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new 7323bae6df1 [MINOR][DOC] Fix typo under python directory
7323bae6df1 is described below
commit 7323bae6df1def2cfcf9509baf699ca6d0ba20f5
Author: Kazuaki Ishizaki <ishiz...@jp.ibm.com>
AuthorDate: Sun Dec 4 20:29:38 2022 +0900
[MINOR][DOC] Fix typo under python directory
### What changes were proposed in this pull request?
Fix typo in pydoc and messages under `python` directory
### Why are the changes needed?
Better documentation
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
N/A
Closes #38900 from kiszk/typo-pydocs.
Authored-by: Kazuaki Ishizaki <ishiz...@jp.ibm.com>
Signed-off-by: Hyukjin Kwon <gurwls...@apache.org>
---
python/pyspark/ml/tuning.py | 2 +-
python/pyspark/pandas/frame.py | 2 +-
python/pyspark/pandas/groupby.py | 4 ++--
python/pyspark/pandas/spark/accessors.py | 2 +-
python/pyspark/pandas/supported_api_gen.py | 10 +++++-----
python/pyspark/rdd.py | 4 ++--
python/pyspark/sql/catalog.py | 4 ++--
python/pyspark/sql/connect/function_builder.py | 2 +-
python/pyspark/sql/dataframe.py | 6 +++---
python/pyspark/sql/functions.py | 4 ++--
python/pyspark/sql/session.py | 2 +-
python/pyspark/sql/streaming/query.py | 4 ++--
python/pyspark/sql/streaming/readwriter.py | 4 ++--
13 files changed, 25 insertions(+), 25 deletions(-)
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 44a8b51ef8e..0dabcdd7f27 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -405,7 +405,7 @@ class _ValidatorSharedReadWrite:
elif isinstance(v, MLWritable):
raise RuntimeError(
"ValidatorSharedReadWrite.saveImpl does not handle
parameters of type: "
- "MLWritable that are not
Estimaor/Evaluator/Transformer, and if parameter "
+ "MLWritable that are not
Estimator/Evaluator/Transformer, and if parameter "
"is estimator, it cannot be meta estimator such as
Validator or OneVsRest"
)
else:
diff --git a/python/pyspark/pandas/frame.py b/python/pyspark/pandas/frame.py
index 835c13d6fdd..f044634da0b 100644
--- a/python/pyspark/pandas/frame.py
+++ b/python/pyspark/pandas/frame.py
@@ -382,7 +382,7 @@ class DataFrame(Frame, Generic[T]):
.. versionchanged:: 3.4.0
Since 3.4.0, it deals with `data` and `index` in this approach:
1, when `data` is a distributed dataset (Internal DataFrame/Spark
DataFrame/
- pandas-on-Spark DataFrame/pandas-on-Spark Series), it will first
parallize
+ pandas-on-Spark DataFrame/pandas-on-Spark Series), it will first
parallelize
the `index` if necessary, and then try to combine the `data` and
`index`;
Note that if `data` and `index` doesn't have the same anchor, then
`compute.ops_on_diff_frames` should be turned on;
diff --git a/python/pyspark/pandas/groupby.py b/python/pyspark/pandas/groupby.py
index c5dbcb79710..baa5f0ae146 100644
--- a/python/pyspark/pandas/groupby.py
+++ b/python/pyspark/pandas/groupby.py
@@ -1989,7 +1989,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
if should_infer_schema:
# Here we execute with the first 1000 to get the return type.
log_advice(
- "If the type hints is not specified for `grouby.apply`, "
+ "If the type hints is not specified for `groupby.apply`, "
"it is expensive to infer the data type internally."
)
limit = get_option("compute.shortcut_limit")
@@ -3107,7 +3107,7 @@ class GroupBy(Generic[FrameLike], metaclass=ABCMeta):
# Here we execute with the first 1000 to get the return type.
# If the records were less than 1000, it uses pandas API directly
for a shortcut.
log_advice(
- "If the type hints is not specified for `grouby.transform`, "
+ "If the type hints is not specified for `groupby.transform`, "
"it is expensive to infer the data type internally."
