def test_categorical_warnings_and_errors(self):
# Warning for non-string labels
# Error for labels too long
original = pd.DataFrame.from_records(
[['a' * 10000],
['b' * 10000],
['c' * 10000],
['d' * 10000]],
columns=['Too_long'])
original = pd.concat([original[col].astype('category') for col in original], axis=1)
with tm.ensure_clean() as path:
tm.assertRaises(ValueError, original.to_stata, path)
original = pd.DataFrame.from_records(
[['a'],
['b'],
['c'],
['d'],
[1]],
columns=['Too_long'])
original = pd.concat([original[col].astype('category') for col in original], axis=1)
with warnings.catch_warnings(record=True) as w:
original.to_stata(path)
tm.assert_equal(len(w), 1) # should get a warning for mixed content
def test_series_groupby_errors():
s = pd.Series([1, 2, 2, 1, 1])
ss = dd.from_pandas(s, npartitions=2)
msg = "Grouper for '1' not 1-dimensional"
with tm.assertRaisesRegexp(ValueError, msg):
s.groupby([1, 2]) # pandas
with tm.assertRaisesRegexp(ValueError, msg):
ss.groupby([1, 2]) # dask should raise the same error
msg = "Grouper for '2' not 1-dimensional"
with tm.assertRaisesRegexp(ValueError, msg):
s.groupby([2]) # pandas
with tm.assertRaisesRegexp(ValueError, msg):
ss.groupby([2]) # dask should raise the same error
msg = "No group keys passed!"
with tm.assertRaisesRegexp(ValueError, msg):
s.groupby([]) # pandas
with tm.assertRaisesRegexp(ValueError, msg):
ss.groupby([]) # dask should raise the same error
sss = dd.from_pandas(s, npartitions=3)
assert raises(NotImplementedError, lambda: ss.groupby(sss))
with tm.assertRaises(KeyError):
s.groupby('x') # pandas
with tm.assertRaises(KeyError):
ss.groupby('x') # dask should raise the same error
def test_loc2d():
# index indexer is always regarded as slice for duplicated values
assert_eq(d.loc[5, 'a'], full.loc[5:5, 'a'])
# assert_eq(d.loc[[5], 'a'], full.loc[[5], 'a'])
assert_eq(d.loc[5, ['a']], full.loc[5:5, ['a']])
# assert_eq(d.loc[[5], ['a']], full.loc[[5], ['a']])
assert_eq(d.loc[3:8, 'a'], full.loc[3:8, 'a'])
assert_eq(d.loc[:8, 'a'], full.loc[:8, 'a'])
assert_eq(d.loc[3:, 'a'], full.loc[3:, 'a'])
assert_eq(d.loc[[8], 'a'], full.loc[[8], 'a'])
assert_eq(d.loc[3:8, ['a']], full.loc[3:8, ['a']])
assert_eq(d.loc[:8, ['a']], full.loc[:8, ['a']])
assert_eq(d.loc[3:, ['a']], full.loc[3:, ['a']])
assert_eq(d.loc[[3, 4, 3], ['a']], full.loc[[3, 4, 3], ['a']])
# 3d
with tm.assertRaises(pd.core.indexing.IndexingError):
d.loc[3, 3, 3]
# Series should raise
with tm.assertRaises(pd.core.indexing.IndexingError):
d.a.loc[3, 3]
with tm.assertRaises(pd.core.indexing.IndexingError):
d.a.loc[3:, 3]
with tm.assertRaises(pd.core.indexing.IndexingError):
d.a.loc[d.a % 2 == 0, 3]
def test_errorbar_plot(self):
s = Series(np.arange(10))
s_err = np.random.randn(10)
# test line and bar plots
kinds = ['line', 'bar']
for kind in kinds:
_check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
_check_plot_works(s.plot, yerr=s_err, kind=kind)
_check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
_check_plot_works(s.plot, xerr=s_err)
# test time series plotting
ix = date_range('1/1/2000', '1/1/2001', freq='M')
ts = Series(np.arange(12), index=ix)
ts_err = Series(np.random.randn(12), index=ix)
_check_plot_works(ts.plot, yerr=ts_err)
# check incorrect lengths and types
with tm.assertRaises(ValueError):
s.plot(yerr=np.arange(11))
s_err = ['zzz']*10
with tm.assertRaises(TypeError):
s.plot(yerr=s_err)
def test_errorbar_plot(self):
d = {'x': np.arange(12), 'y': np.arange(12, 0, -1)}
df = DataFrame(d)
d_err = {'x': np.ones(12)*0.2, 'y': np.ones(12)*0.4}
df_err = DataFrame(d_err)
# check line plots
_check_plot_works(df.plot, yerr=df_err, logy=True)
_check_plot_works(df.plot, yerr=df_err, logx=True, logy=True)
kinds = ['line', 'bar', 'barh']
for kind in kinds:
_check_plot_works(df.plot, yerr=df_err['x'], kind=kind)
_check_plot_works(df.plot, yerr=d_err, kind=kind)
_check_plot_works(df.plot, yerr=df_err, xerr=df_err, kind=kind)
_check_plot_works(df.plot, yerr=df_err['x'], xerr=df_err['x'], kind=kind)
