def test_tab_completion(self):
# GH 9910
s = Series(list('abcd'))
# Series of str values should have .str but not .dt/.cat in __dir__
assert 'str' in dir(s)
assert 'dt' not in dir(s)
assert 'cat' not in dir(s)
# similarly for .dt
s = Series(date_range('1/1/2015', periods=5))
assert 'dt' in dir(s)
assert 'str' not in dir(s)
assert 'cat' not in dir(s)
# Similarly for .cat, but with the twist that str and dt should be
# there if the categories are of that type first cat and str.
s = Series(list('abbcd'), dtype="category")
assert 'cat' in dir(s)
assert 'str' in dir(s) # as it is a string categorical
assert 'dt' not in dir(s)
# similar to cat and str
s = Series(date_range('1/1/2015', periods=5)).astype("category")
assert 'cat' in dir(s)
assert 'str' not in dir(s)
assert 'dt' in dir(s) # as it is a datetime categorical
def test_series(self):
# GH6407
# inferring series
# invalid type of Series
for s in [ Series(np.arange(10)),
Series(np.arange(10.))]:
self.assertRaises(TypeError, lambda : infer_freq(s))
# a non-convertible string
self.assertRaises(ValueError, lambda : infer_freq(Series(['foo','bar'])))
# cannot infer on PeriodIndex
for freq in [None, 'L', 'Y']:
s = Series(period_range('2013',periods=10,freq=freq))
self.assertRaises(TypeError, lambda : infer_freq(s))
# DateTimeIndex
for freq in ['M', 'L', 'S']:
s = Series(date_range('20130101',periods=10,freq=freq))
inferred = infer_freq(s)
self.assertEqual(inferred,freq)
s = Series(date_range('20130101','20130110'))
inferred = infer_freq(s)
self.assertEqual(inferred,'D')
def test_astype_str(self):
# test astype string - #10442
result = date_range('2012-01-01', periods=4,
name='test_name').astype(str)
expected = Index(['2012-01-01', '2012-01-02', '2012-01-03',
'2012-01-04'], name='test_name', dtype=object)
tm.assert_index_equal(result, expected)
# test astype string with tz and name
result = date_range('2012-01-01', periods=3, name='test_name',
tz='US/Eastern').astype(str)
expected = Index(['2012-01-01 00:00:00-05:00',
'2012-01-02 00:00:00-05:00',
'2012-01-03 00:00:00-05:00'],
name='test_name', dtype=object)
tm.assert_index_equal(result, expected)
# test astype string with freqH and name
result = date_range('1/1/2011', periods=3, freq='H',
name='test_name').astype(str)
expected = Index(['2011-01-01 00:00:00', '2011-01-01 01:00:00',
'2011-01-01 02:00:00'],
name='test_name', dtype=object)
tm.assert_index_equal(result, expected)
# test astype string with freqH and timezone
result = date_range('3/6/2012 00:00', periods=2, freq='H',
tz='Europe/London', name='test_name').astype(str)
expected = Index(['2012-03-06 00:00:00+00:00',
'2012-03-06 01:00:00+00:00'],
dtype=object, name='test_name')
tm.assert_index_equal(result, expected)
def test_tz_range_is_utc(self):
exp = '["2013-01-01T05:00:00.000Z","2013-01-02T05:00:00.000Z"]'
dfexp = ('{"DT":{'
'"0":"2013-01-01T05:00:00.000Z",'
'"1":"2013-01-02T05:00:00.000Z"}}')
tz_range = pd.date_range('2013-01-01 05:00:00Z', periods=2)
self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True))
dti = pd.DatetimeIndex(tz_range)
self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True))
df = DataFrame({'DT': dti})
self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True))
tz_range = pd.date_range('2013-01-01 00:00:00', periods=2,
tz='US/Eastern')
self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True))
dti = pd.DatetimeIndex(tz_range)
self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True))
df = DataFrame({'DT': dti})
self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True))
tz_range = pd.date_range('2013-01-01 00:00:00-0500', periods=2)
self.assertEqual(exp, pd.json.dumps(tz_range, iso_dates=True))
dti = pd.DatetimeIndex(tz_range)
self.assertEqual(exp, pd.json.dumps(dti, iso_dates=True))
df = DataFrame({'DT': dti})
self.assertEqual(dfexp, pd.json.dumps(df, iso_dates=True))
def _check_generated_range(self, start, freq):
freq = freq.upper()
gen = date_range(start, periods=7, freq=freq)
index = _dti(gen.values)
if not freq.startswith('Q-'):
self.assertEqual(infer_freq(index), gen.freqstr)
else:
inf_freq = infer_freq(index)
self.assertTrue((inf_freq == 'Q-DEC' and
gen.freqstr in ('Q', 'Q-DEC', 'Q-SEP', 'Q-JUN',
'Q-MAR'))
or
(inf_freq == 'Q-NOV' and
gen.freqstr in ('Q-NOV', 'Q-AUG', 'Q-MAY', 'Q-FEB'))
or
(inf_freq == 'Q-OCT' and
gen.freqstr in ('Q-OCT', 'Q-JUL', 'Q-APR', 'Q-JAN')))
gen = date_range(start, periods=5, freq=freq)
index = _dti(gen.values)
if not freq.startswith('Q-'):
self.assertEqual(infer_freq(index), gen.freqstr)
else:
inf_freq = infer_freq(index)
self.assertTrue((inf_freq == 'Q-DEC' and
gen.freqstr in ('Q', 'Q-DEC', 'Q-SEP', 'Q-JUN',
'Q-MAR'))
or
(inf_freq == 'Q-NOV' and
gen.freqstr in ('Q-NOV', 'Q-AUG', 'Q-MAY', 'Q-FEB'))
or
(inf_freq == 'Q-OCT' and
gen.freqstr in ('Q-OCT', 'Q-JUL', 'Q-APR', 'Q-JAN')))
def test_to_period_tz_explicit_pytz(self):
xp = date_range('1/1/2000', '4/1/2000').to_period()
ts = date_range('1/1/2000', '4/1/2000', tz=pytz.timezone('US/Eastern'))
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=pytz.utc)
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=tzlocal())
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
def test_to_period_tz_dateutil(self):
xp = date_range('1/1/2000', '4/1/2000').to_period()
ts = date_range('1/1/2000', '4/1/2000', tz='dateutil/US/Eastern')
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=dateutil.tz.tzutc())
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=tzlocal())
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
def test_groupby_groups_datetimeindex(self):
# GH#1430
periods = 1000
ind = pd.date_range(start='2012/1/1', freq='5min', periods=periods)
df = DataFrame({'high': np.arange(periods),
'low': np.arange(periods)}, index=ind)
grouped = df.groupby(lambda x: datetime(x.year, x.month, x.day))
# it works!
groups = grouped.groups
assert isinstance(list(groups.keys())[0], datetime)
# GH#11442
index = pd.date_range('2015/01/01', periods=5, name='date')
df = pd.DataFrame({'A': [5, 6, 7, 8, 9],
'B': [1, 2, 3, 4, 5]}, index=index)
result = df.groupby(level='date').groups
dates = ['2015-01-05', '2015-01-04', '2015-01-03',
'2015-01-02', '2015-01-01']
expected = {pd.Timestamp(date): pd.DatetimeIndex([date], name='date')
for date in dates}
tm.assert_dict_equal(result, expected)
grouped = df.groupby(level='date')
for date in dates:
result = grouped.get_group(date)
data = [[df.loc[date, 'A'], df.loc[date, 'B']]]
expected_index = pd.DatetimeIndex([date], name='date')
expected = pd.DataFrame(data,
columns=list('AB'),
index=expected_index)
tm.assert_frame_equal(result, expected)
def test_to_period_tz_pytz(self):
from pytz import utc as UTC
xp = date_range('1/1/2000', '4/1/2000').to_period()
ts = date_range('1/1/2000', '4/1/2000', tz='US/Eastern')
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=UTC)
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
ts = date_range('1/1/2000', '4/1/2000', tz=tzlocal())
result = ts.to_period()[0]
expected = ts[0].to_period()
assert result == expected
tm.assert_index_equal(ts.to_period(), xp)
def test_iteration_preserves_tz(self):
# see gh-8890
index = date_range("2012-01-01", periods=3, freq='H', tz='US/Eastern')
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result == expected
index = date_range("2012-01-01", periods=3, freq='H',
tz=dateutil.tz.tzoffset(None, -28800))
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result._repr_base == expected._repr_base
assert result == expected
# 9100
index = pd.DatetimeIndex(['2014-12-01 03:32:39.987000-08:00',
'2014-12-01 04:12:34.987000-08:00'])
for i, ts in enumerate(index):
result = ts
expected = index[i]
assert result._repr_base == expected._repr_base
assert result == expected
def test_categorical_series_repr_datetime_ordered(self):
idx = date_range('2011-01-01 09:00', freq='H', periods=5)
s = Series(Categorical(idx, ordered=True))
exp = """0 2011-01-01 09:00:00
1 2011-01-01 10:00:00
2 2011-01-01 11:00:00
3 2011-01-01 12:00:00
4 2011-01-01 13:00:00
dtype: category
Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa
assert repr(s) == exp
idx = date_range('2011-01-01 09:00', freq='H', periods=5,
tz='US/Eastern')
s = Series(Categorical(idx, ordered=True))
exp = """0 2011-01-01 09:00:00-05:00
1 2011-01-01 10:00:00-05:00
2 2011-01-01 11:00:00-05:00
3 2011-01-01 12:00:00-05:00
4 2011-01-01 13:00:00-05:00
dtype: category
Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
2011-01-01 13:00:00-05:00]""" # noqa
assert repr(s) == exp
def test_join_aware(self):
rng = date_range('1/1/2011', periods=10, freq='H')
ts = Series(np.random.randn(len(rng)), index=rng)
ts_utc = ts.tz_localize('utc')
self.assertRaises(Exception, ts.__add__, ts_utc)
self.assertRaises(Exception, ts_utc.__add__, ts)
test1 = DataFrame(np.zeros((6,3)),
index=date_range("2012-11-15 00:00:00", periods=6,
freq="100L", tz="US/Central"))
test2 = DataFrame(np.zeros((3,3)),
index=date_range("2012-11-15 00:00:00", periods=3,
freq="250L", tz="US/Central"),
columns=range(3,6))
result = test1.join(test2, how='outer')
ex_index = test1.index.union(test2.index)
self.assertTrue(result.index.equals(ex_index))
self.assertTrue(result.index.tz.zone == 'US/Central')
# non-overlapping
rng = date_range("2012-11-15 00:00:00", periods=6,
freq="H", tz="US/Central")
rng2 = date_range("2012-11-15 12:00:00", periods=6,
freq="H", tz="US/Eastern")
result = rng.union(rng2)
self.assertTrue(result.tz.zone == 'UTC')
def test_ufunc_coercions(self):
idx = date_range('2011-01-01', periods=3, freq='2D', name='x')
delta = np.timedelta64(1, 'D')
for result in [idx + delta, np.add(idx, delta)]:
assert isinstance(result, DatetimeIndex)
exp = date_range('2011-01-02', periods=3, freq='2D', name='x')
tm.assert_index_equal(result, exp)
assert result.freq == '2D'
for result in [idx - delta, np.subtract(idx, delta)]:
assert isinstance(result, DatetimeIndex)
exp = date_range('2010-12-31', periods=3, freq='2D', name='x')
tm.assert_index_equal(result, exp)
assert result.freq == '2D'
delta = np.array([np.timedelta64(1, 'D'), np.timedelta64(2, 'D'),
np.timedelta64(3, 'D')])
for result in [idx + delta, np.add(idx, delta)]:
assert isinstance(result, DatetimeIndex)
exp = DatetimeIndex(['2011-01-02', '2011-01-05', '2011-01-08'],
freq='3D', name='x')
tm.assert_index_equal(result, exp)
assert result.freq == '3D'
for result in [idx - delta, np.subtract(idx, delta)]:
assert isinstance(result, DatetimeIndex)
exp = DatetimeIndex(['2010-12-31', '2011-01-01', '2011-01-02'],
