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new 4d21b94 [SPARK-35475][PYTHON] Fix disallow_untyped_defs mypy checks
4d21b94 is described below
commit 4d21b94d13daf958edf6bbf49c4aa17f5cb012b5
Author: Takuya UESHIN <ues...@databricks.com>
AuthorDate: Fri Jun 11 11:07:11 2021 -0700
[SPARK-35475][PYTHON] Fix disallow_untyped_defs mypy checks
### What changes were proposed in this pull request?
Adds more type annotations in the file `python/pyspark/pandas/namespace.py`
and fixes the mypy check failures.
### Why are the changes needed?
We should enable more disallow_untyped_defs mypy checks.
### Does this PR introduce _any_ user-facing change?
Yes.
This PR adds more type annotations in pandas APIs on Spark module, which
can impact interaction with development tools for users.
### How was this patch tested?
The mypy check with a new configuration and existing tests should pass.
Closes #32871 from ueshin/issues/SPARK-35475/disallow_untyped_defs.
Authored-by: Takuya UESHIN <ues...@databricks.com>
Signed-off-by: Takuya UESHIN <ues...@databricks.com>
---
python/mypy.ini | 3 -
python/pyspark/pandas/frame.py | 8 +-
python/pyspark/pandas/namespace.py | 281 ++++++++++++++++++++++---------------
python/pyspark/pandas/utils.py | 2 +-
4 files changed, 175 insertions(+), 119 deletions(-)
diff --git a/python/mypy.ini b/python/mypy.ini
index 86156fa..ae76f41 100644
--- a/python/mypy.ini
+++ b/python/mypy.ini
@@ -174,9 +174,6 @@ disallow_untyped_defs = False
[mypy-pyspark.pandas.groupby]
disallow_untyped_defs = False
-[mypy-pyspark.pandas.namespace]
-disallow_untyped_defs = False
-
[mypy-pyspark.pandas.series]
disallow_untyped_defs = False
diff --git a/python/pyspark/pandas/frame.py b/python/pyspark/pandas/frame.py
index d61b80c..0773901 100644
--- a/python/pyspark/pandas/frame.py
+++ b/python/pyspark/pandas/frame.py
@@ -8929,7 +8929,13 @@ defaultdict(<class 'list'>, {'col..., 'col...})]
else:
raise TypeError("other must be a pandas-on-Spark DataFrame")
- def melt(self, id_vars=None, value_vars=None, var_name=None,
value_name="value") -> "DataFrame":
+ def melt(
+ self,
+ id_vars: Optional[Union[Any, Tuple, List[Union[Any, Tuple]]]] = None,
+ value_vars: Optional[Union[Any, Tuple, List[Union[Any, Tuple]]]] =
None,
+ var_name: Optional[Union[str, List[str]]] = None,
+ value_name: str = "value",
+ ) -> "DataFrame":
"""
Unpivot a DataFrame from wide format to long format, optionally
leaving identifier variables set.
diff --git a/python/pyspark/pandas/namespace.py
b/python/pyspark/pandas/namespace.py
index 090a1bf..41ba186 100644
--- a/python/pyspark/pandas/namespace.py
+++ b/python/pyspark/pandas/namespace.py
@@ -20,6 +20,7 @@ Wrappers around spark that correspond to common pandas
functions.