_check_plot_works(df.plot, yerr=df_err, xerr=df_err, subplots=True, kind=kind)
_check_plot_works((df+1).plot, yerr=df_err, xerr=df_err, kind='bar', log=True)
# yerr is raw error values
_check_plot_works(df['y'].plot, yerr=np.ones(12)*0.4)
_check_plot_works(df.plot, yerr=np.ones((2, 12))*0.4)
# yerr is column name
df['yerr'] = np.ones(12)*0.2
_check_plot_works(df.plot, y='y', x='x', yerr='yerr')
with tm.assertRaises(ValueError):
df.plot(yerr=np.random.randn(11))
df_err = DataFrame({'x': ['zzz']*12, 'y': ['zzz']*12})
with tm.assertRaises(TypeError):
df.plot(yerr=df_err)
def test_drop_column(self):
expected = self.read_csv(self.csv15)
expected["byte_"] = expected["byte_"].astype(np.int8)
expected["int_"] = expected["int_"].astype(np.int16)
expected["long_"] = expected["long_"].astype(np.int32)
expected["float_"] = expected["float_"].astype(np.float32)
expected["double_"] = expected["double_"].astype(np.float64)
expected["date_td"] = expected["date_td"].apply(datetime.strptime, args=("%Y-%m-%d",))
columns = ["byte_", "int_", "long_"]
expected = expected[columns]
dropped = read_stata(self.dta15_117, convert_dates=True, columns=columns)
tm.assert_frame_equal(expected, dropped)
# See PR 10757
columns = ["int_", "long_", "byte_"]
expected = expected[columns]
reordered = read_stata(self.dta15_117, convert_dates=True, columns=columns)
tm.assert_frame_equal(expected, reordered)
with tm.assertRaises(ValueError):
columns = ["byte_", "byte_"]
read_stata(self.dta15_117, convert_dates=True, columns=columns)
with tm.assertRaises(ValueError):
columns = ["byte_", "int_", "long_", "not_found"]
read_stata(self.dta15_117, convert_dates=True, columns=columns)
def test_invalid_encoding(self):
# test case for testing invalid encoding
data = self.data['string']
with tm.assertRaises(ValueError):
data.to_clipboard(encoding='ascii')
with tm.assertRaises(NotImplementedError):
pd.read_clipboard(encoding='ascii')
def test_drop_column(self):
expected = self.read_csv(self.csv15)
expected['byte_'] = expected['byte_'].astype(np.int8)
expected['int_'] = expected['int_'].astype(np.int16)
expected['long_'] = expected['long_'].astype(np.int32)
expected['float_'] = expected['float_'].astype(np.float32)
expected['double_'] = expected['double_'].astype(np.float64)
expected['date_td'] = expected['date_td'].apply(datetime.strptime,
args=('%Y-%m-%d',))
columns = ['byte_', 'int_', 'long_']
expected = expected[columns]
dropped = read_stata(self.dta15_117, convert_dates=True,
columns=columns)
tm.assert_frame_equal(expected, dropped)
# See PR 10757
columns = ['int_', 'long_', 'byte_']
expected = expected[columns]
reordered = read_stata(self.dta15_117, convert_dates=True,
columns=columns)
tm.assert_frame_equal(expected, reordered)
with tm.assertRaises(ValueError):
columns = ['byte_', 'byte_']
read_stata(self.dta15_117, convert_dates=True, columns=columns)
with tm.assertRaises(ValueError):
columns = ['byte_', 'int_', 'long_', 'not_found']
read_stata(self.dta15_117, convert_dates=True, columns=columns)
def test_nested_scope(self):
from pandas.computation.ops import UndefinedVariableError
engine = self.engine
parser = self.parser
# smoke test
x = 1 # noqa
result = pd.eval('x + 1', engine=engine, parser=parser)
self.assertEqual(result, 2)
df = DataFrame(np.random.randn(5, 3))
df2 = DataFrame(np.random.randn(5, 3))
# don't have the pandas parser
with tm.assertRaises(SyntaxError):
df.query('(@df>0) & (@df2>0)', engine=engine, parser=parser)
with tm.assertRaises(UndefinedVariableError):
df.query('(df>0) & (df2>0)', engine=engine, parser=parser)
expected = df[(df > 0) & (df2 > 0)]
result = pd.eval('df[(df > 0) & (df2 > 0)]', engine=engine,
parser=parser)
assert_frame_equal(expected, result)
expected = df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]
result = pd.eval('df[(df > 0) & (df2 > 0) & (df[df > 0] > 0)]',
engine=engine, parser=parser)
assert_frame_equal(expected, result)
def test_getitem_day(self):
