dataframe
Here are 478 public repositories matching this topic...
-
Updated
Jun 4, 2021 - Python
-
Updated
Jun 5, 2021 - Java
-
Updated
Jun 1, 2021 - Java
-
Updated
Apr 20, 2021 - Rust
Implements {DataFrame,Series}.empty
This is mostly a mechanical copying of other trait implementations.
- Add struct datatype
- Make
ChunkedArray<StructType>
an iterator - Implement
dyn SeriesTrait
and required subtraits
We need a filter function that can accept multiple logical operators and also be able to filter by a boolean mask, just like pandas as shown here. There are numerous use cases of this, and we certainly have to implement it.
See discussion opensource9ja/danfojs#170 and opensource9ja/danfojs#179
Acceptance
- We can filter a
-
Updated
Jan 29, 2021 - C#
-
Updated
Jun 4, 2021 - C++
Hi ,
I am using some basic functions from pyjanitor such as - clean_names() , collapse_levels() in one of my code which I want to productionise.
And there are limitations on the size of the production code base.
Currently ,if I just look at the requirements.txt for just "pyjanitor" , its huge .
I don't think I require all the dependencies in my code.
How can I remove the unnecessary ones ?
I'm using black and isort for other projects (see e.g. https://github.com/hyperopt/hyperopt/pull/748/files) and find them quite useful to have more consistent codebase. I think you should drop python3.5 support though, as black is python3.6+. Is this something you would be open to consider?
-
Updated
Jan 6, 2019 - Python
-
Updated
Apr 22, 2021 - Go
-
Updated
Jun 4, 2021 - Python
-
Updated
Apr 12, 2021 - Python
-
Updated
Oct 25, 2020 - JavaScript
-
Updated
May 31, 2021 - Clojure
-
Updated
May 6, 2020 - Python
-
Updated
May 30, 2021 - Go
Improve this page
Add a description, image, and links to the dataframe topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the dataframe topic, visit your repo's landing page and select "manage topics."
Hello,
Considering your amazing efficiency on pandas, numpy, and more, it would seem to make sense for your module to work with even bigger data, such as Audio (for example .mp3 and .wav). This is something that would help a lot considering the nature audio (ie. where one of the lowest and most common sampling rates is still 44,100 samples/sec). For a use case, I would consider vaex.open('Hu