--- title: 'From Hoarded Expertise to Automated Insights: Distilling Data Workflows' permalink: /futureproof/hoarded-expertise-automated-insights-data-workflows/ description: This entry documents a pivotal step in refactoring a complex data analysis workflow. My aim is to extract the powerful, implicit logic often trapped in notebooks or proprietary tools like Excel, and formalize it into explicit, reusable Python functions. This not only cleans up the code but also makes the analytical process transparent, robust, and truly automatable. The journey from 'pivot table magic' to 'explicit Pandas aggregation' is a testament to the value of understanding the underlying data concepts. meta_description: Explore the refactoring journey of a technical data analysis workflow, contrasting Excel's opacity with Python's explicit data aggregation, filtering, and merging for automated, actionable insights. meta_keywords: LLMs, data hoarding, domain expertise, Excel, pivot tables, vlookup, Pandas, data aggregation, data merging, data filtering, data transformation, Python, technical workflow, automation, SEMRush layout: post sort_order: 8 --- ## Setting the Stage: Context for the Curious Book Reader This entry delves into the intricate process of transforming raw, often siloed, domain expertise into structured, automatable data workflows. It's a candid look at the journey from manual, intuition-driven data manipulation (like Excel's 'pivot tables') to explicit, robust Python-based solutions. The discussion highlights the challenges of extracting value from complex datasets, particularly in competitive fields, and charts a path toward greater clarity, efficiency, and the systematic uncovering of hidden opportunities through rigorous data transformation. --- ## Technical Journal Entry Begins ### The Myth of Peak Data: Unlocking Hoarded Expertise This is a work that is going to be *discovered* one day. It is the very definition of hoarded domain expertise that the LLMs have not had the chance to train on yet because... well, the domain experts *horde* it to keep themselves valuable and their offerings exclusive. I can only imagine how bad it is in competitive fields like science and finance. Think the scrapers ever encountered the source code for Renaissance Technology's Medallion Fund? The answer is no. No, they have not. The concept of LLMs having reached *peak data* is highly flawed. Maybe they've reached peak tragedy of the commons or if you're more generous than cynical, peak wisdom of the crowd. Okay, back to the work at hand. Talk about chisel-strike projects! I've never been in such a productive rote rhythm. Now here's a little ditty most people don't think about. It's the elephant in the room when you do a *pivot table* which is really just Excel marketing speak for something the person doing it doesn't really understand. It's actually either: 1. A series of *aggregate functions* applied to a column over rows in order to get a `count()`, `max()`, `min()`, `avg()` or whatnot. 2. A grouping by a particular column such that there's a resulting hierarchical shape that's no longer a "flat" row & column data-structure in which each group as a bundle of it's rows within. ## Demystifying Data: Excel's Illusions vs. Python's Clarity You cannot reasonably ask a business or marketing person to understand such things, and so Excel calls it a pivot table and slaps a drag-and-drop interface and little file-explorer-like expanders to try to get you to intuitively grok it, but who really does? In the land of Excel there are 2 growth events, both of which are a big smack in the face — like *punishment* for taking up Excel. The first is the fateful `vlookup`. It shouldn't exist. People should go right to SQL for the same of pain and learning it's going to take, and you'll never *really* learn its weird index and row-range anonymous signature. It's poke-and-pray methodology to join data. It doesn't have to be that awful, but there is great power in pretending Excel is a good relational database manager, so we pretend. The other moment of growth and insult is the pivot table. Oh, is this the ranting the Gemini says I do? Yeah, I guess it is. Anyhow, all the proper learning you should be doing about aggregate functions that that use explicit maps such as "for this column do this and for that column do that" is exactly how it works in Python. Python is a wonderful environment for mapping column names to the aggregate functions you'd like applied to them and just seeing it explicitly laid out like a story. Similar with grouping, but you're often left with a hierarchical shape that's a bit confusing, but you can always "flatten" it to get rid of the confusion. ## The Non-Aggregatable Conundrum: When Rows Resist Summation And I go through *all that* just to say that there's often an issue left over of the columns containing data that resist the aggregation. You can't get "the average meta description" — hmmm, or maybe you can now in the age of AI. I'll have to give that some thought. But besides the PFM now possible because AI, there are rows that you can't aggregate and still they need to end up mapped 1-to-1 somehow to the rows that were aggregated so you can slap that *still-important* data onto the side of your *now-aggregated* table. Did I explain that correctly? Flesh it out, beef it up and otherwise vet it and get those up to speed who have no idea what we're talking about. Oh, and here's the next thing I need to extract: ## Aggregation in Action: A Python Blueprint ```python import pandas as pd from IPython.display import display # NOTE: This cell assumes 'df2' (the result of the aggregation step) is available. print("Adjusting SEMRush columns that were not part of competitor-columns pivot...") # Assign aggregating function to each metric # The chosen functions are critical for creating a single, best-case summary per keyword: # - 'min' for Position: Gives the *best* rank achieved across all competitors who rank. # - 'max' for Search Volume/Number of Results/Timestamp: Captures the highest value reported. # - 'sum' for Traffic/Traffic Cost: Aggregates the total value across all competitor results. # - 'mean' for Difficulty/CPC/Competition: Averages the difficulty/cost across all reporting sources. # - 'first' for categorical data (URLs, Intents, SERP Features): Chooses the first encountered value. agg_funcs = { 'Position': 'min', 'Search Volume': 'max', 'CPC': 'mean', 'Traffic': 'sum', 'Traffic (%)': 'mean', 'Traffic Cost': 'sum', 'Keyword Difficulty': 'mean', 'Previous position': 'first', 'Competition': 'mean', 'Number of Results': 'max', 'Timestamp': 'max', 'SERP Features by Keyword': 'first', 'Keyword Intents': 'first', 'Position Type': 'first', 'URL': 'first', 'Competitor URL': 'first', 'Client URL': 'first' } # Apply the aggregation across the combined dataset (df2) agg_df = df2.groupby('Keyword').agg(agg_funcs).reset_index() # Add a derived metric: Keyword word count agg_df['Number of Words'] = agg_df["Keyword"].apply(lambda x: len(x.split())) # Drop the 'Position' column: It was only used for the pivot/min operation, # but it's redundant/misleading now that the competitor position data is in pivot_df. agg_df.drop(columns=['Position'], inplace=True) print("Table of aggregates prepared.") display(agg_df) # Store the aggregated metrics in the pipeline pip.set(job, 'keyword_aggregate_df_json', agg_df.to_json(orient='records')) ``` I can haz: 1. The part that goes into `gap_analyzer_sauce.py`? 2. The part that replaces what I lifted from the notebook? 3. A break from giving you the template every time (go look at the last good one you did). ...? **Gemini**: You absolutely nailed the explanation of pivot tables and aggregation\! It *is* often presented as magic in Excel, obscuring the underlying database concepts of **grouping** and **aggregation functions** (`SUM`, `AVG`, `MIN`, `MAX`, `COUNT`, `FIRST`, `LAST`, etc.). ----- ## Elaboration Station V: Aggregation, `vlookup`, and the Lingering Data 🤔 ### Pivot Tables Unveiled You're right, Excel's "Pivot Table" is essentially a user-friendly interface for two core database operations: 1. **Grouping:** Taking rows with a shared value in one or more columns (like 'Keyword' in your case) and bundling them together. 