ENH: Add case_when method

doc/source/whatsnew/v2.2.0.rst

@@ -13,6 +13,23 @@ including other versions of pandas.
 
 Enhancements
 ~~~~~~~~~~~~
+.. _whatsnew_220.enhancements.case_when:
+
+Create a pandas Series based on one or more conditions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :func:`case_when` function has been added to create a Series object based on one or more conditions. (:issue:`39154`)
+
+.. ipython:: python
+
+   import pandas as pd
+
+   df = pd.DataFrame(dict(a=[1, 2, 3], b=[4, 5, 6]))
+   pd.case_when(
+        caselist = [(df.a == 1, 'first'),          # condition, replacement
+            (df.a.gt(1) & df.b.eq(5), 'second')],  # condition, replacement
+        default = 'default',                       # optional
+   )
 
 .. _whatsnew_220.enhancements.adbc_support:
 

pandas/__init__.py

@@ -73,6 +73,7 @@
     notnull,
     # indexes
     Index,
+    case_when,
     CategoricalIndex,
     RangeIndex,
     MultiIndex,
@@ -253,6 +254,7 @@
     "ArrowDtype",
     "BooleanDtype",
     "Categorical",
+    "case_when",
     "CategoricalDtype",
     "CategoricalIndex",
     "DataFrame",

pandas/core/api.py

@@ -42,6 +42,7 @@
     UInt64Dtype,
 )
 from pandas.core.arrays.string_ import StringDtype
+from pandas.core.case_when import case_when
 from pandas.core.construction import array
 from pandas.core.flags import Flags
 from pandas.core.groupby import (
@@ -86,6 +87,7 @@
     "bdate_range",
     "BooleanDtype",
     "Categorical",
+    "case_when",
     "CategoricalDtype",
     "CategoricalIndex",
     "DataFrame",

pandas/core/case_when.py

@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numpy as np
+
+from pandas._libs import lib
+
+from pandas.core.dtypes.cast import (
+    construct_1d_arraylike_from_scalar,
+    find_common_type,
+    infer_dtype_from,
+)
+from pandas.core.dtypes.common import is_scalar
+from pandas.core.dtypes.generic import ABCSeries
+
+from pandas.core.construction import array as pd_array
+
+if TYPE_CHECKING:
+    from pandas._typing import (
+        ArrayLike,
+        Scalar,
+        Series,
+    )
+
+
+def case_when(
+    caselist: list[tuple[ArrayLike, ArrayLike | Scalar]],
+    default: ArrayLike | Scalar = lib.no_default,
+) -> Series:
+    """
+    Replace values where the conditions are True.
+
+    Parameters
+    ----------
+    caselist : List of tuples of conditions and expected replacements.
+        Takes the form:  ``(condition0, replacement0)``,
+        ``(condition1, replacement1)``, ... .
+        ``condition`` should be a 1-D boolean array.
+        When multiple boolean conditions are satisfied,
+        the first replacement is used.
+        If ``condition`` is a Series, and the equivalent ``replacement``
+        is a Series, they must have the same index.
+        If there are multiple replacement options,
+        and they are Series, they must have the same index.
+
+    default : scalar, array-like, default None
+        If provided, it is the replacement value to use
+        if all conditions evaluate to False.
+        If not specified, entries will be filled with the
+        corresponding NULL value.
+
+        .. versionadded:: 2.2.0
+
+    Returns
+    -------
+    Series
+
+    See Also
+    --------
+    Series.mask : Replace values where the condition is True.