# GH 6716
# Confirm DatetimeIndex and PeriodIndex works identically
didx = DatetimeIndex(start='2013/01/01', freq='D', periods=400)
pidx = PeriodIndex(start='2013/01/01', freq='D', periods=400)
for idx in [didx, pidx]:
# getitem against index should raise ValueError
values = ['2014', '2013/02', '2013/01/02', '2013/02/01 9H',
'2013/02/01 09:00']
for v in values:
if _np_version_under1p9:
with tm.assertRaises(ValueError):
idx[v]
else:
# GH7116
# these show deprecations as we are trying
# to slice with non-integer indexers
# with tm.assertRaises(IndexError):
# idx[v]
continue
s = Series(np.random.rand(len(idx)), index=idx)
tm.assert_series_equal(s['2013/01'], s[0:31])
tm.assert_series_equal(s['2013/02'], s[31:59])
tm.assert_series_equal(s['2014'], s[365:])
invalid = ['2013/02/01 9H', '2013/02/01 09:00']
for v in invalid:
with tm.assertRaises(KeyError):
s[v]
def test_implementation_limits(self):
min_td = Timedelta(Timedelta.min)
max_td = Timedelta(Timedelta.max)
# GH 12727
# timedelta limits correspond to int64 boundaries
self.assertTrue(min_td.value == np.iinfo(np.int64).min + 1)
self.assertTrue(max_td.value == np.iinfo(np.int64).max)
# Beyond lower limit, a NAT before the Overflow
self.assertIsInstance(min_td - Timedelta(1, 'ns'),
pd.tslib.NaTType)
with tm.assertRaises(OverflowError):
min_td - Timedelta(2, 'ns')
with tm.assertRaises(OverflowError):
max_td + Timedelta(1, 'ns')
# Same tests using the internal nanosecond values
td = Timedelta(min_td.value - 1, 'ns')
self.assertIsInstance(td, pd.tslib.NaTType)
with tm.assertRaises(OverflowError):
Timedelta(min_td.value - 2, 'ns')
with tm.assertRaises(OverflowError):
Timedelta(max_td.value + 1, 'ns')
def test_hist_layout(self):
df = DataFrame(randn(100, 3))
layout_to_expected_size = (
{'layout': None, 'expected_size': (2, 2)}, # default is 2x2
{'layout': (2, 2), 'expected_size': (2, 2)},
{'layout': (4, 1), 'expected_size': (4, 1)},
{'layout': (1, 4), 'expected_size': (1, 4)},
{'layout': (3, 3), 'expected_size': (3, 3)},
{'layout': (-1, 4), 'expected_size': (1, 4)},
{'layout': (4, -1), 'expected_size': (4, 1)},
{'layout': (-1, 2), 'expected_size': (2, 2)},
{'layout': (2, -1), 'expected_size': (2, 2)}
)
for layout_test in layout_to_expected_size:
axes = df.hist(layout=layout_test['layout'])
expected = layout_test['expected_size']
self._check_axes_shape(axes, axes_num=3, layout=expected)
# layout too small for all 4 plots
with tm.assertRaises(ValueError):
df.hist(layout=(1, 1))
# invalid format for layout
with tm.assertRaises(ValueError):
df.hist(layout=(1,))
with tm.assertRaises(ValueError):
df.hist(layout=(-1, -1))
def test_no_order(self):
_skip_if_no_scipy()
s = Series([0, 1, np.nan, 3])
with tm.assertRaises(ValueError):
s.interpolate(method='polynomial')
with tm.assertRaises(ValueError):
s.interpolate(method='spline')
def test_hist_layout(self):
df = self.hist_df
with tm.assertRaises(ValueError):
df.height.hist(layout=(1, 1))
with tm.assertRaises(ValueError):
df.height.hist(layout=[1, 1])
def test_take_filling_fill_value(self):
# same tests as GH 12631
sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([0, np.nan, 4], fill_value=0)
tm.assert_sp_array_equal(result, expected)
# fill_value
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([0, np.nan, 0], fill_value=0)
tm.assert_sp_array_equal(result, expected)
# allow_fill=False
result = sparse.take(np.array([1, 0, -1]),
allow_fill=False, fill_value=True)
expected = SparseArray([0, np.nan, 4], fill_value=0)
tm.assert_sp_array_equal(result, expected)
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
with tm.assertRaisesRegexp(ValueError, msg):
sparse.take(np.array([1, 0, -2]), fill_value=True)
with tm.assertRaisesRegexp(ValueError, msg):
sparse.take(np.array([1, 0, -5]), fill_value=True)
with tm.assertRaises(IndexError):
sparse.take(np.array([1, -6]))
with tm.assertRaises(IndexError):
sparse.take(np.array([1, 5]))
with tm.assertRaises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
def test_nonunique_raises(self):
