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test_io.py
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test_io.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pytest
import numpy as np
import pandas
from collections import OrderedDict
from modin.pandas.utils import to_pandas
from pathlib import Path
import pyarrow as pa
import os
import shutil
import sys
from .utils import df_equals
from modin import __execution_engine__
if os.environ.get("MODIN_BACKEND", "Pandas").lower() == "pandas":
import modin.pandas as pd
else:
import modin.experimental.pandas as pd
# needed to resolve ray-project/ray#3744
pa.__version__ = "0.11.0"
pd.DEFAULT_NPARTITIONS = 4
PY2 = sys.version_info[0] == 2
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_READ_HDF_FILENAME = "test.hdf"
TEST_WRITE_HDF_FILENAME_MODIN = "test_write_modin.hdf"
TEST_WRITE_HDF_FILENAME_PANDAS = "test_write_pandas.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_FWF_FILENAME = "test_fwf.txt"
TEST_GBQ_FILENAME = "test_gbq."
SMALL_ROW_SIZE = 2000
def modin_df_equals_pandas(modin_df, pandas_df):
df1 = to_pandas(modin_df).sort_index()
df2 = pandas_df.sort_index()
if os.environ.get("MODIN_BACKEND", "Pandas").lower() == "pyarrow":
if not df1.dtypes.equals(df2.dtypes):
return df2.astype(df1.dtypes).equals(df1)
return df1.equals(df2)
@pytest.fixture
def make_parquet_file():
"""Pytest fixture factory that makes a parquet file/dir for testing.
Yields:
Function that generates a parquet file/dir
"""
def _make_parquet_file(
row_size=SMALL_ROW_SIZE, force=False, directory=False, partitioned_columns=[]
):
"""Helper function to generate parquet files/directories.
Args:
row_size: Number of rows for the dataframe.
force: Create a new file/directory even if one already exists.
directory: Create a partitioned directory using pyarrow.
partitioned_columns: Create a partitioned directory using pandas.
Will be ignored if directory=True.
"""
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
if os.path.exists(TEST_PARQUET_FILENAME) and not force:
pass
elif directory:
if os.path.exists(TEST_PARQUET_FILENAME):
shutil.rmtree(TEST_PARQUET_FILENAME)
else:
os.mkdir(TEST_PARQUET_FILENAME)
table = pa.Table.from_pandas(df)
pa.parquet.write_to_dataset(table, root_path=TEST_PARQUET_FILENAME)
elif len(partitioned_columns) > 0:
df.to_parquet(TEST_PARQUET_FILENAME, partition_cols=partitioned_columns)
else:
df.to_parquet(TEST_PARQUET_FILENAME)
# Return function that generates csv files
yield _make_parquet_file
# Delete parquet file that was created
if os.path.exists(TEST_PARQUET_FILENAME):
if os.path.isdir(TEST_PARQUET_FILENAME):
shutil.rmtree(TEST_PARQUET_FILENAME)
else:
os.remove(TEST_PARQUET_FILENAME)
def create_test_ray_dataframe():
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],
}
)
return df
def create_test_pandas_dataframe():
df = pandas.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],
}
)
return df
@pytest.fixture
def test_files_eq(path1, path2):
with open(path1, "rb") as file1, open(path2, "rb") as file2:
file1_content = file1.read()
file2_content = file2.read()
if file1_content == file2_content:
return True
else:
return False
def teardown_test_file(test_path):
if os.path.exists(test_path):
os.remove(test_path)
@pytest.fixture
def make_csv_file(delimiter=","):
"""Pytest fixture factory that makes temp csv files for testing.
