mirror of
https://github.com/wassname/ray.git
synced 2026-06-28 12:28:10 +08:00
[DataFrame] Adding read methods and tests (#1712)
* Adding read methods and tests * Referencing internal partition method so constructors are more canonical with Pandas * Fixing to reference from_pandas in utils * Cleaning up unused imports * rerunning tests * fixing flake8 * resolving errors * Added sql and sas test * updating * Temporarily phasing out read_csv code for wrapper while diagnosing, added io tests to travis * Adding travis * restoring distributed read csv * resolving rebases * lint * Sampling out HD test * adding dep * fix pathing * Flagging out tests * resolving read_method issues * fix build issue * move additional dependencies to extras * fixing lint * removing IO dependencies * updated requirements doc
This commit is contained in:
adgirish
committed by
Devin Petersohn
Devin Petersohn
parent
cffda73da1
commit
3c48783a16
@@ -6,12 +6,14 @@ from itertools import chain
|
||||
from io import BytesIO
|
||||
import os
|
||||
import re
|
||||
import warnings
|
||||
|
||||
from pyarrow.parquet import ParquetFile
|
||||
import pandas as pd
|
||||
|
||||
from .dataframe import ray, DataFrame
|
||||
from . import get_npartitions
|
||||
from .utils import from_pandas
|
||||
|
||||
|
||||
# Parquet
|
||||
@@ -189,6 +191,7 @@ def read_csv(filepath,
|
||||
We only support local files for now.
|
||||
kwargs: Keyword arguments in pandas::from_csv
|
||||
"""
|
||||
|
||||
kwargs = dict(
|
||||
sep=sep,
|
||||
delimiter=delimiter,
|
||||
@@ -260,3 +263,212 @@ def read_csv(filepath,
|
||||
df_obj_ids.append(df)
|
||||
|
||||
return DataFrame(row_partitions=df_obj_ids, columns=columns)
|
||||
|
||||
|
||||
def read_json(path_or_buf=None,
|
||||
orient=None,
|
||||
typ='frame',
|
||||
dtype=True,
|
||||
convert_axes=True,
|
||||
convert_dates=True,
|
||||
keep_default_dates=True,
|
||||
numpy=False,
|
||||
precise_float=False,
|
||||
date_unit=None,
|
||||
encoding=None,
|
||||
lines=False,
|
||||
chunksize=None,
|
||||
compression='infer'):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_json(path_or_buf, orient, typ, dtype,
|
||||
convert_axes, convert_dates, keep_default_dates,
|
||||
numpy, precise_float, date_unit, encoding,
|
||||
lines, chunksize, compression)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_html(io,
|
||||
match='.+',
|
||||
flavor=None,
|
||||
header=None,
|
||||
index_col=None,
|
||||
skiprows=None,
|
||||
attrs=None,
|
||||
parse_dates=False,
|
||||
tupleize_cols=None,
|
||||
thousands=',',
|
||||
encoding=None,
|
||||
decimal='.',
|
||||
converters=None,
|
||||
na_values=None,
|
||||
keep_default_na=True):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_html(io, match, flavor, header, index_col,
|
||||
skiprows, attrs, parse_dates, tupleize_cols,
|
||||
thousands, encoding, decimal, converters,
|
||||
na_values, keep_default_na)
|
||||
ray_frame = from_pandas(port_frame[0], get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_clipboard(sep=r'\s+'):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_clipboard(sep)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_excel(io,
|
||||
sheet_name=0,
|
||||
header=0,
|
||||
skiprows=None,
|
||||
skip_footer=0,
|
||||
index_col=None,
|
||||
names=None,
|
||||
usecols=None,
|
||||
parse_dates=False,
|
||||
date_parser=None,
|
||||
na_values=None,
|
||||
thousands=None,
|
||||
convert_float=True,
|
||||
converters=None,
|
||||
dtype=None,
|
||||
true_values=None,
|
||||
false_values=None,
|
||||
engine=None,
|
||||
squeeze=False):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_excel(io, sheet_name, header, skiprows, skip_footer,
|
||||
index_col, names, usecols, parse_dates,
|
||||
date_parser, na_values, thousands,
|
||||
convert_float, converters, dtype, true_values,
|
||||
false_values, engine, squeeze)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_hdf(path_or_buf,
|
||||
key=None,
|
||||
mode='r'):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_hdf(path_or_buf, key, mode)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_feather(path,
|
||||
nthreads=1):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_feather(path)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_msgpack(path_or_buf,
|
||||
encoding='utf-8',
|
||||
iterator=False):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_msgpack(path_or_buf, encoding, iterator)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_stata(filepath_or_buffer,
|
||||
convert_dates=True,
|
||||
convert_categoricals=True,
|
||||
encoding=None,
|
||||
index_col=None,
|
||||
convert_missing=False,
|
||||
preserve_dtypes=True,
|
||||