freq='D', name='x')
tm.assert_index_equal(result, exp)
assert result.freq == 'D'
def test_date_range_localize(self):
rng = date_range('3/11/2012 03:00', periods=15, freq='H', tz='US/Eastern')
rng2 = DatetimeIndex(['3/11/2012 03:00', '3/11/2012 04:00'],
tz='US/Eastern')
rng3 = date_range('3/11/2012 03:00', periods=15, freq='H')
rng3 = rng3.tz_localize('US/Eastern')
self.assert_(rng.equals(rng3))
# DST transition time
val = rng[0]
exp = Timestamp('3/11/2012 03:00', tz='US/Eastern')
self.assertEquals(val.hour, 3)
self.assertEquals(exp.hour, 3)
self.assertEquals(val, exp) # same UTC value
self.assert_(rng[:2].equals(rng2))
# Right before the DST transition
rng = date_range('3/11/2012 00:00', periods=2, freq='H', tz='US/Eastern')
rng2 = DatetimeIndex(['3/11/2012 00:00', '3/11/2012 01:00'],
tz='US/Eastern')
self.assert_(rng.equals(rng2))
exp = Timestamp('3/11/2012 00:00', tz='US/Eastern')
self.assertEquals(exp.hour, 0)
self.assertEquals(rng[0], exp)
exp = Timestamp('3/11/2012 01:00', tz='US/Eastern')
self.assertEquals(exp.hour, 1)
self.assertEquals(rng[1], exp)
rng = date_range('3/11/2012 00:00', periods=10, freq='H',
tz='US/Eastern')
self.assert_(rng[2].hour == 3)
def test_tz_localize_naive(self):
rng = date_range('1/1/2011', periods=100, freq='H')
conv = rng.tz_localize('US/Pacific')
exp = date_range('1/1/2011', periods=100, freq='H', tz='US/Pacific')
self.assert_(conv.equals(exp))
def test_categorical_repr_datetime_ordered(self):
idx = date_range('2011-01-01 09:00', freq='H', periods=5)
c = Categorical(idx, ordered=True)
exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa
assert repr(c) == exp
c = Categorical(idx.append(idx), categories=idx, ordered=True)
exp = """[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00, 2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00]
Categories (5, datetime64[ns]): [2011-01-01 09:00:00 < 2011-01-01 10:00:00 < 2011-01-01 11:00:00 <
2011-01-01 12:00:00 < 2011-01-01 13:00:00]""" # noqa
assert repr(c) == exp
idx = date_range('2011-01-01 09:00', freq='H', periods=5,
tz='US/Eastern')
c = Categorical(idx, ordered=True)
exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
2011-01-01 13:00:00-05:00]""" # noqa
assert repr(c) == exp
c = Categorical(idx.append(idx), categories=idx, ordered=True)
exp = """[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00, 2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00]
Categories (5, datetime64[ns, US/Eastern]): [2011-01-01 09:00:00-05:00 < 2011-01-01 10:00:00-05:00 <
2011-01-01 11:00:00-05:00 < 2011-01-01 12:00:00-05:00 <
2011-01-01 13:00:00-05:00]""" # noqa
assert repr(c) == exp
def test_categorical_index_repr_datetime_ordered(self):
idx = date_range('2011-01-01 09:00', freq='H', periods=5)
i = CategoricalIndex(Categorical(idx, ordered=True))
exp = """CategoricalIndex(['2011-01-01 09:00:00', '2011-01-01 10:00:00',
'2011-01-01 11:00:00', '2011-01-01 12:00:00',
'2011-01-01 13:00:00'],
categories=[2011-01-01 09:00:00, 2011-01-01 10:00:00, 2011-01-01 11:00:00, 2011-01-01 12:00:00, 2011-01-01 13:00:00], ordered=True, dtype='category')""" # noqa
assert repr(i) == exp
idx = date_range('2011-01-01 09:00', freq='H', periods=5,
tz='US/Eastern')
i = CategoricalIndex(Categorical(idx, ordered=True))
exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
'2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
'2011-01-01 13:00:00-05:00'],
categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa
assert repr(i) == exp
i = CategoricalIndex(Categorical(idx.append(idx), ordered=True))
exp = """CategoricalIndex(['2011-01-01 09:00:00-05:00', '2011-01-01 10:00:00-05:00',
'2011-01-01 11:00:00-05:00', '2011-01-01 12:00:00-05:00',
'2011-01-01 13:00:00-05:00', '2011-01-01 09:00:00-05:00',
'2011-01-01 10:00:00-05:00', '2011-01-01 11:00:00-05:00',
'2011-01-01 12:00:00-05:00', '2011-01-01 13:00:00-05:00'],
categories=[2011-01-01 09:00:00-05:00, 2011-01-01 10:00:00-05:00, 2011-01-01 11:00:00-05:00, 2011-01-01 12:00:00-05:00, 2011-01-01 13:00:00-05:00], ordered=True, dtype='category')""" # noqa
assert repr(i) == exp
def test_constructor_with_datetimelike(self):
# 12077
# constructor wwth a datetimelike and NaT
for dtl in [date_range('1995-01-01 00:00:00', periods=5, freq='s'),
date_range('1995-01-01 00:00:00', periods=5,
freq='s', tz='US/Eastern'),
timedelta_range('1 day', periods=5, freq='s')]:
s = Series(dtl)
c = Categorical(s)
expected = type(dtl)(s)
expected.freq = None
tm.assert_index_equal(c.categories, expected)
tm.assert_numpy_array_equal(c.codes, np.arange(5, dtype='int8'))
# with NaT
s2 = s.copy()
s2.iloc[-1] = NaT
c = Categorical(s2)
expected = type(dtl)(s2.dropna())
expected.freq = None
tm.assert_index_equal(c.categories, expected)
exp = np.array([0, 1, 2, 3, -1], dtype=np.int8)
tm.assert_numpy_array_equal(c.codes, exp)
result = repr(c)
assert 'NaT' in result
def test_date_range_businesshour(self):
idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00',
'2014-07-04 11:00',
'2014-07-04 12:00', '2014-07-04 13:00',
'2014-07-04 14:00',
'2014-07-04 15:00', '2014-07-04 16:00'],
freq='BH')
rng = date_range('2014-07-04 09:00', '2014-07-04 16:00', freq='BH')
tm.assert_index_equal(idx, rng)
idx = DatetimeIndex(
['2014-07-04 16:00', '2014-07-07 09:00'], freq='BH')
rng = date_range('2014-07-04 16:00', '2014-07-07 09:00', freq='BH')
tm.assert_index_equal(idx, rng)
idx = DatetimeIndex(['2014-07-04 09:00', '2014-07-04 10:00',
'2014-07-04 11:00',
'2014-07-04 12:00', '2014-07-04 13:00',
'2014-07-04 14:00',
'2014-07-04 15:00', '2014-07-04 16:00',
'2014-07-07 09:00', '2014-07-07 10:00',
'2014-07-07 11:00',
'2014-07-07 12:00', '2014-07-07 13:00',
'2014-07-07 14:00',
'2014-07-07 15:00', '2014-07-07 16:00',
'2014-07-08 09:00', '2014-07-08 10:00',
'2014-07-08 11:00',
'2014-07-08 12:00', '2014-07-08 13:00',
'2014-07-08 14:00',
'2014-07-08 15:00', '2014-07-08 16:00'],
freq='BH')
rng = date_range('2014-07-04 09:00', '2014-07-08 16:00', freq='BH')
tm.assert_index_equal(idx, rng)
def test_select_dtypes_include_exclude_mixed_scalars_lists(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})
ri = df.select_dtypes(include=np.number,
exclude=['floating', 'timedelta'])
ei = df[['b', 'c']]
assert_frame_equal(ri, ei)
ri = df.select_dtypes(include=[np.number, 'category'],
exclude='floating')
ei = df[['b', 'c', 'f', 'k']]
assert_frame_equal(ri, ei)
def test_astype(self):
# astype
expected = np.array([[Timestamp('2013-01-01 00:00:00'),
Timestamp('2013-01-02 00:00:00'),
Timestamp('2013-01-03 00:00:00')],
[Timestamp('2013-01-01 00:00:00-0500',
tz='US/Eastern'),
pd.NaT,
Timestamp('2013-01-03 00:00:00-0500',
tz='US/Eastern')],
[Timestamp('2013-01-01 00:00:00+0100', tz='CET'),
pd.NaT,
Timestamp('2013-01-03 00:00:00+0100',
tz='CET')]],
dtype=object).T
result = self.tzframe.astype(object)
assert_frame_equal(result, DataFrame(
expected, index=self.tzframe.index, columns=self.tzframe.columns))
result = self.tzframe.astype('datetime64[ns]')
expected = DataFrame({'A': date_range('20130101', periods=3),
'B': (date_range('20130101', periods=3,
tz='US/Eastern')
.tz_convert('UTC')
.tz_localize(None)),
'C': (date_range('20130101', periods=3,
tz='CET')
.tz_convert('UTC')
.tz_localize(None))})
expected.iloc[1, 1] = pd.NaT
expected.iloc[1, 2] = pd.NaT
assert_frame_equal(result, expected)
def test_at_time_frame(self):
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
ts = DataFrame(np.random.randn(len(rng), 2), index=rng)
rs = ts.at_time(rng[1])
self.assertTrue((rs.index.hour == rng[1].hour).all())
self.assertTrue((rs.index.minute == rng[1].minute).all())
self.assertTrue((rs.index.second == rng[1].second).all())
result = ts.at_time('9:30')
expected = ts.at_time(time(9, 30))
assert_frame_equal(result, expected)
result = ts.loc[time(9, 30)]
expected = ts.loc[(rng.hour == 9) & (rng.minute == 30)]
assert_frame_equal(result, expected)
# midnight, everything
rng = date_range('1/1/2000', '1/31/2000')
ts = DataFrame(np.random.randn(len(rng), 3), index=rng)
result = ts.at_time(time(0, 0))
assert_frame_equal(result, ts)
# time doesn't exist
rng = date_range('1/1/2012', freq='23Min', periods=384)
ts = DataFrame(np.random.randn(len(rng), 2), rng)
rs = ts.at_time('16:00')
self.assertEqual(len(rs), 0)
def test_date_range_nat(self):
# GH#11587
msg = "Neither `start` nor `end` can be NaT"
with pytest.raises(ValueError, match=msg):
date_range(start='2016-01-01', end=pd.NaT, freq='D')
with pytest.raises(ValueError, match=msg):
date_range(start=pd.NaT, end='2016-01-01', freq='D')
def test_select_x():
assert utils.select_x(None) is None
def _check(d, expected):
x = utils.select_x(d)
assert x == expected
data = dict(col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A')) # T
_check(data, 'col3')
data = dict(col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C']) # N
_check(data, 'col2')
data = dict(col1=[1.0, 2.0, 3.0]) # Q
_check(data, 'col1')
# Custom order
data = dict(col1=[1.0, 2.0, 3.0], # Q
col2=['A', 'B', 'C'], # N
col3=pd.date_range('2012', periods=3, freq='A'), # T
col4=pd.date_range('2012', periods=3, freq='A')) # T
selected_x = utils.select_x(data, ['N', 'T', 'Q', 'O'])
assert selected_x == "col2"
# Len < 1
assert utils.select_x(dict()) is None
def test_CalendarDay_range_with_dst_crossing(self):
# GH 20596
result = date_range('2018-10-23', '2018-11-06', freq='7CD',
tz='Europe/Paris')
expected = date_range('2018-10-23', '2018-11-06',
freq=pd.DateOffset(days=7), tz='Europe/Paris')
tm.assert_index_equal(result, expected)
def test_is_datetime_dtypes(self):
ts = pd.date_range('20130101', periods=3)
tsa = pd.date_range('20130101', periods=3, tz='US/Eastern')
self.assertTrue(is_datetime64_dtype('datetime64'))
self.assertTrue(is_datetime64_dtype('datetime64[ns]'))
self.assertTrue(is_datetime64_dtype(ts))
self.assertFalse(is_datetime64_dtype(tsa))
self.assertFalse(is_datetime64_ns_dtype('datetime64'))
self.assertTrue(is_datetime64_ns_dtype('datetime64[ns]'))
self.assertTrue(is_datetime64_ns_dtype(ts))
self.assertTrue(is_datetime64_ns_dtype(tsa))
self.assertTrue(is_datetime64_any_dtype('datetime64'))
self.assertTrue(is_datetime64_any_dtype('datetime64[ns]'))
self.assertTrue(is_datetime64_any_dtype(ts))
self.assertTrue(is_datetime64_any_dtype(tsa))
self.assertFalse(is_datetime64tz_dtype('datetime64'))
self.assertFalse(is_datetime64tz_dtype('datetime64[ns]'))
self.assertFalse(is_datetime64tz_dtype(ts))
self.assertTrue(is_datetime64tz_dtype(tsa))
for tz in ['US/Eastern', 'UTC']:
dtype = 'datetime64[ns, {}]'.format(tz)
self.assertFalse(is_datetime64_dtype(dtype))
