move to_numeric to pandas.core.tools.numeric

Author: jreback

pandas/core/api.py

@@ -28,7 +28,7 @@
     lreshape, wide_to_long)
 
 from pandas.core.indexing import IndexSlice
-from pandas.core.dtypes.cast import to_numeric
+from pandas.core.tools.numeric import to_numeric
 from pandas.tseries.offsets import DateOffset
 from pandas.core.tools.datetimes import to_datetime
 from pandas.core.tools.timedeltas import to_timedelta

pandas/core/dtypes/cast.py

@@ -5,7 +5,6 @@
 import numpy as np
 import warnings
 
-import pandas as pd
 from pandas._libs import tslib, lib
 from pandas._libs.tslib import iNaT
 from pandas.compat import string_types, text_type, PY3
@@ -19,8 +18,6 @@
                      is_integer_dtype,
                      is_datetime_or_timedelta_dtype,
                      is_bool_dtype, is_scalar,
-                     is_numeric_dtype, is_decimal,
-                     is_number,
                      _string_dtypes,
                      _coerce_to_dtype,
                      _ensure_int8, _ensure_int16,
@@ -29,7 +26,7 @@
                      _POSSIBLY_CAST_DTYPES)
 from .dtypes import ExtensionDtype, DatetimeTZDtype, PeriodDtype
 from .generic import (ABCDatetimeIndex, ABCPeriodIndex,
-                      ABCSeries, ABCIndexClass)
+                      ABCSeries)
 from .missing import isnull, notnull
 from .inference import is_list_like
 
@@ -1029,161 +1026,3 @@ def find_common_type(types):
             return np.object
 
