GeezerVison is showing github.com — datafusion-python/examples/sql-using-python-udf.py at main · apache/datafusion-python

Expand file tree

sql-using-python-udf.py

Copy path

More file actions

Latest commit

History

64 lines (55 loc) · 1.78 KB

sql-using-python-udf.py

Copy path

File metadata and controls

64 lines (55 loc) · 1.78 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements. See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership. The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License. You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing,

# software distributed under the License is distributed on an

# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

# KIND, either express or implied. See the License for the

# specific language governing permissions and limitations

# under the License.

import pyarrow as pa

from datafusion import SessionContext, udf

# Define a user-defined function (UDF)

def is_null(array: pa.Array) -> pa.Array:

return array.is_null()

is_null_arr = udf(

is_null,

[pa.int64()],

pa.bool_(),

"stable",

# This will be the name of the UDF in SQL

# If not specified it will by default the same as Python function name

name="is_null",

)

# Create a context

ctx = SessionContext()

# Create a datafusion DataFrame from a Python dictionary

ctx.from_pydict({"a": [1, 2, 3], "b": [4, None, 6]}, name="t")

# Dataframe:

# +---+---+

# | a | b |

# +---+---+

# | 1 | 4 |

# | 2 | |

# | 3 | 6 |

# +---+---+

# Register UDF for use in SQL

ctx.register_udf(is_null_arr)

# Query the DataFrame using SQL

result_df = ctx.sql("select a, is_null(b) as b_is_null from t")

# Dataframe:

# +---+-----------+

# | a | b_is_null |

# +---+-----------+

# | 1 | false |

# | 2 | true |

# | 3 | false |

# +---+-----------+

assert result_df.to_pydict()["b_is_null"] == [False, True, False]