Importing Data into Mito

This documentation will show you how to import your data to Mito so you can start saving yourself time.

Mito can handle any tabular data. If it's in a dataframe, then it's ready for Mito! There are two ways to get data into Mito, passing a dataframe to the Mitosheet and using the in-app import popup.

Passing Dataframes to Mito

Mito displays any dataframe that is passed directly to a mitosheet.sheet(df1, df2). To see an example of displaying a dataframe in a Mitosheet, copy and run the code below in a JupyterLab cell:

# import Python packages
import mitosheet
import pandas as pd
# create some simple data to display
train_stations = pd.DataFrame({'Zip': [21001, 97321, 49224, 87102, 24910, 22301], 'City': ['Aberdeen', 'Albany', 'Albion', 'Albuquerque', 'Alderson', 'Alexandria'], 'State': ['MD', 'OR', 'MI', 'NM', 'WV', 'VA'], 'Ticket_Office': ['N', 'Y', 'N', 'Y', 'N', 'Y']})
demographics = pd.DataFrame({'Zip': [21001, 97321, 49224, 87102, 24910, 22301], 'Median_Income': [53979.0, 112924.0, 37556.0, 28388.0, 30914.0, 54087.0], 'Mean_Income': [66169.0, 147076.0, 50371.0, 39529.0, 40028.0, 64068.0], 'Pop': [18974.0, 11162.0, 14900.0, 22204.0, 5383.0, 19504.0]})
# render the Mitosheet with the data
mitosheet.sheet(train_stations, demographics)

Using Pandas, you can create dataframes by reading csv files, Excel files, or even querying an SQL database. If you have dataframes you want to interact with and manipulate, pass them into a Mitosheet!

If a Mitosheet does not appear, make sure that you followed the Installation Instructions. Not downloading the Jupyter Lab extension manager is a common mistake.

Using the Import Task-pane

Mito also has a point and click method for importing CSV files.

  1. Click on the import button in the Mito toolbar.

  2. Select the files you want to import, and Mito will import them into the sheet.

Want help? Book a call with our support team.