Merge branch 'master' into cast-nat

Author: Daniel Saxton

ci/code_checks.sh

@@ -188,9 +188,9 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
     set -o pipefail
     if [[ "$AZURE" == "true" ]]; then
         # we exclude all c/cpp files as the c/cpp files of pandas code base are tested when Linting .c and .h files
-        ! grep -n '--exclude=*.'{svg,c,cpp,html} --exclude-dir=env -RI "\s$" * | awk -F ":" '{print "##vso[task.logissue type=error;sourcepath=" $1 ";linenumber=" $2 ";] Tailing whitespaces found: " $3}'
+        ! grep -n '--exclude=*.'{svg,c,cpp,html,js} --exclude-dir=env -RI "\s$" * | awk -F ":" '{print "##vso[task.logissue type=error;sourcepath=" $1 ";linenumber=" $2 ";] Tailing whitespaces found: " $3}'
     else
-        ! grep -n '--exclude=*.'{svg,c,cpp,html} --exclude-dir=env -RI "\s$" * | awk -F ":" '{print $1 ":" $2 ":Tailing whitespaces found: " $3}'
+        ! grep -n '--exclude=*.'{svg,c,cpp,html,js} --exclude-dir=env -RI "\s$" * | awk -F ":" '{print $1 ":" $2 ":Tailing whitespaces found: " $3}'
     fi
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 fi

doc/redirects.csv

@@ -503,7 +503,6 @@ generated/pandas.DataFrame.to_parquet,../reference/api/pandas.DataFrame.to_parqu
 generated/pandas.DataFrame.to_period,../reference/api/pandas.DataFrame.to_period
 generated/pandas.DataFrame.to_pickle,../reference/api/pandas.DataFrame.to_pickle
 generated/pandas.DataFrame.to_records,../reference/api/pandas.DataFrame.to_records
-generated/pandas.DataFrame.to_sparse,../reference/api/pandas.DataFrame.to_sparse
 generated/pandas.DataFrame.to_sql,../reference/api/pandas.DataFrame.to_sql
 generated/pandas.DataFrame.to_stata,../reference/api/pandas.DataFrame.to_stata
 generated/pandas.DataFrame.to_string,../reference/api/pandas.DataFrame.to_string
@@ -1432,7 +1431,6 @@ generated/pandas.Series.to_msgpack,../reference/api/pandas.Series.to_msgpack
 generated/pandas.Series.to_numpy,../reference/api/pandas.Series.to_numpy
 generated/pandas.Series.to_period,../reference/api/pandas.Series.to_period
 generated/pandas.Series.to_pickle,../reference/api/pandas.Series.to_pickle
-generated/pandas.Series.to_sparse,../reference/api/pandas.Series.to_sparse
 generated/pandas.Series.to_sql,../reference/api/pandas.Series.to_sql
 generated/pandas.Series.to_string,../reference/api/pandas.Series.to_string
 generated/pandas.Series.to_timestamp,../reference/api/pandas.Series.to_timestamp

doc/source/reference/frame.rst

@@ -356,15 +356,7 @@ Serialization / IO / conversion
    DataFrame.to_msgpack
    DataFrame.to_gbq
    DataFrame.to_records
-   DataFrame.to_sparse
    DataFrame.to_dense
    DataFrame.to_string
    DataFrame.to_clipboard
    DataFrame.style
-
-Sparse
-~~~~~~
-.. autosummary::
-   :toctree: api/
-
-   SparseDataFrame.to_coo

doc/source/reference/series.rst

@@ -576,18 +576,7 @@ Serialization / IO / conversion
    Series.to_sql
    Series.to_msgpack
    Series.to_json
-   Series.to_sparse
    Series.to_dense
    Series.to_string
    Series.to_clipboard
    Series.to_latex
-
-
-Sparse
-------
-
-.. autosummary::
-   :toctree: api/
-
-   SparseSeries.to_coo
-   SparseSeries.from_coo

doc/source/user_guide/io.rst

@@ -4641,6 +4641,14 @@ Several caveats.
 
 See the `Full Documentation <https://github.com/wesm/feather>`__.
 
