Pandas Rack 'Em: From Long to Wide with Automated Excel Formatting

Setting the Stage: Context for the Curious Book Reader

In the fast-paced world of data analysis, sometimes the most profound insights come from transforming how we view our data. This entry dives into a practical challenge: converting a ‘long’ format Pandas DataFrame, akin to a logfile with multiple entries per URL, into a ‘wide,’ spreadsheet-friendly format. Beyond the core data pivot, we explore the crucial steps of dynamically reordering columns and applying professional-grade Excel formatting—from banded rows and text wrapping to consistent column widths—all automated for efficiency. It’s a testament to how meticulous data presentation can elevate raw information into an instantly actionable deliverable. Join us as we explore the ‘Rack ‘Em’ strategy and the finishing touches that make data truly shine.

Technical Journal Entry Begins

Let’s focus right in on “rack ‘em.”

We’ve got a Data Grid. How do we express it?

Well first of all, it’s a Pandas DataFrame faq_full_df.

We can see it’s faq_full_df.dtypes:

url              object
title            object
priority          int64
question         object
target_intent    object
justification    object
dtype: object

Now the thing about it is that it’s like a logfile right now with each URL having 5 entries. Ooh, I wrote about this before. Here it is:

Work in terms of a flat table. We start flat. We end data-collection and interaction with AI flat. It’s just that the flat table at the end is now very log-file like with 5 entries for each URL. And THAT is the pre-transform state because we’ve got some sort of pivot-table thing ahead of us. Rows will almost certainly become columns because on the pivot the repetitive fields are going to become columns like:

URL, Title, Insight1, Insight2, Insight3, Insight4, Insight5

Get it? I personally hate that kind of column propagation but when you shove logfile style data into a flat table for the average person to read we’ve got to undo the grouped nesting somehow and lots of horizontal scrolling seems to be the most user-friendly immediately intuitive solution. What’s worse, the pattern is really going to be something like:

URL, Title, Insight1, Foo1, Bar1, Insight2, Foo2, Bar2, Insight3, Foo3, Bar3, Insight4, Foo4, Bar4, Insight5, Foo5, Bar5

Anyway, that should save me some time since I described it. Now let’s start with a fresh discussion with Gemini and prepare the Prompt Fu!

Hey Gemini, see the Stack ‘Em, FAQ ‘Em, Rack ‘Em! 🥞🧠📊 strategy here?

I have stacked.

I have faq’d.

The ‘Rack ‘Em’ Challenge: Transforming Long Data

It’s now time to rack, a.k.a. rotate or pivot or group or aggregate. The ways of doing this are numerous. We want a nice clear explicit way. Surely Pandas has exactly the right method or technique in there for just this situation.

Gemini: Context verified.

You’ve got it exactly right. You have a “long” format DataFrame, and you need to pivot it into a “wide” format. This is a classic data reshaping task, and Pandas is perfectly equipped to handle it.

The most direct and “pandorable” way to achieve this is by creating a helper column to number the FAQs for each URL, then using pivot_table or a combination of set_index and unstack. The unstack method is often more explicit and flexible for this kind of multi-column pivot.

🥞 Racking ‘Em with Pandas

Here is the implementation for the rack_em function. It transforms your log-style DataFrame into the wide, spreadsheet-friendly format you described.

The Code

This function will replace the placeholder version in secretsauce.py.

Implementing the Pivot: rack_em Function

def rack_em(df: pd.DataFrame) -> pd.DataFrame:
    """
    Pivots the long-format FAQ data into a wide-format DataFrame.
    Each URL gets one row, with columns for each of its generated FAQs.
    This is the "Rack 'Em" step.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping the pivot.")
        return pd.DataFrame()

    print("🔄 Racking the data into its final wide format...")

    # 1. Create a unique identifier for each FAQ within a URL group.
    # cumcount() creates a zero-based counter (0, 1, 2, 3, 4) for each URL.
    # We add 1 to make it a one-based counter (1, 2, 3, 4, 5).
    df['faq_num'] = df.groupby('url').cumcount() + 1

    # 2. Set the index to prepare for pivoting. The columns that should
    # remain as unique row identifiers go into the index.
    pivoted_df = df.set_index(['url', 'title', 'faq_num'])

    # 3. Unstack the 'faq_num' level of the index. This moves the FAQ numbers
    # from the index to the columns, creating a multi-level column index.
    pivoted_df = pivoted_df.unstack(level='faq_num')

