fix issue #8000 #8010
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fix issue #8000 #8010
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This commit changes `np.interp()` arguments to include the default values of the left and right parameters as np.nan. In effect, when pandas interpolates a Series with trailing missing data, missing data values are preserved rather than being overwritten with the default value (last non-missing value).
Added a test that confirms that linear interpolation of a Series does not extrapolate over missing data that trails the last known value.
| def test_interpolate_linear(): | ||
| a = Series([np.nan, 1, np.nan, 3, np.nan])) | ||
| b = a.interpolate() | ||
| assert(b[4] == np.nan) |
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construct an expected series and use assert_series equal
move this test to test_generic (u will see a whole bunch of other interpolation tests)
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resubmitted as PR #8013 with suggested edits
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FYI, in general to simply rebase the PR rather than submit a new one |
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This pull request is in response to issue #8000.
Changes to core/common.py add np.nan as the default value for missing values to the left and right non-missing values during interpolation. This prevents DataFrame.interpolate() from extrapolating the last non-missing value over all trailing missing values (the default).
Changes to tests/test_common.py add test coverage to the above change. A passing test is where an interpolated series with a trailing missing value maintains that trailing missing value after interpolation.