808 lines
30 KiB
Python
808 lines
30 KiB
Python
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import numpy as np
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import pytest
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import Index, MultiIndex, Series, date_range, isna
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import pandas._testing as tm
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@pytest.fixture(
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params=[
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"linear",
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"index",
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"values",
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"nearest",
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"slinear",
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"zero",
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"quadratic",
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"cubic",
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"barycentric",
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"krogh",
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"polynomial",
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"spline",
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"piecewise_polynomial",
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"from_derivatives",
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"pchip",
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"akima",
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"cubicspline",
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]
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)
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def nontemporal_method(request):
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"""Fixture that returns an (method name, required kwargs) pair.
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This fixture does not include method 'time' as a parameterization; that
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method requires a Series with a DatetimeIndex, and is generally tested
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separately from these non-temporal methods.
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"""
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method = request.param
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kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
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return method, kwargs
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@pytest.fixture(
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params=[
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"linear",
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"slinear",
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"zero",
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"quadratic",
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"cubic",
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"barycentric",
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"krogh",
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"polynomial",
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"spline",
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"piecewise_polynomial",
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"from_derivatives",
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"pchip",
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"akima",
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"cubicspline",
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]
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)
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def interp_methods_ind(request):
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"""Fixture that returns a (method name, required kwargs) pair to
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be tested for various Index types.
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This fixture does not include methods - 'time', 'index', 'nearest',
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'values' as a parameterization
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"""
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method = request.param
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kwargs = {"order": 1} if method in ("spline", "polynomial") else {}
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return method, kwargs
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class TestSeriesInterpolateData:
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def test_interpolate(self, datetime_series, string_series):
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ts = Series(np.arange(len(datetime_series), dtype=float), datetime_series.index)
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ts_copy = ts.copy()
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ts_copy[5:10] = np.NaN
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linear_interp = ts_copy.interpolate(method="linear")
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tm.assert_series_equal(linear_interp, ts)
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ord_ts = Series(
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[d.toordinal() for d in datetime_series.index], index=datetime_series.index
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).astype(float)
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ord_ts_copy = ord_ts.copy()
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ord_ts_copy[5:10] = np.NaN
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time_interp = ord_ts_copy.interpolate(method="time")
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tm.assert_series_equal(time_interp, ord_ts)
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def test_interpolate_time_raises_for_non_timeseries(self):
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# When method='time' is used on a non-TimeSeries that contains a null
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# value, a ValueError should be raised.
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non_ts = Series([0, 1, 2, np.NaN])
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msg = "time-weighted interpolation only works on Series.* with a DatetimeIndex"
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with pytest.raises(ValueError, match=msg):
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non_ts.interpolate(method="time")
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@td.skip_if_no_scipy
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def test_interpolate_cubicspline(self):
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ser = Series([10, 11, 12, 13])
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expected = Series(
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[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
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index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
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)
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# interpolate at new_index
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new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
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float
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)
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result = ser.reindex(new_index).interpolate(method="cubicspline")[1:3]
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tm.assert_series_equal(result, expected)
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@td.skip_if_no_scipy
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def test_interpolate_pchip(self):
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ser = Series(np.sort(np.random.uniform(size=100)))
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# interpolate at new_index
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new_index = ser.index.union(
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Index([49.25, 49.5, 49.75, 50.25, 50.5, 50.75])
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).astype(float)
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interp_s = ser.reindex(new_index).interpolate(method="pchip")
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# does not blow up, GH5977
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interp_s[49:51]
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@td.skip_if_no_scipy
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def test_interpolate_akima(self):
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ser = Series([10, 11, 12, 13])
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# interpolate at new_index where `der` is zero
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expected = Series(
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[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
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index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
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)
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new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
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float
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)
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interp_s = ser.reindex(new_index).interpolate(method="akima")
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tm.assert_series_equal(interp_s[1:3], expected)
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# interpolate at new_index where `der` is a non-zero int
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expected = Series(
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[11.0, 1.0, 1.0, 1.0, 12.0, 1.0, 1.0, 1.0, 13.0],
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index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
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)
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new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
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float
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)
