Resolving pre-commit errors

Author: ammar-qazi

pandas/core/arrays/base.py

@@ -1005,17 +1005,21 @@ def interpolate(
         ----------
         method : str, default 'linear'
             Interpolation technique to use. One of:
-            * 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.
-            * 'time': Works on daily and higher resolution data to interpolate given length of interval.
+            * 'linear': Ignore the index and treat the values as equally spaced. 
+            This is the only method supported on MultiIndexes.
+            * 'time': Works on daily and higher resolution data to interpolate 
+            given length of interval.
             * 'index', 'values': use the actual numerical values of the index.
             * 'pad': Fill in NaNs using existing values.
-            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 'polynomial': 
-            Passed to scipy.interpolate.interp1d, whereas 'spline' is passed to 
-            scipy.interpolate.UnivariateSpline. These methods use the numerical values of the index. 
-            Both 'polynomial' and 'spline' require that you also specify an order (int), 
-            e.g. arr.interpolate(method='polynomial', order=5).
-            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 'cubicspline': 
-            Wrappers around the SciPy interpolation methods of similar names. See Notes.
+            * 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'barycentric', 
+            'polynomial': Passed to scipy.interpolate.interp1d, whereas 'spline' 
+            is passed to scipy.interpolate.UnivariateSpline. These methods use 
+            the numerical values of the index.
+            Both 'polynomial' and 'spline' require that you also specify an 
+            order (int), e.g. arr.interpolate(method='polynomial', order=5).
+            * 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima', 
+            'cubicspline': Wrappers around the SciPy interpolation methods 
+            of similar names. See Notes.
             * 'from_derivatives': Refers to scipy.interpolate.BPoly.from_derivatives.
         axis : int
             Axis to interpolate along. For 1D NumpyExtensionArray, use 0.
@@ -1026,11 +1030,15 @@ def interpolate(
         limit_direction : {'forward', 'backward', 'both'}
             Consecutive NaNs will be filled in this direction.
             * If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
-            * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 'backward'.
-            Raises ValueError if limit_direction is 'forward' or 'both' and method is 'backfill' or 'bfill'.
-            Raises ValueError if limit_direction is 'backward' or 'both' and method is 'pad' or 'ffill'.
+            * If 'method' is 'backfill' or 'bfill', 'limit_direction' must be 
+            'backward'.
+            Raises ValueError if limit_direction is 'forward' or 'both' and method 
+            is 'backfill' or 'bfill'.
+            Raises ValueError if limit_direction is 'backward' or 'both' and method 
+            is 'pad' or 'ffill'.
         limit_area : {'inside', 'outside'} or None
-            If limit is specified, consecutive NaNs will be filled with this restriction.
+            If limit is specified, consecutive NaNs will be filled with this 
+            restriction.
             * None: No fill restriction.
             * 'inside': Only fill NaNs surrounded by valid values (interpolate).
             * 'outside': Only fill NaNs outside valid values (extrapolate).