661 lines
24 KiB
Python
661 lines
24 KiB
Python
import polars as pl
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from pathlib import Path
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import pandas as pd
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from typing import Union
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import json
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import re
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from plots import JPMCPlotsMixin
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import marimo as mo
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def extract_voice_label(html_str: str) -> str:
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"""
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Extract voice label from HTML string and convert to short format.
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Parameters:
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html_str (str): HTML string containing voice label in format "Voice N"
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Returns:
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str: Voice label in format "VN" (e.g., "V14")
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Example:
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>>> extract_voice_label('<span style="...">Voice 14<br />...')
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'V14'
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"""
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match = re.search(r'Voice (\d+)', html_str)
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return f"V{match.group(1)}" if match else None
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def extract_qid(val):
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"""Extracts the 'ImportId' from a string representation of a dictionary."""
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if isinstance(val, str) and val.startswith('{') and val.endswith('}'):
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val = eval(val)
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return val['ImportId']
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def combine_exclusive_columns(df: pl.DataFrame, id_col: str = "_recordId", target_col_name: str = "combined_value") -> pl.DataFrame:
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"""
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Combines all columns except id_col into a single column.
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Raises ValueError if more than one column is populated in a single row.
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"""
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merge_cols = [c for c in df.columns if c != id_col]
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# Validate: count non-nulls horizontally
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row_counts = df.select(
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pl.sum_horizontal(pl.col(merge_cols).is_not_null())
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).to_series()
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if (row_counts > 1).any():
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raise ValueError("Invalid Data: Multiple columns populated for a single record row.")
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# Merge columns using coalesce
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return df.select([
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pl.col(id_col),
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pl.coalesce(merge_cols).alias(target_col_name)
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])
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def calculate_weighted_ranking_scores(df: pl.LazyFrame) -> pl.DataFrame:
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"""
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Calculate weighted scores for character or voice rankings.
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Points system: 1st place = 3 pts, 2nd place = 2 pts, 3rd place = 1 pt.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing character/ voice ranking columns.
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Returns
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-------
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pl.DataFrame
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DataFrame with columns 'Character' and 'Weighted Score', sorted by score.
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"""
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if isinstance(df, pl.LazyFrame):
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df = df.collect()
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scores = []
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# Identify ranking columns (assume all columns except _recordId)
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ranking_cols = [c for c in df.columns if c != '_recordId']
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for col in ranking_cols:
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# Calculate score:
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# (Count of Rank 1 * 3) + (Count of Rank 2 * 2) + (Count of Rank 3 * 1)
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r1_count = df.filter(pl.col(col) == 1).height
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r2_count = df.filter(pl.col(col) == 2).height
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r3_count = df.filter(pl.col(col) == 3).height
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weighted_score = (r1_count * 3) + (r2_count * 2) + (r3_count * 1)
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# Clean name
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clean_name = col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip()
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scores.append({
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'Character': clean_name,
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'Weighted Score': weighted_score
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})
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return pl.DataFrame(scores).sort('Weighted Score', descending=True)
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class JPMCSurvey(JPMCPlotsMixin):
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"""Class to handle JPMorgan Chase survey data."""
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def __init__(self, data_path: Union[str, Path], qsf_path: Union[str, Path]):
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if isinstance(data_path, str):
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data_path = Path(data_path)
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if isinstance(qsf_path, str):
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qsf_path = Path(qsf_path)
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self.data_filepath = data_path
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self.qsf_filepath = qsf_path
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self.qid_descr_map = self._extract_qid_descr_map()
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self.qsf:dict = self._load_qsf()
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# get export directory name for saving figures ie if data_path='data/exports/OneDrive_2026-01-21/...' should be 'figures/OneDrive_2026-01-21'
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self.fig_save_dir = Path('figures') / self.data_filepath.parts[2]
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if not self.fig_save_dir.exists():
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self.fig_save_dir.mkdir(parents=True, exist_ok=True)
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self.data_filtered = None
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self.plot_height = 500
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self.plot_width = 1000
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# Filter values
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self.filter_age:list = None
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self.filter_gender:list = None
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self.filter_consumer:list = None
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self.filter_ethnicity:list = None
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self.filter_income:list = None
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def _extract_qid_descr_map(self) -> dict:
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"""Extract mapping of Qualtrics ImportID to Question Description from results file."""