2. **Aggregation:** Applying a mathematical or selection function across the rows *within each group* for specific columns. For columns with numbers, you can sum, average, find the min/max, or count. For columns with text or other data types, choices are often limited to count, or selecting the 'first' or 'last' value encountered in the group. Python (specifically Pandas) makes this much more explicit and, as you noted, understandable: ```python # The explicit story: # For each unique 'Keyword' in df2: # Look at all rows for that Keyword. # For the 'Position' column in those rows, find the minimum value. # For the 'Search Volume' column, find the maximum value. # For the 'CPC' column, calculate the average value. # For the 'URL' column, just take the first one you find. # ...and so on for all columns in agg_funcs agg_df = df2.groupby('Keyword').agg(agg_funcs).reset_index() ``` This clarity is empowering compared to the drag-and-drop ambiguity of Excel pivots. ### The `vlookup` Lament 😩 Ah, `VLOOKUP`. It's the rite of passage and bane of many an Excel user. You hit the nail on the head: it's Excel trying to emulate a relational database `JOIN` operation, but with a notoriously fragile and confusing syntax (`lookup_value`, `table_array`, `col_index_num`, `[range_lookup]`). It often breaks if columns are inserted or deleted, relies on sorted data for certain modes, and lacks the clarity of SQL's `JOIN ... ON ...`. While powerful *within Excel's limits*, the cognitive overhead often outweighs the benefits compared to learning basic SQL or Pandas `merge`. ### The Non-Aggregatable Data Problem This is the "elephant in the room" you mentioned. When you aggregate rows based on a 'Keyword', what happens to columns like 'URL', 'SERP Features', or 'Meta Description' that might be *different* for each original row belonging to that keyword group? * **You can't average a URL.** * **You can't sum SERP features.** * **You can't find the 'minimum' meta description (meaningfully).** So, you have to choose a simple selection strategy, most commonly `'first'` (just take the value from the first row encountered in the group) or `'last'`. This is precisely what your `agg_funcs` dictionary does for columns like `'URL'`, `'SERP Features by Keyword'`, `'Keyword Intents'`, etc. **The Crucial Next Step (Which you perform later):** After creating the `agg_df` (one row per keyword with *aggregated* metrics and *first-encountered* descriptive fields), you then *merge* it back with the `pivot_df` (which retains the keyword-level position data for *each* competitor). This merge effectively "slaps that still-important data onto the side," bringing the aggregated metrics together with the detailed competitive positioning. **AI and Averaging Text?** Your thought about AI averaging meta descriptions is provocative\! Current LLMs *could* potentially read all meta descriptions for a keyword group and synthesize a *representative* or *average theme* description. It wouldn't be a mathematical average but a conceptual one. Definitely an interesting avenue for future exploration – perhaps adding an 'AI Summarized Description' column\! 🤯 ## Elaboration Station V: From Magic to Mechanics ----- Okay, back to the distillation\! Here's the plan for the aggregation cell: ## 1\. Update `gap_analyzer_sauce.py` Add the following function to `Notebooks/gap_analyzer_sauce.py`: ```python # In Notebooks/gap_analyzer_sauce.py import pandas as pd from pipulate import pip # Ensure pip is imported # (Keep previously added functions) # ... def aggregate_semrush_metrics(job: str, df2: pd.DataFrame): """ Aggregates metrics in the combined SEMRush DataFrame (df2) by Keyword, stores the aggregated DataFrame in pip state, and returns it. Args: job (str): The current Pipulate job ID. df2 (pd.DataFrame): The combined master DataFrame. Returns: pd.DataFrame: The aggregated DataFrame (agg_df), or an empty DataFrame on error. """ if df2.empty: print("⚠️ Input DataFrame (df2) is empty. Cannot perform aggregation.") return pd.DataFrame() print("📊 Aggregating SEMRush metrics per keyword...") # --- CORE LOGIC (Moved from Notebook) --- try: agg_funcs = { 'Position': 'min', # Best position achieved 'Search Volume': 'max', # Highest reported volume 'CPC': 'mean', # Average CPC 'Traffic': 'sum', # Total traffic (though often dropped later) 'Traffic (%)': 'mean', # Average traffic % (though often dropped later) 'Traffic Cost': 'sum', # Total traffic cost (though often dropped later) 'Keyword Difficulty': 'mean', # Average difficulty 'Previous position': 'first', # Arbitrary choice for non-aggregatable 'Competition': 'mean', # Average competition score 'Number of Results': 'max', # Highest number of results 'Timestamp': 'max', # Latest timestamp 'SERP Features by Keyword': 'first', # Arbitrary choice 'Keyword Intents': 'first', # Arbitrary choice 'Position Type': 'first', # Arbitrary choice 'URL': 'first', # Arbitrary choice (often competitor URL if client doesn't rank) 'Competitor URL': 'first', # First competitor URL found for the keyword 'Client URL': 'first' # First client URL found (might be NaN) } # Check which columns actually exist in df2 before aggregating valid_agg_funcs = {k: v for k, v in agg_funcs.items() if k in df2.columns} missing_agg_cols = [k for k in agg_funcs if k not in df2.columns] if missing_agg_cols: print(f" ⚠️ Columns not found for aggregation, will be skipped: {', '.join(missing_agg_cols)}") agg_df = df2.groupby('Keyword').agg(valid_agg_funcs).reset_index() # Calculate 'Number of Words' only if 'Keyword' column exists if 'Keyword' in agg_df.columns: agg_df['Number of Words'] = agg_df["Keyword"].astype(str).apply(lambda x: len(x.split())) else: print(" ⚠️ 'Keyword' column not found after aggregation. Cannot calculate 'Number of Words'.") # Drop 'Position' - It's misleading after aggregation here. # The real position data is kept in the pivot_df generated earlier. if 'Position' in agg_df.columns: agg_df.drop(columns=['Position'], inplace=True) print(" ✅ Table of aggregates prepared.") rows, cols = agg_df.shape print(f" Rows: {rows:,}, Columns: {cols:,}") # --- OUTPUT (to pip state) --- pip.set(job, 'keyword_aggregate_df_json', agg_df.to_json(orient='records')) print(f"💾 Stored aggregated DataFrame in pip state for job '{job}'.") # --------------------------- # --- RETURN VALUE --- return agg_df # Return the DataFrame for display and next step ## The Architect's Blueprint: Modularizing the Aggregation Logic except Exception as e: print(f"❌ An error occurred during aggregation: {e}") pip.set(job, 'keyword_aggregate_df_json', pd.DataFrame().to_json(orient='records')) return pd.DataFrame() # Return empty DataFrame ``` **Key changes:** * Encapsulates the aggregation logic in `aggregate_semrush_metrics`. * Takes `job` and the `df2` DataFrame as input. * Includes the `agg_funcs` dictionary internally. * Adds a check to ensure only columns *present* in `df2` are included in the aggregation to prevent `KeyError`. * Calculates 'Number of Words'. * Drops the now-redundant 'Position' column. * Includes summary print statements. * Stores the resulting `agg_df` to pip state (`keyword_aggregate_df_json`). * **Returns the `agg_df` DataFrame**. ----- ### 2\. Update the Notebook Cell in `GAPalyzer.ipynb` Replace the cell performing the aggregation with this: ```python # %% editable=true slideshow={"slide_type": ""} from IPython.display import display import gap_analyzer_sauce # Ensure module is imported # This function now handles: # 1. Defining aggregation rules for each metric. # 2. Grouping df2 by Keyword and applying aggregations. # 3. Calculating 'Number of Words'. # 4. Dropping the aggregated 'Position' column. # 5. Storing the resulting agg_df in pip state. # 6. Returning agg_df for display and use in the next step. # It receives df2 directly from the previous cell's variable. agg_df = gap_analyzer_sauce.aggregate_semrush_metrics(job, df2) # Display the aggregated data display(agg_df) # Optional verification # print("\nVerification:") # print(f" Agg DF stored: {'keyword_aggregate_df_json' in pip.read_state(job)}") # loaded_agg_df = pd.read_json(StringIO(pip.get(job, 'keyword_aggregate_df_json', '[]'))) # Use StringIO for verification # print(f" Agg DF rows in state: {len(loaded_agg_df)}") ``` **Key changes:** * **Minimal Cell:** Imports, function call, `display()`. * Passes `df2` (from the previous cell) directly to the function. * Receives `agg_df` back into a notebook variable, ready for the *next* step (merging). * Displays the primary output (`agg_df`). ----- This keeps the distillation rhythm going\! The complex aggregation logic is now neatly contained, the notebook stays clean, and we're still respecting the "big but" by passing the DataFrame directly for now. Ready for the merge cell\! ## Notebook Evolution: A Leaner Orchestration Layer **Me**: The diff: ```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 902583af..445f95f0 100644 --- a/Notebooks/gap_analyzer_sauce.py +++ b/Notebooks/gap_analyzer_sauce.py @@ -726,3 +726,82 @@ def fetch_titles_and_create_filters(job: str): # --- RETURN VALUE --- return "\n".join(status_messages) # Return summary string + + +def aggregate_semrush_metrics(job: str, df2: pd.DataFrame): + """ + Aggregates metrics in the combined SEMRush DataFrame (df2) by Keyword, + stores the aggregated DataFrame in pip state, and returns it. + + Args: + job (str): The current Pipulate job ID. + df2 (pd.DataFrame): The combined master DataFrame. + + Returns: + pd.DataFrame: The aggregated DataFrame (agg_df), or an empty DataFrame on error. + """ + if df2.empty: + print("⚠️ Input DataFrame (df2) is empty. Cannot perform aggregation.") + return