self.assertTrue(is_datetime64tz_dtype(dtype))
self.assertTrue(is_datetime64_ns_dtype(dtype))
self.assertTrue(is_datetime64_any_dtype(dtype))
def setup(self):
N = 25000
index = tm.makeStringIndex(N)
self.df = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N)},
index=index)
self.df_mixed = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N),
'string1': ['foo'] * N,
'bool1': [True] * N,
'int1': np.random.randint(0, N, size=N)},
index=index)
self.df_wide = DataFrame(np.random.randn(N, 100))
self.start_wide = self.df_wide.index[10000]
self.stop_wide = self.df_wide.index[15000]
self.df2 = DataFrame({'float1': np.random.randn(N),
'float2': np.random.randn(N)},
index=date_range('1/1/2000', periods=N))
self.start = self.df2.index[10000]
self.stop = self.df2.index[15000]
self.df_wide2 = DataFrame(np.random.randn(N, 100),
index=date_range('1/1/2000', periods=N))
self.df_dc = DataFrame(np.random.randn(N, 10),
columns=['C%03d' % i for i in range(10)])
self.fname = '__test__.h5'
self.store = HDFStore(self.fname)
self.store.put('fixed', self.df)
self.store.put('fixed_mixed', self.df_mixed)
self.store.append('table', self.df2)
self.store.append('table_mixed', self.df_mixed)
self.store.append('table_wide', self.df_wide)
self.store.append('table_wide2', self.df_wide2)
def test_with_tz_ambiguous_times(self):
tz = pytz.timezone('US/Eastern')
rng = bdate_range(datetime(2009, 1, 1), datetime(2010, 1, 1))
# regular no problem
self.assert_(rng.tz_validate())
# March 13, 2011, spring forward, skip from 2 AM to 3 AM
dr = date_range(datetime(2011, 3, 13, 1, 30), periods=3,
freq=datetools.Hour())
self.assertRaises(pytz.AmbiguousTimeError, dr.tz_localize, tz)
# after dst transition, it works
dr = date_range(datetime(2011, 3, 13, 3, 30), periods=3,
freq=datetools.Hour(), tz=tz)
# November 6, 2011, fall back, repeat 2 AM hour
dr = date_range(datetime(2011, 11, 6, 1, 30), periods=3,
freq=datetools.Hour())
self.assertRaises(pytz.AmbiguousTimeError, dr.tz_localize, tz)
# UTC is OK
dr = date_range(datetime(2011, 3, 13), periods=48,
freq=datetools.Minute(30), tz=pytz.utc)
def setUp(self):
date_index = date_range(datetime(2009, 12, 11), periods=3,
freq=datetools.bday)
ts = Series([3, 1, 4], index=date_index)
self.TS1 = ts
date_index = date_range(datetime(2009, 12, 11), periods=5,
freq=datetools.bday)
ts = Series([1, 5, 9, 2, 6], index=date_index)
self.TS2 = ts
date_index = date_range(datetime(2009, 12, 11), periods=3,
freq=datetools.bday)
ts = Series([5, np.nan, 3], index=date_index)
self.TS3 = ts
date_index = date_range(datetime(2009, 12, 11), periods=5,
freq=datetools.bday)
ts = Series([np.nan, 5, 8, 9, 7], index=date_index)
self.TS4 = ts
data = {'x1': self.TS2, 'x2': self.TS4}
self.DF1 = DataFrame(data=data)
data = {'x1': self.TS2, 'x2': self.TS4}
self.DICT1 = data
def test_select_dtypes_exclude_using_scalars(self):
df = DataFrame({'a': list('abc'),
'b': list(range(1, 4)),
'c': np.arange(3, 6).astype('u1'),
'd': np.arange(4.0, 7.0, dtype='float64'),
'e': [True, False, True],
'f': pd.Categorical(list('abc')),
'g': pd.date_range('20130101', periods=3),
'h': pd.date_range('20130101', periods=3,
tz='US/Eastern'),
'i': pd.date_range('20130101', periods=3,
tz='CET'),
'j': pd.period_range('2013-01', periods=3,
freq='M'),
'k': pd.timedelta_range('1 day', periods=3)})
ri = df.select_dtypes(exclude=np.number)
ei = df[['a', 'e', 'f', 'g', 'h', 'i', 'j']]
assert_frame_equal(ri, ei)
ri = df.select_dtypes(exclude='category')
ei = df[['a', 'b', 'c', 'd', 'e', 'g', 'h', 'i', 'j', 'k']]
assert_frame_equal(ri, ei)
pytest.raises(NotImplementedError,
lambda: df.select_dtypes(exclude='period'))
def test_TimeSeries_deprecation(self):
# deprecation TimeSeries, #10890
with tm.assert_produces_warning(FutureWarning):
pd.SparseTimeSeries(1, index=pd.date_range('20130101', periods=3))
def ugh(exp):
file = open_ncfile('../../'+exp+'/landCoverFrac/LPX-Bern_'+exp+
'_landCoverFrac_original.nc')
lat = file.variables['latitude'][92:160]
lon = file.variables['longitude'][584:668]
time = pd.date_range(start='1/1/2001', end='1/01/2019', freq='M')
veg1 = file.variables['landCoverFrac'][584:668,92:160,3612:,0]
veg2 = file.variables['landCoverFrac'][584:668,92:160,3612:,1]
veg3 = file.variables['landCoverFrac'][584:668,92:160,3612:,2]
veg4 = file.variables['landCoverFrac'][584:668,92:160,3612:,3]
veg5 = file.variables['landCoverFrac'][584:668,92:160,3612:,4]
veg6 = file.variables['landCoverFrac'][584:668,92:160,3612:,5]
veg7 = file.variables['landCoverFrac'][584:668,92:160,3612:,6]
veg8 = file.variables['landCoverFrac'][584:668,92:160,3612:,7]
veg9 = file.variables['landCoverFrac'][584:668,92:160,3612:,8]
veg10 = file.variables['landCoverFrac'][584:668,92:160,3612:,9]
veg11 = file.variables['landCoverFrac'][584:668,92:160,3612:,10]
veg12 = file.variables['landCoverFrac'][584:668,92:160,3612:,11]
veg13 = file.variables['landCoverFrac'][584:668,92:160,3612:,12]
veg14 = file.variables['landCoverFrac'][584:668,92:160,3612:,13]
veg15 = file.variables['landCoverFrac'][584:668,92:160,3612:,14]
veg16 = file.variables['landCoverFrac'][584:668,92:160,3612:,15]
veg17 = file.variables['landCoverFrac'][584:668,92:160,3612:,16]
veg18 = file.variables['landCoverFrac'][584:668,92:160,3612:,17]
veg19 = file.variables['landCoverFrac'][584:668,92:160,3612:,18]
veg20 = file.variables['landCoverFrac'][584:668,92:160,3612:,19]
pft_short = ['TrBR', 'TeNE', 'TeBE', 'TeBS', 'BNE', 'BNS', 'BBS', 'C3G',
'C4G', 'PeatGr', 'PeatSM', 'PeatTrBE', 'PeatTrBR', 'PeatHerb',
'C3Crop', 'C4Crop', 'C3Past', 'C4Past', 'UrbanBare']
pfts = ['Tropical broad raingreen', 'Temperate needle evergreen',
'Temperate broad evergeen', 'Temperate broad summergreen',
'Boreal needle evergreen', 'Boreal needle summergreen',
'Boreal broad summergreen', 'C3 herbaceous', 'C4 herbaceous',
'Peat graminoid', 'Peat sphagnum moss',
'Peat flood tolerant tropical broad evergreen',
'Peat flood tolerant tropical broad raingreen',
'Peat flood tolerant herbaceous', 'Cropland C3 herbaceous',
'Cropland C4 herbaceous', 'Pasture C3 herbaceous',
'Pasture C4 herbaceous', 'Urban Bare']
vegs = [veg2, veg3, veg4, veg5, veg6, veg7, veg8, veg9, veg10, veg11, veg12,
veg13, veg14, veg15, veg16, veg17, veg18, veg19, veg20]
# create dataset
ds = xr.Dataset({
'TrBE': xr.DataArray(data = np.transpose(veg1),
dims = ['time', 'latitude', 'longitude'],
coords = [time, lat, lon],
attrs = {'long_name': 'Tropical broad evergreen',
'units' : '%'})},
attrs = {'Conventions':'CF-1.6',
'Institution':'Climate and Environmental Physics, '
'University of Bern',
'Source': 'Extracted from LPX-Bern_S3_01 at '
'2019-08-13T17:58:26.478921',
'Title':'Fractional Land Cover of PFT output from '
'LPX-Bern for GCP201',
'Contact': '*****@*****.**'}
)
for v, ps, p in zip(vegs, pft_short, pfts):
ds[ps] = xr.DataArray(data = np.transpose(v),
dims = ['time', 'latitude', 'longitude'],
coords = [time, lat, lon],
attrs = {'long_name': p,
'units' : '%'})
ds['latitude'].attrs={'units':'degrees_north', 'long_name':'latitude',
'standard_name':'latitude', 'axis':'Y'}
ds['longitude'].attrs={'units':'degrees_east', 'long_name':'longitude',
'standard_name':'longitude', 'axis':'X'}
ds.to_netcdf('LPX-Bern_'+exp+'_landCoverFrac.nc',
encoding={'latitude':{'dtype': 'double'},
'longitude':{'dtype': 'double'},
'time':{'dtype': 'double'},
'TrBE':{'dtype': 'float32'},
'TrBR':{'dtype': 'float32'},
'TeNE':{'dtype': 'float32'},
'TeBE':{'dtype': 'float32'},
'TeBS':{'dtype': 'float32'},
'BNE':{'dtype': 'float32'},
'BNS':{'dtype': 'float32'},
'BBS':{'dtype': 'float32'},
'C3G':{'dtype': 'float32'},
'C4G':{'dtype': 'float32'},
'PeatGr':{'dtype': 'float32'},
'PeatSM':{'dtype': 'float32'},
'PeatTrBE':{'dtype': 'float32'},
'PeatTrBR':{'dtype': 'float32'},
'PeatHerb':{'dtype': 'float32'},
'C3Crop':{'dtype': 'float32'},
'C4Crop':{'dtype': 'float32'},
'C3Past':{'dtype': 'float32'},
'C4Past':{'dtype': 'float32'},
'UrbanBare':{'dtype': 'float32'}})
def lag():
os.chdir(dir_in)
#get names
tg_list_name = sorted(os.listdir())
x = 91
y = 92
for tg in range(x, y):
os.chdir(dir_in)
tg_name = tg_list_name[tg]
print(tg_name, '\n')
pred = pd.read_csv(tg_name)
#create a daily time series - date_range
#get only the ymd of the start and end times
start_time = pred['date'][0].split(' ')[0]
end_time = pred['date'].iloc[-1].split(' ')[0]
print(start_time, ' - ', end_time, '\n')
date_range = pd.date_range(start_time, end_time, freq='D')
#defining time changing lambda functions
time_str = lambda x: str(x)
time_converted_str = pd.DataFrame(map(time_str, date_range),
columns=['date'])
time_converted_stamp = pd.DataFrame(date_range, columns=['timestamp'])
"""
first prepare the six time lagging dataframes
then use the merge function to merge the original
predictor with the lagging dataframes
"""
#prepare lagged time series for time only
#note here that since MERRA has 3hrly data
#the lag_hrs is increased from 6(eraint) to 31(MERRA)
time_lagged = pd.DataFrame()
lag_hrs = list(range(0, 31))
for lag in lag_hrs:
lag_name = 'lag' + str(lag)
lam_delta = lambda x: str(x - dt.timedelta(hours=lag))
lag_new = pd.DataFrame(map(lam_delta, time_converted_stamp['timestamp']), \
columns = [lag_name])
time_lagged = pd.concat([time_lagged, lag_new], axis=1)
#datafrmae that contains all lagged time series (just time)
time_all = pd.concat([time_converted_str, time_lagged], axis=1)
pred_lagged = pd.DataFrame()
for ii in range(
1, time_all.shape[1]): #to loop through the lagged time series
print(time_all.columns[ii])
#extracting corresponding tag time series
lag_ts = pd.DataFrame(time_all.iloc[:, ii])
lag_ts.columns = ['date']
#merge the selected tlagged time with the predictor on = "date"
pred_new = pd.merge(pred, lag_ts, on=['date'], how='right')
pred_new.drop('Unnamed: 0', axis=1, inplace=True)
#sometimes nan values go to the bottom of the dataframe
#sort df by date -> reset the index -> remove old index
pred_new.sort_values(by='date', inplace=True)
pred_new.reset_index(inplace=True)
pred_new.drop('index', axis=1, inplace=True)
#concatenate lagged dataframe
if ii == 1:
pred_lagged = pred_new
else:
pred_lagged = pd.concat([pred_lagged, pred_new.iloc[:, 1:]],
axis=1)
#cd to saving directory
os.chdir(dir_out)
pred_lagged.to_csv(tg_name)
os.chdir(dir_in)
def obtain_data(config_file_path, metadata_file_path=None):
"""
Uses read_config() to acquire a full dictionary of the config file and then uses the values contained within it
to direct how data is processed and what variables are obtained.
