Merge remote-tracking branch 'upstream/master' into unique-index

Author: TomAugspurger

MANIFEST.in

@@ -15,6 +15,7 @@ graft pandas
 global-exclude *.bz2
 global-exclude *.csv
 global-exclude *.dta
+global-exclude *.feather
 global-exclude *.gz
 global-exclude *.h5
 global-exclude *.html
@@ -24,7 +25,10 @@ global-exclude *.pickle
 global-exclude *.png
 global-exclude *.pyc
 global-exclude *.pyd
+global-exclude *.ods
+global-exclude *.odt
 global-exclude *.sas7bdat
+global-exclude *.sav
 global-exclude *.so
 global-exclude *.xls
 global-exclude *.xlsm

doc/source/development/contributing.rst

@@ -208,7 +208,7 @@ We'll now kick off a three-step process:
 
    # Build and install pandas
    python setup.py build_ext --inplace -j 4
-   python -m pip install -e --no-build-isolation .
+   python -m pip install -e . --no-build-isolation
 
 At this point you should be able to import pandas from your locally built version::
 
@@ -252,7 +252,7 @@ You'll need to have at least python3.5 installed on your system.
 
    # Build and install pandas
    python setup.py build_ext --inplace -j 4
-   python -m pip install -e --no-build-isolation .
+   python -m pip install -e . --no-build-isolation
 
 Creating a branch
 -----------------

doc/source/user_guide/io.rst

@@ -4845,7 +4845,7 @@ The above example creates a partitioned dataset that may look like:
    from shutil import rmtree
    try:
        rmtree('test')
-   except Exception:
+   except OSError:
        pass
 
 .. _io.sql:

doc/source/user_guide/options.rst

@@ -353,7 +353,7 @@ display.max_colwidth                    50           The maximum width in charac
                                                      a column in the repr of a pandas
                                                      data structure. When the column overflows,
                                                      a "..." placeholder is embedded in
-                                                     the output.
+                                                     the output. 'None' value means unlimited.
 display.max_info_columns                100          max_info_columns is used in DataFrame.info
                                                      method to decide if per column information
                                                      will be printed.

doc/source/whatsnew/v1.0.0.rst

@@ -21,6 +21,8 @@ including other versions of pandas.
 Enhancements
 ~~~~~~~~~~~~
 
+- :meth:`DataFrame.to_string` added the ``max_colwidth`` parameter to control when wide columns are truncated (:issue:`9784`)
+-
 
 .. _whatsnew_1000.enhancements.other:
 
@@ -35,6 +37,15 @@ Other enhancements
   (:issue:`28368`)
 -
 
+
+Build Changes
+^^^^^^^^^^^^^
+
+Pandas has added a `pyproject.toml <https://www.python.org/dev/peps/pep-0517/>`_ file and will no longer include
+cythonized files in the source distribution uploaded to PyPI (:issue:`28341`, :issue:`20775`). If you're installing
+a built distribution (wheel) or via conda, this shouldn't have any effect on you. If you're building pandas from
+source, you should no longer need to install Cython into your build environment before calling ``pip install pandas``.
+
 .. _whatsnew_1000.api_breaking:
 
 Backwards incompatible API changes
@@ -105,6 +116,7 @@ Performance improvements
 Bug fixes
 ~~~~~~~~~
 
+- Bug in :meth:`DataFrame.to_html` when using ``formatters=<list>`` and ``max_cols`` together. (:issue:`25955`)
 
 Categorical
 ^^^^^^^^^^^
@@ -169,6 +181,7 @@ Indexing
 ^^^^^^^^
 
 - Bug in assignment using a reverse slicer (:issue:`26939`)
+- Bug in :meth:`DataFrame.explode` would duplicate frame in the presence of duplicates in the index (:issue:`28010`)
 - Bug in reindexing a :meth:`PeriodIndex` with another type of index that contained a `Period` (:issue:`28323`) (:issue:`28337`)
 - Fix assignment of column via `.loc` with numpy non-ns datetime type (:issue:`27395`)
 
@@ -191,6 +204,7 @@ I/O
 - Bug in :meth:`DataFrame.to_json` where using a Tuple as a column or index value and using ``orient="columns"`` or ``orient="index"`` would produce invalid JSON (:issue:`20500`)
 - Improve infinity parsing. :meth:`read_csv` now interprets ``Infinity``, ``+Infinity``, ``-Infinity`` as floating point values (:issue:`10065`)
 - Bug in :meth:`DataFrame.to_csv` where values were truncated when the length of ``na_rep`` was shorter than the text input data. (:issue:`25099`)
+- Bug in :func:`DataFrame.to_string` where values were truncated using display options instead of outputting the full content (:issue:`9784`)
 
