Source code for lux.action.row_group

#  Copyright 2019-2020 The Lux Authors.
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  See the License for the specific language governing permissions and
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import lux
from lux.interestingness.interestingness import interestingness
from lux.processor.Compiler import Compiler
from lux.utils import utils

from lux.vis.Vis import Vis
from lux.vis.VisList import VisList
import pandas as pd

[docs]def row_group(ldf): recommendation = { "action": "Row Groups", "description": "Shows charts of possible visualizations with respect to the row-wise index.", "long_description": 'A row index can be thought of as an extra row that indicates the values that the user is interested in. \ Lux focuses on visualizing named dataframe indices, i.e., indices with a non-null name property, as a proxy of the attribute \ that the user is interested in or have operated on (e.g., group-by attribute). In particular, dataframes with named indices \ are often pre-aggregated, so Lux visualizes exactly the values that the dataframe portrays. \ <a href="" target="_blank">More details</a>', } collection = [] if ldf.index.nlevels == 1: if is not None: dim_name = else: dim_name = "index" for row_id in range(len(ldf)): row = ldf.iloc[ row_id, ] rowdf = row.reset_index() # if (dim_name =="index"): #TODO: need to change this to auto-detect # rowdf.data_type_lookup["index"]="nominal" # rowdf.data_model_lookup["index"]="dimension" # rowdf.cardinality["index"]=len(rowdf) # if isinstance(ldf.columns,pd.DatetimeIndex): # rowdf.data_type_lookup[dim_name]="temporal" vis = Vis( [ dim_name, lux.Clause(, data_model="measure", aggregation=None), ], rowdf, ) collection.append(vis) vlst = VisList(collection) # Note that we are not computing interestingness score here because we want to preserve the arrangement of the aggregated data recommendation["collection"] = vlst return recommendation