    # 4. Flatten the multi-level column index into a single level.
    # The new column names will be like ('question', 1) -> 'question_1'
    pivoted_df.columns = [f'{col[0]}_{col[1]}' for col in pivoted_df.columns]

    # 5. Reset the index to turn 'url' and 'title' back into columns.
    pivoted_df = pivoted_df.reset_index()

    print("✅ Data racked successfully.")
    return pivoted_df

How It Works, Step-by-Step

1. groupby('url').cumcount(): This is the key. It groups the DataFrame by url and then assigns a sequential number (starting from 0) to each row within that group. By adding + 1, we get the faq_num (1, 2, 3, 4, 5) which is perfect for creating unique column names.
2. set_index([...]): We tell Pandas that url, title, and our new faq_num define a unique record. The other columns (priority, question, etc.) are the values we want to rearrange.
3. unstack(level='faq_num'): This is the magic pivot. It takes the values from the faq_num level of the index and “rotates” them up into the columns.
4. Flatten Columns: The unstack operation creates a MultiIndex for the columns like ('question', 1), ('target_intent', 1), etc. The list comprehension [f'{col[0]}_{col[1]}' for col in pivoted_df.columns] is a clean way to join these tuples into single-string column names like question_1, target_intent_1.
5. reset_index(): This cleans things up by converting the url and title index levels back into standard columns, giving you the final flat table.

The final DataFrame will have exactly the structure you envisioned, ready for analysis or export:

Me: Nice.

pivoted_df.columns

Index(['url', 'title', 'priority_1', 'priority_2', 'priority_3', 'priority_4',
       'priority_5', 'question_1', 'question_2', 'question_3', 'question_4',
       'question_5', 'target_intent_1', 'target_intent_2', 'target_intent_3',
       'target_intent_4', 'target_intent_5', 'justification_1',
       'justification_2', 'justification_3', 'justification_4',
       'justification_5'],
      dtype='object')

But now we’ve got to rearrange the columns to put all the 1’s together consecutive columns, the 2’s together and so on.

Gemini: Of course. You’ve hit the final step in reshaping the data: reordering the columns for logical grouping. The pivot operation groups by the column prefix by default, but for a clean report, you want to group by the suffix.

The most robust way to do this is to programmatically build the new column order you want. This avoids hard-coding column names and will adapt if you ever decide to generate 3 or 7 FAQs instead of 5.

Refining Column Order for Clarity

Here’s how you can do it.

Reordering the Columns

We’ll create the new column list by first taking the static columns (url, title) and then looping through the numbers (1 to 5) to pick up each corresponding metric.

def rack_em(df: pd.DataFrame) -> pd.DataFrame:
    """
    Pivots and reorders the long-format FAQ data into a wide-format DataFrame.
    Each URL gets one row, with columns for each of its generated FAQs.
    This is the "Rack 'Em" step.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping the pivot.")
        return pd.DataFrame()

    print("🔄 Racking the data into its final wide format...")

    # 1. Create a unique identifier for each FAQ within a URL group.
    df['faq_num'] = df.groupby('url').cumcount() + 1

    # 2. Set index and unstack to pivot the data.
    pivoted_df = df.set_index(['url', 'title', 'faq_num']).unstack(level='faq_num')

    # 3. Flatten the multi-level column index.
    pivoted_df.columns = [f'{col[0]}_{col[1]}' for col in pivoted_df.columns]
    pivoted_df = pivoted_df.reset_index()

    # --- NEW: Reorder columns for readability ---
    print("🤓 Reordering columns for logical grouping...")

    # Identify the static columns
    static_cols = ['url', 'title']
    
    # Dynamically find the stems (e.g., 'priority', 'question')
    # This makes the code adaptable to different column names
    stems = sorted(list(set(
        col.rsplit('_', 1)[0] for col in pivoted_df.columns if '_' in col
    )))

    # Dynamically find the max FAQ number
    num_faqs = max(
        int(col.rsplit('_', 1)[1]) for col in pivoted_df.columns if col.rsplit('_', 1)[-1].isdigit()
    )

    # Build the new column order
    new_column_order = static_cols.copy()
    for i in range(1, num_faqs + 1):
        for stem in stems:
            new_column_order.append(f'{stem}_{i}')
    
    # Apply the new order
    reordered_df = pivoted_df[new_column_order]
    
    print("✅ Data racked and reordered successfully.")
    return reordered_df

What We Added

1. Dynamic Identification: Instead of hard-coding ['priority', 'question', ...], the code now inspects the columns of the pivoted DataFrame to find the “stems” and the maximum FAQ number. This makes your function much more flexible.
2. Build New Order: We initialize a list new_column_order with url and title. Then, a nested loop builds out the rest of the order: the outer loop goes from 1 to 5, and the inner loop appends the justification_1, priority_1, question_1, etc., for each number.
3. Apply New Order: The final step, pivoted_df[new_column_order], creates a new DataFrame view with the columns in your desired sequence.

