character refine

01_ingest_qualtrics_export.py

@@ -15,6 +15,7 @@ def _():
     return (
         JPMCSurvey,
         mo,
+        pl,
         plot_average_scores_with_counts,
         plot_top3_ranking_distribution,
     )
@@ -31,29 +32,88 @@ def _():
 @app.cell
 def _(JPMCSurvey, QSF_FILE, RESULTS_FILE):
     survey = JPMCSurvey(RESULTS_FILE, QSF_FILE)
-    survey.qid_descr_map
-    return (survey,)
-
-
-@app.cell
-def _(survey):
     data = survey.load_data()
-    df = data.collect()
+    data.collect()
+    return data, survey
 
 
-    df.select([q for q in df.columns if 'QID98' in q])
+@app.cell
-
+def _():
-    return (data,)
+    # survey.qid_descr_map
+    return
 
 
 @app.cell
 def _(mo):
     mo.md(r"""
-    # Data Cleanup
+    # Data Validation
+    """)
+    return
 
-    - Remove incomplete responses (progress < 100)
+
-    - Flag outliers based on duration (add column)
+@app.cell
-    - Flag responses that give the same rating for everything (indicates lack of engagement)
+def _(data, mo, pl):
+    # all progress is 100
+    def check_progress(data):
+        if data.collect().select(pl.col('progress').unique()).shape[0] == 1:
+            return mo.md("""## ✅ All responses are complete (progress = 100) """)
+    
+        return mo.md("## ⚠️ There are incomplete responses (progress < 100) ⚠️")
+
+    check_progress(data)
+
+    return
+
+
+@app.cell
+def _(data, mo, pl):
+
+    def duration_validation(data):
+        # Identify any outliers in duration
+        duration_stats = data.select(
+            pl.col('duration').mean().alias('mean_duration'),
+            pl.col('duration').std().alias('std_duration')
+        ).collect()
+        mean_duration = duration_stats['mean_duration'][0]
+        std_duration = duration_stats['std_duration'][0]
+        upper_outlier_threshold = mean_duration + 3 * std_duration
+        lower_outlier_threshold = mean_duration - 3 * std_duration
+    
+        _d = data.with_columns(
+            ((pl.col('duration') > upper_outlier_threshold) | (pl.col('duration') < lower_outlier_threshold)).alias('outlier_duration')
+        )
+    
+        # Show durations with outlier flag is true
+        outlier_data = _d.filter(pl.col('outlier_duration') == True).collect()
+
+        if outlier_data.shape[0] == 0:
+            return mo.md("## ✅ No duration outliers detected")
+
+        return mo.md(f"""
+        ## ⚠️ Duration Outliers Detected ⚠️
+        - Mean Duration: {mean_duration:.2f} seconds (approximately {mean_duration/60:.2f} minutes)
+        - Standard Deviation of Duration: {std_duration:.2f} seconds
+        - Upper Outlier Threshold (Mean + 3*Std): {upper_outlier_threshold:.2f} seconds
+        - Lower Outlier Threshold (Mean - 3*Std): {lower_outlier_threshold:.2f} seconds
+        - Number of Outlier Responses: {outlier_data.shape[0]}
+    
+        Outliers:
+    
+        {mo.ui.table(outlier_data)}
+    
+    
+        **⚠️ NOTE: These have not been removed from the dataset ⚠️**
+    
+        """)
+    
+    duration_validation(data)
+    return
+
+
+@app.cell
+def _(mo):
+    mo.md(r"""
+    # Data Analysis
     """)
     return
 
@@ -61,64 +121,67 @@ def _(mo):
 @app.cell
 def _(mo):
     mo.md(r"""
-    # Answers Decoding
+    ## Demographics
-
-    Pipeline to decode the ranking of voices. Currently saved as QID's, they need to be remapped back to their actual values so that the analysis can be performed. ie:
-
-    `GQIK26_G0_x8_RANK` -> Refers to question `Top 3 Traits_0_8_RANK - What are the important traits for the Chase AI virtual assistant?` and thus the #8 option
     """)
     return
 
