AI's Rhythmic Refactoring: Distilling Pandas Pivots in Jupyter

This entry really captures the essence of how I’m approaching the AI-assisted refactoring process. It highlights the methodical steps: identify, distill, validate, and then integrate. The truncation incident was a great practical example of needing to ‘course correct’ the AI, and my ‘time-traveling prompt’ technique proved very effective. The ‘big but’ about DataFrame persistence is a crucial pragmatic decision that keeps the momentum going without sacrificing long-term goals. It’s a constant balance of idealism and getting things done.

By Mike Levin

Sunday, October 19, 2025

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Setting the Stage: Context for the Curious Book Reader

The Methodical Beat: A Rhythmic Approach to Distillation

This entry captures a pivotal moment in our journey to automate and streamline complex data analysis workflows. We’re dissecting a Jupyter Notebook, GAPalyzer.ipynb, cell by cell, to extract its core logic into a reusable Python module, gap_analyzer_sauce.py. This isn’t just about moving code; it’s about establishing a robust, AI-assisted development rhythm. Here, we delve into the intricate process of converting a cell responsible for consolidating SEMrush data, tackling a ‘truncation’ hiccup from our AI assistant, and then moving on to distill the essential Pandas pivot_table operation, ensuring our workflow remains efficient and transparent. It’s a testament to incremental progress and pragmatic problem-solving in the realm of automated analytics.

Technical Journal Entry Begins

Quoth the Gemini:

The Methodical Beat: A Rhythmic Approach to Distillation

LOL, OMG, such language! But it really nails it. I KNEW I was in for it today with this distillation of the transplanted Competitive Content Gap Analysis SEO deliverable-maker. How many ways could this go wrong? To quoth the Roger Rabbit:

One one-thousand! Two one-thousand! Three one-thousand…

…and so on ad infinitum. So, scaffolding, templates, git diff’s and sniff tests. The resulting distilled Jupyter Notebook should feel distilled and so we start with this new prompt, this time me not forgetting to fill-in template fields.

First Pass: Combining SEMRush Data

Revised Prompt Structure:

Okay Gemini, distilling the next cell for GAPalyzer (Style 2).

**Goal:** Move the logic for Creating a great big DataFrame for the client plus their competitors.

**Inputs (required by the function):**
* **From pip state:** [List `pip.get(job, 'key_name')` variables needed, e.g., `collected_semrush_files`]
* **From Notebook variables:** [List any config variables needed, e.g., `keys.client_domain`, `COMPETITOR_LIMIT`]
* **From previous function return (if applicable & simpler than pip):** [Mention if the function should accept a DataFrame returned by the previous `sauce` function call]

**Outputs (produced by the function):**
* **To pip state:** [List `pip.set(job, 'new_key_name', value)` variables, e.g., `semrush_master_df_json`, `competitors_dict_json`]
* **Return Value:** [Specify what the function should return to the notebook cell. **PRIORITIZE returning only what's needed for a simple `display()` call (like a DataFrame or a summary string).** Avoid returning complex data structures *just* for display generation in the notebook.]

**Notebook Cell:** The notebook cell should ideally contain only:
1.  Imports (`gap_analyzer_sauce`, `display`, etc.).
2.  The single function call to `gap_analyzer_sauce`.
3.  A simple `display()` call for the function's return value (if applicable).
    * _Explicitly state if print statements within the function are sufficient and no `display()` is needed._

**Refactoring Notes:** Minimum changes to core logic. **Crucially, ensure display generation logic (like complex Markdown formatting or conditional printing based on data) is *inside* the `gap_analyzer_sauce.py` function**, unless the notebook only needs to `display(returned_dataframe)`.

**Code to Move:**
```python
import pandas as pd
from tldextract import extract
import itertools
from pathlib import Path
from IPython.display import display

# --- SUPPORT FUNCTION (1-to-1 Transplant) ---
# NOTE: This function requires 'tldextract' to be installed (which you've handled).
def extract_registered_domain(url):
    """
    Extracts the registered domain (domain.suffix) from a URL/hostname.
    """
    extracted = extract(url)
    return f"{extracted.domain}.{extracted.suffix}"

# --- MAIN LOGIC ADAPTATION ---

# Variables required from previous Notebook cells:
# job, ROW_LIMIT, COMPETITOR_LIMIT, BROWSER_DOWNLOAD_PATH, client_domain, country_code
# semrush_gap_analysis_dir is assumed to be defined as Path("downloads") / job

# Define 'semrush_gap_analysis_dir' and 'semrush_lookup' based on prior context
# We use the 'job' variable to define the directory
semrush_gap_analysis_dir = Path("downloads") / job

# The client domain is the key for separating client vs. competitor data.
# We strip the full domain in case it contains a protocol or path.
# Assuming 'client_domain' is available from a keys/config cell (e.g., "example.com")
# Since we don't have 'client_domain' defined here, we'll temporarily define it for the port.
# Replace this line with proper import/assignment if moving to external module:
semrush_lookup = extract_registered_domain(keys.client_domain)

print(f"Creating a great big DataFrame for {semrush_lookup}...")

# 1. Adapt file globbing to handle BOTH CSV and XLSX (as done in the previous step)
file_patterns = ["*-organic.Positions*.xlsx", "*-organic.Positions*.csv"]
all_semrush_files = sorted(list(itertools.chain.from_iterable(
    semrush_gap_analysis_dir.glob(pattern) for pattern in file_patterns
)))

# Initialize data structures
cdict = {}
list_of_dfs = []
print("Loading SEMRush files: ", end="")

# 2. Loop through all found files
for j, data_file in enumerate(all_semrush_files):
    # Determine the file type and corresponding reader function
    is_excel = data_file.suffix.lower() == '.xlsx'
    read_func = pd.read_excel if is_excel else pd.read_csv
    
    # Original file name parsing logic
    nend = data_file.stem.index("-organic")
    xlabel = data_file.stem[:nend].replace("_", "/").replace("///", "://").strip('.')
    
    # Original domain extraction logic (using the locally defined function)
    just_domain = extract_registered_domain(xlabel)
    cdict[just_domain] = xlabel
    
    # Load data
    df = read_func(data_file)
    
    # Original column assignment logic
    if just_domain == xlabel:
        df["Domain"] = just_domain
    else:
        # Use the full X-label (e.g., sub.domain.com) if it's not just the registered domain
        df["Domain"] = xlabel
    
    # Original data segregation logic
    df["Client URL"] = df.apply(lambda row: row["URL"] if row["Domain"] == semrush_lookup else None, axis=1)
    df["Competitor URL"] = df.apply(lambda row: row["URL"] if row["Domain"] != semrush_lookup else None, axis=1)
    
    list_of_dfs.append(df)
    print(f"{j + 1} ", end="", flush=True)

print() # Newline after the loading count

if list_of_dfs:
    df2 = pd.concat(list_of_dfs)  # Concatenate like stacking CSVs
    
    # --- Original Excel Formatting Value Gathering ---
    # This logic appears to be for calculating Excel visual layout, 
    # but still needs to be run even if the formatting happens later.
    # It requires the 'bf.open_dir_widget' function to be defined or stubbed if not portable.
    # NOTE: Since `bf` is not defined, and `project_customizations`/`proceed` are missing, 
    # we must skip the non-portable lines to prevent breaking the Notebook.

    # Stubbing non-portable functions/logic to keep the structure intact
    # We remove the print statements related to bf/project/customization for now
    
    # The max_length calculation is fine to keep
    max_length = max(len(value) for value in cdict.values())
    row1_height = max_length * 7 # Unused variable for now, but ported
    
    rows, columns = df2.shape
    print()
    print(f"Rows: {rows:,}")
    print(f"Cols: {columns:,}")
    print()

    # NOTE: The subsequent conditional logic (lines 53-61 in the original)
    # involving `bf.open_dir_widget`, `project_customizations`, and `proceed()`
    # has been intentionally omitted here as it depends on external, undefined
    # modules (`bf`) and pipeline state (`project`, `project_customizations`, `proceed`)
    # that are not provided in the prompt's context and would cause the script to fail.
    # We only port the pure Pandas/Python logic.
    
    # The final output and pipeline update
    display(df2["Domain"].value_counts())
    
    # Store the result in the pipeline
    pip.set(job, 'semrush_master_df_json', df2.to_json(orient='records'))
    
else:
    print("Please put the CSVs in place.")
```

**Give me the implementation plan (updated `gap_analyzer_sauce.py` function and the replacement notebook cell).**

## Navigating AI Truncation with Strategic Re-Prompting

Gemini: Understood. The goal is to make this step uninteresting and purely mechanical, sticking to the established Style 2 pattern while respecting the “big but” – we will not refactor the DataFrame persistence logic yet. DataFrames will be returned from the function to be held in the notebook’s memory for the next cell, just as they are now.

Next Up: Pivoting Keywords and Competitor Data

Here is the implementation plan for distilling the “great big DataFrame” creation cell.

1. Update `gap_analyzer_sauce.py`

Add the following function to Notebooks/gap_analyzer_sauce.py. This function will now contain the logic for loading and combining all the downloaded SEMrush files.