"""
from typing import ( # noqa: F401 (SPARK-34943)
Any,
+ Callable,
Dict,
List,
Optional,
@@ -28,9 +29,11 @@ from typing import ( # noqa: F401 (SPARK-34943)
Type,
Union,
cast,
+ no_type_check,
)
from collections import OrderedDict
from collections.abc import Iterable
+from datetime import tzinfo
from distutils.version import LooseVersion
from functools import reduce
from io import BytesIO
@@ -39,6 +42,7 @@ import json
import numpy as np
import pandas as pd
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype,
is_list_like
+from pandas.tseries.offsets import DateOffset
import pyarrow as pa
import pyarrow.parquet as pq
from pyspark import sql as spark
@@ -79,6 +83,7 @@ from pyspark.pandas.internal import (
)
from pyspark.pandas.series import Series, first_series
from pyspark.pandas.spark.utils import as_nullable_spark_type,
force_decimal_precision_scale
+from pyspark.pandas.typedef.typehints import Dtype
from pyspark.pandas.indexes import Index, DatetimeIndex
@@ -197,21 +202,21 @@ def range(
def read_csv(
- path,
- sep=",",
- header="infer",
- names=None,
- index_col=None,
- usecols=None,
- squeeze=False,
- mangle_dupe_cols=True,
- dtype=None,
- nrows=None,
- parse_dates=False,
- quotechar=None,
- escapechar=None,
- comment=None,
- **options
+ path: str,
+ sep: str = ",",
+ header: Union[str, int, None] = "infer",
+ names: Optional[Union[str, List[str]]] = None,
+ index_col: Optional[Union[str, List[str]]] = None,
+ usecols: Optional[Union[List[int], List[str], Callable[[str], bool]]] =
None,
+ squeeze: bool = False,
+ mangle_dupe_cols: bool = True,
+ dtype: Optional[Union[str, Dtype, Dict[str, Union[str, Dtype]]]] = None,
+ nrows: Optional[int] = None,
+ parse_dates: bool = False,
+ quotechar: Optional[str] = None,
+ escapechar: Optional[str] = None,
+ comment: Optional[str] = None,
+ **options: Any
) -> Union[DataFrame, Series]:
"""Read CSV (comma-separated) file into DataFrame or Series.
@@ -221,7 +226,7 @@ def read_csv(
The path string storing the CSV file to be read.
sep : str, default ‘,’
Delimiter to use. Must be a single character.
- header : int, list of int, default ‘infer’
+ header : int, default ‘infer’
Whether to to use as the column names, and the start of the data.
Default behavior is to infer the column names: if no names are passed
the behavior is identical to `header=0` and column names are inferred
from
@@ -287,7 +292,7 @@ def read_csv(
raise ValueError("parse_dates can only be `False`: %s" % parse_dates)
if usecols is not None and not callable(usecols):
- usecols = list(usecols)
+ usecols = list(usecols) # type: ignore
if usecols is None or callable(usecols) or len(usecols) > 0:
reader = default_session().read
@@ -341,18 +346,21 @@ def read_csv(
column_labels = OrderedDict(
(label, col) for label, col in column_labels.items() if
usecols(label)
)
- missing = []
+ missing = [] # type: List[Union[int, str]]
elif all(isinstance(col, int) for col in usecols):
+ usecols_ints = cast(List[int], usecols)
new_column_labels = OrderedDict(
(label, col)
for i, (label, col) in enumerate(column_labels.items())
- if i in usecols
+ if i in usecols_ints
)
missing = [
col
- for col in usecols
- if col >= len(column_labels)
- or list(column_labels)[col] not in new_column_labels
+ for col in usecols_ints
+ if (
+ col >= len(column_labels)
+ or list(column_labels)[col] not in new_column_labels
+ )
]
column_labels = new_column_labels
elif all(isinstance(col, str) for col in usecols):
@@ -424,7 +432,7 @@ def read_csv(
def read_json(
- path: str, lines: bool = True, index_col: Optional[Union[str, List[str]]]
= None, **options
+ path: str, lines: bool = True, index_col: Optional[Union[str, List[str]]]
= None, **options: Any
) -> DataFrame:
"""
Convert a JSON string to DataFrame.
@@ -486,7 +494,7 @@ def read_delta(
version: Optional[str] = None,
timestamp: Optional[str] = None,
index_col: Optional[Union[str, List[str]]] = None,
- **options
+ **options: Any
) -> DataFrame:
"""
Read a Delta Lake table on some file system and return a DataFrame.
@@ -615,7 +623,7 @@ def read_spark_io(
format: Optional[str] = None,
schema: Union[str, "StructType"] = None,
index_col: Optional[Union[str, List[str]]] = None,
- **options
+ **options: Any
) -> DataFrame:
"""Load a DataFrame from a Spark data source.
@@ -694,7 +702,13 @@ def read_spark_io(
)
-def read_parquet(path, columns=None, index_col=None, pandas_metadata=False,
**options) -> DataFrame:
+def read_parquet(
+ path: str,
+ columns: Optional[List[str]] = None,
+ index_col: Optional[List[str]] = None,
+ pandas_metadata: bool = False,
+ **options: Any
+) -> DataFrame:
"""Load a parquet object from the file path, returning a DataFrame.
