def test_constructor_range(self):
msg = "Value needs to be a scalar value, was type <class 'range'>"
with pytest.raises(TypeError, match=msg):
result = RangeIndex(range(1, 5, 2))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
tm.assert_index_equal(result, expected, exact=True)
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
tm.assert_index_equal(result, expected, exact=True)
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
with pytest.raises(TypeError):
Index(range(1, 5, 2), dtype='float64')
msg = r'^from_range\(\) got an unexpected keyword argument'
with pytest.raises(TypeError, match=msg):
pd.RangeIndex.from_range(range(10), copy=True)
def test_constructor_range(self):
pytest.raises(TypeError, lambda: RangeIndex(range(1, 5, 2)))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
tm.assert_index_equal(result, expected, exact=True)
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
tm.assert_index_equal(result, expected, exact=True)
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
pytest.raises(TypeError,
lambda: Index(range(1, 5, 2), dtype='float64'))
def test_symmetric_difference(self):
# GH#12034 Cases where we operate against another RangeIndex and may
# get back another RangeIndex
left = RangeIndex.from_range(range(1, 10), name="foo")
result = left.symmetric_difference(left)
expected = RangeIndex.from_range(range(0), name="foo")
tm.assert_index_equal(result, expected)
result = left.symmetric_difference(expected.rename("bar"))
tm.assert_index_equal(result, left.rename(None))
result = left[:-2].symmetric_difference(left[2:])
expected = Int64Index([1, 2, 8, 9], name="foo")
tm.assert_index_equal(result, expected)
right = RangeIndex.from_range(range(10, 15))
result = left.symmetric_difference(right)
expected = RangeIndex.from_range(range(1, 15))
tm.assert_index_equal(result, expected)
result = left.symmetric_difference(right[1:])
expected = Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14])
tm.assert_index_equal(result, expected)
def test_constructor_range(self):
pytest.raises(TypeError, lambda: RangeIndex(range(1, 5, 2)))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
tm.assert_index_equal(result, expected, exact=True)
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
tm.assert_index_equal(result, expected, exact=True)
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
pytest.raises(TypeError,
lambda: Index(range(1, 5, 2), dtype='float64'))
def test_difference(self):
# GH#12034 Cases where we operate against another RangeIndex and may
# get back another RangeIndex
obj = RangeIndex.from_range(range(1, 10), name="foo")
result = obj.difference(obj)
expected = RangeIndex.from_range(range(0), name="foo")
tm.assert_index_equal(result, expected, exact=True)
result = obj.difference(expected.rename("bar"))
tm.assert_index_equal(result, obj.rename(None), exact=True)
result = obj.difference(obj[:3])
tm.assert_index_equal(result, obj[3:], exact=True)
result = obj.difference(obj[-3:])
tm.assert_index_equal(result, obj[:-3], exact=True)
result = obj[::-1].difference(obj[-3:])
tm.assert_index_equal(result, obj[:-3][::-1], exact=True)
result = obj[::-1].difference(obj[-3:][::-1])
tm.assert_index_equal(result, obj[:-3][::-1], exact=True)
result = obj.difference(obj[2:6])
expected = Int64Index([1, 2, 7, 8, 9], name="foo")
tm.assert_index_equal(result, expected)
def test_constructor_range(self):
self.assertRaises(TypeError, lambda: RangeIndex(range(1, 5, 2)))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
self.assertTrue(result.equals(expected))
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
self.assertTrue(result.equals(expected))
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
self.assertTrue(result.equals(expected))
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
self.assertTrue(result.equals(expected))
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
self.assertTrue(result.equals(expected))
self.assertRaises(TypeError,
lambda: Index(range(1, 5, 2), dtype='float64'))
def test_constructor_range(self):
self.assertRaises(TypeError, lambda: RangeIndex(range(1, 5, 2)))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
self.assertTrue(result.equals(expected))
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
self.assertTrue(result.equals(expected))
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
self.assertTrue(result.equals(expected))
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
self.assertTrue(result.equals(expected))
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
self.assertTrue(result.equals(expected))
self.assertRaises(TypeError,
lambda: Index(range(1, 5, 2), dtype='float64'))
def to_parquet(df, path, engine='auto', compression='snappy', **kwargs):
"""
Write a DataFrame to the parquet format.
