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feat: linear imputation in Resample (#19393)

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Yongjie Zhao
2022-03-28 22:30:45 +08:00
committed by GitHub
parent 6b9113a17b
commit a39dd4493e
4 changed files with 56 additions and 0 deletions
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46
tests/unit_tests/pandas_postprocessing/test_resample.py
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@@ -14,8 +14,10 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import numpy as np
import pandas as pd
import pytest
from pandas import to_datetime
from superset.exceptions import InvalidPostProcessingError
from superset.utils import pandas_postprocessing as pp
@@ -151,3 +153,47 @@ def test_resample_should_raise_ex():
pp.resample(
df=categories_df, rule="1D", method="asfreq",
)
with pytest.raises(InvalidPostProcessingError):
pp.resample(
df=timeseries_df, rule="1D", method="foobar",
)
def test_resample_linear():
df = pd.DataFrame(
index=to_datetime(["2019-01-01", "2019-01-05", "2019-01-08"]),
data={"label": ["a", "e", "j"], "y": [1.0, 5.0, 8.0]},
)
post_df = pp.resample(df=df, rule="1D", method="linear")
"""
label y
2019-01-01 a 1.0
2019-01-02 NaN 2.0
2019-01-03 NaN 3.0
2019-01-04 NaN 4.0
2019-01-05 e 5.0
2019-01-06 NaN 6.0
2019-01-07 NaN 7.0
2019-01-08 j 8.0
"""
assert post_df.equals(
pd.DataFrame(
index=pd.to_datetime(
[
"2019-01-01",
"2019-01-02",
"2019-01-03",
"2019-01-04",
"2019-01-05",
"2019-01-06",
"2019-01-07",
"2019-01-08",
]
),
data={
"label": ["a", np.NaN, np.NaN, np.NaN, "e", np.NaN, np.NaN, "j"],
"y": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0],
},
)
)