def __init__(self, *args, **kw):
self._history = History()
self._intent = []
self._inferred_intent = []
self._recommendation = {}
self._saved_export = None
self._current_vis = []
self._prev = None
self._widget = None
super(LuxDataFrame, self).__init__(*args, **kw)
self.table_name = ""
if lux.config.SQLconnection == "":
from lux.executor.PandasExecutor import PandasExecutor
lux.config.executor = PandasExecutor()
else:
from lux.executor.SQLExecutor import SQLExecutor
lux.config.executor = SQLExecutor()
self._sampled = None
self._toggle_pandas_display = True
self._message = Message()
self._pandas_only = False
# Metadata
self._data_type = {}
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
self._type_override = {}
warnings.formatwarning = lux.warning_format
def __init__(self, *args, **kw):
from lux.executor.PandasExecutor import PandasExecutor
self._history = History()
self._intent = []
self._recommendation = {}
self._saved_export = None
self._current_vis = []
self._prev = None
super(LuxDataFrame, self).__init__(*args, **kw)
self.executor_type = "Pandas"
self.executor = PandasExecutor()
self.SQLconnection = ""
self.table_name = ""
self._sampled = None
self._toggle_pandas_display = True
self._message = Message()
self._pandas_only = False
# Metadata
self.data_type_lookup = None
self.data_type = None
self.data_model_lookup = None
self.data_model = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
warnings.formatwarning = lux.warning_format
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata() # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
id_fields_str = ""
if len(rec_df.data_type["id"]) > 0:
for id_field in rec_df.data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.add(f"{id_fields_str} is not visualized since it resembles an ID field.")
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
rec_infolist = []
from lux.action.custom import custom
from lux.action.custom import custom_actions
from lux.action.correlation import correlation
from lux.action.univariate import univariate
from lux.action.enhance import enhance
from lux.action.filter import filter
from lux.action.generalize import generalize
from lux.action.row_group import row_group
from lux.action.column_group import column_group
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if rec_df.recommendation == {}:
# display conditions for default actions
no_vis = lambda ldf: (ldf.current_vis is None) or (
ldf.current_vis is not None and len(ldf.current_vis) == 0
)
one_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) == 1
)
multiple_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) > 1
)
# globally register default actions
lux.register_action("correlation", correlation, no_vis)
lux.register_action("distribution", univariate, no_vis, "quantitative")
lux.register_action("occurrence", univariate, no_vis, "nominal")
lux.register_action("temporal", univariate, no_vis, "temporal")
lux.register_action("Enhance", enhance, one_current_vis)
lux.register_action("Filter", filter, one_current_vis)
lux.register_action("Generalize", generalize, one_current_vis)
lux.register_action("Custom", custom, multiple_current_vis)
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df.recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
self._widget = rec_df.render_widget()
self._recs_fresh = True
class LuxDataFrame(pd.DataFrame):
"""
A subclass of pd.DataFrame that supports all dataframe operations while housing other variables and functions for generating visual recommendations.
"""
# MUST register here for new properties!!
_metadata = [
"_intent",
"data_type_lookup",
"data_type",
"data_model_lookup",
"data_model",
"unique_values",
"cardinality",
"_rec_info",
"_min_max",
"_current_vis",
"_widget",
"_recommendation",
"_prev",
"_history",
"_saved_export",
"_sampled",
"_toggle_pandas_display",
"_message",
"_pandas_only",
"pre_aggregated",
]
def __init__(self, *args, **kw):
from lux.executor.PandasExecutor import PandasExecutor
self._history = History()
self._intent = []
self._recommendation = {}
self._saved_export = None
self._current_vis = []
self._prev = None
super(LuxDataFrame, self).__init__(*args, **kw)
self.executor_type = "Pandas"
self.executor = PandasExecutor()
self.SQLconnection = ""
self.table_name = ""
self._sampled = None
self._toggle_pandas_display = True
self._message = Message()
self._pandas_only = False
# Metadata
self.data_type_lookup = None
self.data_type = None
self.data_model_lookup = None
self.data_model = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
warnings.formatwarning = lux.warning_format
@property
def _constructor(self):
return LuxDataFrame
@property
def _constructor_sliced(self):
def f(*args, **kwargs):
s = LuxSeries(*args, **kwargs)
for attr in self._metadata: # propagate metadata
s.__dict__[attr] = getattr(self, attr, None)
return s
return f
@property
def history(self):
return self._history
def maintain_metadata(self):
# Check that metadata has not yet been computed
if not hasattr(self, "_metadata_fresh") or not self._metadata_fresh:
# only compute metadata information if the dataframe is non-empty
if len(self) > 0:
self.executor.compute_stats(self)
self.executor.compute_dataset_metadata(self)
self._infer_structure()
self._metadata_fresh = True
def expire_recs(self):
self._recs_fresh = False
self.recommendation = {}
self.current_vis = None
self._widget = None
self._rec_info = None
self._sampled = None
def expire_metadata(self):
# Set metadata as null
self._metadata_fresh = False
self.data_type_lookup = None
self.data_type = None
self.data_model_lookup = None
self.data_model = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
#####################
## Override Pandas ##
#####################
def __getattr__(self, name):
ret_value = super(LuxDataFrame, self).__getattr__(name)
self.expire_metadata()
self.expire_recs()
return ret_value
def _set_axis(self, axis, labels):
super(LuxDataFrame, self)._set_axis(axis, labels)
self.expire_metadata()
self.expire_recs()
def _update_inplace(self, *args, **kwargs):
super(LuxDataFrame, self)._update_inplace(*args, **kwargs)
self.expire_metadata()
self.expire_recs()
def _set_item(self, key, value):
super(LuxDataFrame, self)._set_item(key, value)
self.expire_metadata()
self.expire_recs()
def _infer_structure(self):
# If the dataframe is very small and the index column is not a range index, then it is likely that this is an aggregated data
is_multi_index_flag = self.index.nlevels != 1
not_int_index_flag = self.index.dtype != "int64"
small_df_flag = len(self) < 100
self.pre_aggregated = (is_multi_index_flag or not_int_index_flag) and small_df_flag
if "Number of Records" in self.columns:
self.pre_aggregated = True
very_small_df_flag = len(self) <= 10
if very_small_df_flag:
self.pre_aggregated = True
def set_executor_type(self, exe):
if exe == "SQL":
import pkgutil
if pkgutil.find_loader("psycopg2") is None:
raise ImportError(
"psycopg2 is not installed. Run `pip install psycopg2' to install psycopg2 to enable the Postgres connection."
)
else:
import psycopg2
from lux.executor.SQLExecutor import SQLExecutor
self.executor = SQLExecutor
else:
from lux.executor.PandasExecutor import PandasExecutor
self.executor = PandasExecutor()
self.executor_type = exe
@property
def intent(self):
return self._intent
@intent.setter
def intent(self, intent_input: Union[List[Union[str, Clause]], Vis]):
is_list_input = isinstance(intent_input, list)
is_vis_input = isinstance(intent_input, Vis)
if not (is_list_input or is_vis_input):
raise TypeError(
"Input intent must be either a list (of strings or lux.Clause) or a lux.Vis object."
