subdtype

.github/workflows/docs.yml

@@ -0,0 +1,50 @@
+name: Documentation Build
+
+on: [push, pull_request]
+
+jobs:
+  docbuild:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 100
+
+      - name: Get tags
+        run: git fetch --depth=1 origin +refs/tags/*:refs/tags/*
+
+      - name: Set up minimal Python version
+        uses: actions/setup-python@v2
+        with:
+          python-version: "3.10"
+
+      - name: Get pip cache dir
+        id: pip-cache
+        run: echo "::set-output name=dir::$(pip cache dir)"
+
+      - name: Setup pip cache
+        uses: actions/cache@v2
+        with:
+          path: ${{ steps.pip-cache.outputs.dir }}
+          key: pip-docs
+          restore-keys: pip-docs
+
+      - name: Install locales
+        run: |
+          sudo apt-get install language-pack-fr
+          sudo localedef -i fr_FR -f UTF-8 fr_FR
+
+      - name: Install dependencies
+        run: |
+          sudo apt install -y pandoc
+          pip install --upgrade pip setuptools wheel
+          pip install -r "requirements_docs.txt"
+          pip install docutils==0.14 commonmark==0.8.1 recommonmark==0.5.0 babel==2.8
+          pip install .
+
+      - name: Build documentation
+        run: sphinx-build -n -j auto -b html -d build/doctrees docs build/html
+
+      - name: Doc Tests
+        run: sphinx-build -a -j auto -b doctest -d build/doctrees docs build/doctest

.gitignore

@@ -11,6 +11,7 @@ build/
 dist/
 MANIFEST
 *pytest_cache*
+*mypy_cache*
 .eggs
 
 # WebDAV file system cache files

docs/getting/tutorial.rst

@@ -1,5 +1,3 @@
-.. _tutorial:
-
 **************************
 Tutorial
 **************************

docs/index.rst

@@ -1,7 +1,7 @@
 :orphan:
 
 Pint-pandas: Unit support for pandas
-======================
+=====================================
 
 **Useful links**:
 `Code Repository <https://github.com/hgrecco/pint-pandas>`__ |
@@ -66,9 +66,7 @@ Pint-pandas: Unit support for pandas
 
     Getting started <getting/index>
     User Guide <user/index>
-    Advanced topics <advanced/index>
     ecosystem
-    API Reference <api/index>
 
 .. toctree::
     :maxdepth: 1

docs/user/common.rst

@@ -58,8 +58,9 @@ Creating DataFrames from Series
 The default operation of Pandas `pd.concat` function is to perform row-wise concatenation.  When given a list of Series, each of which is backed by a PintArray, this will inefficiently convert all the PintArrays to arrays of `object` type, concatenate the several series into a DataFrame with that many rows, and then leave it up to you to convert that DataFrame back into column-wise PintArrays.  A much more efficient approach is to concatenate Series in a column-wise fashion:
 
 .. ipython:: python
-    :suppress:
     :okwarning:
+
+        list_of_series = [pd.Series([1.0, 2.0], dtype="pint[m]") for i in range(0, 10)]
         df = pd.concat(list_of_series, axis=1)
 
 

docs/user/initializing.rst

@@ -4,35 +4,50 @@
 Initializing data
 **************************
 
-There are several ways to initialize PintArrays in a DataFrame. Here's the most common methods. We'll use `PA_` and `Q_` as shorthand for PintArray and Quantity.
+There are several ways to initialize a `PintArray`s` in a `DataFrame`. Here's the most common methods. We'll use `PA_` and `Q_` as shorthand for `PintArray` and `Quantity`.
 
 
 
 .. ipython:: python
+    :okwarning:
 
     import pandas as pd
     import pint
     import pint_pandas
-    import io
 
     PA_ = pint_pandas.PintArray
     ureg = pint_pandas.PintType.ureg
     Q_ = ureg.Quantity
 
     df = pd.DataFrame(
         {
-            "A": pd.Series([1.0, 2.0], dtype="pint[m]"),
-            "B": pd.Series([1.0, 2.0]).astype("pint[m]"),
-            "C": PA_([2.0, 3.0], dtype="pint[m]"),
-            "D": PA_([2.0, 3.0], dtype="m"),
-            "E": PA_([2.0, 3.0], dtype=ureg.m),
-            "F": PA_.from_1darray_quantity(Q_([2, 3], ureg.m)),
-            "G": PA_(Q_([2.0, 3.0], ureg.m)),
+            "Ser1": pd.Series([1, 2], dtype="pint[m]"),
+            "Ser2": pd.Series([1, 2]).astype("pint[m]"),
+            "Ser3": pd.Series([1, 2], dtype="pint[m][Int64]"),
+            "Ser4": pd.Series([1, 2]).astype("pint[m][Int64]"),
+            "PArr1": PA_([1, 2], dtype="pint[m]"),
+            "PArr2": PA_([1, 2], dtype="pint[m][Int64]"),
+            "PArr3": PA_([1, 2], dtype="m"),
+            "PArr4": PA_([1, 2], dtype=ureg.m),
+            "PArr5": PA_(Q_([1, 2], ureg.m)),
+            "PArr6": PA_([1, 2],"m"),
         }
     )
     df
 
 
+In the first two Series examples above, the data was converted to Float64.
+
+.. ipython:: python
+
+    df.dtypes
+
+
+To avoid this conversion, specify the subdtype (dtype of the magnitudes) in the dtype `"pint[m][Int64]"` when constructing using a `Series`. The default data dtype that pint-pandas converts to can be changed by modifying `pint_pandas.DEFAULT_SUBDTYPE`.
+
+`PintArray` infers the subdtype from the data passed into it when there is no subdtype specified in the dtype. It also accepts a pint `Unit`` or unit string as the dtype.
+
+
 .. note::
 
-   "pint[unit]" must be used for the Series or DataFrame constuctor.
+   `"pint[unit]"` or `"pint[unit][subdtype]"` must be used for the Series or DataFrame constuctor.

docs/user/reading.rst

@@ -40,7 +40,10 @@ Let's read that into a DataFrame. Here io.StringIO is used in place of reading a
     df = pd.read_csv(io.StringIO(test_data), header=[0, 1], index_col=[0, 1]).T
     # df = pd.read_csv("/path/to/test_data.csv", header=[0, 1])
     for col in df.columns:
-        df[col] = pd.to_numeric(df[col], errors="ignore")
+        try:
+            df[col] = pd.to_numeric(df[col])
+        except:
+            pass
     df.dtypes