def test_annual_upsample(self):
targets = ["D", "B", "M"]
for month in MONTHS:
ts = _simple_pts("1/1/1990", "12/31/1995", freq="A-%s" % month)
for targ, conv, meth in product(targets, ["start", "end"], ["ffill", "bfill"]):
result = ts.resample(targ, fill_method=meth, convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, meth).to_period()
assert_series_equal(result, expected)
df = DataFrame({"a": ts})
rdf = df.resample("D", fill_method="ffill")
exp = df["a"].resample("D", fill_method="ffill")
assert_series_equal(rdf["a"], exp)
rng = period_range("2000", "2003", freq="A-DEC")
ts = Series([1, 2, 3, 4], index=rng)
result = ts.resample("M", fill_method="ffill")
ex_index = period_range("2000-01", "2003-12", freq="M")
expected = ts.asfreq("M", how="start").reindex(ex_index, method="ffill")
assert_series_equal(result, expected)
def test_annual_upsample(self):
targets = ['D', 'B', 'M']
for month in MONTHS:
ts = _simple_pts('1/1/1990', '12/31/1995', freq='A-%s' % month)
for targ, conv, meth in product(targets, ['start', 'end'],
['ffill', 'bfill']):
result = ts.resample(targ, fill_method=meth,
convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, meth).to_period()
assert_series_equal(result, expected)
df = DataFrame({'a' : ts})
rdf = df.resample('D', fill_method='ffill')
exp = df['a'].resample('D', fill_method='ffill')
assert_series_equal(rdf['a'], exp)
rng = period_range('2000', '2003', freq='A-DEC')
ts = Series([1, 2, 3, 4], index=rng)
result = ts.resample('M', fill_method='ffill')
ex_index = period_range('2000-01', '2003-12', freq='M')
expected = ts.asfreq('M', how='start').reindex(ex_index,
method='ffill')
assert_series_equal(result, expected)
def test_between_time(self):
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
ts = Series(np.random.randn(len(rng)), index=rng)
stime = time(0, 0)
etime = time(1, 0)
close_open = itertools.product([True, False], [True, False])
for inc_start, inc_end in close_open:
filtered = ts.between_time(stime, etime, inc_start, inc_end)
exp_len = 13 * 4 + 1
if not inc_start:
exp_len -= 5
if not inc_end:
exp_len -= 4
self.assert_(len(filtered) == exp_len)
for rs in filtered.index:
t = rs.time()
if inc_start:
self.assert_(t >= stime)
else:
self.assert_(t > stime)
if inc_end:
self.assert_(t <= etime)
else:
self.assert_(t < etime)
def test_monthly_upsample(self):
targets = ['D', 'B']
ts = _simple_pts('1/1/1990', '12/31/1995', freq='M')
for targ, conv in product(targets, ['start', 'end']):
result = ts.resample(targ, fill_method='ffill', convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, 'ffill').to_period()
assert_series_equal(result, expected)
def test_monthly_upsample(self):
targets = ["D", "B"]
ts = _simple_pts("1/1/1990", "12/31/1995", freq="M")
for targ, conv in product(targets, ["start", "end"]):
result = ts.resample(targ, fill_method="ffill", convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, "ffill").to_period()
assert_series_equal(result, expected)
def test_monthly_upsample(self):
targets = ['D', 'B']
ts = _simple_pts('1/1/1990', '12/31/1995', freq='M')
for targ, conv in product(targets, ['start', 'end']):
result = ts.resample(targ, fill_method='ffill',
convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, 'ffill').to_period()
assert_series_equal(result, expected)
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
from pandas.util.compat import product
import datetime
import pandas
d = datetime.date.min
data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
[d + datetime.timedelta(i) for i in xrange(20)], [1.0]))
df = pandas.DataFrame(data)
table = df.pivot_table(values=4, rows=[0, 1, 3], cols=[2])
df2 = df.rename(columns=str)
table2 = df2.pivot_table(values='4', rows=['0', '1', '3'], cols=['2'])
tm.assert_frame_equal(table, table2, check_names=False)
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
from pandas.util.compat import product
import datetime
import pandas
d = datetime.date.min
data = list(product(['foo', 'bar'], ['A', 'B', 'C'], ['x1', 'x2'],
[d + datetime.timedelta(i) for i in xrange(20)], [1.0]))
df = pandas.DataFrame(data)
table = df.pivot_table(values=4, rows=[0,1,3],cols=[2])
df2 = df.rename(columns=str)
table2 = df2.pivot_table(values='4', rows=['0','1','3'], cols=['2'])
tm.assert_frame_equal(table, table2)
def test_rank_tie_methods(self):
s = self.s
def _check(s, expected, method='average'):
result = s.rank(method=method)
assert_almost_equal(result, expected)
dtypes = [None, object]
disabled = set([(object, 'first')])
results = self.results
for method, dtype in product(results, dtypes):
if (dtype, method) in disabled:
continue
series = s if dtype is None else s.astype(dtype)
_check(series, results[method], method=method)
def test_rank_tie_methods(self):
s = self.s
def _check(s, expected, method='average'):
result = s.rank(method=method)
