Merge pull request #102582 from changeworld/patch-3

Fix typo

Author: Jak-MS

articles/purview/create-microsoft-purview-python.md

@@ -12,7 +12,7 @@ ms.custom: mode-api
 
 # Quickstart: Create a Microsoft Purview (formerly Azure Purview) account using Python
 
-In this quickstart, you’ll create a Microsoft Purview (formerly Azure Purview) account programatically using Python. [The python reference for Microsoft Purview](/python/api/azure-mgmt-purview/) is available, but this article will take you through all the steps needed to create an account with Python.
+In this quickstart, you’ll create a Microsoft Purview (formerly Azure Purview) account programatically using Python. [The Python reference for Microsoft Purview](/python/api/azure-mgmt-purview/) is available, but this article will take you through all the steps needed to create an account with Python.
 
 The Microsoft Purview governance portal surfaces tools like the Microsoft Purview Data Map and Microsoft Purview Data Catalog that help you manage and govern your data landscape. By connecting to data across your on-premises, multi-cloud, and software-as-a-service (SaaS) sources, the Microsoft Purview Data Map creates an up-to-date map of your information. It identifies and classifies sensitive data, and provides end-to-end linage. Data consumers are able to discover data across your organization, and data administrators are able to audit, secure, and ensure right use of your data.
 

articles/purview/tutorial-using-python-sdk.md

@@ -116,7 +116,7 @@ Microsoft Purview now has the required reading right to scan your Blob Storage.
 
 ## Create Python script file
 
-Create a plain text file, and save it as a python script with the suffix .py.
+Create a plain text file, and save it as a Python script with the suffix .py.
 For example: tutorial.py.
 
 ## Instantiate a Scanning, Catalog, and Administration client
@@ -194,7 +194,7 @@ In this section, you'll register your Blob Storage.
 
 1. Gather the resource ID for your storage account by following this guide: [get the resource ID for a storage account.](../storage/common/storage-account-get-info.md#get-the-resource-id-for-a-storage-account)
 
-1. Then, in your python file, define the following information to be able to register the Blob storage programmatically:
+1. Then, in your Python file, define the following information to be able to register the Blob storage programmatically:
 
     ```python
     storage_name = "<name of your Storage Account>"

articles/synapse-analytics/quickstart-transform-data-using-spark-job-definition.md

@@ -86,7 +86,7 @@ On this panel, you can reference to the Spark job definition to run.
      |Main class name| The fully qualified identifier or the main class that is in the main definition file. <br> Sample: `WordCount`|
      |Command-line arguments| You can add command-line arguments by clicking the **New** button. It should be noted that adding command-line arguments will override the command-line arguments defined by the Spark job definition. <br> *Sample: `abfss://…/path/to/shakespeare.txt` `abfss://…/path/to/result`* <br> |
      |Apache Spark pool| You can select Apache Spark pool from the list.|
-     |Python code reference| Additional python code files used for reference in the main definition file. <br> It supports passing files (.py, .py3, .zip) to the "pyFiles" property. It will override the "pyFiles" property defined in Spark job definition. <br>|
+     |Python code reference| Additional Python code files used for reference in the main definition file. <br> It supports passing files (.py, .py3, .zip) to the "pyFiles" property. It will override the "pyFiles" property defined in Spark job definition. <br>|
      |Reference files | Additional files used for reference in the main definition file. |
      |Dynamically allocate executors| This setting maps to the dynamic allocation property in Spark configuration for Spark Application executors allocation.|
      |Min executors| Min number of executors to be allocated in the specified Spark pool for the job.|

articles/synapse-analytics/spark/apache-spark-development-using-notebooks.md

@@ -450,7 +450,7 @@ You can access data in the primary storage account directly. There's no need to
 
 ## IPython Widgets
 
-Widgets are eventful python objects that have a representation in the browser, often as a control like a slider, textbox etc. IPython Widgets only works in Python environment, it's not supported in other languages (e.g. Scala, SQL, C#) yet. 
+Widgets are eventful Python objects that have a representation in the browser, often as a control like a slider, textbox etc. IPython Widgets only works in Python environment, it's not supported in other languages (e.g. Scala, SQL, C#) yet. 
 
 ### To use IPython Widget
 1. You need to import `ipywidgets` module first to use the Jupyter Widget framework.

articles/synapse-analytics/spark/data-sources/apache-spark-cdm-connector.md

@@ -62,7 +62,7 @@ When reading CSV data, the connector uses the Spark FAILFAST option by default.
   .option("entity", "permissive") or .option("mode", "failfast")
 ```
 
-For example, [here's an example python sample.](https://github.com/Azure/spark-cdm-connector/blob/master/samples/SparkCDMsamplePython.ipynb)
+For example, [here's an example Python sample.](https://github.com/Azure/spark-cdm-connector/blob/master/samples/SparkCDMsamplePython.ipynb)
 
 ## Writing data
 

articles/virtual-machines/workloads/oracle/oracle-database-backup-azure-backup.md

@@ -1083,7 +1083,7 @@ Now the database has been restored you must recover the database. Please follow
 
 1. Unmount the restore point.
 
-   When all databases on the VM have been successfully recovered you may unmount the restore point. This can be done on the VM using the `unmount` command or in Azure portal from the File Recovery blade. You can also unmount the recovery volumes by running the python script again with the **-clean** option.
+   When all databases on the VM have been successfully recovered you may unmount the restore point. This can be done on the VM using the `unmount` command or in Azure portal from the File Recovery blade. You can also unmount the recovery volumes by running the Python script again with the **-clean** option.
 
    In the VM using unmount:
    ```bash