Parameters
@@ -748,6 +762,7 @@ def read_parquet(path, columns=None, index_col=None,
pandas_metadata=False, **op
if index_col is None and pandas_metadata:
# Try to read pandas metadata
+ @no_type_check
@pandas_udf("index_col array<string>, index_names array<string>",
PandasUDFType.SCALAR)
def read_index_metadata(pser):
binary = pser.iloc[0]
@@ -801,7 +816,7 @@ def read_parquet(path, columns=None, index_col=None,
pandas_metadata=False, **op
return psdf
-def read_clipboard(sep=r"\s+", **kwargs) -> DataFrame:
+def read_clipboard(sep: str = r"\s+", **kwargs: Any) -> DataFrame:
r"""
Read text from clipboard and pass to read_csv. See read_csv for the
full argument list
@@ -824,31 +839,31 @@ def read_clipboard(sep=r"\s+", **kwargs) -> DataFrame:
def read_excel(
- io,
- sheet_name=0,
- header=0,
- names=None,
- index_col=None,
- usecols=None,
- squeeze=False,
- dtype=None,
- engine=None,
- converters=None,
- true_values=None,
- false_values=None,
- skiprows=None,
- nrows=None,
- na_values=None,
- keep_default_na=True,
- verbose=False,
- parse_dates=False,
- date_parser=None,
- thousands=None,
- comment=None,
- skipfooter=0,
- convert_float=True,
- mangle_dupe_cols=True,
- **kwds
+ io: Union[str, Any],
+ sheet_name: Union[str, int, List[Union[str, int]], None] = 0,
+ header: Union[int, List[int]] = 0,
+ names: Optional[List] = None,
+ index_col: Optional[List[int]] = None,
+ usecols: Optional[Union[int, str, List[Union[int, str]], Callable[[str],
bool]]] = None,
+ squeeze: bool = False,
+ dtype: Optional[Dict[str, Union[str, Dtype]]] = None,
+ engine: Optional[str] = None,
+ converters: Optional[Dict] = None,
+ true_values: Optional[Any] = None,
+ false_values: Optional[Any] = None,
+ skiprows: Optional[Union[int, List[int]]] = None,
+ nrows: Optional[int] = None,
+ na_values: Optional[Any] = None,
+ keep_default_na: bool = True,
+ verbose: bool = False,
+ parse_dates: Union[bool, List, Dict] = False,
+ date_parser: Optional[Callable] = None,
+ thousands: Optional[str] = None,
+ comment: Optional[str] = None,
+ skipfooter: int = 0,
+ convert_float: bool = True,
+ mangle_dupe_cols: bool = True,
+ **kwds: Any
) -> Union[DataFrame, Series, OrderedDict]:
"""
Read an Excel file into a pandas-on-Spark DataFrame or Series.
@@ -1050,7 +1065,9 @@ def read_excel(
2 None NaN
"""
- def pd_read_excel(io_or_bin, sn, sq):
+ def pd_read_excel(
+ io_or_bin: Any, sn: Union[str, int, List[Union[str, int]], None], sq:
bool
+ ) -> pd.DataFrame:
return pd.read_excel(
io=BytesIO(io_or_bin) if isinstance(io_or_bin, (bytes, bytearray))
else io_or_bin,
sheet_name=sn,
@@ -1099,8 +1116,10 @@ def read_excel(
return cast(Union[DataFrame, Series], from_pandas(pdf_or_psers))
else:
- def read_excel_on_spark(pdf_or_pser, sn):
-
+ def read_excel_on_spark(
+ pdf_or_pser: Union[pd.DataFrame, pd.Series],
+ sn: Union[str, int, List[Union[str, int]], None],
+ ) -> Union[DataFrame, Series]:
if isinstance(pdf_or_pser, pd.Series):
pdf = pdf_or_pser.to_frame()
else:
@@ -1111,7 +1130,7 @@ def read_excel(
as_nullable_spark_type(psdf._internal.spark_frame.drop(*HIDDEN_COLUMNS).schema)
)
- def output_func(pdf):
+ def output_func(pdf: pd.DataFrame) -> pd.DataFrame:
pdf = pd.concat(
[pd_read_excel(bin, sn=sn, sq=False) for bin in
pdf[pdf.columns[0]]]
)
@@ -1153,21 +1172,21 @@ def read_excel(
def read_html(
- io,
- match=".+",
- flavor=None,
- header=None,
- index_col=None,
- skiprows=None,
- attrs=None,
- parse_dates=False,
- thousands=",",
- encoding=None,