 
-@app.cell(hide_code=True)
-def _(mo):
-    mo.md(r"""
-    ## TODO:
-
-    Create a python function for each of the questions. ie `def QID63()`. Each function should return a Polars query, that can be added to an existing query.
-
-    Ideas:
-    - Map column name to include the Voice number (VID) (ie the questions that only have 1 voice). The VID is in this case often included in the question description
-    - `QID_x_GROUP` Contains the rankings of the values, stored in order. The following columns (ie `QID26_G0_x1_RANK`) are redundant and not necessary for us. The function should drop the unnecessary columns  to clean up
-    <!-- - Translate the RANK values back to the actual VID, and create an aggregate column that contains a list of the VIDs in order. ie: [V34, V56, V81].
-      - Use the first line of the question description (see `qid_descr_map`) to get the `"DataExportTag"`, which is a property that can be found in the `.qsf` file to inspect the choice number and it's corresponding VID
-      - "`VOICE SEL. 8-3_0_5_RANK`" refers to `"DataExportTag": "VOICE SEL. 8-3"`, `Group 0` (not important for this), `Choice 5`, and the value in the cell refers to the Rank assigned to that voice
-      - QSF file example to retrieve the VID: `"SurveyElements" -> (Find item where "Payload"["DataExportTag"] == "VOICE SEL. 8-3") -> "Payload" -> "Choices" -> "5" -> "Display" -> (Extract 'Voice <xx>' from the HTML)` -->
-    """)
-    return
-
-
-@app.cell
-def _(survey):
-    cfg = survey._get_qsf_question_by_QID('QID36')['Payload']
-    cfg
-    return
-
-
 @app.cell
 def _(data, survey):
     survey.get_demographics(data)[0].collect()
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Top 8 traits
+    """)
+    return
+
+
 @app.cell
 def _(data, survey):
     survey.get_top_8_traits(data)[0].collect()
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Top 3 traits
+    """)
+    return
+
+
 @app.cell
 def _(data, survey):
     survey.get_top_3_traits(data)[0].collect()
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Character Ranking
+    """)
+    return
+
+
 @app.cell
 def _(data, survey):
     survey.get_character_ranking(data)[0].collect()
     return
 
 
+@app.cell
+def _(mo):
+    mo.md(r"""
+    ## Voices 18 -> 8 -> 3
+    """)
+    return
+
+
 @app.cell
 def _(data, survey):
     survey.get_18_8_3(data)[0].collect()
@@ -128,7 +191,7 @@ def _(data, survey):
 @app.cell
 def _(mo):
     mo.md(r"""
-    # Voice Scales 1-10
+    ## Voice Scales 1-10
     """)
     return
 
@@ -149,7 +212,7 @@ def _(plot_average_scores_with_counts, vscales):
 @app.cell
 def _(mo):
     mo.md(r"""
-    # SS Green Blue
+    ## SS Green Blue
     """)
     return
 
@@ -164,7 +227,7 @@ def _(data, survey):
 @app.cell
 def _(mo):
     mo.md(r"""
-    # Top 3 Voices
+    ## Top 3 Voices
     """)
     return
 
@@ -177,9 +240,9 @@ def _(data, survey):
 
 
 @app.cell
-def _(top3_voices):
+def _():
 
-    print(top3_voices.head())
+    # print(top3_voices.head())
     return
 
 
@@ -192,7 +255,7 @@ def _(plot_top3_ranking_distribution, top3_voices):
 @app.cell
 def _(mo):
     mo.md(r"""
-    # SS Orange / Red
+    ## SS Orange / Red
     """)
     return
 
@@ -205,5 +268,35 @@ def _(data, survey):
     return
 
 
+@app.cell(hide_code=True)
+def _(mo):
+    mo.md(r"""
+    ## Character Refine
+    """)
+    return
+
+
+@app.cell
+def _(data, survey):
+    traits_original = survey.get_top_8_traits(data)[0]
+    traits_original.collect()
+    return (traits_original,)
+
+
+@app.cell
+def _(data, survey):
+    traits_refined = survey.get_character_refine(data)[0]
+    traits_refined.collect()
+    return (traits_refined,)
+
+
+@app.cell
+def _(traits_original, traits_refined):
+    # merge the two dataframes side by side for comparison
+    traits_comparison = traits_original.join(traits_refined, on='_recordId')
+    traits_comparison.collect()
+    return
+
+
 if __name__ == "__main__":
     app.run()

