def test_ax_plot(self):
import matplotlib.pyplot as plt
x = DatetimeIndex(start='2012-01-02', periods=10, freq='D')
y = lrange(len(x))
fig = plt.figure()
ax = fig.add_subplot(111)
lines = ax.plot(x, y, label='Y')
tm.assert_index_equal(DatetimeIndex(lines[0].get_xdata()), x)
def test_frame_inferred(self):
# inferred freq
import matplotlib.pyplot as plt # noqa
idx = date_range('1/1/1987', freq='MS', periods=100)
idx = DatetimeIndex(idx.values, freq=None)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df.plot)
# axes freq
idx = idx[0:40].union(idx[45:99])
df2 = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df2.plot)
# N > 1
idx = date_range('2008-1-1 00:15:00', freq='15T', periods=10)
idx = DatetimeIndex(idx.values, freq=None)
df = DataFrame(np.random.randn(len(idx), 3), index=idx)
_check_plot_works(df.plot)
def test_resample_weekly_bug_1726(self):
# 8/6/12 is a Monday
ind = DatetimeIndex(start="8/6/2012", end="8/26/2012", freq="D")
n = len(ind)
data = [[x] * 5 for x in range(n)]
df = DataFrame(data,
columns=['open', 'high', 'low', 'close', 'vol'],
index=ind)
# it works!
df.resample('W-MON', closed='left', label='left').first()
def test_default_left_closed_label(self):
others = ['MS', 'AS', 'QS', 'D', 'H']
others_freq = ['D', 'Q', 'M', 'H', 'T']
for from_freq, to_freq in zip(others_freq, others):
idx = DatetimeIndex(start='8/15/2012', periods=100, freq=from_freq)
df = DataFrame(np.random.randn(len(idx), 2), idx)
resampled = df.resample(to_freq).mean()
assert_frame_equal(
resampled,
df.resample(to_freq, closed='left', label='left').mean())
def test_default_right_closed_label(self):
end_freq = ['D', 'Q', 'M', 'D']
end_types = ['M', 'A', 'Q', 'W']
for from_freq, to_freq in zip(end_freq, end_types):
idx = DatetimeIndex(start='8/15/2012', periods=100, freq=from_freq)
df = DataFrame(np.random.randn(len(idx), 2), idx)
resampled = df.resample(to_freq).mean()
assert_frame_equal(
resampled,
df.resample(to_freq, closed='right', label='right').mean())
def test_nat_handling(self):
fig = self.plt.gcf()
# self.plt.clf()
ax = fig.add_subplot(111)
dti = DatetimeIndex(['2015-01-01', NaT, '2015-01-03'])
s = Series(range(len(dti)), dti)
s.plot(ax=ax)
xdata = ax.get_lines()[0].get_xdata()
# plot x data is bounded by index values
assert s.index.min() <= Series(xdata).min()
assert Series(xdata).max() <= s.index.max()
def test_resample_with_nat():
# GH 13020
index = DatetimeIndex([
pd.NaT, '1970-01-01 00:00:00', pd.NaT, '1970-01-01 00:00:01',
'1970-01-01 00:00:02'
])
frame = DataFrame([2, 3, 5, 7, 11], index=index)
index_1s = DatetimeIndex(
['1970-01-01 00:00:00', '1970-01-01 00:00:01', '1970-01-01 00:00:02'])
frame_1s = DataFrame([3, 7, 11], index=index_1s)
assert_frame_equal(frame.resample('1s').mean(), frame_1s)
index_2s = DatetimeIndex(['1970-01-01 00:00:00', '1970-01-01 00:00:02'])
frame_2s = DataFrame([5, 11], index=index_2s)
assert_frame_equal(frame.resample('2s').mean(), frame_2s)
index_3s = DatetimeIndex(['1970-01-01 00:00:00'])
frame_3s = DataFrame([7], index=index_3s)
assert_frame_equal(frame.resample('3s').mean(), frame_3s)
assert_frame_equal(frame.resample('60s').mean(), frame_3s)
def test_resample_apply_product():
# GH 5586
index = date_range(start="2012-01-31", freq="M", periods=12)