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interp_s = ser.reindex(new_index).interpolate(method="akima", der=1)
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tm.assert_series_equal(interp_s[1:3], expected)
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@td.skip_if_no_scipy
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def test_interpolate_piecewise_polynomial(self):
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ser = Series([10, 11, 12, 13])
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expected = Series(
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[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
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index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
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)
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# interpolate at new_index
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new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
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float
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)
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interp_s = ser.reindex(new_index).interpolate(method="piecewise_polynomial")
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tm.assert_series_equal(interp_s[1:3], expected)
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@td.skip_if_no_scipy
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def test_interpolate_from_derivatives(self):
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ser = Series([10, 11, 12, 13])
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expected = Series(
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[11.00, 11.25, 11.50, 11.75, 12.00, 12.25, 12.50, 12.75, 13.00],
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index=Index([1.0, 1.25, 1.5, 1.75, 2.0, 2.25, 2.5, 2.75, 3.0]),
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)
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# interpolate at new_index
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new_index = ser.index.union(Index([1.25, 1.5, 1.75, 2.25, 2.5, 2.75])).astype(
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float
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)
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interp_s = ser.reindex(new_index).interpolate(method="from_derivatives")
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tm.assert_series_equal(interp_s[1:3], expected)
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@pytest.mark.parametrize(
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"kwargs",
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[
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{},
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pytest.param(
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{"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
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),
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],
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)
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def test_interpolate_corners(self, kwargs):
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s = Series([np.nan, np.nan])
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tm.assert_series_equal(s.interpolate(**kwargs), s)
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s = Series([], dtype=object).interpolate()
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tm.assert_series_equal(s.interpolate(**kwargs), s)
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def test_interpolate_index_values(self):
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s = Series(np.nan, index=np.sort(np.random.rand(30)))
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s[::3] = np.random.randn(10)
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vals = s.index.values.astype(float)
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result = s.interpolate(method="index")
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expected = s.copy()
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bad = isna(expected.values)
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good = ~bad
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expected = Series(
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np.interp(vals[bad], vals[good], s.values[good]), index=s.index[bad]
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)
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tm.assert_series_equal(result[bad], expected)
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# 'values' is synonymous with 'index' for the method kwarg
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other_result = s.interpolate(method="values")
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tm.assert_series_equal(other_result, result)
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tm.assert_series_equal(other_result[bad], expected)
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def test_interpolate_non_ts(self):
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s = Series([1, 3, np.nan, np.nan, np.nan, 11])
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msg = (
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"time-weighted interpolation only works on Series or DataFrames "
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"with a DatetimeIndex"
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)
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with pytest.raises(ValueError, match=msg):
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s.interpolate(method="time")
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@pytest.mark.parametrize(
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"kwargs",
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[
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{},
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pytest.param(
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{"method": "polynomial", "order": 1}, marks=td.skip_if_no_scipy
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),
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],
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)
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def test_nan_interpolate(self, kwargs):
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s = Series([0, 1, np.nan, 3])
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result = s.interpolate(**kwargs)
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expected = Series([0.0, 1.0, 2.0, 3.0])
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tm.assert_series_equal(result, expected)
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def test_nan_irregular_index(self):
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s = Series([1, 2, np.nan, 4], index=[1, 3, 5, 9])
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result = s.interpolate()
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expected = Series([1.0, 2.0, 3.0, 4.0], index=[1, 3, 5, 9])
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tm.assert_series_equal(result, expected)
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def test_nan_str_index(self):
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s = Series([0, 1, 2, np.nan], index=list("abcd"))
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result = s.interpolate()
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expected = Series([0.0, 1.0, 2.0, 2.0], index=list("abcd"))
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tm.assert_series_equal(result, expected)
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@td.skip_if_no_scipy
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def test_interp_quad(self):
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sq = Series([1, 4, np.nan, 16], index=[1, 2, 3, 4])
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result = sq.interpolate(method="quadratic")
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expected = Series([1.0, 4.0, 9.0, 16.0], index=[1, 2, 3, 4])
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tm.assert_series_equal(result, expected)
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@td.skip_if_no_scipy
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def test_interp_scipy_basic(self):
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s = Series([1, 3, np.nan, 12, np.nan, 25])
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# slinear
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expected = Series([1.0, 3.0, 7.5, 12.0, 18.5, 25.0])
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result = s.interpolate(method="slinear")
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tm.assert_series_equal(result, expected)
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result = s.interpolate(method="slinear", downcast="infer")
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tm.assert_series_equal(result, expected)
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# nearest
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expected = Series([1, 3, 3, 12, 12, 25])
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result = s.interpolate(method="nearest")
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tm.assert_series_equal(result, expected.astype("float"))
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result = s.interpolate(method="nearest", downcast="infer")
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tm.assert_series_equal(result, expected)
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# zero
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expected = Series([1, 3, 3, 12, 12, 25])
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result = s.interpolate(method="zero")
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tm.assert_series_equal(result, expected.astype("float"))
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result = s.interpolate(method="zero", downcast="infer")
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tm.assert_series_equal(result, expected)
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# quadratic
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# GH #15662.
