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_sources/autoapi/arkouda/index.rst.txt

@@ -179,6 +179,7 @@ Functions
    arkouda.corr
    arkouda.cos
    arkouda.cosh
+   arkouda.count_nonzero
    arkouda.cov
    arkouda.create_pdarray
    arkouda.create_pdarray
@@ -269,6 +270,7 @@ Functions
    arkouda.max
    arkouda.maxk
    arkouda.mean
+   arkouda.median
    arkouda.merge
    arkouda.min
    arkouda.mink
@@ -28088,6 +28090,29 @@ Attributes
    :raises TypeError: Raised if the parameter is not a pdarray
 
 
+.. py:function:: count_nonzero(pda)
+
+   Compute the nonzero count of a given array. 1D case only, for now.
+
+   :param pda: The input data, in pdarray form, numeric, bool, or str
+   :type pda: pdarray
+
+   :returns: The nonzero count of the entire pdarray
+   :rtype: np.int64
+
+   .. rubric:: Examples
+
+   >>> pda = ak.array([0,4,7,8,1,3,5,2,-1])
+   >>> ak.count_nonzero(pda)
+   9
+   >>> pda = ak.array([False,True,False,True,False])
+   >>> ak.count_nonzero(pda)
+   3
+   >>> pda = ak.array(["hello","","there"])
+   >>> ak.count_nonzero(pda)
+   2
+
+
 .. py:function:: cov(x: pdarray, y: pdarray) -> numpy.float64
 
    Return the covariance of x and y
@@ -30270,6 +30295,30 @@ Attributes
    :raises RuntimeError: Raised if there's a server-side error thrown
 
 
+.. py:function:: median(pda)
+
+   Compute the median of a given array.  1d case only, for now.
+
+   :param pda: The input data, in pdarray form, numeric type or boolean
+   :type pda: pdarray
+
+   :returns: The median of the entire pdarray
+             The array is sorted, and then if the number of elements is odd,
+                 the return value is the middle element.  If even, then the
+                 mean of the two middle elements.
+   :rtype: np.float64
+
+   .. rubric:: Examples
+
+   >>> import arkouda as ak
+   >>> arkouda.connect()
+   >>> pda = ak.array ([0,4,7,8,1,3,5,2,-1])
+   >>> ak.median(pda)
+   3
+   >>> pda = ak.array([0,1,3,3,1,2,3,4,2,3])
+   2.5
+
+
 .. py:function:: merge(left: DataFrame, right: DataFrame, on: Optional[Union[str, List[str]]] = None, how: str = 'inner', left_suffix: str = '_x', right_suffix: str = '_y', convert_ints: bool = True, sort: bool = True) -> DataFrame
 
    Merge Arkouda DataFrames with a database-style join.

_sources/autoapi/arkouda/numeric/index.rst.txt

@@ -34,6 +34,7 @@ Functions
    arkouda.numeric.clip
    arkouda.numeric.cos
    arkouda.numeric.cosh
+   arkouda.numeric.count_nonzero
    arkouda.numeric.cumprod
    arkouda.numeric.cumsum
    arkouda.numeric.deg2rad
@@ -51,6 +52,7 @@ Functions
    arkouda.numeric.log10
    arkouda.numeric.log1p
    arkouda.numeric.log2
+   arkouda.numeric.median
    arkouda.numeric.rad2deg
    arkouda.numeric.round
    arkouda.numeric.sign
@@ -399,6 +401,29 @@ Functions
    :raises TypeError: Raised if the parameter is not a pdarray
 
 
+.. py:function:: count_nonzero(pda)
+
+   Compute the nonzero count of a given array. 1D case only, for now.
+
+   :param pda: The input data, in pdarray form, numeric, bool, or str
+   :type pda: pdarray
+
+   :returns: The nonzero count of the entire pdarray
+   :rtype: np.int64
+
+   .. rubric:: Examples
+
+   >>> pda = ak.array([0,4,7,8,1,3,5,2,-1])
+   >>> ak.count_nonzero(pda)
+   9
+   >>> pda = ak.array([False,True,False,True,False])
+   >>> ak.count_nonzero(pda)
+   3
+   >>> pda = ak.array(["hello","","there"])
+   >>> ak.count_nonzero(pda)
+   2
+
+
 .. py:function:: cumprod(pda: arkouda.pdarrayclass.pdarray) -> arkouda.pdarrayclass.pdarray
 
