docs: update code examples

source/documentation/data-docs/amazon-s3.md

@@ -48,8 +48,8 @@ Every bucket has three data access levels:
 
 - Read only
 - Read/write
-- Admin 
-   - this provides read/write access and allows the user to add and remove other users from the bucket's data access group
+- Admin
+  - this provides read/write access and allows the user to add and remove other users from the bucket's data access group
 
 ### Path-specific access
 
@@ -146,10 +146,14 @@ For further details, see the relevant sections for [`Rs3tools`](#rs3tools), [`bo
 
 #### JupyterLab / VSCode
 
-The main options for interacting with files stored in AWS S3 buckets on the Analytical Platform via JupyterLab and VSCode are :
+The main options for interacting with files stored in AWS S3 buckets on the Analytical Platform via JupyterLab and VSCode include:
 
-- Reading files : [`polars`](https://docs.pola.rs/), [`pandas`](https://pandas.pydata.org/docs/) , [`awswrangler`](https://pypi.org/project/awswrangler/), [`mojap-arrow-pd-parser`](https://github.com/moj-analytical-services/mojap-arrow-pd-parser?tab=readme-ov-file#mojap-arrow-pd-parser)
-- Downloading / Uploading files : [`awswrangler`](https://pypi.org/project/awswrangler/), [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html)
+- [`polars`](https://docs.pola.rs/)
+- [`pandas`](https://pandas.pydata.org/docs/)
+- [`awswrangler`](https://pypi.org/project/awswrangler/)
+- [`mojap-arrow-pd-parser`](https://github.com/moj-analytical-services/mojap-arrow-pd-parser?tab=readme-ov-file#mojap-arrow-pd-parser)
+[`awswrangler`](https://pypi.org/project/awswrangler/)
+- [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html)
 
 ### Installation and usage
 
@@ -268,93 +272,147 @@ s3browser::file_explorer_s3()
 
 You can find out more about how to use `s3browser` on [GitHub](https://github.com/moj-analytical-services/s3browser).
 
-### JupyterLab
+### JupyterLab and VSCode (python)
 
-You can read/write directly from s3 using [pandas](https://pandas.pydata.org/docs/user_guide/index.html). However, to get the best representation of the column types in the resulting Pandas dataframe(s), you may wish to use [mojap-arrow-pd-parser](https://github.com/moj-analytical-services/mojap-arrow-pd-parser).
+You can read/write directly from s3 using a variety of tools: [awswrangler](https://pypi.org/project/awswrangler/#quick-start), [polars](https://docs.pola.rs/), [boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html), [pandas](https://pandas.pydata.org/docs/user_guide/index.html), or [mojap-arrow-pd-parser](https://github.com/moj-analytical-services/mojap-arrow-pd-parser?tab=readme-ov-file#mojap-arrow-pd-parser)
 
-#### `mojap-arrow-pd-parser`
+#### `AWS Data Wrangler`
 
-`mojap-arrow-pd-parser` provides easy csv, jsonl and parquet file readers. To install in terminal:
+You can also use [`awswrangler`](https://pypi.org/project/awswrangler/#quick-start) to work with data stored in Amazon S3.
+
+To install AWS Wrangler, run the following code in a terminal:
+
+```sh
+python -m pip install awswrangler
+```
+
+To read a CSV file from S3 using AWS Wrangler:
+
+```py
+import awswrangler as wr
+
+# Retrieving the data directly from Amazon S3
+df = wr.s3.read_csv("s3://bucket/dataset/", dataset=True)
+```
+
+More information can be found in the [product documentation](https://aws-data-wrangler.readthedocs.io/en/stable/tutorials/003%20-%20Amazon%20S3.html).
+
+#### `boto3`
+
+You can also download or read objects using the [`boto3`](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html) package.
+
+You can install `boto3` by running the following code in a terminal:
 
 ```bash
-pip install arrow-pd-parser
+python -m pip install boto3
 ```
 
-To read/write a csv file from s3:
+To [download a file from Amazon S3](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-download-file.html#downloading-files), you should use the following code:
 
 ```python
-from arrow_pd_parser import reader, writer
+import boto3
 
-# Specifying the reader Both reader statements are equivalent and call the same readers under the hood
-df1 = reader.read("s3://bucket_name/data/all_types.csv", file_format="csv")
-df2 = reader.csv.read("s3://bucket_name/data/all_types.csv")
+s3_client = boto3.client('s3')
+s3_client.download_file('bucket_name', 'key', 'local_path')
+```
 
-# You can also pass the reader args to the reader as kwargs
-df3 = reader.csv.read("s3://bucket_name/data/all_types.csv", nrows = 2)
-# The writer API has the same functionality
-writer.write(df1, file_format="parquet")
-writer.parquet.write(df1)
+If you receive an `ImportError`, try restarting your kernel, so that Python recognises your `boto3` installation.
+
+Here, you should substitute `'bucket_name'` with the name of the bucket, `'key'` with the path of the object in Amazon S3 and `local_path` with the local path where you would like to save the downloaded file.
+
+To upload a file to Amazon S3, you should use the following code:
+
+```python
+#Upload sample contents to s3
+s3_client = boto3.client('s3')
+
+data = b'This is the content of the file uploaded from python boto3'
+file_name = 'your_file_name.txt'
+
+response = s3_client.put_object(Bucket=your_bucket_name, Body=data, Key=file_name)
+print(f"AWS response code for uploading file is {(response['ResponseMetadata']['HTTPStatusCode'])}")
 ```
 
-`mojap-arrow-pd-parser` infers the file type from the extension, so for example `reader.read("s3://bucket_name/file.parquet")` would read a parquet file without need for specifying the file type.
+You can find more information in the [package documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Object.download_file).
 