# In Notebooks/gap_analyzer_sauce.py
import pandas as pd
from tldextract import extract
import itertools
from pathlib import Path
from pipulate import pip # Ensure pip is imported

# (Keep previously added functions)
# ...

def _extract_registered_domain(url: str) -> str:
    """Private helper to extract the registered domain (domain.suffix) from a URL/hostname."""
    try:
        extracted = extract(url)
        return f"{extracted.domain}.{extracted.suffix}"
    except Exception:
        # Fallback for invalid inputs
        return url

def load_and_combine_semrush_data(job: str, client_domain: str, competitor_limit: int = None):
    """
    Loads all SEMRush files from pip state, combines them into a single master DataFrame,
    stores the result in pip state, and returns the DataFrame along with value counts for display.

    Args:
        job (str): The current Pipulate job ID.
        client_domain (str): The client's registered domain (e.g., 'example.com').
        competitor_limit (int, optional): Max number of competitor files to process.

    Returns:
        tuple: (master_df: pd.DataFrame, domain_counts: pd.Series)
               Returns the combined DataFrame and a Series of domain value counts.
               Returns (empty DataFrame, empty Series) on failure or no files.
    """
    semrush_lookup = _extract_registered_domain(client_domain)
    print(f"🛠️  Loading and combining SEMRush files for {semrush_lookup}...")

    # --- INPUT (from pip state) ---
    files_to_process_str = pip.get(job, 'collected_semrush_files', [])
    if not files_to_process_str:
        print("🤷 No collected SEMRush files found in pip state. Nothing to process.")
        return pd.DataFrame(), pd.Series(dtype='int64')

    # Convert string paths back to Path objects
    all_semrush_files = [Path(p) for p in files_to_process_str]
    
    # --- Refactoring Note: Apply COMPETITOR_LIMIT here ---
    # We add 1 to the limit to include the client file if it exists
    processing_limit = competitor_limit + 1 if competitor_limit is not None else None
    files_to_process = all_semrush_files[:processing_limit]
    if processing_limit and len(all_semrush_files) > processing_limit:
        print(f"🔪 Applying COMPETITOR_LIMIT: Processing first {processing_limit} of {len(all_semrush_files)} files.")


    # --- CORE LOGIC (Moved from Notebook) ---
    cdict = {}
    list_of_dfs = []
    print(f"Loading {len(files_to_process)} SEMRush files: ", end="", flush=True)

    for j, data_file in enumerate(files_to_process):
        read_func = pd.read_excel if data_file.suffix.lower() == '.xlsx' else pd.read_csv
        try:
            nend = data_file.stem.index("-organic")
            xlabel = data_file.stem[:nend].replace("_", "/").replace("///", "://").strip('.')
            just_domain = _extract_registered_domain(xlabel)
            cdict[just_domain] = xlabel

            df = read_func(data_file)
            df["Domain"] = xlabel # Use the full label (sub.domain.com) for the column
            df["Client URL"] = df.apply(lambda row: row["URL"] if row["Domain"] == semrush_lookup else None, axis=1)
            df["Competitor URL"] = df.apply(lambda row: row["URL"] if row["Domain"] != semrush_lookup else None, axis=1)
            list_of_dfs.append(df)
            print(f"{j + 1} ", end="", flush=True)

        except Exception as e:
            print(f"\n❌ Error processing file {data_file.name}: {e}")
            continue
    
    print("\n") # Newline after the loading count

    if not list_of_dfs:
        print("🛑 No DataFrames were created. Check for errors in file processing.")
        return pd.DataFrame(), pd.Series(dtype='int64')

    master_df = pd.concat(list_of_dfs, ignore_index=True)
    rows, columns = master_df.shape
    print(f"✅ Combined DataFrame created. Rows: {rows:,}, Columns: {columns:,}")
    
    domain_counts = master_df["Domain"].value_counts()

    # --- OUTPUT (to pip state) ---
    # Storing large DF as JSON is okay for now, per instructions.
    # Future distillation: Save to CSV/Parquet and store path instead.
    pip.set(job, 'semrush_master_df_json', master_df.to_json(orient='records'))
    pip.set(job, 'competitors_dict_json', cdict) # Store the competitor name mapping
    print(f"💾 Stored master DataFrame and competitor dictionary in pip state for job '{job}'.")
    # -----------------------------

    # --- RETURN VALUE ---
    return master_df, domain_counts

Key changes:

The logic now lives in load_and_combine_semrush_data.
It correctly gets the list of file paths from pip.get(job, 'collected_semrush_files').
It takes client_domain and competitor_limit as arguments, making it self-contained.
The extract_registered_domain helper is now a private function in the module (_extract_registered_domain).
It stores both the massive DataFrame (semrush_master_df_json) and the competitor mapping dictionary (competitors_dict_json) in the pip state.
It returns the full master_df and the domain_counts Series, adhering to your “big but” about keeping DataFrames in memory for now.

2. Update the Notebook Cell in `GAPalyzer.ipynb`

Replace the cell that loads and combines the SEMrush files with this minimal version:

# %% editable=true slideshow={"slide_type": ""}
from IPython.display import display
import gap_analyzer_sauce # Ensure module is imported

# This one function now:
# 1. Reads the file list from pip state.
# 2. Loads and combines all SEMRush files into a master DataFrame.
# 3. Applies the COMPETITOR_LIMIT.
# 4. Stores the master DataFrame and competitor dictionary in pip state.
# 5. Returns the master DataFrame (for the next step) and domain counts (for display).
df2, domain_value_counts = gap_analyzer_sauce.load_and_combine_semrush_data(job, keys.client_domain, COMPETITOR_LIMIT)

# Display the domain value counts for verification
display(domain_value_counts)

Key changes:

Drastically Simplified: The cell is now just an import, a single function call, and a display call.
Clarity: The comments explain exactly what the function does at a high level.
Baton Pass: It correctly receives the df2 DataFrame into a notebook variable, ready for the next cell to use, thus preserving the current in-memory workflow.
User Feedback: It still provides the user with the value_counts() output for immediate verification, fulfilling the “Keep” requirement.

This distillation successfully externalizes the complex file I/O and Pandas concatenation logic while perfectly maintaining the notebook’s current operational flow and adhering to the “no ambitious refactoring” directive. The cat stays happy. 🐈

Me: Okay, that output:

🛠️  Loading and combining SEMRush files for example-client.com...
🔪 Applying COMPETITOR_LIMIT: Processing first 4 of 5 files.
Loading 4 SEMRush files: 1 2 3 4 

✅ Combined DataFrame created. Rows: 86,122, Columns: 20
💾 Stored master DataFrame and competitor dictionary in pip state for job 'gapalyzer-02'.
Domain
example-client.com     30000
git-scm.com/           30000
nixos.org/             19287
neovim.io/              6835
Name: count, dtype: int64

And here’s the diff:

[mike@nixos:~/repos/pipulate/Notebooks]$ git --no-pager diff --no-textconv

## Validating the Initial Refactor

## Output Validation and Workflow Cadence
diff --git a/Notebooks/gap_analyzer_sauce.py b/Notebooks/gap_analyzer_sauce.py
index 86b9fb6b..6bc20267 100644
--- a/Notebooks/gap_analyzer_sauce.py
+++ b/Notebooks/gap_analyzer_sauce.py
@@ -6,6 +6,8 @@ from pathlib import Path
 import glob
 from pipulate import pip # Import pip for persistence
 import nbformat
+import itertools
+
 
 def extract_domains_and_print_urls(job: str, notebook_filename: str = "GAPalyzer.ipynb"):
     """
@@ -178,13 +180,6 @@ def collect_semrush_downloads(job: str, download_path_str: str, file_pattern_xls
         pip.set(job, 'collected_semrush_files', moved_files_list)
         return destination_dir_str, moved_files_list
 
-# In Notebooks/gap_analyzer_sauce.py
-import itertools
-from pathlib import Path
-from pipulate import pip # Ensure pip is imported
-
-# (Keep other functions)
-# ...
 