    Return the cumulative product over the array.
@@ -830,6 +855,30 @@ Functions
    :rtype: pdarray contain values of the base 2 log
 
 
+.. py:function:: median(pda)
+
+   Compute the median of a given array.  1d case only, for now.
+
+   :param pda: The input data, in pdarray form, numeric type or boolean
+   :type pda: pdarray
+
+   :returns: The median of the entire pdarray
+             The array is sorted, and then if the number of elements is odd,
+                 the return value is the middle element.  If even, then the
+                 mean of the two middle elements.
+   :rtype: np.float64
+
+   .. rubric:: Examples
+
+   >>> import arkouda as ak
+   >>> arkouda.connect()
+   >>> pda = ak.array ([0,4,7,8,1,3,5,2,-1])
+   >>> ak.median(pda)
+   3
+   >>> pda = ak.array([0,1,3,3,1,2,3,4,2,3])
+   2.5
+
+
 .. py:function:: rad2deg(pda: arkouda.pdarrayclass.pdarray, where: Union[bool, arkouda.pdarrayclass.pdarray] = True) -> arkouda.pdarrayclass.pdarray
 
    Converts angles element-wise from radians to degrees.

genindex.html

@@ -1681,6 +1681,12 @@ <h2>C</h2>
           <li><a href="autoapi/arkouda/index.html#arkouda.GroupBy.count">(arkouda.GroupBy method)</a>, <a href="autoapi/arkouda/index.html#id252">[1]</a>, <a href="autoapi/arkouda/index.html#id297">[2]</a>, <a href="autoapi/arkouda/index.html#id342">[3]</a>, <a href="autoapi/arkouda/index.html#id387">[4]</a>, <a href="autoapi/arkouda/index.html#id432">[5]</a>, <a href="usage/groupby.html#arkouda.GroupBy.count">[6]</a>
 </li>
           <li><a href="autoapi/arkouda/groupbyclass/index.html#arkouda.groupbyclass.GroupBy.count">(arkouda.groupbyclass.GroupBy method)</a>
+</li>
+        </ul></li>
+        <li><a href="autoapi/arkouda/index.html#arkouda.count_nonzero">count_nonzero() (in module arkouda)</a>
+
+        <ul>
+          <li><a href="autoapi/arkouda/numeric/index.html#arkouda.numeric.count_nonzero">(in module arkouda.numeric)</a>
 </li>
         </ul></li>
         <li><a href="autoapi/arkouda/array_api/_set_functions/index.html#arkouda.array_api._set_functions.UniqueAllResult.counts">counts (arkouda.array_api._set_functions.UniqueAllResult attribute)</a>
@@ -3561,6 +3567,10 @@ <h2>M</h2>
           <li><a href="autoapi/arkouda/index.html#arkouda.GroupBy.median">(arkouda.GroupBy method)</a>, <a href="autoapi/arkouda/index.html#id258">[1]</a>, <a href="autoapi/arkouda/index.html#id303">[2]</a>, <a href="autoapi/arkouda/index.html#id348">[3]</a>, <a href="autoapi/arkouda/index.html#id393">[4]</a>, <a href="autoapi/arkouda/index.html#id438">[5]</a>, <a href="usage/groupby.html#arkouda.GroupBy.median">[6]</a>
 </li>
           <li><a href="autoapi/arkouda/groupbyclass/index.html#arkouda.groupbyclass.GroupBy.median">(arkouda.groupbyclass.GroupBy method)</a>
+</li>
+          <li><a href="autoapi/arkouda/index.html#arkouda.median">(in module arkouda)</a>
+</li>
+          <li><a href="autoapi/arkouda/numeric/index.html#arkouda.numeric.median">(in module arkouda.numeric)</a>
 </li>
         </ul></li>
         <li><a href="autoapi/arkouda/index.html#arkouda.DataFrame.memory_usage">memory_usage() (arkouda.DataFrame method)</a>, <a href="autoapi/arkouda/index.html#id137">[1]</a>