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expected = Series([1, 3.0, 6.823529, 12.0, 18.058824, 25.0])
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result = s.interpolate(method="quadratic")
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tm.assert_series_equal(result, expected)
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result = s.interpolate(method="quadratic", downcast="infer")
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tm.assert_series_equal(result, expected)
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# cubic
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expected = Series([1.0, 3.0, 6.8, 12.0, 18.2, 25.0])
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result = s.interpolate(method="cubic")
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tm.assert_series_equal(result, expected)
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def test_interp_limit(self):
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s = Series([1, 3, np.nan, np.nan, np.nan, 11])
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expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
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result = s.interpolate(method="linear", limit=2)
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tm.assert_series_equal(result, expected)
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@pytest.mark.parametrize("limit", [-1, 0])
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def test_interpolate_invalid_nonpositive_limit(self, nontemporal_method, limit):
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# GH 9217: make sure limit is greater than zero.
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s = Series([1, 2, np.nan, 4])
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method, kwargs = nontemporal_method
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with pytest.raises(ValueError, match="Limit must be greater than 0"):
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s.interpolate(limit=limit, method=method, **kwargs)
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def test_interpolate_invalid_float_limit(self, nontemporal_method):
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# GH 9217: make sure limit is an integer.
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s = Series([1, 2, np.nan, 4])
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method, kwargs = nontemporal_method
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limit = 2.0
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with pytest.raises(ValueError, match="Limit must be an integer"):
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s.interpolate(limit=limit, method=method, **kwargs)
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@pytest.mark.parametrize("invalid_method", [None, "nonexistent_method"])
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def test_interp_invalid_method(self, invalid_method):
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s = Series([1, 3, np.nan, 12, np.nan, 25])
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msg = f"method must be one of.* Got '{invalid_method}' instead"
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with pytest.raises(ValueError, match=msg):
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s.interpolate(method=invalid_method)
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# When an invalid method and invalid limit (such as -1) are
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|
|
# provided, the error message reflects the invalid method.
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method=invalid_method, limit=-1)
|
||
|
|
|
||
|
|
def test_interp_invalid_method_and_value(self):
|
||
|
|
# GH#36624
|
||
|
|
ser = Series([1, 3, np.nan, 12, np.nan, 25])
|
||
|
|
|
||
|
|
msg = "Cannot pass both fill_value and method"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
ser.interpolate(fill_value=3, method="pad")
|
||
|
|
|
||
|
|
def test_interp_limit_forward(self):
|
||
|
|
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
|
||
|
|
|
||
|
|
# Provide 'forward' (the default) explicitly here.
|
||
|
|
expected = Series([1.0, 3.0, 5.0, 7.0, np.nan, 11.0])
|
||
|
|
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="forward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="FORWARD")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_unlimited(self):
|
||
|
|
# these test are for issue #16282 default Limit=None is unlimited
|
||
|
|
s = Series([np.nan, 1.0, 3.0, np.nan, np.nan, np.nan, 11.0, np.nan])
|
||
|
|
expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
|
||
|
|
result = s.interpolate(method="linear", limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 11.0])
|
||
|
|
result = s.interpolate(method="linear", limit_direction="forward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([1.0, 1.0, 3.0, 5.0, 7.0, 9.0, 11.0, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit_direction="backward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_limit_bad_direction(self):
|
||
|
|
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
|
||
|
|
|
||
|
|
msg = (
|
||
|
|
r"Invalid limit_direction: expecting one of \['forward', "
|
||
|
|
r"'backward', 'both'\], got 'abc'"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="linear", limit=2, limit_direction="abc")