- decimal=".",
- converters=None,
- na_values=None,
- keep_default_na=True,
- displayed_only=True,
+ io: Union[str, Any],
+ match: str = ".+",
+ flavor: Optional[str] = None,
+ header: Optional[Union[int, List[int]]] = None,
+ index_col: Optional[Union[int, List[int]]] = None,
+ skiprows: Optional[Union[int, List[int], slice]] = None,
+ attrs: Optional[Dict[str, str]] = None,
+ parse_dates: bool = False,
+ thousands: str = ",",
+ encoding: Optional[str] = None,
+ decimal: str = ".",
+ converters: Optional[Dict] = None,
+ na_values: Optional[Any] = None,
+ keep_default_na: bool = True,
+ displayed_only: bool = True,
) -> List[DataFrame]:
r"""Read HTML tables into a ``list`` of ``DataFrame`` objects.
@@ -1290,7 +1309,12 @@ def read_html(
# TODO: add `coerce_float` and 'parse_dates' parameters
def read_sql_table(
- table_name, con, schema=None, index_col=None, columns=None, **options
+ table_name: str,
+ con: str,
+ schema: Optional[str] = None,
+ index_col: Optional[Union[str, List[str]]] = None,
+ columns: Optional[Union[str, List[str]]] = None,
+ **options: Any
) -> DataFrame:
"""
Read SQL database table into a DataFrame.
@@ -1355,7 +1379,9 @@ def read_sql_table(
# TODO: add `coerce_float`, `params`, and 'parse_dates' parameters
-def read_sql_query(sql, con, index_col=None, **options) -> DataFrame:
+def read_sql_query(
+ sql: str, con: str, index_col: Optional[Union[str, List[str]]] = None,
**options: Any
+) -> DataFrame:
"""Read SQL query into a DataFrame.
Returns a DataFrame corresponding to the result set of the query
@@ -1408,7 +1434,13 @@ def read_sql_query(sql, con, index_col=None, **options)
-> DataFrame:
# TODO: add `coerce_float`, `params`, and 'parse_dates' parameters
-def read_sql(sql, con, index_col=None, columns=None, **options) -> DataFrame:
+def read_sql(
+ sql: str,
+ con: str,
+ index_col: Optional[Union[str, List[str]]] = None,
+ columns: Optional[Union[str, List[str]]] = None,
+ **options: Any
+) -> DataFrame:
"""
Read SQL query or database table into a DataFrame.
@@ -1462,8 +1494,14 @@ def read_sql(sql, con, index_col=None, columns=None,
**options) -> DataFrame:
return read_sql_query(sql, con, index_col=index_col, **options)
+@no_type_check
def to_datetime(
- arg, errors="raise", format=None, unit=None, infer_datetime_format=False,
origin="unix"
+ arg,
+ errors: str = "raise",
+ format: Optional[str] = None,
+ unit: Optional[str] = None,
+ infer_datetime_format: bool = False,
+ origin: str = "unix",
):
"""
Convert argument to datetime.
@@ -1579,7 +1617,7 @@ def to_datetime(
DatetimeIndex(['1960-01-02', '1960-01-03', '1960-01-04'],
dtype='datetime64[ns]', freq=None)
"""
- def pandas_to_datetime(pser_or_pdf) -> Series[np.datetime64]:
+ def pandas_to_datetime(pser_or_pdf: Union[pd.DataFrame, pd.Series]) ->
Series[np.datetime64]:
if isinstance(pser_or_pdf, pd.DataFrame):
pser_or_pdf = pser_or_pdf[["year", "month", "day"]]
return pd.to_datetime(
@@ -1607,15 +1645,15 @@ def to_datetime(
def date_range(
- start=None,
- end=None,
- periods=None,
- freq=None,
- tz=None,
- normalize=False,
- name=None,
- closed=None,
- **kwargs
+ start: Union[str, Any] = None,
+ end: Union[str, Any] = None,
+ periods: Optional[int] = None,
+ freq: Optional[Union[str, DateOffset]] = None,
+ tz: Optional[Union[str, tzinfo]] = None,
+ normalize: bool = False,
+ name: Optional[str] = None,
+ closed: Optional[str] = None,
+ **kwargs: Any
) -> DatetimeIndex:
"""
Return a fixed frequency DatetimeIndex.