df = pd.DataFrame([[1, 2]], columns=['A', 'A'])
with tm.assertRaises(ValueError):
df.style
with tm.assertRaises(ValueError):
Styler(df)
def test_get_division():
pdf = pd.DataFrame(np.random.randn(10, 5), columns=list("abcde"))
ddf = dd.from_pandas(pdf, 3)
assert ddf.divisions == (0, 4, 8, 9)
# DataFrame
div1 = ddf.get_division(0)
assert isinstance(div1, dd.DataFrame)
assert eq(div1, pdf.loc[0:3])
div2 = ddf.get_division(1)
assert eq(div2, pdf.loc[4:7])
div3 = ddf.get_division(2)
assert eq(div3, pdf.loc[8:9])
assert len(div1) + len(div2) + len(div3) == len(pdf)
# Series
div1 = ddf.a.get_division(0)
assert isinstance(div1, dd.Series)
assert eq(div1, pdf.a.loc[0:3])
div2 = ddf.a.get_division(1)
assert eq(div2, pdf.a.loc[4:7])
div3 = ddf.a.get_division(2)
assert eq(div3, pdf.a.loc[8:9])
assert len(div1) + len(div2) + len(div3) == len(pdf.a)
with tm.assertRaises(ValueError):
ddf.get_division(-1)
with tm.assertRaises(ValueError):
ddf.get_division(3)
def check_str_query_method(self, parser, engine):
tm.skip_if_no_ne(engine)
df = DataFrame(randn(10, 1), columns=['b'])
df['strings'] = Series(list('aabbccddee'))
expect = df[df.strings == 'a']
if parser != 'pandas':
col = 'strings'
lst = '"a"'
lhs = [col] * 2 + [lst] * 2
rhs = lhs[::-1]
eq, ne = '==', '!='
ops = 2 * ([eq] + [ne])
for lhs, op, rhs in zip(lhs, ops, rhs):
ex = '{lhs} {op} {rhs}'.format(lhs=lhs, op=op, rhs=rhs)
assertRaises(NotImplementedError, df.query, ex, engine=engine,
parser=parser, local_dict={'strings': df.strings})
else:
res = df.query('"a" == strings', engine=engine, parser=parser)
assert_frame_equal(res, expect)
res = df.query('strings == "a"', engine=engine, parser=parser)
assert_frame_equal(res, expect)
assert_frame_equal(res, df[df.strings.isin(['a'])])
expect = df[df.strings != 'a']
res = df.query('strings != "a"', engine=engine, parser=parser)
assert_frame_equal(res, expect)
res = df.query('"a" != strings', engine=engine, parser=parser)
assert_frame_equal(res, expect)
assert_frame_equal(res, df[~df.strings.isin(['a'])])
def test_hist_layout(self):
import matplotlib.pyplot as plt
df = DataFrame(randn(100, 4))
layout_to_expected_size = (
{"layout": None, "expected_size": (2, 2)}, # default is 2x2
{"layout": (2, 2), "expected_size": (2, 2)},
{"layout": (4, 1), "expected_size": (4, 1)},
{"layout": (1, 4), "expected_size": (1, 4)},
{"layout": (3, 3), "expected_size": (3, 3)},
)
for layout_test in layout_to_expected_size:
ax = df.hist(layout=layout_test["layout"])
self.assertEqual(len(ax), layout_test["expected_size"][0])
self.assertEqual(len(ax[0]), layout_test["expected_size"][1])
# layout too small for all 4 plots
with tm.assertRaises(ValueError):
df.hist(layout=(1, 1))
# invalid format for layout
with tm.assertRaises(ValueError):
df.hist(layout=(1,))
def test_nat_arithmetic(self):
# GH 6873
nat = tslib.NaT
t = Timestamp('2014-01-01')
dt = datetime.datetime(2014, 1, 1)
delta = datetime.timedelta(3600)
# Timestamp / datetime
for (left, right) in [(nat, nat), (nat, t), (dt, nat)]:
# NaT + Timestamp-like should raise TypeError
with tm.assertRaises(TypeError):
left + right
with tm.assertRaises(TypeError):
right + left
# NaT - Timestamp-like (or inverse) returns NaT
self.assertTrue((left - right) is tslib.NaT)
self.assertTrue((right - left) is tslib.NaT)
# timedelta-like
# offsets are tested in test_offsets.py
for (left, right) in [(nat, delta)]:
# NaT + timedelta-like returns NaT
self.assertTrue((left + right) is tslib.NaT)
# timedelta-like + NaT should raise TypeError
with tm.assertRaises(TypeError):
right + left
self.assertTrue((left - right) is tslib.NaT)
with tm.assertRaises(TypeError):
right - left
def test_unstack_non_unique_index_names(self):
idx = MultiIndex.from_tuples([("a", "b"), ("c", "d")], names=["c1", "c1"])
df = DataFrame([1, 2], index=idx)
with tm.assertRaises(ValueError):
df.unstack("c1")
with tm.assertRaises(ValueError):
df.T.stack("c1")
def test_s3_fails(self):
with tm.assertRaises(IOError):
read_csv('s3://nyqpug/asdf.csv')
