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# 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 io
try:
import numpy as np
except ImportError:
np = None
import pyarrow as pa
from pyarrow.tests import util
def _write_table(table, path, **kwargs):
# So we see the ImportError somewhere
import pyarrow.parquet as pq
from pyarrow.pandas_compat import _pandas_api
if _pandas_api.is_data_frame(table):
table = pa.Table.from_pandas(table)
pq.write_table(table, path, **kwargs)
return table
def _read_table(*args, **kwargs):
import pyarrow.parquet as pq
table = pq.read_table(*args, **kwargs)
table.validate(full=True)
return table
def _roundtrip_table(table, read_table_kwargs=None,
write_table_kwargs=None):
read_table_kwargs = read_table_kwargs or {}
write_table_kwargs = write_table_kwargs or {}
writer = pa.BufferOutputStream()
_write_table(table, writer, **write_table_kwargs)
reader = pa.BufferReader(writer.getvalue())
return _read_table(reader, **read_table_kwargs)
def _check_roundtrip(table, expected=None, read_table_kwargs=None,
**write_table_kwargs):
if expected is None:
expected = table
read_table_kwargs = read_table_kwargs or {}
# intentionally check twice
result = _roundtrip_table(table, read_table_kwargs=read_table_kwargs,
write_table_kwargs=write_table_kwargs)
assert result.schema == expected.schema
assert result.equals(expected)
result = _roundtrip_table(result, read_table_kwargs=read_table_kwargs,
write_table_kwargs=write_table_kwargs)
assert result.schema == expected.schema
assert result.equals(expected)
def _roundtrip_pandas_dataframe(df, write_kwargs):
table = pa.Table.from_pandas(df)
result = _roundtrip_table(
table, write_table_kwargs=write_kwargs)
return result.to_pandas()
def _random_integers(size, dtype):
# We do not generate integers outside the int64 range
platform_int_info = np.iinfo('int_')
iinfo = np.iinfo(dtype)
return np.random.randint(max(iinfo.min, platform_int_info.min),
min(iinfo.max, platform_int_info.max),
size=size, dtype=dtype)
def _range_integers(size, dtype):
return pa.array(np.arange(size, dtype=dtype))
def _test_dict(size=10000, seed=0):
np.random.seed(seed)
return {
'uint8': _random_integers(size, np.uint8),
'uint16': _random_integers(size, np.uint16),
'uint32': _random_integers(size, np.uint32),
'uint64': _random_integers(size, np.uint64),
'int8': _random_integers(size, np.int8),
'int16': _random_integers(size, np.int16),
'int32': _random_integers(size, np.int32),
'int64': _random_integers(size, np.int64),
'float32': np.random.randn(size).astype(np.float32),
'float64': np.arange(size, dtype=np.float64),
'bool': np.random.randn(size) > 0,
'strings': [util.rands(10) for i in range(size)],
'all_none': [None] * size,
'all_none_category': [None] * size
}
def _test_dataframe(size=10000, seed=0):
import pandas as pd
df = pd.DataFrame(_test_dict(size, seed))
# TODO(PARQUET-1015)
# df['all_none_category'] = df['all_none_category'].astype('category')
return df
def _test_table(size=10000, seed=0):
return pa.Table.from_pydict(_test_dict(size, seed))
def make_sample_file(table_or_df):
import pyarrow.parquet as pq
if isinstance(table_or_df, pa.Table):
a_table = table_or_df
else:
a_table = pa.Table.from_pandas(table_or_df)
buf = io.BytesIO()
_write_table(a_table, buf, compression='SNAPPY', version='2.6')
buf.seek(0)
return pq.ParquetFile(buf)
def alltypes_sample(size=10000, seed=0, categorical=False):
import pandas as pd
np.random.seed(seed)
arrays = {
'uint8': np.arange(size, dtype=np.uint8),
'uint16': np.arange(size, dtype=np.uint16),
'uint32': np.arange(size, dtype=np.uint32),
'uint64': np.arange(size, dtype=np.uint64),
'int8': np.arange(size, dtype=np.int16),
'int16': np.arange(size, dtype=np.int16),
'int32': np.arange(size, dtype=np.int32),
'int64': np.arange(size, dtype=np.int64),
'float16': np.arange(size, dtype=np.float16),
'float32': np.arange(size, dtype=np.float32),
'float64': np.arange(size, dtype=np.float64),
'bool': np.random.randn(size) > 0,
'datetime_ms': np.arange("2016-01-01T00:00:00.001", size,
dtype='datetime64[ms]'),
'datetime_us': np.arange("2016-01-01T00:00:00.000001", size,
dtype='datetime64[us]'),
'datetime_ns': np.arange("2016-01-01T00:00:00.000000001", size,
dtype='datetime64[ns]'),
'timedelta': np.arange(0, size, dtype="timedelta64[s]"),
'str': pd.Series([str(x) for x in range(size)]),
'empty_str': [''] * size,
'str_with_nulls': [None] + [str(x) for x in range(size - 2)] + [None],
'null': [None] * size,
'null_list': [None] * 2 + [[None] * (x % 4) for x in range(size - 2)],
}
if categorical:
arrays['str_category'] = arrays['str'].astype('category')
return pd.DataFrame(arrays)
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