lux.core package¶

Submodules¶

lux.core.frame module¶

class lux.core.frame.LuxDataFrame(*args, **kw)[source]¶

A subclass of pd.DataFrame that supports all dataframe operations while housing other variables and functions for generating visual recommendations.

expire_metadata()[source]¶: Expire all saved metadata to trigger a recomputation the next time the data is required.

expire_recs()[source]¶: Expires and resets all recommendations

groupby(*args, **kwargs)[source]¶

Group DataFrame using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Parameters:	by (mapping, function, label, or list of labels) – Used to determine the groups for the groupby. If `by` is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see `.align()` method). If an ndarray is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in `self`. Notice that a tuple is interpreted as a (single) key. axis ({0 or 'index', 1 or 'columns'}, default 0) – Split along rows (0) or columns (1). level (int, level name, or sequence of such, default None) – If the axis is a MultiIndex (hierarchical), group by a particular level or levels. as_index (bool, default True) – For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output. sort (bool, default True) – Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group. group_keys (bool, default True) – When calling apply, add group keys to index to identify pieces. squeeze (bool, default False) – Reduce the dimensionality of the return type if possible, otherwise return a consistent type. Deprecated since version 1.1.0. observed (bool, default False) – This only applies if any of the groupers are Categoricals. If True: only show observed values for categorical groupers. If False: show all values for categorical groupers. dropna (bool, default True) – If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups New in version 1.1.0.
Returns:	Returns a groupby object that contains information about the groups.
Return type:	DataFrameGroupBy

See also

resample(): Convenience method for frequency conversion and resampling of time series.

Notes

See the user guide for more.

Examples

>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0

Hierarchical Indexes

We can groupby different levels of a hierarchical index using the level parameter:

>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...           ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
...                   index=index)
>>> df
                Max Speed
Animal Type
Falcon Captive      390.0
       Wild         350.0
Parrot Captive       30.0
       Wild          20.0
>>> df.groupby(level=0).mean()
        Max Speed
Animal
Falcon      370.0
Parrot       25.0
>>> df.groupby(level="Type").mean()
         Max Speed
Type
Captive      210.0
Wild         185.0

We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:

>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by=["b"]).sum()
    a   c
b
1.0 2   3
2.0 2   5

>>> df.groupby(by=["b"], dropna=False).sum()
    a   c
b
1.0 2   3
2.0 2   5
NaN 1   4

>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])

>>> df.groupby(by="a").sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0

>>> df.groupby(by="a", dropna=False).sum()
    b     c
a
a   13.0   13.0
b   12.3  123.0
NaN 12.3   33.0

render_widget(renderer: str = 'altair', input_current_vis='')[source]¶

Generate a LuxWidget based on the LuxDataFrame

Structure of widgetJSON:

{

‘current_vis’: {}, ‘recommendation’: [

{

‘action’: ‘Correlation’, ‘description’: “some description”, ‘vspec’: [

{Vega-Lite spec for vis 1}, {Vega-Lite spec for vis 2}, …

]

}, … repeat for other actions

]

}

Parameters:	renderer (str, optional) – Choice of visualization rendering library, by default “altair” input_current_vis (lux.LuxDataFrame, optional) – User-specified current vis to override default Current Vis, by default

save_as_html(filename: str = 'export.html') → None[source]¶

Save dataframe widget as static HTML file

Parameters:	filename (str) – Filename for the output HTML file

set_data_type(types: dict)[source]¶

Set the data type for a particular attribute in the dataframe overriding the automatically-detected type inferred by Lux

Parameters:	types (dict) – Dictionary that maps attribute/column name to a specified Lux Type. Possible options: “nominal”, “quantitative”, “id”, and “temporal”.

Example

df = pd.read_csv(“https://raw.githubusercontent.com/lux-org/lux-datasets/master/data/absenteeism.csv”) df.set_data_type({“ID”:”id”,

“Reason for absence”:”nominal”})

set_intent_as_vis(vis: lux.vis.Vis.Vis)[source]¶

Set intent of the dataframe based on the intent of a Vis

Parameters:	vis (Vis) – Input Vis object

exported¶

Get selected visualizations as exported Vis List

Notes

Convert the _selectedVisIdxs dictionary into a programmable VisList Example _selectedVisIdxs :

{‘Correlation’: [0, 2], ‘Occurrence’: [1]}

indicating the 0th and 2nd vis from the Correlation tab is selected, and the 1st vis from the Occurrence tab is selected.

Returns:	When there are no exported vis, return empty list -> [] When all the exported vis is from the same tab, return a VisList of selected visualizations. -> VisList(v1, v2…) When the exported vis is from the different tabs, return a dictionary with the action name as key and selected visualizations in the VisList. -> {“Enhance”: VisList(v1, v2…), “Filter”: VisList(v5, v7…), ..}
Return type:	Union[Dict[str,VisList], VisList]

intent¶

Main function to set the intent of the dataframe. The intent input goes through the parser, so that the string inputs are parsed into a lux.Clause object.

Parameters:	intent (List[str,Clause]) – intent list, can be a mix of string shorthand or a lux.Clause object

Notes

Specifying Intent in Lux

lux.core.series module¶

class lux.core.series.LuxSeries(*args, **kw)[source]¶

A subclass of pd.Series that supports all 1-D Series operations

to_pandas() → pandas.core.series.Series[source]¶

Convert Lux Series to Pandas Series

Returns:
Return type:	pd.Series