     return np.find_common_type(types, [])
-
-
-def to_numeric(arg, errors='raise', downcast=None):
-    """
-    Convert argument to a numeric type.
-
-    Parameters
-    ----------
-    arg : list, tuple, 1-d array, or Series
-    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
-        - If 'raise', then invalid parsing will raise an exception
-        - If 'coerce', then invalid parsing will be set as NaN
-        - If 'ignore', then invalid parsing will return the input
-    downcast : {'integer', 'signed', 'unsigned', 'float'} , default None
-        If not None, and if the data has been successfully cast to a
-        numerical dtype (or if the data was numeric to begin with),
-        downcast that resulting data to the smallest numerical dtype
-        possible according to the following rules:
-
-        - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
-        - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
-        - 'float': smallest float dtype (min.: np.float32)
-
-        As this behaviour is separate from the core conversion to
-        numeric values, any errors raised during the downcasting
-        will be surfaced regardless of the value of the 'errors' input.
-
-        In addition, downcasting will only occur if the size
-        of the resulting data's dtype is strictly larger than
-        the dtype it is to be cast to, so if none of the dtypes
-        checked satisfy that specification, no downcasting will be
-        performed on the data.
-
-        .. versionadded:: 0.19.0
-
-    Returns
-    -------
-    ret : numeric if parsing succeeded.
-        Return type depends on input.  Series if Series, otherwise ndarray
-
-    Examples
-    --------
-    Take separate series and convert to numeric, coercing when told to
-
-    >>> import pandas as pd
-    >>> s = pd.Series(['1.0', '2', -3])
-    >>> pd.to_numeric(s)
-    0    1.0
-    1    2.0
-    2   -3.0
-    dtype: float64
-    >>> pd.to_numeric(s, downcast='float')
-    0    1.0
-    1    2.0
-    2   -3.0
-    dtype: float32
-    >>> pd.to_numeric(s, downcast='signed')
-    0    1
-    1    2
-    2   -3
-    dtype: int8
-    >>> s = pd.Series(['apple', '1.0', '2', -3])
-    >>> pd.to_numeric(s, errors='ignore')
-    0    apple
-    1      1.0
-    2        2
-    3       -3
-    dtype: object
-    >>> pd.to_numeric(s, errors='coerce')
-    0    NaN
-    1    1.0
-    2    2.0
-    3   -3.0
-    dtype: float64
-    """
-    if downcast not in (None, 'integer', 'signed', 'unsigned', 'float'):
-        raise ValueError('invalid downcasting method provided')
-
-    is_series = False
-    is_index = False
-    is_scalars = False
-
-    if isinstance(arg, ABCSeries):
-        is_series = True
-        values = arg.values
-    elif isinstance(arg, ABCIndexClass):
-        is_index = True
-        values = arg.asi8
-        if values is None:
-            values = arg.values
-    elif isinstance(arg, (list, tuple)):
-        values = np.array(arg, dtype='O')
-    elif is_scalar(arg):
-        if is_decimal(arg):
-            return float(arg)
-        if is_number(arg):
-            return arg
-        is_scalars = True
-        values = np.array([arg], dtype='O')
-    elif getattr(arg, 'ndim', 1) > 1:
-        raise TypeError('arg must be a list, tuple, 1-d array, or Series')
-    else:
-        values = arg
-
-    try:
-        if is_numeric_dtype(values):
-            pass
-        elif is_datetime_or_timedelta_dtype(values):
-            values = values.astype(np.int64)
-        else:
-            values = _ensure_object(values)
-            coerce_numeric = False if errors in ('ignore', 'raise') else True
-            values = lib.maybe_convert_numeric(values, set(),
-                                               coerce_numeric=coerce_numeric)
-
-    except Exception:
-        if errors == 'raise':
-            raise
-
-    # attempt downcast only if the data has been successfully converted
-    # to a numerical dtype and if a downcast method has been specified
-    if downcast is not None and is_numeric_dtype(values):
-        typecodes = None
-
-        if downcast in ('integer', 'signed'):
-            typecodes = np.typecodes['Integer']
-        elif downcast == 'unsigned' and np.min(values) >= 0:
-            typecodes = np.typecodes['UnsignedInteger']
-        elif downcast == 'float':
-            typecodes = np.typecodes['Float']
-
-            # pandas support goes only to np.float32,
-            # as float dtypes smaller than that are
-            # extremely rare and not well supported
-            float_32_char = np.dtype(np.float32).char
-            float_32_ind = typecodes.index(float_32_char)
-            typecodes = typecodes[float_32_ind:]
-
-        if typecodes is not None:
-            # from smallest to largest
-            for dtype in typecodes:
-                if np.dtype(dtype).itemsize <= values.dtype.itemsize:
-                    values = maybe_downcast_to_dtype(values, dtype)
-
-                    # successful conversion
-                    if values.dtype == dtype:
-                        break
-
-    if is_series:
-        return pd.Series(values, index=arg.index, name=arg.name)
-    elif is_index:
-        # because we want to coerce to numeric if possible,
-        # do not use _shallow_copy_with_infer
-        return pd.Index(values, name=arg.name)
-    elif is_scalars:
-        return values[0]
-    else:
-        return values