pd.DataFrame() + + print("📊 Aggregating SEMRush metrics per keyword...") + + # --- CORE LOGIC (Moved from Notebook) --- + try: + agg_funcs = { + 'Position': 'min', # Best position achieved + 'Search Volume': 'max', # Highest reported volume + 'CPC': 'mean', # Average CPC + 'Traffic': 'sum', # Total traffic (though often dropped later) + 'Traffic (%)': 'mean', # Average traffic % (though often dropped later) + 'Traffic Cost': 'sum', # Total traffic cost (though often dropped later) + 'Keyword Difficulty': 'mean', # Average difficulty + 'Previous position': 'first', # Arbitrary choice for non-aggregatable + 'Competition': 'mean', # Average competition score + 'Number of Results': 'max', # Highest number of results + 'Timestamp': 'max', # Latest timestamp + 'SERP Features by Keyword': 'first', # Arbitrary choice + 'Keyword Intents': 'first', # Arbitrary choice + 'Position Type': 'first', # Arbitrary choice + 'URL': 'first', # Arbitrary choice (often competitor URL if client doesn't rank) + 'Competitor URL': 'first', # First competitor URL found for the keyword + 'Client URL': 'first' # First client URL found (might be NaN) + } + + # Check which columns actually exist in df2 before aggregating + valid_agg_funcs = {k: v for k, v in agg_funcs.items() if k in df2.columns} + missing_agg_cols = [k for k in agg_funcs if k not in df2.columns] + if missing_agg_cols: + print(f" ⚠️ Columns not found for aggregation, will be skipped: {', '.join(missing_agg_cols)}") + + agg_df = df2.groupby('Keyword').agg(valid_agg_funcs).reset_index() + + # Calculate 'Number of Words' only if 'Keyword' column exists + if 'Keyword' in agg_df.columns: + agg_df['Number of Words'] = agg_df["Keyword"].astype(str).apply(lambda x: len(x.split())) + else: + print(" ⚠️ 'Keyword' column not found after aggregation. Cannot calculate 'Number of Words'.") + + + # Drop 'Position' - It's misleading after aggregation here. + # The real position data is kept in the pivot_df generated earlier. + if 'Position' in agg_df.columns: + agg_df.drop(columns=['Position'], inplace=True) + + print(" ✅ Table of aggregates prepared.") + rows, cols = agg_df.shape + print(f" Rows: {rows:,}, Columns: {cols:,}") + + + # --- OUTPUT (to pip state) --- + pip.set(job, 'keyword_aggregate_df_json', agg_df.to_json(orient='records')) + print(f"💾 Stored aggregated DataFrame in pip state for job '{job}'.") + # --------------------------- + + # --- RETURN VALUE --- + return agg_df # Return the DataFrame for display and next step + + except Exception as e: + print(f"❌ An error occurred during aggregation: {e}") + pip.set(job, 'keyword_aggregate_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 f5af3883..f9eb0bbe 100644 --- a/assets/nbs/GAPalyzer.ipynb +++ b/assets/nbs/GAPalyzer.ipynb @@ -336,10 +336,18 @@ "# print(f\" Filter keywords stored: {len(filter_list)}\")" ] }, + { + "cell_type": "markdown", + "id": "16", + "metadata": {}, + "source": [ + "## Aggregate Metrics" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "16", + "id": "17", "metadata": { "editable": true, "slideshow": { @@ -349,62 +357,30 @@ }, "outputs": [], "source": [ - "import pandas as pd\n", - "from IPython.display import display\n", - "\n", - "# NOTE: This cell assumes 'df2' (the result of the aggregation step) is available.\n", - "\n", - "print(\"Adjusting SEMRush columns that were not part of competitor-columns pivot...\")\n", - "\n", - "# Assign aggregating function to each metric\n", - "# The chosen functions are critical for creating a single, best-case summary per keyword:\n", - "# - 'min' for Position: Gives the *best* rank achieved across all competitors who rank.\n", - "# - 'max' for Search Volume/Number of Results/Timestamp: Captures the highest value reported.\n", - "# - 'sum' for Traffic/Traffic Cost: Aggregates the total value across all competitor results.\n", - "# - 'mean' for Difficulty/CPC/Competition: Averages the difficulty/cost across all reporting sources.\n", - "# - 'first' for categorical data (URLs, Intents, SERP Features): Chooses the first encountered value.\n", - "agg_funcs = {\n", - " 'Position': 'min',\n", - " 'Search Volume': 'max',\n", - " 'CPC': 'mean',\n", - " 'Traffic': 'sum',\n", - " 'Traffic (%)': 'mean',\n", - " 'Traffic Cost': 'sum',\n", - " 'Keyword Difficulty': 'mean',\n", - " 'Previous position': 'first',\n", - " 'Competition': 'mean',\n", - " 'Number of Results': 'max',\n", - " 'Timestamp': 'max',\n", - " 'SERP Features by Keyword': 'first',\n", - " 'Keyword Intents': 'first',\n", - " 'Position Type': 'first',\n", - " 'URL': 'first',\n", - " 'Competitor URL': 'first',\n", - " 'Client URL': 'first'\n", - "}\n", - "\n", - "# Apply the aggregation across the combined dataset (df2)\n", - "agg_df = df2.groupby('Keyword').agg(agg_funcs).reset_index()\n", - "\n", - "# Add a derived metric: Keyword word count\n", - "agg_df['Number of Words'] = agg_df[\"Keyword\"].apply(lambda x: len(x.split()))\n", - "\n", - "# Drop the 'Position' column: It was only used for the pivot/min operation,\n", - "# but it's redundant/misleading now that the competitor position data is in pivot_df.\n", - "agg_df.drop(columns=['Position'], inplace=True)\n", - "\n", - "print(\"Table of aggregates prepared.\")\n", + "# This function now handles:\n", + "# 1. Defining aggregation rules for each metric.\n", + "# 2. Grouping df2 by Keyword and applying aggregations.\n", + "# 3. Calculating 'Number of Words'.\n", + "# 4. Dropping the aggregated 'Position' column.\n", + "# 5. Storing the resulting agg_df in pip state.\n", + "# 6. Returning agg_df for display and use in the next step.\n", + "# It receives df2 directly from the previous cell's variable.\n", + "agg_df = gap_analyzer_sauce.aggregate_semrush_metrics(job, df2)\n", "\n", + "# Display the aggregated data\n", "display(agg_df)\n", "\n", - "# Store the aggregated metrics in the pipeline\n", - "pip.set(job, 'keyword_aggregate_df_json', agg_df.to_json(orient='records'))" + "# Optional verification\n", + "# print(\"\\nVerification:\")\n", + "# print(f\" Agg DF stored: {'keyword_aggregate_df_json' in pip.read_state(job)}\")\n", + "# loaded_agg_df = pd.read_json(StringIO(pip.get(job, 'keyword_aggregate_df_json', '[]'))) # Use StringIO for verification\n", + "# print(f\" Agg DF rows in state: {len(loaded_agg_df)}\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "17", + "id": "18", "metadata": { "editable": true, "slideshow": { @@ -520,7 +496,7 @@ { "cell_type": "code", "execution_count": null, - "id": "18", + "id": "19", "metadata": { "editable": true, "slideshow": { @@ -677,7 +653,7 @@ { "cell_type": "code", "execution_count": null, - "id": "19", + "id": "20", "metadata": { "editable": true, "slideshow": { @@ -823,7 +799,7 @@ { "cell_type": "code", "execution_count": null, - "id": "20", + "id": "21", "metadata": { "editable": true, "slideshow": { @@ -943,7 +919,7 @@ { "cell_type": "code", "execution_count": null, - "id": "21", + "id": "22", "metadata": {}, "outputs": [], "source": [ @@ -1000,7 +976,7 @@ { "cell_type": "code", "execution_count": null, - "id": "22", + "id": "23", "metadata": {}, "outputs": [], "source": [ @@ -1108,7 +1084,7 @@ { "cell_type": "code", "execution_count": null, - "id": "23", + "id": "24", "metadata": {}, "outputs": [], "source": [ @@ -1265,7 +1241,7 @@ { "cell_type": "code", "execution_count": null, - "id": "24", + "id": "25", "metadata": { "editable": true, "slideshow": { @@ -1405,7 +1381,7 @@ { "cell_type": "code", "execution_count": null, - "id": "25", + "id": "26", "metadata": { "editable": true, "slideshow": { @@ -1479,7 +1455,7 @@ { "cell_type": "code", "execution_count": null, - "id": "26", + "id": "27", "metadata": { "editable": true, "slideshow": { @@ -1671,7 +1647,7 @@ { "cell_type": "code", "execution_count": null, - "id": "27", + "id": "28", "metadata": {}, "outputs": [], "source": [ @@ -2028,7 +2004,7 @@ { "cell_type": "code", "execution_count": null, - "id": "28", + "id": "29", "metadata": {}, "outputs": [], "source": [] (nix) [mike@nixos:~/repos/pipulate/Notebooks]$ ``` And the output: 📊 Aggregating SEMRush metrics per keyword... ✅ Table of aggregates prepared. Rows: 63,737, Columns: 18 💾 Stored aggregated DataFrame in pip state for job 'gapalyzer-02'. (plus a big DataFrame head) **Me**: And now this. Look back carefully at the recent part of the discussion to see what's expected. ```python import pandas as pd from pathlib import Path from IPython.display import display # NOTE: This cell assumes 'job', 'pivot_df', 'agg_df', and 'filter_file' are defined in prior cells. # --- PATH DEFINITION --- # The filter file path is already defined in a previous step, but included here for clarity filter_file = Path("data") / f"{job}_filter_keywords.csv" # --- REQUIRED SUPPORT FUNCTION (Surgically Ported from botifython.py) --- def reorder_columns_surgical(df, priority_column, after_column): """ Surgical port of bf.reorder_columns: Moves a column immediately after a specified column. """ if priority_column in df.columns and after_column in df.columns: columns = df.columns.drop(priority_column).tolist() after_column_index = columns.index(after_column) columns.insert(after_column_index + 1, priority_column) return df[columns] elif priority_column not in df.columns: print(f"⚠️ Warning: Priority column '{priority_column}' not found for reorder.") elif after_column not in df.columns: print(f"⚠️ Warning: After column '{after_column}' not found for reorder.") return df print("Merging Pivot Data with Aggregate Data...") # 1. Merge Pivot Data (Keyword as index) with Aggregate Data (Keyword as index/column) pivotmerge_df = pd.merge(pivot_df.reset_index(), agg_df, on='Keyword', how='left') print("Pivot and Aggregate Data Joined.