|
||
|
|
|
||
|
|
# raises an error even if no limit is specified.
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="linear", limit_direction="abc")
|
||
|
|
|
||
|
|
# limit_area introduced GH #16284
|
||
|
|
def test_interp_limit_area(self):
|
||
|
|
# These tests are for issue #9218 -- fill NaNs in both directions.
|
||
|
|
s = Series([np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan])
|
||
|
|
|
||
|
|
expected = Series([np.nan, np.nan, 3.0, 4.0, 5.0, 6.0, 7.0, np.nan, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit_area="inside")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series(
|
||
|
|
[np.nan, np.nan, 3.0, 4.0, np.nan, np.nan, 7.0, np.nan, np.nan]
|
||
|
|
)
|
||
|
|
result = s.interpolate(method="linear", limit_area="inside", limit=1)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, np.nan, 3.0, 4.0, np.nan, 6.0, 7.0, np.nan, np.nan])
|
||
|
|
result = s.interpolate(
|
||
|
|
method="linear", limit_area="inside", limit_direction="both", limit=1
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0])
|
||
|
|
result = s.interpolate(method="linear", limit_area="outside")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series(
|
||
|
|
[np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan]
|
||
|
|
)
|
||
|
|
result = s.interpolate(method="linear", limit_area="outside", limit=1)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan])
|
||
|
|
result = s.interpolate(
|
||
|
|
method="linear", limit_area="outside", limit_direction="both", limit=1
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan])
|
||
|
|
result = s.interpolate(
|
||
|
|
method="linear", limit_area="outside", limit_direction="backward"
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# raises an error even if limit type is wrong.
|
||
|
|
msg = r"Invalid limit_area: expecting one of \['inside', 'outside'\], got abc"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="linear", limit_area="abc")
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"method, limit_direction, expected",
|
||
|
|
[
|
||
|
|
("pad", "backward", "forward"),
|
||
|
|
("ffill", "backward", "forward"),
|
||
|
|
("backfill", "forward", "backward"),
|
||
|
|
("bfill", "forward", "backward"),
|
||
|
|
("pad", "both", "forward"),
|
||
|
|
("ffill", "both", "forward"),
|
||
|
|
("backfill", "both", "backward"),
|
||
|
|
("bfill", "both", "backward"),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_interp_limit_direction_raises(self, method, limit_direction, expected):
|
||
|
|
# https://github.com/pandas-dev/pandas/pull/34746
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
|
||
|
|
msg = f"`limit_direction` must be '{expected}' for method `{method}`"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method=method, limit_direction=limit_direction)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, expected_data, kwargs",
|
||
|
|
(
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, 3.0, 3.0, 3.0, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "pad", "limit_area": "inside"},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, 3.0, np.nan, np.nan, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "pad", "limit_area": "inside", "limit": 1},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, 7.0],
|
||
|
|
{"method": "pad", "limit_area": "outside"},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, np.nan, np.nan, np.nan, 7.0, 7.0, np.nan],
|
||
|
|
{"method": "pad", "limit_area": "outside", "limit": 1},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
|
||
|
|
{"method": "pad", "limit_area": "outside", "limit": 1},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
range(5),
|
||
|
|
range(5),
|
||
|
|
{"method": "pad", "limit_area": "outside", "limit": 1},
|
||
|
|
),
|
||
|
|
),
|
||
|
|
)
|
||
|
|
def test_interp_limit_area_with_pad(self, data, expected_data, kwargs):
|
||
|
|
# GH26796
|
||
|
|
|
||
|
|
s = Series(data)
|
||
|
|
expected = Series(expected_data)
|
||
|
|
result = s.interpolate(**kwargs)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, expected_data, kwargs",
|
||
|
|
(
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, 7.0, 7.0, 7.0, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "bfill", "limit_area": "inside"},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, np.nan, 3.0, np.nan, np.nan, 7.0, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "bfill", "limit_area": "inside", "limit": 1},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[3.0, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "bfill", "limit_area": "outside"},
|
||
|
|
),
|
||
|
|
(
|
||
|
|
[np.nan, np.nan, 3, np.nan, np.nan, np.nan, 7, np.nan, np.nan],
|
||
|
|
[np.nan, 3.0, 3.0, np.nan, np.nan, np.nan, 7.0, np.nan, np.nan],
|
||
|
|
{"method": "bfill", "limit_area": "outside", "limit": 1},
|
||
|
|
),
|
||
|
|
),
|
||
|
|
)
|
||
|
|
def test_interp_limit_area_with_backfill(self, data, expected_data, kwargs):
|
||
|
|
# GH26796
|
||
|
|
|
||
|
|
s = Series(data)
|
||
|
|
expected = Series(expected_data)
|
||
|
|
result = s.interpolate(**kwargs)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_limit_direction(self):
