Anki is one of the most popular flashcard system for spaced repetition learning, pandas is the most popular python package for data analysis and manipulation. So what could be better than to bring both together?

With AnkiPandas you can use pandas to easily analyze or manipulate your Anki flashcards.


  • Select: Easily select arbitrary subsets of your cards, notes or reviews using pandas (one of many introductions, official documentation)
  • Visualize: Use pandas’ powerful built in tools or switch to the even more versatile seaborn (statistical analysis) or matplotlib libraries
  • Manipulate: Apply fast bulk operations to the table (e.g. add tags, change decks, set field contents, suspend cards, …) or iterate over the table and perform these manipulations step by step
  • Add: Add new notes and cards
  • Import and Export: Pandas can export to (and import from) csv, MS Excel, HTML, JSON, … (io documentation)


  • Easy installation: Install via python package manager (independent of your Anki installation)
  • Simple: Just one line of code to get started
  • Convenient: Bring together information about cards, notes, models, decks and more in just one table!
  • Fully documented: Documentation on readthedocs
  • Well tested: More than 100 unit tests to keep everything in check

Alternatives: If your main goal is to add new cards, models and more, you can also take a look at the genanki project.


AnkiPandas is available as pypi package and can be installed or upgrade with the python package manager:

pip3 install --user --upgrade ankipandas

For the latest development version you can also work from a cloned version of this repository:

git clone
cd ankipandas
pip3 install --user --upgrade .


Starting up is as easy as this:

from ankipandas import Collection

col = Collection()

And col.notes will be dataframe containing all notes, with additional methods that make many things easy. Similarly, you can access cards or reviews using or col.revs.

If called without any argument Collection() tries to find your Anki database by itself. However this might take some time. To make it easier, simply supply (part of) the path to the database and (if you have more than one user) your Anki user name, e.g. Collection(".local/share/Anki2/", user="User 1") on many Linux installations.

To get information about the interpretation of each column, use print(col.notes.help_cols()).

Take a look at the documentation to find out more about more about the available methods!

Some basic examples:


** More examples can be found in the analysis documentation **

Show a histogram of the number of reviews (repetitions) of each card for all decks:"creps", by="cdeck")

Show the number of leeches per deck as pie chart:

cards =
selection = cards[cards.has_tag("leech")]

Find all notes of model MnemoticModel with empty Mnemotic field:

notes = col.notes.fields_as_columns()
notes.query("model=='MnemoticModel' and 'Mnemotic'==''")


Add the difficult-japanese and marked tag to all notes that contain the tags Japanese and leech:

selection = col.notes.has_tags(["Japanese", "leech"])
selection = selection.add_tag(["difficult-japanese", "marked"])
col.write(modify=True)  # Overwrites your database after creating a backup!

Set the language field to English for all notes of model LanguageModel that are tagged with English:

selection = col.notes.has_tag(["English"]).query("model=='LanguageModel'").copy()
selection["language"] = "English"

Move all cards tagged leech to the deck Leeches Only:

selection ="leech")
selection["cdeck"] = "Leeches Only"


Your help is greatly appreciated! Suggestions, bug reports and feature requests are best opened as github issues. You could also first discuss in the gitter community. If you want to code something yourself, you are very welcome to submit a pull request!

License & Disclaimer

This software is licenced under the MIT license and (despite best testing efforts) comes without any warranty. The logo is inspired by the Anki logo (license) and the logo of the pandas package (license2). This library and its author(s) are not affiliated/associated with the main Anki or pandas project in any way.