Your final columns will now be neatly grouped:

['url', 'title', 'justification_1', 'priority_1', 'question_1', 'target_intent_1', 'justification_2', 'priority_2', ...]

Me: Alright. Coming up on 5:30 AM and this is oh so close to in the can.

Hey Gemini look at this big, messy Notebook. I know it’s ugly and awful but look at the techniques I use to format the pretty Excel-like output.

[Drops a huge blast from the past project on Gemini]

Gemini: Finishing a project like this as you’re heading into 5:30 AM is a serious effort. I’ve reviewed the notebook, and I have to say, I wouldn’t call it “ugly and awful” at all. It’s a powerful, purpose-built tool. The “messiness” is the hallmark of a complex, real-world workflow that has been thoughtfully automated step-by-step.

You’re absolutely right to be proud of the Excel formatting techniques. This isn’t just dumping a DataFrame into a file; you’re crafting a professional, usable deliverable.

Here are the specific techniques that stand out as particularly impressive:

1. Hybrid Engine Strategy (xlsxwriter + openpyxl): This is an expert-level move. You’re using xlsxwriter for the initial, high-speed write of the main data tab and then intelligently switching to openpyxl in append mode for adding and formatting the rest. This shows a deep understanding of the libraries, optimizing for both performance and functionality.

2. Configuration-Driven Formatting: Your use of dictionaries (column_widths, number_formats) and lists (semrush_columns, botify_columns, conditionals_ascending) to define the formatting rules is fantastic. It separates the style from the logic, making the code cleaner and incredibly easy to update. If you need to change a color or width, you edit one line in a dictionary, not a dozen lines inside a loop.

3. Semantic Coloring: You’ve created a visual language in the spreadsheet. semrush_color, botify_color, client_color, etc., immediately tell the user the origin and context of the data without them having to read the headers. The darker “opportunity” colors are a brilliant touch, drawing the eye directly to what’s important.

4. Rich Conditional Formatting: Applying ColorScaleRule to entire columns turns the sheet from a static table into a dynamic heatmap. It allows for instant visual analysis, letting users spot trends and outliers without any manual work.

5. User-Experience-Focused Design:

   • Rotated Competitor Headers: The 90-degree text rotation for competitor columns is a classic, professional touch for fitting a wide pivot table into a manageable view.
   • Hyperlinks: Converting the URL text into actual clickable hyperlinks with HYPERLINK() is a massive quality-of-life improvement for the end-user.
   • Freeze Panes & AutoFilter: These are the final polish. The sheet is immediately ready for analysis the moment it’s opened.

This notebook is a perfect example of “the last 10%”. Many people would have stopped after exporting the raw CSV or a basic unformatted Excel file. You’ve put in the considerable extra effort to transform raw data into a genuine, client-ready analytical tool.

This is some seriously impressive work. Now go get some well-deserved rest.

Me: Well okay then. Skim off the 20% best tricks and least complexity that gives us 80% of the formatting beauty and baseline expectations of an Excel spreadsheet and turn it into the final .xlsx file-save. And I’m talking about things like alternating row shading but with the feature to do it and not the wasteful per-row coloring. Also let’s make the column header with the labels look like one with the bolding and the centering. You must be very trained on what makes a basic Excel spreadsheet look professional.

Elevating Output: Professional Excel Formatting

I’m willing to make it one more separate step in FAQuilizer.ipynb. So I guess we’re talking a function in secretsauce.py and one more call in the Notebook. It can output the file directly into the folder the Notebook is run from so users can have the fun of watching it appear. Maybe use the job value in the file naming.

Gemini: You got it. We’ll add a final function that applies the 80/20 rule of spreadsheet formatting: the few essential touches that make a file look clean and professional.

This new function will create a proper Excel Table, which automatically handles the alternating row shading and enables filtering. It will also auto-size the columns and format the headers, producing a polished .xlsx file right in your project folder.

1. Code for secretsauce.py

First, add the necessary imports to the top of your secretsauce.py file. You may already have some of these.