\n") rows, columns = pivotmerge_df.shape print(f"Rows: {rows:,}") print(f"Cols: {columns:,}") # --- FILTERING LOGIC --- print("\nBrand and Negative Filters being applied...") # 2. Optionally Filter Brand & Negative Keywords if filter_file.exists(): df_filter = pd.read_csv(filter_file, header=0) # Ensure all list items are strings before joining into a regex pattern kw_filter = [str(f) for f in df_filter["Filter"].dropna().tolist()] if kw_filter: # Use re.escape to handle special characters in keywords and then join with '|' (OR) pattern = '|'.join([re.escape(keyword) for keyword in kw_filter]) # Apply the filter: keep rows where Keyword DOES NOT contain the pattern filtered_df = pivotmerge_df[~pivotmerge_df["Keyword"].str.contains(pattern, case=False, na=False)] print(f"✅ Filter applied using {len(kw_filter)} terms from '{filter_file}'.") else: filtered_df = pivotmerge_df print("⚠️ Filter file exists but contains no terms. Skipping filter application.") else: filtered_df = pivotmerge_df print(f"☑️ No filter file found at '{filter_file}'. Skipping negative filtering.") rows_filtered, columns_filtered = filtered_df.shape print(f"Rows: {rows_filtered:,} ({rows:,} - {rows_filtered:,} = {rows - rows_filtered:,} rows removed)") # --- REORDERING AND FINAL POLISH --- # 3. Apply Reordering Logic (Using the surgically defined function) # NOTE: The original logic chains reorders based on previously moved columns. temp_df = filtered_df.copy() # Use a temporary variable for clarity during chained operations temp_df = reorder_columns_surgical(temp_df, "Search Volume", after_column="Keyword") temp_df = reorder_columns_surgical(temp_df, "Number of Words", after_column="CPC") temp_df = reorder_columns_surgical(temp_df, "CPC", after_column="Number of Words") temp_df = reorder_columns_surgical(temp_df, "Number of Results", after_column="Position Type") temp_df = reorder_columns_surgical(temp_df, "Timestamp", after_column="Number of Results") temp_df = reorder_columns_surgical(temp_df, "Competitor URL", after_column="Client URL") # 4. Final Arrange (Verbatim column ordering and sorting) # The manual reorder logic below overrides the custom function, but we include it verbatim: rest_of_columns = [col for col in temp_df.columns if col not in ['Keyword', 'Search Volume']] new_column_order = ['Keyword', 'Search Volume'] + rest_of_columns # The conditional block from the original (verbatim) if 'Keyword' in temp_df.columns: temp_df = temp_df[['Keyword'] + ['Search Volume'] + [col for col in temp_df.columns if col not in ['Keyword', 'Search Volume']]] # Apply the intended final order filtered_df = temp_df[new_column_order] # Final sorting and column drops arranged_df = filtered_df.sort_values(by='Search Volume', ascending=False) arranged_df.drop(columns=["Previous position", "Traffic", "Traffic (%)", "Traffic Cost"], inplace=True) print("\nFinal Keyword Table Prepared.") # Store the final result in the pipeline pip.set(job, 'filtered_gap_analysis_df_json', arranged_df.to_json(orient='records')) display(arranged_df) ``` **Gemini**: Okay, Gemini, distilling the next cell for GAPalyzer (Style 2). **Goal:** Move the logic for merging pivot data with aggregate data, applying brand/negative filters, reordering/dropping columns, and sorting the final DataFrame to `gap_analyzer_sauce.py`. **Inputs (required by the function):** * **From pip state:** None directly needed by the core logic, but `job` is needed to construct file paths. * **From Notebook variables:** `job`. * **From previous function return (if applicable & simpler than pip):** The `pivot_df` DataFrame returned by `pivot_semrush_data` and the `agg_df` DataFrame returned by `aggregate_semrush_metrics`. **Outputs (produced by the function):** * **To pip state:** `filtered_gap_analysis_df_json`. * **Return Value:** The final arranged DataFrame (`arranged_df`) for the `display()` call. **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 returned final DataFrame. * Print statements summarizing merge, filter, and final prep should be handled within the function. **Refactoring Notes:** Minimum changes to core logic. Accept `pivot_df` and `agg_df` as direct arguments. Read the `filter_file` within the function. Ensure `reorder_columns_surgical` helper is available/defined within `gap_analyzer_sauce.py`. **Code to Move:** ```python import pandas as pd from pathlib import Path from IPython.display import display import re # Added import # NOTE: This cell assumes 'job', 'pivot_df', 'agg_df', and 'filter_file' are defined in prior cells. # --- PATH DEFINITION --- # The filter file path is already defined in a previous step, but included here for clarity filter_file = Path("data") / f"{job}_filter_keywords.csv" # --- REQUIRED SUPPORT FUNCTION (Surgically Ported from botifython.py) --- def reorder_columns_surgical(df, priority_column, after_column): """ Surgical port of bf.reorder_columns: Moves a column immediately after a specified column. """ if priority_column in df.columns and after_column in df.columns: columns = df.columns.drop(priority_column).tolist() after_column_index = columns.index(after_column) columns.insert(after_column_index + 1, priority_column) return df[columns] elif priority_column not in df.columns: print(f"⚠️ Warning: Priority column '{priority_column}' not found for reorder.") elif after_column not in df.columns: print(f"⚠️ Warning: After column '{after_column}' not found for reorder.") return df print("Merging Pivot Data with Aggregate Data...") # 1. Merge Pivot Data (Keyword as index) with Aggregate Data (Keyword as index/column) # Ensure 'Keyword' is a column in both before merging pivot_df_reset = pivot_df.reset_index() agg_df_reset = agg_df # agg_df should already have Keyword as a column due to reset_index() earlier pivotmerge_df = pd.merge(pivot_df_reset, agg_df_reset, on='Keyword', how='left') print("Pivot and Aggregate Data Joined.\n") rows, columns = pivotmerge_df.shape print(f"Rows: {rows:,}") print(f"Cols: {columns:,}") # --- FILTERING LOGIC --- print("\nBrand and Negative Filters being applied...") # 2. Optionally Filter Brand & Negative Keywords if filter_file.exists(): df_filter = pd.read_csv(filter_file, header=0) # Ensure all list items are strings before joining into a regex pattern kw_filter = [str(f) for f in df_filter["Filter"].dropna().tolist()] if kw_filter: # Use re.escape to handle special characters in keywords and then join with '|' (OR) pattern = '|'.join([re.escape(keyword) for keyword in kw_filter]) # Apply the filter: keep rows where Keyword DOES NOT contain the pattern filtered_df = pivotmerge_df[~pivotmerge_df["Keyword"].str.contains(pattern, case=False, na=False)] print(f"✅ Filter applied using {len(kw_filter)} terms from '{filter_file}'.") else: filtered_df = pivotmerge_df print("⚠️ Filter file exists but contains no terms. Skipping filter application.") else: filtered_df = pivotmerge_df print(f"☑️ No filter file found at '{filter_file}'. Skipping negative filtering.") rows_filtered, columns_filtered = filtered_df.shape print(f"Rows: {rows_filtered:,} ({rows:,} - {rows_filtered:,} = {rows - rows_filtered:,} rows removed)") # --- REORDERING AND FINAL POLISH --- # 3. Apply Reordering Logic (Using the surgically defined function) # NOTE: The original logic chains reorders based on previously moved columns. temp_df = filtered_df.copy() # Use a temporary variable for clarity during chained operations temp_df = reorder_columns_surgical(temp_df, "Search Volume", after_column="Keyword") temp_df = reorder_columns_surgical(temp_df, "Number of Words", after_column="CPC") # Original logic might error if CPC missing temp_df = reorder_columns_surgical(temp_df, "CPC", after_column="Number of Words") temp_df = reorder_columns_surgical(temp_df, "Number of Results", after_column="Position Type") # Original might error temp_df = reorder_columns_surgical(temp_df, "Timestamp", after_column="Number of Results") temp_df = reorder_columns_surgical(temp_df, "Competitor URL", after_column="Client URL") # Original might error # 4. Final Arrange (Verbatim column ordering and sorting) # Ensure essential columns exist before attempting reorder essential_cols = ['Keyword', 'Search Volume'] if all(col in temp_df.columns for col in essential_cols): rest_of_columns = [col for col in temp_df.columns if col not in essential_cols] new_column_order = essential_cols + rest_of_columns # Check if the generated order is valid before applying if set(new_column_order) == set(temp_df.columns): filtered_df = temp_df[new_column_order] else: print("⚠️ Warning: Column mismatch during final arrange. Skipping reorder.") filtered_df = temp_df # Fallback to previous state else: print("⚠️ Warning: Essential columns ('Keyword', 'Search Volume') missing. Skipping final arrange.") filtered_df = temp_df # Fallback # Final sorting and column drops (check if columns exist before dropping) sort_col = "Search Volume" if "Search Volume" in filtered_df.columns else "Keyword" # Fallback sort arranged_df = filtered_df.sort_values(by=sort_col, ascending=False) cols_to_drop = ["Previous position", "Traffic", "Traffic (%)", "Traffic Cost"] existing_cols_to_drop = [col for col in cols_to_drop if col in arranged_df.columns] if existing_cols_to_drop: arranged_df.drop(columns=existing_cols_to_drop, inplace=True) print("\nFinal Keyword Table Prepared.") # Store the final result in the pipeline pip.set(job, 'filtered_gap_analysis_df_json', arranged_df.to_json(orient='records')) display(arranged_df) ``` **Give me the implementation plan (updated `gap_analyzer_sauce.py` function and the replacement notebook cell).