columns=None,
|
||||
order_categoricals=True,
|
||||
chunksize=None,
|
||||
iterator=False):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_stata(filepath_or_buffer, convert_dates,
|
||||
convert_categoricals, encoding, index_col,
|
||||
convert_missing, preserve_dtypes, columns,
|
||||
order_categoricals, chunksize, iterator)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_sas(filepath_or_buffer,
|
||||
format=None,
|
||||
index=None,
|
||||
encoding=None,
|
||||
chunksize=None,
|
||||
iterator=False):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_sas(filepath_or_buffer, format, index, encoding,
|
||||
chunksize, iterator)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_pickle(path,
|
||||
compression='infer'):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_pickle(path, compression)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
|
||||
def read_sql(sql,
|
||||
con,
|
||||
index_col=None,
|
||||
coerce_float=True,
|
||||
params=None,
|
||||
parse_dates=None,
|
||||
columns=None,
|
||||
chunksize=None):
|
||||
|
||||
warnings.warn("Defaulting to Pandas implementation",
|
||||
PendingDeprecationWarning)
|
||||
|
||||
port_frame = pd.read_sql(sql, con, index_col, coerce_float, params,
|
||||
parse_dates, columns, chunksize)
|
||||
ray_frame = from_pandas(port_frame, get_npartitions())
|
||||
|
||||
return ray_frame
|
||||
|
||||
@@ -5,13 +5,23 @@ from __future__ import print_function
|
||||
import pytest
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from ray.dataframe.utils import to_pandas
|
||||
import ray.dataframe.io as io
|
||||
import os
|
||||
|
||||
from ray.dataframe.utils import to_pandas
|
||||
import sqlite3
|
||||
|
||||
TEST_PARQUET_FILENAME = 'test.parquet'
|
||||
TEST_CSV_FILENAME = 'test.csv'
|
||||
TEST_JSON_FILENAME = 'test.json'
|
||||
TEST_HTML_FILENAME = 'test.html'
|
||||
TEST_EXCEL_FILENAME = 'test.xlsx'
|
||||
TEST_FEATHER_FILENAME = 'test.feather'
|
||||
TEST_HDF_FILENAME = 'test.hdf'
|
||||
TEST_MSGPACK_FILENAME = 'test.msg'
|
||||
TEST_STATA_FILENAME = 'test.dta'
|
||||
TEST_PICKLE_FILENAME = 'test.pkl'
|
||||
TEST_SAS_FILENAME = os.getcwd() + '/data/test1.sas7bdat'
|
||||
TEST_SQL_FILENAME = 'test.db'
|
||||
SMALL_ROW_SIZE = 2000
|
||||
LARGE_ROW_SIZE = 7e6
|
||||
|
||||
@@ -57,6 +67,178 @@ def teardown_csv_file():
|
||||
os.remove(TEST_CSV_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_json_file(row_size, force=False):
|
||||
if os.path.exists(TEST_JSON_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_json(TEST_JSON_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_json_file():
|
||||
if os.path.exists(TEST_JSON_FILENAME):
|
||||
os.remove(TEST_JSON_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_html_file(row_size, force=False):
|
||||
if os.path.exists(TEST_HTML_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_html(TEST_HTML_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_html_file():
|
||||
if os.path.exists(TEST_HTML_FILENAME):
|
||||
os.remove(TEST_HTML_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_clipboard(row_size, force=False):
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_clipboard()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_excel_file(row_size, force=False):
|
||||
if os.path.exists(TEST_EXCEL_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_excel(TEST_EXCEL_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_excel_file():
|
||||
if os.path.exists(TEST_EXCEL_FILENAME):
|
||||
os.remove(TEST_EXCEL_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_feather_file(row_size, force=False):
|
||||
if os.path.exists(TEST_FEATHER_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_feather(TEST_FEATHER_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_feather_file():
|
||||
if os.path.exists(TEST_FEATHER_FILENAME):
|
||||
os.remove(TEST_FEATHER_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_hdf_file(row_size, force=False):
|
||||
if os.path.exists(TEST_HDF_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_hdf(TEST_HDF_FILENAME, 'test')
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_hdf_file():
|
||||
if os.path.exists(TEST_HDF_FILENAME):
|
||||
os.remove(TEST_HDF_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_msgpack_file(row_size, force=False):
|
||||
if os.path.exists(TEST_MSGPACK_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_msgpack(TEST_MSGPACK_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_msgpack_file():
|
||||
if os.path.exists(TEST_MSGPACK_FILENAME):
|
||||
os.remove(TEST_MSGPACK_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_stata_file(row_size, force=False):
|
||||