"\nSee more at: https://lux-api.readthedocs.io/en/latest/source/guide/intent.html"
)
if is_list_input:
self.set_intent(intent_input)
elif is_vis_input:
self.set_intent_as_vis(intent_input)
def clear_intent(self):
self.intent = []
self.expire_recs()
def set_intent(self, intent: List[Union[str, Clause]]):
"""
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
-----
:doc:`../guide/clause`
"""
self.expire_recs()
self._intent = intent
self._parse_validate_compile_intent()
def _parse_validate_compile_intent(self):
self.maintain_metadata()
from lux.processor.Parser import Parser
from lux.processor.Validator import Validator
self._intent = Parser.parse(self._intent)
Validator.validate_intent(self._intent, self)
self.maintain_metadata()
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(self, self._intent)
def copy_intent(self):
# creates a true copy of the dataframe's intent
output = []
for clause in self._intent:
temp_clause = clause.copy_clause()
output.append(temp_clause)
return output
def set_intent_as_vis(self, vis: Vis):
"""
Set intent of the dataframe as the Vis
Parameters
----------
vis : Vis
"""
self.expire_recs()
self._intent = vis._inferred_intent
self._parse_validate_compile_intent()
def to_pandas(self):
import lux.core
return lux.core.originalDF(self, copy=False)
@property
def recommendation(self):
return self._recommendation
@recommendation.setter
def recommendation(self, recommendation: Dict):
self._recommendation = recommendation
@property
def current_vis(self):
return self._current_vis
@current_vis.setter
def current_vis(self, current_vis: Dict):
self._current_vis = current_vis
def __repr__(self):
# TODO: _repr_ gets called from _repr_html, need to get rid of this call
return ""
#######################################################
########## SQL Metadata, type, model schema ###########
#######################################################
def set_SQL_connection(self, connection, t_name):
self.SQLconnection = connection
self.table_name = t_name
self.compute_SQL_dataset_metadata()
self.set_executor_type("SQL")
def compute_SQL_dataset_metadata(self):
self.get_SQL_attributes()
for attr in list(self.columns):
self[attr] = None
self.data_type_lookup = {}
self.data_type = {}
#####NOTE: since we aren't expecting users to do much data processing with the SQL database, should we just keep this
##### in the initialization and do it just once
self.compute_SQL_data_type()
self.compute_SQL_stats()
self.data_model_lookup = {}
self.data_model = {}
self.compute_data_model()
def compute_SQL_stats(self):
# precompute statistics
self.unique_values = {}
self._min_max = {}
self.get_SQL_unique_values()
# self.get_SQL_cardinality()
for attribute in self.columns:
if self.data_type_lookup[attribute] == "quantitative":
self._min_max[attribute] = (
self[attribute].min(),
self[attribute].max(),
)
def get_SQL_attributes(self):
if "." in self.table_name:
table_name = self.table_name[self.table_name.index(".") + 1 :]
else:
table_name = self.table_name
query = f"SELECT column_name FROM INFORMATION_SCHEMA.COLUMNS where TABLE_NAME = '{table_name}'"
attributes = list(pd.read_sql(query, self.SQLconnection)["column_name"])
for attr in attributes:
self[attr] = None
def get_SQL_cardinality(self):
cardinality = {}
for attr in list(self.columns):
card_query = pd.read_sql(
f"SELECT Count(Distinct({attr})) FROM {self.table_name}",
self.SQLconnection,
)
cardinality[attr] = list(card_query["count"])[0]
self.cardinality = cardinality
def get_SQL_unique_values(self):
unique_vals = {}
for attr in list(self.columns):
unique_query = pd.read_sql(
f"SELECT Distinct({attr}) FROM {self.table_name}",
self.SQLconnection,
)
unique_vals[attr] = list(unique_query[attr])
self.unique_values = unique_vals
def compute_SQL_data_type(self):
data_type_lookup = {}
sql_dtypes = {}
self.get_SQL_cardinality()
if "." in self.table_name:
table_name = self.table_name[self.table_name.index(".") + 1 :]
else:
table_name = self.table_name
# get the data types of the attributes in the SQL table
for attr in list(self.columns):
query = f"SELECT DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = '{table_name}' AND COLUMN_NAME = '{attr}'"
datatype = list(pd.read_sql(query, self.SQLconnection)["data_type"])[0]
sql_dtypes[attr] = datatype
data_type = {"quantitative": [], "nominal": [], "temporal": []}
for attr in list(self.columns):
if str(attr).lower() in ["month", "year"]:
data_type_lookup[attr] = "temporal"
data_type["temporal"].append(attr)
elif sql_dtypes[attr] in [
"character",
"character varying",
"boolean",
"uuid",
"text",
]:
data_type_lookup[attr] = "nominal"
data_type["nominal"].append(attr)
elif sql_dtypes[attr] in [
"integer",
"real",
"smallint",
"smallserial",
"serial",
]:
if self.cardinality[attr] < 13:
data_type_lookup[attr] = "nominal"
data_type["nominal"].append(attr)
else:
data_type_lookup[attr] = "quantitative"
data_type["quantitative"].append(attr)
elif "time" in sql_dtypes[attr] or "date" in sql_dtypes[attr]:
data_type_lookup[attr] = "temporal"
data_type["temporal"].append(attr)
self.data_type_lookup = data_type_lookup
self.data_type = data_type
def _append_rec(self, rec_infolist, recommendations: Dict):
if recommendations["collection"] is not None and len(recommendations["collection"]) > 0:
rec_infolist.append(recommendations)
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata() # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
id_fields_str = ""
if len(rec_df.data_type["id"]) > 0:
for id_field in rec_df.data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.add(f"{id_fields_str} is not visualized since it resembles an ID field.")
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
rec_infolist = []
from lux.action.custom import custom
from lux.action.custom import custom_actions
from lux.action.correlation import correlation
from lux.action.univariate import univariate
from lux.action.enhance import enhance
from lux.action.filter import filter
from lux.action.generalize import generalize
from lux.action.row_group import row_group
from lux.action.column_group import column_group
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if rec_df.recommendation == {}:
# display conditions for default actions
no_vis = lambda ldf: (ldf.current_vis is None) or (
ldf.current_vis is not None and len(ldf.current_vis) == 0
)
one_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) == 1
)
multiple_current_vis = (
lambda ldf: ldf.current_vis is not None and len(ldf.current_vis) > 1
)
# globally register default actions
lux.register_action("correlation", correlation, no_vis)
lux.register_action("distribution", univariate, no_vis, "quantitative")
lux.register_action("occurrence", univariate, no_vis, "nominal")
lux.register_action("temporal", univariate, no_vis, "temporal")
lux.register_action("Enhance", enhance, one_current_vis)
lux.register_action("Filter", filter, one_current_vis)
lux.register_action("Generalize", generalize, one_current_vis)
lux.register_action("Custom", custom, multiple_current_vis)
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df.recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
self._widget = rec_df.render_widget()
self._recs_fresh = True
#######################################################
############## LuxWidget Result Display ###############
#######################################################
@property
def widget(self):
if self._widget:
return self._widget
@property
def exported(self) -> Union[Dict[str, VisList], VisList]:
"""
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
-------
Union[Dict[str,VisList], VisList]
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...), ..}
"""
if not hasattr(self, "_widget"):
warnings.warn(
"\nNo widget attached to the dataframe."
