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eland/eland/series.py
Stephen Dodson 86686ebb18 Reformat and cleanup based on PyCharm
2019-11-26 11:02:46 +00:00

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"""
Series
---------
One-dimensional ndarray with axis labels (including time series).
The underlying data resides in Elasticsearch and the API aligns as much as
possible with pandas.DataFrame API.
This allows the eland.Series to access large datasets stored in Elasticsearch,
without storing the dataset in local memory.
Implementation Details
----------------------
Based on NDFrame which underpins eland.DataFrame
"""
import sys
import warnings
from io import StringIO
import numpy as np
import pandas as pd
from pandas.io.common import _expand_user, _stringify_path
from eland import NDFrame
from eland.common import DEFAULT_NUM_ROWS_DISPLAYED, docstring_parameter
from eland.filter import NotFilter, Equal, Greater, Less, GreaterEqual, LessEqual, ScriptFilter, IsIn
def _get_method_name():
return sys._getframe(1).f_code.co_name
class Series(NDFrame):
"""
pandas.Series like API that proxies into Elasticsearch index(es).
Parameters
----------
client : eland.Client
A reference to a Elasticsearch python client
index_pattern : str
An Elasticsearch index pattern. This can contain wildcards.
index_field : str
The field to base the series on
Notes
-----
If the Elasticsearch index is deleted or index mappings are changed after this
object is created, the object is not rebuilt and so inconsistencies can occur.
See Also
--------
:pandas_api_docs:`pandas.Series`
Examples
--------
>>> ed.Series(client='localhost', index_pattern='flights', name='Carrier')
0 Kibana Airlines
1 Logstash Airways
2 Logstash Airways
3 Kibana Airlines
4 Kibana Airlines
...
13054 Logstash Airways
13055 Logstash Airways
13056 Logstash Airways
13057 JetBeats
13058 JetBeats
Name: Carrier, Length: 13059, dtype: object
"""
def __init__(self,
client=None,
index_pattern=None,
name=None,
index_field=None,
query_compiler=None):
# Series has 1 column
if name is None:
columns = None
else:
columns = [name]
super().__init__(
client=client,
index_pattern=index_pattern,
columns=columns,
index_field=index_field,
query_compiler=query_compiler)
@property
def empty(self):
"""Determines if the Series is empty.
Returns:
True if the Series is empty.
False otherwise.
"""
return len(self.index) == 0
@property
def shape(self):
"""
Return a tuple representing the dimensionality of the Series.
Returns
-------
shape: tuple
0. number of rows
1. number of columns
Notes
-----
- number of rows ``len(series)`` queries Elasticsearch
- number of columns == 1
Examples
--------
>>> df = ed.Series('localhost', 'ecommerce', name='total_quantity')
>>> df.shape
(4675, 1)
"""
num_rows = len(self)
num_columns = 1
return num_rows, num_columns
def _get_name(self):
return self._query_compiler.columns[0]
def _set_name(self, name):
self._query_compiler.rename({self.name: name}, inplace=True)
name = property(_get_name, _set_name)
def rename(self, new_name):
"""
Rename name of series. Only column rename is supported. This does not change the underlying
Elasticsearch index, but adds a symbolic link from the new name (column) to the Elasticsearch field name.
For instance, if a field was called 'tot_quan' it could be renamed 'Total Quantity'.
Parameters
----------
new_name: str
Returns
-------
eland.Series
eland.Series with new name.
See Also
--------
:pandas_api_docs:`pandas.Series.rename`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df.Carrier
0 Kibana Airlines
1 Logstash Airways
2 Logstash Airways
3 Kibana Airlines
4 Kibana Airlines
...
13054 Logstash Airways
13055 Logstash Airways
13056 Logstash Airways
13057 JetBeats
13058 JetBeats
Name: Carrier, Length: 13059, dtype: object
>>> df.Carrier.rename('Airline')
0 Kibana Airlines
1 Logstash Airways
2 Logstash Airways
3 Kibana Airlines
4 Kibana Airlines
...
13054 Logstash Airways
13055 Logstash Airways
13056 Logstash Airways
13057 JetBeats
13058 JetBeats
Name: Airline, Length: 13059, dtype: object
"""
return Series(query_compiler=self._query_compiler.rename({self.name: new_name}))
def head(self, n=5):
return Series(query_compiler=self._query_compiler.head(n))
def tail(self, n=5):
return Series(query_compiler=self._query_compiler.tail(n))
def value_counts(self, es_size=10):
"""
Return the value counts for the specified field.
**Note we can only do this for aggregatable Elasticsearch fields - (in general) numeric and keyword
rather than text fields**
TODO - implement remainder of pandas arguments
Parameters
----------
es_size: int, default 10
Number of buckets to return counts for, automatically sorts by count descending.
This parameter is specific to `eland`, and determines how many term buckets
elasticsearch should return out of the overall terms list.
