> For the complete documentation index, see [llms.txt](https://docs.trymito.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.trymito.io/mito-for-streamlit/common-design-patterns/using-mito-for-final-mile-data-cleaning.md).

# Using Mito for Final Mile Data Cleaning

## Final Mile Data Cleaning in a Streamlit Application

Streamlit dashboards allow non-technical users to interact with data. Often, as a Streamlit developer, you may want to expose data to these non-technical users so that they can check and clean the data before it is finalized or used in production.

This final step data cleaning can take a variety of forms, but in general it requires users to be able to:

1. See the raw tabular data
2. Sort, filter, and explore the data to find incorrect, incomplete or missing values
3. Remove invalid columns or rows
4. Add new columns or rows
5. Edit specific values

## Why choose Mito for final mile data cleaning?

Mito is naturally a good fit for all of these operations, and supports most data cleaning applications better than other Streamlit grids.

Because Mito provides an interface for data cleaning that matches the other spreadsheets that users are used to, users will be able to perform the necessary data cleaning without as much user education required -- as it's an interface they expect. 

Furthermore, Mito provides features required by users for data cleaning out of the box, with no configuration required. Users can see, sort, filter and explore the data easily, before adding and removing columns and rows, and editing specific values. See the [Streamlit Grid Comparison App](https://github.com/mito-ds/streamlit-grid-comparison) for more feature comparison on different Streamlit grids.

See a useful data cleaning demo here:

{% embed url="<https://github.com/mito-ds/data-cleaning-demo>" %}


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