@@ -1770,14 +1808,14 @@ def date_range(
def get_dummies(
- data,
- prefix=None,
- prefix_sep="_",
- dummy_na=False,
- columns=None,
- sparse=False,
- drop_first=False,
- dtype=None,
+ data: Union[DataFrame, Series],
+ prefix: Optional[Union[str, List[str], Dict[str, str]]] = None,
+ prefix_sep: str = "_",
+ dummy_na: bool = False,
+ columns: Optional[Union[Any, Tuple, List[Union[Any, Tuple]]]] = None,
+ sparse: bool = False,
+ drop_first: bool = False,
+ dtype: Optional[Union[str, Dtype]] = None,
) -> DataFrame:
"""
Convert categorical variable into dummy/indicator variables, also
@@ -1976,11 +2014,11 @@ def get_dummies(
if drop_first:
values = values[1:]
- def column_name(value):
- if prefix is None or prefix[i] == "":
+ def column_name(value: str) -> str:
+ if prefix is None or cast(List[str], prefix)[i] == "":
return value
else:
- return "{}{}{}".format(prefix[i], prefix_sep, value)
+ return "{}{}{}".format(cast(List[str], prefix)[i], prefix_sep,
value)
for value in values:
remaining_columns.append(
@@ -1995,7 +2033,13 @@ def get_dummies(
# TODO: there are many parameters to implement and support. See pandas's
pd.concat.
-def concat(objs, axis=0, join="outer", ignore_index=False, sort=False) ->
Union[Series, DataFrame]:
+def concat(
+ objs: List[Union[DataFrame, Series]],
+ axis: Union[int, str] = 0,
+ join: str = "outer",
+ ignore_index: bool = False,
+ sort: bool = False,
+) -> Union[Series, DataFrame]:
"""
Concatenate pandas-on-Spark objects along a particular axis with optional
set logic
along the other axes.
@@ -2206,6 +2250,7 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
if len(psdfs_not_same_anchor) > 0:
+ @no_type_check
def resolve_func(psdf, this_column_labels, that_column_labels):
raise AssertionError("This should not happen.")
@@ -2252,16 +2297,15 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
series_names.add(obj.name)
obj = obj.to_frame(DEFAULT_SERIES_NAME)
new_objs.append(obj)
- objs = new_objs
- column_labels_levels = set(obj._internal.column_labels_level for obj in
objs)
+ column_labels_levels = set(obj._internal.column_labels_level for obj in
new_objs)
if len(column_labels_levels) != 1:
raise ValueError("MultiIndex columns should have the same levels")
# DataFrame, DataFrame, ...
# All Series are converted into DataFrame and then compute concat.
if not ignore_index:
- indices_of_psdfs = [psdf.index for psdf in objs]
+ indices_of_psdfs = [psdf.index for psdf in new_objs]
index_of_first_psdf = indices_of_psdfs[0]
for index_of_psdf in indices_of_psdfs:
if index_of_first_psdf.names != index_of_psdf.names:
@@ -2274,20 +2318,20 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
)
)
- column_labels_of_psdfs = [psdf._internal.column_labels for psdf in objs]
+ column_labels_of_psdfs = [psdf._internal.column_labels for psdf in
new_objs]
if ignore_index:
- index_names_of_psdfs = [[] for _ in objs] # type: List
+ index_names_of_psdfs = [[] for _ in new_objs] # type: List
else:
- index_names_of_psdfs = [psdf._internal.index_names for psdf in objs]
+ index_names_of_psdfs = [psdf._internal.index_names for psdf in
new_objs]
if all(name == index_names_of_psdfs[0] for name in index_names_of_psdfs)
and all(
idx == column_labels_of_psdfs[0] for idx in column_labels_of_psdfs
):