)
limit = get_option("compute.shortcut_limit")
diff --git a/python/pyspark/pandas/spark/accessors.py
b/python/pyspark/pandas/spark/accessors.py
index c8e7f507c6f..4e1caa2432b 100644
--- a/python/pyspark/pandas/spark/accessors.py
+++ b/python/pyspark/pandas/spark/accessors.py
@@ -144,7 +144,7 @@ class SparkSeriesMethods(SparkIndexOpsMethods["ps.Series"]):
.. note:: It forces to lose the index and end up with using default
index. It is
preferred to use :meth:`Series.spark.transform` or
`:meth:`DataFrame.spark.apply`
- with specifying the `inedx_col`.
+ with specifying the `index_col`.
.. note:: It does not require to have the same length of the input and
output.
However, it requires to create a new DataFrame internally which
will require
diff --git a/python/pyspark/pandas/supported_api_gen.py
b/python/pyspark/pandas/supported_api_gen.py
index e60cba204f9..301e6a2f9b7 100644
--- a/python/pyspark/pandas/supported_api_gen.py
+++ b/python/pyspark/pandas/supported_api_gen.py
@@ -171,8 +171,8 @@ def _organize_by_implementation_status(
"""
Check the implementation status and parameters of both modules.
- Parmeters
- ---------
+ Parameters
+ ----------
module_name : str
Class name that exists in the path of the module.
pd_funcs: Dict[str, Callable]
@@ -284,11 +284,11 @@ def _update_all_supported_status(
"""
Updates supported status across multiple module paths.
- Parmeters
- ---------
+ Parameters
+ ----------
all_supported_status: Dict[Tuple[str, str], Dict[str, SupportedStatus]]
Data that stores the supported status across multiple module paths.
- pd_modles: List[str]
+ pd_modules: List[str]
Name list of pandas modules.
pd_module_group : Any
Specific path of importable pandas module.
diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py
index 7f5e4e603f4..829f3d08b63 100644
--- a/python/pyspark/rdd.py
+++ b/python/pyspark/rdd.py
@@ -4289,7 +4289,7 @@ class RDD(Generic[T_co]):
Returns
-------
:class:`RDD`
- a :class:`RDD` containing the keys and cogouped values
+ a :class:`RDD` containing the keys and cogrouped values
See Also
--------
@@ -4330,7 +4330,7 @@ class RDD(Generic[T_co]):
Returns
-------
:class:`RDD`
- a :class:`RDD` containing the keys and cogouped values
+ a :class:`RDD` containing the keys and cogrouped values
See Also
--------
diff --git a/python/pyspark/sql/catalog.py b/python/pyspark/sql/catalog.py
index 4a49ef1fa04..6b97aa1db48 100644
--- a/python/pyspark/sql/catalog.py
+++ b/python/pyspark/sql/catalog.py
@@ -803,7 +803,7 @@ class Catalog:
--------
>>> spark.createDataFrame([(1, 1)]).createTempView("my_table")
- Droppping the temporary view.
+ Dropping the temporary view.
>>> spark.catalog.dropTempView("my_table")
True
@@ -840,7 +840,7 @@ class Catalog:
--------
>>> spark.createDataFrame([(1, 1)]).createGlobalTempView("my_table")
- Droppping the global view.
+ Dropping the global view.
>>> spark.catalog.dropGlobalTempView("my_table")
True
diff --git a/python/pyspark/sql/connect/function_builder.py
b/python/pyspark/sql/connect/function_builder.py
index 1b3450786a3..3c59312888a 100644
--- a/python/pyspark/sql/connect/function_builder.py
+++ b/python/pyspark/sql/connect/function_builder.py
@@ -64,7 +64,7 @@ functions = FunctionBuilder()
class UserDefinedFunction(Expression):
- """A user defied function is an expresison that has a reference to the
actual
+ """A user defied function is an expression that has a reference to the
actual
Python callable attached. During plan generation, the client sends a
command to
the server to register the UDF before execution. The expression object can
be
reused and is not attached to a specific execution. If the internal name of
diff --git a/python/pyspark/sql/dataframe.py b/python/pyspark/sql/dataframe.py
index f3873e3c8cd..bd5df07a50f 100644
--- a/python/pyspark/sql/dataframe.py
+++ b/python/pyspark/sql/dataframe.py
@@ -2375,7 +2375,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
| 2|Alice|
+---+-----+
- Specify miltiple columns
+ Specify multiple columns
>>> df = spark.createDataFrame([
... (2, "Alice"), (2, "Bob"), (5, "Bob")], schema=["age", "name"])
@@ -2388,7 +2388,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
| 2| Bob|
+---+-----+
- Specify miltiple columns for sorting order at `ascending`.
