def __repr__(self): arr_repr = ndarray.__repr__(self) weights = self._weights if weights is not None: weights_repr = repr(weights) arr_repr += '\nweights=\n{0}'.format(weights_repr) return arr_repr
def __repr__(self): """Clean string representation of a Series""" if len(self.index) > 500: return self._make_repr(50) elif len(self.index) > 0: return _seriesRepr(self.index, self.values) else: return '%s' % ndarray.__repr__(self)
def __repr__(self): """Clean string representation of a Series""" vals = self.values index = self.index if len(index) > 500: head = _seriesRepr(index[:50], vals[:50]) tail = _seriesRepr(index[-50:], vals[-50:]) return head + '\n...\n' + tail + '\nlength: %d' % len(vals) elif len(index) > 0: return _seriesRepr(index, vals) else: return '%s' % ndarray.__repr__(self)
def __repr__(self): return ndarray.__repr__(self)[:-1] + \ ",\n freq='%s')" % self.freqstr
def __repr__(self): return "%s\n axis info: %s" % (ndarray.__repr__(self), str(self._info))
def __repr__(self): return "%s\n axis info: %s" % (ndarray.__repr__(self), str( self._info))
def __repr__(a): s = ndarray.__repr__(a).replace( '\n', '\n' + ' ' * (len(a.__class__.__name__) - len('array')) ) if a.units != dimensionless: s += ' * ' + a.dimensionality.string return s.replace( 'nan', '...' )