[WIP][SPARK-46620][PS][CONNECT] Implement Frame.asfreq

[zhengruifeng]

What changes were proposed in this pull request?

Implement Frame.asfreq

Why are the changes needed?

for feature parity

Does this PR introduce any user-facing change?

yes

In [1]: import pyspark.pandas as ps

In [2]: import pandas as pd

In [3]: index = pd.date_range('1/1/2000', periods=4, freq='min')

In [4]: series = pd.Series([0.0, None, 2.0, 3.0], index=index)

In [5]: pdf = pd.DataFrame({'s': series})

In [6]: psdf = ps.from_pandas(pdf)
24/01/08 17:25:19 WARN CheckAllocator: More than one DefaultAllocationManager on classpath. Choosing first found

In [7]: psdf.asfreq(freq='30s')
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: `frame.asfreq` loads partial data into the driver's memory to infer the schema, and loads all data into one executor's memory to compute. It should only be used if the pandas DataFrame is expected to be small.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: If the type hints is not specified for `groupby.apply`, it is expensive to infer the data type internally.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
Out[7]: 
                       s
2000-01-01 00:00:00  0.0
2000-01-01 00:00:30  NaN
2000-01-01 00:01:00  NaN
2000-01-01 00:01:30  NaN
2000-01-01 00:02:00  2.0
2000-01-01 00:02:30  NaN
2000-01-01 00:03:00  3.0

In [8]: psdf.asfreq(freq='30s', fill_value=9.0)
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: `frame.asfreq` loads partial data into the driver's memory to infer the schema, and loads all data into one executor's memory to compute. It should only be used if the pandas DataFrame is expected to be small.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: If the type hints is not specified for `groupby.apply`, it is expensive to infer the data type internally.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
Out[8]: 
                       s
2000-01-01 00:00:00  0.0
2000-01-01 00:00:30  9.0
2000-01-01 00:01:00  NaN
2000-01-01 00:01:30  9.0
2000-01-01 00:02:00  2.0
2000-01-01 00:02:30  9.0
2000-01-01 00:03:00  3.0

In [9]: psdf.asfreq(freq='30s', method='bfill')
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: `frame.asfreq` loads partial data into the driver's memory to infer the schema, and loads all data into one executor's memory to compute. It should only be used if the pandas DataFrame is expected to be small.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015: PandasAPIOnSparkAdviceWarning: If the type hints is not specified for `groupby.apply`, it is expensive to infer the data type internally.
  warnings.warn(message, PandasAPIOnSparkAdviceWarning)
Out[9]: 
                       s
2000-01-01 00:00:00  0.0
2000-01-01 00:00:30  NaN
2000-01-01 00:01:00  NaN
2000-01-01 00:01:30  2.0
2000-01-01 00:02:00  2.0
2000-01-01 00:02:30  3.0
2000-01-01 00:03:00  3.0

How was this patch tested?

doc test and ut

Was this patch authored or co-authored using generative AI tooling?

no

[github-actions]

We're closing this PR because it hasn't been updated in a while. This isn't a judgement on the merit of the PR in any way. It's just a way of keeping the PR queue manageable.
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