lux.executor.PandasExecutor.PandasExecutor

class lux.executor.PandasExecutor.PandasExecutor

Given a Vis objects with complete specifications, fetch and process data using Pandas dataframe operations.

__init__()

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__()	Initialize self.
apply_filter(df, attribute, op, val)	Helper function for applying filter to a dataframe
compute_data_model(data_type)
compute_data_model_lookup(data_type)
compute_data_type(ldf)
compute_dataset_metadata(ldf)
compute_stats(ldf)
execute(vislist, ldf[, approx])	Given a VisList, fetch the data required to render the vis.
execute_2D_binning(vis)	Apply 2D binning (heatmap) to vis.data
execute_aggregate(vis[, isFiltered])	Aggregate data points on an axis for bar or line charts
execute_approx_sample(ldf)	Compute and cache an approximate sample of the overall dataframe for the purpose of early pruning of the visualization search space
execute_binning(ldf, vis)	Binning of data points for generating histograms
execute_filter(vis)	Apply a Vis’s filter to vis.data
execute_sampling(ldf)	Compute and cache a sample for the overall dataframe
invert_data_type(data_type)
mapping(rmap)
reverseMapping(map)

static apply_filter(df: lux.core.frame.LuxDataFrame, attribute: str, op: str, val: object) → lux.core.frame.LuxDataFrame

Helper function for applying filter to a dataframe

Parameters:

• df (pandas.DataFrame) – Dataframe to filter on
• attribute (str) – Filter attribute
• op (str) – Filter operation, ‘=’, ‘<’, ‘>’, ‘<=’, ‘>=’, ‘!=’
• val (object) – Filter value

Returns:

df – Dataframe resulting from the filter operation

Return type:

pandas.DataFrame

static execute(vislist: lux.vis.VisList.VisList, ldf: lux.core.frame.LuxDataFrame, approx=False)

Given a VisList, fetch the data required to render the vis. 1) Apply filters 2) Retrieve relevant attribute 3) Perform vis-related processing (aggregation, binning) 4) return a DataFrame with relevant results

Parameters:

• vislist (list[lux.Vis]) – vis list that contains lux.Vis objects for visualization.
• ldf (lux.core.frame) – LuxDataFrame with specified intent.

Returns:

Return type:

None

static execute_2D_binning(vis: lux.vis.Vis.Vis) → None

Apply 2D binning (heatmap) to vis.data

Parameters:vis (Vis) –

static execute_aggregate(vis: lux.vis.Vis.Vis, isFiltered=True)

Aggregate data points on an axis for bar or line charts

Parameters:

• vis (lux.Vis) – lux.Vis object that represents a visualization
• ldf (lux.core.frame) – LuxDataFrame with specified intent.

Returns:

Return type:

None

static execute_approx_sample(ldf: lux.core.frame.LuxDataFrame)

Compute and cache an approximate sample of the overall dataframe for the purpose of early pruning of the visualization search space

Parameters:ldf (LuxDataFrame) –

static execute_binning(ldf: lux.core.frame.LuxDataFrame, vis: lux.vis.Vis.Vis)

Binning of data points for generating histograms

Parameters:

• vis (lux.Vis) – lux.Vis object that represents a visualization
• ldf (lux.core.frame) – LuxDataFrame with specified intent.

Returns:

Return type:

None

static execute_filter(vis: lux.vis.Vis.Vis) → bool

Apply a Vis’s filter to vis.data

Parameters:vis (Vis) – Returns:Boolean flag indicating if any filter was applied Return type:bool

static execute_sampling(ldf: lux.core.frame.LuxDataFrame)

Compute and cache a sample for the overall dataframe

• When # of rows exceeds lux.config.sampling_start, take 75% df as sample
• When # of rows exceeds lux.config.sampling_cap, cap the df at {lux.config.sampling_cap} rows

lux.config.sampling_start = 100k rows lux.config.sampling_cap = 1M rows

Parameters:ldf (LuxDataFrame) –