+.. ipython:: python
+   :suppress:
+
+   import warnings
+   # This can be removed once building with pyarrow >=0.15.0
+   warnings.filterwarnings("ignore", "The Sparse", FutureWarning)
+
+
 .. ipython:: python
 
    df = pd.DataFrame({'a': list('abc'),

doc/source/user_guide/sparse.rst

@@ -6,12 +6,6 @@
 Sparse data structures
 **********************
 
-.. note::
-
-   ``SparseSeries`` and ``SparseDataFrame`` have been deprecated. Their purpose
-   is served equally well by a :class:`Series` or :class:`DataFrame` with
-   sparse values. See :ref:`sparse.migration` for tips on migrating.
-
 Pandas provides data structures for efficiently storing sparse data.
 These are not necessarily sparse in the typical "mostly 0". Rather, you can view these
 objects as being "compressed" where any data matching a specific value (``NaN`` / missing value, though any value
@@ -168,6 +162,11 @@ the correct dense result.
 Migrating
 ---------
 
+.. note::
+
+   ``SparseSeries`` and ``SparseDataFrame`` were removed in pandas 1.0.0. This migration
+   guide is present to aid in migrating from previous versions.
+
 In older versions of pandas, the ``SparseSeries`` and ``SparseDataFrame`` classes (documented below)
 were the preferred way to work with sparse data. With the advent of extension arrays, these subclasses
 are no longer needed. Their purpose is better served by using a regular Series or DataFrame with
@@ -366,12 +365,3 @@ row and columns coordinates of the matrix. Note that this will consume a signifi
 
    ss_dense = pd.Series.sparse.from_coo(A, dense_index=True)
    ss_dense
-
-
-.. _sparse.subclasses:
-
-Sparse subclasses
------------------
-
-The :class:`SparseSeries` and :class:`SparseDataFrame` classes are deprecated. Visit their
-API pages for usage.

doc/source/whatsnew/v0.16.0.rst

@@ -91,8 +91,7 @@ Interaction with scipy.sparse
 
 Added :meth:`SparseSeries.to_coo` and :meth:`SparseSeries.from_coo` methods (:issue:`8048`) for converting to and from ``scipy.sparse.coo_matrix`` instances (see :ref:`here <sparse.scipysparse>`). For example, given a SparseSeries with MultiIndex we can convert to a `scipy.sparse.coo_matrix` by specifying the row and column labels as index levels:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
    s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
@@ -121,8 +120,7 @@ Added :meth:`SparseSeries.to_coo` and :meth:`SparseSeries.from_coo` methods (:is
 The from_coo method is a convenience method for creating a ``SparseSeries``
 from a ``scipy.sparse.coo_matrix``:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    from scipy import sparse
    A = sparse.coo_matrix(([3.0, 1.0, 2.0], ([1, 0, 0], [0, 2, 3])),

doc/source/whatsnew/v0.18.1.rst

@@ -393,8 +393,7 @@ used in the ``pandas`` implementation (:issue:`12644`, :issue:`12638`, :issue:`1
 
 An example of this signature augmentation is illustrated below:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    sp = pd.SparseDataFrame([1, 2, 3])
    sp
@@ -409,8 +408,7 @@ Previous behaviour:
 
 New behaviour:
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    np.cumsum(sp, axis=0)
 

doc/source/whatsnew/v0.19.0.rst

@@ -1235,8 +1235,7 @@ Operators now preserve dtypes
 
 - Sparse data structure now can preserve ``dtype`` after arithmetic ops (:issue:`13848`)
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    s = pd.SparseSeries([0, 2, 0, 1], fill_value=0, dtype=np.int64)
    s.dtype
@@ -1245,8 +1244,7 @@ Operators now preserve dtypes
 
 - Sparse data structure now support ``astype`` to convert internal ``dtype`` (:issue:`13900`)
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    s = pd.SparseSeries([1., 0., 2., 0.], fill_value=0)
    s

doc/source/whatsnew/v0.20.0.rst

@@ -338,8 +338,7 @@ See the :ref:`documentation <sparse.scipysparse>` for more information. (:issue:
 
 All sparse formats are supported, but matrices that are not in :mod:`COOrdinate <scipy.sparse>` format will be converted, copying data as needed.
 
-.. ipython:: python
-   :okwarning:
+.. code-block:: python
 
    from scipy.sparse import csr_matrix
    arr = np.random.random(size=(1000, 5))
@@ -351,7 +350,7 @@ All sparse formats are supported, but matrices that are not in :mod:`COOrdinate
 
 To convert a ``SparseDataFrame`` back to sparse SciPy matrix in COO format, you can use:
 
-.. ipython:: python
+.. code-block:: python
 
    sdf.to_coo()