layouts/01_ingest_qualtrics_export.slides.json

@@ -0,0 +1,4 @@
+{
+  "type": "slides",
+  "data": {}
+}

plots.py

@@ -40,8 +40,9 @@ def plot_average_scores_with_counts(
         Plotly figure object.
     """
     # Calculate average and count of non-null values for each column
+    # Exclude _recordId column
     stats = []
-    for col in df.columns:
+    for col in [c for c in df.columns if c != '_recordId']:
         avg_score = df[col].mean()
         non_null_count = df[col].drop_nulls().len()
         stats.append({
@@ -126,8 +127,9 @@ def plot_top3_ranking_distribution(
     go.Figure
         Plotly figure object.
     """
+    # Exclude _recordId column
     stats = []
-    for col in df.columns:
+    for col in [c for c in df.columns if c != '_recordId']:
         # Count occurrences of each rank (1, 2, 3)
         # We ensure we're just counting the specific integer values
         rank1 = df.filter(pl.col(col) == 1).height

pyproject.toml

@@ -5,6 +5,7 @@ description = "Add your description here"
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
+    "altair>=6.0.0",
     "marimo>=0.18.0",
     "matplotlib>=3.10.8",
     "modin[dask]>=0.37.1",

utils.py

@@ -131,17 +131,21 @@ class JPMCSurvey:
         
         return df.lazy()
     
-    def _get_subset(self, q: pl.LazyFrame, QIDs, rename_cols=True) -> pl.LazyFrame:
+    def _get_subset(self, q: pl.LazyFrame, QIDs, rename_cols=True, include_record_id=True) -> pl.LazyFrame:
         """Extract subset of data based on specific questions."""
+        
+        if include_record_id and '_recordId' not in QIDs:
+            QIDs = ['_recordId'] + QIDs
+        
         if not rename_cols:
             return q.select(QIDs)
         
-        rename_dict = {qid: self.qid_descr_map[qid]['QName'] for qid in QIDs if qid in self.qid_descr_map}
+        rename_dict = {qid: self.qid_descr_map[qid]['QName'] for qid in QIDs if qid in self.qid_descr_map and qid != '_recordId'}
         
         return q.select(QIDs).rename(rename_dict)
 
 
-    def get_demographics(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_demographics(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the demographics. 
         
         Renames columns using qid_descr_map if provided.
@@ -150,7 +154,7 @@ class JPMCSurvey:
         return self._get_subset(q, QIDs), None
 
 
-    def get_top_8_traits(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_top_8_traits(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the top 8 characteristics are most important for this Chase virtual assistant to have. 
         
         Returns subquery that can be chained with other polars queries.
@@ -160,7 +164,7 @@ class JPMCSurvey:
 
 
 
-    def get_top_3_traits(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_top_3_traits(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the top 3 characteristics that the Chase virtual assistant should prioritize. 
         
         Returns subquery that can be chained with other polars queries.
@@ -169,7 +173,7 @@ class JPMCSurvey:
         return self._get_subset(q, QIDs, rename_cols=False).rename({'QID26_0_GROUP': 'Top_3_Traits'}), None
     
     
-    def get_character_ranking(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_character_ranking(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the ranking of characteristics for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -186,7 +190,7 @@ class JPMCSurvey:
         return self._get_subset(q, list(QIDs_rename.keys()), rename_cols=False).rename(QIDs_rename), None
 
     
-    def get_18_8_3(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_18_8_3(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the 18-8-3 feedback for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -201,7 +205,7 @@ class JPMCSurvey:
         return self._get_subset(q, QIDs, rename_cols=False).rename(rename_dict), None
     
     
-    def get_voice_scale_1_10(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_voice_scale_1_10(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the Voice Scale 1-10 ratings for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -218,7 +222,7 @@ class JPMCSurvey:
     
     
     
-    def get_ss_green_blue(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_ss_green_blue(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, dict]:
         """Extract columns containing the SS Green/Blue ratings for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -245,7 +249,7 @@ class JPMCSurvey:
         return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), choices_map
     
     
-    def get_top_3_voices(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_top_3_voices(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the top 3 voice choices for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -276,7 +280,7 @@ class JPMCSurvey:
         return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), None
     
     
-    def get_ss_orange_red(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_ss_orange_red(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, dict]:
         """Extract columns containing the SS Orange/Red ratings for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
@@ -303,15 +307,11 @@ class JPMCSurvey:
         return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), choices_map
     
     
-    def get_character_refine(self, q: pl.LazyFrame) -> pl.LazyFrame:
+    def get_character_refine(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the character refine feedback for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
         """
-        QIDs = ['QID29', 'QID101', 'QID36_0_GROUP']
+        QIDs = ['QID44', 'QID97', 'QID95', 'QID96']
         
