How to Export Your Spotify History to Excel and Uncover Hidden Patterns

What You Need Before You Start

So you want to dig into your Spotify history export and actually make sense of it? Good call. Most people just glance at their yearly Wrapped and call it a day. But the real gold is in your full streaming history—every track, every skip, every 3 AM deep dive into obscure synth-pop.

Before we jump in, let's get the basics straight. You'll need three things: a Spotify account (Free or Premium both work), access to the email tied to that account, and about 15 minutes of patience. That's it. No coding experience required—though I'll show you the manual route if you're feeling adventurous.

Prerequisites for Spotify Data Export

First things first: you must request your data through Spotify's privacy settings. This isn't something you can scrape from the app. Spotify holds your listening history hostage until you formally ask for it. Annoying? A little. But the payoff is worth it.

You'll also need a tool to convert the raw data into something Excel can read. Spotify hands you JSON files—great for developers, terrible for spreadsheet lovers. That's where rigtch.fm comes in. It's built specifically for Spotify data analytics and turns those messy JSON files into clean Excel spreadsheets. Honestly, it's the easiest path.

Understanding the Data You'll Get

Here's the catch: Spotify gives you two types of data. The basic "Account data" export includes your profile info and a limited play history. Useless for real analysis. You want the "Extended streaming history"—that's the one with timestamps, track names, artists, and ms_played (milliseconds listened). Without timestamps, you can't do music analytics worth a damn.

The extended history covers everything you've streamed since you created your account. But there's a limit: Spotify only keeps data going back about 12 months in the detailed format. Older stuff gets rolled up into aggregates. So if you've been on Spotify for years, you'll get the last year of granular data plus summary stats for the rest.

Pro tip: Request your data every 6 months to build a continuous record. Otherwise you'll have gaps when Spotify purges old logs.

Step 1: Request Your Spotify Data from the Privacy Settings

This is the part where most people screw up. They click the wrong button, get the wrong data, and wonder why their Excel file is empty. Don't be that person.

How to Submit a Data Request

Open a desktop browser (this won't work on mobile) and log into your Spotify account. Click your profile picture in the top-right corner, then select "Account". From there, navigate to "Privacy Settings"—usually under the "Security and privacy" section.

Scroll down to the "Download your data" area. You'll see a list of checkboxes. Check only "Extended streaming history". Ignore everything else unless you want your saved playlists and search history too. Click the big green "Request" button.

Spotify will send you a confirmation email. Don't delete it. You'll need to verify the request by clicking a link inside.

Waiting for Spotify's Response

Here's where patience kicks in. Spotify says it takes up to 30 days. In practice, I've seen it arrive in as little as 5 days. But plan for the worst. Check your spam folder daily—the email often lands there because it comes from a noreply address.

If 30 days pass and nothing shows up, submit another request. Sometimes their system drops the ball. I've had to request twice before getting my data.

Step 2: Download and Unzip Your Spotify Data

The email finally arrives. You click the link, and a ZIP file downloads. Now what?

Locating the Downloaded Files

The ZIP file is usually named "Spotify Data.zip" or something similar. Download it to a folder you can find easily—your desktop works fine. Right-click and extract the contents. On Windows, use "Extract All." On Mac, just double-click.

Inside, you'll see a folder with several files. The one you want is "StreamingHistory_music.json". If you have years of data, there might be multiple files like "StreamingHistory_music_0.json", "StreamingHistory_music_1.json", etc. That's normal—Spotify splits large exports into chunks.

Understanding the JSON File Structure

Open one of those JSON files in a text editor (Notepad works, but VS Code or Sublime Text is better). You'll see something like this:

[
  {"endTime": "2026-05-15 14:32:10", "artistName": "Radiohead", "trackName": "Karma Police", "msPlayed": 234000},
  {"endTime": "2026-05-15 14:36:04", "artistName": "Radiohead", "trackName": "No Surprises", "msPlayed": 198000}
]

Each row is one stream. endTime is when the track finished playing. msPlayed is how many milliseconds you actually listened. If you skipped a song after 10 seconds, msPlayed will be around 10000. This is your raw material for Spotify user analytics.

Do NOT edit these files directly. One stray comma and the whole thing breaks. Use a conversion tool instead.

Step 3: Convert JSON to Excel Using the Best Tools

This is where the magic happens. You've got raw JSON. You want clean Excel rows. Let me show you the smartest way.

Why rigtch.fm Is the Easiest Option

Look, I could walk you through writing Python scripts or using clunky online converters. But why bother when rigtch.fm does it in two clicks? Upload your JSON files, and it spits out a perfectly formatted Excel spreadsheet with columns for track name, artist, play time, date, and more.

What sets rigtch.fm apart is the extras. It automatically calculates Spotify stats like your peak listening hours, most-played tracks by month, and even genre breakdowns (if your data includes that). These insights get baked right into the Excel file as additional sheets. You're not just converting data—you're getting a Spotify analytics dashboard in spreadsheet form.

And no, I'm not shilling. I've used Python, I've used online converters, and I keep coming back to rigtch.fm because it saves me an hour every time.

Manual Conversion with Python or Online Converters

If you're a DIY type, here's the Python route. Install pandas (pip install pandas openpyxl), then run this:

import pandas as pd
df = pd.read_json('StreamingHistory_music.json')
df.to_excel('spotify_history.xlsx', index=False)

That's it. Three lines of code. But you'll need to handle multiple JSON files manually, and you won't get any of the analytics extras.