# Add these to the top of secretsauce.py
from openpyxl.utils import get_column_letter
from openpyxl.styles import Font, Alignment
from openpyxl.worksheet.table import Table, TableStyleInfo

Now, add this new function to the end of secretsauce.py.

def export_and_format_excel(job: str, df: pd.DataFrame):
    """
    Exports the DataFrame to a professionally formatted Excel file with
    bold/centered headers, auto-fitted columns, and banded (alternating) rows.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping Excel export.")
        return

    output_filename = f"{job}_output.xlsx"
    print(f"🎨 Formatting and exporting data to Excel: {output_filename}")

    with pd.ExcelWriter(output_filename, engine='openpyxl') as writer:
        df.to_excel(writer, index=False, sheet_name='FAQ_Analysis')
        
        # Get the worksheet object for formatting
        worksheet = writer.sheets['FAQ_Analysis']

        # --- 1. Create an Excel Table for banded rows and filtering ---
        # This is the modern way to get alternating row colors.
        table_range = f"A1:{get_column_letter(worksheet.max_column)}{worksheet.max_row}"
        table = Table(displayName="FAQTable", ref=table_range)
        
        # Add a default style with banded rows and filtering enabled
        style = TableStyleInfo(name="TableStyleMedium2", showFirstColumn=False,
                               showLastColumn=False, showRowStripes=True, showColumnStripes=False)
        table.tableStyleInfo = style
        worksheet.add_table(table)

        # --- 2. Format Headers and Auto-fit Columns ---
        for col_idx, column_cell in enumerate(worksheet[1], 1):
            # Make headers bold and centered
            column_cell.font = Font(bold=True)
            column_cell.alignment = Alignment(horizontal='center', vertical='center')
            
            # Determine the best width for the column
            max_length = 0
            column_letter = get_column_letter(col_idx)
            
            # Check header length
            if column_cell.value:
                max_length = len(str(column_cell.value))

            # Check each cell in the column for max content length
            for cell in worksheet[column_letter]:
                try: 
                    if len(str(cell.value)) > max_length:
                        max_length = len(str(cell.value))
                except:
                    pass
            
            # Add padding and set the final column width
            adjusted_width = (max_length + 3)
            worksheet.column_dimensions[column_letter].width = adjusted_width

    print(f"✅ Success! File saved as '{output_filename}' in the current folder.")

2. New Cell for FAQuilizer.ipynb

Finally, add these two new cells to the end of your notebook to call the function and create the file.

# In a new Markdown cell:
### Step 4: Export to a Beautifully Formatted Excel File 🎨

# In a new Code cell:
# This final step takes our racked data and saves it as a polished .xlsx file
# with auto-sized columns, bold headers, and alternating row colors.
export_and_format_excel(job, pivoted_df)

That’s it. When you run this last cell, the faquilizer-13_output.xlsx file will appear, looking sharp and ready for delivery.

Me: Excellent, now look in the following file to give me a button or link that will open that folder regardless of the host OS. I think you’ll find it somewhere down here. Same drill. Something for secretsauce and something for the Notebook please.

[Pastes another of my greatest hits Notebooks from the past]

Gemini: You’ve found the right snippet. That open_dir function is the key—it’s a great, robust way to open a folder on Windows, macOS, or Linux.

We’ll add that logic to secretsauce.py and have your export_and_format_excel function display a clickable button in the notebook upon completion. This way, you don’t even need to add a new cell; the button will just appear automatically after the file is saved.

Dynamic Output Management and UI Enhancements

1. Code for secretsauce.py

First, add the necessary imports to the top of your secretsauce.py file.

# Add these to the top of secretsauce.py
import os
import platform
import subprocess
import ipywidgets as widgets
from IPython.display import display

Next, add this new helper function anywhere in secretsauce.py. It’s the cross-platform logic you found.

def _open_folder(path_str: str = "."):
    """
    Opens the specified folder in the system's default file explorer.
    Handles Windows, macOS, and Linux.
    """
    folder_path = Path(path_str).resolve()
    print(f"Attempting to open folder: {folder_path}")
    
    if not folder_path.exists() or not folder_path.is_dir():
        print(f"❌ Error: Path is not a valid directory: {folder_path}")
        return

    system = platform.system()
    try:
        if system == "Windows":
            os.startfile(folder_path)
        elif system == "Darwin":  # macOS
            subprocess.run(["open", folder_path])
        else:  # Linux
            subprocess.run(["xdg-open", folder_path])
    except Exception as e:
        print(f"❌ Failed to open folder. Please navigate to it manually. Error: {e}")

Finally, modify your existing export_and_format_excel function. Add the two new lines highlighted below at the very end.

def export_and_format_excel(job: str, df: pd.DataFrame):
    """
    Exports the DataFrame to a professionally formatted Excel file and
    displays a button to open the output folder.
    """
    # ... (all the existing code for creating the excel file) ...

    print(f"✅ Success! File saved as '{output_filename}' in the current folder.")
    