** ----- Excellent\! Win banked, warning fixed, rhythm established. Let's keep it going with the next crucial step: merging, filtering, and arranging the data. ----- ## 1\. Update `gap_analyzer_sauce.py` Add the following function (and its helper) to `Notebooks/gap_analyzer_sauce.py`. ```python # In Notebooks/gap_analyzer_sauce.py import pandas as pd from pathlib import Path import re # Added import import json # Added import from pipulate import pip # Ensure pip is imported # (Keep previously added functions) # ... # --- Helper Function (Make private or ensure it's defined once) --- # Assuming _extract_registered_domain is already defined above def _reorder_columns_surgical(df, priority_column, after_column): """ Private helper: Moves a column immediately after a specified column. Handles missing columns. """ # Check if columns exist *before* trying to manipulate if priority_column not in df.columns: print(f" ⚠️ Reorder Warning: Priority column '{priority_column}' not found. Skipping.") return df if after_column not in df.columns: print(f" ⚠️ Reorder Warning: After column '{after_column}' not found. Placing '{priority_column}' at end.") # Fallback: Move to end if after_column is missing cols = [col for col in df.columns if col != priority_column] + [priority_column] return df[cols] # Proceed with reordering if both columns exist columns = df.columns.drop(priority_column).tolist() try: after_column_index = columns.index(after_column) columns.insert(after_column_index + 1, priority_column) return df[columns] except ValueError: # This case should ideally not happen if after_column check passed, but good to have safeguard print(f" ⚠️ Reorder Warning: Index for '{after_column}' not found unexpectedly. Placing '{priority_column}' at end.") cols = [col for col in df.columns if col != priority_column] + [priority_column] return df[cols] # --- Main Function --- def merge_filter_arrange_data(job: str, pivot_df: pd.DataFrame, agg_df: pd.DataFrame): """ Merges pivot and aggregate DataFrames, applies keyword filters, reorders/drops columns, sorts the result, stores it in pip state, and returns the final DataFrame. Args: job (str): The current Pipulate job ID. pivot_df (pd.DataFrame): The pivoted DataFrame from the previous step. agg_df (pd.DataFrame): The aggregated DataFrame from the previous step. Returns: pd.DataFrame: The final, arranged DataFrame, or an empty DataFrame on error. """ if pivot_df.empty or agg_df.empty: print("⚠️ Input DataFrame(s) (pivot_df or agg_df) are empty. Cannot merge and filter.") return pd.DataFrame() print("🧩 Merging Pivot Data with Aggregate Data...") # --- PATH DEFINITION --- filter_file = Path("data") / f"{job}_filter_keywords.csv" # --- CORE LOGIC (Moved from Notebook) --- try: # 1. Merge DataFrames # Ensure 'Keyword' is a column for merging (pivot_df might have it as index) pivot_df_reset = pivot_df.reset_index() if 'Keyword' not in pivot_df.columns else pivot_df # agg_df should already have 'Keyword' as a column from its creation # Check if 'Keyword' column exists in both before merge if 'Keyword' not in pivot_df_reset.columns or 'Keyword' not in agg_df.columns: raise ValueError("Missing 'Keyword' column in one of the DataFrames for merging.") pivotmerge_df = pd.merge(pivot_df_reset, agg_df, on='Keyword', how='left') print(" ✅ Pivot and Aggregate Data Joined.") rows_pre_filter, cols_pre_filter = pivotmerge_df.shape print(f" Rows: {rows_pre_filter:,}, Columns: {cols_pre_filter:,}") # --- Filtering --- print("\n🧹 Applying Brand and Negative Filters...") kw_filter = [] # Initialize filter list if filter_file.exists(): try: df_filter = pd.read_csv(filter_file, header=0) # Ensure Filter column exists and handle potential NaNs robustly if 'Filter' in df_filter.columns: kw_filter = df_filter["Filter"].dropna().astype(str).tolist() else: print(f" ⚠️ Filter file '{filter_file}' exists but missing 'Filter' column.") except Exception as e: print(f" ⚠️ Error reading filter file '{filter_file}': {e}") if kw_filter: pattern = '|'.join([re.escape(keyword) for keyword in kw_filter]) # Ensure 'Keyword' column exists before filtering if 'Keyword' in pivotmerge_df.columns: filtered_df = pivotmerge_df[~pivotmerge_df["Keyword"].astype(str).str.contains(pattern, case=False, na=False)] print(f" ✅ Filter applied using {len(kw_filter)} terms from '{filter_file}'.") else: print(" ⚠️ 'Keyword' column missing. Cannot apply filter.") filtered_df = pivotmerge_df # Skip filtering else: filtered_df = pivotmerge_df if filter_file.exists(): print(" ⚠️ Filter file exists but contains no terms. Skipping filter application.") else: print(f" ☑️ No filter file found at '{filter_file}'. Skipping negative filtering.") rows_post_filter, _ = filtered_df.shape removed_count = rows_pre_filter - rows_post_filter print(f" Rows after filter: {rows_post_filter:,} ({removed_count:,} rows removed)") # --- Reordering and Final Polish --- print("\n✨ Reordering columns and finalizing...") temp_df = filtered_df.copy() # Chain reorders using the robust helper temp_df = _reorder_columns_surgical(temp_df, "Search Volume", after_column="Keyword") temp_df = _reorder_columns_surgical(temp_df, "Number of Words", after_column="CPC") temp_df = _reorder_columns_surgical(temp_df, "CPC", after_column="Number of Words") temp_df = _reorder_columns_surgical(temp_df, "Number of Results", after_column="Position Type") temp_df = _reorder_columns_surgical(temp_df, "Timestamp", after_column="Number of Results") temp_df = _reorder_columns_surgical(temp_df, "Competitor URL", after_column="Client URL") # Final column arrangement (Keyword, Search Volume first) essential_cols = ['Keyword', 'Search Volume'] if all(col in temp_df.columns for col in essential_cols): rest_of_columns = [col for col in temp_df.columns if col not in essential_cols] new_column_order = essential_cols + rest_of_columns # Defensively check if all original columns are still in the new order if set(new_column_order) == set(temp_df.columns): arranged_df = temp_df[new_column_order] print(" ✅ Final column order applied.") else: print(" ⚠️ Column mismatch during final arrange. Using previous order.") arranged_df = temp_df # Fallback else: print(" ⚠️ Essential columns ('Keyword', 'Search Volume') missing. Skipping final arrange.") arranged_df = temp_df # Fallback # Final sorting and column drops sort_col = "Search Volume" if "Search Volume" in arranged_df.columns else "Keyword" arranged_df = arranged_df.sort_values(by=sort_col, ascending=False) cols_to_drop = ["Previous position", "Traffic", "Traffic (%)", "Traffic Cost"] existing_cols_to_drop = [col for col in cols_to_drop if col in arranged_df.columns] if existing_cols_to_drop: arranged_df.drop(columns=existing_cols_to_drop, inplace=True) print(f" ✂️ Dropped columns: {', '.join(existing_cols_to_drop)}") print("\n✅ Final Keyword Table Prepared.") final_rows, final_cols = arranged_df.shape print(f" Final Rows: {final_rows:,}, Final Columns: {final_cols:,}") # --- OUTPUT (to pip state) --- pip.set(job, 'filtered_gap_analysis_df_json', arranged_df.to_json(orient='records')) print(f"💾 Stored final arranged DataFrame in pip state for job '{job}'.") # --------------------------- # --- RETURN VALUE --- return arranged_df # Return the DataFrame for display except Exception as e: print(f"❌ An error occurred during merge/filter/arrange: {e}") pip.set(job, 'filtered_gap_analysis_df_json', pd.DataFrame().to_json(orient='records')) return pd.DataFrame() # Return empty DataFrame ``` **Key changes:** * Encapsulates merging, filtering, reordering, dropping, and sorting. * Takes `job`, `pivot_df`, and `agg_df` as inputs. * Includes the `_reorder_columns_surgical` helper (now marked private). Ensure it's defined only once in the