Online JSON-to-Excel converters exist too. Most are free but sketchy—they upload your data to unknown servers. I don't recommend it for privacy reasons. Your Spotify history is personal.

Step 4: Import the Excel File and Clean Your Data

Now you've got an Excel file. Open it up. It probably looks messy. That's normal. Let's clean it.

Opening the File in Excel or Google Sheets

Double-click the .xlsx file to open it in Excel. If you don't have Excel, upload it to Google Sheets (free with a Google account). Either works fine for what we're doing.

You should see columns like endTime, artistName, trackName, and msPlayed. If you used rigtch.fm, there might be extra columns like minutesPlayed or hourOfDay. Nice touch—saves you from doing the math.

Removing Duplicates and Errors

Spotify's data isn't perfect. Sometimes the same track appears twice in quick succession—maybe you restarted it or the app glitched. Here's how to clean it:

• Remove duplicate rows: Select all columns, then go to Data > Remove Duplicates. Check all columns and click OK. This eliminates exact duplicates.
• Filter for nulls: Click the filter icon on each column header. Look for blank cells in artistName or trackName. Delete those rows—they're errors.
• Check msPlayed: Any row with msPlayed under 30000 (30 seconds) is probably a skip. Decide if you want to keep or remove those. For accurate music analytics, I remove everything under 30 seconds.

This cleaning step takes 5 minutes but makes your analysis 10x more accurate. Don't skip it.

Step 5: Analyze Your Listening Patterns Like a Pro

Clean data in hand? Now the fun begins. Let's find the hidden patterns.

Creating Pivot Tables for Top Artists and Tracks

Pivot tables are your best friend. Here's the setup for top artists:

1. Select all your data (Ctrl+A or Cmd+A).
2. Go to Insert > PivotTable.
3. Drag artistName into the "Rows" box.
4. Drag trackName (or msPlayed) into the "Values" box. If using msPlayed, change the value setting to "Sum" or "Count."
5. Sort descending by the count or sum column.

Boom—you've got your most-streamed artists of all time. Want tracks instead? Swap artistName for trackName in the rows.

For a deeper Spotify analytics dashboard, add endTime to the "Filters" box. Now you can filter by month, week, or even day to see how your taste evolved.

Visualizing Trends with Charts

Numbers are great. Charts tell the story. Here's what I do:

• Line chart for listening over time: Create a pivot table with endTime (group by month) in rows and Count of trackName in values. Insert a line chart. You'll instantly see your peak months.
• Bar chart for peak listening hours: Add a column called hourOfDay (extracted from endTime using =HOUR() formula). Pivot by that column. Most people peak between 8-10 PM.
• Conditional formatting for intensity: Select your msPlayed column, go to Home > Conditional Formatting > Color Scales. Darker cells = more listening. It's a quick visual of your most-listened moments.

Honestly, once you start looking at this data, you'll notice patterns you never expected. I discovered I listen to jazz only on Sunday mornings. Weird, right?

What to Do Next: Share, Compare, or Automate

You've got your analysis. Now what? Three options, depending on your goals.

Sharing Your Insights on Social Media

Export your top stats as images or PDFs directly from Excel. Select the pivot table or chart, then File > Export. Post your "Top 10 Artists of 2026" on Instagram or Twitter. People eat this stuff up—especially if you add commentary like "Apparently I'm still not over my emo phase."

For cleaner visuals, use rigtch.fm's built-in export feature. It generates social-ready graphics without the Excel hassle.

Setting Up Automatic Exports with rigtch.fm

Here's the game-changer: rigtch.fm offers recurring exports. Set it once, and it emails you updated Excel files every month. No more manual data requests every 6 months. No more forgetting your password. The service handles the Spotify API integration so you get fresh data automatically.

This is huge for ongoing Spotify user analytics. You can track how your listening changes with seasons, new album releases, or life events. Did you listen to more sad music after a breakup? The data will confirm it.

Comparing Your Data with Friends

Merge multiple Excel files from different people. Use Power Query (Excel's Get & Transform Data tool) to combine them into one table. Add a column for "Person" to track who's who. Then create pivot tables comparing top artists across your friend group. Winner gets bragging rights.

Final Tips for Getting the Most from Your Spotify History

You've made it through the tutorial. Here's what I've learned from doing this dozens of times:

Common Mistakes to Avoid

• Requesting basic data instead of extended. The basic export skips timestamps. You get track names but no dates. Useless for trend analysis.
• Editing JSON files manually. One misplaced comma corrupts the entire file. Always use a converter.
• Forgetting to clean data. Skips and duplicates will inflate your stats. Remove them first.
• Not backing up your original ZIP. Keep the original download in case you need to redo the conversion.

When to Request a New Data Export

Set a calendar reminder for every 6 months. Spotify's extended history covers roughly the last year, so requesting twice a year keeps your data continuous. If you're using rigtch.fm's automatic export, you don't need to worry about this—it handles the timing.

One more thing: rigtch.fm has a guide on interpreting listening data that goes deeper than this tutorial. If you want to understand why you listen to certain genres at specific times or how your music taste correlates with your mood, check it out. It's the difference between knowing your top artist and understanding your listening psychology.

Now go export that history. Your music tells a story—it's time to read it.