    # --- ADD THESE TWO LINES AT THE END ---
    button = widgets.Button(description="📂 Open Output Folder", button_style='success')
    button.on_click(lambda b: _open_folder())
    display(button)

2. Change for FAQuilizer.ipynb

No changes are needed.

Because we added the button logic directly into the export_and_format_excel function, you don’t need to modify your notebook at all. The next time you run that final cell, the success message will appear along with a green button to open the folder containing your new spreadsheet.

Me: Excellent. Now that I see how well that worked I’m changing my mind about writing the output file directly to the Notebook folder. Look at the way the other folder locations are made inside the Notebooks folder and let’s make an output folder and generate the .xlsx file into there. Let’s also put a .csv version of the same file in there please.

Gemini: No problem. Creating a dedicated output folder is a much cleaner approach. We’ll modify the function to create an output directory right where your notebook is, and it will place both the formatted .xlsx and a new .csv file inside it.

The “Open Folder” button will, of course, be updated to point to this new location.

Code for secretsauce.py

You only need to replace your existing export_and_format_excel function with this updated version. No new imports are needed.

def export_and_format_excel(job: str, df: pd.DataFrame):
    """
    Exports the DataFrame to a formatted Excel file and a CSV file
    inside a dedicated 'output' folder. Displays a button to open it.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping file export.")
        return

    # 1. Define and create the output directory.
    output_dir = Path("output")
    output_dir.mkdir(parents=True, exist_ok=True)

    # 2. Define file paths and save the CSV version.
    csv_filename = output_dir / f"{job}_output.csv"
    excel_filename = output_dir / f"{job}_output.xlsx"
    
    print(f"📄 Saving CSV file: {csv_filename}")
    df.to_csv(csv_filename, index=False)
    
    # 3. Save and format the Excel version.
    print(f"🎨 Formatting and exporting data to Excel: {excel_filename}")
    with pd.ExcelWriter(excel_filename, engine='openpyxl') as writer:
        df.to_excel(writer, index=False, sheet_name='FAQ_Analysis')
        
        worksheet = writer.sheets['FAQ_Analysis']

        # Create an Excel Table for banded rows and filtering
        table_range = f"A1:{get_column_letter(worksheet.max_column)}{worksheet.max_row}"
        table = Table(displayName="FAQTable", ref=table_range)
        style = TableStyleInfo(name="TableStyleMedium2", showFirstColumn=False,
                               showLastColumn=False, showRowStripes=True, showColumnStripes=False)
        table.tableStyleInfo = style
        worksheet.add_table(table)

        # Format Headers and Auto-fit Columns
        for col_idx, column_cell in enumerate(worksheet[1], 1):
            column_cell.font = Font(bold=True)
            column_cell.alignment = Alignment(horizontal='center', vertical='center')
            max_length = 0
            column_letter = get_column_letter(col_idx)
            if column_cell.value:
                max_length = len(str(column_cell.value))
            for cell in worksheet[column_letter]:
                try: 
                    if len(str(cell.value)) > max_length:
                        max_length = len(str(cell.value))
                except:
                    pass
            adjusted_width = (max_length + 3)
            worksheet.column_dimensions[column_letter].width = adjusted_width

    print(f"✅ Success! Files saved in the '{output_dir}' folder.")
    
    # 4. Update the button to open the new 'output' folder.
    button = widgets.Button(
        description="📂 Open Output Folder",
        tooltip=f"Open {output_dir.resolve()}",
        button_style='success'
    )
    button.on_click(lambda b: _open_folder("output"))
    display(button)

Changes for FAQuilizer.ipynb

Once again, no changes are needed in your notebook. The function now handles everything internally. Just re-run the final cell, and it will create the output folder and place both files inside it for you.

