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
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
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