|
||
|
|
# These tests are for issue #9218 -- fill NaNs in both directions.
|
||
|
|
s = Series([1, 3, np.nan, np.nan, np.nan, 11])
|
||
|
|
|
||
|
|
expected = Series([1.0, 3.0, np.nan, 7.0, 9.0, 11.0])
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="backward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([1.0, 3.0, 5.0, np.nan, 9.0, 11.0])
|
||
|
|
result = s.interpolate(method="linear", limit=1, limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# Check that this works on a longer series of nans.
|
||
|
|
s = Series([1, 3, np.nan, np.nan, np.nan, 7, 9, np.nan, np.nan, 12, np.nan])
|
||
|
|
|
||
|
|
expected = Series([1.0, 3.0, 4.0, 5.0, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0])
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series(
|
||
|
|
[1.0, 3.0, 4.0, np.nan, 6.0, 7.0, 9.0, 10.0, 11.0, 12.0, 12.0]
|
||
|
|
)
|
||
|
|
result = s.interpolate(method="linear", limit=1, limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_limit_to_ends(self):
|
||
|
|
# These test are for issue #10420 -- flow back to beginning.
|
||
|
|
s = Series([np.nan, np.nan, 5, 7, 9, np.nan])
|
||
|
|
|
||
|
|
expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="backward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([5.0, 5.0, 5.0, 7.0, 9.0, 9.0])
|
||
|
|
result = s.interpolate(method="linear", limit=2, limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_limit_before_ends(self):
|
||
|
|
# These test are for issue #11115 -- limit ends properly.
|
||
|
|
s = Series([np.nan, np.nan, 5, 7, np.nan, np.nan])
|
||
|
|
|
||
|
|
expected = Series([np.nan, np.nan, 5.0, 7.0, 7.0, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit=1, limit_direction="forward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, 5.0, 5.0, 7.0, np.nan, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit=1, limit_direction="backward")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
expected = Series([np.nan, 5.0, 5.0, 7.0, 7.0, np.nan])
|
||
|
|
result = s.interpolate(method="linear", limit=1, limit_direction="both")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_interp_all_good(self):
|
||
|
|
s = Series([1, 2, 3])
|
||
|
|
result = s.interpolate(method="polynomial", order=1)
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
# non-scipy
|
||
|
|
result = s.interpolate()
|
||
|
|
tm.assert_series_equal(result, s)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"check_scipy", [False, pytest.param(True, marks=td.skip_if_no_scipy)]
|
||
|
|
)
|
||
|
|
def test_interp_multiIndex(self, check_scipy):
|
||
|
|
idx = MultiIndex.from_tuples([(0, "a"), (1, "b"), (2, "c")])
|
||
|
|
s = Series([1, 2, np.nan], index=idx)
|
||
|
|
|
||
|
|
expected = s.copy()
|
||
|
|
expected.loc[2] = 2
|
||
|
|
result = s.interpolate()
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
msg = "Only `method=linear` interpolation is supported on MultiIndexes"
|
||
|
|
if check_scipy:
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="polynomial", order=1)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_interp_nonmono_raise(self):
|
||
|
|
s = Series([1, np.nan, 3], index=[0, 2, 1])
|
||
|
|
msg = "krogh interpolation requires that the index be monotonic"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="krogh")
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
@pytest.mark.parametrize("method", ["nearest", "pad"])
|
||
|
|
def test_interp_datetime64(self, method, tz_naive_fixture):
|
||
|
|
df = Series(
|
||
|
|