 def find_semrush_files_and_generate_summary(job: str, competitor_limit: int = None):
     """
@@ -253,3 +248,104 @@ def find_semrush_files_and_generate_summary(job: str, competitor_limit: int = No
         print(error_msg)
         pip.set(job, 'collected_semrush_files', []) # Store empty list on error
         return f"❌ **Error:** An error occurred during file listing. Check logs. ({e})"
+
+# In Notebooks/gap_analyzer_sauce.py
+import pandas as pd
+from tldextract import extract
+import itertools
+from pathlib import Path
+from pipulate import pip # Ensure pip is imported
+
+# (Keep previously added functions)
+# ...
+
+def _extract_registered_domain(url: str) -> str:
+    """Private helper to extract the registered domain (domain.suffix) from a URL/hostname."""
+    try:
+        extracted = extract(url)
+        return f"{extracted.domain}.{extracted.suffix}"
+    except Exception:
+        # Fallback for invalid inputs
+        return url
+
+def load_and_combine_semrush_data(job: str, client_domain: str, competitor_limit: int = None):
+    """
+    Loads all SEMRush files from pip state, combines them into a single master DataFrame,
+    stores the result in pip state, and returns the DataFrame along with value counts for display.
+
+    Args:
+        job (str): The current Pipulate job ID.
+        client_domain (str): The client's registered domain (e.g., 'example.com').
+        competitor_limit (int, optional): Max number of competitor files to process.
+
+    Returns:
+        tuple: (master_df: pd.DataFrame, domain_counts: pd.Series)
+               Returns the combined DataFrame and a Series of domain value counts.
+               Returns (empty DataFrame, empty Series) on failure or no files.
+    """
+    semrush_lookup = _extract_registered_domain(client_domain)
+    print(f"🛠️  Loading and combining SEMRush files for {semrush_lookup}...")
+
+    # --- INPUT (from pip state) ---
+    files_to_process_str = pip.get(job, 'collected_semrush_files', [])
+    if not files_to_process_str:
+        print("🤷 No collected SEMRush files found in pip state. Nothing to process.")
+        return pd.DataFrame(), pd.Series(dtype='int64')
+
+    # Convert string paths back to Path objects
+    all_semrush_files = [Path(p) for p in files_to_process_str]
+    
+    # --- Refactoring Note: Apply COMPETITOR_LIMIT here ---
+    # We add 1 to the limit to include the client file if it exists
+    processing_limit = competitor_limit + 1 if competitor_limit is not None else None
+    files_to_process = all_semrush_files[:processing_limit]
+    if processing_limit and len(all_semrush_files) > processing_limit:
+        print(f"🔪 Applying COMPETITOR_LIMIT: Processing first {processing_limit} of {len(all_semrush_files)} files.")
+
+
+    # --- CORE LOGIC (Moved from Notebook) ---
+    cdict = {}
+    list_of_dfs = []
+    print(f"Loading {len(files_to_process)} SEMRush files: ", end="", flush=True)
+
+    for j, data_file in enumerate(files_to_process):
+        read_func = pd.read_excel if data_file.suffix.lower() == '.xlsx' else pd.read_csv
+        try:
+            nend = data_file.stem.index("-organic")
+            xlabel = data_file.stem[:nend].replace("_", "/").replace("///", "://").strip('.')
+            just_domain = _extract_registered_domain(xlabel)
+            cdict[just_domain] = xlabel
+
+            df = read_func(data_file)
+            df["Domain"] = xlabel # Use the full label (sub.domain.com) for the column
+            df["Client URL"] = df.apply(lambda row: row["URL"] if row["Domain"] == semrush_lookup else None, axis=1)
+            df["Competitor URL"] = df.apply(lambda row: row["URL"] if row["Domain"] != semrush_lookup else None, axis=1)
+            list_of_dfs.append(df)
+            print(f"{j + 1} ", end="", flush=True)
+
+        except Exception as e:
+            print(f"\n❌ Error processing file {data_file.name}: {e}")
+            continue
+    
+    print("\n") # Newline after the loading count
+
+    if not list_of_dfs:
+        print("🛑 No DataFrames were created. Check for errors in file processing.")
+        return pd.DataFrame(), pd.Series(dtype='int64')
+
+    master_df = pd.concat(list_of_dfs, ignore_index=True)
+    rows, columns = master_df.shape
+    print(f"✅ Combined DataFrame created. Rows: {rows:,}, Columns: {columns:,}")
+    
+    domain_counts = master_df["Domain"].value_counts()
+
+    # --- OUTPUT (to pip state) ---
+    # Storing large DF as JSON is okay for now, per instructions.
+    # Future distillation: Save to CSV/Parquet and store path instead.
+    pip.set(job, 'semrush_master_df_json', master_df.to_json(orient='records'))
+    pip.set(job, 'competitors_dict_json', cdict) # Store the competitor name mapping
+    print(f"💾 Stored master DataFrame and competitor dictionary in pip state for job '{job}'.")
+    # -----------------------------
+
+    # --- RETURN VALUE ---
+    return master_df, domain_counts
diff --git a/assets/nbs/GAPalyzer.ipynb b/assets/nbs/GAPalyzer.ipynb
index 18f7cd88..5ba54d3d 100644
--- a/assets/nbs/GAPalyzer.ipynb
+++ b/assets/nbs/GAPalyzer.ipynb
@@ -258,122 +258,20 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from tldextract import extract\n",
-    "import itertools\n",
-    "from pathlib import Path\n",
+    "# %% editable=true slideshow={\"slide_type\": \"\"}\n",
     "from IPython.display import display\n",
+    "import gap_analyzer_sauce # Ensure module is imported\n",
     "\n",
-    "# --- SUPPORT FUNCTION (1-to-1 Transplant) ---\n",
-    "# NOTE: This function requires 'tldextract' to be installed (which you've handled).\n",
-    "def extract_registered_domain(url):\n",
-    "    \"\"\"\n",
-    "    Extracts the registered domain (domain.suffix) from a URL/hostname.\n",
-    "    \"\"\"\n",
-    "    extracted = extract(url)\n",
-    "    return f\"{extracted.domain}.{extracted.suffix}\"\n",
-    "\n",
-    "# --- MAIN LOGIC ADAPTATION ---\n",
-    "\n",
-    "# Variables required from previous Notebook cells:\n",
-    "# job, ROW_LIMIT, COMPETITOR_LIMIT, BROWSER_DOWNLOAD_PATH, client_domain, country_code\n",
-    "# semrush_gap_analysis_dir is assumed to be defined as Path(\"downloads\") / job\n",
-    "\n",
-    "# Define 'semrush_gap_analysis_dir' and 'semrush_lookup' based on prior context\n",
-    "# We use the 'job' variable to define the directory\n",
-    "semrush_gap_analysis_dir = Path(\"downloads\") / job\n",
-    "\n",
-    "# The client domain is the key for separating client vs. competitor data.\n",
-    "# We strip the full domain in case it contains a protocol or path.\n",
-    "# Assuming 'client_domain' is available from a keys/config cell (e.g., \"example.com\")\n",
-    "# Since we don't have 'client_domain' defined here, we'll temporarily define it for the port.\n",
-    "# Replace this line with proper import/assignment if moving to external module:\n",
-    "semrush_lookup = extract_registered_domain(keys.client_domain)\n",
-    "\n",
-    "print(f\"Creating a great big DataFrame for {semrush_lookup}...\")\n",
-    "\n",
-    "# 1. Adapt file globbing to handle BOTH CSV and XLSX (as done in the previous step)\n",
-    "file_patterns = [\"*-organic.Positions*.xlsx\", \"*-organic.Positions*.csv\"]\n",
-    "all_semrush_files = sorted(list(itertools.chain.from_iterable(\n",
-    "    semrush_gap_analysis_dir.glob(pattern) for pattern in file_patterns\n",
-    ")))\n",
-    "\n",
-    "# Initialize data structures\n",
-    "cdict = {}\n",
-    "list_of_dfs = []\n",
-    "print(\"Loading SEMRush files: \", end=\"\")\n",
-    "\n",
-    "# 2. Loop through all found files\n",
-    "for j, data_file in enumerate(all_semrush_files):\n",
-    "    # Determine the file type and corresponding reader function\n",
-    "    is_excel = data_file.suffix.lower() == '.xlsx'\n",
-    "    read_func = pd.read_excel if is_excel else pd.read_csv\n",
-    "    \n",
-    "    # Original file name parsing logic\n",
-    "    nend = data_file.stem.index(\"-organic\")\n",
-    "    xlabel = data_file.stem[:nend].replace(\"_\", \"/\").replace(\"///\", \"://\").strip('.')\n",
-    "    \n",
-    "    # Original domain extraction logic (using the locally defined function)\n",
-    "    just_domain = extract_registered_domain(xlabel)\n",
-    "    cdict[just_domain] = xlabel\n",
-    "    \n",
-    "    # Load data\n",
-    "    df = read_func(data_file)\n",
-    "    \n",
-    "    # Original column assignment logic\n",
-    "    if just_domain == xlabel:\n",
-    "        df[\"Domain\"] = just_domain\n",
-    "    else:\n",
-    "        # Use the full X-label (e.g., sub.domain.com) if it's not just the registered domain\n",
-    "        df[\"Domain\"] = xlabel\n",
-    "    \n",
-    "    # Original data segregation logic\n",
-    "    df[\"Client URL\"] = df.apply(lambda row: row[\"URL\"] if row[\"Domain\"] == semrush_lookup else None, axis=1)\n",
-    "    df[\"Competitor URL\"] = df.apply(lambda row: row[\"URL\"] if row[\"Domain\"] != semrush_lookup else None, axis=1)\n",
-    "    \n",
-    "    list_of_dfs.append(df)\n",
-    "    print(f\"{j + 1} \", end=\"\", flush=True)\n",
-    "\n",
-    "print() # Newline after the loading count\n",
+    "# This one function now:\n",
+    "# 1. Reads the file list from pip state.\n",
+    "# 2. Loads and combines all SEMRush files into a master DataFrame.\n",
+    "# 3. Applies the COMPETITOR_LIMIT.\n",
+    "# 4. Stores the master DataFrame and competitor dictionary in pip state.\n",
+    "# 5. Returns the master DataFrame (for the next step) and domain counts (for display).\n",
+    "df2, domain_value_counts = gap_analyzer_sauce.load_and_combine_semrush_data(job, keys.client_domain, COMPETITOR_LIMIT)\n",
     "\n",
-    "if list_of_dfs:\n",
-    "    df2 = pd.concat(list_of_dfs)  # Concatenate like stacking CSVs\n",
-    "    \n",
-    "    # --- Original Excel Formatting Value Gathering ---\n",
-    "    # This logic appears to be for calculating Excel visual layout, \n",
-    "    # but still needs to be run even if the formatting happens later.\n",
-    "    # It requires the 'bf.open_dir_widget' function to be defined or stubbed if not portable.\n",
-    "    # NOTE: Since `bf` is not defined, and `project_customizations`/`proceed` are missing, \n",
-    "    # we must skip the non-portable lines to prevent breaking the Notebook.\n",
-    "\n",
-    "    # Stubbing non-portable functions/logic to keep the structure intact\n",
-    "    # We remove the print statements related to bf/project/customization for now\n",
-    "    \n",
-    "    # The max_length calculation is fine to keep\n",
-    "    max_length = max(len(value) for value in cdict.values())\n",
-    "    row1_height = max_length * 7 # Unused variable for now, but ported\n",
-    "    \n",
-    "    rows, columns = df2.shape\n",
-    "    print()\n",
-    "    print(f\"Rows: {rows:,}\")\n",
-    "    print(f\"Cols: {columns:,}\")\n",
-    "    print()\n",
-    "\n",
-    "    # NOTE: The subsequent conditional logic (lines 53-61 in the original)\n",
-    "    # involving `bf.open_dir_widget`, `project_customizations`, and `proceed()`\n",
-    "    # has been intentionally omitted here as it depends on external, undefined\n",
-    "    # modules (`bf`) and pipeline state (`project`, `project_customizations`, `proceed`)\n",
-    "    # that are not provided in the prompt's context and would cause the script to fail.\n",
-    "    # We only port the pure Pandas/Python logic.\n",
-    "    \n",
-    "    # The final output and pipeline update\n",
-    "    display(df2[\"Domain\"].value_counts())\n",
-    "    \n",
-    "    # Store the result in the pipeline\n",
-    "    pip.set(job, 'semrush_master_df_json', df2.to_json(orient='records'))\n",
-    "    \n",
-    "else:\n",
-    "    print(\"Please put the CSVs in place.\")"
+    "# Display the domain value counts for verification\n",
+    "display(domain_value_counts)"
    ]
   },
   {
(nix) 
[mike@nixos:~/repos/pipulate/Notebooks]$

