Bump version up to v1.0.27

histogrammar · May 20, 2022 · 8c82095 · 8c82095
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diff --git a/CHANGES.rst b/CHANGES.rst
@@ -2,6 +2,17 @@
 Release notes
 =============
 
+Version 1.0.27, May 2022
+------------------------
+* Multiple performance updates, thanks to Simon Brugman.
+* Use pandas functions to infer datatypes and return numpy arrays.
+* Turn of unnecessary specialize function (slow) for Count objects.
+
+Version 1.0.26, Apr 2022
+------------------------
+* Added tutorial notebook with exercises.
+* Fixed 2d heatmap for categorical histograms, where one column was accidentally dropped.
+
 Version 1.0.25, Apr 2021
 ------------------------
 * Improve null handling in pandas dataframes, by inferring datatype using pandas' infer_dtype function.

diff --git a/README.rst b/README.rst
@@ -17,9 +17,10 @@ more quickly via Numpy commands, rather than Python for loops. If PyROOT is avai
 aggregators can be filled from ROOT TTrees hundreds of times more quickly by JIT-compiling a specialized C++ filler.
 Histograms and other aggregators may also be converted into CUDA code for inclusion in a GPU workflow. And if
 PyCUDA is available, they can also be filled from Numpy arrays by JIT-compiling the CUDA code.
+
 This Python implementation of histogrammar been tested to guarantee compatibility with its Scala implementation.
 
-Latest Python release: v1.0.26 (April 2022).
+Latest Python release: v1.0.27 (May 2022).
 
 Announcements
 =============

diff --git a/histogrammar/version.py b/histogrammar/version.py
@@ -3,9 +3,9 @@
 import re
 
 name = "histogrammar"
-__version__ = "1.0.26"
-version = "1.0.26"
-full_version = "1.0.26"
+__version__ = "1.0.27"
+version = "1.0.27"
+full_version = "1.0.27"
 release = True
 
 version_info = tuple(re.split(r"[-\.]", __version__))

diff --git a/setup.py b/setup.py
@@ -22,7 +22,7 @@
 
 MAJOR = 1
 REVISION = 0
-PATCH = 26
+PATCH = 27
 DEV = False
 # NOTE: also update version at: README.rst