Python pandas.core.dtypes.cast.maybe_upcast() Examples
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code examples of pandas.core.dtypes.cast.maybe_upcast().
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Example #1
Source File: frame.py From recruit with Apache License 2.0 | 5 votes |
def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): if level is not None: raise TypeError('Reindex by level not supported for sparse') if self.index.equals(index): if copy: return self.copy() else: return self if len(self.index) == 0: return self._constructor( index=index, columns=self.columns).__finalize__(self) indexer = self.index.get_indexer(index, method, limit=limit) indexer = ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() new_series = {} for col, series in self.iteritems(): if mask.all(): continue values = series.values # .take returns SparseArray new = values.take(indexer) if need_mask: new = new.values # convert integer to float if necessary. need to do a lot # more than that, handle boolean etc also new, fill_value = maybe_upcast(new, fill_value=fill_value) np.putmask(new, mask, fill_value) new_series[col] = new return self._constructor( new_series, index=index, columns=self.columns, default_fill_value=self._default_fill_value).__finalize__(self)
Example #2
Source File: frame.py From vnpy_crypto with MIT License | 5 votes |
def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): if level is not None: raise TypeError('Reindex by level not supported for sparse') if self.index.equals(index): if copy: return self.copy() else: return self if len(self.index) == 0: return self._constructor( index=index, columns=self.columns).__finalize__(self) indexer = self.index.get_indexer(index, method, limit=limit) indexer = _ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() new_series = {} for col, series in self.iteritems(): if mask.all(): continue values = series.values # .take returns SparseArray new = values.take(indexer) if need_mask: new = new.values # convert integer to float if necessary. need to do a lot # more than that, handle boolean etc also new, fill_value = maybe_upcast(new, fill_value=fill_value) np.putmask(new, mask, fill_value) new_series[col] = new return self._constructor( new_series, index=index, columns=self.columns, default_fill_value=self._default_fill_value).__finalize__(self)
Example #3
Source File: frame.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): if level is not None: raise TypeError('Reindex by level not supported for sparse') if self.index.equals(index): if copy: return self.copy() else: return self if len(self.index) == 0: return self._constructor( index=index, columns=self.columns).__finalize__(self) indexer = self.index.get_indexer(index, method, limit=limit) indexer = ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() new_series = {} for col, series in self.iteritems(): if mask.all(): continue values = series.values # .take returns SparseArray new = values.take(indexer) if need_mask: new = new.values # convert integer to float if necessary. need to do a lot # more than that, handle boolean etc also new, fill_value = maybe_upcast(new, fill_value=fill_value) np.putmask(new, mask, fill_value) new_series[col] = new return self._constructor( new_series, index=index, columns=self.columns, default_fill_value=self._default_fill_value).__finalize__(self)
Example #4
Source File: frame.py From Splunking-Crime with GNU Affero General Public License v3.0 | 5 votes |
def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): if level is not None: raise TypeError('Reindex by level not supported for sparse') if self.index.equals(index): if copy: return self.copy() else: return self if len(self.index) == 0: return self._constructor( index=index, columns=self.columns).__finalize__(self) indexer = self.index.get_indexer(index, method, limit=limit) indexer = _ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() new_series = {} for col, series in self.iteritems(): if mask.all(): continue values = series.values # .take returns SparseArray new = values.take(indexer) if need_mask: new = new.values # convert integer to float if necessary. need to do a lot # more than that, handle boolean etc also new, fill_value = maybe_upcast(new, fill_value=fill_value) np.putmask(new, mask, fill_value) new_series[col] = new return self._constructor( new_series, index=index, columns=self.columns, default_fill_value=self._default_fill_value).__finalize__(self)
Example #5
Source File: frame.py From elasticintel with GNU General Public License v3.0 | 5 votes |
def _reindex_index(self, index, method, copy, level, fill_value=np.nan, limit=None, takeable=False): if level is not None: raise TypeError('Reindex by level not supported for sparse') if self.index.equals(index): if copy: return self.copy() else: return self if len(self.index) == 0: return self._constructor( index=index, columns=self.columns).__finalize__(self) indexer = self.index.get_indexer(index, method, limit=limit) indexer = _ensure_platform_int(indexer) mask = indexer == -1 need_mask = mask.any() new_series = {} for col, series in self.iteritems(): if mask.all(): continue values = series.values # .take returns SparseArray new = values.take(indexer) if need_mask: new = new.values # convert integer to float if necessary. need to do a lot # more than that, handle boolean etc also new, fill_value = maybe_upcast(new, fill_value=fill_value) np.putmask(new, mask, fill_value) new_series[col] = new return self._constructor( new_series, index=index, columns=self.columns, default_fill_value=self._default_fill_value).__finalize__(self)