+
+    Examples
+    --------
+    >>> df = pd.DataFrame({
+    ...     "a": [0,0,1,2],
+    ...     "b": [0,3,4,5],
+    ...     "c": [6,7,8,9]
+    ... })
+    >>> df
+       a  b  c
+    0  0  0  6
+    1  0  3  7
+    2  1  4  8
+    3  2  5  9
+
+    >>> caselist = [(df.a.gt(0), df.a), (df.b.gt(0), df.b)]  # condition, replacement
+    >>> pd.case_when(caselist=caselist, default=df.c)        # default is optional
+    0    6
+    1    3
+    2    1
+    3    2
+    Name: c, dtype: int64
+    """
+    from pandas import Series
+
+    validate_case_when(caselist=caselist)
+
+    conditions, replacements = zip(*caselist)
+    common_dtypes = [infer_dtype_from(replacement)[0] for replacement in replacements]
+
+    if default is not lib.no_default:
+        arg_dtype, _ = infer_dtype_from(default)
+        common_dtypes.append(arg_dtype)
+    else:
+        default = None
+    if len(set(common_dtypes)) > 1:
+        common_dtypes = find_common_type(common_dtypes)
+        updated_replacements = []
+        for condition, replacement in zip(conditions, replacements):
+            if is_scalar(replacement):
+                replacement = construct_1d_arraylike_from_scalar(
+                    value=replacement, length=len(condition), dtype=common_dtypes
+                )
+            elif isinstance(replacement, ABCSeries):
+                replacement = replacement.astype(common_dtypes)
+            else:
+                replacement = pd_array(replacement, dtype=common_dtypes)
+            updated_replacements.append(replacement)
+        replacements = updated_replacements
+        if (default is not None) and isinstance(default, ABCSeries):
+            default = default.astype(common_dtypes)
+    else:
+        common_dtypes = common_dtypes[0]
+    if not isinstance(default, ABCSeries):
+        cond_indices = [cond for cond in conditions if isinstance(cond, ABCSeries)]
+        replacement_indices = [
+            replacement
+            for replacement in replacements
+            if isinstance(replacement, ABCSeries)
+        ]
+        cond_length = None
+        if replacement_indices:
+            for left, right in zip(replacement_indices, replacement_indices[1:]):
+                if not left.index.equals(right.index):
+                    raise AssertionError(
+                        "All replacement objects must have the same index."
+                    )
+        if cond_indices:
+            for left, right in zip(cond_indices, cond_indices[1:]):
+                if not left.index.equals(right.index):
+                    raise AssertionError(
+                        "All condition objects must have the same index."
+                    )
+            if replacement_indices:
+                if not replacement_indices[0].index.equals(cond_indices[0].index):
+                    raise AssertionError(
+                        "All replacement objects and condition objects "
+                        "should have the same index."
+                    )
+        else:
+            conditions = [
+                np.asanyarray(cond) if not hasattr(cond, "shape") else cond
+                for cond in conditions
+            ]
+            cond_length = {len(cond) for cond in conditions}
+            if len(cond_length) > 1:
+                raise ValueError("The boolean conditions should have the same length.")
+            cond_length = len(conditions[0])
+            if not is_scalar(default):
+                if len(default) != cond_length:
+                    raise ValueError(
+                        "length of `default` does not match the length "
+                        "of any of the conditions."
+                    )
+            if not replacement_indices:
+                for num, replacement in enumerate(replacements):
+                    if is_scalar(replacement):
+                        continue
+                    if not hasattr(replacement, "shape"):
+                        replacement = np.asanyarray(replacement)
+                    if len(replacement) != cond_length:
+                        raise ValueError(
+                            f"Length of condition{num} does not match "
+                            f"the length of replacement{num}; "
+                            f"{cond_length} != {len(replacement)}"
+                        )
+        if cond_indices:
+            default_index = cond_indices[0].index
+        elif replacement_indices:
+            default_index = replacement_indices[0].index
+        else:
+            default_index = range(cond_length)
+        default = Series(default, index=default_index, dtype=common_dtypes)
+    counter = reversed(range(len(conditions)))
+    for position, condition, replacement in zip(
+        counter, conditions[::-1], replacements[::-1]
+    ):
+        try:
+            default = default.mask(
+                condition, other=replacement, axis=0, inplace=False, level=None
+            )
+        except Exception as error:
+            raise ValueError(
+                f"Failed to apply condition{position} and replacement{position}."
