Víctor Zabalza
Víctor Zabalza
This PR implements handling of the SU and SD CSI commands in `pyte.Stream` and the corresponding `scroll_up` and `scroll_down` methods in `pyte.Screen`.
This is work in progress: DO NOT MERGE This PR adapts the summarise functionality to be able to take a [dask dataframe](https://dask.pydata.org/en/latest/dataframe.html), which will allow to take in larger-than-memory datasets...
Plotly has the advantage of resulting in interactive plots in a jupyter notebook, but it is does not result in easily portable plots. We should consider ways of making the...
Right now the [t-digest](https://github.com/tdunning/t-digest) computation (done using a [python t-digest implementation](https://github.com/CamDavidsonPilon/tdigest)) takes most of the time in generating a summary. The initial motivation to include it was for it to...
The dask distributed scheduler is generally an improvement over the multiprocessing scheduler even in individual multicore machines because of its improved awareness of data locality, so we should consider adding...
For large datasets where computing the summary may be expensive, it would be useful to compute only part of it, be able to explore it, and then compute other parts...
Performing dimensionality analysis with PCA as part of the summary computation would be a useful tool to better understand the data.
Currently all of the metrics computed are independent of a target variable or column, but if `lens.summarise` took the name of a column as the target variable, the output of...
Right now, creating and exploring a lens report is always a two step process: `lens.summarise` and `lens.explore` or `lens.interactive_explore`. It would be useful to have a single function that directly...