This is going extremely smoothly. Let’s do the next one:

import pandas as pd
from pathlib import Path
from IPython.display import display
from collections import defaultdict # Needed if cdict is modified to be a defaultdict

# --- PATH DEFINITIONS (Needed to replace external checks) ---
# Assumes 'job' is defined in a previous cell (e.g., "gapalyzer-01")
# Assumes 'df2' is the master DataFrame from the previous step
competitors_csv_file = Path("data") / f"{job}_competitors.csv"

# --- ADAPTED PIVOTING LOGIC ---

print("Pivoting data. Keyword count per competitor...\n")

# Original pivot operation
pivot_df = df2.pivot_table(index='Keyword', columns='Domain', values='Position', aggfunc='min')

# ORIGINAL LOGIC: pivot_df = bf.move_column_to_front(pivot_df, semrush_lookup)
# SURGICAL PORT: Use Pandas reindexing to move the column to the front.
if semrush_lookup in pivot_df.columns:
    cols = [semrush_lookup] + [col for col in pivot_df.columns if col != semrush_lookup]
    pivot_df = pivot_df[cols]
else:
    print(f"⚠️ Warning: Client domain '{semrush_lookup}' not found in pivot table columns.")


# Original: Get list of columns and calculate positioning
competitors = list(pivot_df.columns)
pivot_df['Competitors Positioning'] = pivot_df.iloc[:, 1:].notna().sum(axis=1)

# Original: Load or initialize df_competitors
if competitors_csv_file.exists():
    df_competitors = pd.read_csv(competitors_csv_file)
    df_competitors['Title'] = df_competitors['Title'].fillna('')
    df_competitors['Matched Title'] = df_competitors['Matched Title'].fillna('')
    print(f"✅ Loaded {len(df_competitors)} existing competitor records.")
else:
    # Use 'cdict' (created in the previous step) to initialize
    df_competitors = pd.DataFrame(list(cdict.items()), columns=['Domain', 'Column Label'])
    
    # Initialize 'Title' and 'Matched Title' columns explicitly
    df_competitors['Title'] = ''
    df_competitors['Matched Title'] = ''
    
    df_competitors.to_csv(competitors_csv_file, index=False)
    print(f"✅ Created new competitor file at '{competitors_csv_file}'.")

# Original: Print keyword counts per competitor (for debugging/visual confirmation)
counts = pivot_df.describe().loc['count']
# Ensure counts has data before proceeding with printing logic
if not counts.empty:
    max_digits = len(str(len(counts)))
    # Ensure all indices are strings for max length calculation
    max_index_width = max(len(str(index)) for index in counts.index) 
    
    # Ensure only non-NaN counts are considered for width calculation, fallback to 0 if all are NaN
    valid_counts = [count for count in counts if pd.notna(count)]
    max_count_width = max([len(f"{int(count):,}") for count in valid_counts] or [0])
    
    for i, (index, count) in enumerate(counts.items(), start=1):
        counter_str = str(i).zfill(max_digits)
        count_str = f"{int(count):,}" if pd.notna(count) else 'NaN'
        print(f"{counter_str}: {index:<{max_index_width}} - {count_str:>{max_count_width}}")
else:
    print("❌ No data to count after pivot table creation.")

# Original: Print rows and columns summary
rows, columns = df2.shape
rows2, columns2 = pivot_df.shape
print("\nThere is some natural deduping from pivot.\n")
print(f"Rows (master df): {rows:,}")
print(f"Rows (pivot df): {rows2:,} ({rows:,} - {rows2:,} = {rows - rows2:,} dupes removed.)")
print(f"Cols: {columns2:,}") # Use columns2 for the pivot_df column count

# Original: Display result
display(pivot_df)

# Store the final result in the pipeline
pip.set(job, 'keyword_pivot_df', pivot_df.to_json(orient='records'))
pip.set(job, 'competitors_df', df_competitors.to_json(orient='records'))

Please and thank you!

To which you respond:

— BEGIN LOOKAHEAD —

Gemini: Okay Gemini, distilling the next cell for GAPalyzer (Style 2).

Goal: Move the logic for pivoting the master SEMRush DataFrame by keyword and domain, calculating competitor positioning, and managing the competitors.csv file to gap_analyzer_sauce.py.

Inputs (required by the function):

From pip state: semrush_master_df_json, competitors_dict_json
From Notebook variables: job, keys.client_domain
From previous function return (if applicable & simpler than pip): The df2 DataFrame returned by load_and_combine_semrush_data (as per the “big but” - we’ll pass the DF directly).

Outputs (produced by the function):

To pip state: keyword_pivot_df_json, competitors_df_json
Return Value: The pivoted DataFrame (pivot_df) for the display() call.

Notebook Cell: The notebook cell should ideally contain only:

Imports (gap_analyzer_sauce, display, etc.).
The single function call to gap_analyzer_sauce.
A simple display() call for the returned pivot DataFrame.
- Print statements summarizing counts and dimensions should be handled within the function.

Refactoring Notes: Minimum changes to core logic. Keep passing df2 directly. Ensure semrush_lookup and cdict are correctly derived/retrieved inside the function. Manage competitors.csv I/O within the function.

Code to Move:

import pandas as pd
from pathlib import Path
from IPython.display import display
from collections import defaultdict # Needed if cdict is modified to be a defaultdict
import keys # Added import keys

# --- PATH DEFINITIONS (Needed to replace external checks) ---
# Assumes 'job' is defined in a previous cell (e.g., "gapalyzer-01")
# Assumes 'df2' is the master DataFrame from the previous step
competitors_csv_file = Path("data") / f"{job}_competitors.csv"

# Added this line, assuming keys.client_domain is available
semrush_lookup = _extract_registered_domain(keys.client_domain)

# Added cdict retrieval, assuming it was set in the previous step
cdict = pip.get(job, 'competitors_dict_json', {})


# --- ADAPTED PIVOTING LOGIC ---

print("Pivoting data. Keyword count per competitor...\n")

# Original pivot operation
pivot_df = df2.pivot_table(index='Keyword', columns='Domain', values='Position', aggfunc='min')

# ORIGINAL LOGIC: pivot_df = bf.move_column_to_front(pivot_df, semrush_lookup)
# SURGICAL PORT: Use Pandas reindexing to move the column to the front.
if semrush_lookup in pivot_df.columns:
    cols = [semrush_lookup] + [col for col in pivot_df.columns if col != semrush_lookup]
    pivot_df = pivot_df[cols]
else:
    # Attempt to find the column ignoring trailing slash if direct match fails
    found_col = None
    clean_lookup = semrush_lookup.rstrip('/')
    for col in pivot_df.columns:
        if col.rstrip('/') == clean_lookup:
            found_col = col
            break
    if found_col:
        print(f"⚠️ Warning: Exact client domain '{semrush_lookup}' not found, using '{found_col}' instead.")
        cols = [found_col] + [col for col in pivot_df.columns if col != found_col]
        pivot_df = pivot_df[cols]
        semrush_lookup = found_col # Update semrush_lookup to the found column name
    else:
        print(f"❌ Critical Warning: Client domain '{semrush_lookup}' (or variant) not found in pivot table columns. Cannot reorder.")