"Please assign dataframe to an output variable.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
exported_vis_lst = self._widget._selectedVisIdxs
exported_vis = []
if exported_vis_lst == {}:
if self._saved_export:
return self._saved_export
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
if len(exported_vis_lst) == 1 and "currentVis" in exported_vis_lst:
return self.current_vis
elif len(exported_vis_lst) > 1:
exported_vis = {}
if "currentVis" in exported_vis_lst:
exported_vis["Current Vis"] = self.current_vis
for export_action in exported_vis_lst:
if export_action != "currentVis":
exported_vis[export_action] = VisList(
list(
map(
self.recommendation[export_action].__getitem__,
exported_vis_lst[export_action],
)
)
)
return exported_vis
elif len(exported_vis_lst) == 1 and ("currentVis" not in exported_vis_lst):
export_action = list(exported_vis_lst.keys())[0]
exported_vis = VisList(
list(
map(
self.recommendation[export_action].__getitem__,
exported_vis_lst[export_action],
)
)
)
self._saved_export = exported_vis
return exported_vis
else:
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
def remove_deleted_recs(self, change):
for action in self._widget.deletedIndices:
deletedSoFar = 0
for index in self._widget.deletedIndices[action]:
self.recommendation[action].remove_index(index - deletedSoFar)
deletedSoFar += 1
def set_intent_on_click(self, change):
from IPython.display import display, clear_output
from lux.processor.Compiler import Compiler
intent_action = list(self._widget.selectedIntentIndex.keys())[0]
vis = self.recommendation[intent_action][self._widget.selectedIntentIndex[intent_action][0]]
self.set_intent_as_vis(vis)
self.maintain_metadata()
self.current_vis = Compiler.compile_intent(self, self._intent)
self.maintain_recs()
with self.output:
clear_output()
display(self._widget)
self._widget.observe(self.remove_deleted_recs, names="deletedIndices")
self._widget.observe(self.set_intent_on_click, names="selectedIntentIndex")
def _repr_html_(self):
from IPython.display import display
from IPython.display import clear_output
import ipywidgets as widgets
try:
if self._pandas_only:
display(self.display_pandas())
self._pandas_only = False
else:
if self.index.nlevels >= 2 or self.columns.nlevels >= 2:
warnings.warn(
"\nLux does not currently support dataframes "
"with hierarchical indexes.\n"
"Please convert the dataframe into a flat "
"table via `pandas.DataFrame.reset_index`.\n",
stacklevel=2,
)
display(self.display_pandas())
return
if len(self) <= 0:
warnings.warn(
"\nLux can not operate on an empty dataframe.\nPlease check your input again.\n",
stacklevel=2,
)
display(self.display_pandas())
return
self.maintain_metadata()
if self._intent != [] and (not hasattr(self, "_compiled") or not self._compiled):
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(self, self._intent)
if lux.config.default_display == "lux":
self._toggle_pandas_display = False
else:
self._toggle_pandas_display = True
# df_to_display.maintain_recs() # compute the recommendations (TODO: This can be rendered in another thread in the background to populate self._widget)
self.maintain_recs()
# Observers(callback_function, listen_to_this_variable)
self._widget.observe(self.remove_deleted_recs, names="deletedIndices")
self._widget.observe(self.set_intent_on_click, names="selectedIntentIndex")
if len(self.recommendation) > 0:
# box = widgets.Box(layout=widgets.Layout(display='inline'))
button = widgets.Button(
description="Toggle Pandas/Lux",
layout=widgets.Layout(width="140px", top="5px"),
)
self.output = widgets.Output()
# box.children = [button,output]
# output.children = [button]
# display(box)
display(button, self.output)
def on_button_clicked(b):
with self.output:
if b:
self._toggle_pandas_display = not self._toggle_pandas_display
clear_output()
if self._toggle_pandas_display:
display(self.display_pandas())
else:
# b.layout.display = "none"
display(self._widget)
# b.layout.display = "inline-block"
button.on_click(on_button_clicked)
on_button_clicked(None)
else:
warnings.warn(
"\nLux defaults to Pandas when there are no valid actions defined.",
stacklevel=2,
)
display(self.display_pandas())
except (KeyboardInterrupt, SystemExit):
raise
except Exception:
warnings.warn(
"\nUnexpected error in rendering Lux widget and recommendations. "
"Falling back to Pandas display.\n"
"Please report the following issue on Github: https://github.com/lux-org/lux/issues \n",
stacklevel=2,
)
warnings.warn(traceback.format_exc())
display(self.display_pandas())
def display_pandas(self):
return self.to_pandas()
def render_widget(self, renderer: str = "altair", input_current_vis=""):
"""
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
"""
check_import_lux_widget()
import luxwidget
widgetJSON = self.to_JSON(self._rec_info, input_current_vis=input_current_vis)
return luxwidget.LuxWidget(
currentVis=widgetJSON["current_vis"],
recommendations=widgetJSON["recommendation"],
intent=LuxDataFrame.intent_to_string(self._intent),
message=self._message.to_html(),
)
@staticmethod
def intent_to_JSON(intent):
from lux.utils import utils
filter_specs = utils.get_filter_specs(intent)
attrs_specs = utils.get_attrs_specs(intent)
intent = {}
intent["attributes"] = [clause.attribute for clause in attrs_specs]
intent["filters"] = [clause.attribute for clause in filter_specs]
return intent
@staticmethod
def intent_to_string(intent):
if intent:
return ", ".join([clause.to_string() for clause in intent])
else:
return ""
def to_JSON(self, rec_infolist, input_current_vis=""):
widget_spec = {}
if self.current_vis:
self.executor.execute(self.current_vis, self)
widget_spec["current_vis"] = LuxDataFrame.current_vis_to_JSON(
self.current_vis, input_current_vis
)
else:
widget_spec["current_vis"] = {}
widget_spec["recommendation"] = []
# Recommended Collection
recCollection = LuxDataFrame.rec_to_JSON(rec_infolist)
widget_spec["recommendation"].extend(recCollection)
return widget_spec
@staticmethod
def current_vis_to_JSON(vlist, input_current_vis=""):
current_vis_spec = {}
numVC = len(vlist) # number of visualizations in the vis list
if numVC == 1:
current_vis_spec = vlist[0].to_code(prettyOutput=False)
elif numVC > 1:
pass
return current_vis_spec
@staticmethod
def rec_to_JSON(recs):
rec_lst = []
import copy
rec_copy = copy.deepcopy(recs)
for idx, rec in enumerate(rec_copy):
if len(rec["collection"]) > 0:
rec["vspec"] = []
for vis in rec["collection"]:
chart = vis.to_code(prettyOutput=False)
rec["vspec"].append(chart)
rec_lst.append(rec)
# delete since not JSON serializable
del rec_lst[idx]["collection"]
return rec_lst
# Overridden Pandas Functions
def head(self, n: int = 5):
self._prev = self
self._history.append_event("head", n=5)
return super(LuxDataFrame, self).head(n)
def tail(self, n: int = 5):
self._prev = self
self._history.append_event("tail", n=5)
return super(LuxDataFrame, self).tail(n)
def info(self, *args, **kwargs):
self._pandas_only = True
self._history.append_event("info", *args, **kwargs)
return super(LuxDataFrame, self).info(*args, **kwargs)
def describe(self, *args, **kwargs):
self._pandas_only = True
self._history.append_event("describe", *args, **kwargs)
return super(LuxDataFrame, self).describe(*args, **kwargs)
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata(
) # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.append(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
id_fields_str = ""
if (len(rec_df.data_type["id"]) > 0):
for id_field in rec_df.data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.append(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
if (not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh
): # Check that recs has not yet been computed
rec_infolist = []
from lux.action.custom import custom
from lux.action.correlation import correlation
from lux.action.univariate import univariate
from lux.action.enhance import enhance
from lux.action.filter import filter
from lux.action.generalize import generalize
from lux.action.row_group import row_group
from lux.action.column_group import column_group
if (rec_df.pre_aggregated):
if (rec_df.columns.name is not None):
rec_df._append_rec(rec_infolist, row_group(rec_df))
if (rec_df.index.name is not None):
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if (rec_df.current_vis is None):
no_vis = True
one_current_vis = False
multiple_current_vis = False
else:
no_vis = len(rec_df.current_vis) == 0
one_current_vis = len(rec_df.current_vis) == 1
multiple_current_vis = len(rec_df.current_vis) > 1
if (no_vis):
rec_df._append_rec(rec_infolist, correlation(rec_df))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "quantitative"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "nominal"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "temporal"))
elif (one_current_vis):
rec_df._append_rec(rec_infolist, enhance(rec_df))
rec_df._append_rec(rec_infolist, filter(rec_df))
rec_df._append_rec(rec_infolist, generalize(rec_df))
elif (multiple_current_vis):
rec_df._append_rec(rec_infolist, custom(rec_df))
# Store _rec_info into a more user-friendly dictionary form
rec_df.recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if (rec_df._plot_config):
for vis in rec_df.current_vis:
vis._plot_config = rec_df.plot_config
for vis in vlist:
vis._plot_config = rec_df.plot_config
if (len(vlist) > 0):
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
elif (
show_prev
): # re-render widget for the current dataframe if previous rec is not recomputed
self._widget = rec_df.render_widget()
self._recs_fresh = True
class LuxDataFrame(pd.DataFrame):
'''
A subclass of pd.DataFrame that supports all dataframe operations while housing other variables and functions for generating visual recommendations.
'''
