COMPAT: rename isnull -> isna, notnull -> notna

existing isnull, notnull remain user facing

closes #15001

doc/source/10min.rst

@@ -373,7 +373,7 @@ To get the boolean mask where values are ``nan``
 
 .. ipython:: python
 
-   pd.isnull(df1)
+   pd.isna(df1)
 
 
 Operations

doc/source/api.rst

@@ -187,8 +187,8 @@ Top-level missing data
 .. autosummary::
    :toctree: generated/
 
-   isnull
-   notnull
+   isna
+   notna
 
 Top-level conversions
 ~~~~~~~~~~~~~~~~~~~~~
@@ -272,8 +272,8 @@ Conversion
    Series.astype
    Series.infer_objects
    Series.copy
-   Series.isnull
-   Series.notnull
+   Series.isna
+   Series.notna
 
 Indexing, iteration
 ~~~~~~~~~~~~~~~~~~~
@@ -780,8 +780,8 @@ Conversion
    DataFrame.convert_objects
    DataFrame.infer_objects
    DataFrame.copy
-   DataFrame.isnull
-   DataFrame.notnull
+   DataFrame.isna
+   DataFrame.notna
 
 Indexing, iteration
 ~~~~~~~~~~~~~~~~~~~
@@ -1098,8 +1098,8 @@ Conversion
 
    Panel.astype
    Panel.copy
-   Panel.isnull
-   Panel.notnull
+   Panel.isna
+   Panel.notna
 
 Getting and setting
 ~~~~~~~~~~~~~~~~~~~
@@ -1342,8 +1342,8 @@ Missing Values
 
    Index.fillna
    Index.dropna
-   Index.isnull
-   Index.notnull
+   Index.isna
+   Index.notna
 
 Conversion
 ~~~~~~~~~~

doc/source/basics.rst

@@ -444,7 +444,7 @@ So, for instance, to reproduce :meth:`~DataFrame.combine_first` as above:
 
 .. ipython:: python
 
-   combiner = lambda x, y: np.where(pd.isnull(x), y, x)
+   combiner = lambda x, y: np.where(pd.isna(x), y, x)
    df1.combine(df2, combiner)
 
 .. _basics.stats:
@@ -511,7 +511,7 @@ optional ``level`` parameter which applies only if the object has a
     :header: "Function", "Description"
     :widths: 20, 80
 
-    ``count``, Number of non-null observations
+    ``count``, Number of non-na observations
     ``sum``, Sum of values
     ``mean``, Mean of values
     ``mad``, Mean absolute deviation
@@ -541,7 +541,7 @@ will exclude NAs on Series input by default:
    np.mean(df['one'].values)
 
 ``Series`` also has a method :meth:`~Series.nunique` which will return the
-number of unique non-null values:
+number of unique non-na values:
 
 .. ipython:: python
 

doc/source/categorical.rst

@@ -863,14 +863,14 @@ a code of ``-1``.
     s.cat.codes
 
 
-Methods for working with missing data, e.g. :meth:`~Series.isnull`, :meth:`~Series.fillna`,
+Methods for working with missing data, e.g. :meth:`~Series.isna`, :meth:`~Series.fillna`,
 :meth:`~Series.dropna`, all work normally:
 
 .. ipython:: python
 
     s = pd.Series(["a", "b", np.nan], dtype="category")
     s
-    pd.isnull(s)
+    pd.isna(s)
     s.fillna("a")
 
 Differences to R's `factor`

doc/source/comparison_with_sas.rst

@@ -444,13 +444,13 @@ For example, in SAS you could do this to filter missing values.
        if value_x ^= .;
    run;
 
-Which doesn't work in in pandas.  Instead, the ``pd.isnull`` or ``pd.notnull`` functions
+Which doesn't work in in pandas.  Instead, the ``pd.isna`` or ``pd.notna`` functions
 should be used for comparisons.
 
 .. ipython:: python
 
-   outer_join[pd.isnull(outer_join['value_x'])]
-   outer_join[pd.notnull(outer_join['value_x'])]
+   outer_join[pd.isna(outer_join['value_x'])]
+   outer_join[pd.notna(outer_join['value_x'])]
 
 pandas also provides a variety of methods to work with missing data - some of
 which would be challenging to express in SAS. For example, there are methods to
@@ -570,15 +570,15 @@ machine's memory, but also that the operations on that data may be faster.
 
 If out of core processing is needed, one possibility is the
 `dask.dataframe <http://dask.pydata.org/en/latest/dataframe.html>`_
-library (currently in development) which 
+library (currently in development) which
 provides a subset of pandas functionality for an on-disk ``DataFrame``
 
 Data Interop
 ~~~~~~~~~~~~
 
