parameters: (arg, errors="raise", downcast=None)
docstring: Convert argument to a numeric type.
  
      The default return dtype is `float64` or `int64`
      depending on the data supplied. Use the `downcast` parameter
      to obtain other dtypes.
  
      Please note that precision loss may occur if really large numbers
      are passed in. Due to the internal limitations of `ndarray`, if
      numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min)
      or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are
      passed in, it is very likely they will be converted to float so that
      they can stored in an `ndarray`. These warnings apply similarly to
      `Series` since it internally leverages `ndarray`.
  
      Parameters
      ----------
      arg : scalar, 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.
  
      Returns
      -------
      ret : numeric if parsing succeeded.
          Return type depends on input.  Series if Series, otherwise ndarray.
  
      See Also
      --------
      DataFrame.astype : Cast argument to a specified dtype.
      to_datetime : Convert argument to datetime.
      to_timedelta : Convert argument to timedelta.
      numpy.ndarray.astype : Cast a numpy array to a specified type.
      convert_dtypes : Convert dtypes.
  
      Examples
      --------
      Take separate series and convert to numeric, coercing when told to
  
      >>> 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
docstring_summary: Convert argument to a numeric type.
docstring_tokens: Convert argument to a numeric type .
function: def to_numeric(arg, errors="raise", downcast=None):
      """
      Convert argument to a numeric type.
  
      The default return dtype is `float64` or `int64`
      depending on the data supplied. Use the `downcast` parameter
      to obtain other dtypes.
  
      Please note that precision loss may occur if really large numbers
      are passed in. Due to the internal limitations of `ndarray`, if
      numbers smaller than `-9223372036854775808` (np.iinfo(np.int64).min)
      or larger than `18446744073709551615` (np.iinfo(np.uint64).max) are
      passed in, it is very likely they will be converted to float so that
      they can stored in an `ndarray`. These warnings apply similarly to
      `Series` since it internally leverages `ndarray`.
  
      Parameters
      ----------
      arg : scalar, 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.
  
      Returns
      -------
      ret : numeric if parsing succeeded.
          Return type depends on input.  Series if Series, otherwise ndarray.
  
      See Also
      --------
      DataFrame.astype : Cast argument to a specified dtype.
      to_datetime : Convert argument to datetime.
      to_timedelta : Convert argument to timedelta.
      numpy.ndarray.astype : Cast a numpy array to a specified type.
      convert_dtypes : Convert dtypes.
  
      Examples
      --------
      Take separate series and convert to numeric, coercing when told to
  
      >>> 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")
  
      if errors not in ("ignore", "raise", "coerce"):
          raise ValueError("invalid error value specified")
  
      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
  
      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 = errors not in ("ignore", "raise")
          try:
              values = lib.maybe_convert_numeric(
                  values, set(), coerce_numeric=coerce_numeric
              )
          except (ValueError, TypeError):
              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
function_tokens: def to_numeric ( arg , errors = "raise" , downcast = None ) : if downcast not in ( None , "integer" , "signed" , "unsigned" , "float" ) : raise ValueError ( "invalid downcasting method provided" ) if errors not in ( "ignore" , "raise" , "coerce" ) : raise ValueError ( "invalid error value specified" ) 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 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 = errors not in ( "ignore" , "raise" ) try : values = lib . maybe_convert_numeric ( values , set ( ) , coerce_numeric = coerce_numeric ) except ( ValueError , TypeError ) : 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