Metadata-Version: 2.1
Name: ssmode
Version: 0.1.6
Summary: Tools for use in Mode Analytics Jupyter Notebooks
Home-page: https://github.com/skyselect/ssmode
Author: SkySelect, Inc.
Author-email: oss@skyselect.com
License: UNKNOWN
Description: # ssmode
        
        ### Installing & Importing
        To use the helper functions in a Mode Python Notebook, first install the package by adding this cell:
        ```python
        pip install ssmode -t "/tmp" > /dev/null 2>&1
        ```
        You can then import various functions like:
        ```python
        from ssmode.tables import style_table
        ```
        
        ### Styling Documentation
        The functions in files `bar_chart.py`, `kpi.py`, `tables.py` are designed to "fake" the Mode Analytics built-in widgets from the Notebook.
        
        ##### Styling Tables
        `style_table(df, hl_type=None, n=3, bar_cols=[])`
        - `df`: Pandas Dataframe (index will not be displayed)
        - `hl_type`: `None` or string `'nlargest'`, `'gradient'` or `'bars'` specifying cell highlighting type
        - `n`: integer > 0 specifying how many greatest cells will be highlighted (only applicable for `hl_type='nlargest'`), all numeric columns will get this style
        - `bar_cols`: array of strings specifying which columns will get the "bar charty style" (only applicable for `hl_type='bars'`)
        
        ##### Styling Bar & Line Charts
        `style_bar_chart(ptl_fig, ytitle='')` OR `style_line_chart(ptl_fig, ytitle='')`
        - `ptl_fig`: plotly chart with bars
        - `ytitle`: title on y-axis
        
        ##### Displaying KPI Widget
        `display_as_kpi(kpi_name, value)`
        - `kpi_name`: string specifying KPI title/name (displayed on top)
        - `value`: value of the KPI (large value)
        
        ### Processing Functions Documentation
        
        ##### Outlier Removal
        `prune_quotes(df, variable_col, group_cols, log_scale=True, k=1.5, max_diffs=[(2,3),(3,5),(4,10)])`
        - `df`: Pandas Dataframe with data to remove outliers from
        - `variable_col`: string with column name based on which outliers will be removed (e.g. `'price'`)
        - `group_cols`: array of string(s) with columns to group `df` by for quartile calculation purposes (e.g. `['item_id']`)
        - `log_scale`: boolean specifying whether to use logarithmic scale for outlier removal
        - `k`: float specifying limit ranges `Q1-k*IQR` and `Q3+k*IQR`
        - `max_diffs`: list of tuples with two values, each tuple specifies total number of quotes and the maximal allowed ratio between max/min quotes (if violated, RFQ will be removed before the IQR outlier detection method)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