Parameters
----------
df : DataFrame
path : string
File path
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet reader library to use. If 'auto', then the option
'io.parquet.engine' is used. If 'auto', then the first
library to be installed is used.
compression : str, optional, default 'snappy'
compression method, includes {'gzip', 'snappy', 'brotli'}
kwargs
Additional keyword arguments passed to the engine
"""
impl = get_engine(engine)
if not isinstance(df, DataFrame):
raise ValueError("to_parquet only support IO with DataFrames")
valid_types = {'string', 'unicode'}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
raise ValueError("parquet does not support serializing {} "
"for the index; you can .reset_index()"
"to make the index into column(s)".format(
type(df.index)))
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError("parquet does not support serializing a "
"non-default index for the index; you "
"can .reset_index() to make the index "
"into column(s)")
if df.index.name is not None:
raise ValueError("parquet does not serialize index meta-data on a "
"default index")
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("parquet must have string column names")
return impl.write(df, path, compression=compression, **kwargs)
def to_parquet(df, path, engine='auto', compression='snappy', **kwargs):
"""
Write a DataFrame to the parquet format.
Parameters
----------
df : DataFrame
path : string
File path
engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'
Parquet reader library to use. If 'auto', then the option
'io.parquet.engine' is used. If 'auto', then the first
library to be installed is used.
compression : str, optional, default 'snappy'
compression method, includes {'gzip', 'snappy', 'brotli'}
kwargs
Additional keyword arguments passed to the engine
"""
impl = get_engine(engine)
if not isinstance(df, DataFrame):
raise ValueError("to_parquet only support IO with DataFrames")
valid_types = {'string', 'unicode'}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
raise ValueError("parquet does not support serializing {} "
"for the index; you can .reset_index()"
"to make the index into column(s)".format(
type(df.index)))
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError("parquet does not support serializing a "
"non-default index for the index; you "
"can .reset_index() to make the index "
"into column(s)")
if df.index.name is not None:
raise ValueError("parquet does not serialize index meta-data on a "
"default index")
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("parquet must have string column names")
return impl.write(df, path, compression=compression, **kwargs)
def test_difference_mismatched_step(self):
obj = RangeIndex.from_range(range(1, 10), name="foo")
result = obj.difference(obj[::2])
expected = obj[1::2]._int64index
tm.assert_index_equal(result, expected, exact=True)
result = obj.difference(obj[1::2])
expected = obj[::2]._int64index
tm.assert_index_equal(result, expected, exact=True)
def to_feather(df: DataFrame, path):
"""
Write a DataFrame to the feather-format
Parameters
----------
df : DataFrame
path : string file path, or file-like object
"""
import_optional_dependency("pyarrow")
from pyarrow import feather
path = stringify_path(path)
if not isinstance(df, DataFrame):
raise ValueError("feather only support IO with DataFrames")
valid_types = {"string", "unicode"}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
typ = type(df.index)
raise ValueError(
f"feather does not support serializing {typ} "
"for the index; you can .reset_index() "
"to make the index into column(s)"
)
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError(
"feather does not support serializing a "
"non-default index for the index; you "
"can .reset_index() to make the index "
"into column(s)"
)
if df.index.name is not None:
raise ValueError(
"feather does not serialize index meta-data on a default index"
)
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("feather must have string column names")
feather.write_feather(df, path)
def test_constructor_range(self):
msg = "Value needs to be a scalar value, was type <class 'range'>"
with pytest.raises(TypeError, match=msg):
result = RangeIndex(range(1, 5, 2))
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
tm.assert_index_equal(result, expected, exact=True)
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
tm.assert_index_equal(result, expected, exact=True)
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
with pytest.raises(
ValueError,
match="Incorrect `dtype` passed: expected signed integer, received float64",
):
Index(range(1, 5, 2), dtype="float64")
msg = r"^from_range\(\) got an unexpected keyword argument"
with pytest.raises(TypeError, match=msg):
RangeIndex.from_range(range(10), copy=True)
def test_constructor_range(self):
result = RangeIndex.from_range(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5, 6))
expected = RangeIndex(5, 6, 1)
tm.assert_index_equal(result, expected, exact=True)
# an invalid range
result = RangeIndex.from_range(range(5, 1))
expected = RangeIndex(0, 0, 1)
tm.assert_index_equal(result, expected, exact=True)
result = RangeIndex.from_range(range(5))
expected = RangeIndex(0, 5, 1)
tm.assert_index_equal(result, expected, exact=True)
result = Index(range(1, 5, 2))
expected = RangeIndex(1, 5, 2)
tm.assert_index_equal(result, expected, exact=True)
msg = (
r"(RangeIndex.)?from_range\(\) got an unexpected keyword argument( 'copy')?"