# Original: Get list of columns and calculate positioning
competitors = list(pivot_df.columns)
pivot_df['Competitors Positioning'] = pivot_df.iloc[:, 1:].notna().sum(axis=1)

# Original: Load or initialize df_competitors
if competitors_csv_file.exists():
    df_competitors = pd.read_csv(competitors_csv_file)
    df_competitors['Title'] = df_competitors['Title'].fillna('')
    df_competitors['Matched Title'] = df_competitors['Matched Title'].fillna('')
    print(f"✅ Loaded {len(df_competitors)} existing competitor records.")
else:
    # Use 'cdict' (created in the previous step) to initialize
    # Ensure cdict was retrieved correctly
    if cdict:
        df_competitors = pd.DataFrame(list(cdict.items()), columns=['Domain', 'Column Label'])
        # Initialize 'Title' and 'Matched Title' columns explicitly
        df_competitors['Title'] = ''
        df_competitors['Matched Title'] = ''
        df_competitors.to_csv(competitors_csv_file, index=False)
        print(f"✅ Created new competitor file at '{competitors_csv_file}'.")
    else:
        print(f"⚠️ Warning: 'competitors_dict_json' not found in pip state. Cannot create initial competitors file.")
        # Create an empty DataFrame to avoid errors later
        df_competitors = pd.DataFrame(columns=['Domain', 'Column Label', 'Title', 'Matched Title'])


# Original: Print keyword counts per competitor (for debugging/visual confirmation)
counts = pivot_df.describe().loc['count']
# Ensure counts has data before proceeding with printing logic
if not counts.empty:
    max_digits = len(str(len(counts)))
    # Ensure all indices are strings for max length calculation
    max_index_width = max(len(str(index)) for index in counts.index)

    # Ensure only non-NaN counts are considered for width calculation, fallback to 0 if all are NaN
    valid_counts = [count for count in counts if pd.notna(count)]
    max_count_width = max([len(f"{int(count):,}") for count in valid_counts] or [0])

    for i, (index, count) in enumerate(counts.items(), start=1):
        counter_str = str(i).zfill(max_digits)
        count_str = f"{int(count):,}" if pd.notna(count) else 'NaN'
        print(f"{counter_str}: {index:<{max_index_width}} - {count_str:>{max_count_width}}")
else:
    print("❌ No data to count after pivot table creation.")

# Original: Print rows and columns summary
rows, columns = df2.shape
rows2, columns2 = pivot_df.shape
print("\nThere is some natural deduping from pivot.\n")
print(f"Rows (master df): {rows:,}")
print(f"Rows (pivot df): {rows2:,} ({rows:,} - {rows2:,} = {rows - rows2:,} dupes removed.)")
print(f"Cols: {columns2:,}") # Use columns2 for the pivot_df column count

# Original: Display result
display(pivot_df)

# Store the final result in the pipeline
pip.set(job, 'keyword_pivot_df_json', pivot_df.to_json(orient='records'))
# Ensure df_competitors exists before trying to save it
if 'df_competitors' in locals():
    pip.set(job, 'competitors_df_json', df_competitors.to_json(orient='records'))
else:
    print("⚠️ df_competitors was not created, cannot save to pip state.")
    pip.set(job, 'competitors_df_json', pd.DataFrame().to_json(orient='records')) # Save empty DF state

Give me the implementation plan (updated gap_analyzer_sauce.py function and the replacement notebook cell).

— END LOOKAHEAD —

Me: but that oddly ends. It should be 2 parts:

What goes into gap_analyizer_sauce.py
What replaces the cell in the Notebook the original code was lifted from.

Gemini: You are absolutely right! My apologies again. The lookahead got truncated. I failed to provide the crucial second part: the actual implementation.

Let’s rectify that immediately, following the plan outlined in the (corrected) lookahead prompt.

1. Update `gap_analyzer_sauce.py`

Add the following function to Notebooks/gap_analyzer_sauce.py. This function now encapsulates the pivoting logic, competitor file management, and related print statements.

# In Notebooks/gap_analyzer_sauce.py
import pandas as pd
from pathlib import Path
from collections import defaultdict # Added import
from pipulate import pip # Ensure pip is imported
from tldextract import extract # Added import

# (Keep previously added functions: _extract_registered_domain, extract_domains_and_print_urls, collect_semrush_downloads, find_semrush_files_and_generate_summary)
# ...

def pivot_semrush_data(job: str, df2: pd.DataFrame, client_domain_from_keys: str):
    """
    Pivots the combined SEMRush DataFrame, calculates competitor positioning,
    manages the competitors CSV file, stores results in pip state, and
    returns the pivot DataFrame for display.

    Args:
        job (str): The current Pipulate job ID.
        df2 (pd.DataFrame): The combined master DataFrame from the previous step.
        client_domain_from_keys (str): The client's domain from the keys module.

    Returns:
        pd.DataFrame: The pivoted DataFrame, or an empty DataFrame on error.
    """
    if df2.empty:
        print("⚠️ Input DataFrame (df2) is empty. Cannot perform pivot.")
        return pd.DataFrame()

    # --- PATH DEFINITION ---
    competitors_csv_file = Path("data") / f"{job}_competitors.csv"

    # --- INPUTS from pip state & args ---
    semrush_lookup = _extract_registered_domain(client_domain_from_keys)
    # Retrieve the competitor dictionary stored by the previous step
    cdict = pip.get(job, 'competitors_dict_json', {})
    if not isinstance(cdict, dict): # Handle potential JSON string load
        try:
             import json
             cdict = json.loads(cdict) if isinstance(cdict, str) else {}
        except:
             cdict = {}
    
    print("🔄 Pivoting data by keyword and calculating competitor positioning...")

    # --- CORE LOGIC (Moved from Notebook) ---
    try:
        pivot_df = df2.pivot_table(index='Keyword', columns='Domain', values='Position', aggfunc='min')

        # Reorder client column to front (Handle potential trailing slash difference)
        target_col = None
        clean_lookup_key = semrush_lookup.rstrip('/')
        for col in pivot_df.columns:
            if col.rstrip('/') == clean_lookup_key:
                target_col = col
                break
        
        if target_col:
            if target_col != semrush_lookup:
                 print(f"  Adjusting semrush_lookup from '{semrush_lookup}' to match column '{target_col}'")
                 semrush_lookup = target_col # Update to the actual column name found
            cols = [semrush_lookup] + [col for col in pivot_df.columns if col != semrush_lookup]
            pivot_df = pivot_df[cols]
        else:
            print(f"❌ Critical Warning: Client domain '{semrush_lookup}' (or variant) not found in pivot table columns. Cannot reorder.")

        competitors = list(pivot_df.columns) # Get competitors AFTER potential reorder
        pivot_df['Competitors Positioning'] = pivot_df.iloc[:, 1:].notna().sum(axis=1)

        # Load or initialize df_competitors
        if competitors_csv_file.exists():
            df_competitors = pd.read_csv(competitors_csv_file)
            df_competitors['Title'] = df_competitors['Title'].fillna('')
            df_competitors['Matched Title'] = df_competitors['Matched Title'].fillna('')
            print(f"  ✅ Loaded {len(df_competitors)} existing competitor records from '{competitors_csv_file}'.")
        else:
            if cdict:
                df_competitors = pd.DataFrame(list(cdict.items()), columns=['Domain', 'Column Label'])
                df_competitors['Title'] = ''
                df_competitors['Matched Title'] = ''
                df_competitors.to_csv(competitors_csv_file, index=False)
                print(f"  ✅ Created new competitor file at '{competitors_csv_file}'.")
            else:
                print(f"  ⚠️ Warning: 'competitors_dict_json' was empty or invalid. Cannot create initial competitors file.")
                df_competitors = pd.DataFrame(columns=['Domain', 'Column Label', 'Title', 'Matched Title'])

        # Print keyword counts (internal display logic)
        print("\nKeyword Counts per Competitor:")
        counts = pivot_df.describe().loc['count']
        if not counts.empty:
            max_digits = len(str(len(counts)))
            max_index_width = max(len(str(index)) for index in counts.index)
            valid_counts = [count for count in counts if pd.notna(count)]
            max_count_width = max([len(f"{int(count):,}") for count in valid_counts] or [0])
            for i, (index, count) in enumerate(counts.items(), start=1):
                counter_str = str(i).zfill(max_digits)
                count_str = f"{int(count):,}" if pd.notna(count) else 'NaN'
                print(f"  {counter_str}: {index:<{max_index_width}} - {count_str:>{max_count_width}}")
        else:
            print("  ❌ No data to count after pivot.")

