Drop missing values ✂
With the new transformation "Dropping missing (NA) values", you can quickly drop all missing values in one or multiple columns
Reorder columns 🔁
Move columns to the front or back of your DataFrame or place them wherever you want.
Categorise numeric values with your new binning feature
Groupby / aggregate
We added new selection options. Apply groupby functions on all columns, on all columns matching a data type or on all columns matching a regular expression.
We enlarged the code box in the "Add Python code" transformation so that you can add larger snippets more easily
Create Pivot Tables📑
Create full fletched pivot tables, including code export.
Create multiple aggregations for one column faster than ever 💪
Apply multiple aggregation functions on a column quickly through a multi-select dropdown
Support for categorical dtype
Convert string columns into categorical ones to save memory.
Rename columns during groupby / aggregate
Quick edit the last transformation from the main control panel
We added a new plot creator that lets you quickly build interactive plotly express graphs. You can also export the code for further customization. The creator works with the most important plot figure types. In case you are missing a specific figure type, please let us know.
Public license file for automatic activation
We provide a public license file under
~/.bamboolib/LICENSE that can be used for automatic license activation in docker or on other computers / VMs
We replaced the close, back, and delete buttons with icons, among others
Added a README to pypi.org. It was about time! 🙂
Keyboard support - Part 2⌨ In order to further improve the user experience when working with the keyboard, we replaced all transformation buttons from the main panel with one text input field, which means that all transformations are now fully searchable via the keyboard.
Better experience when working with the keyboard
When starting a transformation, the first input is always focused. Also, users can select values from the dropdown using tab.
Live code export
We made improvements on the user experience based on user feedback.
More plugin examples We added more examples to our plugin docs, e.g. a plugin example for how to write a custom aggregation function and a plugin showing how you can extract attributes from time delta columns (If you these plugins to become part of our core functionalities, please send us an email).
If the user selects a key for
df_left and there is a key with the same name in
df_right, we will automatically select that key. Also, the exported code simplifies when a merge with same keys has been carried out.
Text transformations (such as "filter" and "replace values") support case-sensitivity and regular expressions now.
"rename", "string manipulations", and "extract datetime attributes" are top-level transformations now and available via search.
After importing bamboolib, you don't need to call
bam.enable() anymore in order to show bamboolib when printing
We removed the normalization step which made sure that the dataframe index is always a RangeIndex.
Keyboard support ⌨ Transformations can now be created via typing on the keyboard - including auto-completion at every step. Mouse is still possible of course.
Plugin - beta🔌 We started implementing a plugin architecture. Starting with this release, you can write your own custom transformations. Please note that we are in beta mode currently, so the API may change over time.
Fixed an issue that caused an error when filtering values in a column that contains NAs
Fixed some CSS specificity issues in JupyterLab
Fixed an issue in JupyterLab that made bamboolib multi-select dropdowns not work properly
some users couldn't use bamboolib for free on Kaggle. We fixed that, because we love our users :)
Rename transformation results🔤 You can now rename the dataframe after a transformation (e.g. name the result of a filter "df_subset")
Edit last transformation ✏ You can edit the last transformation when looking at the history of you transformations.
Increase memory efficiency with numpy dtypes 💾 New support of numpy data types (e.g. int8, int16, ...) so that you can reduce the memory space of your dataframes
Live code export now also works on Firefox
We now support JupyterLab
Fixed an issue that made the Copy-Code-Button not work
Changed the styling of our buttons