PERF: nancorr pearson (pandas-dev#42761)

doc/source/whatsnew/v1.4.0.rst

@@ -171,6 +171,8 @@ Performance improvements
 - Performance improvement in :meth:`.GroupBy.transform` for user-defined functions (:issue:`41598`)
 - Performance improvement in constructing :class:`DataFrame` objects (:issue:`42631`)
 - Performance improvement in :meth:`GroupBy.shift` when ``fill_value`` argument is provided (:issue:`26615`)
+- Performance improvement in :meth:`DataFrame.corr` for ``method=pearson`` on data without missing values (:issue:`40956`)
+-
 
 .. ---------------------------------------------------------------------------
 

pandas/_libs/algos.pyx

@@ -326,8 +326,12 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
         Py_ssize_t i, j, xi, yi, N, K
         bint minpv
         float64_t[:, ::1] result
+        # Initialize to None since we only use in the no missing value case
+        float64_t[::1] means=None, ssqds=None
         ndarray[uint8_t, ndim=2] mask
+        bint no_nans
         int64_t nobs = 0
+        float64_t mean, ssqd, val
         float64_t vx, vy, dx, dy, meanx, meany, divisor, ssqdmx, ssqdmy, covxy
 
     N, K = (<object>mat).shape
@@ -339,25 +343,57 @@ def nancorr(const float64_t[:, :] mat, bint cov=False, minp=None):
 
     result = np.empty((K, K), dtype=np.float64)
     mask = np.isfinite(mat).view(np.uint8)
+    no_nans = mask.all()
+
+    # Computing the online means and variances is expensive - so if possible we can
+    # precompute these and avoid repeating the computations each time we handle
+    # an (xi, yi) pair
+    if no_nans:
+        means = np.empty(K, dtype=np.float64)
+        ssqds = np.empty(K, dtype=np.float64)
+
+        with nogil:
+            for j in range(K):
+                ssqd = mean = 0
+                for i in range(N):
+                    val = mat[i, j]
+                    dx = val - mean
+                    mean += 1 / (i + 1) * dx
+                    ssqd += (val - mean) * dx
+
+                means[j] = mean
+                ssqds[j] = ssqd
 
     with nogil:
         for xi in range(K):
             for yi in range(xi + 1):
-                # Welford's method for the variance-calculation
-                # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
-                nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
-                for i in range(N):
-                    if mask[i, xi] and mask[i, yi]:
+                covxy = 0
+                if no_nans:
+                    for i in range(N):
                         vx = mat[i, xi]
                         vy = mat[i, yi]
-                        nobs += 1
-                        dx = vx - meanx
-                        dy = vy - meany
-                        meanx += 1 / nobs * dx
-                        meany += 1 / nobs * dy
-                        ssqdmx += (vx - meanx) * dx
-                        ssqdmy += (vy - meany) * dy
-                        covxy += (vx - meanx) * dy
+                        covxy += (vx - means[xi]) * (vy - means[yi])
+
+                    ssqdmx = ssqds[xi]
+                    ssqdmy = ssqds[yi]
+                    nobs = N
+
+                else:
+                    nobs = ssqdmx = ssqdmy = covxy = meanx = meany = 0
+                    for i in range(N):
+                        # Welford's method for the variance-calculation
+                        # https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
+                        if mask[i, xi] and mask[i, yi]:
+                            vx = mat[i, xi]
+                            vy = mat[i, yi]
+                            nobs += 1
+                            dx = vx - meanx
+                            dy = vy - meany
+                            meanx += 1 / nobs * dx
+                            meany += 1 / nobs * dy
+                            ssqdmx += (vx - meanx) * dx
+                            ssqdmy += (vy - meany) * dy
+                            covxy += (vx - meanx) * dy
 
                 if nobs < minpv:
                     result[xi, yi] = result[yi, xi] = NaN