# MUST register here for new properties!!
_metadata = [
'_intent', 'data_type_lookup', 'data_type', 'data_model_lookup',
'data_model', 'unique_values', 'cardinality', '_rec_info',
'_pandas_only', '_min_max', 'plot_config', '_current_vis', '_widget',
'_recommendation', '_prev', '_history'
]
def __init__(self, *args, **kw):
from lux.executor.PandasExecutor import PandasExecutor
self._history = History()
self._intent = []
self._recommendation = {}
self._current_vis = []
self._prev = None
super(LuxDataFrame, self).__init__(*args, **kw)
self.executor_type = "Pandas"
self.executor = PandasExecutor()
self.SQLconnection = ""
self.table_name = ""
self._sampled = None
self._default_pandas_display = True
self._toggle_pandas_display = True
self._plot_config = None
self._message = Message()
self._pandas_only = False
# Metadata
self.data_type_lookup = None
self.data_type = None
self.data_model_lookup = None
self.data_model = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
@property
def _constructor(self):
return LuxDataFrame
# @property
# def _constructor_sliced(self):
# def f(*args, **kwargs):
# # adapted from https://github.com/pandas-dev/pandas/issues/13208#issuecomment-326556232
# return LuxSeries(*args, **kwargs).__finalize__(self, method='inherit')
# return f
@property
def history(self):
return self._history
def maintain_metadata(self):
if (not hasattr(self, "_metadata_fresh") or not self._metadata_fresh
): # Check that metadata has not yet been computed
if (
len(self) > 0
): #only compute metadata information if the dataframe is non-empty
self.executor.compute_stats(self)
self.executor.compute_dataset_metadata(self)
self._infer_structure()
self._metadata_fresh = True
def expire_recs(self):
self._recs_fresh = False
self.recommendation = None
self.current_vis = None
self._widget = None
self._rec_info = None
self._sampled = None
def expire_metadata(self):
# Set metadata as null
self._metadata_fresh = False
self.data_type_lookup = None
self.data_type = None
self.data_model_lookup = None
self.data_model = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
#####################
## Override Pandas ##
#####################
# def __finalize__(self,other, method: Optional[str] = None, **kwargs):
# print ("lux finalize")
# super(LuxDataFrame, self).__finalize__(other,method,**kwargs)
# self.expire_metadata()
def __getattr__(self, name):
super(LuxDataFrame, self).__getattr__(name)
self.expire_metadata()
self.expire_recs()
def _set_axis(self, axis, labels):
super(LuxDataFrame, self)._set_axis(axis, labels)
self.expire_metadata()
self.expire_recs()
def _update_inplace(self, *args, **kwargs):
super(LuxDataFrame, self)._update_inplace(*args, **kwargs)
self.expire_metadata()
self.expire_recs()
@property
def default_display(self):
if (self._default_pandas_display):
return "pandas"
else:
return "lux"
@default_display.setter
def default_display(self, type: str) -> None:
"""
Set the widget display to show Pandas by default or Lux by default
Parameters
----------
type : str
Default display type, can take either the string `lux` or `pandas` (regardless of capitalization)
"""
if (type.lower() == "lux"):
self._default_pandas_display = False
elif (type.lower() == "pandas"):
self._default_pandas_display = True
else:
warnings.warn(
"Unsupported display type. Default display option should either be `lux` or `pandas`.",
stacklevel=2)
def _infer_structure(self):
# If the dataframe is very small and the index column is not a range index, then it is likely that this is an aggregated data
is_multi_index_flag = self.index.nlevels != 1
not_int_index_flag = self.index.dtype != 'int64'
small_df_flag = len(self) < 100
self.pre_aggregated = (is_multi_index_flag
or not_int_index_flag) and small_df_flag
if ("Number of Records" in self.columns):
self.pre_aggregated = True
very_small_df_flag = len(self) <= 10
if (very_small_df_flag):
self.pre_aggregated = True
def set_executor_type(self, exe):
if (exe == "SQL"):
import pkgutil
if (pkgutil.find_loader("psycopg2") is None):
raise ImportError(
"psycopg2 is not installed. Run `pip install psycopg2' to install psycopg2 to enable the Postgres connection."
)
else:
import psycopg2
from lux.executor.SQLExecutor import SQLExecutor
self.executor = SQLExecutor
else:
from lux.executor.PandasExecutor import PandasExecutor
self.executor = PandasExecutor()
self.executor_type = exe
@property
def plot_config(self):
return self._plot_config
@plot_config.setter
def plot_config(self, config_func: Callable):
"""
Modify plot aesthetic settings to all visualizations in the dataframe display
Currently only supported for Altair visualizations
Parameters
----------
config_func : Callable
A function that takes in an AltairChart (https://altair-viz.github.io/user_guide/generated/toplevel/altair.Chart.html) as input and returns an AltairChart as output
Example
----------
Changing the color of marks and adding a title for all charts displayed for this dataframe
>>> df = pd.read_csv("lux/data/car.csv")
>>> def changeColorAddTitle(chart):
chart = chart.configure_mark(color="red") # change mark color to red
chart.title = "Custom Title" # add title to chart
return chart
>>> df.plot_config = changeColorAddTitle
>>> df
Change the opacity of all scatterplots displayed for this dataframe
>>> df = pd.read_csv("lux/data/olympic.csv")
>>> def changeOpacityScatterOnly(chart):
if chart.mark=='circle':
chart = chart.configure_mark(opacity=0.1) # lower opacity
return chart
>>> df.plot_config = changeOpacityScatterOnly
>>> df
"""
self._plot_config = config_func
self._recs_fresh = False
def clear_plot_config(self):
self._plot_config = None
self._recs_fresh = False
@property
def intent(self):
return self._intent
@intent.setter
def intent(self, intent_input: Union[List[Union[str, Clause]], Vis]):
is_list_input = isinstance(intent_input, list)
is_vis_input = isinstance(intent_input, Vis)
if not (is_list_input or is_vis_input):
raise TypeError(
"Input intent must be either a list (of strings or lux.Clause) or a lux.Vis object."
"\nSee more at: https://lux-api.readthedocs.io/en/latest/source/guide/intent.html"
)
if is_list_input:
self.set_intent(intent_input)
elif is_vis_input:
self.set_intent_as_vis(intent_input)
def clear_intent(self):
self.intent = []
def set_intent(self, intent: List[Union[str, Clause]]):
"""
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
-----
:doc:`../guide/clause`
"""
self.expire_recs()
self._intent = intent
self._parse_validate_compile_intent()
def _parse_validate_compile_intent(self):
from lux.processor.Parser import Parser
from lux.processor.Validator import Validator
self._intent = Parser.parse(self._intent)
Validator.validate_intent(self._intent, self)
self.maintain_metadata()
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(self, self._intent)
def copy_intent(self):
#creates a true copy of the dataframe's intent
output = []
for clause in self._intent:
temp_clause = clause.copy_clause()
output.append(temp_clause)
return (output)
def set_intent_as_vis(self, vis: Vis):
"""
Set intent of the dataframe as the Vis
Parameters
----------
vis : Vis
"""
self.expire_recs()
self._intent = vis._inferred_intent
self._parse_validate_compile_intent()
def to_pandas(self):
import lux.core
return lux.core.originalDF(self, copy=False)
@property
def recommendation(self):
return self._recommendation
@recommendation.setter
def recommendation(self, recommendation: Dict):
self._recommendation = recommendation
@property
def current_vis(self):
return self._current_vis
@current_vis.setter
def current_vis(self, current_vis: Dict):
self._current_vis = current_vis
def __repr__(self):
# TODO: _repr_ gets called from _repr_html, need to get rid of this call
return ""
#######################################################
########## SQL Metadata, type, model schema ###########
#######################################################
def set_SQL_connection(self, connection, t_name):
self.SQLconnection = connection
self.table_name = t_name
self.compute_SQL_dataset_metadata()
self.set_executor_type("SQL")
def compute_SQL_dataset_metadata(self):
self.get_SQL_attributes()
for attr in list(self.columns):
self[attr] = None
self.data_type_lookup = {}
self.data_type = {}
#####NOTE: since we aren't expecting users to do much data processing with the SQL database, should we just keep this
##### in the initialization and do it just once
self.compute_SQL_data_type()
self.compute_SQL_stats()
self.data_model_lookup = {}
self.data_model = {}
self.compute_data_model()
def compute_SQL_stats(self):
# precompute statistics
self.unique_values = {}
self._min_max = {}
self.get_SQL_unique_values()
#self.get_SQL_cardinality()
for attribute in self.columns:
if self.data_type_lookup[attribute] == 'quantitative':
self._min_max[attribute] = (self[attribute].min(),
self[attribute].max())
def get_SQL_attributes(self):
if "." in self.table_name:
table_name = self.table_name[self.table_name.index(".") + 1:]
else:
table_name = self.table_name
attr_query = "SELECT column_name FROM INFORMATION_SCHEMA.COLUMNS where TABLE_NAME = '{}'".format(
table_name)
attributes = list(
pd.read_sql(attr_query, self.SQLconnection)['column_name'])
for attr in attributes:
self[attr] = None
def get_SQL_cardinality(self):
cardinality = {}
for attr in list(self.columns):
card_query = pd.read_sql(
"SELECT Count(Distinct({})) FROM {}".format(
attr, self.table_name), self.SQLconnection)
cardinality[attr] = list(card_query["count"])[0]
self.cardinality = cardinality
def get_SQL_unique_values(self):
unique_vals = {}
for attr in list(self.columns):
unique_query = pd.read_sql(
"SELECT Distinct({}) FROM {}".format(attr, self.table_name),
self.SQLconnection)
unique_vals[attr] = list(unique_query[attr])
self.unique_values = unique_vals
def compute_SQL_data_type(self):
data_type_lookup = {}
sql_dtypes = {}
self.get_SQL_cardinality()
if "." in self.table_name:
table_name = self.table_name[self.table_name.index(".") + 1:]
else:
table_name = self.table_name
#get the data types of the attributes in the SQL table
for attr in list(self.columns):
datatype_query = "SELECT DATA_TYPE FROM INFORMATION_SCHEMA.COLUMNS WHERE TABLE_NAME = '{}' AND COLUMN_NAME = '{}'".format(
table_name, attr)
datatype = list(
pd.read_sql(datatype_query,
self.SQLconnection)['data_type'])[0]
sql_dtypes[attr] = datatype
data_type = {
"quantitative": [],
"ordinal": [],
"nominal": [],