Yields:
Function that generates csv files
"""
filenames = []
def _make_csv_file(
filename=TEST_CSV_FILENAME,
row_size=SMALL_ROW_SIZE,
force=False,
delimiter=delimiter,
encoding=None,
):
if os.path.exists(filename) and not force:
pass
else:
dates = pandas.date_range("2000", freq="h", periods=row_size)
df = pandas.DataFrame(
{
"col1": np.arange(row_size),
"col2": [str(x.date()) for x in dates],
"col3": np.arange(row_size),
"col4": [str(x.time()) for x in dates],
}
)
df.to_csv(filename, sep=delimiter, encoding=encoding)
filenames.append(filename)
return df
# Return function that generates csv files
yield _make_csv_file
# Delete csv files that were created
for filename in filenames:
if os.path.exists(filename):
os.remove(filename)
def setup_json_file(row_size, force=False):
if os.path.exists(TEST_JSON_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_json(TEST_JSON_FILENAME)
def teardown_json_file():
if os.path.exists(TEST_JSON_FILENAME):
os.remove(TEST_JSON_FILENAME)
def setup_html_file(row_size, force=False):
if os.path.exists(TEST_HTML_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_html(TEST_HTML_FILENAME)
def teardown_html_file():
if os.path.exists(TEST_HTML_FILENAME):
os.remove(TEST_HTML_FILENAME)
def setup_clipboard(row_size, force=False):
df = pandas.DataFrame({"col1": np.arange(row_size), "col2": np.arange(row_size)})
df.to_clipboard()
def setup_excel_file(row_size, force=False):
if os.path.exists(TEST_EXCEL_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_excel(TEST_EXCEL_FILENAME)
def teardown_excel_file():
if os.path.exists(TEST_EXCEL_FILENAME):
os.remove(TEST_EXCEL_FILENAME)
def setup_feather_file(row_size, force=False):
if os.path.exists(TEST_FEATHER_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_feather(TEST_FEATHER_FILENAME)
def teardown_feather_file():
if os.path.exists(TEST_FEATHER_FILENAME):
os.remove(TEST_FEATHER_FILENAME)
def setup_hdf_file(row_size, force=False, format=None):
if os.path.exists(TEST_READ_HDF_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_hdf(TEST_READ_HDF_FILENAME, key="df", format=format)
def teardown_hdf_file():
if os.path.exists(TEST_READ_HDF_FILENAME):
os.remove(TEST_READ_HDF_FILENAME)
def setup_msgpack_file(row_size, force=False):
if os.path.exists(TEST_MSGPACK_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_msgpack(TEST_MSGPACK_FILENAME)
def teardown_msgpack_file():
if os.path.exists(TEST_MSGPACK_FILENAME):
os.remove(TEST_MSGPACK_FILENAME)
def setup_stata_file(row_size, force=False):
if os.path.exists(TEST_STATA_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_stata(TEST_STATA_FILENAME)
def teardown_stata_file():
if os.path.exists(TEST_STATA_FILENAME):
os.remove(TEST_STATA_FILENAME)
def setup_pickle_file(row_size, force=False):
if os.path.exists(TEST_PICKLE_FILENAME) and not force:
pass
else:
df = pandas.DataFrame(
{"col1": np.arange(row_size), "col2": np.arange(row_size)}
)
df.to_pickle(TEST_PICKLE_FILENAME)
def teardown_pickle_file():
if os.path.exists(TEST_PICKLE_FILENAME):
os.remove(TEST_PICKLE_FILENAME)
@pytest.fixture
def make_sql_connection():
"""Sets up sql connections and takes them down after the caller is done.