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if '1_1-16-2026' in self.data_filepath.as_posix():
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df_questions = pd.read_csv(self.data_filepath, nrows=1)
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df_questions
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return df_questions.iloc[0].to_dict()
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else:
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# First row contains Qualtrics Editor question names (ie 'B_VOICE SEL. 18-8')
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# Second row which contains the question content
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# Third row contains the Export Metadata (ie '{"ImportId":"startDate","timeZone":"America/Denver"}')
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df_questions = pd.read_csv(self.data_filepath, nrows=2)
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# transpose df_questions
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df_questions = df_questions.T.reset_index()
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df_questions.columns = ['QName', 'Description', 'export_metadata']
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df_questions['ImportID'] = df_questions['export_metadata'].apply(extract_qid)
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df_questions = df_questions[['ImportID', 'QName', 'Description']]
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# return dict as {ImportID: [QName, Description]}
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return df_questions.set_index('ImportID')[['QName', 'Description']].T.to_dict()
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def _load_qsf(self) -> dict:
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"""Load QSF file to extract question metadata if needed."""
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with open(self.qsf_filepath, 'r', encoding='utf-8') as f:
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qsf_data = json.load(f)
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return qsf_data
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def _get_qsf_question_by_QID(self, QID: str) -> dict:
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"""Get question metadata from QSF using the Question ID."""
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q_elem = [elem for elem in self.qsf['SurveyElements'] if elem['PrimaryAttribute'] == QID]
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if len(q_elem) == 0:
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raise ValueError(f"SurveyElement with 'PrimaryAttribute': '{QID}' not found in QSF.")
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if len(q_elem) > 1:
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raise ValueError(f"Multiple SurveyElements with 'PrimaryAttribute': '{QID}' found in QSF: \n{q_elem}")
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return q_elem[0]
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def load_data(self) -> pl.LazyFrame:
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"""
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Load CSV where column headers are in row 3 as dict strings with ImportId.
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The 3rd row contains metadata like '{"ImportId":"startDate","timeZone":"America/Denver"}'.
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This function extracts the ImportId from each column and uses it as the column name.
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Parameters:
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file_path (Path): Path to the CSV file to load.
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Returns:
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pl.LazyFrame: Polars LazyFrame with ImportId as column names.
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"""
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if '1_1-16-2026' in self.data_filepath.as_posix():
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raise NotImplementedError("This method does not support the '1_1-16-2026' export format.")
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# Read the 3rd row (index 2) which contains the metadata dictionaries
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# Use header=None to get raw values instead of treating them as column names
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df_meta = pd.read_csv(self.data_filepath, nrows=1, skiprows=2, header=None)
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# Extract ImportIds from each column value in this row
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new_columns = [extract_qid(val) for val in df_meta.iloc[0]]
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# Now read the actual data starting from row 4 (skip first 3 rows)
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df = pl.read_csv(self.data_filepath, skip_rows=3)
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# Rename columns with the extracted ImportIds
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df.columns = new_columns
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# Store unique values for filters (ignoring nulls) to detect "all selected" state
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self.options_age = sorted(df['QID1'].drop_nulls().unique().to_list()) if 'QID1' in df.columns else []
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self.options_gender = sorted(df['QID2'].drop_nulls().unique().to_list()) if 'QID2' in df.columns else []
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self.options_consumer = sorted(df['Consumer'].drop_nulls().unique().to_list()) if 'Consumer' in df.columns else []
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self.options_ethnicity = sorted(df['QID3'].drop_nulls().unique().to_list()) if 'QID3' in df.columns else []
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self.options_income = sorted(df['QID15'].drop_nulls().unique().to_list()) if 'QID15' in df.columns else []
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return df.lazy()
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def _get_subset(self, q: pl.LazyFrame, QIDs, rename_cols=True, include_record_id=True) -> pl.LazyFrame:
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"""Extract subset of data based on specific questions."""
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if include_record_id and '_recordId' not in QIDs:
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QIDs = ['_recordId'] + QIDs
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if not rename_cols:
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return q.select(QIDs)
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rename_dict = {qid: self.qid_descr_map[qid]['QName'] for qid in QIDs if qid in self.qid_descr_map and qid != '_recordId'}
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return q.select(QIDs).rename(rename_dict)
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def filter_data(self, q: pl.LazyFrame, age:list=None, gender:list=None, consumer:list=None, ethnicity:list=None, income:list=None) -> pl.LazyFrame:
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"""Filter data based on provided parameters
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Possible parameters:
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- age: list of age groups to include
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- gender: list
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- consumer: list
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- ethnicity: list
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- income: list
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Also saves the result to self.data_filtered.
