PNDSPY1126¶

Description¶

This issue appears when the SMA detects the use of a pandas element that has a direct equivalent in Snowpark pandas, but some scenarios might behave differently than pandas.

Missing or Unsupported Parameters: axis other than 0 is not implemented.

Reason: Y if the following are true, otherwise N: - func is a callable that always returns either a pandas DataFrame, a pandas Series, or objects that are neither DataFrame nor Series. - grouping on axis=0 - Not applying transform to a dataframe with a non-unique index - Not applying func that returns two dataframes that have different labels for the column at a given position - Not applying func that returns two dataframes that have different names for a given index label - Not applying func that returns two Series that have different labels for the row at a given position - Not applying func that returns two Series that have different names - Not grouping by an “external” by, i.e. an object that is not a label for a column or level of the dataframe.

Scenario¶

A method with a few scenarios that aren’t supported in Snowpark.

import pandas as pd

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar'], 'B': [1, 2, 3, 4]})
grouped = df.groupby('A')
result = grouped.apply()

import snowflake.snowpark.modin.pandas as pd

#EWI: PNDSPY1126 => pandas.core.groupby.groupby.GroupBy.apply has a partial mapping, with few scenarios not supported. Check Snowpark pandas documentation for more detail.
df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar'], 'B': [1, 2, 3, 4]})
grouped = df.groupby('A')
result = grouped.apply()

Recommended fix¶

The parameter axis other than 0 is not implemented. is not supported in Snowpark pandas. If your code uses this parameter, consider one of these approaches:

1. Remove the parameter: If the parameter is not essential for your use case, simply remove it from the function call.

2. Use default behavior: The function will work with default values for the unsupported parameter.

3. Post-process with native pandas: If the parameter is critical, collect the result using .to_pandas() and apply the operation with native pandas:

   # Convert to native pandas for unsupported parameter
   result = df.to_pandas().apply(axis other than 0 is not implemented.=value)
   

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NULL/NaN handling difference: Y if the following are true, otherwise N: - func is a callable that always returns either a pandas DataFrame, a pandas Series, or objects that are neither DataFrame nor Series. - grouping on axis=0 - Not applying transform to a dataframe with a non-unique index - Not applying func that returns two dataframes that have different labels for the column at a given position - Not applying func that returns two dataframes that have different names for a given index label - Not applying func that returns two Series that have different labels for the row at a given position - Not applying func that returns two Series that have different names - Not grouping by an “external” by, i.e. an object that is not a label for a column or level of the dataframe.

Snowpark pandas may handle NULL/NaN values differently:

• Pre-filter NULL values using .dropna() or .fillna() before the operation

• Verify NULL handling behavior with a small sample dataset

• Use explicit NULL checks: df[df['column'].notna()]

Additional recommendations¶

Check the Snowpark pandas documentation to verify which scenarios aren’t supported for that specific element.