Merge remote-tracking branch 'upstream/master' into io_csv_docstring_fixed

* upstream/master:
  DOC: Removing rpy2 dependencies, and converting examples using it to regular code blocks (pandas-dev#23737)
  BUG: Fix dtype=str converts NaN to 'n' (pandas-dev#22564)
  DOC: update pandas.core.resample.Resampler.nearest docstring (pandas-dev#20381)
  REF/TST: Add more pytest idiom to parsers tests (pandas-dev#23810)
  Added support for Fraction and Number (PEP 3141) to pandas.api.types.is_scalar (pandas-dev#22952)
  DOC: Updating to_timedelta docstring (pandas-dev#23259)

Author: thoo

ci/deps/travis-36-doc.yaml

@@ -2,7 +2,6 @@ name: pandas
 channels:
   - defaults
   - conda-forge
-  - r
 dependencies:
   - beautifulsoup4
   - bottleneck
@@ -31,14 +30,11 @@ dependencies:
   - python-snappy
   - python=3.6*
   - pytz
-  - r
-  - rpy2
   - scipy
   - seaborn
   - sphinx
   - sqlalchemy
   - statsmodels
-  - tzlocal
   - xarray
   - xlrd
   - xlsxwriter

doc/source/r_interface.rst

@@ -33,21 +33,28 @@ See also the documentation of the `rpy2 <http://rpy2.bitbucket.org/>`__ project:
 
 In the remainder of this page, a few examples of explicit conversion is given. The pandas conversion of rpy2 needs first to be activated:
 
-.. ipython:: python
+.. code-block:: python
 
-   from rpy2.robjects import r, pandas2ri
-   pandas2ri.activate()
+    >>> from rpy2.robjects import pandas2ri  # doctest: +SKIP
+    >>> pandas2ri.activate()  # doctest: +SKIP
 
 Transferring R data sets into Python
 ------------------------------------
 
 Once the pandas conversion is activated (``pandas2ri.activate()``), many conversions
 of R to pandas objects will be done automatically. For example, to obtain the 'iris' dataset as a pandas DataFrame:
 
-.. ipython:: python
+.. code-block:: python
 
-    r.data('iris')
-    r['iris'].head()
+    >>> from rpy2.robjects import r  # doctest: +SKIP
+    >>> r.data('iris')  # doctest: +SKIP
+    >>> r['iris'].head()  # doctest: +SKIP
+        Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species
+    0           5.1          3.5           1.4          0.2  setosa
+    1           4.9          3.0           1.4          0.2  setosa
+    2           4.7          3.2           1.3          0.2  setosa
+    3           4.6          3.1           1.5          0.2  setosa
+    4           5.0          3.6           1.4          0.2  setosa
 
 If the pandas conversion was not activated, the above could also be accomplished
 by explicitly converting it with the ``pandas2ri.ri2py`` function
@@ -59,13 +66,19 @@ Converting DataFrames into R objects
 The ``pandas2ri.py2ri`` function support the reverse operation to convert
 DataFrames into the equivalent R object (that is, **data.frame**):
 
-.. ipython:: python
+.. code-block:: python
+
+   >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]},
+   ...                   index=["one", "two", "three"])  # doctest: +SKIP
+   >>> r_dataframe = pandas2ri.py2ri(df)  # doctest: +SKIP
+   >>> print(type(r_dataframe))  # doctest: +SKIP
+   <class 'rpy2.robjects.vectors.DataFrame'>
+   >>> print(r_dataframe)  # doctest: +SKIP
+         A B C
+   one   1 4 7
+   two   2 5 8
+   three 3 6 9
 
-   df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C':[7,8,9]},
-                     index=["one", "two", "three"])
-   r_dataframe = pandas2ri.py2ri(df)
-   print(type(r_dataframe))
-   print(r_dataframe)
 
 The DataFrame's index is stored as the ``rownames`` attribute of the
 data.frame instance.

doc/source/whatsnew/v0.24.0.rst

@@ -466,6 +466,7 @@ For situations where you need an ``ndarray`` of ``Interval`` objects, use
    np.asarray(idx)
    idx.values.astype(object)
 