 Plotting
 ^^^^^^^^
@@ -223,13 +237,6 @@ Sparse
 -
 -
 
-
-Build Changes
-^^^^^^^^^^^^^
-- Fixed pyqt development dependency issue because of different pyqt package name in conda and PyPI (:issue:`26838`)
-- Added a `pyproject.toml <https://www.python.org/dev/peps/pep-0517/>`_ file (:issue:`20775`)
-
-
 ExtensionArray
 ^^^^^^^^^^^^^^
 

pandas/core/arrays/sparse.py

@@ -245,10 +245,10 @@ def construct_from_string(cls, string):
         if string.startswith("Sparse"):
             try:
                 sub_type, has_fill_value = cls._parse_subtype(string)
-                result = SparseDtype(sub_type)
-            except Exception:
+            except ValueError:
                 raise TypeError(msg)
             else:
+                result = SparseDtype(sub_type)
                 msg = (
                     "Could not construct SparseDtype from '{}'.\n\nIt "
                     "looks like the fill_value in the string is not "

pandas/core/base.py

@@ -1289,17 +1289,17 @@ def value_counts(
 
         Parameters
         ----------
-        normalize : boolean, default False
+        normalize : bool, default False
             If True then the object returned will contain the relative
             frequencies of the unique values.
-        sort : boolean, default True
+        sort : bool, default True
             Sort by frequencies.
-        ascending : boolean, default False
+        ascending : bool, default False
             Sort in ascending order.
-        bins : integer, optional
+        bins : int, optional
             Rather than count values, group them into half-open bins,
             a convenience for ``pd.cut``, only works with numeric data.
-        dropna : boolean, default True
+        dropna : bool, default True
             Don't include counts of NaN.
 
         Returns
@@ -1496,7 +1496,7 @@ def memory_usage(self, deep=False):
         size_hint="",
         sort=textwrap.dedent(
             """\
-            sort : boolean, default False
+            sort : bool, default False
                 Sort `uniques` and shuffle `labels` to maintain the
                 relationship.
             """

pandas/core/common.py

@@ -445,15 +445,15 @@ def pipe(obj, func, *args, **kwargs):
 
     Parameters
     ----------
-    func : callable or tuple of (callable, string)
+    func : callable or tuple of (callable, str)
         Function to apply to this object or, alternatively, a
         ``(callable, data_keyword)`` tuple where ``data_keyword`` is a
         string indicating the keyword of `callable`` that expects the
         object.
-    args : iterable, optional
-        positional arguments passed into ``func``.
-    kwargs : dict, optional
-        a dictionary of keyword arguments passed into ``func``.
+    *args : iterable, optional
+        Positional arguments passed into ``func``.
+    **kwargs : dict, optional
+        A dictionary of keyword arguments passed into ``func``.
 
     Returns
     -------

pandas/core/config_init.py

@@ -148,10 +148,10 @@ def use_numexpr_cb(key):
 """
 
 max_colwidth_doc = """
-: int
+: int or None
     The maximum width in characters of a column in the repr of
     a pandas data structure. When the column overflows, a "..."
-    placeholder is embedded in the output.
+    placeholder is embedded in the output. A 'None' value means unlimited.
 """
 
 colheader_justify_doc = """
@@ -340,7 +340,9 @@ def is_terminal():
         validator=is_instance_factory([type(None), int]),
     )
     cf.register_option("max_categories", 8, pc_max_categories_doc, validator=is_int)
-    cf.register_option("max_colwidth", 50, max_colwidth_doc, validator=is_int)
+    cf.register_option(
+        "max_colwidth", 50, max_colwidth_doc, validator=is_nonnegative_int
+    )
     if is_terminal():
         max_cols = 0  # automatically determine optimal number of columns
     else:

pandas/core/dtypes/common.py

@@ -2049,10 +2049,8 @@ def pandas_dtype(dtype):
     # raise a consistent TypeError if failed
     try:
         npdtype = np.dtype(dtype)
-    except Exception:
-        # we don't want to force a repr of the non-string
-        if not isinstance(dtype, str):
-            raise TypeError("data type not understood")
+    except SyntaxError:
+        # np.dtype uses `eval` which can raise SyntaxError
         raise TypeError("data type '{}' not understood".format(dtype))
 
     # Any invalid dtype (such as pd.Timestamp) should raise an error.