)
with pytest.raises(TypeError, match=msg):
RangeIndex.from_range(range(10), copy=True)
def to_feather(df, path):
"""
Write a DataFrame to the feather-format
Parameters
----------
df : DataFrame
path : string
File path
"""
path = _stringify_path(path)
if not isinstance(df, DataFrame):
raise ValueError("feather only support IO with DataFrames")
feather = _try_import()
valid_types = {'string', 'unicode'}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
raise ValueError("feather does not support serializing {} "
"for the index; you can .reset_index()"
"to make the index into column(s)".format(
type(df.index)))
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError("feather does not support serializing a "
"non-default index for the index; you "
"can .reset_index() to make the index "
"into column(s)")
if df.index.name is not None:
raise ValueError("feather does not serialize index meta-data on a "
"default index")
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("feather must have string column names")
feather.write_dataframe(df, path)
def to_feather(
df: DataFrame,
path: FilePathOrBuffer[AnyStr],
storage_options: StorageOptions = None,
**kwargs,
):
"""
Write a DataFrame to the binary Feather format.
Parameters
----------
df : DataFrame
path : string file path, or file-like object
storage_options : dict, optional
Extra options that make sense for a particular storage connection, e.g.
host, port, username, password, etc., if using a URL that will
be parsed by ``fsspec``, e.g., starting "s3://", "gcs://". An error
will be raised if providing this argument with a local path or
a file-like buffer. See the fsspec and backend storage implementation
docs for the set of allowed keys and values.
.. versionadded:: 1.2.0
**kwargs :
Additional keywords passed to `pyarrow.feather.write_feather`.
.. versionadded:: 1.1.0
"""
import_optional_dependency("pyarrow")
from pyarrow import feather
ioargs = get_filepath_or_buffer(path, mode="wb", storage_options=storage_options)
if not isinstance(df, DataFrame):
raise ValueError("feather only support IO with DataFrames")
valid_types = {"string", "unicode"}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
typ = type(df.index)
raise ValueError(
f"feather does not support serializing {typ} "
"for the index; you can .reset_index() to make the index into column(s)"
)
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError(
"feather does not support serializing a non-default index for the index; "
"you can .reset_index() to make the index into column(s)"
)
if df.index.name is not None:
raise ValueError(
"feather does not serialize index meta-data on a default index"
)
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("feather must have string column names")
feather.write_feather(df, ioargs.filepath_or_buffer, **kwargs)
if ioargs.should_close:
assert not isinstance(ioargs.filepath_or_buffer, str)
ioargs.filepath_or_buffer.close()
def to_feather(
df: DataFrame,
path: FilePathOrBuffer[AnyStr],
storage_options: StorageOptions = None,
**kwargs,
):
"""
Write a DataFrame to the binary Feather format.
Parameters
----------
df : DataFrame
path : string file path, or file-like object
{storage_options}
.. versionadded:: 1.2.0
**kwargs :
Additional keywords passed to `pyarrow.feather.write_feather`.
.. versionadded:: 1.1.0
"""
import_optional_dependency("pyarrow")
from pyarrow import feather
if not isinstance(df, DataFrame):
raise ValueError("feather only support IO with DataFrames")
valid_types = {"string", "unicode"}
# validate index
# --------------
# validate that we have only a default index
# raise on anything else as we don't serialize the index
if not isinstance(df.index, Int64Index):
typ = type(df.index)
raise ValueError(
f"feather does not support serializing {typ} "
"for the index; you can .reset_index() to make the index into column(s)"
)
if not df.index.equals(RangeIndex.from_range(range(len(df)))):
raise ValueError(
"feather does not support serializing a non-default index for the index; "
"you can .reset_index() to make the index into column(s)")
if df.index.name is not None:
raise ValueError(
"feather does not serialize index meta-data on a default index")
# validate columns
# ----------------
# must have value column names (strings only)
if df.columns.inferred_type not in valid_types:
raise ValueError("feather must have string column names")
with get_handle(path, "wb", storage_options=storage_options,
is_text=False) as handles:
feather.write_feather(df, handles.handle, **kwargs)
