5 Tools to Use With Excel: Maps, ChatGPT, VS Code, Notion and Power Query

The ideas are good. The details matter more, because each tool has limits that decide whether the workflow works for you. Here is each pairing, with the setup and the gotchas.

Google My Maps: turn location rows into a map​

Phillips describes building a spreadsheet of major New York-area airports. He found that rows of names and coordinates didn't show how JFK, LaGuardia, Newark and the smaller airports relate to each other. Importing the sheet into Google My Maps turned it into an interactive map. He also uses it for trip planning and customer locations.

How the import works, per Google's My Maps help:

• Not applicableGoogle's help page lists the accepted inputs: CSV, TSV, KML, KMZ, GPX, XLSX, and Google Sheets.
• Unless the file is a photo, the first row should hold column titles.
• You need at least one column that locates each row. That can be latitude and longitude, addresses, place names, or WKT geometry.
• You add the data by opening or creating a map, choosing Add layer, naming the layer, and choosing Import.

The limit to know: Google's guidance says not to import files with more than 2,000 rows. A list of airports, stores or trip stops fits easily. A large customer or sensor dataset does not fit in one layer. The sources don't establish a workaround, so split big data or test it first.

A useful extra: My Maps can re-import a layer from a CSV or Google Sheet and keep your styling. The options are replace all items, update matching items, add more items, and merge matching items. That helps if your Excel list keeps changing.

ChatGPT: a sounding board before you build​

Phillips says he has tested ChatGPT, Claude, Gemini and Copilot extensively with Excel. He credits Claude with large automation projects, Gemini with precise implementation plans, and Copilot with growing ability inside Excel. He still returns to ChatGPT when working through a problem. His example was comparing formulas, helper columns, PivotTables and Power Query before choosing an approach. These rankings are his personal impressions, not benchmarks.

What OpenAI documents:

• ChatGPT can analyze uploaded spreadsheets, including .xls, .xlsx and .csv files, and it can create tables and charts.
• It works best with clear column headers, one record per row, and no empty rows or columns splitting the data.
• File types and limits vary by model, plan and workspace settings.
• For some tasks it writes and runs Python. OpenAI says to review the generated code, outputs and assumptions before relying on the result.
• A file can upload but still be too large or complex for complete analysis. In that case OpenAI suggests asking about specific sheets or splitting the file.

Practical guidance:

• Describe the goal, and say what must not change.
• Ask for alternatives and trade-offs, as Phillips does. Don't ask for just one answer.
• Test any formula against a few rows you can check by hand.
• Think about data sensitivity before uploading a workbook. Phillips doesn't discuss privacy. OpenAI's help page points to its retention and data-control documentation for how uploaded files are handled. For confidential company data, check your organization's policy first.

VS Code: handle the CSV as the text file it is​

A CSV is a text file, and Phillips found that VS Code helped once he treated it that way. He used it to inspect a CSV of more than two million rows, which Excel couldn't show in full. He also uses it to compare file versions, fix encoding problems, search large data and make bulk edits. His claim that it opened the file "without complaint" is his own experience. The sources here don't verify that VS Code handles every file that size smoothly.

Excel's side of the limit, per Microsoft:

• A worksheet holds at most 1,048,576 rows by 16,384 columns, so a two-million-row file cannot fit on one sheet.
• Opening such a file directly can produce a warning that the data set is too large for the Excel grid. If you save over the original, you lose the data that wasn't loaded.
• Microsoft's safer route is to use Data > From Text/CSV in Excel for PC, choose Load To, and pick PivotTable Report. The PivotTable then works with the whole data set.
• Save a truncated copy under a different name if you must save.
• Cross-check that all the rows arrived. Compare the source row count with the count shown in Excel's status bar.

The takeaway: a text editor is good for looking at and fixing raw files. Opening a file there doesn't mean you've analyzed it. For analysis beyond the grid, use Power Query and a PivotTable.

Notion: the same rows in different views​

Phillips imported a task-tracker CSV into Notion and made it a database. The rows became a table, a status board and a due-date calendar. His reasoning is that Excel is better for entering, cleaning and analyzing data, while Notion is better for exploring it.

How Notion handles it, per its help pages:

• Go to Settings, then Import, then CSV. You can create a new database or import into an existing one.
• Rows become database items and columns become properties.
• Make sure the first row holds headers.
• Dates import in MM/DD/YYYY format. Columns with mixed data types may import as text.
• CSV upload limits are 5 MB per file on Free and 50 MB on paid plans.
• A new CSV import can't create formulas, rollups or relations. Rebuild them in Notion.
• If numbers such as currency, percentages or negatives import badly, Notion suggests mapping that column to Text.
• For garbled characters, re-export the CSV as UTF-8.

The catch for ongoing use: CSV imports add rows. They don't update existing ones, so repeated imports create duplicates. Notion isn't a live link to your workbook. Treat it as a one-way copy unless you plan to manage updates.

File Explorer and Power Query: drop a file, refresh, done​

Phillips calls this the least glamorous item and the one that saved him the most repetitive work. The consolidation is done by Power Query, and the folder is where new files land. He adds a report by dropping it into the folder and refreshing the workbook. He later set the report to refresh when it opens.

Setup, per Microsoft Support:

1. In Excel, select Data > Get Data > From File > From Folder.
2. Browse to the folder and confirm the listed files are the ones you want.
3. Choose a command such as Combine > Combine & Load, or Combine & Transform Data if you want to shape the data first.
4. In the Combine Files dialog, pick the sample file and check settings such as delimiter and data type detection. You can also choose to skip files with errors.
5. Excel builds the helper queries and loads the combined table. After that, refresh to include new files.

Before you start:

• Use a dedicated folder. Without one, every file in the folder and any selected subfolders is included.
• Files should share a schema with consistent headers, data types and column counts. Column order doesn't matter, because matching is by name.
• If a folder mixes file types or has subfolders, open Transform Data and filter on columns such as Extension or Folder Path before combining.

Refresh on open: Microsoft's refresh documentation describes a "Refresh data when opening the file" checkbox in the connection properties. Third-party Excel guides say it is off by default, so you have to enable it. Excel Off The Grid says Power Query only recalculates when told to. The setting runs when the workbook opens. It does not watch the folder while the file is closed. A community answer on the Microsoft Fabric forum says new files only trigger an automatic refresh through something like a Power Automate flow. That claim comes from a forum thread, so treat it as a lead, not Microsoft guidance.

Reading the list critically​

Phillips writes from one person's experience, and the article is a personal roundup. Some of its specifics, like the two-million-row file and the time savings, can't be independently checked. The workflows are real and supported by vendor documentation, though.

A fair counterpoint is that every added tool adds a place for data to drift out of sync. The Notion duplication behavior and the manual nature of My Maps re-imports are examples. The tools that read from the source file, like Power Query, avoid that problem. The ones that take a copy, like Notion, don't.

Quick start​

1. Pick the one task that costs you the most time each week.
2. Match it to the pairing above and read that tool's limit first.
3. Keep the original Excel file as the source of truth.
4. Verify row counts and formulas after every import or AI-assisted change.

Excel stays at the center. The list is a reminder that a tool for the job next to it can save you real time.