If a metadata file is provided, the config file will still be used for data organization, but the metadata will
be pulled from the metadata file.
Args:
config_file_path : string of path to config file, should work with absolute or relative path
metadata_file_path : string of path to metadata file if provided
Returns:
extracted_data : pandas dataframe of entire dataset, with the variables being organized into columns
col_df : pandas series of what variables are stored in what columns, used to track which vars are provided
station_name : string of file, including path, that was provided to dataset
log_file : string of log file, including path, that was provided to dataset
station_lat : station latitude in decimal degrees
station_elev : station elevation in meters
anemom_height : height of anemometer in meters
fill_value : value pulled from config file that indicates missing data in output file
script_mode : boolean flag for if user wants to correct data or not
gen_bokeh : boolean flag for if user wants to plot graphs or not
"""
# Open config file
validate_file(config_file_path, ['ini'])
config_dict = read_config(config_file_path)
print('\nSuccessfully opened config file at %s' % config_file_path)
# Open metadata file
# If a metadata file is provided we will open it and overwrite values in config_dict with its values
if metadata_file_path is not None:
validate_file(metadata_file_path, 'xlsx') # Validate file to make sure it exists and is the right type
metadata_df = pd.read_excel(metadata_file_path, sheet_name=0, index_col=0, engine='openpyxl',
keep_default_na=True, na_filter=True, verbose=True)
print('\nSuccessfully opened metadata file at %s' % metadata_file_path)
current_row = metadata_df.run_count.ne(2).idxmax() - 1
metadata_series = metadata_df.iloc[current_row]
config_dict['data_file_path'] = metadata_series.input_path
config_dict['station_latitude'] = metadata_series.latitude
config_dict['station_longitude'] = metadata_series.longitude
config_dict['station_elevation'] = metadata_series.elev_m
config_dict['anemometer_height'] = metadata_series.anemom_height_m
config_dict['corr_flag'] = metadata_series.run_count
# split file string on extension
(file_name, station_extension) = os.path.splitext(config_dict['data_file_path'])
# check to see if file is in a subdirectory in the same folder as the script
if '/' in file_name:
(folder_path, delimiter, _station_name) = file_name.rpartition('/')
elif '\\' in file_name:
(folder_path, delimiter, _station_name) = file_name.rpartition('\\')
else:
folder_path = os.getcwd()
# Add new keys to config_dict for directory and file information to save files later on
config_dict['station_name'] = str(metadata_series.id)
config_dict['file_name'] = file_name
config_dict['station_extension'] = station_extension
config_dict['folder_path'] = folder_path
else: # No metadata file was provided, use the path info of the data file to construct path variables
metadata_df = None
metadata_series = None
(file_name, station_extension) = os.path.splitext(config_dict['data_file_path'])
# check to see if file is in a subdirectory or by itself
if '/' in file_name:
(folder_path, delimiter, station_name) = file_name.rpartition('/')
elif '\\' in file_name:
(folder_path, delimiter, station_name) = file_name.rpartition('\\')
else:
station_name = file_name
folder_path = os.getcwd()
# Add new keys to config_dict for directory and file information to save files later on
config_dict['station_name'] = station_name
config_dict['file_name'] = file_name
config_dict['station_extension'] = station_extension
config_dict['folder_path'] = folder_path
# Check lines_of_header value, if 0 change it to NONE, if nonzero minus it by one
if config_dict['lines_of_header'] == 0:
config_dict['lines_of_header'] = None
else:
config_dict['lines_of_header'] = config_dict['lines_of_header'] - 1
# Open data file
validate_file(config_dict['data_file_path'], ['csv', 'xls', 'xlsx'])
if station_extension == '.csv': # csv file provided
raw_data = pd.read_csv(config_dict['data_file_path'], delimiter=',', header=config_dict['lines_of_header'],
index_col=None, engine='python', skipfooter=config_dict['lines_of_footer'],
na_values=config_dict['missing_data_value'], keep_default_na=True,
na_filter=True, verbose=True, skip_blank_lines=True)
elif station_extension == '.xlsx':
raw_data = pd.read_excel(config_dict['data_file_path'], sheet_name=0, header=config_dict['lines_of_header'],
index_col=None, engine='openpyxl', skipfooter=config_dict['lines_of_footer'],
na_values=config_dict['missing_data_value'], keep_default_na=True,
na_filter=True, verbose=True)
elif station_extension == '.xls':
raw_data = pd.read_excel(config_dict['data_file_path'], sheet_name=0, header=config_dict['lines_of_header'],
index_col=None, engine='xlrd', skipfooter=config_dict['lines_of_footer'],
na_values=config_dict['missing_data_value'], keep_default_na=True,
na_filter=True, verbose=True)
else:
# This script is only handles csv and excel files. Validate_file() already catches this case
raise IOError('\n\nProvided file was of type \'{}\' but script was expecting type \'{}\'.'
.format(station_extension, ['csv', 'xls', 'xlsx']))
print('\nSuccessfully opened data file at %s' % config_dict['data_file_path'])
# Handle any for network-specific oddities that may have slipped through
raw_data = raw_data.replace(to_replace='NO RECORD ', value=np.nan) # catch for whitespaces on agriment
# check for the existence of 'correction_files' folder and if not present make one
if not os.path.exists(folder_path + '/correction_files'):
os.makedirs(folder_path + '/correction_files')
os.makedirs(folder_path + '/correction_files/before_graphs/')
os.makedirs(folder_path + '/correction_files/after_graphs/')
os.makedirs(folder_path + '/correction_files/histograms/')
else:
pass
# Create log file for this new data file
config_dict['log_file_path'] = config_dict['folder_path'] + \
'/correction_files/' + config_dict['station_name'] + '_changes_log' + '.txt'
log.basicConfig()
logger = open(config_dict['log_file_path'], 'w')
logger.write('The raw data for %s has been successfully read in at %s. \n \n' %
(config_dict['station_name'], dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
logger.close()
print('\nSuccessfully created log file at %s.' % config_dict['log_file_path'])
# Date handling, figures out the date format and extracts from string if needed
if config_dict['date_format'] == 1:
# Date is provided as a string, expected format is MM/DD/YYYY, time can be included as well.
if config_dict['string_date_col'] != -1:
data_date = np.array(raw_data.iloc[:, config_dict['string_date_col']])
dt_date = pd.to_datetime(data_date, errors='raise')
data_day = np.array(dt_date.day.astype('int'))
data_month = np.array(dt_date.month.astype('int'))
data_year = np.array(dt_date.year.astype('int'))
else:
# date format was provided as a string date but no string date was given
raise ValueError('Date format parameter indicated a string date but none was provided')
elif config_dict['date_format'] == 2:
if config_dict['month_col'] != -1 and config_dict['day_col'] != -1 and config_dict['year_col'] != -1:
data_month = np.array(raw_data.iloc[:, config_dict['month_col']].astype('int'))
data_day = np.array(raw_data.iloc[:, config_dict['day_col']].astype('int'))
data_year = np.array(raw_data.iloc[:, config_dict['year_col']].astype('int'))
else:
# date format was provided as separate columns but some were missing
raise ValueError('Date format parameter indicated separate y/m/d columns but some or all were missing')
elif config_dict['date_format'] == 3:
# Date is pre-split between year column and DOY column
if config_dict['day_of_year_col'] != -1 and config_dict['year_col'] != -1:
data_doy = np.array(raw_data.iloc[:, config_dict['day_of_year_col']].astype('int'))
data_year = np.array(raw_data.iloc[:, config_dict['year_col']].astype('int'))
else:
# date format was provided as separate year and doy columns but some were missing
raise ValueError('Date format parameter indicated year and DOY columns but some or all were missing')
dt_date = pd.to_datetime(data_year * 1000 + data_doy, format='%Y%j', errors='raise')
data_day = np.array(dt_date.day.astype('int'))
data_month = np.array(dt_date.month.astype('int'))
data_year = np.array(dt_date.year.astype('int'))
else:
# Script cannot function without a time variable
raise ValueError('Parameter error: date_format is set to an unexpected value.')
