Merge branch 'bugs/4522' of https://github.com/devin-petersohn/modin into bugs/4522

Signed-off-by: Rehan Durrani <[email protected]>

Author: Rehan Durrani

docs/development/contributing.rst

@@ -13,7 +13,7 @@ want to review in order to get started.
 
 Also, feel free to join the discussions on the `developer mailing list`_.
 
-If you want a quick guide to getting your development enviroment setup, please
+If you want a quick guide to getting your development environment setup, please
 use `the contributing instructions on GitHub`_.
 
 Certificate of Origin

docs/development/partition_api.rst

@@ -9,7 +9,7 @@ from raw futures objects.
 Partition IPs
 -------------
 For finer grained placement control, Modin also provides an API to get the IP addresses of the nodes that hold each partition.
-You can pass the partitions having needed IPs to your function. It can help with minimazing of data movement between nodes.
+You can pass the partitions having needed IPs to your function. It can help with minimizing of data movement between nodes.
 
 Partition API implementations
 -----------------------------

docs/flow/modin/config.rst

@@ -5,7 +5,7 @@ Modin Configuration Settings
 
 To adjust Modin's default behavior, you can set the value of Modin
 configs by setting an environment variable or by using the
-``modin.config`` API. To list all avaliable configs in Modin, please
+``modin.config`` API. To list all available configs in Modin, please
 run ``python -m modin.config`` to print all
 Modin configs with descriptions.
 

docs/flow/modin/core/dataframe/index.rst

@@ -3,7 +3,7 @@
 Core Modin Dataframe Objects
 ============================
 
-Modin paritions data to scale efficiently.
+Modin partitions data to scale efficiently.
 To keep track of everything a few key classes are introduced: ``Dataframe``, ``Partition``, ``AxisPartiton`` and ``PartitionManager``.
 
 * ``Dataframe`` is the class conforming to Dataframe Algebra.

docs/flow/modin/core/dataframe/pandas/dataframe.rst

@@ -8,7 +8,7 @@ The class serves as the intermediate level
 between ``pandas`` query compiler and conforming partition manager. All queries formed
 at the query compiler layer are ingested by this class and then conveyed jointly with the stored partitions
 into the partition manager for processing. Direct partitions manipulation by this class is prohibited except
-cases if an operation is striclty private or protected and called inside of the class only. The class provides
+cases if an operation is strictly private or protected and called inside of the class only. The class provides
 significantly reduced set of operations that fit plenty of pandas operations.
 
 Main tasks of :py:class:`~modin.core.dataframe.pandas.dataframe.dataframe.PandasDataframe` are storage of partitions, manipulation with labels of axes and

docs/flow/modin/core/dataframe/pandas/partitioning/partition_manager.rst

@@ -18,7 +18,7 @@ Partition manager can apply user-passed (arbitrary) function in different modes:
 
 * block-wise (apply a function to individual block partitions):
 
-  * optinally accepting partition indices along each axis
+  * optionally accepting partition indices along each axis
   * optionally accepting an item to be split so parts of it would be sent to each partition
 
 * along a full axis (apply a function to an entire column or row made up of block partitions when user function needs information about the whole axis)

docs/flow/modin/core/execution/dask/implementations/pandas_on_dask/partitioning/partition_manager.rst

@@ -2,7 +2,7 @@ PandasOnDaskDataframePartitionManager
 """""""""""""""""""""""""""""""""""""
 
 This class is the specific implementation of :py:class:`~modin.core.dataframe.pandas.partitioning.partition_manager.PandasDataframePartitionManager`
-using Dask as the execution engine. This class is responsible for partition manipulation and applying a funcion to
+using Dask as the execution engine. This class is responsible for partition manipulation and applying a function to
 block/row/column partitions.
 
 Public API

docs/flow/modin/core/execution/python/implementations/pandas_on_python/index.rst

@@ -33,7 +33,7 @@ PandasOnPython Dataframe implementation
 This page describes implementation of :doc:`Modin PandasDataframe Objects </flow/modin/core/dataframe/pandas/index>`
 specific for `PandasOnPython` execution. Since Python engine doesn't allow computation parallelization,
 operations on partitions are performed sequentially. The absence of parallelization doesn't give any
-perfomance speed-up, so ``PandasOnPython`` is used for testing purposes only.
+performance speed-up, so ``PandasOnPython`` is used for testing purposes only.
 
 * :doc:`PandasOnPythonDataframe <dataframe>`
 * :doc:`PandasOnPythonDataframePartition <partitioning/partition>`

docs/flow/modin/core/execution/python/implementations/pandas_on_python/partitioning/partition_manager.rst

@@ -3,7 +3,7 @@ PandasOnPythonDataframePartition
 
 The class is specific implementation of :py:class:`~modin.core.dataframe.pandas.partitioning.partition_manager.PandasDataframePartitionManager`
 using Python as the execution engine. This class is responsible for partitions manipulation and applying
-a funcion to block/row/column partitions.
+a function to block/row/column partitions.
 
 Public API
 ----------

docs/flow/modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition_manager.rst

@@ -2,7 +2,7 @@ PandasOnRayDataframePartitionManager
 """"""""""""""""""""""""""""""""""""
 
 This class is the specific implementation of :py:class:`~modin.core.execution.ray.generic.partitioning.GenericRayDataframePartitionManager`
-using Ray distributed engine. This class is responsible for partition manipulation and applying a funcion to
+using Ray distributed engine. This class is responsible for partition manipulation and applying a function to
 block/row/column partitions.
 
 Public API