pandas/core/tools/numeric.py

@@ -0,0 +1,170 @@
+import numpy as np
+import pandas as pd
+from pandas.core.dtypes.common import (
+    is_scalar,
+    is_numeric_dtype,
+    is_decimal,
+    is_datetime_or_timedelta_dtype,
+    is_number,
+    _ensure_object)
+from pandas.core.dtypes.generic import ABCSeries, ABCIndexClass
+from pandas.core.dtypes.cast import maybe_downcast_to_dtype
+from pandas._libs import lib
+
+
+def to_numeric(arg, errors='raise', downcast=None):
+    """
+    Convert argument to a numeric type.
+
+    Parameters
+    ----------
+    arg : list, tuple, 1-d array, or Series
+    errors : {'ignore', 'raise', 'coerce'}, default 'raise'
+        - If 'raise', then invalid parsing will raise an exception
+        - If 'coerce', then invalid parsing will be set as NaN
+        - If 'ignore', then invalid parsing will return the input
+    downcast : {'integer', 'signed', 'unsigned', 'float'} , default None
+        If not None, and if the data has been successfully cast to a
+        numerical dtype (or if the data was numeric to begin with),
+        downcast that resulting data to the smallest numerical dtype
+        possible according to the following rules:
+
+        - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
+        - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
+        - 'float': smallest float dtype (min.: np.float32)
+
+        As this behaviour is separate from the core conversion to
+        numeric values, any errors raised during the downcasting
+        will be surfaced regardless of the value of the 'errors' input.
+
+        In addition, downcasting will only occur if the size
+        of the resulting data's dtype is strictly larger than
+        the dtype it is to be cast to, so if none of the dtypes
+        checked satisfy that specification, no downcasting will be
+        performed on the data.
+
+        .. versionadded:: 0.19.0
+
+    Returns
+    -------
+    ret : numeric if parsing succeeded.
+        Return type depends on input.  Series if Series, otherwise ndarray
+
+    Examples
+    --------
+    Take separate series and convert to numeric, coercing when told to
+
+    >>> import pandas as pd
+    >>> s = pd.Series(['1.0', '2', -3])
+    >>> pd.to_numeric(s)
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float64
+    >>> pd.to_numeric(s, downcast='float')
+    0    1.0
+    1    2.0
+    2   -3.0
+    dtype: float32
+    >>> pd.to_numeric(s, downcast='signed')
+    0    1
+    1    2
+    2   -3
+    dtype: int8
+    >>> s = pd.Series(['apple', '1.0', '2', -3])
+    >>> pd.to_numeric(s, errors='ignore')
+    0    apple
+    1      1.0
+    2        2
+    3       -3
+    dtype: object
+    >>> pd.to_numeric(s, errors='coerce')
+    0    NaN
+    1    1.0
+    2    2.0
+    3   -3.0
+    dtype: float64
+    """
+    if downcast not in (None, 'integer', 'signed', 'unsigned', 'float'):
+        raise ValueError('invalid downcasting method provided')
+
+    is_series = False
+    is_index = False
+    is_scalars = False
+
+    if isinstance(arg, ABCSeries):
+        is_series = True
+        values = arg.values
+    elif isinstance(arg, ABCIndexClass):
+        is_index = True
+        values = arg.asi8
+        if values is None:
+            values = arg.values
+    elif isinstance(arg, (list, tuple)):
+        values = np.array(arg, dtype='O')
+    elif is_scalar(arg):
+        if is_decimal(arg):
+            return float(arg)
+        if is_number(arg):
+            return arg
+        is_scalars = True
+        values = np.array([arg], dtype='O')
+    elif getattr(arg, 'ndim', 1) > 1:
+        raise TypeError('arg must be a list, tuple, 1-d array, or Series')
+    else:
+        values = arg
+
+    try:
+        if is_numeric_dtype(values):
+            pass
+        elif is_datetime_or_timedelta_dtype(values):
+            values = values.astype(np.int64)
+        else:
+            values = _ensure_object(values)
+            coerce_numeric = False if errors in ('ignore', 'raise') else True
+            values = lib.maybe_convert_numeric(values, set(),
+                                               coerce_numeric=coerce_numeric)
+
+    except Exception:
+        if errors == 'raise':
+            raise
+
+    # attempt downcast only if the data has been successfully converted
+    # to a numerical dtype and if a downcast method has been specified
+    if downcast is not None and is_numeric_dtype(values):
+        typecodes = None
+
+        if downcast in ('integer', 'signed'):
+            typecodes = np.typecodes['Integer']
+        elif downcast == 'unsigned' and np.min(values) >= 0:
+            typecodes = np.typecodes['UnsignedInteger']
+        elif downcast == 'float':
+            typecodes = np.typecodes['Float']
+
+            # pandas support goes only to np.float32,
+            # as float dtypes smaller than that are
+            # extremely rare and not well supported
+            float_32_char = np.dtype(np.float32).char
+            float_32_ind = typecodes.index(float_32_char)
+            typecodes = typecodes[float_32_ind:]
+
+        if typecodes is not None:
+            # from smallest to largest
+            for dtype in typecodes:
+                if np.dtype(dtype).itemsize <= values.dtype.itemsize:
+                    values = maybe_downcast_to_dtype(values, dtype)
+
+                    # successful conversion
+                    if values.dtype == dtype:
+                        break
+
+    if is_series:
+        return pd.Series(values, index=arg.index, name=arg.name)
+    elif is_index:
+        # because we want to coerce to numeric if possible,
+        # do not use _shallow_copy_with_infer
+        return pd.Index(values, name=arg.name)
+    elif is_scalars:
+        return values[0]
+    else:
+        return values