PNDSPY1033¶

Applies to¶

This EWI applies to the following elements (same implementation):

• pandas.core.frame.DataFrame.align

• pandas.core.generic.NDFrame.align

• pandas.core.series.Series.align

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: copy, level, fill_value

Reason: N for MultiIndex, for deprecated parameters method, limit, fill_axis, broadcast_axis, or if fill_value is not default of np.nan.

Scenario¶

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

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = df.align()

import snowflake.snowpark.modin.pandas as pd

#EWI: PNDSPY1033 => pandas.core.frame.DataFrame.align has a partial mapping, with few scenarios not supported. Check Snowpark pandas documentation for more detail.
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
result = df.align()

Recommended fix¶

The following parameters are not supported in Snowpark pandas: copy, level, fill_value.

Recommended approaches:

1. Avoid unsupported parameters: Modify your code to not use these parameters if they are not essential.

2. Use .to_pandas() for full compatibility: If you need these parameters, convert to native pandas first:

   # Convert to native pandas when unsupported parameters are needed
   native_df = df.to_pandas()
   result = native_df.align(...)  # Use all parameters
   

   Copy

3. Split the operation: Perform supported operations in Snowpark pandas, then use native pandas only for the unsupported functionality.

NULL/NaN handling difference: N for MultiIndex, for deprecated parameters method, limit, fill_axis, broadcast_axis, or if fill_value is not default of np.nan.

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.