 pandas provides a :func:`read_sas` method that can read SAS data saved in
 the XPORT or SAS7BDAT binary format.
- 
+
 .. code-block:: none
 
    libname xportout xport 'transport-file.xpt';
@@ -613,4 +613,3 @@ to interop data between SAS and pandas is to serialize to csv.
 
    In [9]: %time df = pd.read_csv('big.csv')
    Wall time: 4.86 s
- 

doc/source/comparison_with_sql.rst

@@ -101,7 +101,7 @@ Just like SQL's OR and AND, multiple conditions can be passed to a DataFrame usi
     # tips by parties of at least 5 diners OR bill total was more than $45
     tips[(tips['size'] >= 5) | (tips['total_bill'] > 45)]
 
-NULL checking is done using the :meth:`~pandas.Series.notnull` and :meth:`~pandas.Series.isnull`
+NULL checking is done using the :meth:`~pandas.Series.notna` and :meth:`~pandas.Series.isna`
 methods.
 
 .. ipython:: python
@@ -121,9 +121,9 @@ where ``col2`` IS NULL with the following query:
 
 .. ipython:: python
 
-    frame[frame['col2'].isnull()]
+    frame[frame['col2'].isna()]
 
-Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.notnull`.
+Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.notna`.
 
 .. code-block:: sql
 
@@ -133,7 +133,7 @@ Getting items where ``col1`` IS NOT NULL can be done with :meth:`~pandas.Series.
 
 .. ipython:: python
 
-    frame[frame['col1'].notnull()]
+    frame[frame['col1'].notna()]
 
 
 GROUP BY

doc/source/conf.py

@@ -238,8 +238,8 @@
 # https://github.com/pandas-dev/pandas/issues/16186
 
 moved_api_pages = [
-    ('pandas.core.common.isnull', 'pandas.isnull'),
-    ('pandas.core.common.notnull', 'pandas.notnull'),
+    ('pandas.core.common.isnull', 'pandas.isna'),
+    ('pandas.core.common.notnull', 'pandas.notna'),
     ('pandas.core.reshape.get_dummies', 'pandas.get_dummies'),
     ('pandas.tools.merge.concat', 'pandas.concat'),
     ('pandas.tools.merge.merge', 'pandas.merge'),

doc/source/gotchas.rst

@@ -202,7 +202,7 @@ For many reasons we chose the latter. After years of production use it has
 proven, at least in my opinion, to be the best decision given the state of
 affairs in NumPy and Python in general. The special value ``NaN``
 (Not-A-Number) is used everywhere as the ``NA`` value, and there are API
-functions ``isnull`` and ``notnull`` which can be used across the dtypes to
+functions ``isna`` and ``notna`` which can be used across the dtypes to
 detect NA values.
 