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"""
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# Apply filters
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self.filter_age = age
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if age is not None:
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q = q.filter(pl.col('QID1').is_in(age))
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self.filter_gender = gender
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if gender is not None:
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q = q.filter(pl.col('QID2').is_in(gender))
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self.filter_consumer = consumer
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if consumer is not None:
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q = q.filter(pl.col('Consumer').is_in(consumer))
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self.filter_ethnicity = ethnicity
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if ethnicity is not None:
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q = q.filter(pl.col('QID3').is_in(ethnicity))
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self.filter_income = income
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if income is not None:
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q = q.filter(pl.col('QID15').is_in(income))
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self.data_filtered = q
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return self.data_filtered
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def get_demographics(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the demographics.
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Renames columns using qid_descr_map if provided.
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"""
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QIDs = ['QID1', 'QID2', 'QID3', 'QID4', 'QID13', 'QID14', 'QID15', 'QID16', 'QID17', 'Consumer']
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return self._get_subset(q, QIDs), None
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def get_top_8_traits(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the top 8 characteristics are most important for this Chase virtual assistant to have.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs = ['QID25']
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return self._get_subset(q, QIDs, rename_cols=False).rename({'QID25': 'Top_8_Traits'}), None
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def get_top_3_traits(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the top 3 characteristics that the Chase virtual assistant should prioritize.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs = ['QID26_0_GROUP']
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return self._get_subset(q, QIDs, rename_cols=False).rename({'QID26_0_GROUP': 'Top_3_Traits'}), None
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def get_character_ranking(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the ranking of characteristics for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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# Requires QSF to map "Character Ranking_2" to the actual character
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cfg = self._get_qsf_question_by_QID('QID27')['Payload']
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QIDs_map = {f'QID27_{v}': cfg['VariableNaming'][k] for k,v in cfg['RecodeValues'].items()}
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QIDs_rename = {qid: f'Character_Ranking_{QIDs_map[qid].replace(" ", "_")}' for qid in QIDs_map}
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return self._get_subset(q, list(QIDs_rename.keys()), rename_cols=False).rename(QIDs_rename), None
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def get_18_8_3(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the 18-8-3 feedback for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs = ['QID29', 'QID101', 'QID36_0_GROUP']
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rename_dict = {
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'QID29': '18-8_Set-A',
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'QID101': '18-8_Set-B',
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'QID36_0_GROUP': '3_Ranked'
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}
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subset = self._get_subset(q, QIDs, rename_cols=False).rename(rename_dict)
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# Combine 18-8 Set A and Set B into single column
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subset = subset.with_columns(
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pl.coalesce(['18-8_Set-A', '18-8_Set-B']).alias('8_Combined')
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)
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# Change order of columns
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subset = subset.select(['_recordId', '18-8_Set-A', '18-8_Set-B', '8_Combined', '3_Ranked'])
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return subset, None
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def get_voice_scale_1_10(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the Voice Scale 1-10 ratings for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs_map = {}
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for qid, val in self.qid_descr_map.items():
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if 'Scale 1-10_1' in val['QName']:
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# Convert "Voice 16 Scale 1-10_1" to "Scale_1_10__Voice_16"
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QIDs_map[qid] = f"Voice_Scale_1_10__V{val['QName'].split()[1]}"
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return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), None
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def get_ss_green_blue(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, dict]:
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"""Extract columns containing the SS Green/Blue ratings for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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cfg = self._get_qsf_question_by_QID('QID35')['Payload']
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QIDs_map = {}
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choices_map = {}
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for qid, val in self.qid_descr_map.items():
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if 'SS Green-Blue' in val['QName']:
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cfg = self._get_qsf_question_by_QID(qid.split('_')[0])['Payload']
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# ie: "V14 SS Green-Blue_1"
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qname_parts = val['QName'].split()
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voice = qname_parts[0]
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trait_num = qname_parts[-1].split('_')[-1]
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QIDs_map[qid] = f"SS_Green_Blue__{voice}__Choice_{trait_num}"
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choices_map[f"SS_Green_Blue__{voice}__Choice_{trait_num}"] = cfg['Choices'][trait_num]['Display']
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return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), choices_map
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def get_top_3_voices(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the top 3 voice choices for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs_map = {}
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cfg36 = self._get_qsf_question_by_QID('QID36')['Payload']
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choice_voice_map = {k: extract_voice_label(v['Display']) for k,v in cfg36['Choices'].items()}
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for qid, val in self.qid_descr_map.items():
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if 'Rank Top 3 Voices' in val['QName']:
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cfg = self._get_qsf_question_by_QID(qid.split('_')[0])['Payload']
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voice_num = val['QName'].split('_')[-1]
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# Validate that the DynamicChoices Locator is as expected
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if cfg['DynamicChoices']['Locator'] != r"q://QID36/ChoiceGroup/SelectedChoicesInGroup/1":
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raise ValueError(f"Unexpected DynamicChoices Locator for QID '{qid}': {cfg['DynamicChoices']['Locator']}")
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# extract the voice from the QID36 config
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voice = choice_voice_map[voice_num]