"temporal": []
}
for attr in list(self.columns):
if str(attr).lower() in ["month", "year"]:
data_type_lookup[attr] = "temporal"
data_type["temporal"].append(attr)
elif sql_dtypes[attr] in [
"character", "character varying", "boolean", "uuid", "text"
]:
data_type_lookup[attr] = "nominal"
data_type["nominal"].append(attr)
elif sql_dtypes[attr] in [
"integer", "real", "smallint", "smallserial", "serial"
]:
if self.cardinality[attr] < 13:
data_type_lookup[attr] = "nominal"
data_type["nominal"].append(attr)
else:
data_type_lookup[attr] = "quantitative"
data_type["quantitative"].append(attr)
elif "time" in sql_dtypes[attr] or "date" in sql_dtypes[attr]:
data_type_lookup[attr] = "temporal"
data_type["temporal"].append(attr)
self.data_type_lookup = data_type_lookup
self.data_type = data_type
def _append_rec(self, rec_infolist, recommendations: Dict):
if (recommendations["collection"] is not None
and len(recommendations["collection"]) > 0):
rec_infolist.append(recommendations)
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata(
) # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.append(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
id_fields_str = ""
if (len(rec_df.data_type["id"]) > 0):
for id_field in rec_df.data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.append(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
if (not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh
): # Check that recs has not yet been computed
rec_infolist = []
from lux.action.custom import custom
from lux.action.correlation import correlation
from lux.action.univariate import univariate
from lux.action.enhance import enhance
from lux.action.filter import filter
from lux.action.generalize import generalize
from lux.action.row_group import row_group
from lux.action.column_group import column_group
if (rec_df.pre_aggregated):
if (rec_df.columns.name is not None):
rec_df._append_rec(rec_infolist, row_group(rec_df))
if (rec_df.index.name is not None):
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
if (rec_df.current_vis is None):
no_vis = True
one_current_vis = False
multiple_current_vis = False
else:
no_vis = len(rec_df.current_vis) == 0
one_current_vis = len(rec_df.current_vis) == 1
multiple_current_vis = len(rec_df.current_vis) > 1
if (no_vis):
rec_df._append_rec(rec_infolist, correlation(rec_df))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "quantitative"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "nominal"))
rec_df._append_rec(rec_infolist,
univariate(rec_df, "temporal"))
elif (one_current_vis):
rec_df._append_rec(rec_infolist, enhance(rec_df))
rec_df._append_rec(rec_infolist, filter(rec_df))
rec_df._append_rec(rec_infolist, generalize(rec_df))
elif (multiple_current_vis):
rec_df._append_rec(rec_infolist, custom(rec_df))
# Store _rec_info into a more user-friendly dictionary form
rec_df.recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if (rec_df._plot_config):
for vis in rec_df.current_vis:
vis._plot_config = rec_df.plot_config
for vis in vlist:
vis._plot_config = rec_df.plot_config
if (len(vlist) > 0):
rec_df.recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
elif (
show_prev
): # re-render widget for the current dataframe if previous rec is not recomputed
self._widget = rec_df.render_widget()
self._recs_fresh = True
#######################################################
############## LuxWidget Result Display ###############
#######################################################
@property
def widget(self):
if (self._widget):
return self._widget
@property
def exported(self) -> Union[Dict[str, VisList], VisList]:
"""
Get selected visualizations as exported Vis List
Notes
-----
Convert the _exportedVisIdxs dictionary into a programmable VisList
Example _exportedVisIdxs :
{'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
-------
Union[Dict[str,VisList], VisList]
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...), ..}
"""
if not hasattr(self, "_widget"):
warnings.warn(
"\nNo widget attached to the dataframe."
"Please assign dataframe to an output variable.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2)
return []
exported_vis_lst = self._widget._exportedVisIdxs
exported_vis = []
if (exported_vis_lst == {}):
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2)
return []
if len(exported_vis_lst) == 1 and "currentVis" in exported_vis_lst:
return self.current_vis
elif len(exported_vis_lst) > 1:
exported_vis = {}
if ("currentVis" in exported_vis_lst):
exported_vis["Current Vis"] = self.current_vis
for export_action in exported_vis_lst:
if (export_action != "currentVis"):
exported_vis[export_action] = VisList(
list(
map(self.recommendation[export_action].__getitem__,
exported_vis_lst[export_action])))
return exported_vis
elif len(exported_vis_lst) == 1 and ("currentVis"
not in exported_vis_lst):
export_action = list(exported_vis_lst.keys())[0]
exported_vis = VisList(
list(
map(self.recommendation[export_action].__getitem__,
exported_vis_lst[export_action])))
return exported_vis
else:
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2)
return []
def _repr_html_(self):
from IPython.display import display
from IPython.display import clear_output
import ipywidgets as widgets
try:
if (self._pandas_only):
display(self.display_pandas())
self._pandas_only = False
else:
if (self.index.nlevels >= 2 or self.columns.nlevels >= 2):
warnings.warn(
"\nLux does not currently support dataframes "
"with hierarchical indexes.\n"
"Please convert the dataframe into a flat "
"table via `pandas.DataFrame.reset_index`.\n",
stacklevel=2,
)
display(self.display_pandas())
return
if (len(self) <= 0):
warnings.warn(
"\nLux can not operate on an empty dataframe.\nPlease check your input again.\n",
stacklevel=2)
display(self.display_pandas())
return
if (len(self.columns) <= 1):
warnings.warn(
"\nLux defaults to Pandas when there is only a single column.",
stacklevel=2)
display(self.display_pandas())
return
self.maintain_metadata()
if (self._intent != [] and
(not hasattr(self, "_compiled") or not self._compiled)):
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(
self, self._intent)
self._toggle_pandas_display = self._default_pandas_display # Reset to Pandas Vis everytime
# df_to_display.maintain_recs() # compute the recommendations (TODO: This can be rendered in another thread in the background to populate self._widget)
self.maintain_recs()
# box = widgets.Box(layout=widgets.Layout(display='inline'))
button = widgets.Button(description="Toggle Pandas/Lux",
layout=widgets.Layout(width='140px',
top='5px'))
output = widgets.Output()
# box.children = [button,output]
# output.children = [button]
# display(box)
display(button, output)
def on_button_clicked(b):
with output:
if (b):
self._toggle_pandas_display = not self._toggle_pandas_display
clear_output()
if (self._toggle_pandas_display):
display(self.display_pandas())
else:
# b.layout.display = "none"
display(self._widget)
# b.layout.display = "inline-block"
button.on_click(on_button_clicked)
on_button_clicked(None)
except (KeyboardInterrupt, SystemExit):
raise
except:
warnings.warn(
"\nUnexpected error in rendering Lux widget and recommendations. "
"Falling back to Pandas display.\n\n"
"Please report this issue on Github: https://github.com/lux-org/lux/issues ",
stacklevel=2)
display(self.display_pandas())
def display_pandas(self):
return self.to_pandas()
def render_widget(self, renderer: str = "altair", input_current_vis=""):
"""
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
"""
check_import_lux_widget()
import luxWidget
widgetJSON = self.to_JSON(self._rec_info,
input_current_vis=input_current_vis)
return luxWidget.LuxWidget(
currentVis=widgetJSON["current_vis"],
recommendations=widgetJSON["recommendation"],
intent=LuxDataFrame.intent_to_string(self._intent),
message=self._message.to_html())
@staticmethod
def intent_to_JSON(intent):
from lux.utils import utils
filter_specs = utils.get_filter_specs(intent)
attrs_specs = utils.get_attrs_specs(intent)
intent = {}
intent['attributes'] = [clause.attribute for clause in attrs_specs]
intent['filters'] = [clause.attribute for clause in filter_specs]
return intent
@staticmethod
def intent_to_string(intent):
if (intent):
return ", ".join([clause.to_string() for clause in intent])
else:
return ""
def to_JSON(self, rec_infolist, input_current_vis=""):
widget_spec = {}
if (self.current_vis):
self.executor.execute(self.current_vis, self)
widget_spec["current_vis"] = LuxDataFrame.current_vis_to_JSON(
self.current_vis, input_current_vis)
else:
widget_spec["current_vis"] = {}
widget_spec["recommendation"] = []
# Recommended Collection
recCollection = LuxDataFrame.rec_to_JSON(rec_infolist)
widget_spec["recommendation"].extend(recCollection)
return widget_spec
@staticmethod
def current_vis_to_JSON(vlist, input_current_vis=""):
current_vis_spec = {}
numVC = len(vlist) #number of visualizations in the vis list
if (numVC == 1):
current_vis_spec = vlist[0].render_VSpec()
elif (numVC > 1):
pass
return current_vis_spec
@staticmethod
def rec_to_JSON(recs):
rec_lst = []
import copy
rec_copy = copy.deepcopy(recs)
for idx, rec in enumerate(rec_copy):
if (len(rec["collection"]) > 0):
rec["vspec"] = []
for vis in rec["collection"]:
chart = vis.render_VSpec()
rec["vspec"].append(chart)
rec_lst.append(rec)
# delete DataObjectCollection since not JSON serializable
del rec_lst[idx]["collection"]
return rec_lst
# Overridden Pandas Functions
def head(self, n: int = 5):
self._prev = self
self._history.append_event("head", n=5)
return super(LuxDataFrame, self).head(n)
def tail(self, n: int = 5):
self._prev = self
self._history.append_event("tail", n=5)
return super(LuxDataFrame, self).tail(n)
def info(self, *args, **kwargs):
self._pandas_only = True
self._history.append_event("info", *args, **kwargs)
return super(LuxDataFrame, self).info(*args, **kwargs)
def describe(self, *args, **kwargs):
self._pandas_only = True
self._history.append_event("describe", *args, **kwargs)
return super(LuxDataFrame, self).describe(*args, **kwargs)
def maintain_recs(self, is_series="DataFrame"):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.config.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata(
) # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
if len(rec_df) == 0:
rec_df._message.add(f"Lux cannot operate on an empty {is_series}.")
