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1import numpy as np
3from pandas._libs import lib
5from pandas.core.dtypes.cast import maybe_downcast_to_dtype
6from pandas.core.dtypes.common import (
7 ensure_object,
8 is_datetime_or_timedelta_dtype,
9 is_decimal,
10 is_number,
11 is_numeric_dtype,
12 is_scalar,
13)
14from pandas.core.dtypes.generic import ABCIndexClass, ABCSeries
16import pandas as pd
19def to_numeric(arg, errors="raise", downcast=None):
20 """
21 Convert argument to a numeric type.
23 The default return dtype is `float64` or `int64`
24 depending on the data supplied. Use the `downcast` parameter
25 to obtain other dtypes.
27 Please note that precision loss may occur if really large numbers
28 are passed in. Due to the internal limitations of `ndarray`, if
29 numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min)
30 or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are
31 passed in, it is very likely they will be converted to float so that
32 they can stored in an `ndarray`. These warnings apply similarly to
33 `Series` since it internally leverages `ndarray`.
35 Parameters
36 ----------
37 arg : scalar, list, tuple, 1-d array, or Series
38 errors : {'ignore', 'raise', 'coerce'}, default 'raise'
39 - If 'raise', then invalid parsing will raise an exception.
40 - If 'coerce', then invalid parsing will be set as NaN.
41 - If 'ignore', then invalid parsing will return the input.
42 downcast : {'integer', 'signed', 'unsigned', 'float'}, default None
43 If not None, and if the data has been successfully cast to a
44 numerical dtype (or if the data was numeric to begin with),
45 downcast that resulting data to the smallest numerical dtype
46 possible according to the following rules:
48 - 'integer' or 'signed': smallest signed int dtype (min.: np.int8)
49 - 'unsigned': smallest unsigned int dtype (min.: np.uint8)
50 - 'float': smallest float dtype (min.: np.float32)
52 As this behaviour is separate from the core conversion to
53 numeric values, any errors raised during the downcasting
54 will be surfaced regardless of the value of the 'errors' input.
56 In addition, downcasting will only occur if the size
57 of the resulting data's dtype is strictly larger than
58 the dtype it is to be cast to, so if none of the dtypes
59 checked satisfy that specification, no downcasting will be
60 performed on the data.
62 Returns
63 -------
64 ret : numeric if parsing succeeded.
65 Return type depends on input. Series if Series, otherwise ndarray.
67 See Also
68 --------
69 DataFrame.astype : Cast argument to a specified dtype.
70 to_datetime : Convert argument to datetime.
71 to_timedelta : Convert argument to timedelta.
72 numpy.ndarray.astype : Cast a numpy array to a specified type.
73 convert_dtypes : Convert dtypes.
75 Examples
76 --------
77 Take separate series and convert to numeric, coercing when told to
79 >>> s = pd.Series(['1.0', '2', -3])
80 >>> pd.to_numeric(s)
81 0 1.0
82 1 2.0
83 2 -3.0
84 dtype: float64
85 >>> pd.to_numeric(s, downcast='float')
86 0 1.0
87 1 2.0
88 2 -3.0
89 dtype: float32
90 >>> pd.to_numeric(s, downcast='signed')
91 0 1
92 1 2
93 2 -3
94 dtype: int8
95 >>> s = pd.Series(['apple', '1.0', '2', -3])
96 >>> pd.to_numeric(s, errors='ignore')
97 0 apple
98 1 1.0
99 2 2
100 3 -3
101 dtype: object
102 >>> pd.to_numeric(s, errors='coerce')
103 0 NaN
104 1 1.0
105 2 2.0
106 3 -3.0
107 dtype: float64
108 """
109 if downcast not in (None, "integer", "signed", "unsigned", "float"):
110 raise ValueError("invalid downcasting method provided")
112 if errors not in ("ignore", "raise", "coerce"):
113 raise ValueError("invalid error value specified")
115 is_series = False
116 is_index = False
117 is_scalars = False
119 if isinstance(arg, ABCSeries):
120 is_series = True
121 values = arg.values
122 elif isinstance(arg, ABCIndexClass):
123 is_index = True
124 values = arg.asi8
125 if values is None:
126 values = arg.values
127 elif isinstance(arg, (list, tuple)):
128 values = np.array(arg, dtype="O")
129 elif is_scalar(arg):
130 if is_decimal(arg):
131 return float(arg)
132 if is_number(arg):
133 return arg
134 is_scalars = True
135 values = np.array([arg], dtype="O")
136 elif getattr(arg, "ndim", 1) > 1:
137 raise TypeError("arg must be a list, tuple, 1-d array, or Series")
138 else:
139 values = arg
141 if is_numeric_dtype(values):
142 pass
143 elif is_datetime_or_timedelta_dtype(values):
144 values = values.astype(np.int64)
145 else:
146 values = ensure_object(values)
147 coerce_numeric = errors not in ("ignore", "raise")
148 try:
149 values = lib.maybe_convert_numeric(
150 values, set(), coerce_numeric=coerce_numeric
151 )
152 except (ValueError, TypeError):
153 if errors == "raise":
154 raise
156 # attempt downcast only if the data has been successfully converted
157 # to a numerical dtype and if a downcast method has been specified
158 if downcast is not None and is_numeric_dtype(values):
159 typecodes = None
161 if downcast in ("integer", "signed"):
162 typecodes = np.typecodes["Integer"]
163 elif downcast == "unsigned" and np.min(values) >= 0:
164 typecodes = np.typecodes["UnsignedInteger"]
165 elif downcast == "float":
166 typecodes = np.typecodes["Float"]
168 # pandas support goes only to np.float32,
169 # as float dtypes smaller than that are
170 # extremely rare and not well supported
171 float_32_char = np.dtype(np.float32).char
172 float_32_ind = typecodes.index(float_32_char)
173 typecodes = typecodes[float_32_ind:]
175 if typecodes is not None:
176 # from smallest to largest
177 for dtype in typecodes:
178 if np.dtype(dtype).itemsize <= values.dtype.itemsize:
179 values = maybe_downcast_to_dtype(values, dtype)
181 # successful conversion
182 if values.dtype == dtype:
183 break
185 if is_series:
186 return pd.Series(values, index=arg.index, name=arg.name)
187 elif is_index:
188 # because we want to coerce to numeric if possible,
189 # do not use _shallow_copy_with_infer
190 return pd.Index(values, name=arg.name)
191 elif is_scalars:
192 return values[0]
193 else:
194 return values