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# Convert "Top 3 Voices_1" to "Top_3_Voices__V14"
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QIDs_map[qid] = f"Top_3_Voices_ranking__{voice}"
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return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), None
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def get_ss_orange_red(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, dict]:
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"""Extract columns containing the SS Orange/Red ratings for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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cfg = self._get_qsf_question_by_QID('QID40')['Payload']
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QIDs_map = {}
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choices_map = {}
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for qid, val in self.qid_descr_map.items():
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if 'SS Orange-Red' in val['QName']:
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cfg = self._get_qsf_question_by_QID(qid.split('_')[0])['Payload']
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# ie: "V14 SS Orange-Red_1"
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qname_parts = val['QName'].split()
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voice = qname_parts[0]
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trait_num = qname_parts[-1].split('_')[-1]
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QIDs_map[qid] = f"SS_Orange_Red__{voice}__Choice_{trait_num}"
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choices_map[f"SS_Orange_Red__{voice}__Choice_{trait_num}"] = cfg['Choices'][trait_num]['Display']
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return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), choices_map
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def get_character_refine(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
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"""Extract columns containing the character refine feedback for the Chase virtual assistant.
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Returns subquery that can be chained with other polars queries.
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"""
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QIDs = ['QID44', 'QID97', 'QID95', 'QID96']
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return self._get_subset(q, QIDs, rename_cols=True), None
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def process_speaking_style_data(
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df: Union[pl.LazyFrame, pl.DataFrame],
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trait_map: dict[str, str]
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) -> pl.DataFrame:
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"""
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Process speaking style columns from wide to long format and map trait descriptions.
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Parses columns with format: SS_{StyleGroup}__{Voice}__{ChoiceID}
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Example: SS_Orange_Red__V14__Choice_1
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Parameters
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----------
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df : pl.LazyFrame or pl.DataFrame
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Input dataframe containing SS_* columns.
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trait_map : dict
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Dictionary mapping column names to trait descriptions.
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Keys should be full column names like "SS_Orange_Red__V14__Choice_1".
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Returns
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-------
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pl.DataFrame
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Long-format dataframe with columns:
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_recordId, Voice, Style_Group, Choice_ID, Description, Score, Left_Anchor, Right_Anchor
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"""
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# Normalize input to LazyFrame
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lf = df.lazy() if isinstance(df, pl.DataFrame) else df
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# 1. Melt SS_ columns
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melted = lf.melt(
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id_vars=["_recordId"],
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value_vars=pl.col("^SS_.*$"),
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variable_name="full_col_name",
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value_name="score"
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)
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# 2. Extract components from column name
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# Regex captures: Style_Group (e.g. SS_Orange_Red), Voice (e.g. V14), Choice_ID (e.g. Choice_1)
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pattern = r"^(?P<Style_Group>SS_.+?)__(?P<Voice>.+?)__(?P<Choice_ID>Choice_\d+)$"
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processed = melted.with_columns(
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pl.col("full_col_name").str.extract_groups(pattern)
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).unnest("full_col_name")
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# 3. Create Mapping Lookup from the provided dictionary
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# We map (Style_Group, Choice_ID) -> Description
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mapping_data = []
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seen = set()
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for col_name, desc in trait_map.items():
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match = re.match(pattern, col_name)
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if match:
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groups = match.groupdict()
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key = (groups["Style_Group"], groups["Choice_ID"])
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if key not in seen:
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# Parse description into anchors if possible (Left : Right)
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parts = desc.split(':')
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left_anchor = parts[0].strip() if len(parts) > 0 else ""
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right_anchor = parts[1].strip() if len(parts) > 1 else ""
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mapping_data.append({
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"Style_Group": groups["Style_Group"],
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"Choice_ID": groups["Choice_ID"],
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"Description": desc,
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"Left_Anchor": left_anchor,
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"Right_Anchor": right_anchor
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})
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seen.add(key)
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if not mapping_data:
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return processed.collect()
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mapping_lf = pl.LazyFrame(mapping_data)
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# 4. Join Data with Mapping
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result = processed.join(
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mapping_lf,
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on=["Style_Group", "Choice_ID"],
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how="left"
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)
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# 5. Cast score to Int
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result = result.with_columns(
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pl.col("score").cast(pl.Int64, strict=False)
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)
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return result.collect()
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def process_voice_scale_data(
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df: Union[pl.LazyFrame, pl.DataFrame]
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) -> pl.DataFrame:
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"""
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Process Voice Scale columns from wide to long format.
