelda LinearRegression(copy_X=True, fit_intercept=Tr...
dtype: object