zhengruifeng opened a new pull request, #44869:
URL: https://github.com/apache/spark/pull/44869
### What changes were proposed in this pull request?
1, Introduce a basic fallback mechanism for frame methods, with a new option
`compute.pandas_fallback` default false;
2, implement `Frame.asfreq` and `Frame.asof`
### Why are the changes needed?
for pandas parity
### Does this PR introduce _any_ user-facing change?
yes
```
In [1]: import pyspark.pandas as ps
...: import pandas as pd
...:
...: index = pd.date_range('1/1/2000', periods=4, freq='min')
...: series = pd.Series([0.0, None, 2.0, 3.0], index=index)
...: pdf = pd.DataFrame({'s': series})
...: psdf = ps.from_pandas(pdf)
In [2]: psdf.asfreq(freq='30s')
---------------------------------------------------------------------------
PandasNotImplementedError Traceback (most recent call last)
Cell In[2], line 1
----> 1 psdf.asfreq(freq='30s')
File ~/Dev/spark/python/pyspark/pandas/missing/__init__.py:23, in
unsupported_function.<locals>.unsupported_function(*args, **kwargs)
22 def unsupported_function(*args, **kwargs):
---> 23 raise PandasNotImplementedError(
24 class_name=class_name, method_name=method_name, reason=reason
25 )
PandasNotImplementedError: The method `pd.DataFrame.asfreq()` is not
implemented yet.
In [3]: ps.set_option("compute.pandas_fallback", True)
In [4]: psdf.asfreq(freq='30s')
/Users/ruifeng.zheng/Dev/spark/python/pyspark/pandas/utils.py:1015:
PandasAPIOnSparkAdviceWarning: `asfreq` is executed in fallback mode. It 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[4]:
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
```
### How was this patch tested?
added ut
### Was this patch authored or co-authored using generative AI tooling?
no
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