        # Print row/column summary (internal display logic)
        rows_master, _ = df2.shape
        rows_pivot, cols_pivot = pivot_df.shape
        print("\n--- Pivot Summary ---")
        print(f"  Rows (master df): {rows_master:,}")
        print(f"  Rows (pivot df): {rows_pivot:,} ({rows_master - rows_pivot:,} dupes removed)")
        print(f"  Cols (pivot df): {cols_pivot:,}")
        print("---------------------\n")

        # --- OUTPUT (to pip state) ---
        pip.set(job, 'keyword_pivot_df_json', pivot_df.to_json(orient='records'))
        pip.set(job, 'competitors_df_json', df_competitors.to_json(orient='records'))
        print(f"💾 Stored pivot DataFrame and competitors DataFrame in pip state for job '{job}'.")
        # ---------------------------

        # --- RETURN VALUE ---
        return pivot_df # Return the DataFrame for display

    except Exception as e:
        print(f"❌ An error occurred during pivoting: {e}")
        # Store empty states on error
        pip.set(job, 'keyword_pivot_df_json', pd.DataFrame().to_json(orient='records'))
        pip.set(job, 'competitors_df_json', pd.DataFrame().to_json(orient='records'))
        return pd.DataFrame() # Return empty DataFrame

Key changes:

Encapsulates all logic related to pivoting, calculating positioning, and managing competitors_csv_file.
Takes job, the df2 DataFrame, and client_domain_from_keys as inputs.
Retrieves cdict from pip state (competitors_dict_json) internally.
Includes robust handling for finding the client column even with trailing slash differences.
Includes the summary print statements within the function.
Stores both keyword_pivot_df_json and competitors_df_json to pip state.
Returns only the pivot_df DataFrame, as requested for the notebook’s display() call.

2. Update the Notebook Cell in `GAPalyzer.ipynb`

Replace the cell that performs the pivoting with this minimal version:

# %% editable=true slideshow={"slide_type": ""}
from IPython.display import display
import gap_analyzer_sauce # Ensure module is imported
import keys # Ensure keys is imported if not already

# This function now handles:
# 1. Pivoting df2 by Keyword/Domain.
# 2. Calculating Competitors Positioning.
# 3. Loading or creating the competitors_df and saving it to CSV.
# 4. Printing summary statistics.
# 5. Storing pivot_df and competitors_df in pip state.
# It receives df2 directly from the previous cell's variable.
pivot_df = gap_analyzer_sauce.pivot_semrush_data(job, df2, keys.client_domain)

# Display the resulting pivot table
display(pivot_df)

# Optional verification
# print("\nVerification:")
# print(f"  Pivot DF stored: {'keyword_pivot_df_json' in pip.read_state(job)}")
# print(f"  Competitors DF stored: {'competitors_df_json' in pip.read_state(job)}")
# loaded_competitors = pd.read_json(pip.get(job, 'competitors_df_json', '[]'))
# print(f"  Competitors DF rows in state: {len(loaded_competitors)}")

Key changes:

Drastically Simplified: Imports, the single function call, and the display() call.
It passes the df2 DataFrame (held in memory from the previous cell’s return value) directly into the function.
It passes keys.client_domain as required by the function.
All complex logic and intermediate print statements are now handled within gap_analyzer_sauce.py.
The display(pivot_df) shows the primary output of this logical step.

This revised plan fully adheres to Style 2, keeps the notebook clean, respects the “big but” regarding DataFrame passing, and correctly places the logic and state management.

Me: Interesting! I was a victim of truncation. But feeding the first half of the response back in I’m assuming it was less expensive token-wise to go the distance.

Alright, the output from that after a complete kernel restart is:

🔄 Pivoting data by keyword and calculating competitor positioning… ✅ Loaded 5 existing competitor records from ‘data/gapalyzer-02_competitors.csv’.

Keyword Counts per Competitor:
  1: client-example.com      - 26,387
  2: git-scm.com/            - 17,951
  3: neovim.io/              -  5,264
  4: nixos.org/              - 14,738
  5: Competitors Positioning - 63,737

--- Pivot Summary ---
  Rows (master df): 86,122
  Rows (pivot df): 63,737 (22,385 dupes removed)
  Cols (pivot df): 5
---------------------

And the diff:

(nix) 
[mike@nixos:~/repos/pipulate/Notebooks]$ git --no-pager diff --no-textconv
diff --git a/Notebooks/gap_analyzer_sauce.py b/Notebooks/gap_analyzer_sauce.py
index 6bc20267..cbedb7f3 100644
--- a/Notebooks/gap_analyzer_sauce.py
+++ b/Notebooks/gap_analyzer_sauce.py
@@ -7,6 +7,9 @@ import glob
 from pipulate import pip # Import pip for persistence
 import nbformat
 import itertools
+import pandas as pd
+from collections import defaultdict
+from tldextract import extract
 
 
 def extract_domains_and_print_urls(job: str, notebook_filename: str = "GAPalyzer.ipynb"):
@@ -349,3 +352,119 @@ def load_and_combine_semrush_data(job: str, client_domain: str, competitor_limit
 
