# Quick Start

**Only works for paying subscribers, purchase your API key** [<mark style="color:purple;">**`here`**</mark>](https://sov.ai/subscribe)**.**

<pre class="language-python"><code class="lang-python"><strong>import sovai as sov
</strong><strong>sov.token_auth(token="add_your_subscriber_token_here")
</strong></code></pre>

## Installation

To use Sovai's python module you can install `sovai`.

```
pip install sovai[full]
```

{% hint style="success" %}
**The best way to familiarize yourself with this powerful library is to head straight to the**[ **tutorial section.**](/get-started/tutorials.md)
{% endhint %}

### Data

If you only want to download data into pandas please use the much lighter package.

```
pip install sovai
```

```python
import sovai as sov
sov.data("query")
```

## Commands

Utilize various commands to interact with datasets:, [`data`](#download-datasets) ,[`plots`](#visualizing-data), and [`reports`](#running-reports)`.`

* `sov.data('query')`: Retrieve data based on the specified query.
* `sov.plots('query')`: Generate plots for visual analysis.
* `sov.reports('query')`: Access reports summarizing predictions.

## Authenticate Account: <mark style="color:blue;">`token_auth()`</mark>

There are two ways to authenticate your requests. Get your token [here](https://sov.ai/home).

<pre class="language-python"><code class="lang-python">import sovai as sov

# 1. Method 1: Configuration API connection
<strong>sov.token_auth(token="add_your_token_here")
</strong>
# 2. Method 2: Or read token from .env file e.g API_TOKEN=super_secret_token
sov.read_key('.env')
</code></pre>

## Download Data: : <mark style="color:blue;">`data()`</mark>

Once authenticated, downloading datasets becomes easy.

```python
# Example data retrieval
gs_df = sov.data("bankruptcy/monthly")
```

## Visualize Data: <mark style="color:blue;">`plot()`</mark>

#### Unique Plots

Certain datasets have unique visualizations that you access from their respective pages.

```python
# Calls the 'bankruptcy' dataset and the associated chart_type
sov.plot('bankruptcy', chart_type='compare')
```

#### Universal Plots

Other datasets can use panda's built-in plots.

```python
df_risks = sov.data("risks")
df_risks[["MARKET_RISK","TURING_RISK"]].tail(15400).plot()
```

## Running Reports: <mark style="color:blue;">`report()`</mark>

Run report to explore the dataset.

```python
sov.report("corprisk/accounting",report_type="sector-top")
```

We can also make use of pandas' built-in functions to run queries on top of the data.

```python
df_risks.query("ticker == 'CGRNQ'")
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.sov.ai/get-started/quickstart.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.