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Parses columns with format: Voice_Scale_1_10__V{Voice}
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Example: Voice_Scale_1_10__V14
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Returns
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-------
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pl.DataFrame
|
|
Long-format dataframe with columns:
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|
_recordId, Voice, Voice_Scale_Score
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"""
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lf = df.lazy() if isinstance(df, pl.DataFrame) else df
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# Melt
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melted = lf.melt(
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id_vars=["_recordId"],
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value_vars=pl.col("^Voice_Scale_1_10__V.*$"),
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variable_name="full_col_name",
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value_name="Voice_Scale_Score"
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)
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# Extract Voice
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processed = melted.with_columns(
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pl.col("full_col_name").str.extract(r"V(\d+)", 1).alias("Voice_Num")
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).with_columns(
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("V" + pl.col("Voice_Num")).alias("Voice")
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)
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# Keep Score as Float (original data is f64)
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result = processed.select([
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"_recordId",
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"Voice",
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pl.col("Voice_Scale_Score").cast(pl.Float64, strict=False)
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])
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|
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return result.collect()
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def join_voice_and_style_data(
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processed_style_data: pl.DataFrame,
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|
processed_voice_data: pl.DataFrame
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|
) -> pl.DataFrame:
|
|
"""
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|
Joins processed Speaking Style data with Voice Scale 1-10 data.
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|
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|
Parameters
|
|
----------
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|
processed_style_data : pl.DataFrame
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|
Result of process_speaking_style_data
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|
processed_voice_data : pl.DataFrame
|
|
Result of process_voice_scale_data
|
|
|
|
Returns
|
|
-------
|
|
pl.DataFrame
|
|
Merged dataframe with columns from both, joined on _recordId and Voice.
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|
"""
|
|
|
|
return processed_style_data.join(
|
|
processed_voice_data,
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|
on=["_recordId", "Voice"],
|
|
how="inner"
|
|
)
|
|
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def process_voice_ranking_data(
|
|
df: Union[pl.LazyFrame, pl.DataFrame]
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|
) -> pl.DataFrame:
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"""
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Process Voice Ranking columns from wide to long format and convert ranks to points.
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|
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|
Parses columns with format: Top_3_Voices_ranking__V{Voice}
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|
Converts ranks to points: 1st place = 3 pts, 2nd place = 2 pts, 3rd place = 1 pt
|
|
|
|
Returns
|
|
-------
|
|
pl.DataFrame
|
|
Long-format dataframe with columns:
|
|
_recordId, Voice, Ranking_Points
|
|
"""
|
|
lf = df.lazy() if isinstance(df, pl.DataFrame) else df
|
|
|
|
# Melt
|
|
melted = lf.melt(
|
|
id_vars=["_recordId"],
|
|
value_vars=pl.col("^Top_3_Voices_ranking__V.*$"),
|
|
variable_name="full_col_name",
|
|
value_name="rank"
|
|
)
|
|
|
|
# Extract Voice
|
|
processed = melted.with_columns(
|
|
pl.col("full_col_name").str.extract(r"V(\d+)", 1).alias("Voice_Num")
|
|
).with_columns(
|
|
("V" + pl.col("Voice_Num")).alias("Voice")
|
|
)
|
|
|
|
# Convert rank to points: 1st=3, 2nd=2, 3rd=1, null=0 (not ranked)
|
|
# Rank values are 1, 2, 3 for position in top 3
|
|
result = processed.with_columns(
|
|
pl.when(pl.col("rank") == 1).then(3)
|
|
.when(pl.col("rank") == 2).then(2)
|
|
.when(pl.col("rank") == 3).then(1)
|
|
.otherwise(0)
|
|
.alias("Ranking_Points")
|
|
).select([
|
|
"_recordId",
|
|
"Voice",
|
|
"Ranking_Points"
|
|
])
|
|
|
|
return result.collect()
|