> For the complete documentation index, see [llms.txt](https://docs.alkemi.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.alkemi.ai/documentation/getting-started/quickstart.md).

# Quickstart

{% embed url="<https://www.loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?sid=4363404f-9d62-446d-a8fe-93a55b1e3794>" %}

This guide outlines the steps to upload a CSV file and analyze data using the Alkemi DataLab interface. You can download the CSV we use in this guide from data.gov [here](https://ers.usda.gov/sites/default/files/_laserfiche/DataFiles/50673/CPIHistoricalForecast.csv?v=85900) or utilize any of the data products that are offered for free with your account.

### Key Steps

The steps below outline the quickstart video above with links to where each step is discussed in the video.

{% stepper %}
{% step %}

## **Accessing Alkemi DataLab** [**0:00**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=0)

* Log In to [Alkemi DataLab.](https://datalab.alkemi.ai/?request_access=true)
* Familiarize yourself with the layout:
  * Left sidebar: Chat history and API endpoints.
  * Main chat input area for queries and commands.
  * Right sidebar: Credit balance, integrations, and current data slices.
    {% endstep %}

{% step %}

## **Preparing to Upload a CSV File** [**1:11**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=71)

* Ensure you have a CSV file ready for upload (e.g., consumer price index historical data).
  {% endstep %}

{% step %}

## **Uploading the CSV File** [**1:22**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=82)

* Use the upload functionality in Alkemi DataLab to upload your CSV file.
* Review the columns in your CSV file, noting important attributes.
  {% endstep %}

{% step %}

## **Creating a New Table from the CSV Data** [**2:11**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=131)

* Construct a query to filter and group data:
  * Filter by the attribute column (e.g., midpoint of prediction interval).
  * Group by consumer price index item.
  * Calculate a new column for forecast percent change.
    {% endstep %}

{% step %}

## **Querying Results** [**2:50**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=170)

* Send the query to the Alkemi agent.
* Wait for the results to be assembled and saved as a data asset.
  {% endstep %}

{% step %}

## **Accessing and Reviewing the Results** [**3:06**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=186)

* Use the mentioning functionality to reference the new table in chat threads.
* Click to expand and view the data, or download it as a CSV.
  {% endstep %}

{% step %}

## **Cleaning and Sorting the Data** [**4:00**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=240)

* Remove any irrelevant categories (e.g., 'all food').
* Sort the results by total increase in descending order.
  {% endstep %}

{% step %}

## **Visualizing the Data** [**4:37**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=277)

* Request a visualization of the data (e.g., as a bar chart or pie chart).
* Interact with the chart to view details and download it for presentations.
  {% endstep %}

{% step %}

## **Next Steps** [**5:16**](https://loom.com/share/4d49ebbc73364a2bb2ffb7172e367f9a?t=316)

* Consider connecting integrations like Snowflake for enhanced data analysis.
  {% endstep %}
  {% endstepper %}


---

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