assert_almost_equal(result, expected)
dtypes = [None, object]
disabled = set([(object, 'first')])
results = self.results
for method, dtype in product(results, dtypes):
if (dtype, method) in disabled:
continue
series = s if dtype is None else s.astype(dtype)
_check(series, results[method], method=method)
def test_annual_upsample(self):
targets = ['D', 'B', 'M']
for month in MONTHS:
ts = _simple_pts('1/1/1990', '12/31/1995', freq='A-%s' % month)
for targ, conv, meth in product(targets, ['start', 'end'],
['ffill', 'bfill']):
result = ts.resample(targ, fill_method=meth, convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, meth).to_period()
assert_series_equal(result, expected)
df = DataFrame({'a': ts})
rdf = df.resample('D', fill_method='ffill')
exp = df['a'].resample('D', fill_method='ffill')
assert_series_equal(rdf['a'], exp)
def test_annual_upsample(self):
targets = ['D', 'B', 'M']
for month in MONTHS:
ts = _simple_pts('1/1/1990', '12/31/1995', freq='A-%s' % month)
for targ, conv, meth in product(targets, ['start', 'end'],
['ffill', 'bfill']):
result = ts.resample(targ, fill_method=meth,
convention=conv)
expected = result.to_timestamp(targ, how=conv)
expected = expected.asfreq(targ, meth).to_period()
assert_series_equal(result, expected)
df = DataFrame({'a' : ts})
rdf = df.resample('D', fill_method='ffill')
exp = df['a'].resample('D', fill_method='ffill')
assert_series_equal(rdf['a'], exp)
def test_pivot_integer_columns(self):
# caused by upstream bug in unstack
from pandas.util.compat import product
import datetime
import pandas
d = datetime.date.min
data = list(
product(
["foo", "bar"], ["A", "B", "C"], ["x1", "x2"], [d + datetime.timedelta(i) for i in xrange(20)], [1.0]
)
)
df = pandas.DataFrame(data)
table = df.pivot_table(values=4, rows=[0, 1, 3], cols=[2])
df2 = df.rename(columns=str)
table2 = df2.pivot_table(values="4", rows=["0", "1", "3"], cols=["2"])
tm.assert_frame_equal(table, table2)
def test_rank_2d_tie_methods(self):
s = self.s
df = self.df
def _check2d(df, expected, method='average', axis=0):
exp_df = DataFrame({'A': expected, 'B': expected})
if axis == 1:
df = df.T
exp_df = exp_df.T
result = df.rank(method=method, axis=axis)
assert_frame_equal(result, exp_df)
dtypes = [None, object]
disabled = set([(object, 'first')])
results = self.results
for method, axis, dtype in product(results, [0, 1], dtypes):
if (dtype, method) in disabled:
continue
frame = df if dtype is None else df.astype(dtype)
_check2d(frame, results[method], method=method, axis=axis)
def test_rank_2d_tie_methods(self):
s = self.s
df = self.df
def _check2d(df, expected, method='average', axis=0):
exp_df = DataFrame({'A': expected, 'B': expected})
if axis == 1:
df = df.T
exp_df = exp_df.T
result = df.rank(method=method, axis=axis)
assert_frame_equal(result, exp_df)
dtypes = [None, object]
disabled = set([(object, 'first')])
results = self.results
for method, axis, dtype in product(results, [0, 1], dtypes):
if (dtype, method) in disabled:
continue
frame = df if dtype is None else df.astype(dtype)
_check2d(frame, results[method], method=method, axis=axis)
def test_rank_descending(self):
dtypes = ['O', 'f8', 'i8']
for dtype, method in product(dtypes, self.results):
if 'i' in dtype:
s = self.s.dropna()
df = self.df.dropna()
else:
s = self.s.astype(dtype)
df = self.df.astype(dtype)
res = s.rank(ascending=False)
expected = (s.max() - s).rank()
assert_series_equal(res, expected)
res = df.rank(ascending=False)
expected = (df.max() - df).rank()
assert_frame_equal(res, expected)
if method == 'first' and dtype == 'O':
continue
expected = (s.max() - s).rank(method=method)
res2 = s.rank(method=method, ascending=False)
assert_series_equal(res2, expected)
expected = (df.max() - df).rank(method=method)
if dtype != 'O':
res2 = df.rank(method=method, ascending=False,
numeric_only=True)
assert_frame_equal(res2, expected)
res3 = df.rank(method=method, ascending=False,
numeric_only=False)
assert_frame_equal(res3, expected)
def test_rank_descending(self):
dtypes = ['O', 'f8', 'i8']
for dtype, method in product(dtypes, self.results):
if 'i' in dtype:
s = self.s.dropna()
df = self.df.dropna()
else:
s = self.s.astype(dtype)
df = self.df.astype(dtype)
res = s.rank(ascending=False)
expected = (s.max() - s).rank()
assert_series_equal(res, expected)
res = df.rank(ascending=False)
expected = (df.max() - df).rank()
assert_frame_equal(res, expected)
if method == 'first' and dtype == 'O':
continue
expected = (s.max() - s).rank(method=method)
res2 = s.rank(method=method, ascending=False)
assert_series_equal(res2, expected)
expected = (df.max() - df).rank(method=method)
if dtype != 'O':
res2 = df.rank(method=method, ascending=False,
numeric_only=True)
assert_frame_equal(res2, expected)
res3 = df.rank(method=method, ascending=False,
numeric_only=False)
assert_frame_equal(res3, expected)