#########################
# Variable processing
# Imports all weather variables, converts them into the correct units, and filters them to remove impossible values
(data_tmax, tmax_col) = process_variable(config_dict, raw_data, 'maximum_temperature')
(data_tmin, tmin_col) = process_variable(config_dict, raw_data, 'minimum_temperature')
(data_tavg, tavg_col) = process_variable(config_dict, raw_data, 'average_temperature')
(data_tdew, tdew_col) = process_variable(config_dict, raw_data, 'dewpoint_temperature')
(data_ea, ea_col) = process_variable(config_dict, raw_data, 'vapor_pressure')
(data_rhmax, rhmax_col) = process_variable(config_dict, raw_data, 'maximum_relative_humidity')
(data_rhmin, rhmin_col) = process_variable(config_dict, raw_data, 'minimum_relative_humidity')
(data_rhavg, rhavg_col) = process_variable(config_dict, raw_data, 'average_relative_humidity')
(data_rs, rs_col) = process_variable(config_dict, raw_data, 'solar_radiation')
(data_ws, ws_col) = process_variable(config_dict, raw_data, 'wind_speed')
(data_precip, precip_col) = process_variable(config_dict, raw_data, 'precipitation')
# HPRCC data reports '0' for missing observations as well as a text column, but this script doesn't interpret text
# columns, so instead we see if both tmax and tmin have the same value (0, or -17.7778 depending on units) and if so
# mark that row as missing
# realistically tmax should never equal tmin, so this is an okay check to have in general
for i in range(len(data_tmax)):
if data_tmax[i] == data_tmin[i]:
data_tmax[i] = np.nan
data_tmin[i] = np.nan
data_tavg[i] = np.nan
data_tdew[i] = np.nan
data_ea[i] = np.nan
data_rhmax[i] = np.nan
data_rhmin[i] = np.nan
data_rhavg[i] = np.nan
data_rs[i] = np.nan
data_ws[i] = np.nan
data_precip[i] = np.nan
else:
pass
#########################
# Dataframe Construction
# In this section we convert the individual numpy arrays into a pandas dataframe to accomplish several goals:
# 1. Make use of the pandas reindexing function to cover literal gaps in the dataset (not just missing values)
# 2. Resample data to remove any duplicate records (same day appears twice in dataset, first instance is kept)
# 3. Cleanly pass extracted data to the main script function
# Create Datetime dataframe for reindexing
datetime_df = pd.DataFrame({'year': data_year, 'month': data_month, 'day': data_day})
datetime_df = pd.to_datetime(datetime_df)
# Create a series of all dates in time series
date_reindex = pd.date_range(datetime_df.iloc[0], datetime_df.iloc[-1])
reindexing_additions = np.setdiff1d(np.array(date_reindex), np.array(datetime_df), assume_unique=False)
logger = open(config_dict['log_file_path'], 'w')
logger.write('The raw data file had %s missing date entries from its time record. \n \n' %
reindexing_additions.size)
logger.close()
print('\nSystem: The input data file had %s missing dates in its time record.' % reindexing_additions.size)
# Create dataframe of data
data_df = pd.DataFrame({'year': data_year, 'month': data_month,
'day': data_day, 'tavg': data_tavg, 'tmax': data_tmax, 'tmin': data_tmin,
'tdew': data_tdew, 'ea': data_ea, 'rhavg': data_rhavg, 'rhmax': data_rhmax,
'rhmin': data_rhmin, 'rs': data_rs, 'ws': data_ws, 'precip': data_precip},
index=datetime_df)
# Create dataframe of column indices for weather variable, to track which ones were provided vs calculated
col_df = pd.Series({'tmax': tmax_col, 'tmin': tmin_col, 'tavg': tavg_col, 'tdew': tdew_col, 'ea': ea_col,
'rhmax': rhmax_col, 'rhmin': rhmin_col, 'rhavg': rhavg_col, 'rs': rs_col, 'ws': ws_col,
'precip': precip_col})
# Check for the existence of duplicate indexes
# if found, since it cannot be determined which value is true, we default to first instance and remove all following
data_df = data_df[~data_df.index.duplicated(keep='first')]
# Reindex data with filled date series in case there are gaps in the data
data_df = data_df.reindex(date_reindex, fill_value=np.nan)
# Now replace M/D/Y columns with reindexed dates so there are no missing days
data_df.year = date_reindex.year
data_df.month = date_reindex.month
data_df.day = date_reindex.day
return data_df, col_df, metadata_df, metadata_series, config_dict
def test_getitem_unrecognized_scalar():
# GH#32684 a scalar key that is not recognized by lib.is_scalar
# a series that might be produced via `frame.dtypes`
ser = Series([1, 2], index=[np.dtype("O"), np.dtype("i8")])
key = ser.index[1]
result = ser[key]
assert result == 2
@pytest.mark.parametrize(
"index",
[
date_range("2014-01-01", periods=20, freq="MS"),
period_range("2014-01", periods=20, freq="M"),
timedelta_range("0", periods=20, freq="H"),
],
)
def test_slice_with_zero_step_raises(index):
ts = Series(np.arange(20), index)
with pytest.raises(ValueError, match="slice step cannot be zero"):
ts[::0]
with pytest.raises(ValueError, match="slice step cannot be zero"):
ts.loc[::0]
with pytest.raises(ValueError, match="slice step cannot be zero"):
ts.iloc[::0]
def test_subexpr_datetime():
data = pd.date_range(start='01/01/2010', end='01/04/2010', freq='D').values
s = symbol('s', discover(data))
result = compute(s.truncate(days=2).day, data)
expected = np.array([31, 2, 2, 4])
np.testing.assert_array_equal(result, expected)
def index(self) -> pd.DatetimeIndex:
if self._index is None:
self._index = pd.date_range(self.start_date,
periods=self.prediction_length,
freq=self.freq)
return self._index
import wooldridge as woo
import pandas as pd
import numpy as np
import statsmodels.api as sm
import patsy as pt
barium = woo.dataWoo('barium')
T = len(barium)
# monthly time series starting Feb. 1978:
barium.index = pd.date_range(start='1978-02', periods=T, freq='M')
# perform the Cochrane-Orcutt estimation (iterative procedure):
y, X = pt.dmatrices(
'np.log(chnimp) ~ np.log(chempi) + np.log(gas) +'
'np.log(rtwex) + befile6 + affile6 + afdec6',
data=barium,
return_type='dataframe')
reg = sm.GLSAR(y, X)
CORC_results = reg.iterative_fit(maxiter=100)
table = pd.DataFrame({
'b_CORC': CORC_results.params,
'se_CORC': CORC_results.bse
})
print(f'reg.rho: {reg.rho}\n')
print(f'table: \n{table}\n')
def test_interp_datetime64(self):
_skip_if_no_scipy()
df = Series([1, np.nan, 3], index=date_range('1/1/2000', periods=3))
result = df.interpolate(method='nearest')
expected = Series([1, 1, 3], index=date_range('1/1/2000', periods=3))
assert_series_equal(result, expected)
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2018-09-14 21:57:25 # @Author : cdl ([email protected]) # @Link : https://github.com/cdlwhm1217096231/python3_spider # @Version : $Id$ from pyecharts import Bar import pandas as pd import numpy as np title = "bar chart" index = pd.date_range('14/9/2018', periods=6, freq='M') df1 = pd.DataFrame(np.random.randn(6), index=index) df2 = pd.DataFrame(np.random.randn(6), index=index) dtvalue1 = [i[0] for i in df1.values] dtvalue2 = [i[0] for i in df2.values] _index = [i for i in df1.index.format()] bar = Bar(title, 'Profit and loss situation') bar.add('profit', _index, dtvalue1) bar.add('loss', _index, dtvalue2)
def add_datetime_index(self, start_date, feature_added_df: pd.DataFrame):
date_range = pd.date_range(start_date, periods=48 * 9, freq='30min')
feature_added_df.index = date_range
def test_equals(self):
s1 = pd.Series([1, 2, 3], index=[0, 2, 1])
s2 = s1.copy()
self.assert_(s1.equals(s2))
s1[1] = 99
self.assert_(not s1.equals(s2))
# NaNs compare as equal
s1 = pd.Series([1, np.nan, 3, np.nan], index=[0, 2, 1, 3])
s2 = s1.copy()
self.assert_(s1.equals(s2))
s2[0] = 9.9
self.assert_(not s1.equals(s2))
idx = MultiIndex.from_tuples([(0, 'a'), (1, 'b'), (2, 'c')])
s1 = Series([1, 2, np.nan], index=idx)
s2 = s1.copy()
self.assert_(s1.equals(s2))
# Add object dtype column with nans
index = np.random.random(10)
df1 = DataFrame(np.random.random(10,), index=index, columns=['floats'])
df1['text'] = 'the sky is so blue. we could use more chocolate.'.split()
df1['start'] = date_range('2000-1-1', periods=10, freq='T')
df1['end'] = date_range('2000-1-1', periods=10, freq='D')
df1['diff'] = df1['end'] - df1['start']
df1['bool'] = (np.arange(10) % 3 == 0)
df1.ix[::2] = nan
df2 = df1.copy()
self.assert_(df1['text'].equals(df2['text']))
self.assert_(df1['start'].equals(df2['start']))
self.assert_(df1['end'].equals(df2['end']))
self.assert_(df1['diff'].equals(df2['diff']))
self.assert_(df1['bool'].equals(df2['bool']))
self.assert_(df1.equals(df2))
self.assert_(not df1.equals(object))
# different dtype
different = df1.copy()
different['floats'] = different['floats'].astype('float32')
self.assert_(not df1.equals(different))
# different index
different_index = -index
different = df2.set_index(different_index)
self.assert_(not df1.equals(different))
# different columns
different = df2.copy()
different.columns = df2.columns[::-1]
self.assert_(not df1.equals(different))
# DatetimeIndex
index = pd.date_range('2000-1-1', periods=10, freq='T')
df1 = df1.set_index(index)
df2 = df1.copy()
self.assert_(df1.equals(df2))
# MultiIndex
df3 = df1.set_index(['text'], append=True)
df2 = df1.set_index(['text'], append=True)
self.assert_(df3.equals(df2))
df2 = df1.set_index(['floats'], append=True)
self.assert_(not df3.equals(df2))
# NaN in index
df3 = df1.set_index(['floats'], append=True)
df2 = df1.set_index(['floats'], append=True)
self.assert_(df3.equals(df2))
def gen_all_quarters(start: datetime.datetime, end: datetime.datetime):
idx = pd.date_range(start=start, end=end, freq="Q")
# dt_list = [dt.strftime("%Y-%m-%d %H:%M:%S") for dt in idx]
dt_list = [dt.to_pydatetime() for dt in idx]
return dt_list
def main():
# Calendar-Spread implementation example
# default market environment
market_env = MarketEnvironment()
print(market_env)
# options expirations
T_short = "31-05-2020"
T_long = "30-08-2020"
# current underlying level
S_t = market_env.get_S()
# calendar-spread portfolio initialized (as empty portfolio)
calendar_spread_ptf = Portfolio(name="Calendar Spread Strategy")
print(calendar_spread_ptf)
# T_long-call
Vanilla_Call_long = PlainVanillaOption(market_env, T=T_long, K=S_t)
print(Vanilla_Call_long)
# T_short-call
Vanilla_Call_short = PlainVanillaOption(market_env, T=T_short, K=S_t)
print(Vanilla_Call_short)
# creation of Calendar-Spread portfolio strategy
calendar_spread_ptf.add_instrument(Vanilla_Call_long, 1)
calendar_spread_ptf.add_instrument(Vanilla_Call_short, -1)
print(calendar_spread_ptf)
# portfolio plotter instance
calendar_spread_ptf_plotter = PortfolioPlotter(calendar_spread_ptf)
# valuation date of the portfolio
valuation_date = calendar_spread_ptf.get_t()
print(valuation_date)
# select metrics to plot
for plot_metrics in ["price", "PnL", "delta", "theta", "gamma", "vega", "rho"]:
plot_details_flag = True if plot_metrics == "price" else False
# time-parameter as a date-range of 5 valuation dates between t and T_short
last_date = T_short if plot_metrics in ["price", "PnL"] else date_string_to_datetime_obj(T_short) - pd.Timedelta(days=1)
multiple_valuation_dates = pd.date_range(start=valuation_date,
end=last_date,
periods=5)
print(multiple_valuation_dates)
# Bull-Spread price plot
calendar_spread_ptf_plotter.plot(t=last_date, plot_metrics=plot_metrics,
plot_details=plot_details_flag)
# Plot at multiple dates
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics)
# Surface plot
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True)
# Surface plot (rotate) - Underlying value side
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True, view=(0,180))
# Price surface plot (rotate) - Date side
calendar_spread_ptf_plotter.plot(t=multiple_valuation_dates, plot_metrics=plot_metrics,
surf_plot=True, view=(0,-90))
def data_read(date_range, crd, utc_offset):
# Number of ASOS observations an hour
interval = 12
freq = ['5min', 'H']
# Adjust datetimes to timezone corresponding to location of interest
date_range = pd.date_range(start=date_range[0], end=date_range[-1], freq=freq[0])
start_date, end_date = [date_range[0], date_range[-1]]
# Generate strings from dates for comparison purposes
date_str = [datetime.datetime.strftime(start_date, '%Y%m%d%H%M'),
datetime.datetime.strftime(end_date, '%Y%m%d%H%M')]
# Initialize DataFrame here to return nan DataFrame in case of failed FTP connection
df = pd.DataFrame(np.nan, index=date_range, columns=['T_air', 'T_dew', 'u_r', 'p_air'])
# Set up URL to appropriate data file
data_url = wfile(date_str[0][0:6] + '.dat', crd)
# Import data to DataFrame 'df'
try:
asos_data = pd.read_table(data_url, header=None)
except:
print('FTP connection failed. Exiting program...')
