Release notes (#80)

doc/source/caveats.rst

@@ -1,5 +1,5 @@
-Usage Limitations
-=================
+Usage Notes
+===========
 
 Type Mapping
 ------------
@@ -43,20 +43,12 @@ pantab maps the following dtypes from pandas to the equivalent column type in Ta
 Any dtype not explicitly listed in the above table will raise a ValueError if trying to write out data.
 
 .. versionadded:: 1.0.0
-   If using pandas 1.0 and above, text data will be read back into a ``"string"`` dtype rather than an ``object`` dtype.
+   If using pandas 1.0 and above, text data will be read back into a ``string`` dtype rather than an ``object`` dtype.
 
 .. note::
 
    Most objects can maintain their type when "round-tripping" to/from Hyper extracts, with the exception of the float32 object as only DOUBLE is available for floating point storage in Hyper. After pandas 1.0 / pantab 1.0, object dtypes written will be read back in as string.
 
-Missing Value Handling
-----------------------
-In the scientific Python community missing values are often notated as ``np.nan``, which is a float value implementing the `IEEE 754 <https://en.wikipedia.org/wiki/IEEE_754>`_ standard for missing value handling. Because NumPy stores homogenous arrays, there has been a long standing issue in pandas where integers could not store missing records, as noted in the `pandas documentation on missing values <https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html#working-with-missing-data>`_.
-
-Complicating factors is the presence of the ``None`` sentinel in Python, which can often times intuitively be represented as a missing value. Unfortunately ``None`` is a Python level construct, and most of the performance of the data stack in Python is implemented in lower-level C routines that cannot take advantage of this value. The Hyper API though uses this as its missing value indicator.
-
-Because of the various missing value indicators, serializing to/from a Hyper extract via pantab cannot be lossless. On write, ``None`` and ``np.nan`` values will both be written in as missing data to the Hyper extract. On read, missing values will be read back as ``np.nan``.
-
 Index / Column Handling
 -----------------------
 A pandas DataFrame always comes with an ``Index`` used to slice / access rows. No such concept exists in Tableau, so this will be implicitly dropped when writing Hyper extracts.

doc/source/changelog.rst

@@ -1,6 +1,27 @@
 Changelog
 ^^^^^^^^^
 
+Pantab 1.0.0 (2020-01-15)
+=========================
+
+Special thanks to `chillerno1 <https://github.com/chillerno1>`_ for contributing to this release.
+
+Features
+--------
+
+- pantab now supports reading/writing pandas 1.0 dtypes, namely the ``boolean`` and ``string`` dtypes. (#20)
+
+  .. important::
+
+     TEXT data read from a Hyper extract will be stored in a ``string`` dtype when using pandas 1.0 or greater in combination with pantab 1.0 or greater. Older versions of either tool will read the data back into a ``object`` dtype.
+
+
+Bugfixes
+--------
+
+- Fixed potential segfault on systems where not all addresses can be expressed in an unsigned long long. (#52)
+
+
 Pantab 0.2.3 (2020-01-02)
 =========================
 

pantab/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.2.3"
+__version__ = "1.0.0"
 
 
 from ._reader import frame_from_hyper, frames_from_hyper

setup.py

@@ -63,7 +63,7 @@
 
 setup(
     name="pantab",
-    version="0.2.3",
+    version="1.0.0",
     description="Converts pandas DataFrames into Tableau Hyper Extracts",
     long_description=long_description,
     long_description_content_type="text/markdown",