+
 .. _whatsnew_0240.api.timezone_offset_parsing:
 
 Parsing Datetime Strings with Timezone Offsets
@@ -1442,6 +1443,7 @@ Reshaping
 - Bug in :func:`merge_asof` where confusing error message raised when attempting to merge with missing values (:issue:`23189`)
 - Bug in :meth:`DataFrame.nsmallest` and :meth:`DataFrame.nlargest` for dataframes that have a :class:`MultiIndex` for columns (:issue:`23033`).
 - Bug in :meth:`DataFrame.append` with a :class:`Series` with a dateutil timezone would raise a ``TypeError`` (:issue:`23682`)
+- Bug in ``Series`` construction when passing no data and ``dtype=str`` (:issue:`22477`)
 
 .. _whatsnew_0240.bug_fixes.sparse:
 
@@ -1474,6 +1476,7 @@ Other
 - :meth:`DataFrame.nlargest` and :meth:`DataFrame.nsmallest` now returns the correct n values when keep != 'all' also when tied on the first columns (:issue:`22752`)
 - :meth:`~pandas.io.formats.style.Styler.bar` now also supports tablewise application (in addition to rowwise and columnwise) with ``axis=None`` and setting clipping range with ``vmin`` and ``vmax`` (:issue:`21548` and :issue:`21526`). ``NaN`` values are also handled properly.
 - Logical operations ``&, |, ^`` between :class:`Series` and :class:`Index` will no longer raise ``ValueError`` (:issue:`22092`)
+- Checking PEP 3141 numbers in :func:`~pandas.api.types.is_scalar` function returns ``True`` (:issue:`22903`)
 - Bug in :meth:`DataFrame.combine_first` in which column types were unexpectedly converted to float (:issue:`20699`)
 