sys.exit()
## Regex patterns for data mining through the .dat file(s)
# Air temperature regex: string of 6 characters "(0-9)(0-9)/(0-9)(0-9)" bounded by 2 spaces
T_pattern = r'\s.?\d\d[+-/].?\d\d\s'
# Wind speed regex: string of 6 characters "(0-9)(0-9)KT " bounded by 2 numbers and a space
# Note: This definition ignores gusts
u_pattern = r"\s\d\d\d\d\d\D"
# Note: This definition allows the gust becomes the effective wind speed
# u_pattern = r"\d\d[K][T]\s\d"
# Air pressure regex: string of 6 characters "SLP(0-9)(0-9)"
p_pattern = r"[S][L][P]\d\d\d"
# Iterate through all rows in ASOS data file. For dates in file that are within date range, extract data.
for row in asos_data.iloc[:, 0]:
if datetime.datetime.strptime(row[13:23], '%Y%m%d%H') in df.index:
# If temperature pattern is found, extract data.
if re.findall(T_pattern, row):
date = datetime.datetime.strptime(row[13:25], '%Y%m%d%H%M')
# Extract air temperature ('M' prefix indicates a negative temperature)
T_air_str = re.findall(T_pattern, row)[0]
if T_air_str[1] == 'M':
df.loc[date, 'T_air'] = CtoK(-int(T_air_str[2:4]))
else:
df.loc[date, 'T_air'] = CtoK(int(T_air_str[1:3]))
# Extract dew point temperature ('M' prefix indicates a negative temperature)
if T_air_str[-4] == 'M':
df.loc[date, 'T_dew'] = CtoK(-int(T_air_str[-3:-1]))
else:
df.loc[date, 'T_dew'] = CtoK(int(T_air_str[-3:-1]))
# Extract wind speed
if re.findall(u_pattern, row):
u_str = re.findall(u_pattern, row)[0]
df.loc[date, 'u_r'] = int(u_str[4:6])
else:
df.loc[date, 'u_r'] = 0
# Extract air pressure
if re.findall(p_pattern, row):
# Convert p_str to pressure in hPa
p_temp = 1000 + int(re.findall(p_pattern, row)[0][-3:])/10
df.loc[date, 'p_air'] = int(p_temp)
else:
df.loc[date, 'p_air'] = 1013.25
# Average over all observations to produce hourly, then re-index to set dates to proper indices.
df = pd.DataFrame(df.values.reshape(-1, interval, df.shape[1]).mean(1), columns=df.columns)
df['date'] = pd.date_range(start=date_range[0], end=date_range[-1], freq=freq[1])
df = df.set_index('date')
# Delete ASOS data folder created locally
shutil.rmtree(os.path.join(os.path.dirname(__file__), data_url.split('/')[-2]))
return df['u_r'], df['T_dew'], df['p_air']
#Dividing the dataset into test and train
y_train=np.array(df['Prediction'][:-15])
X_train=np.array(X[:-15])
X_test=np.array(X[-15:])
#Training the model
model = LinearRegression()
model.fit(X_train,y_train)
forecast_prediction = model.predict(X_test) #Prediction made
#To create a dataframe of predicted price
forecast_prediction=[df['Adj. Close'][-1]]+forecast_prediction.tolist()
Pred_data={"Forecast":forecast_prediction}
dates = pd.date_range('20180327', periods=16)
Pred_df = pd.DataFrame(data=Pred_data, index=dates)
print("Done.\n")
print(Pred_df[-15:]) #Displaying predicted price
#For ploting the graph of Date vs Price
df['Adj. Close'].plot()
Pred_df['Forecast'].plot()
plt.legend(loc=4)
plt.title("Google Stock Price")
plt.xlabel('Date')
plt.ylabel('Price')
plt.show() #To display graph to the user
def test_strftime(self):
# GH 10086
s = Series(date_range("20130101", periods=5))
result = s.dt.strftime("%Y/%m/%d")
expected = Series(
["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
)
tm.assert_series_equal(result, expected)
s = Series(date_range("2015-02-03 11:22:33.4567", periods=5))
result = s.dt.strftime("%Y/%m/%d %H-%M-%S")
expected = Series(
[
"2015/02/03 11-22-33",
"2015/02/04 11-22-33",
"2015/02/05 11-22-33",
"2015/02/06 11-22-33",
"2015/02/07 11-22-33",
]
)
tm.assert_series_equal(result, expected)
s = Series(period_range("20130101", periods=5))
result = s.dt.strftime("%Y/%m/%d")
expected = Series(
["2013/01/01", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
)
tm.assert_series_equal(result, expected)
s = Series(period_range("2015-02-03 11:22:33.4567", periods=5, freq="s"))
result = s.dt.strftime("%Y/%m/%d %H-%M-%S")
expected = Series(
[
"2015/02/03 11-22-33",
"2015/02/03 11-22-34",
"2015/02/03 11-22-35",
"2015/02/03 11-22-36",
"2015/02/03 11-22-37",
]
)
tm.assert_series_equal(result, expected)
s = Series(date_range("20130101", periods=5))
s.iloc[0] = pd.NaT
result = s.dt.strftime("%Y/%m/%d")
expected = Series(
["NaT", "2013/01/02", "2013/01/03", "2013/01/04", "2013/01/05"]
)
tm.assert_series_equal(result, expected)
datetime_index = date_range("20150301", periods=5)
result = datetime_index.strftime("%Y/%m/%d")
expected = Index(
["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
dtype=np.object_,
)
# dtype may be S10 or U10 depending on python version
tm.assert_index_equal(result, expected)
period_index = period_range("20150301", periods=5)
result = period_index.strftime("%Y/%m/%d")
expected = Index(
["2015/03/01", "2015/03/02", "2015/03/03", "2015/03/04", "2015/03/05"],
dtype="=U10",
)
tm.assert_index_equal(result, expected)
s = Series([datetime(2013, 1, 1, 2, 32, 59), datetime(2013, 1, 2, 14, 32, 1)])
result = s.dt.strftime("%Y-%m-%d %H:%M:%S")
expected = Series(["2013-01-01 02:32:59", "2013-01-02 14:32:01"])
tm.assert_series_equal(result, expected)
s = Series(period_range("20130101", periods=4, freq="H"))
result = s.dt.strftime("%Y/%m/%d %H:%M:%S")
expected = Series(
[
"2013/01/01 00:00:00",
"2013/01/01 01:00:00",
"2013/01/01 02:00:00",
"2013/01/01 03:00:00",
]
)
s = Series(period_range("20130101", periods=4, freq="L"))
result = s.dt.strftime("%Y/%m/%d %H:%M:%S.%l")
expected = Series(
[
"2013/01/01 00:00:00.000",
"2013/01/01 00:00:00.001",
"2013/01/01 00:00:00.002",
"2013/01/01 00:00:00.003",
]
)
tm.assert_series_equal(result, expected)
# %%
download_dir = 'output.csv' #where you want the file to be downloaded to
csv = open(download_dir, "w")
#"w" indicates that you're writing strings to the file
columnTitleRow = "datetime, WindSpeed, WindDir\n"
csv.write(columnTitleRow)
for i in range(len(time)):
row=str(time[i])+","+str(speed[i])+","+str(direction[i])+"\n"
print(row)
csv.write(row)
csv.close()
# %%
idx = pd.date_range(time, periods=10, freq='min')
ts = pd.Series(range(len(idx)), index=idx)
ts
ts.resample('10min').mean()
# %%
time[1]
# %%
def test_dt_accessor_no_new_attributes(self):
# https://github.com/pandas-dev/pandas/issues/10673
s = Series(date_range("20130101", periods=5, freq="D"))
with pytest.raises(AttributeError, match="You cannot add any new attribute"):
s.dt.xlabel = "a"
def test_dt_accessor_updates_on_inplace(self):
s = Series(pd.date_range("2018-01-01", periods=10))
s[2] = None
s.fillna(pd.Timestamp("2018-01-01"), inplace=True)
result = s.dt.date
assert result[0] == result[2]
import numpy as np
import pandas as pd
s = pd.Series([1,3,5,np.nan,6,8])
dates = pd.date_range('20200405',periods=13)
df = pd.DataFrame(np.random.randn(13, 4), index=dates, columns=list('ABCD')
df2 = pd.DataFrame({
'A':1.,
'B':pd.Timestamp('20200405'),
'C':pd.Series(1, index=list(range(4)),dtype='float32'),
'D':np.array([3]*4, dtype='int32'),
'E': pd.Categorical(['test','train','test','train']),
'F':'foo'})
print(df.iloc[3])
print(df.iloc[3:5,0:2])
def test_dt_accessor_datetime_name_accessors(self, time_locale):
# Test Monday -> Sunday and January -> December, in that sequence
if time_locale is None:
# If the time_locale is None, day-name and month_name should
# return the english attributes
expected_days = [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday",
]
expected_months = [
"January",
"February",
"March",
"April",
"May",
"June",
"July",
"August",
"September",
"October",
"November",
"December",
]
else:
with tm.set_locale(time_locale, locale.LC_TIME):
expected_days = calendar.day_name[:]
expected_months = calendar.month_name[1:]
s = Series(date_range(freq="D", start=datetime(1998, 1, 1), periods=365))
english_days = [
"Monday",
"Tuesday",
"Wednesday",
"Thursday",
"Friday",
"Saturday",
"Sunday",
]
for day, name, eng_name in zip(range(4, 11), expected_days, english_days):
name = name.capitalize()
assert s.dt.weekday_name[day] == eng_name
assert s.dt.day_name(locale=time_locale)[day] == name
