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https://issues.apache.org/jira/browse/BEAM-9547?focusedWorklogId=507873&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-507873
]
ASF GitHub Bot logged work on BEAM-9547:
----------------------------------------
Author: ASF GitHub Bot
Created on: 05/Nov/20 00:18
Start Date: 05/Nov/20 00:18
Worklog Time Spent: 10m
Work Description: robertwb commented on a change in pull request #13252:
URL: https://github.com/apache/beam/pull/13252#discussion_r517707816
##########
File path: sdks/python/apache_beam/dataframe/pandas_top_level_functions.py
##########
@@ -0,0 +1,148 @@
+#
+# 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.
+
+"""A module providing various functionality from the top-level pandas
namespace.
+"""
+
+import re
+from typing import Mapping
+
+import pandas as pd
+
+from apache_beam.dataframe import expressions
+from apache_beam.dataframe import frame_base
+from apache_beam.dataframe import partitionings
+
+
+def _call_on_first_arg(name):
+ def wrapper(target, *args, **kwargs):
+ if isinstance(target, frame_base.DeferredBase):
+ return getattr(target, name)(*args, **kwargs)
+ else:
+ return getattr(pd, name)(target, *args, **kwargs)
+
+ return staticmethod(wrapper)
+
+
+def _defer_to_pandas(name):
+ def wrapper(*args, **kwargs):
+ res = getattr(pd, name)(*args, **kwargs)
+ if type(res) in frame_base.DeferredBase._pandas_type_map.keys():
+ return DeferredBase.wrap(expressions.ConstantExpression(res, res[0:0]))
+ else:
+ return res
+
+ return staticmethod(wrapper)
+
+
+def _is_top_level_function(o):
+ return (
+ callable(o) and not isinstance(o, type) and hasattr(o, '__name__') and
+ re.match('[a-z].*', o.__name__))
+
+
+class DeferredPandasModule(object):
+ array = _defer_to_pandas('array')
+ bdate_range = _defer_to_pandas('bdate_range')
+
+ @staticmethod
+ @frame_base.args_to_kwargs(pd)
+ @frame_base.populate_defaults(pd)
+ def concat(
+ objs,
+ axis,
+ join,
+ ignore_index,
+ keys,
+ levels,
+ names,
+ verify_integrity,
+ sort,
+ copy):
+
+ if ignore_index:
+ raise NotImplementedError('concat(ignore_index)')
+ if levels:
+ raise NotImplementedError('concat(levels)')
+
+ if isinstance(objs, Mapping):
+ if keys is None:
+ keys = list(objs.keys())
+ objs = [objs[k] for k in keys]
+ else:
+ objs = list(objs)
+ deferred_none = expressions.ConstantExpression(None)
+ exprs = [deferred_none if o is None else o._expr for o in objs]
+
+ if axis in (1, 'columns'):
+ required_partitioning = partitionings.Index()
+ elif verify_integrity:
+ required_partitioning = partitionings.Index()
+ else:
+ required_partitioning = partitionings.Nothing()
+
+ return frame_base.DeferredBase.wrap(
+ expressions.ComputedExpression(
+ 'concat',
+ lambda *objs: pd.concat(
+ objs,
+ axis=axis,
+ join=join,
+ ignore_index=ignore_index,
+ keys=keys,
+ levels=levels,
+ names=names,
+ verify_integrity=verify_integrity), # yapf break
+ exprs,
+ requires_partition_by=required_partitioning,
+ preserves_partition_by=partitionings.Index()))
+
+ date_range = _defer_to_pandas('date_range')
+ describe_option = _defer_to_pandas('describe_option')
+ factorize = _call_on_first_arg('factorize')
+ get_option = _defer_to_pandas('get_option')
+ interval_range = _defer_to_pandas('interval_range')
+ isna = _call_on_first_arg('isna')
+ isnull = _call_on_first_arg('isnull')
+ json_normalize = _defer_to_pandas('json_normalize')
+ melt = _call_on_first_arg('melt')
+ merge = _call_on_first_arg('merge')
+ melt = _call_on_first_arg('melt')
+ merge_ordered = frame_base.wont_implement_method('order-sensitive')
+ notna = _call_on_first_arg('notna')
+ notnull = _call_on_first_arg('notnull')
+ option_context = _defer_to_pandas('option_context')
+ period_range = _defer_to_pandas('period_range')
+ pivot = _call_on_first_arg('pivot')
+ pivot_table = _call_on_first_arg('pivot_table')
+ show_versions = _defer_to_pandas('show_versions')
+ test = frame_base.wont_implement_method('test')
+ timedelta_range = _defer_to_pandas('timedelta_range')
+ to_pickle = frame_base.wont_implement_method('order-sensitive')
+ notna = _call_on_first_arg('notna')
+
+ def __getattr__(self, name):
+ if name.startswith('read_'):
+ return frame_base.wont_implement_method(
+ 'Use p | apache_beam.dataframe.io.%s' % name)
Review comment:
We should probably populate that module with the missing `read_` ones
then. I'll take a pass after this PR.
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Issue Time Tracking
-------------------
Worklog Id: (was: 507873)
Time Spent: 33h (was: 32h 50m)
> Implement all pandas operations (or raise WontImplementError)
> -------------------------------------------------------------
>
> Key: BEAM-9547
> URL: https://issues.apache.org/jira/browse/BEAM-9547
> Project: Beam
> Issue Type: Improvement
> Components: sdk-py-core
> Reporter: Brian Hulette
> Assignee: Robert Bradshaw
> Priority: P2
> Time Spent: 33h
> Remaining Estimate: 0h
>
> We should have an implementation for every DataFrame, Series, and GroupBy
> method. Everything that's not actually implemented should get a default
> implementation that raises WontImplementError
> See https://github.com/apache/beam/pull/10757#discussion_r389132292
> Progress at the individual operation level is tracked in a
> [spreadsheet|https://docs.google.com/spreadsheets/d/1hHAaJ0n0k2tw465ORs5tfdy4Lg0DnGWIQ53cLjAhel0/edit],
> consider requesting edit access if you'd like to help out.
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