panda: FloatingPointError con np.seterr (all = 'raise') e dati mancanti

Question

Ricevo un errore FloatingPointError quando voglio esaminare i dati che coinvolgono dati mancanti.

import numpy as np 
import pandas as pd 
np.seterr(all='raise') 

s = pd.Series([np.nan,np.nan,np.nan],index=[1,2,3]); print(s); print(s.head()) 

Sono sulla nuova versione di pandas, installato tramite

conda install -f pandas 

dopo pkill python e conda remove pandas.

Ecco la parte posteriore traccia:

Out[4]: --------------------------------------------------------------------------- 
FloatingPointError      Traceback (most recent call last) 
/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/IPython/core/formatters.pyc in __call__(self, obj) 
    695     type_pprinters=self.type_printers, 
    696     deferred_pprinters=self.deferred_printers) 
--> 697    printer.pretty(obj) 
    698    printer.flush() 
    699    return stream.getvalue() 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/IPython/lib/pretty.pyc in pretty(self, obj) 
    381        if callable(meth): 
    382         return meth(obj, self, cycle) 
--> 383    return _default_pprint(obj, self, cycle) 
    384   finally: 
    385    self.end_group() 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/IPython/lib/pretty.pyc in _default_pprint(obj, p, cycle) 
    501  if _safe_getattr(klass, '__repr__', None) not in _baseclass_reprs: 
    502   # A user-provided repr. Find newlines and replace them with p.break_() 
--> 503   _repr_pprint(obj, p, cycle) 
    504   return 
    505  p.begin_group(1, '<') 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/IPython/lib/pretty.pyc in _repr_pprint(obj, p, cycle) 
    683  """A pprint that just redirects to the normal repr function.""" 
    684  # Find newlines and replace them with p.break_() 
--> 685  output = repr(obj) 
    686  for idx,output_line in enumerate(output.splitlines()): 
    687   if idx: 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/base.pyc in __repr__(self) 
    61   Yields Bytestring in Py2, Unicode String in py3. 
    62   """ 
---> 63   return str(self) 
    64 
    65 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/base.pyc in __str__(self) 
    41   if compat.PY3: 
    42    return self.__unicode__() 
---> 43   return self.__bytes__() 
    44 
    45  def __bytes__(self): 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/base.pyc in __bytes__(self) 
    53 
    54   encoding = get_option("display.encoding") 
---> 55   return self.__unicode__().encode(encoding, 'replace') 
    56 
    57  def __repr__(self): 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/series.pyc in __unicode__(self) 
    954 
    955   self.to_string(buf=buf, name=self.name, dtype=self.dtype, 
--> 956      max_rows=max_rows) 
    957   result = buf.getvalue() 
    958 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/series.pyc in to_string(self, buf, na_rep, float_format, header, length, dtype, name, max_rows) 
    992   the_repr = self._get_repr(float_format=float_format, na_rep=na_rep, 
    993         header=header, length=length, dtype=dtype, 
--> 994         name=name, max_rows=max_rows) 
    995 
    996   # catch contract violations 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/series.pyc in _get_repr(self, name, header, length, dtype, na_rep, float_format, max_rows) 
    1022           float_format=float_format, 
    1023           max_rows=max_rows) 
-> 1024   result = formatter.to_string() 
    1025 
    1026   # TODO: following check prob. not neces. 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.pyc in to_string(self) 
    223 
    224   fmt_index, have_header = self._get_formatted_index() 
--> 225   fmt_values = self._get_formatted_values() 
    226 
    227   maxlen = max(self.adj.len(x) for x in fmt_index) # max index len 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.pyc in _get_formatted_values(self) 
    213   return format_array(self.tr_series._values, None, 
    214        float_format=self.float_format, 
--> 215        na_rep=self.na_rep) 
    216 
    217  def to_string(self): 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.pyc in format_array(values, formatter, float_format, na_rep, digits, space, justify) 
    1974       justify=justify) 
    1975 
-> 1976  return fmt_obj.get_result() 
    1977 
    1978 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.pyc in get_result(self) 
    1990 
    1991  def get_result(self): 
-> 1992   fmt_values = self._format_strings() 
    1993   return _make_fixed_width(fmt_values, self.justify) 
    1994 

/home/xxx/.conda/envs/myenv2/lib/python2.7/site-packages/pandas/core/format.pyc in _format_strings(self) 
    2085 
    2086    # this is pretty arbitrary for now 
-> 2087    has_large_values = (abs_vals > 1e8).any() 
    2088    has_small_values = ((abs_vals < 10 ** (-self.digits)) & 
    2089         (abs_vals > 0)).any() 

FloatingPointError: invalid value encountered in greater 

Answer 1

Ogni volta che si import pandas, sono impostati tutti gli errori NumPy di ​​essere ignorare. Questo è attualmente un comportamento non documentato.

Questo viene fatto in pandas/compat/numpy_compat.py

# TODO: HACK for NumPy 1.5.1 to suppress warnings 
# is this necessary? 
try: 
    np.seterr(all='ignore') 
except Exception: # pragma: no cover 
    pass 

Ecco come che gioca fuori

In [1]: import numpy as np 

In [2]: np.geterr() 
Out[2]: {'divide': 'warn', 'invalid': 'warn', 'over': 'warn', 'under': 'ignore'} 

In [3]: import pandas as pd 

In [4]: np.geterr() 
Out[4]: {'divide': 'ignore', 'invalid': 'ignore', 'over': 'ignore', 'under': 'ignore'} 

In [5]: s = pd.Series([np.nan,np.nan,np.nan],index=[1,2,3]); print(s); print(s.head()) 
1 NaN 
2 NaN 
3 NaN 
dtype: float64 
1 NaN 
2 NaN 
3 NaN 
dtype: float64 

In [6]: np.seterr(invalid='raise') 
Out[6]: {'divide': 'ignore', 'invalid': 'ignore', 'over': 'ignore', 'under': 'ignore'} 
In [7]: s = pd.Series([np.nan,np.nan,np.nan],index=[1,2,3]); print(s); print(s.head()) 
FloatingPointError: invalid value encountered in greater 

La "soluzione" è, quindi, di non np.seterr(invalid'raise'), ogni volta che si utilizza pandas (soprattutto quando si lavora con i dati mancanti) .