# Receive a permission error when trying to read a private bucket.
# It's irrelevant here that this isn't actually a table.
with tm.assertRaises(IOError):
read_csv('s3://cant_get_it/')
def test_no_index(self):
columns = ["x", "y"]
original = DataFrame(np.reshape(np.arange(10.0), (5, 2)), columns=columns)
original.index.name = "index_not_written"
with tm.ensure_clean() as path:
original.to_stata(path, write_index=False)
written_and_read_again = self.read_dta(path)
tm.assertRaises(KeyError, lambda: written_and_read_again["index_not_written"])
def test_catch_too_many_names(self):
# see gh-5156
data = """\
1,2,3
4,,6
7,8,9
10,11,12\n"""
tm.assertRaises(ValueError, self.read_csv, StringIO(data), header=0, names=["a", "b", "c", "d"])
def test_construction_dti_with_mixed_timezones(self):
# GH 11488 (not changed, added explicit tests)
# no tz results in DatetimeIndex
result = DatetimeIndex(
[Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
exp = DatetimeIndex(
[Timestamp('2011-01-01'), Timestamp('2011-01-02')], name='idx')
self.assert_index_equal(result, exp, exact=True)
self.assertTrue(isinstance(result, DatetimeIndex))
# same tz results in DatetimeIndex
result = DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
Timestamp('2011-01-02 10:00',
tz='Asia/Tokyo')],
name='idx')
exp = DatetimeIndex(
[Timestamp('2011-01-01 10:00'), Timestamp('2011-01-02 10:00')
], tz='Asia/Tokyo', name='idx')
self.assert_index_equal(result, exp, exact=True)
self.assertTrue(isinstance(result, DatetimeIndex))
# same tz results in DatetimeIndex (DST)
result = DatetimeIndex([Timestamp('2011-01-01 10:00', tz='US/Eastern'),
Timestamp('2011-08-01 10:00',
tz='US/Eastern')],
name='idx')
exp = DatetimeIndex([Timestamp('2011-01-01 10:00'),
Timestamp('2011-08-01 10:00')],
tz='US/Eastern', name='idx')
self.assert_index_equal(result, exp, exact=True)
self.assertTrue(isinstance(result, DatetimeIndex))
# different tz coerces tz-naive to tz-awareIndex(dtype=object)
result = DatetimeIndex([Timestamp('2011-01-01 10:00'),
Timestamp('2011-01-02 10:00',
tz='US/Eastern')], name='idx')
exp = DatetimeIndex([Timestamp('2011-01-01 05:00'),
Timestamp('2011-01-02 10:00')],
tz='US/Eastern', name='idx')
self.assert_index_equal(result, exp, exact=True)
self.assertTrue(isinstance(result, DatetimeIndex))
# tz mismatch affecting to tz-aware raises TypeError/ValueError
with tm.assertRaises(ValueError):
DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
Timestamp('2011-01-02 10:00', tz='US/Eastern')],
name='idx')
with tm.assertRaises(TypeError):
DatetimeIndex([Timestamp('2011-01-01 10:00'),
Timestamp('2011-01-02 10:00', tz='US/Eastern')],
tz='Asia/Tokyo', name='idx')
with tm.assertRaises(ValueError):
DatetimeIndex([Timestamp('2011-01-01 10:00', tz='Asia/Tokyo'),
Timestamp('2011-01-02 10:00', tz='US/Eastern')],
tz='US/Eastern', name='idx')
def test_unsortable(self):
# GH 13714
arr = np.array([1, 2, datetime.now(), 0, 3], dtype=object)
if compat.PY2 and not pd._np_version_under1p10:
# RuntimeWarning: tp_compare didn't return -1 or -2 for exception
with tm.assert_produces_warning(RuntimeWarning):
tm.assertRaises(TypeError, algos.safe_sort, arr)
else:
tm.assertRaises(TypeError, algos.safe_sort, arr)
def test_unstack_non_unique_index_names(self):
idx = MultiIndex.from_tuples([('a', 'b'), ('c', 'd')],
names=['c1', 'c1'])
df = DataFrame([1, 2], index=idx)
with tm.assertRaises(ValueError):
df.unstack('c1')
with tm.assertRaises(ValueError):
df.T.stack('c1')
def test_data_fail(self):
from lxml.etree import XMLSyntaxError
spam_data = os.path.join(DATA_PATH, 'spam.html')
banklist_data = os.path.join(DATA_PATH, 'banklist.html')
with tm.assertRaises(XMLSyntaxError):
self.read_html(spam_data)
with tm.assertRaises(XMLSyntaxError):
self.read_html(banklist_data)
def test_spline_error(self):
tm._skip_if_no_scipy()
s = pd.Series(np.arange(10) ** 2)
s[np.random.randint(0, 9, 3)] = np.nan
with tm.assertRaises(ValueError):
s.interpolate(method='spline')
with tm.assertRaises(ValueError):
s.interpolate(method='spline', order=0)
def test_hist_layout(self):
n = 10
gender = tm.choice(['Male', 'Female'], size=n)
df = DataFrame({'gender': gender,
'height': random.normal(66, 4, size=n), 'weight':
random.normal(161, 32, size=n)})