-        rename_dict = {
+        return self._get_subset(q, QIDs, rename_cols=True), None
-            'QID29': '18-8_Set-A',
-            'QID101': '18-8_Set-B',
-            'QID36_0_GROUP': '8-3_Ranked'
-        }

uv.lock

@@ -15,6 +15,22 @@ wheels = [
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 ]
 
+[[package]]
+name = "altair"
+version = "6.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "jinja2" },
+    { name = "jsonschema" },
+    { name = "narwhals" },
+    { name = "packaging" },
+    { name = "typing-extensions", marker = "python_full_version < '3.15'" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f7/c0/184a89bd5feba14ff3c41cfaf1dd8a82c05f5ceedbc92145e17042eb08a4/altair-6.0.0.tar.gz", hash = "sha256:614bf5ecbe2337347b590afb111929aa9c16c9527c4887d96c9bc7f6640756b4", size = 763834, upload-time = "2025-11-12T08:59:11.519Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/db/33/ef2f2409450ef6daa61459d5de5c08128e7d3edb773fefd0a324d1310238/altair-6.0.0-py3-none-any.whl", hash = "sha256:09ae95b53d5fe5b16987dccc785a7af8588f2dca50de1e7a156efa8a461515f8", size = 795410, upload-time = "2025-11-12T08:59:09.804Z" },
+]
+
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -37,6 +53,15 @@ wheels = [
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 ]
 
+[[package]]
+name = "attrs"
+version = "25.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/5c/685e6633917e101e5dcb62b9dd76946cbb57c26e133bae9e0cd36033c0a9/attrs-25.4.0.tar.gz", hash = "sha256:16d5969b87f0859ef33a48b35d55ac1be6e42ae49d5e853b597db70c35c57e11", size = 934251, upload-time = "2025-10-06T13:54:44.725Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/3a/2a/7cc015f5b9f5db42b7d48157e23356022889fc354a2813c15934b7cb5c0e/attrs-25.4.0-py3-none-any.whl", hash = "sha256:adcf7e2a1fb3b36ac48d97835bb6d8ade15b8dcce26aba8bf1d14847b57a3373", size = 67615, upload-time = "2025-10-06T13:54:43.17Z" },
+]
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.14.3"
@@ -641,6 +666,33 @@ wheels = [
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 ]
 
+[[package]]
+name = "jsonschema"
+version = "4.26.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "jsonschema-specifications" },
+    { name = "referencing" },
+    { name = "rpds-py" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/b3/fc/e067678238fa451312d4c62bf6e6cf5ec56375422aee02f9cb5f909b3047/jsonschema-4.26.0.tar.gz", hash = "sha256:0c26707e2efad8aa1bfc5b7ce170f3fccc2e4918ff85989ba9ffa9facb2be326", size = 366583, upload-time = "2026-01-07T13:41:07.246Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/69/90/f63fb5873511e014207a475e2bb4e8b2e570d655b00ac19a9a0ca0a385ee/jsonschema-4.26.0-py3-none-any.whl", hash = "sha256:d489f15263b8d200f8387e64b4c3a75f06629559fb73deb8fdfb525f2dab50ce", size = 90630, upload-time = "2026-01-07T13:41:05.306Z" },
+]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2025.9.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "referencing" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/19/74/a633ee74eb36c44aa6d1095e7cc5569bebf04342ee146178e2d36600708b/jsonschema_specifications-2025.9.1.tar.gz", hash = "sha256:b540987f239e745613c7a9176f3edb72b832a4ac465cf02712288397832b5e8d", size = 32855, upload-time = "2025-09-08T01:34:59.186Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
+]
+
 [[package]]
 name = "kiwisolver"
 version = "1.4.9"
@@ -1271,6 +1323,7 @@ name = "phase-3-quant"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "altair" },
     { name = "marimo" },
     { name = "matplotlib" },
     { name = "modin", extra = ["dask"] },
@@ -1290,6 +1343,7 @@ dependencies = [
 
 [package.metadata]
 requires-dist = [
+    { name = "altair", specifier = ">=6.0.0" },
     { name = "marimo", specifier = ">=0.18.0" },
     { name = "matplotlib", specifier = ">=3.10.8" },
     { name = "modin", extras = ["dask"], specifier = ">=0.37.1" },
@@ -1735,6 +1789,20 @@ wheels = [
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 ]
 
+[[package]]
+name = "referencing"
+version = "0.37.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "attrs" },
+    { name = "rpds-py" },
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