 However, it comes with it a couple of trade-offs which I most certainly have

doc/source/missing_data.rst

@@ -36,7 +36,7 @@ When / why does data become missing?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Some might quibble over our usage of *missing*. By "missing" we simply mean
-**null** or "not present for whatever reason". Many data sets simply arrive with
+**na** or "not present for whatever reason". Many data sets simply arrive with
 missing data, either because it exists and was not collected or it never
 existed. For example, in a collection of financial time series, some of the time
 series might start on different dates. Thus, values prior to the start date
@@ -63,27 +63,27 @@ to handling missing data. While ``NaN`` is the default missing value marker for
 reasons of computational speed and convenience, we need to be able to easily
 detect this value with data of different types: floating point, integer,
 boolean, and general object. In many cases, however, the Python ``None`` will
-arise and we wish to also consider that "missing" or "null".
+arise and we wish to also consider that "missing" or "na".
 
 .. note::
 
    Prior to version v0.10.0 ``inf`` and ``-inf`` were also
-   considered to be "null" in computations. This is no longer the case by
-   default; use the ``mode.use_inf_as_null`` option to recover it.
+   considered to be "na" in computations. This is no longer the case by
+   default; use the ``mode.use_inf_as_na`` option to recover it.
 
-.. _missing.isnull:
+.. _missing.isna:
 
 To make detecting missing values easier (and across different array dtypes),
-pandas provides the :func:`~pandas.core.common.isnull` and
-:func:`~pandas.core.common.notnull` functions, which are also methods on
+pandas provides the :func:`isna` and
+:func:`notna` functions, which are also methods on
 ``Series`` and ``DataFrame`` objects:
 
 .. ipython:: python
 
    df2['one']
-   pd.isnull(df2['one'])
-   df2['four'].notnull()
-   df2.isnull()
+   pd.isna(df2['one'])
+   df2['four'].notna()
+   df2.isna()
 
 .. warning::
 
@@ -206,7 +206,7 @@ with missing data.
 Filling missing values: fillna
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-The **fillna** function can "fill in" NA values with non-null data in a couple
+The **fillna** function can "fill in" NA values with non-na data in a couple
 of ways, which we illustrate:
 
 **Replace NA with a scalar value**
@@ -220,7 +220,7 @@ of ways, which we illustrate:
 **Fill gaps forward or backward**
 
 Using the same filling arguments as :ref:`reindexing <basics.reindexing>`, we
-can propagate non-null values forward or backward:
+can propagate non-na values forward or backward:
 
 .. ipython:: python
 
@@ -288,7 +288,7 @@ a Series in this case.
 
 .. ipython:: python
 
-        dff.where(pd.notnull(dff), dff.mean(), axis='columns')
+        dff.where(pd.notna(dff), dff.mean(), axis='columns')
 
 
 .. _missing_data.dropna:

doc/source/options.rst

@@ -419,10 +419,10 @@ mode.chained_assignment             warn         Raise an exception, warn, or no
                                                  assignment, The default is warn
 mode.sim_interactive                False        Whether to simulate interactive mode
                                                  for purposes of testing.
-mode.use_inf_as_null                False        True means treat None, NaN, -INF,
-                                                 INF as null (old way), False means
+mode.use_inf_as_na                  False        True means treat None, NaN, -INF,
+                                                 INF as NA (old way), False means
                                                  None and NaN are null, but INF, -INF
-                                                 are not null (new way).
+                                                 are not NA (new way).
 compute.use_bottleneck              True         Use the bottleneck library to accelerate
                                                  computation if it is installed.
 compute.use_numexpr                 True         Use the numexpr library to accelerate

doc/source/whatsnew/v0.21.0.txt

@@ -127,8 +127,6 @@ the target. Now, a ``ValueError`` will be raised when such an input is passed in
    ...
    ValueError: Cannot operate inplace if there is no assignment
 
-.. _whatsnew_0210.dtype_conversions:
-
 Dtype Conversions
 ^^^^^^^^^^^^^^^^^
 
@@ -186,6 +184,22 @@ Dtype Conversions
   - Inconsistent behavior in ``.where()`` with datetimelikes which would raise rather than coerce to ``object`` (:issue:`16402`)
    - Bug in assignment against ``int64`` data with ``np.ndarray`` with ``float64`` dtype may keep ``int64`` dtype (:issue:`14001`)
 