Yields:
Factory that generates sql connection objects
"""
filenames = []
def _sql_connection(filename, table=""):
# Remove file if exists
if os.path.exists(filename):
os.remove(filename)
filenames.append(filename)
# Create connection and, if needed, table
conn = "sqlite:///{}".format(filename)
if table:
df = pandas.DataFrame(
{
"col1": [0, 1, 2, 3, 4, 5, 6],
"col2": [7, 8, 9, 10, 11, 12, 13],
"col3": [14, 15, 16, 17, 18, 19, 20],
"col4": [21, 22, 23, 24, 25, 26, 27],
"col5": [0, 0, 0, 0, 0, 0, 0],
}
)
df.to_sql(table, conn)
return conn
yield _sql_connection
# Takedown the fixture
for filename in filenames:
if os.path.exists(filename):
os.remove(filename)
def setup_fwf_file():
if os.path.exists(TEST_FWF_FILENAME):
return
fwf_data = """id8141 360.242940 149.910199 11950.7
id1594 444.953632 166.985655 11788.4
id1849 364.136849 183.628767 11806.2
id1230 413.836124 184.375703 11916.8
id1948 502.953953 173.237159 12468.3"""
with open(TEST_FWF_FILENAME, "w") as f:
f.write(fwf_data)
def teardown_fwf_file():
if os.path.exists(TEST_FWF_FILENAME):
os.remove(TEST_FWF_FILENAME)
def test_from_parquet(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME)
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME)
df_equals(modin_df, pandas_df)
def test_from_parquet_with_columns(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
df_equals(modin_df, pandas_df)
def test_from_parquet_partition(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE, directory=True)
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME)
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME)
df_equals(modin_df, pandas_df)
def test_from_parquet_partition_with_columns(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE, directory=True)
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
df_equals(modin_df, pandas_df)
def test_from_parquet_partitioned_columns(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE, partitioned_columns=["col1"])
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME)
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME)
df_equals(modin_df, pandas_df)
def test_from_parquet_partitioned_columns_with_columns(make_parquet_file):
make_parquet_file(SMALL_ROW_SIZE, partitioned_columns=["col1"])
pandas_df = pandas.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
modin_df = pd.read_parquet(TEST_PARQUET_FILENAME, columns=["col1"])
df_equals(modin_df, pandas_df)
def test_from_json():
setup_json_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_json(TEST_JSON_FILENAME)
modin_df = pd.read_json(TEST_JSON_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_json_file()
def test_from_html():
setup_html_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_html(TEST_HTML_FILENAME)[0]
modin_df = pd.read_html(TEST_HTML_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_html_file()
@pytest.mark.skip(reason="No clipboard on Travis")
def test_from_clipboard():
setup_clipboard(SMALL_ROW_SIZE)
pandas_df = pandas.read_clipboard()
modin_df = pd.read_clipboard()
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_excel():
setup_excel_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_excel(TEST_EXCEL_FILENAME)
modin_df = pd.read_excel(TEST_EXCEL_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_excel_file()
def test_from_excel_all_sheets():
setup_excel_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_excel(TEST_EXCEL_FILENAME, sheet_name=None)
modin_df = pd.read_excel(TEST_EXCEL_FILENAME, sheet_name=None)
assert isinstance(pandas_df, OrderedDict)
assert isinstance(modin_df, OrderedDict)
assert pandas_df.keys() == modin_df.keys()
for key in pandas_df.keys():
assert modin_df_equals_pandas(modin_df.get(key), pandas_df.get(key))
teardown_excel_file()
# @pytest.mark.skip(reason="Arrow version mismatch between Pandas and Feather")
def test_from_feather():
