> For the complete documentation index, see [llms.txt](https://antoinepinto.gitbook.io/easyenvi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://antoinepinto.gitbook.io/easyenvi/easy-environment.md).

# Easy Environment

## Definition

Easy Environment is a Python tool that provides **easy-to-use functionality for managing files and data in different environments**. It offers a class that simplifies file operations on the local disk and cloud services such as Google Cloud (Google Cloud Storage and Big Query) or SharePoint.

## Features

* **Multi-format loading and saving:** Load and save files in various formats with one command line
  * **Default supported formats**: csv, docx, jpg, json, md, parquet, pdf, pickle, png, pptx, sql, toml, txt, xlsx, xml, yaml, yml
  * **Unsupported formats**: Customisable. See [Customise supported formats](/easyenvi/extra/customise-supported-formats.md).
* **Multi-environment management:**
  * **Local disk**: Loading/saving and management.
  * **Google Cloud Storage**: Loading/saving and management.
  * **Big Query**: Append, write, and run queries on Big Query tables.
  * **SharePoint**: Download, upload, and manage files on SharePoint.

<figure><img src="/files/RMS9rFEolWURRrZDowA7" alt=""><figcaption><p>Easy Environment - Features</p></figcaption></figure>

## Initialisation

To use Easy Environment, follow these instructions:

1. Install `easyenvi`

{% code fullWidth="false" %}

```python
pip install easyenvi==1.0.5
```

{% endcode %}

2. Create an instance of the EasyEnvironment class

All the parameters in the `EasyEnvironment`class are optional: it depends on how you use the tool.&#x20;

```python
from easyenvi import EasyEnvironment

envi = EasyEnvironment(
  local_path="", # Optional

  gcloud_project_id="your-project-id", # Optional
  gcloud_credential_path="path/to/credentials.json", # Optional
  GCS_path="gs://your-bucket-name/", # Optional

  sharepoint_site_url="https://{tenant}.sharepoint.com/sites/{site}", # Optional
  sharepoint_client_id="your-client-id", # Optional
  sharepoint_client_secret="your-client-secret", # Optional
                  )
```

Specifying certain parameters means certain dependencies:&#x20;

* For using **local operation**, it is necessary to specify `local_path`, the path from which local operations should be executed - specify an empty string if you want to use the current directory. Additionnaly, the installation of the `fsspec` library is required.
* For using **Google Cloud**, it is necessary to specify the project ID, the path to a credential .json file, and, in case of interaction with Google Cloud Storage, the path to the GCS folder (see [Google Cloud Initialisation](/easyenvi/google-cloud-environment/google-cloud-initialisation.md)). Additionnaly, the installation of the libraries `google-cloud-storage`, `google-cloud-bigquery` and `fsspec` is required.
* For using **SharePoint**, it is necessary to specify the SharePoint site to interact with, as well as authentication credentials: either the client\_id/client\_secret pair or the username/user\_password pair (see [SharePoint Initialisation](/easyenvi/sharepoint-environment/sharepoint-initialisation.md)). Furthermore, the installation of the `Office365-REST-Python-Client` library is required.

## Examples of use

### Local Features

```python
# Load any file format
my_dict = envi.local.load(path='inputs/my_dictionnary.pickle')
my_logo = envi.local.load(path='inputs/my_logo.png')
dataset = envi.local.load(path='inputs/dataset.csv')

# Save any file format
envi.local.save(obj=my_dict, path='outputs/my_dictionnary.pickle')
envi.local.save(obj=my_logo, path='outputs/my_logo.png')
envi.local.save(obj=dataset, path='outputs/dataset.csv')
```

### Google Cloud Storage features

```python
# Load any file format
my_dict = envi.gcloud.GCS.load(path='inputs/my_dictionnary.pickle')
my_logo = envi.gcloud.GCS.load(path='inputs/my_logo.png')
dataset = envi.gcloud.GCS.load(path='inputs/dataset.csv')

# Save any file format
envi.gcloud.GCS.save(obj=my_dict, path='outputs/my_dictionnary.pickle')
envi.gcloud.GCS.save(obj=my_logo, path='outputs/my_logo.png')
envi.gcloud.GCS.save(obj=dataset, path='outputs/dataset.csv')
```

### Big Query features

```python
df = pd.DataFrame(data={'age': [21, 52, 30], 'wage': [12, 17, 11]})

# Create a new table
envi.gcloud.BQ.write(dataset, 'mydata.mytable')

# Append an existing table
envi.gcloud.BQ.append(dataset, 'mydata.mytable')

# Run queries
query = """
SELECT *
FROM mydata.mytable
WHERE age < 40
"""

new_dataset = envi.gcloud.BQ.query(query).to_dataframe()
```

### SharePoint features

```python
# Download a file
envi.sharepoint.download(input_path="/Document partages/folder/my_file.txt",
                        output_path="local_folder/my_file.txt")
                        
# Upload a file
envi.sharepoint.upload(input_path="local_folder/my_file.txt",
                      output_path="Document partages/folder/my_file.txt")
                      
# List files
envi.sharepoint.list_files(folder="local_folder")
```

Browse the following sections to obtain more information about the parameters and functionalities.