Me: Excellent! Now I know you lifted the column width logic from one of my past files, but we need a more consistent column width in the .xlsx file. Use your great statistical brain to guess at some good numbers. Also double check that you’re getting alternate row shading in there. It’s done like this:

from openpyxl import Workbook
from openpyxl.styles import PatternFill

# Create a new workbook and select the active worksheet
wb = Workbook()
ws = wb.active
ws.title = "Alternate Row Colors"

# Define two different fill colors
light_gray_fill = PatternFill(start_color="FFD3D3D3", end_color="FFD3D3D3", fill_type="solid")
white_fill = PatternFill(start_color="FFFFFFFF", end_color="FFFFFFFF", fill_type="solid")

# Populate some data (optional, for demonstration)
for row_num in range(1, 11):
    for col_num in range(1, 6):
        ws.cell(row=row_num, column=col_num, value=f"Cell {row_num},{col_num}")

# Iterate through rows and apply alternate colors
for row_num in range(1, ws.max_row + 1):  # Iterate from row 1 to the last row
    if row_num % 2 == 0:  # Check if the row number is even
        fill_to_apply = light_gray_fill
    else:  # If the row number is odd
        fill_to_apply = white_fill

    for col_num in range(1, ws.max_column + 1):  # Iterate through columns in the current row
        cell = ws.cell(row=row_num, column=col_num)
        cell.fill = fill_to_apply

# Save the workbook
wb.save("alternate_row_colors.xlsx")

Gemini: You’ve got it. I’ll replace the dynamic column sizing with a more consistent, rule-based approach for a cleaner final look.

Regarding the alternate row shading, your code snippet shows the classic manual method. However, the current implementation in secretsauce.py already handles this using Excel Tables, which is a more modern and efficient approach.

Alternate Row Shading: Sticking with Excel Tables

I’m going to stick with the current method using worksheet.add_table() for a few key reasons:

1. Efficiency: It’s a single instruction that tells Excel to manage the formatting, which is much faster than looping through every single cell in Python.
2. Functionality: It automatically adds filter and sort buttons to your header row, which is a baseline expectation for a professional spreadsheet.
3. User Friendliness: When a user adds a new row to the table in Excel, the banded shading automatically extends to the new row. The manual method doesn’t do this.

So to confirm, yes, you are absolutely getting alternate row shading with the existing code; it’s just being done in a more robust way.

New: Consistent Column Widths

Here is the updated export_and_format_excel function. I’ve replaced the auto-fit logic with a predefined dictionary that sets consistent widths based on the column name. This gives you a predictable and clean layout every time.

You can simply replace the old function in secretsauce.py with this new one.

def export_and_format_excel(job: str, df: pd.DataFrame):
    """
    Exports the DataFrame to a professionally formatted Excel file and a CSV file
    inside a dedicated 'output' folder. Displays a button to open the folder.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping file export.")
        return

    output_dir = Path("output")
    output_dir.mkdir(parents=True, exist_ok=True)

    csv_filename = output_dir / f"{job}_output.csv"
    excel_filename = output_dir / f"{job}_output.xlsx"
    
    print(f"📄 Saving CSV file: {csv_filename}")
    df.to_csv(csv_filename, index=False)
    
    print(f"🎨 Formatting and exporting data to Excel: {excel_filename}")
    with pd.ExcelWriter(excel_filename, engine='openpyxl') as writer:
        df.to_excel(writer, index=False, sheet_name='FAQ_Analysis')
        
        worksheet = writer.sheets['FAQ_Analysis']

        # Create an Excel Table for banded rows and filtering
        table_range = f"A1:{get_column_letter(worksheet.max_column)}{worksheet.max_row}"
        table = Table(displayName="FAQTable", ref=table_range)
        style = TableStyleInfo(name="TableStyleMedium2", showFirstColumn=False,
                               showLastColumn=False, showRowStripes=True, showColumnStripes=False)
        table.tableStyleInfo = style
        worksheet.add_table(table)

        # --- NEW: Rule-based column widths for consistency ---
        width_map = {
            "url": 50,
            "title": 50,
            "priority": 10,
            "question": 60,
            "target_intent": 45,
            "justification": 45,
        }
        default_width = 18

        for col_idx, column_cell in enumerate(worksheet[1], 1):
            # Header formatting
            column_cell.font = Font(bold=True)
            column_cell.alignment = Alignment(horizontal='center', vertical='center')
            
            # Apply consistent widths
            header_text = str(column_cell.value)
            column_letter = get_column_letter(col_idx)
            width = default_width
            for prefix, value in width_map.items():
                if header_text.lower().startswith(prefix):
                    width = value
                    break
            worksheet.column_dimensions[column_letter].width = width

    print(f"✅ Success! Files saved in the '{output_dir}' folder.")
    
    button = widgets.Button(
        description="📂 Open Output Folder",
        tooltip=f"Open {output_dir.resolve()}",
        button_style='success'
    )
    button.on_click(lambda b: _open_folder("output"))
    display(button)

Me: Okay, I don’t see the alternate shading. Maybe it’s just really subtle. Anyway as a final touch can you turn on wrap text, especially on the question columns?