setup_feather_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_feather(TEST_FEATHER_FILENAME)
modin_df = pd.read_feather(TEST_FEATHER_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_feather_file()
def test_from_hdf():
setup_hdf_file(SMALL_ROW_SIZE, format=None)
pandas_df = pandas.read_hdf(TEST_READ_HDF_FILENAME, key="df")
modin_df = pd.read_hdf(TEST_READ_HDF_FILENAME, key="df")
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_hdf_file()
def test_from_hdf_format():
setup_hdf_file(SMALL_ROW_SIZE, format="table")
pandas_df = pandas.read_hdf(TEST_READ_HDF_FILENAME, key="df")
modin_df = pd.read_hdf(TEST_READ_HDF_FILENAME, key="df")
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_hdf_file()
def test_from_msgpack():
setup_msgpack_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_msgpack(TEST_MSGPACK_FILENAME)
modin_df = pd.read_msgpack(TEST_MSGPACK_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_msgpack_file()
def test_from_stata():
setup_stata_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_stata(TEST_STATA_FILENAME)
modin_df = pd.read_stata(TEST_STATA_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_stata_file()
def test_from_pickle():
setup_pickle_file(SMALL_ROW_SIZE)
pandas_df = pandas.read_pickle(TEST_PICKLE_FILENAME)
modin_df = pd.read_pickle(TEST_PICKLE_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
teardown_pickle_file()
def test_from_sql(make_sql_connection):
filename = "test_from_sql.db"
table = "test_from_sql"
conn = make_sql_connection(filename, table)
query = "select * from {0}".format(table)
pandas_df = pandas.read_sql(query, conn)
modin_df = pd.read_sql(query, conn)
assert modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_sql(query, conn, index_col="index")
modin_df = pd.read_sql(query, conn, index_col="index")
assert modin_df_equals_pandas(modin_df, pandas_df)
with pytest.warns(UserWarning):
pd.read_sql_query(query, conn)
with pytest.warns(UserWarning):
pd.read_sql_table(table, conn)
@pytest.mark.skip(reason="No SAS write methods in Pandas")
def test_from_sas():
pandas_df = pandas.read_sas(TEST_SAS_FILENAME)
modin_df = pd.read_sas(TEST_SAS_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv(make_csv_file):
make_csv_file()
pandas_df = pandas.read_csv(TEST_CSV_FILENAME)
modin_df = pd.read_csv(TEST_CSV_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
if not PY2:
pandas_df = pandas.read_csv(Path(TEST_CSV_FILENAME))
modin_df = pd.read_csv(Path(TEST_CSV_FILENAME))
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_parse_dates_read_csv():
pandas_df = pandas.read_csv("modin/pandas/test/data/test_time_parsing.csv")
modin_df = pd.read_csv("modin/pandas/test/data/test_time_parsing.csv")
modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=0,
encoding="utf-8",
)
modin_df = pd.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=0,
encoding="utf-8",
)
modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=0,
parse_dates=["timestamp"],
encoding="utf-8",
)
modin_df = pd.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=0,
parse_dates=["timestamp"],
encoding="utf-8",
)
modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=2,
parse_dates=["timestamp"],
encoding="utf-8",
)
modin_df = pd.read_csv(
"modin/pandas/test/data/test_time_parsing.csv",
names=[
"timestamp",
"symbol",
"high",
"low",
"open",
"close",
"spread",
"volume",
],
header=0,
index_col=2,
parse_dates=["timestamp"],
encoding="utf-8",
)
modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_with_args():
pandas_df = pandas.read_csv(
"modin/pandas/test/data/issue_621.csv", header=None, usecols=[0, 7]
)
modin_df = pd.read_csv(
"modin/pandas/test/data/issue_621.csv", header=None, usecols=[0, 7]
)
modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv("modin/pandas/test/data/issue_621.csv", usecols=[0, 7])
modin_df = pd.read_csv("modin/pandas/test/data/issue_621.csv", usecols=[0, 7])
modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv(
