# Create a Mito for Streamlit App

{% hint style="info" %}
Make sure you've installed [Mito for Streamlit](https://docs.trymito.io/getting-started/installing-mito/installing-mito-for-streamlit) before continuing.
{% endhint %}

The easiest way to create your first Streamlit application with Mito is to try things yourself. Streamlit has an awesome developer story, so for each edit you make to your code, you can just refresh the page and see your new changes.

In this guide, we're going to use Mito for Streamlit to create an interactive app that allows users to explore Tesla stock data. We'll do some basic data cleaning, and then perform an analysis that computes the traded volume in the most recent month. 

{% hint style="info" %}
To see the finished script, [scroll down](#putting-it-all-together).
{% endhint %}

## Create your first app

1. First, create a new Python script. Let's call it `stock_data.py`
2. Open `stock_data.py` in your favorite IDE or text editor, then add these lines:

   ```python
   import streamlit as st
   from mitosheet.streamlit.v1 import spreadsheet
   ```
3. Let's make the page wide, and set a title:

   ```python
   st.set_page_config(layout="wide")
   st.title('Tesla Stock Volume Analysis')
   ```
4. Now, run your Streamlit app from the command line:

   ```bash
   streamlit run stock_data.py
   ```
5. The Streamlit app should open in a new tab in your web-browser. Check out that awesome title!

### Add a Mitosheet

1. Now, let's use the Mito spreadsheet to display 

   ```python
   CSV_URL = 'https://raw.githubusercontent.com/plotly/datasets/master/tesla-stock-price.csv'
   new_dfs, code = spreadsheet(CSV_URL)
   ```
2. You'll see a few buttons in the upper-right corner of your app asking if you'd like to rerun the app. Choose **Always rerun**, and you'll see your changes automatically each time you save the `stock_data.py` file.
3. When your app refreshes, you will see the Mito spreadsheet with Tesla stock data. Scroll around like you would in any other spreadsheet!
4. The return value of the `spreadsheet` is each of the tabs of the spreadsheet, as well as the code that corresponds to the edits you make in the spreadsheet. Let's display both:

   ```python
   st.write(new_dfs)
   st.code(code)
   ```

### Use the Mitosheet to Clean the Dataset

1. Take a closer look at this dataset. It looks like the first row has an invalid date. Let's remove it from our analysis by **right clicking on the row label 0** and then clicking **Delete Rows.**
2. Turn the `Date` column into a datetime. Click the **Filter** icon next to the `Date` header, and in the taskpane that opens, use the **Dtype** dropdown to select **datetime.**
3. Pause here and check out the dataframes displayed below the Mitosheet. Note that each 
4. Turn the `Volume` column into a float. Click the **Filter** icon next to the `Volume` header, and in the taskpane that opens, use the **Dtype** dropdown to select **float.**

### Use the Mitosheet to Create a Pivot Table

We're now ready to use more of Mito's advanced functionality. Let's create a pivot table that allows us to understand how the traded volume has changed over time. 

1. Click the **Pivot** button in the Mito toolbar. 
2. Add `Date` to the **Rows** section. Group date by **year-month**, so we can understand volume changes on a monthly lee. 
3. Add `Volume` to the **Values** section. Select an aggregation type of **sum**.
   1. If you cannot select **sum** for `Volume`, you may have forgotten to change `Volume` to a **float.** See step 4 in the section above.
4. This pivot table now displayes the total of traded volume per month. If you scroll below, you will see that this pivot table is returned from the Mito spreadsheet, and the code to generate this pivot table is returned as well.

See the rest of our documentation to see what other functionality is available in Mito, or continue to our [App Gallery](https://docs.trymito.io/mito-for-streamlit/streamlit-app-gallery) to get ideas for how to use Mito in your Streamlit applications.

## Putting it all together

That's it, you made your first app with Mito for Streamlit. Here's our finished script:

```python
import streamlit as st
from mitosheet.streamlit.v1 import spreadsheet

st.set_page_config(layout="wide")
st.title('Tesla Stock Volume Analysis')

CSV_URL = 'https://raw.githubusercontent.com/plotly/datasets/master/tesla-stock-price.csv'
new_dfs, code = spreadsheet(CSV_URL)

st.write(new_dfs)
st.code(code)
```

### Share your app

After you’ve built an app using Mito for Streamlit, it's time to share it! To show it off to the world you can use **Streamlit Community Cloud** to deploy, manage, and share your app for free.

It works in 3 simple steps:

1. Put your app in a public GitHub repo (and make sure it has a requirements.txt!)
2. Sign into [share.streamlit.io](https://share.streamlit.io/)
3. Click 'Deploy an app' and then paste in your GitHub URL

That's it! You now have a publicly deployed app that you can share with the world. Click to learn more about [how to use Streamlit Community Cloud](https://docs.streamlit.io/streamlit-community-cloud).