def test_at_time(self):
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
ts = Series(np.random.randn(len(rng)), index=rng)
df = DataFrame(np.random.randn(len(rng), 3), index=rng)
result = ts[time(9, 30)]
result_df = df.ix[time(9, 30)]
expected = ts[(rng.hour == 9) & (rng.minute == 30)]
exp_df = df[(rng.hour == 9) & (rng.minute == 30)]
# expected.index = date_range('1/1/2000', '1/4/2000')
assert_series_equal(result, expected)
tm.assert_frame_equal(result_df, exp_df)
chunk = df.ix['1/4/2000':]
result = chunk.ix[time(9, 30)]
expected = result_df[-1:]
tm.assert_frame_equal(result, expected)
# midnight, everything
rng = date_range('1/1/2000', '1/31/2000')
ts = Series(np.random.randn(len(rng)), index=rng)
result = ts.at_time(time(0, 0))
assert_series_equal(result, ts)
def test_at_time(self):
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
ts = Series(np.random.randn(len(rng)), index=rng)
rs = ts.at_time(rng[1])
assert (rs.index.hour == rng[1].hour).all()
assert (rs.index.minute == rng[1].minute).all()
assert (rs.index.second == rng[1].second).all()
result = ts.at_time("9:30")
expected = ts.at_time(time(9, 30))
tm.assert_series_equal(result, expected)
df = DataFrame(np.random.randn(len(rng), 3), index=rng)
result = ts[time(9, 30)]
result_df = df.loc[time(9, 30)]
expected = ts[(rng.hour == 9) & (rng.minute == 30)]
exp_df = df[(rng.hour == 9) & (rng.minute == 30)]
result.index = result.index._with_freq(None)
tm.assert_series_equal(result, expected)
tm.assert_frame_equal(result_df, exp_df)
chunk = df.loc["1/4/2000":]
result = chunk.loc[time(9, 30)]
expected = result_df[-1:]
# Without resetting the freqs, these are 5 min and 1440 min, respectively
result.index = result.index._with_freq(None)
expected.index = expected.index._with_freq(None)
tm.assert_frame_equal(result, expected)
# midnight, everything
rng = date_range("1/1/2000", "1/31/2000")
ts = Series(np.random.randn(len(rng)), index=rng)
result = ts.at_time(time(0, 0))
tm.assert_series_equal(result, ts)
# time doesn't exist
rng = date_range("1/1/2012", freq="23Min", periods=384)
ts = Series(np.random.randn(len(rng)), rng)
rs = ts.at_time("16:00")
assert len(rs) == 0
def test_at_time(self):
rng = date_range("1/1/2000", "1/5/2000", freq="5min")
ts = Series(np.random.randn(len(rng)), index=rng)
rs = ts.at_time(rng[1])
assert (rs.index.hour == rng[1].hour).all()
assert (rs.index.minute == rng[1].minute).all()
assert (rs.index.second == rng[1].second).all()
result = ts.at_time("9:30")
expected = ts.at_time(time(9, 30))
tm.assert_series_equal(result, expected)
df = DataFrame(np.random.randn(len(rng), 3), index=rng)
result = ts[time(9, 30)]
result_df = df.loc[time(9, 30)]
expected = ts[(rng.hour == 9) & (rng.minute == 30)]
exp_df = df[(rng.hour == 9) & (rng.minute == 30)]
# FIXME: dont leave commented-out
# expected.index = date_range('1/1/2000', '1/4/2000')
tm.assert_series_equal(result, expected)
tm.assert_frame_equal(result_df, exp_df)
chunk = df.loc["1/4/2000":]
result = chunk.loc[time(9, 30)]
expected = result_df[-1:]
tm.assert_frame_equal(result, expected)
# midnight, everything
rng = date_range("1/1/2000", "1/31/2000")
ts = Series(np.random.randn(len(rng)), index=rng)
result = ts.at_time(time(0, 0))
tm.assert_series_equal(result, ts)
# time doesn't exist
rng = date_range("1/1/2012", freq="23Min", periods=384)
ts = Series(np.random.randn(len(rng)), rng)
rs = ts.at_time("16:00")
assert len(rs) == 0
def test_at_time(self):
rng = date_range('1/1/2000', '1/5/2000', freq='5min')
ts = Series(np.random.randn(len(rng)), 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_series_equal(result, expected)
