Releases: histogrammar/histogrammar-python
Releases · histogrammar/histogrammar-python
v1.0.33
v1.0.32
v1.0.31
v1.0.30
v1.0.29
v1.0.28, June 2022
Version 1.0.28, June 2022
- Multiple performance updates, to Bin, SparselyBin and Categorize histograms.
- SparselyBin, Categorize: optimized filling with 1-d and 2-d numpy arrays
- Bin, SparselyBin, Categorize: (fast) numpy arrays for bin-centers and bin-labels.
- Count: new, fast filling option when float weight is known.
- util.py: faster get_datatype() and get_ndim() functions.
v1.0.27
v1.0.26
v1.0.25
Version 1.0.25, Apr 2021
- Improve null handling in pandas dataframes, by inferring datatype using pandas' infer_dtype function.
- nans in bool columns get converted to "NaN", so the column keeps True and False values in Categorize, not "1" and "0".
- columns of type object get converted to strings using to_string(), of type string uses only_str().
v1.0.24
Version 1.0.24, Apr 2021
- Categorize histogram now handles nones and nans in friendlier way, they are converted to "NaN".
- make_histogram() now casts spark nulls to nan in case of numeric columns. scala interprets null as 0.
- SparselyBin histograms did not add up nanflow when added. Now fixed.
- Added unit test for doing checks on null conversion to nans
- Use new histogrammar-scala jar files, v1.0.20
- Added new histogrammar-scala v1.0.20 jar files to tests/jars/