elif len(rec_df) < 5 and not rec_df.pre_aggregated:
rec_df._message.add(
f"The {is_series} is too small to visualize. To generate visualizations in Lux, the {is_series} must contain at least 5 rows."
)
elif self.index.nlevels >= 2 or self.columns.nlevels >= 2:
rec_df._message.add(
f"Lux does not currently support visualizations in a {is_series} "
f"with hierarchical indexes.\n"
f"Please convert the {is_series} into a flat "
f"table via pandas.DataFrame.reset_index.")
else:
id_fields_str = ""
inverted_data_type = lux.config.executor.invert_data_type(
rec_df.data_type)
if len(inverted_data_type["id"]) > 0:
for id_field in inverted_data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.add(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
is_sql_tbl = lux.config.executor.name == "SQLExecutor"
rec_infolist = []
from lux.action.row_group import row_group
from lux.action.column_group import column_group
# TODO: Rewrite these as register action inside default actions
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
elif not (len(rec_df) < 5 and not rec_df.pre_aggregated
and not is_sql_tbl) and not (self.index.nlevels >= 2 or
self.columns.nlevels >= 2):
from lux.action.custom import custom_actions
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.config.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df._recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df._recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
rec_df.show_all_column_vis()
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
rec_df.show_all_column_vis()
self._widget = rec_df.render_widget()
self._recs_fresh = True
class LuxDataFrame(pd.DataFrame):
"""
A subclass of pd.DataFrame that supports all dataframe operations while housing other variables and functions for generating visual recommendations.
"""
# MUST register here for new properties!!
_metadata = [
"_intent",
"_inferred_intent",
"_data_type",
"unique_values",
"cardinality",
"_rec_info",
"_min_max",
"_current_vis",
"_widget",
"_recommendation",
"_prev",
"_history",
"_saved_export",
"_sampled",
"_toggle_pandas_display",
"_message",
"_pandas_only",
"pre_aggregated",
"_type_override",
]
def __init__(self, *args, **kw):
self._history = History()
self._intent = []
self._inferred_intent = []
self._recommendation = {}
self._saved_export = None
self._current_vis = []
self._prev = None
self._widget = None
super(LuxDataFrame, self).__init__(*args, **kw)
self.table_name = ""
if lux.config.SQLconnection == "":
from lux.executor.PandasExecutor import PandasExecutor
lux.config.executor = PandasExecutor()
else:
from lux.executor.SQLExecutor import SQLExecutor
lux.config.executor = SQLExecutor()
self._sampled = None
self._toggle_pandas_display = True
self._message = Message()
self._pandas_only = False
# Metadata
self._data_type = {}
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
self._type_override = {}
warnings.formatwarning = lux.warning_format
@property
def _constructor(self):
return LuxDataFrame
@property
def _constructor_sliced(self):
def f(*args, **kwargs):
s = LuxSeries(*args, **kwargs)
for attr in self._metadata: # propagate metadata
s.__dict__[attr] = getattr(self, attr, None)
return s
return f
@property
def history(self):
return self._history
@property
def data_type(self):
if not self._data_type:
self.maintain_metadata()
return self._data_type
def maintain_metadata(self):
is_sql_tbl = lux.config.executor.name == "SQLExecutor"
if lux.config.SQLconnection != "" and is_sql_tbl:
from lux.executor.SQLExecutor import SQLExecutor
lux.config.executor = SQLExecutor()
# Check that metadata has not yet been computed
if not hasattr(self, "_metadata_fresh") or not self._metadata_fresh:
# only compute metadata information if the dataframe is non-empty
if is_sql_tbl:
lux.config.executor.compute_dataset_metadata(self)
self._infer_structure()
self._metadata_fresh = True
else:
if len(self) > 0:
lux.config.executor.compute_stats(self)
lux.config.executor.compute_dataset_metadata(self)
self._infer_structure()
self._metadata_fresh = True
def expire_recs(self):
"""
Expires and resets all recommendations
"""
self._recs_fresh = False
self._recommendation = {}
self._widget = None
self._rec_info = None
self._sampled = None
def expire_metadata(self):
"""
Expire all saved metadata to trigger a recomputation the next time the data is required.
"""
self._metadata_fresh = False
self._data_type = None
self.unique_values = None
self.cardinality = None
self._min_max = None
self.pre_aggregated = None
#####################
## Override Pandas ##
#####################
def __getattr__(self, name):
ret_value = super(LuxDataFrame, self).__getattr__(name)
self.expire_metadata()
self.expire_recs()
return ret_value
def _set_axis(self, axis, labels):
super(LuxDataFrame, self)._set_axis(axis, labels)
self.expire_metadata()
self.expire_recs()
def _update_inplace(self, *args, **kwargs):
super(LuxDataFrame, self)._update_inplace(*args, **kwargs)
self.expire_metadata()
self.expire_recs()
def _set_item(self, key, value):
super(LuxDataFrame, self)._set_item(key, value)
self.expire_metadata()
self.expire_recs()
def _infer_structure(self):
# If the dataframe is very small and the index column is not a range index, then it is likely that this is an aggregated data
is_multi_index_flag = self.index.nlevels != 1
not_int_index_flag = not pd.api.types.is_integer_dtype(self.index)
is_sql_tbl = lux.config.executor.name == "SQLExecutor"
small_df_flag = len(self) < 100 and is_sql_tbl
if self.pre_aggregated == None:
self.pre_aggregated = (is_multi_index_flag
or not_int_index_flag) and small_df_flag
if "Number of Records" in self.columns:
self.pre_aggregated = True
self.pre_aggregated = "groupby" in [
event.name for event in self.history
] and not is_sql_tbl
@property
def intent(self):
"""
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
-----
:doc:`../guide/intent`
"""
return self._intent
@intent.setter
def intent(self, intent_input: Union[List[Union[str, Clause]], Vis]):
is_list_input = isinstance(intent_input, list)
is_vis_input = isinstance(intent_input, Vis)
if not (is_list_input or is_vis_input):
raise TypeError(
"Input intent must be either a list (of strings or lux.Clause) or a lux.Vis object."