with tm.assertRaises(ValueError):
df.height.hist(layout=(1, 1))
with tm.assertRaises(ValueError):
df.height.hist(layout=[1, 1])
def test_interp_nan_idx(self):
df = DataFrame({'A': [1, 2, np.nan, 4], 'B': [np.nan, 2, 3, 4]})
df = df.set_index('A')
with tm.assertRaises(NotImplementedError):
df.interpolate(method='values')
def test_to_gbq_with_no_project_id_given_should_fail(self):
with tm.assertRaises(TypeError):
gbq.to_gbq(DataFrame(), 'dataset.tablename')
def test_describe_quantiles_both(self):
with tm.assertRaises(ValueError):
tm.makeDataFrame().describe(percentile_width=50,
percentiles=[25, 75])
def test_describe_percentiles_percent_or_raw(self):
df = tm.makeDataFrame()
with tm.assertRaises(ValueError):
df.describe(percentiles=[10, 50, 100])
def test_describe_raises(self):
with tm.assertRaises(NotImplementedError):
tm.makePanel().describe()
def test_interpolate_non_ts(self):
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
with tm.assertRaises(ValueError):
s.interpolate(method='time')
def test_display_format_raises(self):
df = pd.DataFrame(np.random.randn(2, 2))
with tm.assertRaises(TypeError):
df.style.format(5)
with tm.assertRaises(TypeError):
df.style.format(True)
def test_bad_project_id(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELECT 1",
project_id='001',
private_key=_get_private_key_path())
def test_bad_table_name(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELECT * FROM [publicdata:samples.nope]",
project_id=_get_project_id(),
private_key=_get_private_key_path())
def test_plot_fails_with_dupe_color_and_style(self):
x = Series(randn(2))
with tm.assertRaises(ValueError):
x.plot(style='k--', color='k')
def test_bad_url_protocol(self):
with tm.assertRaises(URLError):
self.read_html('git://github.com', match='.*Water.*')
def test_invalid_flavor():
url = 'google.com'
with tm.assertRaises(ValueError):
read_html(url, 'google', flavor='not a* valid**++ flaver')
def test_fred_multi_bad_series(self):
names = ['NOTAREALSERIES', 'CPIAUCSL', "ALSO FAKE"]
with tm.assertRaises(HTTPError):
DataReader(names, data_source="fred")
def test_bool_header_arg(self):
# GH 6114
for arg in [True, False]:
with tm.assertRaises(TypeError):
read_html(self.spam_data, header=arg)
def test_interp_nonmono_raise(self):
tm._skip_if_no_scipy()
s = Series([1, np.nan, 3], index=[0, 2, 1])
with tm.assertRaises(ValueError):
s.interpolate(method='krogh')
def test_malformed_query(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELCET * FORM [publicdata:samples.shakespeare]",
project_id=_get_project_id(),
private_key=_get_private_key_path())
def test_init_non_pandas(self):
with tm.assertRaises(TypeError):
Styler([1, 2, 3])
def test_malformed_query(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELCET * FORM [publicdata:samples.shakespeare]",
project_id=PROJECT_ID)
def test_bad_table_name(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELECT * FROM [publicdata:samples.nope]",
project_id=PROJECT_ID)
def test_read_gbq_with_empty_private_key_file_should_fail(self):
with tm.ensure_clean() as empty_file_path:
with tm.assertRaises(gbq.InvalidPrivateKeyFormat):
gbq.read_gbq('SELECT 1',
project_id='x',
private_key=empty_file_path)
def test_tz_convert_and_localize(self):
l0 = date_range('20140701', periods=5, freq='D')
# TODO: l1 should be a PeriodIndex for testing
# after GH2106 is addressed
with tm.assertRaises(NotImplementedError):
period_range('20140701', periods=1).tz_convert('UTC')
with tm.assertRaises(NotImplementedError):
period_range('20140701', periods=1).tz_localize('UTC')
# l1 = period_range('20140701', periods=5, freq='D')
l1 = date_range('20140701', periods=5, freq='D')
int_idx = Index(range(5))
for fn in ['tz_localize', 'tz_convert']:
if fn == 'tz_convert':
l0 = l0.tz_localize('UTC')
l1 = l1.tz_localize('UTC')
for idx in [l0, l1]:
l0_expected = getattr(idx, fn)('US/Pacific')
l1_expected = getattr(idx, fn)('US/Pacific')
df1 = DataFrame(np.ones(5), index=l0)
df1 = getattr(df1, fn)('US/Pacific')
self.assertTrue(df1.index.equals(l0_expected))
# MultiIndex
# GH7846
df2 = DataFrame(np.ones(5), MultiIndex.from_arrays([l0, l1]))
df3 = getattr(df2, fn)('US/Pacific', level=0)
self.assertFalse(df3.index.levels[0].equals(l0))
