Funnel Charts

Big Number

Coming soon!

Wordcloud

src.dx.plotting.dex.basic_charts.wordcloud(df, word_column, size, text_format='sentence', word_rotation=None, random_coloring=False, return_view=False, **kwargs)

Generates a DEX wordcloud from the given DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame to plot.	required
word_column	str	The column to use for the words in the cloud.	required
size	str	The column to use for the size of the words in the cloud.	required
text_format	DEXTextDataFormat	The format of the text data. Either "sentence" or "token".	'sentence'
word_rotation	Optional[DEXWordRotate]	The rotation to use for the words in the cloud (45, 90, "jitter", or None).	None
random_coloring	bool	Whether to use random coloring for the words in the cloud.	False
return_view	bool	Whether to return a DEXView object instead of render.	False
**kwargs		Additional keyword arguments to pass to the view metadata.	{}

Source code in src/dx/plotting/dex/basic_charts.py

def wordcloud(
    df: pd.DataFrame,
    word_column: str,
    size: str,
    text_format: options.DEXTextDataFormat = "sentence",
    word_rotation: Optional[options.DEXWordRotate] = None,
    random_coloring: bool = False,
    return_view: bool = False,
    **kwargs,
) -> Optional[DEXWordcloudChartView]:
    """
    Generates a DEX wordcloud from the given DataFrame.

    Parameters
    ----------
    df: pd.DataFrame
        The DataFrame to plot.
    word_column: str
        The column to use for the words in the cloud.
    size: str
        The column to use for the size of the words in the cloud.
    text_format: DEXTextDataFormat
        The format of the text data. Either `"sentence"` or `"token"`.
    word_rotation: Optional[DEXWordRotate]
        The rotation to use for the words in the cloud (`45`, `90`, `"jitter"`, or `None`).
    random_coloring: bool
        Whether to use random coloring for the words in the cloud.
    return_view: bool
        Whether to return a `DEXView` object instead of render.
    **kwargs
        Additional keyword arguments to pass to the view metadata.
    """
    raise_for_missing_columns([word_column, size], df.columns)

    chart_settings = {
        "text_data_format": text_format,
        "token_metric": size,
        "word_data": word_column,
        "word_color": "random" if random_coloring else "none",
        "word_rotate": word_rotation if word_rotation else "none",
    }
    return handle_view(
        df,
        chart_mode="wordcloud",
        chart=chart_settings,
        return_view=return_view,
        **kwargs,
    )

Dimension Matrix

Coming soon!

Violin

src.dx.plotting.dex.summary_charts.violin(df, split_by, metric, bins=30, show_interquartile_range=False, column_sort_order='asc', column_sort_type='string', return_view=False, **kwargs)

Generates a DEX violin plot from the given DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	The DataFrame to plot.	required
split_by	str	The column to use for splitting the data.	required
metric	str	The column to use to show distribution and density.	required
bins	int	The number of bins to use for the violin plot.	30
show_interquartile_range	bool	Whether to show the interquartile range.	False
column_sort_order	DEXSortColumnsByOrder	The order to sort the columns by. ("asc" or "desc")	'asc'
column_sort_type	DEXSortColumnsByType	The type of sorting to use. ("number", "string", or "date")	'string'
return_view	bool	Whether to return a DEXView object instead of render.	False
**kwargs		Additional keyword arguments to pass to the view metadata.	{}

Source code in src/dx/plotting/dex/summary_charts.py

def violin(
    df: pd.DataFrame,
    split_by: str,
    metric: str,
    bins: int = 30,
    show_interquartile_range: bool = False,
    column_sort_order: options.DEXSortColumnsByOrder = "asc",
    column_sort_type: options.DEXSortColumnsByType = "string",
    return_view: bool = False,
    **kwargs,
) -> Optional[DEXViolinChartView]:
    """
    Generates a DEX violin plot from the given DataFrame.

    Parameters
    ----------
    df: pd.DataFrame
        The DataFrame to plot.
    split_by: str
        The column to use for splitting the data.
    metric: str
        The column to use to show distribution and density.
    bins: int
        The number of bins to use for the violin plot.
    show_interquartile_range: bool
        Whether to show the interquartile range.
    column_sort_order: DEXSortColumnsByOrder
        The order to sort the columns by. (`"asc"` or `"desc"`)
    column_sort_type: DEXSortColumnsByType
        The type of sorting to use. (`"number"`, `"string"`, or `"date"`)
    return_view: bool
        Whether to return a `DEXView` object instead of render.
    **kwargs
        Additional keyword arguments to pass to the view metadata.
    """
    if str(column_sort_order).lower() == "asc":
        sort_order = "desc"
    elif str(column_sort_order).lower() == "desc":
        sort_order = "asc"
    chart_params = dict(
        summary_bins=bins,
        sort_columns_by=f"{sort_order}-col-{column_sort_type}",
        violin_iqr=show_interquartile_range,
    )
    return summary(
        df,
        split_by=split_by,
        metric=metric,
        summary_type="violin",
        chart_params=chart_params,
        return_view=return_view,
        **kwargs,
    )

Boxplot

Coming soon!

Heatmap

Coming soon!

Histogram

Coming soon!

Ridgeline

Coming soon!

Horizon

Coming soon!

Hexbin

Coming soon!