df = DataFrame(np.random.randn(len(rng), 3), index=rng)
result = ts[time(9, 30)]
result_df = df.loc[time(9, 30)]
expected = ts[(rng.hour == 9) & (rng.minute == 30)]
exp_df = df[(rng.hour == 9) & (rng.minute == 30)]
# expected.index = date_range('1/1/2000', '1/4/2000')
assert_series_equal(result, expected)
tm.assert_frame_equal(result_df, exp_df)
chunk = df.loc['1/4/2000':]
result = chunk.loc[time(9, 30)]
expected = result_df[-1:]
tm.assert_frame_equal(result, expected)
# midnight, everything
rng = date_range('1/1/2000', '1/31/2000')
ts = Series(np.random.randn(len(rng)), index=rng)
result = ts.at_time(time(0, 0))
assert_series_equal(result, ts)
# time doesn't exist
rng = date_range('1/1/2012', freq='23Min', periods=384)
ts = Series(np.random.randn(len(rng)), rng)
rs = ts.at_time('16:00')
self.assertEqual(len(rs), 0)
def test_localized_at_time(self, tzstr):
tz = timezones.maybe_get_tz(tzstr)
rng = date_range("4/16/2012", "5/1/2012", freq="H")
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize(tzstr)
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize(tzstr)
tm.assert_series_equal(result, expected)
assert timezones.tz_compare(result.index.tz, tz)
def test_localized_at_time_between_time(self):
from datetime import time
rng = date_range('4/16/2012', '5/1/2012', freq='H')
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize('US/Eastern')
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize('US/Eastern')
assert_series_equal(result, expected)
self.assert_(result.index.tz.zone == 'US/Eastern')
t1, t2 = time(10, 0), time(11, 0)
result = ts_local.between_time(t1, t2)
expected = ts.between_time(t1, t2).tz_localize('US/Eastern')
assert_series_equal(result, expected)
self.assert_(result.index.tz.zone == 'US/Eastern')
def test_localized_at_time_between_time(self):
from datetime import time
rng = date_range('4/16/2012', '5/1/2012', freq='H')
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize('US/Eastern')
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize('US/Eastern')
assert_series_equal(result, expected)
self.assert_(result.index.tz.zone == 'US/Eastern')
t1, t2 = time(10, 0), time(11, 0)
result = ts_local.between_time(t1, t2)
expected = ts.between_time(t1, t2).tz_localize('US/Eastern')
assert_series_equal(result, expected)
self.assert_(result.index.tz.zone == 'US/Eastern')
def test_localized_at_time_between_time(self):
from datetime import time
rng = date_range("4/16/2012", "5/1/2012", freq="H")
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize(self.tzstr("US/Eastern"))
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize(self.tzstr("US/Eastern"))
tm.assert_series_equal(result, expected)
self.assertTrue(self.cmptz(result.index.tz, self.tz("US/Eastern")))
t1, t2 = time(10, 0), time(11, 0)
result = ts_local.between_time(t1, t2)
expected = ts.between_time(t1, t2).tz_localize(self.tzstr("US/Eastern"))
tm.assert_series_equal(result, expected)
self.assertTrue(self.cmptz(result.index.tz, self.tz("US/Eastern")))
def test_localized_at_time_between_time(self, tzstr):
from datetime import time
tz = timezones.maybe_get_tz(tzstr)
rng = date_range('4/16/2012', '5/1/2012', freq='H')
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize(tzstr)
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize(tzstr)
tm.assert_series_equal(result, expected)
assert timezones.tz_compare(result.index.tz, tz)
t1, t2 = time(10, 0), time(11, 0)
result = ts_local.between_time(t1, t2)
expected = ts.between_time(t1, t2).tz_localize(tzstr)
tm.assert_series_equal(result, expected)
assert timezones.tz_compare(result.index.tz, tz)
