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from datetime import datetime
import numpy as np
import pytest
import pytz
from pandas.core.dtypes.common import (
is_categorical_dtype,
is_interval_dtype,
is_object_dtype,
)
from pandas import (
DataFrame,
DatetimeIndex,
Index,
IntervalIndex,
Series,
Timestamp,
cut,
date_range,
)
import pandas._testing as tm
class TestDataFrameAlterAxes:
@pytest.fixture
def idx_expected(self):
idx = DatetimeIndex(["2013-1-1 13:00", "2013-1-2 14:00"], name="B").tz_localize(
"US/Pacific"
)
expected = Series(
np.array(
[
Timestamp("2013-01-01 13:00:00-0800", tz="US/Pacific"),
Timestamp("2013-01-02 14:00:00-0800", tz="US/Pacific"),
],
dtype="object",
),
name="B",
)
assert expected.dtype == idx.dtype
return idx, expected
def test_to_series_keep_tz_deprecated_true(self, idx_expected):
# convert to series while keeping the timezone
idx, expected = idx_expected
msg = "stop passing 'keep_tz'"
with tm.assert_produces_warning(FutureWarning) as m:
result = idx.to_series(keep_tz=True, index=[0, 1])
assert msg in str(m[0].message)
tm.assert_series_equal(result, expected)
def test_to_series_keep_tz_deprecated_false(self, idx_expected):
idx, expected = idx_expected
with tm.assert_produces_warning(FutureWarning) as m:
result = idx.to_series(keep_tz=False, index=[0, 1])
tm.assert_series_equal(result, expected.dt.tz_convert(None))
msg = "do 'idx.tz_convert(None)' before calling"
assert msg in str(m[0].message)
def test_setitem_dt64series(self, idx_expected):
# convert to utc
idx, expected = idx_expected
df = DataFrame(np.random.randn(2, 1), columns=["A"])
df["B"] = idx
with tm.assert_produces_warning(FutureWarning) as m:
df["B"] = idx.to_series(keep_tz=False, index=[0, 1])
msg = "do 'idx.tz_convert(None)' before calling"
assert msg in str(m[0].message)
result = df["B"]
comp = Series(idx.tz_convert("UTC").tz_localize(None), name="B")
tm.assert_series_equal(result, comp)
def test_setitem_datetimeindex(self, idx_expected):
# setting a DataFrame column with a tzaware DTI retains the dtype
idx, expected = idx_expected
df = DataFrame(np.random.randn(2, 1), columns=["A"])
# assign to frame
df["B"] = idx
result = df["B"]
tm.assert_series_equal(result, expected)
def test_setitem_object_array_of_tzaware_datetimes(self, idx_expected):
# setting a DataFrame column with a tzaware DTI retains the dtype
idx, expected = idx_expected
df = DataFrame(np.random.randn(2, 1), columns=["A"])
# object array of datetimes with a tz
df["B"] = idx.to_pydatetime()
result = df["B"]
tm.assert_series_equal(result, expected)
def test_constructor_from_tzaware_datetimeindex(self, idx_expected):
# don't cast a DatetimeIndex WITH a tz, leave as object
# GH 6032
idx, expected = idx_expected
# convert index to series
result = Series(idx)
tm.assert_series_equal(result, expected)
def test_set_axis_setattr_index(self):
# GH 6785
# set the index manually
df = DataFrame([{"ts": datetime(2014, 4, 1, tzinfo=pytz.utc), "foo": 1}])
expected = df.set_index("ts")
df.index = df["ts"]
df.pop("ts")
tm.assert_frame_equal(df, expected)
def test_dti_set_index_reindex(self):
# GH 6631
df = DataFrame(np.random.random(6))
idx1 = date_range("2011/01/01", periods=6, freq="M", tz="US/Eastern")
idx2 = date_range("2013", periods=6, freq="A", tz="Asia/Tokyo")
df = df.set_index(idx1)
tm.assert_index_equal(df.index, idx1)
df = df.reindex(idx2)
tm.assert_index_equal(df.index, idx2)
def test_dti_set_index_reindex_with_tz(self):
# GH 11314
# with tz
index = date_range(
datetime(2015, 10, 1), datetime(2015, 10, 1, 23), freq="H", tz="US/Eastern"
)
df = DataFrame(np.random.randn(24, 1), columns=["a"], index=index)
new_index = date_range(
datetime(2015, 10, 2), datetime(2015, 10, 2, 23), freq="H", tz="US/Eastern"
)
result = df.set_index(new_index)
assert result.index.freq == index.freq
# Renaming
def test_assign_columns(self, float_frame):
float_frame["hi"] = "there"
df = float_frame.copy()
df.columns = ["foo", "bar", "baz", "quux", "foo2"]
tm.assert_series_equal(float_frame["C"], df["baz"], check_names=False)
tm.assert_series_equal(float_frame["hi"], df["foo2"], check_names=False)
class TestIntervalIndex:
def test_setitem(self):
df = DataFrame({"A": range(10)})
ser = cut(df["A"], 5)
assert isinstance(ser.cat.categories, IntervalIndex)
# B & D end up as Categoricals
# the remainer are converted to in-line objects
# contining an IntervalIndex.values
df["B"] = ser
df["C"] = np.array(ser)
df["D"] = ser.values
df["E"] = np.array(ser.values)
assert is_categorical_dtype(df["B"].dtype)
assert is_interval_dtype(df["B"].cat.categories)
assert is_categorical_dtype(df["D"].dtype)
assert is_interval_dtype(df["D"].cat.categories)
assert is_object_dtype(df["C"])
assert is_object_dtype(df["E"])
# they compare equal as Index
# when converted to numpy objects
c = lambda x: Index(np.array(x))
tm.assert_index_equal(c(df.B), c(df.B))
tm.assert_index_equal(c(df.B), c(df.C), check_names=False)
tm.assert_index_equal(c(df.B), c(df.D), check_names=False)
tm.assert_index_equal(c(df.C), c(df.D), check_names=False)
# B & D are the same Series
tm.assert_series_equal(df["B"], df["B"])
tm.assert_series_equal(df["B"], df["D"], check_names=False)
# C & E are the same Series
tm.assert_series_equal(df["C"], df["C"])
tm.assert_series_equal(df["C"], df["E"], check_names=False)
def test_set_reset_index(self):
df = DataFrame({"A": range(10)})
s = cut(df.A, 5)
df["B"] = s
df = df.set_index("B")
df = df.reset_index()