pandasticsearch.types module
# -*- coding: UTF-8 -*- from pandasticsearch.operators import * import six class Column(object): def __init__(self, field): self._field = field def __eq__(self, other): return Equal(field=self._field, value=other) def __ne__(self, other): return ~Equal(field=self._field, value=other) def __gt__(self, other): return Greater(field=self._field, value=other) def __lt__(self, other): return Less(field=self._field, value=other) def __ge__(self, other): return GreaterEqual(field=self._field, value=other) def __le__(self, other): return LessEqual(field=self._field, value=other) def isin(self, other): return IsIn(field=self._field, value=other) def field_name(self): return self._field @property def desc(self): return Sorter(self._field) @property def asc(self): return Sorter(self._field, 'asc') @property def max(self): return MetricAggregator(self._field, 'max') @property def min(self): return MetricAggregator(self._field, 'min') @property def avg(self): return MetricAggregator(self._field, 'avg') @property def value_count(self): return MetricAggregator(self._field, 'value_count') count = value_count @property def cardinality(self): return MetricAggregator(self._field, 'cardinality') distinct_count = cardinality @property def percentiles(self): return MetricAggregator(self._field, 'percentiles') @property def percentile_ranks(self): return MetricAggregator(self._field, 'percentile_ranks') class Row(tuple): """ The builtin L{DataFrame} row type for accessing before converted into Pandas DataFrame. The fields will be sorted by names. >>> row = Row(name="Alice", age=12) >>> row Row(age=12, name='Alice') >>> row['name'], row['age'] ('Alice', 12) >>> row.name, row.age ('Alice', 12) >>> 'name' in row True >>> 'wrong_key' in row """ def __new__(cls, **kwargs): names = sorted(kwargs.keys()) row = tuple.__new__(cls, [kwargs[n] for n in names]) row._fields = names return row def __getitem__(self, name): try: idx = self._fields.index(name) return super(Row, self).__getitem__(idx) except IndexError: raise KeyError(name) except ValueError: raise ValueError(name) def __contains__(self, name): return name in self._fields def __repr__(self): return 'Row('+','.join(['{0}={1}'.format(k, Row._stringfy(v)) for k, v in zip(self._fields, tuple(self))])+')' @classmethod def _stringfy(cls, v): b = six.StringIO() b.write(repr(v)) return b.getvalue() def as_dict(self): return dict((x, y) for x, y in zip(self._fields, self))
Classes
class Column
class Column(object): def __init__(self, field): self._field = field def __eq__(self, other): return Equal(field=self._field, value=other) def __ne__(self, other): return ~Equal(field=self._field, value=other) def __gt__(self, other): return Greater(field=self._field, value=other) def __lt__(self, other): return Less(field=self._field, value=other) def __ge__(self, other): return GreaterEqual(field=self._field, value=other) def __le__(self, other): return LessEqual(field=self._field, value=other) def isin(self, other): return IsIn(field=self._field, value=other) def field_name(self): return self._field @property def desc(self): return Sorter(self._field) @property def asc(self): return Sorter(self._field, 'asc') @property def max(self): return MetricAggregator(self._field, 'max') @property def min(self): return MetricAggregator(self._field, 'min') @property def avg(self): return MetricAggregator(self._field, 'avg') @property def value_count(self): return MetricAggregator(self._field, 'value_count') count = value_count @property def cardinality(self): return MetricAggregator(self._field, 'cardinality') distinct_count = cardinality @property def percentiles(self): return MetricAggregator(self._field, 'percentiles') @property def percentile_ranks(self): return MetricAggregator(self._field, 'percentile_ranks')
Ancestors (in MRO)
- Column
- builtins.object
Class variables
var count
var distinct_count
Static methods
def __init__(
self, field)
Initialize self. See help(type(self)) for accurate signature.
def __init__(self, field): self._field = field
def field_name(
self)
def field_name(self): return self._field
def isin(
self, other)
def isin(self, other): return IsIn(field=self._field, value=other)
Instance variables
var asc
var avg
var cardinality
var count
var desc
var distinct_count
var max
var min
var percentile_ranks
var percentiles
var value_count
class Row
The builtin L{DataFrame} row type for accessing before converted into Pandas DataFrame. The fields will be sorted by names.
row = Row(name="Alice", age=12) row Row(age=12, name='Alice') row['name'], row['age'] ('Alice', 12) row.name, row.age ('Alice', 12) 'name' in row True 'wrong_key' in row
class Row(tuple): """ The builtin L{DataFrame} row type for accessing before converted into Pandas DataFrame. The fields will be sorted by names. >>> row = Row(name="Alice", age=12) >>> row Row(age=12, name='Alice') >>> row['name'], row['age'] ('Alice', 12) >>> row.name, row.age ('Alice', 12) >>> 'name' in row True >>> 'wrong_key' in row """ def __new__(cls, **kwargs): names = sorted(kwargs.keys()) row = tuple.__new__(cls, [kwargs[n] for n in names]) row._fields = names return row def __getitem__(self, name): try: idx = self._fields.index(name) return super(Row, self).__getitem__(idx) except IndexError: raise KeyError(name) except ValueError: raise ValueError(name) def __contains__(self, name): return name in self._fields def __repr__(self): return 'Row('+','.join(['{0}={1}'.format(k, Row._stringfy(v)) for k, v in zip(self._fields, tuple(self))])+')' @classmethod def _stringfy(cls, v): b = six.StringIO() b.write(repr(v)) return b.getvalue() def as_dict(self): return dict((x, y) for x, y in zip(self._fields, self))
Ancestors (in MRO)
- Row
- builtins.tuple
- builtins.object
Static methods
def as_dict(
self)
def as_dict(self): return dict((x, y) for x, y in zip(self._fields, self))