"\nSee more at: https://lux-api.readthedocs.io/en/latest/source/guide/intent.html"
)
if is_list_input:
self.set_intent(intent_input)
elif is_vis_input:
self.set_intent_as_vis(intent_input)
def clear_intent(self):
self.intent = []
self.expire_recs()
def set_intent(self, intent: List[Union[str, Clause]]):
self.expire_recs()
self._intent = intent
self._parse_validate_compile_intent()
def _parse_validate_compile_intent(self):
self.maintain_metadata()
from lux.processor.Parser import Parser
from lux.processor.Validator import Validator
self._intent = Parser.parse(self._intent)
Validator.validate_intent(self._intent, self)
self.maintain_metadata()
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(self, self._intent)
def copy_intent(self):
# creates a true copy of the dataframe's intent
output = []
for clause in self._intent:
temp_clause = clause.copy_clause()
output.append(temp_clause)
return output
def set_intent_as_vis(self, vis: Vis):
"""
Set intent of the dataframe based on the intent of a Vis
Parameters
----------
vis : Vis
Input Vis object
"""
self.expire_recs()
self._intent = vis._inferred_intent
self._parse_validate_compile_intent()
def set_data_type(self, types: dict):
"""
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"})
"""
if self._type_override == None:
self._type_override = types
else:
self._type_override = {**self._type_override, **types}
if not self.data_type:
self.maintain_metadata()
for attr in types:
if types[attr] not in [
"nominal", "quantitative", "id", "temporal"
]:
raise ValueError(
f'Invalid data type option specified for {attr}. Please use one of the following supported types: ["nominal", "quantitative", "id", "temporal"]'
)
self.data_type[attr] = types[attr]
self.expire_recs()
def to_pandas(self):
import lux.core
return lux.core.originalDF(self, copy=False)
@property
def recommendation(self):
if self._recommendation is not None and self._recommendation == {}:
from lux.processor.Compiler import Compiler
self.maintain_metadata()
self.current_vis = Compiler.compile_intent(self, self._intent)
self.maintain_recs()
return self._recommendation
@recommendation.setter
def recommendation(self, recommendation: Dict):
self._recommendation = recommendation
@property
def current_vis(self):
from lux.processor.Validator import Validator
# _parse_validate_compile_intent does not call executor,
# we only attach data to current vis when user request current_vis
valid_current_vis = (self._current_vis is not None
and len(self._current_vis) > 0
and self._current_vis[0].data is None
and self._current_vis[0].intent)
if valid_current_vis and Validator.validate_intent(
self._current_vis[0].intent, self):
lux.config.executor.execute(self._current_vis, self)
return self._current_vis
@current_vis.setter
def current_vis(self, current_vis: Dict):
self._current_vis = current_vis
def _append_rec(self, rec_infolist, recommendations: Dict):
if recommendations["collection"] is not None and len(
recommendations["collection"]) > 0:
rec_infolist.append(recommendations)
def show_all_column_vis(self):
if self.intent == [] or self.intent is None:
vis = Vis(list(self.columns), self)
if vis.mark != "":
vis._all_column = True
self.current_vis = VisList([vis])
def maintain_recs(self, is_series="DataFrame"):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.config.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata(
) # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
if len(rec_df) == 0:
rec_df._message.add(f"Lux cannot operate on an empty {is_series}.")
elif len(rec_df) < 5 and not rec_df.pre_aggregated:
rec_df._message.add(
f"The {is_series} is too small to visualize. To generate visualizations in Lux, the {is_series} must contain at least 5 rows."
)
elif self.index.nlevels >= 2 or self.columns.nlevels >= 2:
rec_df._message.add(
f"Lux does not currently support visualizations in a {is_series} "
f"with hierarchical indexes.\n"
f"Please convert the {is_series} into a flat "
f"table via pandas.DataFrame.reset_index.")
else:
id_fields_str = ""
inverted_data_type = lux.config.executor.invert_data_type(
rec_df.data_type)
if len(inverted_data_type["id"]) > 0:
for id_field in inverted_data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.add(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
is_sql_tbl = lux.config.executor.name == "SQLExecutor"
rec_infolist = []
from lux.action.row_group import row_group
from lux.action.column_group import column_group
# TODO: Rewrite these as register action inside default actions
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
elif not (len(rec_df) < 5 and not rec_df.pre_aggregated
and not is_sql_tbl) and not (self.index.nlevels >= 2 or
self.columns.nlevels >= 2):
from lux.action.custom import custom_actions
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.config.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df._recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df._recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
rec_df.show_all_column_vis()
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
rec_df.show_all_column_vis()
self._widget = rec_df.render_widget()
self._recs_fresh = True
#######################################################
############## LuxWidget Result Display ###############
#######################################################
@property
def widget(self):
if self._widget:
return self._widget
@property
def exported(self) -> Union[Dict[str, VisList], VisList]:
"""
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
-------
Union[Dict[str,VisList], VisList]
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...), ..}
"""
if self.widget is None:
warnings.warn(
"\nNo widget attached to the dataframe."
"Please assign dataframe to an output variable.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
exported_vis_lst = self._widget._selectedVisIdxs
exported_vis = []
if exported_vis_lst == {}:
if self._saved_export:
return self._saved_export
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
if len(exported_vis_lst) == 1 and "currentVis" in exported_vis_lst:
return self.current_vis
elif len(exported_vis_lst) > 1:
exported_vis = {}
if "currentVis" in exported_vis_lst:
exported_vis["Current Vis"] = self.current_vis
for export_action in exported_vis_lst:
if export_action != "currentVis":
exported_vis[export_action] = VisList(
list(
map(
self._recommendation[export_action].
__getitem__,
exported_vis_lst[export_action],
)))
return exported_vis
elif len(exported_vis_lst) == 1 and ("currentVis"
not in exported_vis_lst):
export_action = list(exported_vis_lst.keys())[0]
exported_vis = VisList(
list(
map(
self._recommendation[export_action].__getitem__,
exported_vis_lst[export_action],
)))
self._saved_export = exported_vis
return exported_vis
else:
warnings.warn(
"\nNo visualization selected to export.\n"
"See more: https://lux-api.readthedocs.io/en/latest/source/guide/FAQ.html#troubleshooting-tips",
stacklevel=2,
)
return []
def remove_deleted_recs(self, change):
for action in self._widget.deletedIndices:
deletedSoFar = 0
for index in self._widget.deletedIndices[action]:
self._recommendation[action].remove_index(index - deletedSoFar)
deletedSoFar += 1
def set_intent_on_click(self, change):
from IPython.display import display, clear_output
from lux.processor.Compiler import Compiler
intent_action = list(self._widget.selectedIntentIndex.keys())[0]
vis = self._recommendation[intent_action][
self._widget.selectedIntentIndex[intent_action][0]]
self.set_intent_as_vis(vis)
self.maintain_metadata()
self.current_vis = Compiler.compile_intent(self, self._intent)
self.maintain_recs()
with self.output:
clear_output()
display(self._widget)
self._widget.observe(self.remove_deleted_recs, names="deletedIndices")
self._widget.observe(self.set_intent_on_click,
names="selectedIntentIndex")
def _ipython_display_(self):
from IPython.display import display
from IPython.display import clear_output
import ipywidgets as widgets
try:
if self._pandas_only:
display(self.display_pandas())
self._pandas_only = False
else:
if not self.index.nlevels >= 2 or self.columns.nlevels >= 2:
self.maintain_metadata()
if self._intent != [] and (not hasattr(self, "_compiled")
or not self._compiled):
from lux.processor.Compiler import Compiler
self.current_vis = Compiler.compile_intent(
self, self._intent)
if lux.config.default_display == "lux":
self._toggle_pandas_display = False
else:
self._toggle_pandas_display = True
# df_to_display.maintain_recs() # compute the recommendations (TODO: This can be rendered in another thread in the background to populate self._widget)
self.maintain_recs()
# Observers(callback_function, listen_to_this_variable)
self._widget.observe(self.remove_deleted_recs,
names="deletedIndices")
self._widget.observe(self.set_intent_on_click,
names="selectedIntentIndex")
button = widgets.Button(
description="Toggle Pandas/Lux",