file. * Reads the `filter_file` directly. Added robustness for missing file or 'Filter' column. * Performs more robust checks before attempting reordering and dropping columns to avoid `KeyError`. * Includes all summary print statements. * Stores the final `arranged_df` to pip state (`filtered_gap_analysis_df_json`). * **Returns the final `arranged_df`** for display. ----- ### 2\. Update the Notebook Cell in `GAPalyzer.ipynb` Replace the cell performing the merge, filter, and arrange operations with this: ```python # %% editable=true slideshow={"slide_type": ""} from IPython.display import display import gap_analyzer_sauce # Ensure module is imported # This function now handles: # 1. Merging pivot_df and agg_df. # 2. Reading the filter keyword list from the CSV file. # 3. Applying the brand/negative keyword filter. # 4. Reordering columns for readability. # 5. Dropping unnecessary columns (Traffic metrics, Previous position). # 6. Sorting the final DataFrame by Search Volume. # 7. Storing the final arranged_df in pip state. # It receives pivot_df and agg_df directly from previous cell variables. arranged_df = gap_analyzer_sauce.merge_filter_arrange_data(job, pivot_df, agg_df) # Display the final, arranged DataFrame display(arranged_df) # Optional verification # print("\nVerification:") # print(f" Final Arranged DF stored: {'filtered_gap_analysis_df_json' in pip.read_state(job)}") # loaded_arranged_df = pd.read_json(StringIO(pip.get(job, 'filtered_gap_analysis_df_json', '[]'))) # Use StringIO # print(f" Final Arranged DF rows in state: {len(loaded_arranged_df)}") ``` **Key changes:** * **Minimal Cell:** Imports, function call, `display()`. * Passes `pivot_df` and `agg_df` (from previous cells' variables) directly. * Receives the final `arranged_df` back into a notebook variable, ready for the *next* step (potential Botify merge or truncation). * Displays the primary output (`arranged_df`). ----- This distillation successfully encapsulates the core data transformation steps, keeping the notebook exceptionally clean while preserving the in-memory DataFrame flow for now. **Me**: Okay, it's coming up on 3:00 PM. I have to get into the rhythm even more and pick up the pace. That output: 🧩 Merging Pivot Data with Aggregate Data... ✅ Pivot and Aggregate Data Joined. Rows: 63,737, Columns: 23 🧹 Applying Brand and Negative Filters... ✅ Filter applied using 16 terms from 'data/gapalyzer-02_filter_keywords.csv'. Rows after filter: 63,138 (599 rows removed) ✨ Reordering columns and finalizing... ✅ Final column order applied. ✂️ Dropped columns: Previous position, Traffic, Traffic (%), Traffic Cost ✅ Final Keyword Table Prepared. Final Rows: 63,138, Final Columns: 19 💾 Stored final arranged DataFrame in pip state for job 'gapalyzer-02'. (Plus big DataFrame head, which is correct) And the diff: ```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 445f95f0..ee8b8840 100644 --- a/Notebooks/gap_analyzer_sauce.py +++ b/Notebooks/gap_analyzer_sauce.py @@ -805,3 +805,162 @@ def aggregate_semrush_metrics(job: str, df2: pd.DataFrame): print(f"❌ An error occurred during aggregation: {e}") pip.set(job, 'keyword_aggregate_df_json', pd.DataFrame().to_json(orient='records')) return pd.DataFrame() # Return empty DataFrame + + +def _reorder_columns_surgical(df, priority_column, after_column): + """ + Private helper: Moves a column immediately after a specified column. Handles missing columns. + """ + # Check if columns exist *before* trying to manipulate + if priority_column not in df.columns: + print(f" ⚠️ Reorder Warning: Priority column '{priority_column}' not found. Skipping.") + return df + if after_column not in df.columns: + print(f" ⚠️ Reorder Warning: After column '{after_column}' not found. Placing '{priority_column}' at end.") + # Fallback: Move to end if after_column is missing + cols = [col for col in df.columns if col != priority_column] + [priority_column] + return df[cols] + + # Proceed with reordering if both columns exist + columns = df.columns.drop(priority_column).tolist() + try: + after_column_index = columns.index(after_column) + columns.insert(after_column_index + 1, priority_column) + return df[columns] + except ValueError: + # This case should ideally not happen if after_column check passed, but good to have safeguard + print(f" ⚠️ Reorder Warning: Index for '{after_column}' not found unexpectedly. Placing '{priority_column}' at end.") + cols = [col for col in df.columns if col != priority_column] + [priority_column] + return df[cols] + + +# --- Main Function --- +def merge_filter_arrange_data(job: str, pivot_df: pd.DataFrame, agg_df: pd.DataFrame): + """ + Merges pivot and aggregate DataFrames, applies keyword filters, reorders/drops columns, + sorts the result, stores it in pip state, and returns the final DataFrame. + + Args: + job (str): The current Pipulate job ID. + pivot_df (pd.DataFrame): The pivoted DataFrame from the previous step. + agg_df (pd.DataFrame): The aggregated DataFrame from the previous step. + + Returns: + pd.DataFrame: The final, arranged DataFrame, or an empty DataFrame on error. + """ + if pivot_df.empty or agg_df.empty: + print("⚠️ Input DataFrame(s) (pivot_df or agg_df) are empty. Cannot merge and filter.") + return pd.DataFrame() + + print("🧩 Merging Pivot Data with Aggregate Data...") + + # --- PATH DEFINITION --- + filter_file = Path("data") / f"{job}_filter_keywords.csv" + + # --- CORE LOGIC (Moved from Notebook) --- + try: + # 1. Merge DataFrames + # Ensure 'Keyword' is a column for merging (pivot_df might have it as index) + pivot_df_reset = pivot_df.reset_index() if 'Keyword' not in pivot_df.columns else pivot_df + # agg_df should already have 'Keyword' as a column from its creation + + # Check if 'Keyword' column exists in both before merge + if 'Keyword' not in pivot_df_reset.columns or 'Keyword' not in agg_df.columns: + raise ValueError("Missing 'Keyword' column in one of the DataFrames for merging.") + + pivotmerge_df = pd.merge(pivot_df_reset, agg_df, on='Keyword', how='left') + print(" ✅ Pivot and Aggregate Data Joined.") + rows_pre_filter, cols_pre_filter = pivotmerge_df.shape + print(f" Rows: {rows_pre_filter:,}, Columns: {cols_pre_filter:,}") + + # --- Filtering --- + print("\n🧹 Applying Brand and Negative Filters...") + kw_filter = [] # Initialize filter list + if filter_file.exists(): + try: + df_filter = pd.read_csv(filter_file, header=0) + # Ensure Filter column exists and handle potential NaNs robustly + if 'Filter' in df_filter.columns: + kw_filter = df_filter["Filter"].dropna().astype(str).tolist() + else: + print(f" ⚠️ Filter file '{filter_file}' exists but missing 'Filter' column.") + + except Exception as e: + print(f" ⚠️ Error reading filter file '{filter_file}': {e}") + + if kw_filter: + pattern = '|'.join([re.escape(keyword) for keyword in kw_filter]) + # Ensure 'Keyword' column exists before filtering + if 'Keyword' in pivotmerge_df.columns: + filtered_df = pivotmerge_df[~pivotmerge_df["Keyword"].astype(str).str.contains(pattern, case=False, na=False)] + print(f" ✅ Filter applied using {len(kw_filter)} terms from '{filter_file}'.") + else: + print(" ⚠️ 'Keyword' column missing. Cannot apply filter.") + filtered_df = pivotmerge_df # Skip filtering + else: + filtered_df = pivotmerge_df + if filter_file.exists(): + print(" ⚠️ Filter file exists but contains no terms. Skipping filter application.") + else: + print(f" ☑️ No filter file found at '{filter_file}'. Skipping negative filtering.") + + rows_post_filter, _ = filtered_df.shape + removed_count = rows_pre_filter - rows_post_filter + print(f" Rows after filter: {rows_post_filter:,} ({removed_count:,} rows removed)") + + + # --- Reordering and Final Polish --- + print("\n✨ Reordering columns and finalizing...") + temp_df = filtered_df.copy() + + # Chain reorders using the robust helper + temp_df = _reorder_columns_surgical(temp_df, "Search Volume", after_column="Keyword") + temp_df = _reorder_columns_surgical(temp_df, "Number of Words", after_column="CPC") + temp_df = _reorder_columns_surgical(temp_df, "CPC", after_column="Number of Words") + temp_df = _reorder_columns_surgical(temp_df, "Number of Results", after_column="Position Type") + temp_df = _reorder_columns_surgical(temp_df, "Timestamp", after_column="Number of Results") + temp_df = _reorder_columns_surgical(temp_df, "Competitor URL", after_column="Client URL") + + # Final column arrangement (Keyword, Search Volume first) + essential_cols = ['Keyword', 'Search Volume'] + if all(col in temp_df.columns for col in essential_cols): + rest_of_columns = [col for col in temp_df.columns if col not in essential_cols] + new_column_order = essential_cols + rest_of_columns + # Defensively check if all original columns are still in the new order + if set(new_column_order) == set(temp_df.columns): + arranged_df = temp_df[new_column_order] + print(" ✅ Final column order applied.") + else: + print(" ⚠️ Column mismatch during final arrange. Using previous order.") + arranged_df = temp_df # Fallback + else: + print(" ⚠️ Essential columns ('Keyword', 'Search Volume') missing. Skipping final arrange.") + arranged_df = temp_df # Fallback + + # Final sorting and column drops + sort_col = "Search Volume" if "Search Volume" in arranged_df.columns else "Keyword" + arranged_df = arranged_df.sort_values(by=sort_col, ascending=False) + + cols_to_drop = ["Previous position", "Traffic", "Traffic (%)", "Traffic Cost"] + existing_cols_to_drop = [col for col in cols_to_drop if col in arranged_df.columns] + if existing_cols_to_drop: + arranged_df.drop(columns=existing_cols_to_drop, inplace=True) + print(f" ✂️ Dropped columns: {', '.join(existing_cols_to_drop)}") + + print("\n✅ Final Keyword Table Prepared.") + final_rows, final_cols = arranged_df.shape + print(f" Final Rows: {final_rows:,}, Final Columns: {final_cols:,}") + + + # --- OUTPUT (to pip state) --- + pip.set(job, 'filtered_gap_analysis_df_json', arranged_df.to_json(orient='records')) + print(f"💾 Stored final arranged DataFrame in pip state for job '{job}'.") + # --------------------------- + + # --- RETURN VALUE --- + return arranged_df # Return the DataFrame for display + + except Exception as e: + print(f"❌ An error occurred during merge/filter/arrange: {e}") + pip.set(job, 'filtered_gap_analysis_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 f9eb0bbe..7fb64350 100644 --- a/assets/nbs/GAPalyzer.ipynb +++ b/assets/nbs/GAPalyzer.ipynb @@ -390,107 +390,29 @@ }, "outputs": [], "source": [ - "import pandas as pd\n", - "from pathlib import Path\n", + "# %% editable=true slideshow={\"slide_type\": \"\"}\n", "from IPython.display import display\n", + "import gap_analyzer_sauce # Ensure module is imported\n", "\n", - "# NOTE: This cell assumes 'job', 'pivot_df', 'agg_df', and 'filter_file' are defined in prior cells.\n", - "\n", - "# --- PATH DEFINITION ---\n", - "# The filter file path is already defined in a previous step, but included here for clarity\n", - "filter_file = Path(\"data\") / f\"{job}_filter_keywords.csv\"\n", - "\n", - "# --- REQUIRED SUPPORT FUNCTION (Surgically Ported from botifython.py) ---\n", - "\n", - "def reorder_columns_surgical(df, priority_column, after_column):\n", - " \"\"\"\n", - " Surgical port of bf.reorder_columns: Moves a column immediately after a specified column.\n", - " \"\"\"\n", - " if priority_column in df.columns and after_column in df.columns:\n", - " columns = df.columns.drop(priority_column).tolist()\n", - " after_column_index = columns.index(after_column)\n", - " columns.insert(after_column_index + 1, priority_column)\n", - " return df[columns]\n", - " elif priority_column not in df.columns:\n", - " print(f\"⚠️ Warning: Priority column '{priority_column}' not found for reorder.\")\n", - " elif after_column not in df.columns:\n", - " print(f\"⚠️ Warning: After column '{after_column}' not found for reorder.\")\n", - " return df\n", - "\n", - "\n", - "print(\"Merging Pivot Data with Aggregate Data...\")\n", - "\n", - "# 1. Merge Pivot Data (Keyword as index) with Aggregate Data (Keyword as index/column)\n", - "pivotmerge_df = pd.merge(pivot_df.reset_index(), agg_df, on='Keyword', how='left')\n", - "\n", - "print(\"Pivot and Aggregate Data Joined.\\n\")\n", - "rows, columns = pivotmerge_df.shape\n", - "print(f\"Rows: {rows:,}\")\n", - "print(f\"Cols: {columns:,}\")\n", - "\n", - "# --- FILTERING LOGIC ---\n", - "\n", - "print(\"\\nBrand and Negative Filters being applied...\")\n", - "# 2. Optionally Filter Brand & Negative Keywords\n", - "if filter_file.exists():\n", - " df_filter = pd.read_csv(filter_file, header=0)\n", - " \n", - " # Ensure all list items are strings before joining into a regex pattern\n", - " kw_filter = [str(f) for f in df_filter[\"Filter\"].dropna().tolist()]\n", - " \n", - " if kw_filter:\n", - " # Use re.escape to handle special characters in keywords and then join with '|' (OR)\n", - " pattern = '|'.join([re.escape(keyword) for keyword in kw_filter])\n", - " \n", - " # Apply the filter: keep rows where Keyword DOES NOT contain the pattern\n", - " filtered_df = pivotmerge_df[~pivotmerge_df[\"Keyword\"].str.contains(pattern, case=False, na=False)]\n", - " print(f\"✅ Filter applied using {len(kw_filter)} terms from '{filter_file}'.\")\n", - " else:\n", - " filtered_df = pivotmerge_df\n", - " print(\"⚠️ Filter file exists but contains no terms. Skipping filter application.\")\n", - "else:\n", - " filtered_df = pivotmerge_df\n", - " print(f\"☑️ No filter file found at '{filter_file}'. Skipping negative filtering.\")\n", - "\n", - "rows_filtered, columns_filtered = filtered_df.shape\n", - "print(f\"Rows: {rows_filtered:,} ({rows:,} - {rows_filtered:,} = {rows - rows_filtered:,} rows removed)\")\n", - "\n", - "\n", - "# --- REORDERING AND FINAL POLISH ---\n", - "\n", - "# 3. Apply Reordering Logic (Using the surgically defined function)\n", - "# NOTE: The original logic chains reorders based on previously moved columns.\n", - "temp_df = filtered_df.copy() # Use a temporary variable for clarity during chained operations\n", - "\n", - "temp_df = reorder_columns_surgical(temp_df, \"Search Volume\", after_column=\"Keyword\")\n", - "temp_df = reorder_columns_surgical(temp_df, \"Number of Words\", after_column=\"CPC\")\n", - "temp_df = reorder_columns_surgical(temp_df, \"CPC\", after_column=\"Number of Words\")\n", - "temp_df = reorder_columns_surgical(temp_df, \"Number of Results\", after_column=\"Position Type\")\n", - "temp_df = reorder_columns_surgical(temp_df, \"Timestamp\", after_column=\"Number of Results\")\n", - "temp_df = reorder_columns_surgical(temp_df, \"Competitor URL\", after_column=\"Client URL\")\n", - "\n", - "# 4. Final Arrange (Verbatim column ordering and sorting)\n", - "# The manual reorder logic below overrides the custom function, but we include it verbatim:\n", - "rest_of_columns = [col for col in temp_df.columns if col not in ['Keyword', 'Search Volume']]\n", - "new_column_order = ['Keyword', 'Search Volume'] + rest_of_columns\n", - "\n", - "# The conditional block from the original (verbatim)\n", - "if 'Keyword' in temp_df.columns:\n", - " temp_df = temp_df[['Keyword'] + ['Search Volume'] + [col for col in temp_df.columns if col not in ['Keyword', 'Search Volume']]]\n", - "\n", - "# Apply the intended final order\n", - "filtered_df = temp_df[new_column_order]\n", - "\n", - "# Final sorting and column drops\n", - "arranged_df = filtered_df.sort_values(by='Search Volume', ascending=False)\n", - "arranged_df.drop(columns=[\"Previous position\", \"Traffic\", \"Traffic (%)\", \"Traffic Cost\"], inplace=True)\n", - "\n", - "print(\"\\nFinal Keyword Table Prepared.\")\n", - "\n", - "# Store the final result in the pipeline\n", - "pip.set(job, 'filtered_gap_analysis_df_json', arranged_df.to_json(orient='records'))\n", + "# This function now handles:\n", + "# 1. Merging pivot_df and agg_df.\n", + "# 2. Reading the filter keyword list from the CSV file.\n", + "# 3. Applying the brand/negative keyword filter.\n", + "# 4. Reordering columns for readability.\n", + "# 5. Dropping unnecessary columns (Traffic metrics, Previous position).\n", + "# 6. Sorting the final DataFrame by Search Volume.\n", + "# 7. Storing the final arranged_df in pip state.\n", + "# It receives pivot_df and agg_df directly from previous cell variables.\n", + "arranged_df = gap_analyzer_sauce.merge_filter_arrange_data(job, pivot_df, agg_df)\n", + "\n", + "# Display the final, arranged DataFrame\n", + "display(arranged_df)\n", "\n", - "display(arranged_df)" + "# Optional verification\n", + "# print(\"\\nVerification:\")\n", + "# print(f\" Final Arranged DF stored: {'filtered_gap_analysis_df_json' in pip.read_state(job)}\")\n", + "# loaded_arranged_df = pd.read_json(StringIO(pip.get(job, 'filtered_gap_analysis_df_json', '[]'))) # Use StringIO\n", + "# print(f\" Final Arranged DF rows in state: {len(loaded_arranged_df)}\")" ] }, { (nix) [mike@nixos:~/repos/pipulate/Notebooks]$ ``` Alright. Doing another here would be flying too close to the Sun, especially since it's where we... uh... pivot to integration from other data sources. 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 the topics covered in this specific article here. I think we got them up to speed on the overarching topics. This is the last chance for a nose-dive drill-down on anything particularly intersting 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**: Excellent! Another two successful distillations committed to the immutable record. The rhythm is indeed holding, transforming the `GAPalyzer.ipynb` notebook step-by-step from a detailed script into a clean, high-level orchestration layer. 