[1, np.nan, 3], index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture)
|
||
|
|
)
|
||
|
|
result = df.interpolate(method=method)
|
||
|
|
expected = Series(
|
||
|
|
[1.0, 1.0, 3.0],
|
||
|
|
index=date_range("1/1/2000", periods=3, tz=tz_naive_fixture),
|
||
|
|
)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_pad_datetime64tz_values(self):
|
||
|
|
# GH#27628 missing.interpolate_2d should handle datetimetz values
|
||
|
|
dti = pd.date_range("2015-04-05", periods=3, tz="US/Central")
|
||
|
|
ser = Series(dti)
|
||
|
|
ser[1] = pd.NaT
|
||
|
|
result = ser.interpolate(method="pad")
|
||
|
|
|
||
|
|
expected = Series(dti)
|
||
|
|
expected[1] = expected[0]
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_interp_limit_no_nans(self):
|
||
|
|
# GH 7173
|
||
|
|
s = Series([1.0, 2.0, 3.0])
|
||
|
|
result = s.interpolate(limit=1)
|
||
|
|
expected = s
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
@pytest.mark.parametrize("method", ["polynomial", "spline"])
|
||
|
|
def test_no_order(self, method):
|
||
|
|
# see GH-10633, GH-24014
|
||
|
|
s = Series([0, 1, np.nan, 3])
|
||
|
|
msg = "You must specify the order of the spline or polynomial"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method=method)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
@pytest.mark.parametrize("order", [-1, -1.0, 0, 0.0, np.nan])
|
||
|
|
def test_interpolate_spline_invalid_order(self, order):
|
||
|
|
s = Series([0, 1, np.nan, 3])
|
||
|
|
msg = "order needs to be specified and greater than 0"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
s.interpolate(method="spline", order=order)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_spline(self):
|
||
|
|
s = Series([1, 2, np.nan, 4, 5, np.nan, 7])
|
||
|
|
result = s.interpolate(method="spline", order=1)
|
||
|
|
expected = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_spline_extrapolate(self):
|
||
|
|
s = Series([1, 2, 3, 4, np.nan, 6, np.nan])
|
||
|
|
result3 = s.interpolate(method="spline", order=1, ext=3)
|
||
|
|
expected3 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 6.0])
|
||
|
|
tm.assert_series_equal(result3, expected3)
|
||
|
|
|
||
|
|
result1 = s.interpolate(method="spline", order=1, ext=0)
|
||
|
|
expected1 = Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0])
|
||
|
|
tm.assert_series_equal(result1, expected1)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_spline_smooth(self):
|
||
|
|
s = Series([1, 2, np.nan, 4, 5.1, np.nan, 7])
|
||
|
|
assert (
|
||
|
|
s.interpolate(method="spline", order=3, s=0)[5]
|
||
|
|
!= s.interpolate(method="spline", order=3)[5]
|
||
|
|
)
|
||
|
|
|
||
|
|
@td.skip_if_no_scipy
|
||
|
|
def test_spline_interpolation(self):
|
||
|
|
s = Series(np.arange(10) ** 2)
|
||
|
|
s[np.random.randint(0, 9, 3)] = np.nan
|
||
|
|
result1 = s.interpolate(method="spline", order=1)
|
||
|
|
expected1 = s.interpolate(method="spline", order=1)
|
||
|
|
tm.assert_series_equal(result1, expected1)
|
||
|
|
|
||
|
|
def test_interp_timedelta64(self):
|
||
|
|
# GH 6424
|
||
|
|
df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 3]))
|
||
|
|
result = df.interpolate(method="time")
|
||
|
|
expected = Series([1.0, 2.0, 3.0], index=pd.to_timedelta([1, 2, 3]))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
# test for non uniform spacing
|
||
|
|
df = Series([1, np.nan, 3], index=pd.to_timedelta([1, 2, 4]))
|
||
|
|
result = df.interpolate(method="time")
|
||
|
|
expected = Series([1.0, 1.666667, 3.0], index=pd.to_timedelta([1, 2, 4]))