self.assertTrue(df3.index.levels[0].equals(l0_expected))
self.assertTrue(df3.index.levels[1].equals(l1))
self.assertFalse(df3.index.levels[1].equals(l1_expected))
df3 = getattr(df2, fn)('US/Pacific', level=1)
self.assertTrue(df3.index.levels[0].equals(l0))
self.assertFalse(df3.index.levels[0].equals(l0_expected))
self.assertTrue(df3.index.levels[1].equals(l1_expected))
self.assertFalse(df3.index.levels[1].equals(l1))
df4 = DataFrame(np.ones(5),
MultiIndex.from_arrays([int_idx, l0]))
df5 = getattr(df4, fn)('US/Pacific', level=1)
self.assertTrue(df3.index.levels[0].equals(l0))
self.assertFalse(df3.index.levels[0].equals(l0_expected))
self.assertTrue(df3.index.levels[1].equals(l1_expected))
self.assertFalse(df3.index.levels[1].equals(l1))
# Bad Inputs
for fn in ['tz_localize', 'tz_convert']:
# Not DatetimeIndex / PeriodIndex
with tm.assertRaisesRegexp(TypeError, 'DatetimeIndex'):
df = DataFrame(index=int_idx)
df = getattr(df, fn)('US/Pacific')
# Not DatetimeIndex / PeriodIndex
with tm.assertRaisesRegexp(TypeError, 'DatetimeIndex'):
df = DataFrame(np.ones(5),
MultiIndex.from_arrays([int_idx, l0]))
df = getattr(df, fn)('US/Pacific', level=0)
# Invalid level
with tm.assertRaisesRegexp(ValueError, 'not valid'):
df = DataFrame(index=l0)
df = getattr(df, fn)('US/Pacific', level=1)
def test_read_gbq_with_private_key_json_wrong_types_should_fail(self):
with tm.assertRaises(gbq.InvalidPrivateKeyFormat):
gbq.read_gbq(
'SELECT 1',
project_id='x',
private_key='{ "client_email" : 1, "private_key" : True }')
def test_bad_project_id(self):
with tm.assertRaises(gbq.GenericGBQException):
gbq.read_gbq("SELECT 1", project_id='001')
def test_read_gbq_with_empty_private_key_json_should_fail(self):
with tm.assertRaises(gbq.InvalidPrivateKeyFormat):
gbq.read_gbq('SELECT 1', project_id='x', private_key='{}')
def test_read_gbq_with_no_project_id_given_should_fail(self):
with tm.assertRaises(TypeError):
gbq.read_gbq('SELECT "1" as NUMBER_1')
def test_invalid_kind(self):
s = Series([1, 2])
with tm.assertRaises(ValueError):
s.plot(kind='aasdf')
def test_to_csv_multiindex(self):
pname = '__tmp_to_csv_multiindex__'
frame = self.frame
old_index = frame.index
arrays = np.arange(len(old_index) * 2).reshape(2, -1)
new_index = MultiIndex.from_arrays(arrays, names=['first', 'second'])
frame.index = new_index
with ensure_clean(pname) as path:
frame.to_csv(path, header=False)
frame.to_csv(path, columns=['A', 'B'])
# round trip
frame.to_csv(path)
df = DataFrame.from_csv(path, index_col=[0, 1], parse_dates=False)
# TODO to_csv drops column name
assert_frame_equal(frame, df, check_names=False)
self.assertEqual(frame.index.names, df.index.names)
# needed if setUP becomes a classmethod
self.frame.index = old_index
# try multiindex with dates
tsframe = self.tsframe
old_index = tsframe.index
new_index = [old_index, np.arange(len(old_index))]
tsframe.index = MultiIndex.from_arrays(new_index)
tsframe.to_csv(path, index_label=['time', 'foo'])
recons = DataFrame.from_csv(path, index_col=[0, 1])
# TODO to_csv drops column name
assert_frame_equal(tsframe, recons, check_names=False)
# do not load index
tsframe.to_csv(path)
recons = DataFrame.from_csv(path, index_col=None)
np.testing.assert_equal(
len(recons.columns), len(tsframe.columns) + 2)
# no index
tsframe.to_csv(path, index=False)
recons = DataFrame.from_csv(path, index_col=None)
assert_almost_equal(recons.values, self.tsframe.values)
# needed if setUP becomes classmethod
self.tsframe.index = old_index
with ensure_clean(pname) as path:
# GH3571, GH1651, GH3141
def _make_frame(names=None):
if names is True:
names = ['first', 'second']
return DataFrame(np.random.randint(0, 10, size=(3, 3)),
columns=MultiIndex.from_tuples(
[('bah', 'foo'),
('bah', 'bar'),
('ban', 'baz')], names=names),
dtype='int64')
# column & index are multi-index
df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)
df.to_csv(path, tupleize_cols=False)
result = read_csv(path, header=[0, 1, 2, 3], index_col=[
0, 1], tupleize_cols=False)
assert_frame_equal(df, result)
# column is mi
df = mkdf(5, 3, r_idx_nlevels=1, c_idx_nlevels=4)
df.to_csv(path, tupleize_cols=False)
result = read_csv(
path, header=[0, 1, 2, 3], index_col=0, tupleize_cols=False)
assert_frame_equal(df, result)