def test_localized_at_time_between_time(self, tzstr):
from datetime import time
tz = timezones.maybe_get_tz(tzstr)
rng = date_range('4/16/2012', '5/1/2012', freq='H')
ts = Series(np.random.randn(len(rng)), index=rng)
ts_local = ts.tz_localize(tzstr)
result = ts_local.at_time(time(10, 0))
expected = ts.at_time(time(10, 0)).tz_localize(tzstr)
tm.assert_series_equal(result, expected)
assert timezones.tz_compare(result.index.tz, tz)
t1, t2 = time(10, 0), time(11, 0)
result = ts_local.between_time(t1, t2)
expected = ts.between_time(t1, t2).tz_localize(tzstr)
tm.assert_series_equal(result, expected)
assert timezones.tz_compare(result.index.tz, tz)
def test_at_time_raises(self):
# GH20725
ser = Series("a b c".split())
msg = "Index must be DatetimeIndex"
with pytest.raises(TypeError, match=msg):
ser.at_time("00:00")
import numpy as np
from datetime import time
from pandas import Series, DataFrame
# 时间和'最当前'数据选取
# 对于长时间市场数据,选取每天特定时间的价格数据
# 对于数据不规整, 要数据规整
rng = pd.date_range('2012-06-01 09:30', '2012-06-06 15:59', freq='T')
ts = Series(np.arange(len(rng), dtype=float), index=rng)
print ts
# 十点
print ts[time(10, 0)]
# 等价于
# 可以使用实例方法at_time
print ts.at_time(time(10, 0))
# 选取两个Time对象之间的值
# 选取10:00到10:01之间
print ts.between_time(time(10, 0), time(10, 1))
# 得到上午10之前最后出现的那个值
indexer = np.sort(np.random.permutation(len(ts))[700:])
irr_ts = ts.copy()
irr_ts[indexer] = np.nan
print irr_ts['2012-06-01 09:50': '2012-06-01 10:00']
# 如果将一组Timestamp传入asof方法, 就可以得到这些时间点处的有效值(非NA)
selection = pd.date_range('2012-06-01 10:00', periods=4, freq='B')
print irr_ts.asof(selection)
print(infl_q)
#索引匹配并填充缺失值
k = infl_q.reindex(gdp.index, method='ffill')
print(k)
#时间和最当前数据提取
rng = pd.date_range('2012-06-01 09:30', '2012-06-01 15:59', freq='T')
# 交易时段按分钟采样
rng = rng.append([rng + pd.offsets.BDay(i) for i in range(1, 4)])
# 再补4天
ts = Series(np.arange(len(rng), dtype=float), index=rng)
print(ts.head())
print(ts.tail())
#利用python的datetime.time对象进行索引即可抽取出这些时间点上的值
print(ts[time(10, 0)]) #抽取10点的数据
print(ts.at_time(time(10, 0)))
#between_time方法，它用于选取两个Time对象之间的值
j = ts.between_time(time(10, 0), time(10, 1)) #定位到时间段
print(j)
#如果刚好没有数据落在某个具体时间上,希望得到上午10点之前最后出现的值
#根据ts随机选个排列，并选择700条及以后的数据进行排序
indexer = np.sort(np.random.permutation(len(ts))[700:])
irr_ts = ts.copy()
#根据随机下标索引把一部分时间点数据设置为NA
irr_ts[indexer] = np.nan
print(irr_ts['2012-06-01 09:50':'2012-06-01 10:00'])
#如果将一组Timestamp传入asof方法,就能得到这些时间点处的有效值
#连续四个工作日的上午10点
selection = pd.date_range('2012-06-01 10:00', periods=4, freq='B')
#上面随机几次,确保某个10点数据为NA.asof的话会拿最近数据填充
gdp = Series([1.78, 1.94, 2.08, 2.01, 2.15, 2.31, 2.46],
index=pd.period_range('1984Q2', periods=7, freq='Q-SEP'))
inf1 = Series([0.025, 0.045, 0.037, 0.04],
index=pd.period_range('1982', periods=4, freq='A-DEC'))
inf1_q = inf1.asfreq('Q-SEP', how='end')
print(inf1_q)
print(inf1_q.reindex(gdp.index, method='ffill'))
rng = pd.date_range('2012-06-01 09:30', '2012-06-01 15:59', freq='T')
rng = rng.append([rng + pd.offsets.BDay(i) for i in range(1, 4)])
ts = Series(np.arange(len(rng), dtype=float), index=rng)
print(ts)
print(ts[time(10, 0)])
print(ts.at_time(time(10, 0)))
print(ts.between_time(time(10, 0), time(10, 1)))
indexer = np.sort(np.random.permutation(len(ts))[700:])
irr_ts = ts.copy()
irr_ts[indexer] = np.nan
print(irr_ts['2012-06-01 09:50':'2012-06-01 10:00'])
selection = pd.date_range('2012-06-01 10:00', periods=4, freq='B')
print(irr_ts.asof(selection))
data1 = DataFrame(np.ones((6, 3), dtype=float),
columns=['a', 'b', 'c'],
index=pd.date_range('6/12/2012', periods=6))
data2 = DataFrame(np.ones((6, 3), dtype=float) * 2,
columns=['a', 'b', 'c'],