 .. _whatsnew_0.24.0.contributors:
@@ -1482,3 +1485,4 @@ Contributors
 ~~~~~~~~~~~~
 
 .. contributors:: v0.23.4..HEAD
+

pandas/_libs/lib.pyx

@@ -1,5 +1,8 @@
 # -*- coding: utf-8 -*-
 from decimal import Decimal
+from fractions import Fraction
+from numbers import Number
+
 import sys
 
 import cython
@@ -15,7 +18,6 @@ from cpython.datetime cimport (PyDateTime_Check, PyDate_Check,
                                PyDateTime_IMPORT)
 PyDateTime_IMPORT
 
-
 import numpy as np
 cimport numpy as cnp
 from numpy cimport (ndarray, PyArray_GETITEM,
@@ -105,23 +107,54 @@ def is_scalar(val: object) -> bool:
     """
     Return True if given value is scalar.
 
-    This includes:
-    - numpy array scalar (e.g. np.int64)
-    - Python builtin numerics
-    - Python builtin byte arrays and strings
-    - None
-    - instances of datetime.datetime
-    - instances of datetime.timedelta
-    - Period
-    - instances of decimal.Decimal
-    - Interval
-    - DateOffset
+    Parameters
+    ----------
+    val : object
+        This includes:
+
+        - numpy array scalar (e.g. np.int64)
+        - Python builtin numerics
+        - Python builtin byte arrays and strings
+        - None
+        - datetime.datetime
+        - datetime.timedelta
+        - Period
+        - decimal.Decimal
+        - Interval
+        - DateOffset
+        - Fraction
+        - Number
+
+    Returns
+    -------
+    bool
+        Return True if given object is scalar, False otherwise
+
+    Examples
+    --------
+    >>> dt = pd.datetime.datetime(2018, 10, 3)
+    >>> pd.is_scalar(dt)
+    True
+
+    >>> pd.api.types.is_scalar([2, 3])
+    False
+
+    >>> pd.api.types.is_scalar({0: 1, 2: 3})
+    False
+
+    >>> pd.api.types.is_scalar((0, 2))
+    False
+
+    pandas supports PEP 3141 numbers:
 
+    >>> from fractions import Fraction
+    >>> pd.api.types.is_scalar(Fraction(3, 5))
+    True
     """
 
     return (cnp.PyArray_IsAnyScalar(val)
             # As of numpy-1.9, PyArray_IsAnyScalar misses bytearrays on Py3.
-            or isinstance(val, bytes)
+            or isinstance(val, (bytes, Fraction, Number))
             # We differ from numpy (as of 1.10), which claims that None is
             # not scalar in np.isscalar().
             or val is None

pandas/core/dtypes/cast.py

@@ -6,7 +6,7 @@
 
 from pandas._libs import lib, tslib, tslibs
 from pandas._libs.tslibs import OutOfBoundsDatetime, Period, iNaT
-from pandas.compat import PY3, string_types, text_type
+from pandas.compat import PY3, string_types, text_type, to_str
 
 from .common import (
     _INT64_DTYPE, _NS_DTYPE, _POSSIBLY_CAST_DTYPES, _TD_DTYPE, _string_dtypes,
@@ -1216,11 +1216,16 @@ def construct_1d_arraylike_from_scalar(value, length, dtype):
         if not isinstance(dtype, (np.dtype, type(np.dtype))):
             dtype = dtype.dtype
 
-        # coerce if we have nan for an integer dtype
-        # GH 22858: only cast to float if an index
-        # (passed here as length) is specified
         if length and is_integer_dtype(dtype) and isna(value):
-            dtype = np.float64
+            # coerce if we have nan for an integer dtype
+            dtype = np.dtype('float64')
+        elif isinstance(dtype, np.dtype) and dtype.kind in ("U", "S"):
+            # we need to coerce to object dtype to avoid
+            # to allow numpy to take our string as a scalar value
+            dtype = object
+            if not isna(value):
+                value = to_str(value)
+
         subarr = np.empty(length, dtype=dtype)
         subarr.fill(value)
 

pandas/core/dtypes/common.py

@@ -419,7 +419,7 @@ def is_datetime64_dtype(arr_or_dtype):
         return False
     try:
         tipo = _get_dtype_type(arr_or_dtype)
-    except TypeError:
+    except (TypeError, UnicodeEncodeError):
         return False
     return issubclass(tipo, np.datetime64)
 

pandas/core/resample.py

@@ -418,23 +418,63 @@ def pad(self, limit=None):
 
     def nearest(self, limit=None):
         """
-        Fill values with nearest neighbor starting from center
+        Resample by using the nearest value.
+
+        When resampling data, missing values may appear (e.g., when the
+        resampling frequency is higher than the original frequency).
+        The `nearest` method will replace ``NaN`` values that appeared in
+        the resampled data with the value from the nearest member of the
+        sequence, based on the index value.
+        Missing values that existed in the original data will not be modified.
+        If `limit` is given, fill only this many values in each direction for
+        each of the original values.
 
         Parameters
         ----------
-        limit : integer, optional
-            limit of how many values to fill
+        limit : int, optional
+            Limit of how many values to fill.
 