layout=widgets.Layout(width="140px", top="5px"),
)
self.output = widgets.Output()
display(button, self.output)
def on_button_clicked(b):
with self.output:
if b:
self._toggle_pandas_display = not self._toggle_pandas_display
clear_output()
if self._toggle_pandas_display:
display(self.display_pandas())
else:
# b.layout.display = "none"
display(self._widget)
# b.layout.display = "inline-block"
button.on_click(on_button_clicked)
on_button_clicked(None)
except (KeyboardInterrupt, SystemExit):
raise
except Exception:
if lux.config.pandas_fallback:
warnings.warn(
"\nUnexpected error in rendering Lux widget and recommendations. "
"Falling back to Pandas display.\n"
"Please report the following issue on Github: https://github.com/lux-org/lux/issues \n",
stacklevel=2,
)
warnings.warn(traceback.format_exc())
display(self.display_pandas())
else:
raise
def display_pandas(self):
return self.to_pandas()
def render_widget(self, renderer: str = "altair", input_current_vis=""):
"""
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
"""
check_import_lux_widget()
import luxwidget
widgetJSON = self.to_JSON(self._rec_info,
input_current_vis=input_current_vis)
return luxwidget.LuxWidget(
currentVis=widgetJSON["current_vis"],
recommendations=widgetJSON["recommendation"],
intent=LuxDataFrame.intent_to_string(self._intent),
message=self._message.to_html(),
)
@staticmethod
def intent_to_JSON(intent):
from lux.utils import utils
filter_specs = utils.get_filter_specs(intent)
attrs_specs = utils.get_attrs_specs(intent)
intent = {}
intent["attributes"] = [clause.attribute for clause in attrs_specs]
intent["filters"] = [clause.attribute for clause in filter_specs]
return intent
@staticmethod
def intent_to_string(intent):
if intent:
return ", ".join([clause.to_string() for clause in intent])
else:
return ""
def to_JSON(self, rec_infolist, input_current_vis=""):
widget_spec = {}
if self.current_vis:
lux.config.executor.execute(self.current_vis, self)
widget_spec["current_vis"] = LuxDataFrame.current_vis_to_JSON(
self.current_vis, input_current_vis)
else:
widget_spec["current_vis"] = {}
widget_spec["recommendation"] = []
# Recommended Collection
recCollection = LuxDataFrame.rec_to_JSON(rec_infolist)
widget_spec["recommendation"].extend(recCollection)
return widget_spec
@staticmethod
def current_vis_to_JSON(vlist, input_current_vis=""):
current_vis_spec = {}
numVC = len(vlist) # number of visualizations in the vis list
if numVC == 1:
current_vis_spec = vlist[0].to_code(
language=lux.config.plotting_backend, prettyOutput=False)
elif numVC > 1:
pass
if vlist[0]._all_column:
current_vis_spec["allcols"] = True
else:
current_vis_spec["allcols"] = False
return current_vis_spec
@staticmethod
def rec_to_JSON(recs):
rec_lst = []
import copy
rec_copy = copy.deepcopy(recs)
for idx, rec in enumerate(rec_copy):
if len(rec["collection"]) > 0:
rec["vspec"] = []
for vis in rec["collection"]:
chart = vis.to_code(language=lux.config.plotting_backend,
prettyOutput=False)
rec["vspec"].append(chart)
rec_lst.append(rec)
# delete since not JSON serializable
del rec_lst[idx]["collection"]
return rec_lst
def save_as_html(self, filename: str = "export.html") -> None:
"""
Save dataframe widget as static HTML file
Parameters
----------
filename : str
Filename for the output HTML file
"""
if self.widget is None:
self.maintain_metadata()
self.maintain_recs()
from ipywidgets.embed import embed_data
data = embed_data(views=[self.widget])
import json
manager_state = json.dumps(data["manager_state"])
widget_view = json.dumps(data["view_specs"][0])
# Separate out header since CSS file conflict with {} notation in Python format strings
header = """
<head>
<title>Lux Widget</title>
<link rel="lux" type="image/png" sizes="96x96" href="https://github.com/lux-org/lux-resources/blob/master/logo/favicon-96x96.png?raw=True">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" integrity="sha384-TX8t27EcRE3e/ihU7zmQxVncDAy5uIKz4rEkgIXeMed4M0jlfIDPvg6uqKI2xXr2" crossorigin="anonymous">
<!-- Load RequireJS, used by the IPywidgets for dependency management -->
<script
src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"
integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA="
crossorigin="anonymous">
</script>
<!-- Load IPywidgets bundle for embedding. -->
<script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.18.0/dist/embed-amd.js"
crossorigin="anonymous">
</script>
<style type="text/css">
#intentBtn, #warnBtn, #exportBtn{
display: none;
}
#deleteBtn {
right: 10px !important;
}
#footer-description{
margin: 10px;
text-align: right;
}
</style>
</head>
"""
html_template = """
<html>
{header}
<body>
<script type="application/vnd.jupyter.widget-state+json">
{manager_state}
</script>
<script type="application/vnd.jupyter.widget-view+json">
{widget_view}
</script>
<div id="footer-description">
These visualizations were generated by <a href="https://github.com/lux-org/lux/"><img src="https://raw.githubusercontent.com/lux-org/lux-resources/master/logo/logo.png" width="65px" style="vertical-align: middle;"></img></a>
</div>
</body>
</html>
"""
manager_state = json.dumps(data["manager_state"])
widget_view = json.dumps(data["view_specs"][0])
rendered_template = html_template.format(header=header,
manager_state=manager_state,
widget_view=widget_view)
with open(filename, "w") as fp:
fp.write(rendered_template)
print(f"Saved HTML to {filename}")
# Overridden Pandas Functions
def head(self, n: int = 5):
ret_val = super(LuxDataFrame, self).head(n)
ret_val._prev = self
ret_val._history.append_event("head", n=5)
return ret_val
def tail(self, n: int = 5):
ret_val = super(LuxDataFrame, self).tail(n)
ret_val._prev = self
ret_val._history.append_event("tail", n=5)
return ret_val
def describe(self, *args, **kwargs):
ret_val = super(LuxDataFrame, self).describe(*args, **kwargs)
ret_val._pandas_only = True
ret_val._history.append_event("describe", *args, **kwargs)
return ret_val
def groupby(self, *args, **kwargs):
history_flag = False
if "history" not in kwargs or ("history" in kwargs
and kwargs["history"]):
history_flag = True
if "history" in kwargs:
del kwargs["history"]
groupby_obj = super(LuxDataFrame, self).groupby(*args, **kwargs)
for attr in self._metadata:
groupby_obj.__dict__[attr] = getattr(self, attr, None)
if history_flag:
groupby_obj._history = groupby_obj._history.copy()
groupby_obj._history.append_event("groupby", *args, **kwargs)
groupby_obj.pre_aggregated = True
return groupby_obj
def maintain_recs(self):
# `rec_df` is the dataframe to generate the recommendations on
# check to see if globally defined actions have been registered/removed
if lux.config.update_actions["flag"] == True:
self._recs_fresh = False
show_prev = False # flag indicating whether rec_df is showing previous df or current self
if self._prev is not None:
rec_df = self._prev
rec_df._message = Message()
rec_df.maintain_metadata(
) # the prev dataframe may not have been printed before
last_event = self.history._events[-1].name
rec_df._message.add(
f"Lux is visualizing the previous version of the dataframe before you applied <code>{last_event}</code>."
)
show_prev = True
else:
rec_df = self
rec_df._message = Message()
# Add warning message if there exist ID fields
id_fields_str = ""
inverted_data_type = lux.config.executor.invert_data_type(
rec_df.data_type)
if len(inverted_data_type["id"]) > 0:
for id_field in inverted_data_type["id"]:
id_fields_str += f"<code>{id_field}</code>, "
id_fields_str = id_fields_str[:-2]
rec_df._message.add(
f"{id_fields_str} is not visualized since it resembles an ID field."
)
rec_df._prev = None # reset _prev
# Check that recs has not yet been computed
if not hasattr(rec_df, "_recs_fresh") or not rec_df._recs_fresh:
rec_infolist = []
from lux.action.row_group import row_group
from lux.action.column_group import column_group
# TODO: Rewrite these as register action inside default actions
if rec_df.pre_aggregated:
if rec_df.columns.name is not None:
rec_df._append_rec(rec_infolist, row_group(rec_df))
rec_df._append_rec(rec_infolist, column_group(rec_df))
else:
# if rec_df._recommendation == {}:
from lux.action.custom import custom_actions
# generate vis from globally registered actions and append to dataframe
custom_action_collection = custom_actions(rec_df)
for rec in custom_action_collection:
rec_df._append_rec(rec_infolist, rec)
lux.config.update_actions["flag"] = False
# Store _rec_info into a more user-friendly dictionary form
rec_df._recommendation = {}
for rec_info in rec_infolist:
action_type = rec_info["action"]
vlist = rec_info["collection"]
if len(vlist) > 0:
rec_df._recommendation[action_type] = vlist
rec_df._rec_info = rec_infolist
self._widget = rec_df.render_widget()
# re-render widget for the current dataframe if previous rec is not recomputed
elif show_prev:
self._widget = rec_df.render_widget()
self._recs_fresh = True