# If all columns are in the same order and values, use it.
- psdfs = objs
+ psdfs = new_objs
else:
if join == "inner":
- interested_columns = set.intersection(*map(set,
column_labels_of_psdfs))
+ interested_columns = set.intersection(*map(lambda x: set(x),
column_labels_of_psdfs))
# Keep the column order with its firsts DataFrame.
merged_columns = [
label for label in column_labels_of_psdfs[0] if label in
interested_columns
@@ -2299,7 +2343,7 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
# FIXME: better ordering
merged_columns = sorted(merged_columns, key=name_like_string)
- psdfs = [psdf[merged_columns] for psdf in objs]
+ psdfs = [psdf[merged_columns] for psdf in new_objs]
elif join == "outer":
merged_columns = []
for labels in column_labels_of_psdfs:
@@ -2320,7 +2364,7 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
merged_columns = sorted(merged_columns, key=name_like_string)
psdfs = []
- for psdf in objs:
+ for psdf in new_objs:
columns_to_add = list(set(merged_columns) -
set(psdf._internal.column_labels))
# TODO: NaN and None difference for missing values. pandas
seems filling NaN.
@@ -2391,13 +2435,20 @@ def concat(objs, axis=0, join="outer",
ignore_index=False, sort=False) -> Union[
return result_psdf
-def melt(frame, id_vars=None, value_vars=None, var_name=None,
value_name="value") -> DataFrame:
+def melt(
+ frame: DataFrame,
+ id_vars: Optional[Union[Any, Tuple, List[Union[Any, Tuple]]]] = None,
+ value_vars: Optional[Union[Any, Tuple, List[Union[Any, Tuple]]]] = None,
+ var_name: Optional[Union[str, List[str]]] = None,
+ value_name: str = "value",
+) -> DataFrame:
return DataFrame.melt(frame, id_vars, value_vars, var_name, value_name)
melt.__doc__ = DataFrame.melt.__doc__
+@no_type_check
def isna(obj):
"""
Detect missing values for an array-like object.
@@ -2480,6 +2531,7 @@ def isna(obj):
isnull = isna
+@no_type_check
def notna(obj):
"""
Detect existing (non-missing) values.
@@ -2555,8 +2607,8 @@ notnull = notna
def merge(
- obj,
- right: "DataFrame",
+ obj: DataFrame,
+ right: DataFrame,
how: str = "inner",
on: Union[Any, List[Any], Tuple, List[Tuple]] = None,
left_on: Union[Any, List[Any], Tuple, List[Tuple]] = None,
@@ -2687,6 +2739,7 @@ def merge(
)
+@no_type_check
def to_numeric(arg):
"""
Convert argument to a numeric type.
@@ -2759,7 +2812,7 @@ def to_numeric(arg):
return pd.to_numeric(arg)
-def broadcast(obj) -> DataFrame:
+def broadcast(obj: DataFrame) -> DataFrame:
"""
Marks a DataFrame as small enough for use in broadcast joins.
@@ -2801,10 +2854,10 @@ def broadcast(obj) -> DataFrame:
def read_orc(
- path,
+ path: str,
columns: Optional[List[str]] = None,
index_col: Optional[Union[str, List[str]]] = None,
- **options
+ **options: Any
) -> "DataFrame":
"""
Load an ORC object from the file path, returning a DataFrame.
@@ -2892,7 +2945,7 @@ _get_dummies_acceptable_types =
_get_dummies_default_accept_types + (
)
-def _test():
+def _test() -> None:
import os
import doctest
import shutil
diff --git a/python/pyspark/pandas/utils.py b/python/pyspark/pandas/utils.py
index 8acc967..794c61f 100644
--- a/python/pyspark/pandas/utils.py
+++ b/python/pyspark/pandas/utils.py
@@ -600,7 +600,7 @@ def column_labels_level(column_labels: List[Tuple]) -> int:
return list(levels)[0]
-def name_like_string(name: Optional[Union[str, Tuple]]) -> str:
+def name_like_string(name: Optional[Union[Any, Tuple]]) -> str:
"""
Return the name-like strings from str or tuple of str
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