# API Reference ## The Spreadsheet Component To display the Mito spreadsheet in a Streamlit application, use the following code: ```python from mitosheet.streamlit.v1 import spreadsheet # ... rest of your streamlit app spreadsheet() ``` ### Component API {% code fullWidth="false" %} ```python from mitosheet.streamlit.v1 import spreadsheet spreadsheet( *args: , import_folder: Optional[str]=None, df_names: Optional[List[str]]=None, sheet_functions: Optional[List[Callable]]=None, importers: Optional[List[Callable]]=None, code_options: Optional[CodeOptions]=None, column_definitions: Optional[List[ColumnDefinitions]]=None, return_type: Options[str]='default', default_editing_mode: Optional[Literal['cell', 'column']]=None, height: Optional[str]=None, key=None ) -> Tuple[Dict[str, pd.DataFrame], str] ``` {% endcode %}

Argument Name	Type	Explanation
`*args`	`Union[pd.DataFrame, str]`	Pass any number of Pandas dataframes or paths to CSV files that will be displayed by in the Mito spreadsheet.
`import_folder`	`Optional[str]`	A file path to a folder where users can import data from. Any subfolders will be available for navigation and importing within the Mito file browser.
`df_names`	`Optional[List[str]]`	if you pass Pandas dataframes through the `args`, then optionally include the names of these dataframes in this list. This makes Mito generated code more correct.
`sheet_functions`	`Optional[List[Callable]]`	Pass functions that are available as spreadsheet functions in the Mito spreadsheet. See more here. (Mito Enterprise)
`importers`	`Optional[List[Callable]]`	Pass custom data importers that get an auto-generated UI. See more here. (Mito Enterprise)
`editors`	`Optional[List[Callable]]`	Pass Python functions that edit a dataframe to get an auto-generated Ui. See more here. (Mito Enterprise)
`code_options`	`Optional[CodeOptions]`	Pass a `CodeOptions` object to specify how the code should be generated. See more here. (Mito Enterprise)
`column_definitions`	`Optional[List[ColumnDefinitions]]=None`	An optional list of `ColumnDefinitions` to specify conditional formats for a set of columns. See below for usage. See more here. (Mito Enterprise)
`key`	`Optional[str]`	An optional key that uniquely identifies this component. Necessary if you have multiple Mito `spreadsheet` displaying the same data.
`return_type`	`Optional[str]`	An optional string to identify what information you want from the `spreadsheet` function. See below for the options.
`default_editing_mode`	`Optional[Literal['cell', 'column']]`	Sets default formula editing behavior. Formulas will either default to editing the entire column, or the specific cell being edited. If not set, defaults to editing the entire column. (Mito Enterprise)
`height`	`Optional[str]`	An optional string to specify the height in pixels that you want to use for the mitosheet. ie: "900px"

### Return Types By default, the `spreadsheet` component returns: ```python new_dfs, code = spreadsheet() ``` * `new_dfs`: a Python dictionary from dataframe name to dataframe object. There will be one entry for each tab in the Mito spreadsheet. * `code`: a string that corresponds to the edits made by the user of the course of the Mito analysis. #### MitoAnalysis Return Type To **easily rerun your analysis on new data,** return the [MitoAnalysis](/mito-for-streamlit/api-reference/runnableanalysis-class.md) class through the `return_type` parameter: ```python analysis = spreadsheet(return_type='analysis') ``` #### Other return types * `selection`: the current selected cells in the mitosheet * `default_list`: similar to the default return type, but the dataframes are a list instead of a dictionary * `dfs_dict` similar to the default return type, but only returns the dict without the code * `code`: similar to the default return type, but only return the code * `dfs_list`: returns a list of the dataframes generated by the analysis ## Examples Below are examples of common uses of the Mito spreadsheet component in a streamlit application. #### Empty Mito Spreadsheet ```python from mitosheet.streamlit.v1 import spreadsheet # Render an empty spreadsheet that allows users to import from the ./data folder new_dfs, _ = spreadsheet(import_folder='./data') ``` #### Display a Dataframe for Editing ```python import pandas as pd from mitosheet.streamlit.v1 import spreadsheet # Pass a single dataframe for editing df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) new_dfs, _ = spreadsheet(df, df_names=['df']) new_df = list(new_dfs.values())[0] # Note that multiple dataframes can be passed as well # and each will appear as a tab in the Mito spreadsheet new_dfs, _ = spreadsheet(df1, df2, df_names=['df1', 'df2']) ``` #### Display a CSV file for Editing ```python from mitosheet.streamlit.v1 import spreadsheet # Mito will automatically read this file into a dataframe # and display it as a sheet tab dfs, _ = spreadsheet('path-to-file.csv') ``` #### Displaying Mito generated code ```python from mitosheet.streamlit.v1 import spreadsheet # The second return value is Mito generated code new_dfs, code = spreadsheet('path-to-file.csv') # Display the code st.code(code) ``` --- # 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.trymito.io/mito-for-streamlit/api-reference.md?ask= ``` 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.