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test_iter.py
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test_iter.py
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# Licensed to Modin Development Team under one or more contributor license agreements.
# See the NOTICE file distributed with this work for additional information regarding
# copyright ownership. The Modin Development Team 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 pytest
import numpy as np
import pandas
import matplotlib
import modin.pandas as pd
import io
from modin.pandas.test.utils import (
random_state,
RAND_LOW,
RAND_HIGH,
df_equals,
test_data_values,
test_data_keys,
create_test_dfs,
test_data,
)
from modin.config import NPartitions
from modin.test.test_utils import warns_that_defaulting_to_pandas
NPartitions.put(4)
# Force matplotlib to not use any Xwindows backend.
matplotlib.use("Agg")
@pytest.mark.parametrize("method", ["items", "iteritems", "iterrows"])
def test_items_iteritems_iterrows(method):
data = test_data["float_nan_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
for modin_item, pandas_item in zip(
getattr(modin_df, method)(), getattr(pandas_df, method)()
):
modin_index, modin_series = modin_item
pandas_index, pandas_series = pandas_item
df_equals(pandas_series, modin_series)
assert pandas_index == modin_index
@pytest.mark.parametrize("name", [None, "NotPandas"])
def test_itertuples_name(name):
data = test_data["float_nan_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
modin_it_custom = modin_df.itertuples(name=name)
pandas_it_custom = pandas_df.itertuples(name=name)
for modin_row, pandas_row in zip(modin_it_custom, pandas_it_custom):
np.testing.assert_equal(modin_row, pandas_row)
def test_itertuples_multiindex():
data = test_data["int_data"]
modin_df, pandas_df = pd.DataFrame(data), pandas.DataFrame(data)
new_idx = pd.MultiIndex.from_tuples(
[(i // 4, i // 2, i) for i in range(len(modin_df.columns))]
)
modin_df.columns = new_idx
pandas_df.columns = new_idx
modin_it_custom = modin_df.itertuples()
pandas_it_custom = pandas_df.itertuples()
for modin_row, pandas_row in zip(modin_it_custom, pandas_it_custom):
np.testing.assert_equal(modin_row, pandas_row)
def test___iter__():
modin_df = pd.DataFrame(test_data_values[0])
pandas_df = pandas.DataFrame(test_data_values[0])
modin_iterator = modin_df.__iter__()
# Check that modin_iterator implements the iterator interface
assert hasattr(modin_iterator, "__iter__")
assert hasattr(modin_iterator, "next") or hasattr(modin_iterator, "__next__")
pd_iterator = pandas_df.__iter__()
assert list(modin_iterator) == list(pd_iterator)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test___contains__(request, data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
result = False
key = "Not Exist"
assert result == modin_df.__contains__(key)
assert result == (key in modin_df)
if "empty_data" not in request.node.name:
result = True
key = pandas_df.columns[0]
assert result == modin_df.__contains__(key)
assert result == (key in modin_df)
def test__options_display():
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(1000, 102))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
pandas.options.display.max_rows = 10
pandas.options.display.max_columns = 10
x = repr(pandas_df)
pd.options.display.max_rows = 5
pd.options.display.max_columns = 5
y = repr(modin_df)
assert x != y
pd.options.display.max_rows = 10
pd.options.display.max_columns = 10
y = repr(modin_df)
assert x == y
# test for old fixed max values
pandas.options.display.max_rows = 75
pandas.options.display.max_columns = 75
x = repr(pandas_df)
pd.options.display.max_rows = 75
pd.options.display.max_columns = 75
y = repr(modin_df)
assert x == y
def test___finalize__():
data = test_data_values[0]
with warns_that_defaulting_to_pandas():
pd.DataFrame(data).__finalize__(None)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test___copy__(data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
modin_df_copy, pandas_df_copy = modin_df.__copy__(), pandas_df.__copy__()
df_equals(modin_df_copy, pandas_df_copy)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test___deepcopy__(data):
modin_df = pd.DataFrame(data)
pandas_df = pandas.DataFrame(data)
modin_df_copy, pandas_df_copy = (
modin_df.__deepcopy__(),
pandas_df.__deepcopy__(),
)
df_equals(modin_df_copy, pandas_df_copy)
def test___repr__():
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(1000, 100))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(1000, 99))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(1000, 101))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(1000, 102))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
