BUG: fix dirint KeyError with scalar inputs on pandas >= 2.0

docs/sphinx/source/whatsnew/v0.15.2.rst

@@ -14,6 +14,9 @@ Deprecations
 
 Bug fixes
 ~~~~~~~~~
+* Fixed :py:func:`pvlib.irradiance.dirint` raising ``KeyError`` with
+  scalar inputs on pandas >= 2.0. (:pull:`2752`, :issue:`2751`)
+  By :ghuser:`Omesh37`.
 * Corrects a bug in :py:func:`pvlib.temperature.fuentes`. If inputs were
   data type integer, users can expect modeled cell temperature values to
   increase slightly.

pvlib/irradiance.py

@@ -1927,10 +1927,10 @@ def dirint(ghi, solar_zenith, times, pressure=101325., use_delta_kt_prime=True,
 
     Parameters
     ----------
-    ghi : array-like
+    ghi : numeric
         Global horizontal irradiance. See :term:`ghi`. [Wm⁻²]
 
-    solar_zenith : array-like
+    solar_zenith : numeric
         True (not refraction-corrected) solar zenith angles. See
         :term:`solar_zenith`. [°]
 
@@ -1964,9 +1964,9 @@ def dirint(ghi, solar_zenith, times, pressure=101325., use_delta_kt_prime=True,
 
     Returns
     -------
-    dni : array-like
-        The modeled direct normal irradiance, as provided by the
-        DIRINT model. [Wm⁻²]
+    dni : numeric or pd.Series
+        Estimated direct normal irradiance. Returns float if all inputs
+        are scalar, pd.Series otherwise. [Wm⁻²]
 
     Notes
     -----
@@ -1983,6 +1983,7 @@ def dirint(ghi, solar_zenith, times, pressure=101325., use_delta_kt_prime=True,
        Global Horizontal to Direct Normal Insolation", Technical Report No.
        SERI/TR-215-3087, Golden, CO: Solar Energy Research Institute, 1987.
     """
+    scalar_input = np.isscalar(solar_zenith)
 
     disc_out = disc(ghi, solar_zenith, times, pressure=pressure,
                     min_cos_zenith=min_cos_zenith, max_zenith=max_zenith)
@@ -2001,6 +2002,8 @@ def dirint(ghi, solar_zenith, times, pressure=101325., use_delta_kt_prime=True,
     # Perez eqn 5
     dni = disc_out['dni'] * dirint_coeffs
 
+    if scalar_input:
+        return float(dni.iloc[0])
     return dni
 
 
@@ -2109,6 +2112,14 @@ def _dirint_bins(times, kt_prime, zenith, w, delta_kt_prime):
     -------
     tuple of kt_prime_bin, zenith_bin, w_bin, delta_kt_prime_bin
     """
+    # Ensure scalar inputs are converted to Series so that boolean masks
+    # produce a boolean Series rather than a scalar bool.
+    # Scalar bools cause KeyError in pandas >= 2.0. GH #XXXX
+    kt_prime = pd.Series(kt_prime, index=times, dtype=float)
+    zenith = pd.Series(zenith, index=times, dtype=float)
+    w = pd.Series(w, index=times, dtype=float)
+    delta_kt_prime = pd.Series(delta_kt_prime, index=times, dtype=float)
+
     # @wholmgren: the following bin assignments use MATLAB's 1-indexing.
     # Later, we'll subtract 1 to conform to Python's 0-indexing.
 

tests/test_irradiance.py

@@ -1140,6 +1140,36 @@ def test_dirindex_min_cos_zenith_max_zenith():
     assert_series_equal(out, expected)
 
 
+def test_dirint_scalar_inputs():
+    """Scalar numeric inputs return scalar float output. GH #2751"""
+    times = pd.DatetimeIndex(['2023-06-21 12:00'], tz='UTC')
+
+    # scalar int input → should return float (not Series)
+    result = irradiance.dirint(
+        ghi=500, solar_zenith=45, times=times
+    )
+    assert np.isscalar(result), "scalar input should return scalar output"
+    assert isinstance(result, float)
+
+    # scalar float with delta_kt_prime disabled → non-NaN value check
+    result = irradiance.dirint(
+        ghi=500.0, solar_zenith=45.0, times=times,
+        use_delta_kt_prime=False
+    )
+    assert np.isscalar(result)
+    assert isinstance(result, float)
+    assert result > 0  # should be positive DNI
+
+    # Series input → should still return Series (regression check)
+    times2 = pd.date_range('2023-06-21 10:00', periods=3, freq='h', tz='UTC')
+    result_series = irradiance.dirint(
+        ghi=pd.Series([400, 500, 300], index=times2),
+        solar_zenith=pd.Series([50, 40, 60], index=times2),
+        times=times2
+    )
+    assert isinstance(result_series, pd.Series)
+
+
 def test_dni():
     ghi = pd.Series([90, 100, 100, 100, 100])
     dhi = pd.Series([100, 90, 50, 50, 50])