-The package also has a lot of other functionality including specifying data types when reading (or writing). More details can be found in the package [README](https://github.com/moj-analytical-services/mojap-arrow-pd-parser#mojap-arrow-pd-parser).
+#### `polars`
+
+[`polars`](https://docs.pola.rs/) is a fast DataFrame library implemented in Rust and can be used to read/write data from/to Amazon S3. To install polars and s3fs, run the following code in a terminal:
+
+```bash
+python -m pip install polars s3fs
+```
+
+To read a CSV file from S3 using polars (see [reading from Cloud storage](https://docs.pola.rs/user-guide/io/cloud-storage/) in the docs):
+
+```py
+import polars as pl
+
+source = "s3://bucket/*.csv"
+
+df = pl.read_csv(source)
+```
+
+To [write a DataFrame to S3](https://docs.pola.rs/user-guide/io/cloud-storage/#writing-to-cloud-storage#writing-to-cloud-storage):
+
+```py
+import polars as pl
+import s3fs
+
+df = pl.DataFrame({
+    "foo": ["a", "b", "c", "d", "d"],
+    "bar": [1, 2, 3, 4, 5],
+})
+
+fs = s3fs.S3FileSystem()
+destination = "s3://bucket/my_file.parquet"
+
+# write parquet
+with fs.open(destination, mode='wb') as f:
+    df.write_parquet(f)
+```
 
 #### `pandas`
 
 You can use any of the `pandas` read functions (for example, `read_csv` or `read_json`) to download data directly from Amazon S3. This requires that you have installed the `pandas` and `s3fs` packages. To install these, run the following code in a terminal:
 
-```
+```bash
 python -m pip install pandas s3fs
 ```
 
 As an example, to read a CSV, you should run the following code:
 
-```
+```py
 import pandas as pd
 pd.read_csv('s3://bucket_name/key')
 ```
 
 Here, you should substitute `bucket_name` with the name of the bucket and `key` with the path of the object in Amazon S3.
 
-#### `boto3`
-
-You can also download or read objects using the `boto3` package.
+#### `mojap-arrow-pd-parser`
 
-You can install `boto3` by running the following code in a terminal:
+`mojap-arrow-pd-parser` provides easy csv, jsonl and parquet file readers. To install in terminal:
 
-```
-pip install boto3
+```bash
+pip install arrow-pd-parser
 ```
 
-To download a file from Amazon S3, you should use the following code:
+To read/write a csv file from s3:
 
 ```python
-import boto3
-
-s3 = boto3.resource('s3')
-s3.Object('bucket_name', 'key').download_file('local_path')
-```
-
-If you receive an `ImportError`, try restarting your kernel, so that Python recognises your `boto3` installation.
-
-Here, you should substitute `'bucket_name'` with the name of the bucket, `'key'` with the path of the object in Amazon S3 and `local_path` with the local path where you would like to save the downloaded file.
+from arrow_pd_parser import reader, writer
 
-To upload a file to Amazon S3, you should use the following code:
+# Specifying the reader Both reader statements are equivalent and call the same readers under the hood
+df1 = reader.read("s3://bucket_name/data/all_types.csv", file_format="csv")
+df2 = reader.csv.read("s3://bucket_name/data/all_types.csv")
 
-```python
-#Upload sample contents to s3
-s3 = boto3.client('s3')
-data = b'This is the content of the file uploaded from python boto3'
-file_name='your_file_name.txt'
-response =s3.put_object(Bucket= your_bucket_name,Body= data,Key= file_name)
-print('AWS response code for uploading file is '+str(response['ResponseMetadata']['HTTPStatusCode']))
+# You can also pass the reader args to the reader as kwargs
+df3 = reader.csv.read("s3://bucket_name/data/all_types.csv", nrows = 2)
+# The writer API has the same functionality
+writer.write(df1, file_format="parquet")
+writer.parquet.write(df1)
 ```
 
-You can find more information in the [package documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Object.download_file).
-
-#### `AWS Data Wrangler`
-
-You can also use `AWS Wrangler` to work with data stored in Amazon S3.
+`mojap-arrow-pd-parser` infers the file type from the extension, so for example `reader.read("s3://bucket_name/file.parquet")` would read a parquet file without need for specifying the file type.
 
-More information can be found in the [product documentation](https://aws-data-wrangler.readthedocs.io/en/stable/tutorials/003%20-%20Amazon%20S3.html).
+The package also has a lot of other functionality including specifying data types when reading (or writing). More details can be found in the package [README](https://github.com/moj-analytical-services/mojap-arrow-pd-parser#mojap-arrow-pd-parser).