"modin/pandas/test/data/issue_621.csv", usecols=[0, 7], names=[0, 7]
)
modin_df = pd.read_csv(
"modin/pandas/test/data/issue_621.csv", usecols=[0, 7], names=[0, 7]
)
modin_df_equals_pandas(modin_df, pandas_df)
def test_from_table(make_csv_file):
make_csv_file(delimiter="\t")
pandas_df = pandas.read_table(TEST_CSV_FILENAME)
modin_df = pd.read_table(TEST_CSV_FILENAME)
assert modin_df_equals_pandas(modin_df, pandas_df)
if not PY2:
pandas_df = pandas.read_table(Path(TEST_CSV_FILENAME))
modin_df = pd.read_table(Path(TEST_CSV_FILENAME))
assert modin_df_equals_pandas(modin_df, pandas_df)
@pytest.mark.skipif(
__execution_engine__.lower() == "python", reason="Using pandas implementation"
)
def test_from_csv_s3(make_csv_file):
dataset_url = "s3://noaa-ghcn-pds/csv/1788.csv"
pandas_df = pandas.read_csv(dataset_url)
# This first load is to trigger all the import deprecation warnings
modin_df = pd.read_csv(dataset_url)
# This will warn if it defaults to pandas behavior, but it shouldn't
with pytest.warns(None) as record:
modin_df = pd.read_csv(dataset_url)
assert not record.list
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_default(make_csv_file):
# We haven't implemented read_csv from https, but if it's implemented, then this needs to change
dataset_url = "https://raw.githubusercontent.com/modin-project/modin/master/modin/pandas/test/data/blah.csv"
pandas_df = pandas.read_csv(dataset_url)
with pytest.warns(UserWarning):
modin_df = pd.read_csv(dataset_url)
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_chunksize(make_csv_file):
make_csv_file()
# Tests __next__ and correctness of reader as an iterator
# Use larger chunksize to read through file quicker
rdf_reader = pd.read_csv(TEST_CSV_FILENAME, chunksize=500)
pd_reader = pandas.read_csv(TEST_CSV_FILENAME, chunksize=500)
for modin_df, pd_df in zip(rdf_reader, pd_reader):
assert modin_df_equals_pandas(modin_df, pd_df)
# Tests that get_chunk works correctly
rdf_reader = pd.read_csv(TEST_CSV_FILENAME, chunksize=1)
pd_reader = pandas.read_csv(TEST_CSV_FILENAME, chunksize=1)
modin_df = rdf_reader.get_chunk(1)
pd_df = pd_reader.get_chunk(1)
assert modin_df_equals_pandas(modin_df, pd_df)
# Tests that read works correctly
rdf_reader = pd.read_csv(TEST_CSV_FILENAME, chunksize=1)
pd_reader = pandas.read_csv(TEST_CSV_FILENAME, chunksize=1)
modin_df = rdf_reader.read()
pd_df = pd_reader.read()
assert modin_df_equals_pandas(modin_df, pd_df)
def test_from_csv_delimiter(make_csv_file):
make_csv_file(delimiter="|")
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, sep="|")
modin_df = pd.read_csv(TEST_CSV_FILENAME, sep="|")
assert modin_df_equals_pandas(modin_df, pandas_df)
modin_df = pd.DataFrame.from_csv(
TEST_CSV_FILENAME, sep="|", parse_dates=False, header="infer", index_col=None
)
pandas_df = pandas.DataFrame.from_csv(
TEST_CSV_FILENAME, sep="|", parse_dates=False, header="infer", index_col=None
)
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_skiprows(make_csv_file):
make_csv_file()
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, skiprows=2)
modin_df = pd.read_csv(TEST_CSV_FILENAME, skiprows=2)
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_encoding(make_csv_file):
make_csv_file(encoding="latin8")
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, encoding="latin8")
modin_df = pd.read_csv(TEST_CSV_FILENAME, encoding="latin8")
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_default_to_pandas_behavior(make_csv_file):
make_csv_file()
with pytest.warns(UserWarning):
# Test nrows
pd.read_csv(TEST_CSV_FILENAME, nrows=10)
if not PY2:
with pytest.warns(UserWarning):
# This tests that we default to pandas on a buffer
from io import StringIO
pd.read_csv(StringIO(open(TEST_CSV_FILENAME, "r").read()))
with pytest.warns(UserWarning):
pd.read_csv(TEST_CSV_FILENAME, skiprows=lambda x: x in [0, 2])
