one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels powerful Python data analysis toolkit, Release 1.0.0 cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [100]: from pandas.plotting import parallel_coordinates In [101]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [92]: from pandas.plotting import parallel_coordinates In [93]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [94]: from pandas.plotting import parallel_coordinates In [95]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine

one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together. In [94]: from pandas.plotting import parallel_coordinates In [95]: memory datasets in parallel. Dask can use multiple threads or processes on a single machine, or a cluster of machines to process data in parallel. We’ll import dask.dataframe and notice that the API feels do operations in parallel. We can also connect to a cluster to distribute the work on many machines. In this case we’ll connect to a local “cluster” made up of several processes on this single machine