# dup column names?
df = mkdf(5, 3, r_idx_nlevels=3, c_idx_nlevels=4)
df.to_csv(path, tupleize_cols=False)
result = read_csv(path, header=[0, 1, 2, 3], index_col=[
0, 1, 2], tupleize_cols=False)
assert_frame_equal(df, result)
# writing with no index
df = _make_frame()
df.to_csv(path, tupleize_cols=False, index=False)
result = read_csv(path, header=[0, 1], tupleize_cols=False)
assert_frame_equal(df, result)
# we lose the names here
df = _make_frame(True)
df.to_csv(path, tupleize_cols=False, index=False)
result = read_csv(path, header=[0, 1], tupleize_cols=False)
self.assertTrue(all([x is None for x in result.columns.names]))
result.columns.names = df.columns.names
assert_frame_equal(df, result)
# tupleize_cols=True and index=False
df = _make_frame(True)
df.to_csv(path, tupleize_cols=True, index=False)
result = read_csv(
path, header=0, tupleize_cols=True, index_col=None)
result.columns = df.columns
assert_frame_equal(df, result)
# whatsnew example
df = _make_frame()
df.to_csv(path, tupleize_cols=False)
result = read_csv(path, header=[0, 1], index_col=[
0], tupleize_cols=False)
assert_frame_equal(df, result)
df = _make_frame(True)
df.to_csv(path, tupleize_cols=False)
result = read_csv(path, header=[0, 1], index_col=[
0], tupleize_cols=False)
assert_frame_equal(df, result)
# column & index are multi-index (compatibility)
df = mkdf(5, 3, r_idx_nlevels=2, c_idx_nlevels=4)
df.to_csv(path, tupleize_cols=True)
result = read_csv(path, header=0, index_col=[
0, 1], tupleize_cols=True)
result.columns = df.columns
assert_frame_equal(df, result)
# invalid options
df = _make_frame(True)
df.to_csv(path, tupleize_cols=False)
# catch invalid headers
with assertRaisesRegexp(CParserError,
'Passed header=\[0,1,2\] are too many '
'rows for this multi_index of columns'):
read_csv(path, tupleize_cols=False,
header=lrange(3), index_col=0)
with assertRaisesRegexp(CParserError,
'Passed header=\[0,1,2,3,4,5,6\], len of '
'7, but only 6 lines in file'):
read_csv(path, tupleize_cols=False,
header=lrange(7), index_col=0)
for i in [4, 5, 6]:
with tm.assertRaises(CParserError):
read_csv(path, tupleize_cols=False,
header=lrange(i), index_col=0)
# write with cols
with assertRaisesRegexp(TypeError, 'cannot specify cols with a '
'MultiIndex'):
df.to_csv(path, tupleize_cols=False, columns=['foo', 'bar'])
with ensure_clean(pname) as path:
# empty
tsframe[:0].to_csv(path)
recons = DataFrame.from_csv(path)
exp = tsframe[:0]
exp.index = []
self.assertTrue(recons.columns.equals(exp.columns))
self.assertEqual(len(recons), 0)
def test_bad_deprecate_kwarg(self):
with tm.assertRaises(TypeError):
@deprecate_kwarg('old', 'new', 0)
def f4(new=None):
pass
def test_to_gbq_should_fail_if_invalid_table_name_passed(self):
with tm.assertRaises(gbq.NotFoundException):
gbq.to_gbq(DataFrame(), 'invalid_table_name', project_id="1234")
def test_get_loc(self):
idx = pd.date_range('2000-01-01', periods=3)
for method in [None, 'pad', 'backfill', 'nearest']:
self.assertEqual(idx.get_loc(idx[1], method), 1)
self.assertEqual(idx.get_loc(idx[1].to_pydatetime(), method), 1)
self.assertEqual(idx.get_loc(str(idx[1]), method), 1)
if method is not None:
self.assertEqual(
idx.get_loc(idx[1],
method,
tolerance=pd.Timedelta('0 days')), 1)
self.assertEqual(idx.get_loc('2000-01-01', method='nearest'), 0)
self.assertEqual(idx.get_loc('2000-01-01T12', method='nearest'), 1)
self.assertEqual(
idx.get_loc('2000-01-01T12', method='nearest', tolerance='1 day'),
1)
self.assertEqual(
idx.get_loc('2000-01-01T12',
method='nearest',
tolerance=pd.Timedelta('1D')), 1)
self.assertEqual(
idx.get_loc('2000-01-01T12',
method='nearest',
tolerance=np.timedelta64(1, 'D')), 1)
self.assertEqual(
idx.get_loc('2000-01-01T12',
method='nearest',
tolerance=timedelta(1)), 1)
with tm.assertRaisesRegexp(ValueError, 'must be convertible'):
idx.get_loc('2000-01-01T12', method='nearest', tolerance='foo')
with tm.assertRaises(KeyError):
idx.get_loc('2000-01-01T03', method='nearest', tolerance='2 hours')
self.assertEqual(idx.get_loc('2000', method='nearest'), slice(0, 3))
self.assertEqual(idx.get_loc('2000-01', method='nearest'), slice(0, 3))
self.assertEqual(idx.get_loc('1999', method='nearest'), 0)
self.assertEqual(idx.get_loc('2001', method='nearest'), 2)
with tm.assertRaises(KeyError):
idx.get_loc('1999', method='pad')
with tm.assertRaises(KeyError):
idx.get_loc('2001', method='backfill')
with tm.assertRaises(KeyError):
idx.get_loc('foobar')
with tm.assertRaises(TypeError):
idx.get_loc(slice(2))
idx = pd.to_datetime(['2000-01-01', '2000-01-04'])
self.assertEqual(idx.get_loc('2000-01-02', method='nearest'), 0)
self.assertEqual(idx.get_loc('2000-01-03', method='nearest'), 1)
self.assertEqual(idx.get_loc('2000-01', method='nearest'), slice(0, 2))
# time indexing
idx = pd.date_range('2000-01-01', periods=24, freq='H')
tm.assert_numpy_array_equal(idx.get_loc(time(12)),
np.array([12]),
check_dtype=False)
tm.assert_numpy_array_equal(idx.get_loc(time(12, 30)),
np.array([]),
check_dtype=False)
with tm.assertRaises(NotImplementedError):
idx.get_loc(time(12, 30), method='pad')