+.. _whatsnew_0210.api.na_changes:
+
+NA naming Changes
+^^^^^^^^^^^^^^^^^
+
+In orde to promote more consistency among the pandas API, we have added additional top-level
+functions :func:`isna` and :func:`notna` that are the same as :func:`isnull` and :func:`notnull`.
+The naming scheme is now more consistent with methods ``.dropna()`` and ``.fillna()``. Furthermore
+in all cases where ``.isnull()`` and ``.notnull()`` methods are defined, these have additional methods
+named ``.isna()`` and ``.notna()``, these include for classes `Categorical`,
+`Index`, `Series`, and `DataFrame`. (:issue:`15001`).
+
+Using :func:`isnull` and :func:`notnull` will now issue a ``DeprecationWarning`` and recommend using :func:`isna` and :func`notnull` respectively.
+
+The configuration option ``mode.use_inf_as_null``is deprecated, and ``mode.use_inf_as_na`` is added as a replacement.
+
 .. _whatsnew_0210.api:
 
 Other API Changes

pandas/_libs/algos_rank_helper.pxi.in

@@ -83,7 +83,7 @@ def rank_1d_{{dtype}}(object in_arr, ties_method='average', ascending=True,
         nan_value = {{neg_nan_value}}
 
     {{if dtype == 'object'}}
-    mask = lib.isnullobj(values)
+    mask = lib.isnaobj(values)
     {{elif dtype == 'float64'}}
     mask = np.isnan(values)
     {{elif dtype == 'int64'}}
@@ -259,7 +259,7 @@ def rank_2d_{{dtype}}(object in_arr, axis=0, ties_method='average',
         nan_value = {{neg_nan_value}}
 
     {{if dtype == 'object'}}
-    mask = lib.isnullobj2d(values)
+    mask = lib.isnaobj2d(values)
     {{elif dtype == 'float64'}}
     mask = np.isnan(values)
     {{elif dtype == 'int64'}}

pandas/_libs/lib.pyx

@@ -286,7 +286,7 @@ def item_from_zerodim(object val):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def isnullobj(ndarray arr):
+def isnaobj(ndarray arr):
     cdef Py_ssize_t i, n
     cdef object val
     cdef ndarray[uint8_t] result
@@ -303,7 +303,7 @@ def isnullobj(ndarray arr):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def isnullobj_old(ndarray arr):
+def isnaobj_old(ndarray arr):
     cdef Py_ssize_t i, n
     cdef object val
     cdef ndarray[uint8_t] result
@@ -320,7 +320,7 @@ def isnullobj_old(ndarray arr):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def isnullobj2d(ndarray arr):
+def isnaobj2d(ndarray arr):
     cdef Py_ssize_t i, j, n, m
     cdef object val
     cdef ndarray[uint8_t, ndim=2] result
@@ -339,7 +339,7 @@ def isnullobj2d(ndarray arr):
 
 @cython.wraparound(False)
 @cython.boundscheck(False)
-def isnullobj2d_old(ndarray arr):
+def isnaobj2d_old(ndarray arr):
     cdef Py_ssize_t i, j, n, m
     cdef object val
     cdef ndarray[uint8_t, ndim=2] result

pandas/_libs/testing.pyx

@@ -1,7 +1,7 @@
 import numpy as np
 
 from pandas import compat
-from pandas.core.dtypes.missing import isnull, array_equivalent
+from pandas.core.dtypes.missing import isna, array_equivalent
 from pandas.core.dtypes.common import is_dtype_equal
 
 cdef NUMERIC_TYPES = (
@@ -182,7 +182,7 @@ cpdef assert_almost_equal(a, b,
     if a == b:
         # object comparison
         return True
-    if isnull(a) and isnull(b):
+    if isna(a) and isna(b):
         # nan / None comparison
         return True
     if is_comparable_as_number(a) and is_comparable_as_number(b):