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_series_interpolate_method_values(self):
|
||
|
|
# GH#1646
|
||
|
|
rng = date_range("1/1/2000", "1/20/2000", freq="D")
|
||
|
|
ts = Series(np.random.randn(len(rng)), index=rng)
|
||
|
|
|
||
|
|
ts[::2] = np.nan
|
||
|
|
|
||
|
|
result = ts.interpolate(method="values")
|
||
|
|
exp = ts.interpolate()
|
||
|
|
tm.assert_series_equal(result, exp)
|
||
|
|
|
||
|
|
def test_series_interpolate_intraday(self):
|
||
|
|
# #1698
|
||
|
|
index = pd.date_range("1/1/2012", periods=4, freq="12D")
|
||
|
|
ts = Series([0, 12, 24, 36], index)
|
||
|
|
new_index = index.append(index + pd.DateOffset(days=1)).sort_values()
|
||
|
|
|
||
|
|
exp = ts.reindex(new_index).interpolate(method="time")
|
||
|
|
|
||
|
|
index = pd.date_range("1/1/2012", periods=4, freq="12H")
|
||
|
|
ts = Series([0, 12, 24, 36], index)
|
||
|
|
new_index = index.append(index + pd.DateOffset(hours=1)).sort_values()
|
||
|
|
result = ts.reindex(new_index).interpolate(method="time")
|
||
|
|
|
||
|
|
tm.assert_numpy_array_equal(result.values, exp.values)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ind",
|
||
|
|
[
|
||
|
|
["a", "b", "c", "d"],
|
||
|
|
pd.period_range(start="2019-01-01", periods=4),
|
||
|
|
pd.interval_range(start=0, end=4),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_interp_non_timedelta_index(self, interp_methods_ind, ind):
|
||
|
|
# gh 21662
|
||
|
|
df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
|
||
|
|
|
||
|
|
method, kwargs = interp_methods_ind
|
||
|
|
if method == "pchip":
|
||
|
|
pytest.importorskip("scipy")
|
||
|
|
|
||
|
|
if method == "linear":
|
||
|
|
result = df[0].interpolate(**kwargs)
|
||
|
|
expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
else:
|
||
|
|
expected_error = (
|
||
|
|
"Index column must be numeric or datetime type when "
|
||
|
|
f"using {method} method other than linear. "
|
||
|
|
"Try setting a numeric or datetime index column before "
|
||
|
|
"interpolating."
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=expected_error):
|
||
|
|
df[0].interpolate(method=method, **kwargs)
|
||
|
|
|
||
|
|
def test_interpolate_timedelta_index(self, interp_methods_ind):
|
||
|
|
"""
|
||
|
|
Tests for non numerical index types - object, period, timedelta
|
||
|
|
Note that all methods except time, index, nearest and values
|
||
|
|
are tested here.
|
||
|
|
"""
|
||
|
|
# gh 21662
|
||
|
|
ind = pd.timedelta_range(start=1, periods=4)
|
||
|
|
df = pd.DataFrame([0, 1, np.nan, 3], index=ind)
|
||
|
|
|
||
|
|
method, kwargs = interp_methods_ind
|
||
|
|
if method == "pchip":
|
||
|
|
pytest.importorskip("scipy")
|
||
|
|
|
||
|
|
if method in {"linear", "pchip"}:
|
||
|
|
result = df[0].interpolate(method=method, **kwargs)
|
||
|
|
expected = Series([0.0, 1.0, 2.0, 3.0], name=0, index=ind)
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
else:
|
||
|
|
pytest.skip(
|
||
|
|
"This interpolation method is not supported for Timedelta Index yet."
|
||
|
|
)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"ascending, expected_values",
|
||
|
|
[(True, [1, 2, 3, 9, 10]), (False, [10, 9, 3, 2, 1])],
|
||
|
|
)
|
||
|
|
def test_interpolate_unsorted_index(self, ascending, expected_values):
|
||
|
|
# GH 21037
|
||
|
|
ts = Series(data=[10, 9, np.nan, 2, 1], index=[10, 9, 3, 2, 1])
|
||
|
|
result = ts.sort_index(ascending=ascending).interpolate(method="index")
|
||
|
|
expected = Series(data=expected_values, index=expected_values, dtype=float)
|
||
|
|
tm.assert_series_equal(result, expected)
|