if os.path.exists(TEST_STATA_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_stata(TEST_STATA_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_stata_file():
|
||||
if os.path.exists(TEST_STATA_FILENAME):
|
||||
os.remove(TEST_STATA_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_pickle_file(row_size, force=False):
|
||||
if os.path.exists(TEST_PICKLE_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({
|
||||
'col1': np.arange(row_size),
|
||||
'col2': np.arange(row_size)
|
||||
})
|
||||
df.to_pickle(TEST_PICKLE_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_pickle_file():
|
||||
if os.path.exists(TEST_PICKLE_FILENAME):
|
||||
os.remove(TEST_PICKLE_FILENAME)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_sql_file(conn, force=False):
|
||||
if os.path.exists(TEST_SQL_FILENAME) and not force:
|
||||
pass
|
||||
else:
|
||||
df = pd.DataFrame({'col1': [0, 1, 2, 3],
|
||||
'col2': [4, 5, 6, 7],
|
||||
'col3': [8, 9, 10, 11],
|
||||
'col4': [12, 13, 14, 15],
|
||||
'col5': [0, 0, 0, 0]})
|
||||
df.to_sql(TEST_SQL_FILENAME.split(".")[0], conn)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def teardown_sql_file():
|
||||
if os.path.exists(TEST_SQL_FILENAME):
|
||||
os.remove(TEST_SQL_FILENAME)
|
||||
|
||||
|
||||
def test_from_parquet_small():
|
||||
|
||||
setup_parquet_file(SMALL_ROW_SIZE)
|
||||
@@ -90,12 +272,120 @@ def test_from_csv():
|
||||
teardown_csv_file()
|
||||
|
||||
|
||||
def test_from_csv_delimiter():
|
||||
setup_csv_file(SMALL_ROW_SIZE, delimiter='|')
|
||||
def test_from_json():
|
||||
setup_json_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_csv(TEST_CSV_FILENAME)
|
||||
ray_df = io.read_csv(TEST_CSV_FILENAME)
|
||||
pd_df = pd.read_json(TEST_JSON_FILENAME)
|
||||
ray_df = io.read_json(TEST_JSON_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_csv_file()
|
||||
teardown_json_file()
|
||||
|
||||
|
||||
def test_from_html():
|
||||
setup_html_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_html(TEST_HTML_FILENAME)[0]
|
||||
ray_df = io.read_html(TEST_HTML_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_html_file()
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="No clipboard on Travis")
|
||||
def test_from_clipboard():
|
||||
setup_clipboard(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_clipboard()
|
||||
ray_df = io.read_clipboard()
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
|
||||
def test_from_excel():
|
||||
setup_excel_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_excel(TEST_EXCEL_FILENAME)
|
||||
ray_df = io.read_excel(TEST_EXCEL_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_excel_file()
|
||||
|
||||
|
||||
def test_from_feather():
|
||||
setup_feather_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_feather(TEST_FEATHER_FILENAME)
|
||||
ray_df = io.read_feather(TEST_FEATHER_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_feather_file()
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="Memory overflow on Travis")
|
||||
def test_from_hdf():
|
||||
setup_hdf_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_hdf(TEST_HDF_FILENAME, key='test')
|
||||
ray_df = io.read_hdf(TEST_HDF_FILENAME, key='test')
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_hdf_file()
|
||||
|
||||
|
||||
def test_from_msgpack():
|
||||
setup_msgpack_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_msgpack(TEST_MSGPACK_FILENAME)
|
||||
ray_df = io.read_msgpack(TEST_MSGPACK_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_msgpack_file()
|
||||
|
||||
|
||||
def test_from_stata():
|
||||
setup_stata_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_stata(TEST_STATA_FILENAME)
|
||||
ray_df = io.read_stata(TEST_STATA_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_stata_file()
|
||||
|
||||
|
||||
def test_from_pickle():
|
||||
setup_pickle_file(SMALL_ROW_SIZE)
|
||||
|
||||
pd_df = pd.read_pickle(TEST_PICKLE_FILENAME)
|
||||
ray_df = io.read_pickle(TEST_PICKLE_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_pickle_file()
|
||||
|
||||
|
||||
def test_from_sql():
|
||||
conn = sqlite3.connect(TEST_SQL_FILENAME)
|
||||
setup_sql_file(conn, True)
|
||||
|
||||
pd_df = pd.read_sql("select * from test", conn)
|
||||
ray_df = io.read_sql("select * from test", conn)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
teardown_sql_file()
|
||||
|
||||
|
||||
@pytest.mark.skip(reason="No SAS write methods in Pandas")
|
||||
def test_from_sas():
|
||||
pd_df = pd.read_sas(TEST_SAS_FILENAME)
|
||||
ray_df = io.read_sas(TEST_SAS_FILENAME)
|
||||
|
||||
assert ray_df_equals_pandas(ray_df, pd_df)
|
||||
|
||||
Reference in New Issue
Block a user