# ___repr___ method has a different code path depending on
# whether the number of rows is >60; and a different code path
# depending on the number of columns is >20.
# Previous test cases already check the case when cols>20
# and rows>60. The cases that follow exercise the other three
# combinations.
# rows <= 60, cols > 20
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(10, 100))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
# rows <= 60, cols <= 20
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(10, 10))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
# rows > 60, cols <= 20
frame_data = random_state.randint(RAND_LOW, RAND_HIGH, size=(100, 10))
pandas_df = pandas.DataFrame(frame_data)
modin_df = pd.DataFrame(frame_data)
assert repr(pandas_df) == repr(modin_df)
# Empty
pandas_df = pandas.DataFrame(columns=["col{}".format(i) for i in range(100)])
modin_df = pd.DataFrame(columns=["col{}".format(i) for i in range(100)])
assert repr(pandas_df) == repr(modin_df)
# From Issue #1705
string_data = """"time","device_id","lat","lng","accuracy","activity_1","activity_1_conf","activity_2","activity_2_conf","activity_3","activity_3_conf"
"2016-08-26 09:00:00.206",2,60.186805,24.821049,33.6080017089844,"STILL",75,"IN_VEHICLE",5,"ON_BICYCLE",5
"2016-08-26 09:00:05.428",5,60.192928,24.767222,5,"WALKING",62,"ON_BICYCLE",29,"RUNNING",6
"2016-08-26 09:00:05.818",1,60.166382,24.700443,3,"WALKING",75,"IN_VEHICLE",5,"ON_BICYCLE",5
"2016-08-26 09:00:15.816",1,60.166254,24.700671,3,"WALKING",75,"IN_VEHICLE",5,"ON_BICYCLE",5
"2016-08-26 09:00:16.413",5,60.193055,24.767427,5,"WALKING",85,"ON_BICYCLE",15,"UNKNOWN",0
"2016-08-26 09:00:20.578",3,60.152996,24.745216,3.90000009536743,"STILL",69,"IN_VEHICLE",31,"UNKNOWN",0"""
pandas_df = pandas.read_csv(io.StringIO(string_data))
with warns_that_defaulting_to_pandas():
modin_df = pd.read_csv(io.StringIO(string_data))
assert repr(pandas_df) == repr(modin_df)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_inplace_series_ops(data):
pandas_df = pandas.DataFrame(data)
modin_df = pd.DataFrame(data)
if len(modin_df.columns) > len(pandas_df.columns):
col0 = modin_df.columns[0]
col1 = modin_df.columns[1]
pandas_df[col1].dropna(inplace=True)
modin_df[col1].dropna(inplace=True)
df_equals(modin_df, pandas_df)
pandas_df[col0].fillna(0, inplace=True)
modin_df[col0].fillna(0, inplace=True)
df_equals(modin_df, pandas_df)
def test___setattr__():
pandas_df = pandas.DataFrame([1, 2, 3])
modin_df = pd.DataFrame([1, 2, 3])
pandas_df.new_col = [4, 5, 6]
modin_df.new_col = [4, 5, 6]
df_equals(modin_df, pandas_df)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_isin(data):
pandas_df = pandas.DataFrame(data)
modin_df = pd.DataFrame(data)
val = [1, 2, 3, 4]
pandas_result = pandas_df.isin(val)
modin_result = modin_df.isin(val)
df_equals(modin_result, pandas_result)
@pytest.mark.parametrize("data", test_data_values, ids=test_data_keys)
def test_constructor(data):
pandas_df = pandas.DataFrame(data)
modin_df = pd.DataFrame(data)
df_equals(pandas_df, modin_df)
pandas_df = pandas.DataFrame({k: pandas.Series(v) for k, v in data.items()})
modin_df = pd.DataFrame({k: pd.Series(v) for k, v in data.items()})
df_equals(pandas_df, modin_df)
@pytest.mark.parametrize(
"data",
[
np.arange(1, 10000, dtype=np.float32),
[
pd.Series([1, 2, 3], dtype="int32"),
pandas.Series([4, 5, 6], dtype="int64"),
np.array([7, 8, 9], dtype=np.float32),
],
pandas.Categorical([1, 2, 3, 4, 5]),
],
)
def test_constructor_dtypes(data):
md_df, pd_df = create_test_dfs(data)
df_equals(md_df, pd_df)
def test_constructor_columns_and_index():
modin_df = pd.DataFrame(
[[1, 1, 10], [2, 4, 20], [3, 7, 30]],
index=[1, 2, 3],
columns=["id", "max_speed", "health"],
)
pandas_df = pandas.DataFrame(
[[1, 1, 10], [2, 4, 20], [3, 7, 30]],
index=[1, 2, 3],
columns=["id", "max_speed", "health"],
)
df_equals(modin_df, pandas_df)
df_equals(pd.DataFrame(modin_df), pandas.DataFrame(pandas_df))
df_equals(
pd.DataFrame(modin_df, columns=["max_speed", "health"]),
pandas.DataFrame(pandas_df, columns=["max_speed", "health"]),
)
df_equals(
pd.DataFrame(modin_df, index=[1, 2]),
pandas.DataFrame(pandas_df, index=[1, 2]),
)
df_equals(
pd.DataFrame(modin_df, index=[1, 2], columns=["health"]),
pandas.DataFrame(pandas_df, index=[1, 2], columns=["health"]),
)
df_equals(
pd.DataFrame(modin_df.iloc[:, 0], index=[1, 2, 3]),
pandas.DataFrame(pandas_df.iloc[:, 0], index=[1, 2, 3]),
)
df_equals(
pd.DataFrame(modin_df.iloc[:, 0], columns=["NO_EXIST"]),
pandas.DataFrame(pandas_df.iloc[:, 0], columns=["NO_EXIST"]),
)
with pytest.raises(NotImplementedError):
pd.DataFrame(modin_df, index=[1, 2, 99999])
with pytest.raises(NotImplementedError):
pd.DataFrame(modin_df, columns=["NO_EXIST"])