s = s.append(Series([pd.NaT]))
assert np.isnan(s.dt.day_name(locale=time_locale).iloc[-1])
s = Series(date_range(freq="M", start="2012", end="2013"))
result = s.dt.month_name(locale=time_locale)
expected = Series([month.capitalize() for month in expected_months])
# work around https://github.com/pandas-dev/pandas/issues/22342
result = result.str.normalize("NFD")
expected = expected.str.normalize("NFD")
tm.assert_series_equal(result, expected)
for s_date, expected in zip(s, expected_months):
result = s_date.month_name(locale=time_locale)
expected = expected.capitalize()
result = unicodedata.normalize("NFD", result)
expected = unicodedata.normalize("NFD", expected)
assert result == expected
s = s.append(Series([pd.NaT]))
assert np.isnan(s.dt.month_name(locale=time_locale).iloc[-1])
import pandas as pd
import numpy as np
dates = pd.date_range("20191031", periods=6)
df = pd.DataFrame(np.arange(24).reshape(6, 4), index=dates, columns=['A', 'B', 'C', 'D'])
print(df)
'''
A B C D
2019-10-31 0 1 2 3
2019-11-01 4 5 6 7
2019-11-02 8 9 10 11
2019-11-03 12 13 14 15
2019-11-04 16 17 18 19
2019-11-05 20 21 22 23
'''
df.iloc[2, 2] = 1111
print(df)
'''
A B C D
2019-10-31 0 1 2 3
2019-11-01 4 5 6 7
2019-11-02 8 9 1111 11
2019-11-03 12 13 14 15
2019-11-04 16 17 18 19
2019-11-05 20 21 22 23
'''
df.loc['2019-11-02', 'B'] = 2222
print(df)
'''
def test_dt_namespace_accessor(self):
# GH 7207, 11128
# test .dt namespace accessor
ok_for_period = PeriodArray._datetimelike_ops
ok_for_period_methods = ["strftime", "to_timestamp", "asfreq"]
ok_for_dt = DatetimeIndex._datetimelike_ops
ok_for_dt_methods = [
"to_period",
"to_pydatetime",
"tz_localize",
"tz_convert",
"normalize",
"strftime",
"round",
"floor",
"ceil",
"day_name",
"month_name",
]
ok_for_td = TimedeltaIndex._datetimelike_ops
ok_for_td_methods = [
"components",
"to_pytimedelta",
"total_seconds",
"round",
"floor",
"ceil",
]
def get_expected(s, name):
result = getattr(Index(s._values), prop)
if isinstance(result, np.ndarray):
if is_integer_dtype(result):
result = result.astype("int64")
elif not is_list_like(result):
return result
return Series(result, index=s.index, name=s.name)
def compare(s, name):
a = getattr(s.dt, prop)
b = get_expected(s, prop)
if not (is_list_like(a) and is_list_like(b)):
assert a == b
else:
tm.assert_series_equal(a, b)
# datetimeindex
cases = [
Series(date_range("20130101", periods=5), name="xxx"),
Series(date_range("20130101", periods=5, freq="s"), name="xxx"),
Series(date_range("20130101 00:00:00", periods=5, freq="ms"), name="xxx"),
]
for s in cases:
for prop in ok_for_dt:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_dt_methods:
getattr(s.dt, prop)
result = s.dt.to_pydatetime()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.tz_localize("US/Eastern")
exp_values = DatetimeIndex(s.values).tz_localize("US/Eastern")
expected = Series(exp_values, index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
tz_result = result.dt.tz
assert str(tz_result) == "US/Eastern"
freq_result = s.dt.freq
assert freq_result == DatetimeIndex(s.values, freq="infer").freq
# let's localize, then convert
result = s.dt.tz_localize("UTC").dt.tz_convert("US/Eastern")
exp_values = (
DatetimeIndex(s.values).tz_localize("UTC").tz_convert("US/Eastern")
)
expected = Series(exp_values, index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
# datetimeindex with tz
s = Series(date_range("20130101", periods=5, tz="US/Eastern"), name="xxx")
for prop in ok_for_dt:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_dt_methods:
getattr(s.dt, prop)
result = s.dt.to_pydatetime()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.tz_convert("CET")
expected = Series(s._values.tz_convert("CET"), index=s.index, name="xxx")
tm.assert_series_equal(result, expected)
tz_result = result.dt.tz
assert str(tz_result) == "CET"
freq_result = s.dt.freq
assert freq_result == DatetimeIndex(s.values, freq="infer").freq
# timedelta index
cases = [
Series(
timedelta_range("1 day", periods=5), index=list("abcde"), name="xxx"
),
Series(timedelta_range("1 day 01:23:45", periods=5, freq="s"), name="xxx"),
Series(
timedelta_range("2 days 01:23:45.012345", periods=5, freq="ms"),
name="xxx",
),
]
for s in cases:
for prop in ok_for_td:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_td_methods:
getattr(s.dt, prop)
result = s.dt.components
assert isinstance(result, DataFrame)
tm.assert_index_equal(result.index, s.index)
result = s.dt.to_pytimedelta()
assert isinstance(result, np.ndarray)
assert result.dtype == object
result = s.dt.total_seconds()
assert isinstance(result, pd.Series)
assert result.dtype == "float64"
freq_result = s.dt.freq
assert freq_result == TimedeltaIndex(s.values, freq="infer").freq
# both
index = date_range("20130101", periods=3, freq="D")
s = Series(date_range("20140204", periods=3, freq="s"), index=index, name="xxx")
exp = Series(
np.array([2014, 2014, 2014], dtype="int64"), index=index, name="xxx"
)
tm.assert_series_equal(s.dt.year, exp)
exp = Series(np.array([2, 2, 2], dtype="int64"), index=index, name="xxx")
tm.assert_series_equal(s.dt.month, exp)
exp = Series(np.array([0, 1, 2], dtype="int64"), index=index, name="xxx")
tm.assert_series_equal(s.dt.second, exp)
exp = pd.Series([s[0]] * 3, index=index, name="xxx")
tm.assert_series_equal(s.dt.normalize(), exp)
# periodindex
cases = [Series(period_range("20130101", periods=5, freq="D"), name="xxx")]
for s in cases:
for prop in ok_for_period:
# we test freq below
if prop != "freq":
compare(s, prop)
for prop in ok_for_period_methods:
getattr(s.dt, prop)
freq_result = s.dt.freq
assert freq_result == PeriodIndex(s.values).freq
# test limited display api
def get_dir(s):
results = [r for r in s.dt.__dir__() if not r.startswith("_")]
return list(sorted(set(results)))
s = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
results = get_dir(s)
tm.assert_almost_equal(
results, list(sorted(set(ok_for_dt + ok_for_dt_methods)))
)
s = Series(
period_range("20130101", periods=5, freq="D", name="xxx").astype(object)
)
results = get_dir(s)
tm.assert_almost_equal(
results, list(sorted(set(ok_for_period + ok_for_period_methods)))
)
# 11295
# ambiguous time error on the conversions
s = Series(pd.date_range("2015-01-01", "2016-01-01", freq="T"), name="xxx")
s = s.dt.tz_localize("UTC").dt.tz_convert("America/Chicago")
results = get_dir(s)
tm.assert_almost_equal(
results, list(sorted(set(ok_for_dt + ok_for_dt_methods)))
)
exp_values = pd.date_range(
"2015-01-01", "2016-01-01", freq="T", tz="UTC"
).tz_convert("America/Chicago")
expected = Series(exp_values, name="xxx")
tm.assert_series_equal(s, expected)
# no setting allowed
s = Series(date_range("20130101", periods=5, freq="D"), name="xxx")
with pytest.raises(ValueError, match="modifications"):
s.dt.hour = 5
# trying to set a copy
with pd.option_context("chained_assignment", "raise"):
with pytest.raises(com.SettingWithCopyError):
s.dt.hour[0] = 5
def main():
subject_file = open("subjects.txt","r")
n=int(subject_file.readline())
sub=[]
subcode={}
colors=[0]*n
for i in range(n):
x=subject_file.readline().split(" -> ")
sub.append(x)
subcode[x[0]]=[i,x[1]]
graph=[None]*n
for i in range(n):
graph[i]=[0]*n
student_file = open("students.txt" , "r")
n2=int(student_file.readline())
for i in range(n2):
x=student_file.readline().split()
x[1]=x[1].split(",")
for j in x[1]:
for k in x[1]:
if(j==k):
continue
graph[subcode[j][0]][subcode[k][0]]=1
graph[subcode[k][0]][subcode[j][0]]=1
colors[0]=1
colorsused=1
for i in range(1,n):
c=[]
for j in range(n):
if graph[i][j]==1 and colors[j] not in c:
c.append(colors[j])
for col in range(1,colorsused+2):
if col not in c:
colors[i]=col
colorsused=max(colorsused,col)
break
import pandas as pd
import datetime
print("Enter starting of exam in YYYY-MM-DD format: ")
d1,d2="",""
week=['Monday','Tuesday','Wednesday','Thursday', 'Friday', 'Saturday','Sunday']
while(True):
try:
d1=input()
d1=pd.to_datetime(d1)
break
except:
print("Enter Valid Date!")
