Package: pydeephaven
Deephaven Python ClientDeephaven Python Client is a Python package created by Deephaven Data Labs. It is a client API that allows Python applications to remotely access Deephaven data servers.Source DirectoryFrom the deephaven-core repository root(clone fromhttps://github.com/deephaven/deephaven-core)$cdpy/clientDev environment setup$pip3install-rrequirements-dev.txtBuild$python3setup.pybdist_wheelRun tests$python3-munittestdiscovertestsRun examples$python3-mexamples.demo_table_ops
$python3-mexamples.demo_query
$python3-mexamples.demo_run_script
$python3-mexamples.demo_merge_tables
$python3-mexamples.demo_asof_joinInstallNote the actual name of the.whlfile may be different depending on system details.$pip3installdist/pydeephaven-<x.y.z>-py3-none-any.whlQuick start>>>frompydeephavenimportSession>>>session=Session()# assuming Deephaven Community Edition is running locally with the default configuration>>>table1=session.time_table(period=1000000000).update(formulas=["Col1 = i % 2"])>>>df=table1.to_arrow().to_pandas()>>>print(df)TimestampCol1016296815256900000000116296815257000000001216296815257100000000316296815257200000001416296815257300000000.........149816296815406700000000149916296815406800000001150016296815406900000000150116296815407000000001150216296815407100000000>>>session.close()InitializeTheSessionclass is your connection to Deephaven. This is what allows your Python code to interact with a Deephaven server.from pydeephaven import Session

session = Session()Ticking tableTheSessionclass has many methods that create tables. This example creates a ticking time table and binds it to Deephaven.from pydeephaven import Session

session = Session()

table = session.time_table(period=1000000000).update(formulas=["Col1 = i % 2"])
session.bind_table(name="my_table", table=table)This is the general flow of how the Python client interacts with Deephaven. You create a table (new or existing), execute some operations on it, and then bind it to Deephaven. Binding the table gives it a named reference on the Deephaven server, so that it can be used from the Web API or other Sessions.Execute a query on a tabletable.update()can be used to execute an update on a table. This updates a table with a query string.from pydeephaven import Session

session = Session()

# Create a table with no columns and 3 rows
table = session.empty_table(3)
# Create derived table having a new column MyColumn populated with the row index "i"
table = table.update(["MyColumn = i"])
# Update the Deephaven Web Console with this new table
session.bind_table(name="my_table", table=table)Sort a tabletable.sort()can be used to sort a table. This example sorts a table by one of its columns.from pydeephaven import Session

session = Session()

table = session.empty_table(5)
table = table.update(["SortColumn = 4-i"])

table = table.sort(["SortColumn"])
session.bind_table(name="my_table", table=table)Filter a tabletable.where()can be used to filter a table. This example filters a table using a filter string.from pydeephaven import Session

session = Session()

table = session.empty_table(5)
table = table.update(["Values = i"])

table = table.where(["Values % 2 == 1"])
session.bind_table(name="my_table", table=table)Query objectsQuery objects are a way to create and manage a sequence of Deephaven query operations as a single unit. Query objects have the potential to perform better than the corresponding individual queries, because the query object can be transmitted to the server in one request rather than several, and because the system can perform certain optimizations when it is able to see the whole sequence of queries at once. They are similar in spirit to prepared statements in SQL.The general flow of using a query object is to construct a query with a table, call the table operations (sort, filter, update, etc.) on the query object, and then assign your table to the return value ofquery.exec().Any operation that can be executed on a table can also be executed on a query object. This example shows two operations that compute the same result, with the first one using the table updates and the second one using a query object.from pydeephaven import Session

session = Session()

table = session.empty_table(10)

# executed immediately
table1= table.update(["MyColumn = i"]).sort(["MyColumn"]).where(["MyColumn > 5"]);

# create Query Object (execution is deferred until the "exec" statement)
query_obj = session.query(table).update(["MyColumn = i"]).sort(["MyColumn"]).where(["MyColumn > 5"]);
# Transmit the QueryObject to the server and execute it
table2 = query_obj.exec();

session.bind_table(name="my_table1", table=table1)
session.bind_table(name="my_table2", table=table2)Join two tablestable.join()is one of many operations that can join two tables, as shown below.from pydeephaven import Session

session = Session()

table1 = session.empty_table(5)
table1 = table1.update(["Values1 = i", "Group = i"])
table2 = session.empty_table(5)
table2 = table2.update(["Values2 = i + 10", "Group = i"])

table = table1.join(table2, on=["Group"])
session.bind_table(name="my_table", table=table)Perform aggregations on a tableAggregations can be applied on tables in the Python client. This example creates an aggregation that averages
theCountcolumn of a table, and aggregates it by theGroupcolumn.from pydeephaven import Session, agg

session = Session()

table = session.empty_table(10)
table = table.update(["Count = i", "Group = i % 2"])

my_agg = agg.avg(["Count"])

table = table.agg_by(aggs=[my_agg], by=["Group"])
session.bind_table(name="my_table", table=table)Convert a PyArrow table to a Deephaven tableDeephaven natively supportsPyArrow tables. This example converts between a PyArrow table and a Deephaven table.import pyarrow as pa
from pydeephaven import Session

session = Session()

arr = pa.array([4,5,6], type=pa.int32())
pa_table = pa.Table.from_arrays([arr], names=["Integers"])

table = session.import_table(pa_table)
session.bind_table(name="my_table", table=table)

#Convert the Deephaven table back to a pyarrow table
pa_table = table.to_arrow()Execute a script server sidesession.run_script()can be used to execute code on the Deephaven server. This is useful when operations cannot be done on the client-side, such as creating a dynamic table writer. This example shows how to execute a script server-side and retrieve a table generated from the script.from pydeephaven import Session

session = Session()

script = """
from deephaven import empty_table

table = empty_table(8).update(["Index = i"])
"""

session.run_script(script)

table = session.open_table("table")
print(table.to_arrow())Error handlingTheDHErroris thrown whenever the client package encounters an error. This example shows how to catch aDHError.from pydeephaven import Session, DHError

try:
    session = Session(host="invalid_host")
except DHError as e:
    print("Deephaven error when connecting to session")
    print(e)
except Exception as e:
    print("Unknown error")
    print(e)Related documentationhttps://deephaven.io/https://arrow.apache.org/docs/python/index.htmlAPI Reference[start here]https://deephaven.io/core/client-api/python/