PNDSPY1011¶

Message pandas.core.groupby.generic.DataFrameGroupBy.resample has a partial mapping because there is a not supported scenario in Snowpark pandas.

Category Warning

Description¶

This issue appears when the SMA identifies a pandas.core.groupby.generic.DataFrameGroupBy.resample usage. Snowpark pandas currently has limitations with DataFrameGroupBy.resample. The rule parameter only supports s, min, h, and D as frequency values.

Scenario¶

An unsupported use of pandas.core.groupby.generic.DataFrameGroupBy.resample.

Input¶

The following example shows an unsupported use of pandas.core.groupby.generic.DataFrameGroupBy.resample.

import pandas as pd

df = pd.DataFrame({
        "category": ["A", "A", "B", "B"],
        "date": pd.to_datetime(["2023-01-01", "2023-01-15", "2023-01-01", "2023-01-20"]),
        "value": [10, 20, 30, 40]
    })

df = df.set_index("date")
df.groupby("category").resample("ME").sum()

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Output¶

The SMA adds the EWI PNDSPY1011 to the output code to indicate that it has a scenario not supported in Snowpark pandas.

from snowflake.snowpark.modin import plugin
import modin.pandas as pd

df = pd.DataFrame({
        "category": ["A", "A", "B", "B"],
        "date": pd.to_datetime(["2023-01-01", "2023-01-15", "2023-01-01", "2023-01-20"]),
        "value": [10, 20, 30, 40]
    })

df = df.set_index("date")
#EWI: PNDSPY1011 => pandas.core.groupby.generic.DataFrameGroupBy.resample has a partial mapping because there is a not supported scenario in Snowpark pandas.
df.groupby("category").resample("ME").sum()

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Recommended fix¶

This requires a manual adjustment. You can use the pd.Grouper method to create a new column that represents the period you want to group by, and then use the groupby method. To illustrate the recommended fix, here is the output code with the changes applied:

from snowflake.snowpark.modin import plugin
import modin.pandas as pd

df = pd.DataFrame({
        "category": ["A", "A", "B", "B"],
        "date": pd.to_datetime(["2023-01-01", "2023-01-15", "2023-01-01", "2023-01-20"]),
        "value": [10, 20, 30, 40]
    })

df = df.set_index("date")
df.groupby(["category", pd.Grouper(freq="ME")]).sum()

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