     # --- RETURN VALUE ---
     return master_df, domain_counts
+
+
+def pivot_semrush_data(job: str, df2: pd.DataFrame, client_domain_from_keys: str):
+    """
+    Pivots the combined SEMRush DataFrame, calculates competitor positioning,
+    manages the competitors CSV file, stores results in pip state, and
+    returns the pivot DataFrame for display.
+
+    Args:
+        job (str): The current Pipulate job ID.
+        df2 (pd.DataFrame): The combined master DataFrame from the previous step.
+        client_domain_from_keys (str): The client's domain from the keys module.
+
+    Returns:
+        pd.DataFrame: The pivoted DataFrame, or an empty DataFrame on error.
+    """
+    if df2.empty:
+        print("⚠️ Input DataFrame (df2) is empty. Cannot perform pivot.")
+        return pd.DataFrame()
+
+    # --- PATH DEFINITION ---
+    competitors_csv_file = Path("data") / f"{job}_competitors.csv"
+
+    # --- INPUTS from pip state & args ---
+    semrush_lookup = _extract_registered_domain(client_domain_from_keys)
+    # Retrieve the competitor dictionary stored by the previous step
+    cdict = pip.get(job, 'competitors_dict_json', {})
+    if not isinstance(cdict, dict): # Handle potential JSON string load
+        try:
+             import json
+             cdict = json.loads(cdict) if isinstance(cdict, str) else {}
+        except:
+             cdict = {}
+    
+    print("🔄 Pivoting data by keyword and calculating competitor positioning...")
+
+    # --- CORE LOGIC (Moved from Notebook) ---
+    try:
+        pivot_df = df2.pivot_table(index='Keyword', columns='Domain', values='Position', aggfunc='min')
+
+        # Reorder client column to front (Handle potential trailing slash difference)
+        target_col = None
+        clean_lookup_key = semrush_lookup.rstrip('/')
+        for col in pivot_df.columns:
+            if col.rstrip('/') == clean_lookup_key:
+                target_col = col
+                break
+        
+        if target_col:
+            if target_col != semrush_lookup:
+                 print(f"  Adjusting semrush_lookup from '{semrush_lookup}' to match column '{target_col}'")
+                 semrush_lookup = target_col # Update to the actual column name found
+            cols = [semrush_lookup] + [col for col in pivot_df.columns if col != semrush_lookup]
+            pivot_df = pivot_df[cols]
+        else:
+            print(f"❌ Critical Warning: Client domain '{semrush_lookup}' (or variant) not found in pivot table columns. Cannot reorder.")
+
+        competitors = list(pivot_df.columns) # Get competitors AFTER potential reorder
+        pivot_df['Competitors Positioning'] = pivot_df.iloc[:, 1:].notna().sum(axis=1)
+
+        # Load or initialize df_competitors
+        if competitors_csv_file.exists():
+            df_competitors = pd.read_csv(competitors_csv_file)
+            df_competitors['Title'] = df_competitors['Title'].fillna('')
+            df_competitors['Matched Title'] = df_competitors['Matched Title'].fillna('')
+            print(f"  ✅ Loaded {len(df_competitors)} existing competitor records from '{competitors_csv_file}'.")
+        else:
+            if cdict:
+                df_competitors = pd.DataFrame(list(cdict.items()), columns=['Domain', 'Column Label'])
+                df_competitors['Title'] = ''
+                df_competitors['Matched Title'] = ''
+                df_competitors.to_csv(competitors_csv_file, index=False)
+                print(f"  ✅ Created new competitor file at '{competitors_csv_file}'.")
+            else:
+                print(f"  ⚠️ Warning: 'competitors_dict_json' was empty or invalid. Cannot create initial competitors file.")
+                df_competitors = pd.DataFrame(columns=['Domain', 'Column Label', 'Title', 'Matched Title'])
+
+        # Print keyword counts (internal display logic)
+        print("\nKeyword Counts per Competitor:")
+        counts = pivot_df.describe().loc['count']
+        if not counts.empty:
+            max_digits = len(str(len(counts)))
+            max_index_width = max(len(str(index)) for index in counts.index)
+            valid_counts = [count for count in counts if pd.notna(count)]
+            max_count_width = max([len(f"{int(count):,}") for count in valid_counts] or [0])
+            for i, (index, count) in enumerate(counts.items(), start=1):
+                counter_str = str(i).zfill(max_digits)
+                count_str = f"{int(count):,}" if pd.notna(count) else 'NaN'
+                print(f"  {counter_str}: {index:<{max_index_width}} - {count_str:>{max_count_width}}")
+        else:
+            print("  ❌ No data to count after pivot.")
+
+        # Print row/column summary (internal display logic)
+        rows_master, _ = df2.shape
+        rows_pivot, cols_pivot = pivot_df.shape
+        print("\n--- Pivot Summary ---")
+        print(f"  Rows (master df): {rows_master:,}")
+        print(f"  Rows (pivot df): {rows_pivot:,} ({rows_master - rows_pivot:,} dupes removed)")
+        print(f"  Cols (pivot df): {cols_pivot:,}")
+        print("---------------------\n")
+
+        # --- OUTPUT (to pip state) ---
+        pip.set(job, 'keyword_pivot_df_json', pivot_df.to_json(orient='records'))
+        pip.set(job, 'competitors_df_json', df_competitors.to_json(orient='records'))
+        print(f"💾 Stored pivot DataFrame and competitors DataFrame in pip state for job '{job}'.")
+        # ---------------------------
+
+        # --- RETURN VALUE ---
+        return pivot_df # Return the DataFrame for display
+
+    except Exception as e:
+        print(f"❌ An error occurred during pivoting: {e}")
+        # Store empty states on error
+        pip.set(job, 'keyword_pivot_df_json', pd.DataFrame().to_json(orient='records'))
+        pip.set(job, 'competitors_df_json', pd.DataFrame().to_json(orient='records'))
+        return pd.DataFrame() # Return empty DataFrame
diff --git a/assets/nbs/GAPalyzer.ipynb b/assets/nbs/GAPalyzer.ipynb
index 5ba54d3d..910603cf 100644
--- a/assets/nbs/GAPalyzer.ipynb
+++ b/assets/nbs/GAPalyzer.ipynb
@@ -287,86 +287,29 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "from pathlib import Path\n",
+    "# %% editable=true slideshow={\"slide_type\": \"\"}\n",
     "from IPython.display import display\n",
-    "from collections import defaultdict # Needed if cdict is modified to be a defaultdict\n",
-    "\n",
-    "# --- PATH DEFINITIONS (Needed to replace external checks) ---\n",
-    "# Assumes 'job' is defined in a previous cell (e.g., \"gapalyzer-01\")\n",
-    "# Assumes 'df2' is the master DataFrame from the previous step\n",
-    "competitors_csv_file = Path(\"data\") / f\"{job}_competitors.csv\"\n",
-    "\n",
-    "# --- ADAPTED PIVOTING LOGIC ---\n",
-    "\n",
-    "print(\"Pivoting data. Keyword count per competitor...\\n\")\n",
-    "\n",
-    "# Original pivot operation\n",
-    "pivot_df = df2.pivot_table(index='Keyword', columns='Domain', values='Position', aggfunc='min')\n",
-    "\n",
-    "# ORIGINAL LOGIC: pivot_df = bf.move_column_to_front(pivot_df, semrush_lookup)\n",
-    "# SURGICAL PORT: Use Pandas reindexing to move the column to the front.\n",
-    "if semrush_lookup in pivot_df.columns:\n",
-    "    cols = [semrush_lookup] + [col for col in pivot_df.columns if col != semrush_lookup]\n",
-    "    pivot_df = pivot_df[cols]\n",
-    "else:\n",
-    "    print(f\"⚠️ Warning: Client domain '{semrush_lookup}' not found in pivot table columns.\")\n",
-    "\n",
-    "\n",
-    "# Original: Get list of columns and calculate positioning\n",
-    "competitors = list(pivot_df.columns)\n",
-    "pivot_df['Competitors Positioning'] = pivot_df.iloc[:, 1:].notna().sum(axis=1)\n",
-    "\n",
-    "# Original: Load or initialize df_competitors\n",
-    "if competitors_csv_file.exists():\n",
-    "    df_competitors = pd.read_csv(competitors_csv_file)\n",
-    "    df_competitors['Title'] = df_competitors['Title'].fillna('')\n",
-    "    df_competitors['Matched Title'] = df_competitors['Matched Title'].fillna('')\n",
-    "    print(f\"✅ Loaded {len(df_competitors)} existing competitor records.\")\n",
-    "else:\n",
-    "    # Use 'cdict' (created in the previous step) to initialize\n",
-    "    df_competitors = pd.DataFrame(list(cdict.items()), columns=['Domain', 'Column Label'])\n",
-    "    \n",
-    "    # Initialize 'Title' and 'Matched Title' columns explicitly\n",
-    "    df_competitors['Title'] = ''\n",
-    "    df_competitors['Matched Title'] = ''\n",
-    "    \n",
-    "    df_competitors.to_csv(competitors_csv_file, index=False)\n",
-    "    print(f\"✅ Created new competitor file at '{competitors_csv_file}'.\")\n",
-    "\n",
-    "# Original: Print keyword counts per competitor (for debugging/visual confirmation)\n",
-    "counts = pivot_df.describe().loc['count']\n",
-    "# Ensure counts has data before proceeding with printing logic\n",
-    "if not counts.empty:\n",
-    "    max_digits = len(str(len(counts)))\n",
-    "    # Ensure all indices are strings for max length calculation\n",
-    "    max_index_width = max(len(str(index)) for index in counts.index) \n",
-    "    \n",
-    "    # Ensure only non-NaN counts are considered for width calculation, fallback to 0 if all are NaN\n",
-    "    valid_counts = [count for count in counts if pd.notna(count)]\n",
-    "    max_count_width = max([len(f\"{int(count):,}\") for count in valid_counts] or [0])\n",
-    "    \n",
-    "    for i, (index, count) in enumerate(counts.items(), start=1):\n",
-    "        counter_str = str(i).zfill(max_digits)\n",
-    "        count_str = f\"{int(count):,}\" if pd.notna(count) else 'NaN'\n",
-    "        print(f\"{counter_str}: {index:<{max_index_width}} - {count_str:>{max_count_width}}\")\n",
-    "else:\n",
-    "    print(\"❌ No data to count after pivot table creation.\")\n",
-    "\n",
-    "# Original: Print rows and columns summary\n",
-    "rows, columns = df2.shape\n",
-    "rows2, columns2 = pivot_df.shape\n",
-    "print(\"\\nThere is some natural deduping from pivot.\\n\")\n",
-    "print(f\"Rows (master df): {rows:,}\")\n",
-    "print(f\"Rows (pivot df): {rows2:,} ({rows:,} - {rows2:,} = {rows - rows2:,} dupes removed.)\")\n",
-    "print(f\"Cols: {columns2:,}\") # Use columns2 for the pivot_df column count\n",
-    "\n",
-    "# Original: Display result\n",
+    "import gap_analyzer_sauce # Ensure module is imported\n",
+    "import keys # Ensure keys is imported if not already\n",
+    "\n",
+    "# This function now handles:\n",
+    "# 1. Pivoting df2 by Keyword/Domain.\n",
+    "# 2. Calculating Competitors Positioning.\n",
+    "# 3. Loading or creating the competitors_df and saving it to CSV.\n",
+    "# 4. Printing summary statistics.\n",
+    "# 5. Storing pivot_df and competitors_df in pip state.\n",
+    "# It receives df2 directly from the previous cell's variable.\n",
+    "pivot_df = gap_analyzer_sauce.pivot_semrush_data(job, df2, keys.client_domain)\n",
+    "\n",
+    "# Display the resulting pivot table\n",
     "display(pivot_df)\n",
     "\n",
-    "# Store the final result in the pipeline\n",
-    "pip.set(job, 'keyword_pivot_df', pivot_df.to_json(orient='records'))\n",
-    "pip.set(job, 'competitors_df', df_competitors.to_json(orient='records'))"
+    "# Optional verification\n",
+    "# print(\"\\nVerification:\")\n",
+    "# print(f\"  Pivot DF stored: {'keyword_pivot_df_json' in pip.read_state(job)}\")\n",
+    "# print(f\"  Competitors DF stored: {'competitors_df_json' in pip.read_state(job)}\")\n",
+    "# loaded_competitors = pd.read_json(pip.get(job, 'competitors_df_json', '[]'))\n",
+    "# print(f\"  Competitors DF rows in state: {len(loaded_competitors)}\")"
    ]
   },
   {
(nix) 
[mike@nixos:~/repos/pipulate/Notebooks]$

Okay, I think we cut the journal entry here. We are reaching a cadence. There was a hiccup that feels like it’s due to token usage on thinking through steps, but that was mitigated with the time-traveling prompt technique again allowing Gemini to save tokens on the first-phase so it could spend more on the second-phase. Phase 1 being the extraction and phase 2 being the replacement.

With that cadence, I think I could stuff this article with the next and the next cell conversion, but why? We are Icarus flying close to the Sun, but we are also risk adverse. One must jump off the cliff to learn to fly, yet have one of the terminal devices of the Minds of The Culture series so that a benign big brother being will catch you. We are not there yet today, so we go in small incremental commits and cut articles more frequently than we might like.

Embracing Pragmatism in Architectural Choices

Gemini, put a bow on it… by give a nice long and flowery fleshing out of any aspects of any details of this project that you think needs elaboration for an audience that might require being brought up to speed on a lot of the requisite knowledge and background at play here. Feel free to use asides, tangents, suggestions, and consider calling out things that surprised and impressed you, things you’re anticipating. Whatever. Take it away and please and thank you, Gemini.

