[GitHub] [incubator-superset] villebro commented on a change in pull request #8733: Replace pandas.DataFrame with PyArrow.Table for nullable int typing

[GitBox <gi...@apache.org>]

villebro commented on a change in pull request #8733: Replace pandas.DataFrame with PyArrow.Table for nullable int typing
URL: https://github.com/apache/incubator-superset/pull/8733#discussion_r359020382
 
 

 ##########
 File path: superset/table.py
 ##########
 @@ -0,0 +1,158 @@
+# 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.
+# pylint: disable=C,R,W
+""" Superset wrapper around pyarrow.Table.
+"""
+import logging
+import re
+
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+
+
+def dedup(l, suffix="__", case_sensitive=True):
+    """De-duplicates a list of string by suffixing a counter
+
+    Always returns the same number of entries as provided, and always returns
+    unique values. Case sensitive comparison by default.
+
+    >>> print(','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'])))
+    foo,bar,bar__1,bar__2,Bar
+    >>> print(
+        ','.join(dedup(['foo', 'bar', 'bar', 'bar', 'Bar'], case_sensitive=False))
+    )
+    foo,bar,bar__1,bar__2,Bar__3
+    """
+    new_l = []
+    seen = {}
+    for s in l:
+        s_fixed_case = s if case_sensitive else s.lower()
+        if s_fixed_case in seen:
+            seen[s_fixed_case] += 1
+            s += suffix + str(seen[s_fixed_case])
+        else:
+            seen[s_fixed_case] = 0
+        new_l.append(s)
+    return new_l
+
+
+class SupersetTable(object):
+    def __init__(self, data, cursor_description, db_engine_spec):
+        data = data or []
+        column_names = []
+
+        if cursor_description:
+            # get deduped list of column names
+            column_names = dedup([col[0] for col in cursor_description])
+
+            # fix cursor descriptor with the deduped names
+            cursor_description = [
+                tuple([column_name, *list(description)[1:]])
+                for column_name, description in zip(column_names, cursor_description)
+            ]
+
+        # put data in a 2D array so we can efficiently access each column;
+        array = np.array(data, dtype="object")
+        if array.size > 0:
+            data = [pa.array(array[:, i]) for i, column in enumerate(column_names)]
+
+        # workaround for bug converting `psycopg2.tz.FixedOffsetTimezone` tzinfo values.
+        # related: https://issues.apache.org/jira/browse/ARROW-5248
+        if data:
+            for i, column in enumerate(column_names):
+                if pa.types.is_temporal(data[i].type):
+                    series = pd.Series(array[:, i])
+                    data[i] = pa.Array.from_pandas(series)
+
+        self.table = pa.Table.from_arrays(data, names=column_names)
+        self._type_dict = {}
+        try:
+            # The driver may not be passing a cursor.description
+            self._type_dict = {
+                col: db_engine_spec.get_datatype(cursor_description[i][1])
+                for i, col in enumerate(column_names)
+                if cursor_description
+            }
+        except Exception as e:
+            logging.exception(e)
+
+    @classmethod
+    def pa_table_to_df(cls, pa_table):
+        return pa_table.to_pandas(integer_object_nulls=True)
+
+    @staticmethod
+    def is_date(db_type_str):
+        return db_type_str in ("DATETIME", "TIMESTAMP")
+
+    @classmethod
+    def is_id(cls, column_name):
+        return column_name.startswith("id") or column_name.endswith("id")
+
+    def data_type(self, col_name: str, pa_dtype: pa.DataType):
+        """Given a pyarrow data type, Returns a generic database type"""
+        set_type = self._type_dict.get(col_name)
+        if set_type:
+            return set_type
+
+        mapped_type = self.convert_pa_dtype(pa_dtype)
+        if mapped_type:
+            return mapped_type
+
+        return None
+
+    @staticmethod
+    def convert_pa_dtype(pa_dtype: pa.DataType):
+        if pa.types.is_boolean(pa_dtype):
+            return "BOOL"
+        if pa.types.is_integer(pa_dtype):
+            return "INT"
+        if pa.types.is_floating(pa_dtype):
+            return "FLOAT"
+        if pa.types.is_string(pa_dtype):
+            return "STRING"
+        if pa.types.is_temporal(pa_dtype):
+            return "DATETIME"
+        return None
+
+    def to_pandas_df(self):
 
 Review comment:
   `-> pd.DataFrame`

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