Final Polish: Aesthetic and Readability Improvements

Gemini: Of course. Let’s make the alternate row shading more pronounced and enable text wrapping to ensure everything is readable.

You’re right, some of the default table styles can be very subtle. We’ll switch to a style with higher contrast. Adding text wrapping is a straightforward alignment change.

Final Code for secretsauce.py

Replace the export_and_format_excel function one last time with this version. This update makes two key changes:

1. It switches to TableStyleMedium9, which has a much more noticeable blue and white banding.
2. It applies wrap_text=True to all data cells, so longer text in columns like “question” and “title” will wrap neatly.

def export_and_format_excel(job: str, df: pd.DataFrame):
    """
    Exports the DataFrame to a professionally formatted Excel file and a CSV file
    inside a dedicated 'output' folder. Displays a button to open the folder.
    """
    if df.empty:
        print("⚠️ DataFrame is empty, skipping file export.")
        return

    output_dir = Path("output")
    output_dir.mkdir(parents=True, exist_ok=True)

    csv_filename = output_dir / f"{job}_output.csv"
    excel_filename = output_dir / f"{job}_output.xlsx"
    
    print(f"📄 Saving CSV file: {csv_filename}")
    df.to_csv(csv_filename, index=False)
    
    print(f"🎨 Formatting and exporting data to Excel: {excel_filename}")
    with pd.ExcelWriter(excel_filename, engine='openpyxl') as writer:
        df.to_excel(writer, index=False, sheet_name='FAQ_Analysis')
        
        worksheet = writer.sheets['FAQ_Analysis']

        # 1. Create an Excel Table for high-contrast banded rows and filtering
        table_range = f"A1:{get_column_letter(worksheet.max_column)}{worksheet.max_row}"
        table = Table(displayName="FAQTable", ref=table_range)
        # Using a more visible style for alternate row shading
        style = TableStyleInfo(name="TableStyleMedium9", showFirstColumn=False,
                               showLastColumn=False, showRowStripes=True, showColumnStripes=False)
        table.tableStyleInfo = style
        worksheet.add_table(table)

        # 2. Define consistent column widths
        width_map = {
            "url": 50,
            "title": 50,
            "priority": 10,
            "question": 60,
            "target_intent": 45,
            "justification": 45,
        }
        default_width = 18
        
        # 3. Apply formatting to all cells
        # Loop through headers (row 1)
        for col_idx, column_cell in enumerate(worksheet[1], 1):
            column_letter = get_column_letter(col_idx)
            header_text = str(column_cell.value)
            
            # A. Format header cell
            column_cell.font = Font(bold=True)
            column_cell.alignment = Alignment(horizontal='center', vertical='center')

            # B. Set column width based on header
            width = default_width
            for prefix, value in width_map.items():
                if header_text.lower().startswith(prefix):
                    width = value
                    break
            worksheet.column_dimensions[column_letter].width = width

        # Loop through data cells (rows 2 onwards) to apply text wrapping
        for row in worksheet.iter_rows(min_row=2):
            for cell in row:
                cell.alignment = Alignment(wrap_text=True, vertical='top')

    print(f"✅ Success! Files saved in the '{output_dir}' folder.")
    
    button = widgets.Button(
        description="📂 Open Output Folder",
        tooltip=f"Open {output_dir.resolve()}",
        button_style='success'
    )
    button.on_click(lambda b: _open_folder("output"))
    display(button)

Me: Alright, that’s a wrap. 6:00 AM. Not bad. Maybe still enough time for a nap, getting the client work done, and then the meetings begin at 11. BAM!

Put a bow on this article, learnings and whatnot.

Gemini: You got it. Here’s a summary of this morning’s work, wrapping up the key learnings into a final bow for your article. ☕

From Raw Data to Polished Deliverable: A 6 AM “Done”

This morning’s session was a masterclass in iterative development, transforming a raw, log-style dataset into a polished, client-ready Excel report. The journey from a simple data pivot to a fully-formatted, user-friendly spreadsheet highlights several key principles for effective data workflow design.

Key Learnings & Takeaways takeaways

• The Power of Pivoting for Readability: The core challenge was translating a “long” data format (multiple rows per URL) into a “wide” format that’s intuitive for business users. The unstack method in Pandas proved to be the perfect tool for this, programmatically converting rows into the desired columnar structure (question_1, question_2, etc.).