ts = Series(range(12), index=index)
df = DataFrame(dict(A=ts, B=ts + 2))
result = df.resample("Q").apply(np.product)
expected = DataFrame(
np.array([[0, 24], [60, 210], [336, 720], [990, 1716]], dtype=np.int64),
index=DatetimeIndex(
["2012-03-31", "2012-06-30", "2012-09-30", "2012-12-31"], freq="Q-DEC"
),
columns=["A", "B"],
)
tm.assert_frame_equal(result, expected)
def test_downsample_across_dst_weekly():
# GH 9119, GH 21459
df = DataFrame(index=DatetimeIndex([
'2017-03-25', '2017-03-26', '2017-03-27',
'2017-03-28', '2017-03-29'
], tz='Europe/Amsterdam'),
data=[11, 12, 13, 14, 15])
result = df.resample('1W').sum()
expected = DataFrame([23, 42], index=pd.DatetimeIndex([
'2017-03-26', '2017-04-02'
], tz='Europe/Amsterdam'))
tm.assert_frame_equal(result, expected)
idx = pd.date_range("2013-04-01", "2013-05-01", tz='Europe/London',
freq='H')
s = Series(index=idx)
result = s.resample('W').mean()
expected = Series(index=pd.date_range(
'2013-04-07', freq='W', periods=5, tz='Europe/London'
))
tm.assert_series_equal(result, expected)
def test_downsample_across_dst_weekly():
# GH 9119, GH 21459
df = DataFrame(
index=DatetimeIndex(
["2017-03-25", "2017-03-26", "2017-03-27", "2017-03-28", "2017-03-29"],
tz="Europe/Amsterdam",
),
data=[11, 12, 13, 14, 15],
)
result = df.resample("1W").sum()
expected = DataFrame(
[23, 42],
index=pd.DatetimeIndex(["2017-03-26", "2017-04-02"], tz="Europe/Amsterdam"),
)
tm.assert_frame_equal(result, expected)
idx = pd.date_range("2013-04-01", "2013-05-01", tz="Europe/London", freq="H")
s = Series(index=idx)
result = s.resample("W").mean()
expected = Series(
index=pd.date_range("2013-04-07", freq="W", periods=5, tz="Europe/London")
)
tm.assert_series_equal(result, expected)
def test_resample_dst_anchor():
# 5172
dti = DatetimeIndex([datetime(2012, 11, 4, 23)], tz='US/Eastern')
df = DataFrame([5], index=dti)
assert_frame_equal(df.resample(rule='D').sum(),
DataFrame([5], index=df.index.normalize()))
df.resample(rule='MS').sum()
assert_frame_equal(
df.resample(rule='MS').sum(),
DataFrame([5], index=DatetimeIndex([datetime(2012, 11, 1)],
tz='US/Eastern')))
dti = date_range('2013-09-30', '2013-11-02', freq='30Min',
tz='Europe/Paris')
values = range(dti.size)
df = DataFrame({"a": values,
"b": values,
"c": values}, index=dti, dtype='int64')
how = {"a": "min", "b": "max", "c": "count"}
assert_frame_equal(
df.resample("W-MON").agg(how)[["a", "b", "c"]],
DataFrame({"a": [0, 48, 384, 720, 1056, 1394],
"b": [47, 383, 719, 1055, 1393, 1586],
"c": [48, 336, 336, 336, 338, 193]},
index=date_range('9/30/2013', '11/4/2013',
freq='W-MON', tz='Europe/Paris')),
'W-MON Frequency')
assert_frame_equal(
df.resample("2W-MON").agg(how)[["a", "b", "c"]],
DataFrame({"a": [0, 48, 720, 1394],
"b": [47, 719, 1393, 1586],
"c": [48, 672, 674, 193]},
index=date_range('9/30/2013', '11/11/2013',
freq='2W-MON', tz='Europe/Paris')),
'2W-MON Frequency')
assert_frame_equal(
df.resample("MS").agg(how)[["a", "b", "c"]],
DataFrame({"a": [0, 48, 1538],
"b": [47, 1537, 1586],
"c": [48, 1490, 49]},
index=date_range('9/1/2013', '11/1/2013',
freq='MS', tz='Europe/Paris')),
'MS Frequency')
assert_frame_equal(
df.resample("2MS").agg(how)[["a", "b", "c"]],
DataFrame({"a": [0, 1538],
"b": [1537, 1586],
