diff --git a/doc/source/whatsnew/v0.23.0.txt b/doc/source/whatsnew/v0.23.0.txt
index fe5342c520196..5fa0c1ff9253f 100644
--- a/doc/source/whatsnew/v0.23.0.txt
+++ b/doc/source/whatsnew/v0.23.0.txt
@@ -379,6 +379,8 @@ Performance Improvements
 - Improved performance of :func:`IntervalIndex.symmetric_difference()` (:issue:`18475`)
 - Improved performance of ``DatetimeIndex`` and ``Series`` arithmetic operations with Business-Month and Business-Quarter frequencies (:issue:`18489`)
 - :func:`Series` / :func:`DataFrame` tab completion limits to 100 values, for better performance. (:issue:`18587`)
+- Improved performance of :func:`DataFrame.median` with ``axis=1`` when bottleneck is not installed (:issue:`16468`)
+
 
 .. _whatsnew_0230.docs:
 
diff --git a/pandas/_libs/algos.pyx b/pandas/_libs/algos.pyx
index 7b61cd22f45d1..9a7af71e74574 100644
--- a/pandas/_libs/algos.pyx
+++ b/pandas/_libs/algos.pyx
@@ -196,24 +196,6 @@ cpdef numeric kth_smallest(numeric[:] a, Py_ssize_t k) nogil:
     return a[k]
 
 
-cpdef numeric median(numeric[:] arr):
-    """
-    A faster median
-    """
-    cdef Py_ssize_t n = arr.size
-
-    if n == 0:
-        return np.NaN
-
-    arr = arr.copy()
-
-    if n % 2:
-        return kth_smallest(arr, n // 2)
-    else:
-        return (kth_smallest(arr, n // 2) +
-                kth_smallest(arr, n // 2 - 1)) / 2
-
-
 # ----------------------------------------------------------------------
 # Pairwise correlation/covariance
 
diff --git a/pandas/_libs/groupby.pyx b/pandas/_libs/groupby.pyx
index 9d9ac2ef2f5b1..9cc15fb6692d9 100644
--- a/pandas/_libs/groupby.pyx
+++ b/pandas/_libs/groupby.pyx
@@ -118,7 +118,7 @@ def group_last_object(ndarray[object, ndim=2] out,
                 out[i, j] = resx[i, j]
 
 
-cdef inline float64_t _median_linear(float64_t* a, int n) nogil:
+cdef inline float64_t median_linear(float64_t* a, int n) nogil:
     cdef int i, j, na_count = 0
     cdef float64_t result
     cdef float64_t* tmp
diff --git a/pandas/_libs/groupby_helper.pxi.in b/pandas/_libs/groupby_helper.pxi.in
index 14d47398ac1df..a751fadaf48cf 100644
--- a/pandas/_libs/groupby_helper.pxi.in
+++ b/pandas/_libs/groupby_helper.pxi.in
@@ -740,7 +740,7 @@ def group_median_float64(ndarray[float64_t, ndim=2] out,
             ptr += _counts[0]
             for j in range(ngroups):
                 size = _counts[j + 1]
-                out[j, i] = _median_linear(ptr, size)
+                out[j, i] = median_linear(ptr, size)
                 ptr += size
 
 
diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
index 63989304bb5f9..eda86f12d501d 100644
--- a/pandas/core/nanops.py
+++ b/pandas/core/nanops.py
@@ -6,7 +6,7 @@
 
 import numpy as np
 from pandas import compat
-from pandas._libs import tslib, algos, lib
+from pandas._libs import tslib, lib
 from pandas.core.dtypes.common import (
     _get_dtype,
     is_float, is_scalar,
@@ -370,14 +370,13 @@ def nanmean(values, axis=None, skipna=True):
 @bottleneck_switch()
 def nanmedian(values, axis=None, skipna=True):
 
-    values, mask, dtype, dtype_max = _get_values(values, skipna)
-
     def get_median(x):
         mask = notna(x)
         if not skipna and not mask.all():
             return np.nan
-        return algos.median(com._values_from_object(x[mask]))
+        return np.nanmedian(x[mask])
 
+    values, mask, dtype, dtype_max = _get_values(values, skipna)
     if not is_float_dtype(values):
         values = values.astype('f8')
         values[mask] = np.nan
@@ -389,10 +388,15 @@ def get_median(x):
 
     # an array from a frame
     if values.ndim > 1:
+
         # there's a non-empty array to apply over otherwise numpy raises
         if notempty:
-            return _wrap_results(
-                np.apply_along_axis(get_median, axis, values), dtype)
+            if not skipna:
+                return _wrap_results(
+                    np.apply_along_axis(get_median, axis, values), dtype)
+
+            # fastpath for the skipna case
+            return _wrap_results(np.nanmedian(values, axis), dtype)
 
         # must return the correct shape, but median is not defined for the
         # empty set so return nans of shape "everything but the passed axis"