             .. versionadded:: 0.21.0
 
         Returns
         -------
-        an upsampled Series
+        Series or DataFrame
+            An upsampled Series or DataFrame with ``NaN`` values filled with
+            their nearest value.
 
         See Also
         --------
-        Series.fillna
-        DataFrame.fillna
+        backfill : Backward fill the new missing values in the resampled data.
+        pad : Forward fill ``NaN`` values.
+
+        Examples
+        --------
+        >>> s = pd.Series([1, 2],
+        ...               index=pd.date_range('20180101',
+        ...                                   periods=2,
+        ...                                   freq='1h'))
+        >>> s
+        2018-01-01 00:00:00    1
+        2018-01-01 01:00:00    2
+        Freq: H, dtype: int64
+
+        >>> s.resample('15min').nearest()
+        2018-01-01 00:00:00    1
+        2018-01-01 00:15:00    1
+        2018-01-01 00:30:00    2
+        2018-01-01 00:45:00    2
+        2018-01-01 01:00:00    2
+        Freq: 15T, dtype: int64
+
+        Limit the number of upsampled values imputed by the nearest:
+
+        >>> s.resample('15min').nearest(limit=1)
+        2018-01-01 00:00:00    1.0
+        2018-01-01 00:15:00    1.0
+        2018-01-01 00:30:00    NaN
+        2018-01-01 00:45:00    2.0
+        2018-01-01 01:00:00    2.0
+        Freq: 15T, dtype: float64
         """
         return self._upsample('nearest', limit=limit)
 

pandas/core/tools/timedeltas.py

@@ -17,31 +17,43 @@
 
 def to_timedelta(arg, unit='ns', box=True, errors='raise'):
     """
-    Convert argument to timedelta
+    Convert argument to timedelta.
+
+    Timedeltas are absolute differences in times, expressed in difference
+    units (e.g. days, hours, minutes, seconds). This method converts
+    an argument from a recognized timedelta format / value into
+    a Timedelta type.
 
     Parameters
     ----------
-    arg : string, timedelta, list, tuple, 1-d array, or Series
-    unit : str, optional
-        Denote the unit of the input, if input is an integer. Default 'ns'.
-        Possible values:
-        {'Y', 'M', 'W', 'D', 'days', 'day', 'hours', hour', 'hr', 'h',
-        'm', 'minute', 'min', 'minutes', 'T', 'S', 'seconds', 'sec', 'second',
-        'ms', 'milliseconds', 'millisecond', 'milli', 'millis', 'L',
-        'us', 'microseconds', 'microsecond', 'micro', 'micros', 'U',
-        'ns', 'nanoseconds', 'nano', 'nanos', 'nanosecond', 'N'}
-    box : boolean, default True
-        - If True returns a Timedelta/TimedeltaIndex of the results
-        - if False returns a np.timedelta64 or ndarray of values of dtype
-          timedelta64[ns]
+    arg : str, timedelta, list-like or Series
+        The data to be converted to timedelta.
+    unit : str, default 'ns'
+        Denotes the unit of the arg. Possible values:
+        ('Y', 'M', 'W', 'D', 'days', 'day', 'hours', hour', 'hr',
+        'h', 'm', 'minute', 'min', 'minutes', 'T', 'S', 'seconds',
+        'sec', 'second', 'ms', 'milliseconds', 'millisecond',
+        'milli', 'millis', 'L', 'us', 'microseconds', 'microsecond',
+        'micro', 'micros', 'U', 'ns', 'nanoseconds', 'nano', 'nanos',
+        'nanosecond', 'N').
+    box : bool, default True
+        - If True returns a Timedelta/TimedeltaIndex of the results.
+        - If False returns a numpy.timedelta64 or numpy.darray of
+          values of dtype timedelta64[ns].
     errors : {'ignore', 'raise', 'coerce'}, default 'raise'
-        - If 'raise', then invalid parsing will raise an exception
-        - If 'coerce', then invalid parsing will be set as NaT
-        - If 'ignore', then invalid parsing will return the input
+        - If 'raise', then invalid parsing will raise an exception.
+        - If 'coerce', then invalid parsing will be set as NaT.
+        - If 'ignore', then invalid parsing will return the input.
 
     Returns
     -------
-    ret : timedelta64/arrays of timedelta64 if parsing succeeded
+    timedelta64 or numpy.array of timedelta64
+        Output type returned if parsing succeeded.
+
+    See also
+    --------
+    DataFrame.astype : Cast argument to a specified dtype.
+    to_datetime : Convert argument to datetime.
 
     Examples
     --------
@@ -69,10 +81,10 @@ def to_timedelta(arg, unit='ns', box=True, errors='raise'):
     TimedeltaIndex(['0 days', '1 days', '2 days', '3 days', '4 days'],
                    dtype='timedelta64[ns]', freq=None)
 
-    See Also
-    --------
-    pandas.DataFrame.astype : Cast argument to a specified dtype.
-    pandas.to_datetime : Convert argument to datetime.
+    Returning an ndarray by using the 'box' keyword argument:
+
+    >>> pd.to_timedelta(np.arange(5), box=False)
+    array([0, 1, 2, 3, 4], dtype='timedelta64[ns]')
     """
     unit = parse_timedelta_unit(unit)