+ Specify multiple columns for sorting order at `ascending`.
>>> df.orderBy(["age", "name"], ascending=[False, False]).show()
+---+-----+
@@ -3209,7 +3209,7 @@ class DataFrame(PandasMapOpsMixin, PandasConversionMixin):
Parameters
----------
exprs : :class:`Column` or dict of key and value strings
- Columns or expressions to aggreate DataFrame by.
+ Columns or expressions to aggregate DataFrame by.
Returns
-------
diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index 3aeb48adea7..9746196dc94 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -5517,7 +5517,7 @@ def concat_ws(sep: str, *cols: "ColumnOrName") -> Column:
Parameters
----------
sep : str
- words seperator.
+ words separator.
cols : :class:`~pyspark.sql.Column` or str
list of columns to work on.
@@ -7916,7 +7916,7 @@ def arrays_zip(*cols: "ColumnOrName") -> Column:
"""
Collection function: Returns a merged array of structs in which the N-th
struct contains all
N-th values of input arrays. If one of the arrays is shorter than others
then
- resulting struct type value will be a `null` for missing elemets.
+ resulting struct type value will be a `null` for missing elements.
.. versionadded:: 2.4.0
diff --git a/python/pyspark/sql/session.py b/python/pyspark/sql/session.py
index eec3246cac3..ebad3224f02 100644
--- a/python/pyspark/sql/session.py
+++ b/python/pyspark/sql/session.py
@@ -1393,7 +1393,7 @@ class SparkSession(SparkConversionMixin):
>>> spark.readStream
<pyspark.sql.streaming.readwriter.DataStreamReader object ...>
- The example below uses Rate source that generates rows continously.
+ The example below uses Rate source that generates rows continuously.
After that, we operate a modulo by 3, and then write the stream out to
the console.
The streaming query stops in 3 seconds.
diff --git a/python/pyspark/sql/streaming/query.py
b/python/pyspark/sql/streaming/query.py
index 5ee7a4790b0..c1f0e734800 100644
--- a/python/pyspark/sql/streaming/query.py
+++ b/python/pyspark/sql/streaming/query.py
@@ -183,7 +183,7 @@ class StreamingQuery:
>>> sdf = spark.readStream.format("rate").load()
>>> sq =
sdf.writeStream.format('memory').queryName('query_awaitTermination').start()
- Return wheter the query has terminated or not within 5 seconds
+ Return whether the query has terminated or not within 5 seconds
>>> sq.awaitTermination(5)
False
@@ -517,7 +517,7 @@ class StreamingQueryManager:
>>> sdf = spark.readStream.format("rate").load()
>>> sq =
sdf.writeStream.format('memory').queryName('this_query').start()
- Return wheter any of the query on the associated SparkSession
+ Return whether any of the query on the associated SparkSession
has terminated or not within 5 seconds
>>> spark.streams.awaitAnyTermination(5)
diff --git a/python/pyspark/sql/streaming/readwriter.py
b/python/pyspark/sql/streaming/readwriter.py
index ef3b7e525e3..c58848dc508 100644
--- a/python/pyspark/sql/streaming/readwriter.py
+++ b/python/pyspark/sql/streaming/readwriter.py
@@ -52,7 +52,7 @@ class DataStreamReader(OptionUtils):
>>> spark.readStream
<pyspark.sql.streaming.readwriter.DataStreamReader object ...>
- The example below uses Rate source that generates rows continously.
+ The example below uses Rate source that generates rows continuously.
After that, we operate a modulo by 3, and then writes the stream out to
the console.
The streaming query stops in 3 seconds.
@@ -721,7 +721,7 @@ class DataStreamWriter:
Examples
--------
- The example below uses Rate source that generates rows continously.
+ The example below uses Rate source that generates rows continuously.
After that, we operate a modulo by 3, and then writes the stream out to
the console.
The streaming query stops in 3 seconds.
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