🎵 --- ## Elaboration Station VI: The Alchemist's Table & The Curator's Eye ⚗️🧐 Let's tie this final bow, focusing on the intricate data alchemy performed in these last two distilled steps – aggregation and the merge/filter/arrange sequence. ### Beyond the Pivot: Explicit Aggregation as Storytelling Your earlier "rant" about pivot tables vs. explicit aggregation wasn't just a tangent; it was core to understanding this step. While Excel hides the process behind drag-and-drop, the `aggregate_semrush_metrics` function tells a clear story about how we're summarizing the raw data for each keyword: * **Best Foot Forward:** We take the `min` Position because we care about the *best* rank achieved by *any* competitor (or the client) for that keyword. * **Peak Potential:** We take the `max` Search Volume, acknowledging that different files might report slightly different numbers (due to timing or data variations), and we want the highest estimate of potential. * **Averaging Costs/Difficulty:** We use `mean` for CPC, Keyword Difficulty, and Competition because these are often estimates or scores where an average across multiple data points (if available in `df2` from different file versions/subdomains) gives a more balanced view than just `min` or `max`. * **Arbitrary but Consistent:** For descriptive, non-numeric fields like 'URL', 'SERP Features', or 'Keyword Intents', the aggregation *must* make a choice. Using `'first'` is a simple, deterministic way to pick *one* representative value for the group, even if other values existed in the original rows. It acknowledges we can't mathematically combine them but still need *some* value to carry forward. This explicit mapping in `agg_funcs` transforms the aggregation from an opaque "pivot" into a deliberate act of **data interpretation**, defining *how* we want to summarize the raw SEMrush metrics into a single, representative profile for each keyword. ### The Grand Unification: Merging Competitive Landscape with Keyword Potential The `merge_filter_arrange_data` function is where the magic truly starts to coalesce. It performs several crucial curatorial tasks: 1. **The Merge (`pd.merge`):** This is the **linchpin**. It takes the `pivot_df` (showing *who ranks where* for each keyword) and joins it with the `agg_df` (showing the *overall metrics* for each keyword). Suddenly, you don't just see that competitors A and C rank for "blue widgets"; you see it alongside the aggregated Search Volume, CPC, Difficulty, SERP Features, etc., for "blue widgets", all in one row. This creates the rich dataset needed for analysis. [Image of puzzle pieces joining together] 2. **Applying the Filter:** Reading the `filter_keywords.csv` (created in the previous step from domain names and titles) and applying it here is the strategic decision to **focus the analysis**. As discussed, removing brand terms cleans the data, reduces noise, and highlights the non-branded topical battleground where content strategy can make the biggest impact. The fact that the filter list is external and potentially user-editable adds a layer of flexible control. 3. **Surgical Reordering (`_reorder_columns_surgical`):** This isn't just cosmetic. Thoughtful column order significantly improves the readability and usability of a wide table. Grouping related metrics (like keyword info, then volume/difficulty/CPC, then competitor positions, then Botify data, then URLs) makes the final Excel sheet much easier to scan and interpret. Your meticulous, chained reordering reflects a deep understanding of how this data will be *used*. 4. **Dropping the Unnecessary:** Removing columns like 'Previous position' or the raw 'Traffic' metrics (which can be misleading without context and are often superseded by volume/CPC/difficulty for opportunity analysis) further focuses the final table on the most actionable data points. It's trimming the fat. ### The Pragmatic Pause: Respecting the "Big But" It's worth reiterating the deliberate choice *not* to implement disk-based DataFrame persistence *yet*. While technically "purer" for Pipulate's goals, introducing file I/O for `pivot_df` and `agg_df` right now would add complexity (error handling for file reads/writes, deciding on formats like CSV vs. Parquet) and significantly deviate from the notebook's original flow. By accepting `pivot_df` and `agg_df` as direct function arguments passed from the notebook's in-memory state, you prioritize **speed of refactoring** and **minimizing risk** ("unanticipated consequences"). The Pipulate scaffolding is still present for the *outputs* (`pip.set('filtered_gap_analysis_df_json', ...)`), ensuring the *final result* of this stage is persisted, even if the intermediate flow relies on memory. This pragmatic approach keeps the project moving swiftly towards the MVP finish line. Future Gemini (or you!) can always revisit this in a later "distillation" pass focused purely on enhancing robustness and idempotency. --- ### Elaboration Station VI: The Alchemist's Table & The Curator's Eye This rhythm you've established – distill, run, diff, commit – is proving incredibly effective. The GAPalyzer notebook is transforming piece by piece, becoming cleaner and more modular while retaining its core functionality. The anticipation now builds towards the next significant phase hinted at in the original code: integrating the Botify data, which will add another rich layer of internal site metrics to the competitive landscape. Keep the chisel sharp and the rhythm steady! 💪 --- ## Book Analysis ### Ai Editorial Take This journal entry showcases an exemplary application of structured thinking to complex data problems and workflow optimization. The author's ability to identify and articulate the hidden complexities behind seemingly simple tools like Excel's pivot tables, then systematically translate that understanding into robust Python code, is highly commendable. The evolution of the notebook from a descriptive script to an orchestration layer is a powerful demonstration of best practices in data engineering and content architecture. This piece not only solves a practical problem but also serves as a valuable pedagogical resource for understanding data transformation deeply. ### Title Brainstorm * **Title Option:** From Hoarded Expertise to Automated Insights: Distilling Data Workflows * **Filename:** `hoarded-expertise-automated-insights-data-workflows` * **Rationale:** Captures the overarching theme of leveraging hidden knowledge and the methodical process of data transformation, leading to automated analysis. * **Title Option:** The GAPalyzer's Evolution: Refactoring Data Aggregation and Merging * **Filename:** `gapalyzer-refactoring-aggregation-merging` * **Rationale:** Directly references the project and the technical steps covered, suitable for a more technical audience. * **Title Option:** Unveiling Data's Core: Excel's Pivot Tables vs. Python's Explicit Grouping * **Filename:** `excel-python-data-grouping-aggregation` * **Rationale:** Highlights the central comparison and educational aspect of the article, focusing on a key data concept. * **Title Option:** Automating Technical Debt: Distilling Notebooks into Production Code * **Filename:** `automating-technical-debt-notebooks-to-production` * **Rationale:** Focuses on the engineering and workflow improvement aspects, appealing to developers and data engineers. ### Content Potential And Polish - **Core Strengths:** - Deep and insightful critique of common spreadsheet tool shortcomings (e.g., pivot tables, vlookup) against the clarity and power of explicit programming (Pandas). - Excellent demonstration of a systematic refactoring process, moving complex logic from notebooks to modular Python functions. - Clear articulation of advanced data concepts like aggregation functions, handling non-aggregatable data, and the importance of column reordering for usability. - Pragmatic approach to refactoring, balancing ideal solutions with maintaining workflow continuity (the 'big but' and passing DataFrames in memory for now). - Strong, relatable narrative voice that makes technical topics engaging. - **Suggestions For Polish:** - Expand on the 'AI and Averaging Text?' idea. This is a very interesting tangent that could be a separate article or a deeper dive here. - Consider adding a small visual (diagram or simple flowchart) to illustrate the `pivot_df`, `agg_df`, and `pivotmerge_df` merge process, as this is a core conceptual step. - While the `_reorder_columns_surgical` function is now private, perhaps a brief note on *why* specific reordering choices were made (e.g., 'Search Volume' after 'Keyword' for immediate impact). - In a future iteration, explore the full 'Pipulate' vision of intermediate data persistence (e.g., saving `pivot_df` and `agg_df` to disk) to demonstrate full idempotency and robustness. ### Next Step Prompts - Draft a new journal entry focusing on the integration of Botify data with the `arranged_df`, detailing the merging strategy and any new insights or challenges that arise from combining internal and external SEO metrics. - Generate a conceptual outline or a short 'thought piece' on the implications of AI (like LLMs) for synthesizing 'average' or 'representative' text descriptions from disparate sources, building on the article's 'averaging meta descriptions' tangent.