# Best Practices¶

It is easy to get started with Dask DataFrame, but using it well does require some experience. This page contains suggestions for best practices, and includes solutions to common problems.

## Use Pandas¶

For data that fits into RAM, Pandas can often be faster and easier to use than Dask DataFrame. While “Big Data” tools can be exciting, they are almost always worse than normal data tools while those remain appropriate.

## Reduce, and then use Pandas¶

Similar to above, even if you have a large dataset there may be a point in your computation where you’ve reduced things to a more manageable level. You may want to switch to Pandas at this point.

df = dd.read_parquet('my-giant-file.parquet')
df = df[df.name == 'Alice']              # Select a subsection
result = df.groupby('id').value.mean()   # Reduce to a smaller size
result = result.compute()                # Convert to Pandas dataframe
result...                                # Continue working with Pandas


## Pandas Performance Tips Apply to Dask DataFrame¶

Usual Pandas performance tips like avoiding apply, using vectorized operations, using categoricals, etc., all apply equally to Dask DataFrame. See Modern Pandas by Tom Augspurger for a good read on this topic.

## Use the Index¶

Dask DataFrame can be optionally sorted along a single index column. Some operations against this column can be very fast. For example, if your dataset is sorted by time, you can quickly select data for a particular day, perform time series joins, etc. You can check if your data is sorted by looking at the df.known_divisions attribute. You can set an index column using the .set_index(column_name) method. This operation is expensive though, so use it sparingly (see below):

df = df.set_index('timestamp')  # set the index to make some operations fast

df.loc['2001-01-05':'2001-01-12']  # this is very fast if you have an index
df.merge(df2, left_index=True, right_index=True)  # this is also very fast


## Avoid Full-Data Shuffling¶

Setting an index is an important but expensive operation (see above). You should do it infrequently and you should persist afterwards (see below).

Some operations like set_index and merge/join are harder to do in a parallel or distributed setting than if they are in-memory on a single machine. In particular, shuffling operations that rearrange data become much more communication intensive. For example, if your data is arranged by customer ID but now you want to arrange it by time, all of your partitions will have to talk to each other to exchange shards of data. This can be an intensive process, particularly on a cluster.

So, definitely set the index but try do so infrequently. After you set the index, you may want to persist your data if you are on a cluster:

df = df.set_index('column_name')  # do this infrequently


Additionally, set_index has a few options that can accelerate it in some situations. For example, if you know that your dataset is sorted or you already know the values by which it is divided, you can provide these to accelerate the set_index operation. For more information, see the set_index docstring.

df2 = df.set_index(d.timestamp, sorted=True)


## Persist Intelligently¶

Note

This section is only relevant to users on distributed systems.

Often DataFrame workloads look like the following:

2. Filter data to a particular subset
3. Shuffle data to set an intelligent index
4. Several complex queries on top of this indexed data

It is often ideal to load, filter, and shuffle data once and keep this result in memory. Afterwards, each of the several complex queries can be based off of this in-memory data rather than have to repeat the full load-filter-shuffle process each time. To do this, use the client.persist method:

df = dd.read_csv('s3://bucket/path/to/*.csv')
df = df[df.balance < 0]
df = client.persist(df)

df = df.set_index('timestamp')
df = client.persist(df)

>>> df.customer_id.nunique().compute()
18452844

>>> df.groupby(df.city).size().compute()
...


Persist is important because Dask DataFrame is lazy by default. It is a way of telling the cluster that it should start executing the computations that you have defined so far, and that it should try to keep those results in memory. You will get back a new DataFrame that is semantically equivalent to your old DataFrame, but now points to running data. Your old DataFrame still points to lazy computations:

# Don't do this
client.persist(df)  # persist doesn't change the input in-place

df = client.persist(df)  # replace your old lazy DataFrame


Your Dask DataFrame is split up into many Pandas DataFrames. We sometimes call these “partitions”, and often the number of partitions is decided for you. For example, it might be the number of CSV files from which you are reading. However, over time, as you reduce or increase the size of your pandas DataFrames by filtering or joining, it may be wise to reconsider how many partitions you need. There is a cost to having too many or having too few.

Partitions should fit comfortably in memory (smaller than a gigabyte) but also not be too many. Every operation on every partition takes the central scheduler a few hundred microseconds to process. If you have a few thousand tasks this is barely noticeable, but it is nice to reduce the number if possible.

A common situation is that you load lots of data into reasonably sized partitions (Dask’s defaults make decent choices), but then you filter down your dataset to only a small fraction of the original. At this point, it is wise to regroup your many small partitions into a few larger ones. You can do this by using the dask.dataframe.DataFrame.repartition method:

df = dd.read_csv('s3://bucket/path/to/*.csv')
df = df[df.name == 'Alice']  # only 1/100th of the data
df = df.repartition(npartitions=df.npartitions // 100)

df = df.persist()  # if on a distributed system


This helps to reduce overhead and increase the effectiveness of vectorized Pandas operations. You should aim for partitions that have around 100MB of data each.

Additionally, reducing partitions is very helpful just before shuffling, which creates n log(n) tasks relative to the number of partitions. DataFrames with less than 100 partitions are much easier to shuffle than DataFrames with tens of thousands.

## Joins¶

Joining two DataFrames can be either very expensive or very cheap depending on the situation. It is cheap in the following cases:

1. Joining a Dask DataFrame with a Pandas DataFrame
2. Joining a Dask DataFrame with another Dask DataFrame of a single partition
3. Joining Dask DataFrames along their indexes

Also, it is expensive in the following case:

1. Joining Dask DataFrames along columns that are not their index

The expensive case requires a shuffle. This is fine, and Dask DataFrame will complete the job well, but it will be more expensive than a typical linear-time operation:

dd.merge(a, pandas_df)  # fast
dd.merge(a, b, left_index=True, right_index=True)  # fast
dd.merge(a, b, left_index=True, right_on='id')  # half-fast, half-slow
dd.merge(a, b, left_on='id', right_on='id')  # slow


## Store Data in Apache Parquet Format¶

HDF5 is a popular choice for Pandas users with high performance needs. We encourage Dask DataFrame users to store and load data using Parquet instead. Apache Parquet is a columnar binary format that is easy to split into multiple files (easier for parallel loading) and is generally much simpler to deal with than HDF5 (from the library’s perspective). It is also a common format used by other big data systems like Apache Spark and Apache Impala, and so it is useful to interchange with other systems:

df.to_parquet('path/to/my-results/')


Dask supports reading parquet files with different engine implementations of the Apache Parquet format for Python:

df1 = dd.read_parquet('path/to/my-results/', engine='fastparquet')

conda install fastparquet pyarrow -c conda-forge