def test_from_csv_index_col(make_csv_file):
make_csv_file()
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, index_col="col1")
modin_df = pd.read_csv(TEST_CSV_FILENAME, index_col="col1")
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_skipfooter(make_csv_file):
make_csv_file()
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, skipfooter=13)
modin_df = pd.read_csv(TEST_CSV_FILENAME, skipfooter=13)
assert modin_df_equals_pandas(modin_df, pandas_df)
def test_from_csv_parse_dates(make_csv_file):
make_csv_file(force=True)
pandas_df = pandas.read_csv(TEST_CSV_FILENAME, parse_dates=[["col2", "col4"]])
modin_df = pd.read_csv(TEST_CSV_FILENAME, parse_dates=[["col2", "col4"]])
assert modin_df_equals_pandas(modin_df, pandas_df)
pandas_df = pandas.read_csv(
TEST_CSV_FILENAME, parse_dates={"time": ["col2", "col4"]}
)
modin_df = pd.read_csv(TEST_CSV_FILENAME, parse_dates={"time": ["col2", "col4"]})
assert modin_df_equals_pandas(modin_df, pandas_df)
@pytest.mark.skip(reason="No clipboard on Travis")
def test_to_clipboard():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
modin_df.to_clipboard()
modin_as_clip = pandas.read_clipboard()
pandas_df.to_clipboard()
pandas_as_clip = pandas.read_clipboard()
assert modin_as_clip.equals(pandas_as_clip)
def test_to_csv():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
TEST_CSV_DF_FILENAME = "test_df.csv"
TEST_CSV_pandas_FILENAME = "test_pandas.csv"
modin_df.to_csv(TEST_CSV_DF_FILENAME)
pandas_df.to_csv(TEST_CSV_pandas_FILENAME)
assert test_files_eq(TEST_CSV_DF_FILENAME, TEST_CSV_pandas_FILENAME)
teardown_test_file(TEST_CSV_pandas_FILENAME)
teardown_test_file(TEST_CSV_DF_FILENAME)
@pytest.mark.skip(reason="Defaulting to Pandas")
def test_to_dense():
modin_df = create_test_ray_dataframe()
with pytest.raises(NotImplementedError):
modin_df.to_dense()
def test_to_dict():
modin_df = create_test_ray_dataframe()
assert modin_df.to_dict() == to_pandas(modin_df).to_dict()
@pytest.mark.xfail(strict=False, reason="Flaky test, defaults to pandas")
def test_to_excel():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
TEST_EXCEL_DF_FILENAME = "test_df.xlsx"
TEST_EXCEL_pandas_FILENAME = "test_pandas.xlsx"
modin_writer = pandas.ExcelWriter(TEST_EXCEL_DF_FILENAME)
pandas_writer = pandas.ExcelWriter(TEST_EXCEL_pandas_FILENAME)
modin_df.to_excel(modin_writer)
pandas_df.to_excel(pandas_writer)
modin_writer.save()
pandas_writer.save()
assert test_files_eq(TEST_EXCEL_DF_FILENAME, TEST_EXCEL_pandas_FILENAME)
teardown_test_file(TEST_EXCEL_DF_FILENAME)
teardown_test_file(TEST_EXCEL_pandas_FILENAME)
def test_to_feather():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
TEST_FEATHER_DF_FILENAME = "test_df.feather"
TEST_FEATHER_pandas_FILENAME = "test_pandas.feather"
modin_df.to_feather(TEST_FEATHER_DF_FILENAME)
pandas_df.to_feather(TEST_FEATHER_pandas_FILENAME)
assert test_files_eq(TEST_FEATHER_DF_FILENAME, TEST_FEATHER_pandas_FILENAME)
teardown_test_file(TEST_FEATHER_pandas_FILENAME)
teardown_test_file(TEST_FEATHER_DF_FILENAME)
def test_to_gbq():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
# Because we default to pandas, we can just test the equality of the two frames.
assert to_pandas(modin_df).equals(pandas_df)
def test_to_html():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
TEST_HTML_DF_FILENAME = "test_df.html"
TEST_HTML_pandas_FILENAME = "test_pandas.html"
modin_df.to_html(TEST_HTML_DF_FILENAME)
pandas_df.to_html(TEST_HTML_pandas_FILENAME)
assert test_files_eq(TEST_HTML_DF_FILENAME, TEST_HTML_pandas_FILENAME)
teardown_test_file(TEST_HTML_pandas_FILENAME)
teardown_test_file(TEST_HTML_DF_FILENAME)
def test_to_json():
modin_df = create_test_ray_dataframe()
pandas_df = create_test_pandas_dataframe()
TEST_JSON_DF_FILENAME = "test_df.json"
TEST_JSON_pandas_FILENAME = "test_pandas.json"
modin_df.to_json(TEST_JSON_DF_FILENAME)
pandas_df.to_json(TEST_JSON_pandas_FILENAME)