d2=d1 + datetime.timedelta(days=2*colorsused)
daterange = pd.date_range(d1,d2)
currcol=1
for single_date in daterange:
if(single_date.weekday()==6):
continue
else:
s=str(single_date).split()[0]
print("\n-------------------------------------------------")
print(s,'('+week[single_date.weekday()]+')',":")
print("-------------------------------------------------\n")
for i in range(n):
if(currcol==colors[i]):
print(sub[i][0],sub[i][1],end="")
if(currcol>=colorsused):
break
else:
currcol+=1
class TestCategoricalDtypeParametrized:
@pytest.mark.parametrize(
"categories",
[
list("abcd"),
np.arange(1000),
["a", "b", 10, 2, 1.3, True],
[True, False],
pd.date_range("2017", periods=4),
],
)
def test_basic(self, categories, ordered):
c1 = CategoricalDtype(categories, ordered=ordered)
tm.assert_index_equal(c1.categories, pd.Index(categories))
assert c1.ordered is ordered
def test_order_matters(self):
categories = ["a", "b"]
c1 = CategoricalDtype(categories, ordered=True)
c2 = CategoricalDtype(categories, ordered=False)
c3 = CategoricalDtype(categories, ordered=None)
assert c1 is not c2
assert c1 is not c3
@pytest.mark.parametrize("ordered", [False, None])
def test_unordered_same(self, ordered):
c1 = CategoricalDtype(["a", "b"], ordered=ordered)
c2 = CategoricalDtype(["b", "a"], ordered=ordered)
assert hash(c1) == hash(c2)
def test_categories(self):
result = CategoricalDtype(["a", "b", "c"])
tm.assert_index_equal(result.categories, pd.Index(["a", "b", "c"]))
assert result.ordered is False
def test_equal_but_different(self, ordered):
c1 = CategoricalDtype([1, 2, 3])
c2 = CategoricalDtype([1.0, 2.0, 3.0])
assert c1 is not c2
assert c1 != c2
@pytest.mark.parametrize("v1, v2", [([1, 2, 3], [1, 2, 3]), ([1, 2, 3], [3, 2, 1])])
def test_order_hashes_different(self, v1, v2):
c1 = CategoricalDtype(v1, ordered=False)
c2 = CategoricalDtype(v2, ordered=True)
c3 = CategoricalDtype(v1, ordered=None)
assert c1 is not c2
assert c1 is not c3
def test_nan_invalid(self):
msg = "Categorical categories cannot be null"
with pytest.raises(ValueError, match=msg):
CategoricalDtype([1, 2, np.nan])
def test_non_unique_invalid(self):
msg = "Categorical categories must be unique"
with pytest.raises(ValueError, match=msg):
CategoricalDtype([1, 2, 1])
def test_same_categories_different_order(self):
c1 = CategoricalDtype(["a", "b"], ordered=True)
c2 = CategoricalDtype(["b", "a"], ordered=True)
assert c1 is not c2
@pytest.mark.parametrize("ordered1", [True, False, None])
@pytest.mark.parametrize("ordered2", [True, False, None])
def test_categorical_equality(self, ordered1, ordered2):
# same categories, same order
# any combination of None/False are equal
# True/True is the only combination with True that are equal
c1 = CategoricalDtype(list("abc"), ordered1)
c2 = CategoricalDtype(list("abc"), ordered2)
result = c1 == c2
expected = bool(ordered1) is bool(ordered2)
assert result is expected
# same categories, different order
# any combination of None/False are equal (order doesn't matter)
# any combination with True are not equal (different order of cats)
c1 = CategoricalDtype(list("abc"), ordered1)
c2 = CategoricalDtype(list("cab"), ordered2)
result = c1 == c2
expected = (bool(ordered1) is False) and (bool(ordered2) is False)
assert result is expected
# different categories
c2 = CategoricalDtype([1, 2, 3], ordered2)
assert c1 != c2
# none categories
c1 = CategoricalDtype(list("abc"), ordered1)
c2 = CategoricalDtype(None, ordered2)
c3 = CategoricalDtype(None, ordered1)
assert c1 == c2
assert c2 == c1
assert c2 == c3
@pytest.mark.parametrize("categories", [list("abc"), None])
@pytest.mark.parametrize("other", ["category", "not a category"])
def test_categorical_equality_strings(self, categories, ordered, other):
c1 = CategoricalDtype(categories, ordered)
result = c1 == other
expected = other == "category"
assert result is expected
def test_invalid_raises(self):
with pytest.raises(TypeError, match="ordered"):
CategoricalDtype(["a", "b"], ordered="foo")
with pytest.raises(TypeError, match="'categories' must be list-like"):
CategoricalDtype("category")
def test_mixed(self):
a = CategoricalDtype(["a", "b", 1, 2])
b = CategoricalDtype(["a", "b", "1", "2"])
assert hash(a) != hash(b)
def test_from_categorical_dtype_identity(self):
c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
# Identity test for no changes
c2 = CategoricalDtype._from_categorical_dtype(c1)
assert c2 is c1
def test_from_categorical_dtype_categories(self):
c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
# override categories
result = CategoricalDtype._from_categorical_dtype(c1, categories=[2, 3])
assert result == CategoricalDtype([2, 3], ordered=True)
def test_from_categorical_dtype_ordered(self):
c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
# override ordered
result = CategoricalDtype._from_categorical_dtype(c1, ordered=False)
assert result == CategoricalDtype([1, 2, 3], ordered=False)
def test_from_categorical_dtype_both(self):
c1 = Categorical([1, 2], categories=[1, 2, 3], ordered=True)
# override ordered
result = CategoricalDtype._from_categorical_dtype(
c1, categories=[1, 2], ordered=False
)
assert result == CategoricalDtype([1, 2], ordered=False)
def test_str_vs_repr(self, ordered):
c1 = CategoricalDtype(["a", "b"], ordered=ordered)
assert str(c1) == "category"
# Py2 will have unicode prefixes
pat = r"CategoricalDtype\(categories=\[.*\], ordered={ordered}\)"
assert re.match(pat.format(ordered=ordered), repr(c1))
def test_categorical_categories(self):
# GH17884
c1 = CategoricalDtype(Categorical(["a", "b"]))
tm.assert_index_equal(c1.categories, pd.Index(["a", "b"]))
c1 = CategoricalDtype(CategoricalIndex(["a", "b"]))
tm.assert_index_equal(c1.categories, pd.Index(["a", "b"]))
@pytest.mark.parametrize(
"new_categories", [list("abc"), list("cba"), list("wxyz"), None]
)
@pytest.mark.parametrize("new_ordered", [True, False, None])
def test_update_dtype(self, ordered, new_categories, new_ordered):
original_categories = list("abc")
dtype = CategoricalDtype(original_categories, ordered)
new_dtype = CategoricalDtype(new_categories, new_ordered)
result = dtype.update_dtype(new_dtype)
expected_categories = pd.Index(new_categories or original_categories)
expected_ordered = new_ordered if new_ordered is not None else dtype.ordered
tm.assert_index_equal(result.categories, expected_categories)
assert result.ordered is expected_ordered
def test_update_dtype_string(self, ordered):
dtype = CategoricalDtype(list("abc"), ordered)
expected_categories = dtype.categories
expected_ordered = dtype.ordered
result = dtype.update_dtype("category")
tm.assert_index_equal(result.categories, expected_categories)
assert result.ordered is expected_ordered
@pytest.mark.parametrize("bad_dtype", ["foo", object, np.int64, PeriodDtype("Q")])
def test_update_dtype_errors(self, bad_dtype):
dtype = CategoricalDtype(list("abc"), False)
msg = "a CategoricalDtype must be passed to perform an update, "
with pytest.raises(ValueError, match=msg):
dtype.update_dtype(bad_dtype)
def apple_calendar(apple_start, apple_end):
apple_fiscal = pd.read_csv(csv_path, parse_dates=True, index_col=None)
# convert date, start and end date range, pay date and month duration to datetime
apple_fiscal['date'] = pd.to_datetime(apple_fiscal['date'])
apple_fiscal['start_date'] = pd.to_datetime(apple_fiscal['start_date'])
apple_starts = apple_fiscal[['start_date', 'date']]
apple_fiscal['end_date'] = pd.to_datetime(apple_fiscal['end_date'])
apple_ends = apple_fiscal[['end_date', 'date']]
apple_fiscal['pay_date'] = pd.to_datetime(apple_fiscal['pay_date'])
apple_pays = apple_fiscal['pay_date']
apple_duration = apple_fiscal[['date', 'next_month_duration']]
# set parse format / parse date entry string
year, month, day = map(int, apple_start_date.split('-'))
apple_start = datetime.date(year, month, day)
year, month, day = map(int, apple_end_date.split('-'))
apple_end = datetime.date(year, month, day)
# create list of annual dates, based on the start date from date entry variable and convert list to data frame
apple_range = apple_end - apple_start
apple_range = apple_range.days + 1
apple_revenue = list(range(0, apple_range))
start_datelist = pd.date_range(apple_start, apple_end).tolist()
start_apple_table = pd.DataFrame(start_datelist)
# name index 'days' and column 'date'
start_apple_table.index.name = 'days'
start_apple_table.columns = ['date']
# merge csv columns 'start_date', 'end_date', and 'next_month_duration' with data frame
start_apple_table = start_apple_table.merge(apple_starts, how='left', on='date', left_index=True)
start_apple_table = start_apple_table.merge(apple_ends, how='left', on='date', left_index=True)
start_apple_table = start_apple_table.merge(apple_duration, how='left', on='date', left_index=True)
# add 'pay_date' column to list only the days when receive payment from Apple
start_apple_table['date'] = start_apple_table.loc[(start_apple_table['date'].isin(apple_pays)), 'pay_date'] = start_apple_table['date']
# assign 'sample_sales' column to second input revenue
start_apple_table['sample_sales'] = apple_revenue
# change index to column 'date'
start_apple_table = start_apple_table.set_index('date')
# convert 'next_month_duration' from integer to datetime days
start_apple_table['next_month_duration'] = pd.to_timedelta(start_apple_table['next_month_duration'], unit='D')
# create 'monthly_sales' column
start_apple_table['monthly_sales'] = start_apple_table.apply(lambda x: start_apple_table.loc[(start_apple_table['start_date']
<= x.name) & (x.name <= start_apple_table['end_date']), ['sample_sales']].sum(), axis=1)
# create 'monthly_adj' column to move the sales up by next month fiscal duration period
start_apple_table['monthly_adj'] = start_apple_table.apply(lambda x: start_apple_table.loc[(start_apple_table['start_date']
+ start_apple_table['next_month_duration'] <= x.name) & (x.name <= start_apple_table['end_date'] +
start_apple_table['next_month_duration']), ['sample_sales']].sum(), axis=1)
# shift 'monthly_adj' by 7 rows to be captured by 'pay_date'
start_apple_table['monthly_shift'] = start_apple_table['monthly_adj'].shift(7)
# add 'monthly_payment' and show only on 'pay_date' dates
start_apple_table['monthly_payment'] = start_apple_table['monthly_shift'].loc[start_apple_table['pay_date'].notnull()]
# add 'cumulative_payment' column
start_apple_table['cumulative_payment'] = start_apple_table['monthly_payment'].cumsum()
return start_apple_table
# Learning pandas in about 10 minutes # https://pandas.pydata.org/pandas-docs/stable/10min.html # CNA 330 # Mustafa Musa, [email protected] import pandas as pd import numpy as np import matplotlib.pyplot as plt s = pd.Series([1, 3, 5, np.nan, 6, 8]) print(s) dates = pd.date_range('20130101', periods=6) print(dates) df = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list('ABCD')) print(df) df2 = pd.DataFrame({ 'A': 1., 'B': pd.Timestamp('20130102'), 'C': pd.Series(1, index=list(range(4)), dtype='float32'), 'D': np.array([3] * 4, dtype='int32'), 'E': pd.Categorical(["test", "train", "test", "train"]), 'F': 'foo' }) print(df2) print(df2.dtypes) print(df.head()) print(df.tail(3)) print(df.index)
thumbnail_url="caching-example.png",
code_url="caching_example/caching_example.py",
mp4_url="caching-example.mp4",
tags=["Panel", "Caching"],
)
ACCENT_COLOR = "#C01754"
CACHE_EXPIRY = 60 * 60 * 24 # seconds, i.e. one Day
np.random.seed([3, 1415])
PERIODS = 1 * 24 * 60 # minutes. I.e. 1 days
DATA = pd.DataFrame(
{
"time": pd.date_range("2020-01-01", periods=PERIODS, freq="T"),
"price": np.random.randn(PERIODS) + 98,
}
)
def _load_data(frac=0.1):
time.sleep(0.5 + frac * 0.5)
return DATA.sample(frac=frac)
def _plot_data(frac=0.1):
time.sleep(0.5)
data = _load_data(frac)
return data.hvplot(x="time", y="price")
def forecast(self,
start_date:date=None,
end_date:date=None,
fq:str=None,
econ_limit:float=None,
np_limit:float=None,
npi:float=0,
fluid_rate:float=None,
show_water:bool = False,
**kwargs
):
"""
Forecast curve from the declination object.
Input:
start_date -> (datetime.date) Initial date Forecast
end_date -> (datetime.date) end date Forecast
fq -> (str) frequecy for the time table.
Use https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases
econ_limit -> (int,float,np.dnarray) Economic limit Rate. If end_date
Return:
f: DataFrame with t column and curve column
np: Cummulative production
"""
if econ_limit is None:
econ_limit = self.econ_limit
else:
assert isinstance(econ_limit,(int,float,np.ndarray)), 'econ_limit must be a number'
if fq is None:
fq = self.fq
else:
assert isinstance(fq,str), 'fq must be str'
if start_date is None:
if self.start_date is None:
start_date = self.ti
else:
start_date = self.start_date
else:
assert isinstance(start_date,date), 'start_date must be date'
if end_date is None:
if self.end_date is None:
end_date = self.ti + timedelta(days=365) if econ_limit is None else None
else:
end_date = self.end_date
else:
assert isinstance(end_date,date), 'end_date must be date'
if np_limit is None:
np_limit = self.np_limit
else:
assert isinstance(np_limit,(int,float,np.ndarray)), 'econ_limit must be a number'
if fluid_rate is None:
fluid_rate = self.fluid_rate
if econ_limit is None:
time_range = pd.Series(pd.date_range(start=start_date, end=end_date, freq=fq, **kwargs))
f, Np = forecast_curve(time_range,self.qi,self.di,self.ti,self.b,npi=npi, gas=self.gas)
else:
f, Np = forecast_econlimit(start_date,econ_limit,self.qi,self.di,self.ti,self.b, fr=fq,end_date=end_date,npi=npi,gas=self.gas)
if np_limit is not None:
if Np > np_limit:
f = f.loc[f['np']<np_limit,:]
Np = f.iloc[-1,-1]
if show_water and fluid_rate is not None:
f['qw'] = fluid_rate - f['qo']
f['bsw'] = f['qw'] / (f['qw'] + f['qo'])
f['wor'] = f['qw'] / f['qo']
f['wor_1'] = f['wor'] + 1
return f, Np