zhengruifeng opened a new pull request, #44621:
URL: https://github.com/apache/spark/pull/44621
### 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
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