Which of the following situations would not be the best choice for a server core installation?

Dask DataFrame¶

A Dask DataFrame is a large parallel DataFrame composed of many smaller pandas DataFrames, split along the index. These pandas DataFrames may live on disk for larger-than-memory computing on a single machine, or on many different machines in a cluster. One Dask DataFrame operation triggers many operations on the constituent pandas DataFrames.

Design¶

Dask DataFrames coordinate many pandas DataFrames/Series arranged along the index. A Dask DataFrame is partitioned row-wise, grouping rows by index value for efficiency. These pandas objects may live on disk or on other machines.

Dask DataFrame copies the pandas DataFrame API¶

Because the dask.DataFrame application programming interface (API) is a subset of the pd.DataFrame API, it should be familiar to pandas users. There are some slight alterations due to the parallel nature of Dask:

Dask DataFrame API

>>> import dask.dataframe as dd
>>> df = dd.read_csv('2014-*.csv')
>>> df.head()
    x  y
0  1  a
1  2  b
2  3  c
3  4  a
4  5  b
5  6  c

>>> df2 = df[df.y == 'a'].x + 1
>>> df2.compute()
0    2
3    5
Name: x, dtype: int64

pandas DataFrame API

>>> import pandas as pd
>>> df = pd.read_csv('2014-1.csv')
>>> df.head()
    x  y
0  1  a
1  2  b
2  3  c
3  4  a
4  5  b
5  6  c

>>> df2 = df[df.y == 'a'].x + 1
>>> df2
0    2
3    5
Name: x, dtype: int64

As with all Dask collections, you trigger computation by calling the .compute() method.

Common Uses and Anti-Uses¶

Dask DataFrame is used in situations where pandas is commonly needed, usually when pandas fails due to data size or computation speed:

• Manipulating large datasets, even when those datasets don’t fit in memory

• Accelerating long computations by using many cores

• Distributed computing on large datasets with standard pandas operations like groupby, join, and time series computations

Dask DataFrame may not be the best choice in the following situations:

• If your dataset fits comfortably into RAM on your laptop, then you may be better off just using pandas. There may be simpler ways to improve performance than through parallelism

• If your dataset doesn’t fit neatly into the pandas tabular model, then you might find more use in dask.bag or dask.array

• If you need functions that are not implemented in Dask DataFrame, then you might want to look at dask.delayed which offers more flexibility

• If you need a proper database with all that databases offer you might prefer something like Postgres

Scope¶

Dask DataFrame covers a well-used portion of the pandas API. The following class of computations works well:

• Trivially parallelizable operations (fast):

  • Element-wise operations: df.x + df.y, df * df

  • Row-wise selections: df[df.x > 0]

  • Loc: df.loc[4.0:10.5]

  • Common aggregations: df.x.max(), df.max()

  • Is in: df[df.x.isin([1, 2, 3])]

  • Date time/string accessors: df.timestamp.month

• Cleverly parallelizable operations (fast):

  • groupby-aggregate (with common aggregations): df.groupby(df.x).y.max(), df.groupby('x').min() (see Aggregate)

  • groupby-apply on index: df.groupby(['idx', 'x']).apply(myfunc), where idx is the index level name

  • value_counts: df.x.value_counts()

  • Drop duplicates: df.x.drop_duplicates()

  • Join on index: dd.merge(df1, df2, left_index=True, right_index=True) or dd.merge(df1, df2, on=['idx', 'x']) where idx is the index name for both df1 and df2

  • Join with pandas DataFrames: dd.merge(df1, df2, on='id')

  • Element-wise operations with different partitions / divisions: df1.x + df2.y

  • Date time resampling: df.resample(...)

  • Rolling averages: df.rolling(...)

  • Pearson’s correlation: df[['col1', 'col2']].corr()

• Operations requiring a shuffle (slow-ish, unless on index, see Shuffling for GroupBy and Join)

  • Set index: df.set_index(df.x)

  • groupby-apply not on index (with anything): df.groupby(df.x).apply(myfunc)

  • Join not on the index: dd.merge(df1, df2, on='name')

However, Dask DataFrame does not implement the entire pandas interface. Users expecting this will be disappointed. Notably, Dask DataFrame has the following limitations:

1. Setting a new index from an unsorted column is expensive

2. Many operations like groupby-apply and join on unsorted columns require setting the index, which as mentioned above, is expensive

3. The pandas API is very large. Dask DataFrame does not attempt to implement many pandas features or any of the more exotic data structures like NDFrames

4. Operations that were slow on pandas, like iterating through row-by-row, remain slow on Dask DataFrame

See the DataFrame API documentation for a more extensive list.

Execution¶

By default, Dask DataFrame uses the multi-threaded scheduler. This exposes some parallelism when pandas or the underlying NumPy operations release the global interpreter lock (GIL). Generally, pandas is more GIL bound than NumPy, so multi-core speed-ups are not as pronounced for Dask DataFrame as they are for Dask Array. This is particularly true for string-heavy Python DataFrames, as Python strings are GIL bound.

There has been recent work on changing the underlying representation of pandas string data types to be backed by PyArrow Buffers, which should release the GIL, however, this work is still considered experimental.

When dealing with text data, you may see speedups by switching to the distributed scheduler either on a cluster or single machine.

When should Server Core be installed?

When installing Windows Server Why is Server Core installation recommended?

What are the benefits of a Server Core installation?

Which of the following role is available in the core Windows Server installation?