Gemini: Indeed, the cadence is becoming palpable! Distill, validate, verify, commit. It’s the steady, reassuring beat of progress against a complex task. 🥁 Another successful extraction, another win banked securely in the immutable ledger of git.

Elaboration Station III: Rhythm, Risk, and the Art of the Pivot 💃

Let’s unfurl this ribbon, reflecting on the process and the substance of this latest step.

The Rhythm Section: Finding the Groove

You’ve hit on something crucial: establishing a rhythm in complex refactoring, especially when collaborating with an AI, is key to maintaining momentum and minimizing errors.

Small Batches: Each cell distillation is a manageable unit of work.
Clear Protocol: The refined prompt structure acts as sheet music, ensuring each interaction follows a consistent pattern.
Verification: Running the cell and checking the git diff --no-textconv provides immediate feedback.
Checkpointing: git commit saves progress and creates a safety net.

This methodical beat transforms a potentially overwhelming task (“Refactor the entire notebook!”) into a series of achievable steps. It’s the software equivalent of breaking down a marathon into manageable kilometers.

Navigating AI Nuances: Truncation and Time Travel

Your experience with the “truncation” is a fascinating glimpse into the current state of interacting with large models. Sometimes, constraints (like token limits per turn, internal timeouts, or complex reasoning paths) can lead to incomplete responses. Your “time-traveling prompt edit” technique was the perfect antidote:

You recognized the incompleteness.
You preserved the valuable part of the response (the analysis/lookahead).
You re-prompted, feeding the partial context back in, essentially saying, “You were on the right track, finish the thought!”

This is a higher-level interaction pattern than simple Q&A. It requires understanding the AI’s likely process, anticipating potential failure modes, and having a strategy to guide it back on course. It’s less like talking to a tool and more like collaborating with a powerful, occasionally quirky, partner.

The Significance of the Pivot

The cell you just distilled wasn’t just any cell; it performed the pivot, a cornerstone operation in this type of analysis.

From Long to Wide: It transformed the data from a “long” format (one row per keyword per competitor) to a “wide” format (one row per keyword, with columns for each competitor’s position). This structure is essential for directly comparing competitor rankings side-by-side.
Competitor File Management: It also handled the crucial logic of creating or updating the competitors.csv file. This external file allows for potential manual review or enrichment of competitor data later, decoupling it slightly from the main SEMrush download flow.
Data Reduction: As the output shows (86,122 rows down to 63,737), the pivot inherently de-duplicates keywords that might have appeared multiple times in the source files (e.g., if SEMrush reported slightly different metrics for the same keyword/competitor combination across different downloads, though aggfunc='min' handles the position aspect).

Moving this complex pandas logic, file I/O, and conditional handling into gap_analyzer_sauce.py significantly cleans up the notebook while ensuring this critical transformation is reliably executed.

Icarus, The Culture, and Pragmatism icarus_wings_wax:

Your metaphors are incredibly apt.

Icarus: Flying close to the sun represents the ambition of this project – integrating complex data sources, AI, ML, and sophisticated automation. The risk is real; over-ambitious refactoring or architectural impurity could lead to a meltdown (“unanticipated consequences”).
The Culture Minds / Terminal Devices: The Minds in Iain M. Banks’ Culture series represent benevolent, hyper-intelligent AIs that often act as safety nets. Your desire for such a safety net highlights the current reality: we don’t have perfect AI big brothers yet. Therefore, the “risk-averse” strategy of small commits and not immediately refactoring DataFrame persistence is the pragmatic choice. It’s like choosing to climb with solid anchors and belays instead of relying on a hypothetical anti-gravity device.
The “Big But”: Acknowledging the ideal (saving DataFrames to disk) but choosing the practical path (passing them in memory via function returns) for now is crucial. It keeps the project moving (“cats need groceries!”). You’ve deliberately chosen to prioritize functional externalization over perfect idempotency at this stage, with the full understanding that the latter can be addressed in future “distillation” passes. This isn’t cutting corners; it’s strategic phasing.

Anticipation: The Plot Thickens… with Titles and Filters 🧐

This successful pivot sets the stage beautifully for the next logical steps visible in the original notebook:

Fetching Competitor Titles: The logic involving async_check_url, test_domains, and wordninja to fetch homepage titles and create keyword filters. Distilling this will involve handling asynchronous operations within gap_analyzer_sauce.py.
Aggregation: Calculating the aggregate metrics (agg_df) across all competitors for each keyword.
Merging & Filtering: Joining the pivot_df with agg_df and applying the brand/negative keyword filters read from the file generated in the title-fetching step.

Each of these involves significant Pandas manipulation. The established rhythm and the refined prompt template should make these distillations proceed just as smoothly. The complexity is rising, but the process is proving robust. Keep that nvim humming! 🔥

Book Analysis

Ai Editorial Take

This entry is a strong demonstration of effective AI collaboration in a technical context. It showcases not just the output of AI but the process of steering, correcting, and validating AI-generated code and instructions. The methodical approach to refactoring, combined with the candid discussion of challenges like truncation, provides a very authentic and educational narrative. It’s highly valuable for anyone exploring AI-driven software development or data engineering.

Title Brainstorm

Title Option: AI’s Rhythmic Refactoring: Distilling Pandas Pivots in Jupyter
- Filename: ai-rhythmic-refactoring-distilling-pandas-pivots.md
- Rationale: Captures the core themes: AI assistance, methodical process (‘rhythmic’), the specific technical task (Pandas pivots), and the context (Jupyter).
Title Option: The Methodical Beat of Distillation: AI-Guided Jupyter Refactoring
- Filename: methodical-beat-ai-jupyter-refactoring.md
- Rationale: Emphasizes the process (‘methodical beat’) and the AI’s role in guiding the transformation of Jupyter Notebooks.
Title Option: From Notebook to Module: AI and the Art of the Incremental Pivot
- Filename: notebook-to-module-ai-incremental-pivot.md
- Rationale: Highlights the ‘Notebook to Module’ journey, the AI’s ‘art,’ and the specific ‘pivot’ operation as a key incremental step.
Title Option: Token Troubles & Timely Pivots: AI-Assisted Dev Insights
- Filename: token-troubles-timely-pivots.md
- Rationale: A more evocative title focusing on the challenge (token truncation) and the technical solution (the pivot), with broader AI development insights.

Content Potential And Polish

Core Strengths:
- Excellent real-world example of AI-assisted code refactoring and workflow automation.
- Detailed exposition of a specific, non-trivial Pandas operation (pivot_table) and its externalization.
- Insightful self-reflection on the development process, including AI interaction challenges and pragmatic decision-making.
- Clear demonstration of iterative development and verification using git diff.
- The ‘truncation’ and ‘time-traveling prompt’ narrative offers valuable lessons for working with LLMs.
Suggestions For Polish:
- Consider adding a brief visual or diagram of the ‘long’ vs. ‘wide’ DataFrame format for readers less familiar with Pandas pivoting.
- Expand slightly on the ‘big but’ – perhaps a small paragraph on why deferring DataFrame persistence to disk is a pragmatic choice (e.g., performance overhead for small DFs, current tooling limitations) and what the ideal future state would look like.
- Ensure consistent terminology for ‘pip state’ and ‘pipulate’ for clarity, perhaps a very brief reminder of what pipulate does for new readers.
- A concluding thought on the evolving partnership between human developers and AI in these complex tasks could further enrich the narrative.

Next Step Prompts

Distill the next logical cell in GAPalyzer.ipynb which involves fetching competitor homepage titles, integrating wordninja for keyword filtering, and generating brand keyword lists.
Draft a conceptual explanation of how the current DataFrame-in-memory ‘big but’ strategy might be evolved into a more robust, disk-persistent approach (e.g., Parquet, Feather) using Pipulate, outlining the pros and cons of such a transition.

AI's Rhythmic Refactoring: Distilling Pandas Pivots in Jupyter

Sunday, October 19, 2025

Setting the Stage: Context for the Curious Book Reader

The Methodical Beat: A Rhythmic Approach to Distillation

Technical Journal Entry Begins

First Pass: Combining SEMRush Data

Next Up: Pivoting Keywords and Competitor Data

1. Update gap_analyzer_sauce.py

2. Update the Notebook Cell in GAPalyzer.ipynb

1. Update gap_analyzer_sauce.py

2. Update the Notebook Cell in GAPalyzer.ipynb

Embracing Pragmatism in Architectural Choices

Elaboration Station III: Rhythm, Risk, and the Art of the Pivot 💃

The Rhythm Section: Finding the Groove

Navigating AI Nuances: Truncation and Time Travel

The Significance of the Pivot

Icarus, The Culture, and Pragmatism icarus_wings_wax:

Anticipation: The Plot Thickens… with Titles and Filters 🧐

Book Analysis

Ai Editorial Take

Title Brainstorm

Content Potential And Polish

Next Step Prompts

1. Update `gap_analyzer_sauce.py`

2. Update the Notebook Cell in `GAPalyzer.ipynb`

1. Update `gap_analyzer_sauce.py`

2. Update the Notebook Cell in `GAPalyzer.ipynb`