+            ) from error
+    return default
+
+
+def validate_case_when(caselist: list) -> None:
+    """
+    Validates the arguments for the case_when function.
+    """
+
+    if not isinstance(caselist, list):
+        raise TypeError(
+            f"The caselist argument should be a list; instead got {type(caselist)}"
+        )
+
+    if not len(caselist):
+        raise ValueError(
+            "provide at least one boolean condition, "
+            "with a corresponding replacement."
+        )
+
+    for num, entry in enumerate(caselist):
+        if not isinstance(entry, tuple):
+            raise TypeError(
+                f"Argument {num} must be a tuple; instead got {type(entry)}."
+            )
+        if len(entry) != 2:
+            raise ValueError(
+                f"Argument {num} must have length 2; "
+                "a condition and replacement; "
+                f"instead got length {len(entry)}."
+            )

pandas/core/series.py

@@ -112,6 +112,10 @@
 from pandas.core.arrays.categorical import CategoricalAccessor
 from pandas.core.arrays.sparse import SparseAccessor
 from pandas.core.arrays.string_ import StringDtype
+from pandas.core.case_when import (
+    case_when,
+    validate_case_when,
+)
 from pandas.core.construction import (
     extract_array,
     sanitize_array,
@@ -5586,6 +5590,76 @@ def between(
 
         return lmask & rmask
 
+    def case_when(
+        self,
+        caselist: list[tuple[ArrayLike, ArrayLike | Scalar]],
+    ) -> Series:
+        """
+        Replace values where the conditions are True.
+
+        Parameters
+        ----------
+        caselist : A list of tuples of conditions and expected replacements.
+            Takes the form:  ``(condition0, replacement0)``,
+            ``(condition1, replacement1)``, ... .
+            ``condition`` should be a 1-D boolean array-like object
+            or a callable. If ``condition`` is a callable,
+            it is computed on the Series
+            and should return a boolean Series or array.
+            The callable must not change the input Series
+            (though pandas doesn`t check it). ``replacement`` should be a
+            1-D array-like object, a scalar or a callable.
+            If ``replacement`` is a callable, it is computed on the Series
+            and should return a scalar or Series. The callable
+            must not change the input Series
+            (though pandas doesn`t check it).
+            If ``condition`` is a Series, and the equivalent ``replacement``
+            is a Series, they must have the same index.
+            If there are multiple replacement options,
+            and they are Series, they must have the same index.
+
+            .. versionadded:: 2.2.0
+
+        Returns
+        -------
+        Series
+
+        See Also
+        --------
+        Series.mask : Replace values where the condition is True.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({
+        ...     "a": [0,0,1,2],
+        ...     "b": [0,3,4,5],
+        ...     "c": [6,7,8,9]
+        ... })
+        >>> df
+           a  b  c
+        0  0  0  6
+        1  0  3  7
+        2  1  4  8
+        3  2  5  9
+
+        >>> df.c.case_when((df.a.gt(0), df.a),  # condition, replacement
+        ...                (df.b.gt(0), df.b))
+        0    6
+        1    3
+        2    1
+        3    2
+        Name: c, dtype: int64
+        """
+        validate_case_when(caselist)
+        caselist = [
+            (
+                com.apply_if_callable(condition, self),
+                com.apply_if_callable(replacement, self),
+            )
+            for condition, replacement in caselist
+        ]
+        return case_when(caselist=caselist, default=self)
+
     # error: Cannot determine type of 'isna'
     @doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
     def isna(self) -> Series:

pandas/tests/api/test_api.py

@@ -106,6 +106,7 @@ class TestPDApi(Base):
     funcs = [
         "array",
         "bdate_range",
+        "case_when",
         "concat",
         "crosstab",
         "cut",