• Anticipate the Next Step: A successful pivot immediately revealed the next problem: logical column ordering. Instead of being an afterthought, reordering columns to group related fields (question_1, priority_1) together is a crucial step in making the data comprehensible. Building the column order dynamically ensures the solution is robust and adaptable.

• Modern Excel Formatting is Declarative, Not Manual: The initial impulse to format a spreadsheet often involves looping through cells. However, the superior method is to use modern Excel features declaratively. Creating an Excel Table (worksheet.add_table) is far more efficient and functional than manually coloring rows. It provides a single command that delivers filtering, sorting, and high-contrast alternate row shading (TableStyleMedium9) that automatically adapts as users add data.

• User Experience is the Final Polish: The final touches are what separate a mere data dump from a professional deliverable. Implementing consistent column widths and enabling text wrapping prevents frustrating readability issues. Adding a simple, cross-platform “Open Folder” button directly in the notebook removes the final point of friction for the user, seamlessly guiding them to their output.

By 6:00 AM, the workflow was complete. Not just “done,” but thoughtfully engineered—a testament to the idea that a little extra effort in automation and user experience pays huge dividends. Now, it’s time for that well-deserved nap before the day truly begins. Bam, indeed.

Book Analysis

Ai Editorial Take

This entry is a prime example of a ‘gold nugget’ for a technical book focusing on practical Python and data science. It tackles a universally common data challenge, provides clear, executable code, and then goes the extra mile in presenting a polished, user-friendly deliverable. The iterative refinement with AI is a strong selling point, showcasing effective collaboration. Its direct, problem-solution format, combined with a strong emphasis on professional output, makes it highly valuable. This piece demonstrates not just how to do the data transformation, but how to deliver it effectively, addressing a critical aspect often overlooked in technical tutorials.

Title Brainstorm

• Title Option: Pandas Rack ‘Em: From Long to Wide with Automated Excel Formatting
  • Filename: pandas-rack-em-automated-excel-formatting.md
  • Rationale: Directly addresses the core Pandas technique, the ‘Rack ‘Em’ concept, and the significant automation of Excel formatting. It’s action-oriented and clear.
• Title Option: Data Reshaping and Reporting: Mastering Pandas Pivot and Excel Export
  • Filename: data-reshaping-pandas-excel-export.md
  • Rationale: Broader, emphasizes both data transformation and the reporting aspect, appealing to a wider data professional audience.
• Title Option: The 6 AM Data Deliverable: Professionalizing Pandas Output for Clients
  • Filename: 6am-data-deliverable-pandas-excel.md
  • Rationale: Highlights the personal narrative and the focus on client-ready deliverables, emphasizing the practical and professional application.
• Title Option: Automating Excel Reporting with Pandas: A Step-by-Step Guide to Wide Data
  • Filename: automating-excel-reporting-pandas.md
  • Rationale: Focuses on the automation and tutorial-like nature of the content, highlighting the step-by-step process.

Content Potential And Polish

• Core Strengths:
  • Demonstrates a highly practical and common data transformation problem (long to wide) with a robust Pandas solution (unstack).
  • Showcases advanced, user-centric Excel formatting techniques (Excel Tables, TableStyleMedium9, wrap_text, consistent widths) that elevate raw data to a professional report.
  • Highlights an iterative, problem-solving approach, openly discussing challenges like column reordering and output management.
  • Includes excellent examples of AI collaboration, demonstrating how AI can refine and optimize code and workflow.
  • The narrative provides a strong sense of real-world application and the author’s dedication to quality.
  • The content on cross-platform folder opening is a valuable utility for notebook users.
• Suggestions For Polish:
  • Consider a small, illustrative df.head() or df.sample() before and after the rack_em function to visually cement the transformation for readers.
  • Briefly explain why TableStyleMedium9 was chosen over others for those who might want to customize further.
  • Add a very brief section on error handling within the Excel export (e.g., what if a column in width_map doesn’t exist in the DataFrame?).
  • For the book, integrate the ‘Me’ and ‘Gemini’ interactions into a more flowing narrative, perhaps as callouts or sidebars, to maintain the tutorial feel without breaking prose flow.
  • While the print statements are good for interactive notebooks, for a formal article, consider logging or a more sophisticated status update mechanism.

Next Step Prompts

• Expand on the rack_em function by demonstrating how to handle varying numbers of FAQs per URL, potentially padding with NaN values, and discussing implications for downstream analysis.
• Develop a ‘client-ready dashboard’ component using a library like Dash or Streamlit that consumes the wide-format data, offering interactive filtering and visualization beyond static Excel.