"c": [1538, 49]},
index=date_range('9/1/2013', '11/1/2013',
freq='2MS', tz='Europe/Paris')),
'2MS Frequency')
df_daily = df['10/26/2013':'10/29/2013']
assert_frame_equal(
df_daily.resample("D").agg({"a": "min", "b": "max", "c": "count"})
[["a", "b", "c"]],
DataFrame({"a": [1248, 1296, 1346, 1394],
"b": [1295, 1345, 1393, 1441],
"c": [48, 50, 48, 48]},
index=date_range('10/26/2013', '10/29/2013',
freq='D', tz='Europe/Paris')),
'D Frequency')
def test_resample_dst_anchor():
# 5172
dti = DatetimeIndex([datetime(2012, 11, 4, 23)], tz="US/Eastern")
df = DataFrame([5], index=dti)
tm.assert_frame_equal(
df.resample(rule="D").sum(), DataFrame([5], index=df.index.normalize())
)
df.resample(rule="MS").sum()
tm.assert_frame_equal(
df.resample(rule="MS").sum(),
DataFrame([5], index=DatetimeIndex([datetime(2012, 11, 1)], tz="US/Eastern")),
)
dti = date_range("2013-09-30", "2013-11-02", freq="30Min", tz="Europe/Paris")
values = range(dti.size)
df = DataFrame({"a": values, "b": values, "c": values}, index=dti, dtype="int64")
how = {"a": "min", "b": "max", "c": "count"}
tm.assert_frame_equal(
df.resample("W-MON").agg(how)[["a", "b", "c"]],
DataFrame(
{
"a": [0, 48, 384, 720, 1056, 1394],
"b": [47, 383, 719, 1055, 1393, 1586],
"c": [48, 336, 336, 336, 338, 193],
},
index=date_range("9/30/2013", "11/4/2013", freq="W-MON", tz="Europe/Paris"),
),
"W-MON Frequency",
)
tm.assert_frame_equal(
df.resample("2W-MON").agg(how)[["a", "b", "c"]],
DataFrame(
{
"a": [0, 48, 720, 1394],
"b": [47, 719, 1393, 1586],
"c": [48, 672, 674, 193],
},
index=date_range(
"9/30/2013", "11/11/2013", freq="2W-MON", tz="Europe/Paris"
),
),
"2W-MON Frequency",
)
tm.assert_frame_equal(
df.resample("MS").agg(how)[["a", "b", "c"]],
DataFrame(
{"a": [0, 48, 1538], "b": [47, 1537, 1586], "c": [48, 1490, 49]},
index=date_range("9/1/2013", "11/1/2013", freq="MS", tz="Europe/Paris"),
),
"MS Frequency",
)
tm.assert_frame_equal(
df.resample("2MS").agg(how)[["a", "b", "c"]],
DataFrame(
{"a": [0, 1538], "b": [1537, 1586], "c": [1538, 49]},
index=date_range("9/1/2013", "11/1/2013", freq="2MS", tz="Europe/Paris"),
),
"2MS Frequency",
)
df_daily = df["10/26/2013":"10/29/2013"]
tm.assert_frame_equal(
df_daily.resample("D").agg({"a": "min", "b": "max", "c": "count"})[
["a", "b", "c"]
],
DataFrame(
{
"a": [1248, 1296, 1346, 1394],
"b": [1295, 1345, 1393, 1441],
"c": [48, 50, 48, 48],
},
index=date_range("10/26/2013", "10/29/2013", freq="D", tz="Europe/Paris"),
),
"D Frequency",
)
from datetime import datetime
import numpy as np
import pytest
from pandas.compat import OrderedDict, range
import pandas as pd
from pandas import DataFrame, Series
from pandas.core.indexes.datetimes import date_range
from pandas.core.resample import DatetimeIndex
import pandas.util.testing as tm
from pandas.util.testing import assert_frame_equal, assert_series_equal
dti = DatetimeIndex(start=datetime(2005, 1, 1),
end=datetime(2005, 1, 10), freq='Min')
test_series = Series(np.random.rand(len(dti)), dti)
test_frame = DataFrame(
{'A': test_series, 'B': test_series, 'C': np.arange(len(dti))})
def test_str():
r = test_series.resample('H')
assert ('DatetimeIndexResampler [freq=<Hour>, axis=0, closed=left, '
'label=left, convention=start, base=0]' in str(r))
def test_api():