Returns
-------
pandas.Series
number of occurences of each value in the column
See Also
--------
:pandas_api_docs:`pandas.Series.value_counts`
:es_api_docs:`search-aggregations-bucket-terms-aggregation`
Examples
--------
>>> df = ed.DataFrame('localhost', 'flights')
>>> df['Carrier'].value_counts()
Logstash Airways 3331
JetBeats 3274
Kibana Airlines 3234
ES-Air 3220
Name: Carrier, dtype: int64
"""
if not isinstance(es_size, int):
raise TypeError("es_size must be a positive integer.")
if not es_size > 0:
raise ValueError("es_size must be a positive integer.")
return self._query_compiler.value_counts(es_size)
# dtype not implemented for Series as causes query to fail
# in pandas.core.computation.ops.Term.type
# ----------------------------------------------------------------------
# Rendering Methods
def __repr__(self):
"""
Return a string representation for a particular Series.
"""
buf = StringIO()
# max_rows and max_cols determine the maximum size of the pretty printed tabular
# representation of the series. pandas defaults are 60 and 20 respectively.
# series where len(series) > max_rows shows a truncated view with 10 rows shown.
max_rows = pd.get_option("display.max_rows")
min_rows = pd.get_option("display.min_rows")
if len(self) > max_rows:
max_rows = min_rows
show_dimensions = pd.get_option("display.show_dimensions")
self.to_string(
buf=buf,
name=self.name,
dtype=True,
min_rows=min_rows,
max_rows=max_rows,
length=show_dimensions,
)
result = buf.getvalue()
return result
@docstring_parameter(DEFAULT_NUM_ROWS_DISPLAYED)
def to_string(
self,
buf=None,
na_rep="NaN",
float_format=None,
header=True,
index=True,
length=False,
dtype=False,
name=False,
max_rows=None,
min_rows=None,
):
"""
Render a string representation of the Series.
Follows pandas implementation except when ``max_rows=None``. In this scenario, we set ``max_rows={0}`` to avoid
accidentally dumping an entire index. This can be overridden by explicitly setting ``max_rows``.
See Also
--------
:pandas_api_docs:`pandas.Series.to_string`
for argument details.
"""
# In pandas calling 'to_string' without max_rows set, will dump ALL rows - we avoid this
# by limiting rows by default.
num_rows = len(self) # avoid multiple calls
if num_rows <= DEFAULT_NUM_ROWS_DISPLAYED:
if max_rows is None:
max_rows = num_rows
else:
max_rows = min(num_rows, max_rows)
elif max_rows is None:
warnings.warn("Series.to_string called without max_rows set "
"- this will return entire index results. "
"Setting max_rows={default}"
" overwrite if different behaviour is required."
.format(default=DEFAULT_NUM_ROWS_DISPLAYED),
UserWarning)
max_rows = DEFAULT_NUM_ROWS_DISPLAYED
# because of the way pandas handles max_rows=0, not having this throws an error
# see eland issue #56
if max_rows == 0:
max_rows = 1
# Create a slightly bigger dataframe than display
temp_series = self._build_repr(max_rows + 1)
if buf is not None:
_buf = _expand_user(_stringify_path(buf))
else:
_buf = StringIO()
# Create repr of fake series without name, length, dtype summary
temp_str = temp_series.to_string(buf=_buf,
na_rep=na_rep,
float_format=float_format,
header=header,
index=index,
length=False,
dtype=False,
name=False,
max_rows=max_rows)
# Create the summary
footer = ""
if name and self.name is not None:
footer += "Name: {}".format(str(self.name))
if length and len(self) > max_rows:
if footer:
footer += ", "
footer += "Length: {}".format(len(self.index))
if dtype:
if footer:
footer += ", "
footer += "dtype: {}".format(temp_series.dtype)
if len(footer) > 0:
_buf.write("\n{}".format(footer))
if buf is None:
result = _buf.getvalue()
return result
def _to_pandas(self):
return self._query_compiler.to_pandas()[self.name]
@property
def _dtype(self):
# DO NOT MAKE PUBLIC (i.e. def dtype) as this breaks query eval implementation
return self._query_compiler.dtypes[0]
def __gt__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value > doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return Greater(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
def __lt__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value < doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return Less(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
def __ge__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value >= doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return GreaterEqual(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
def __le__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value <= doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return LessEqual(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
def __eq__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value == doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return Equal(field=self.name, value=other)
elif isinstance(other, str):
return Equal(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
def __ne__(self, other):
if isinstance(other, Series):
# Need to use scripted query to compare to values
painless = "doc['{}'].value != doc['{}'].value".format(self.name, other.name)
return ScriptFilter(painless)
elif isinstance(other, (int, float)):
return NotFilter(Equal(field=self.name, value=other))
elif isinstance(other, str):
return NotFilter(Equal(field=self.name, value=other))
else:
raise NotImplementedError(other, type(other))
def isin(self, other):
if isinstance(other, list):
return IsIn(field=self.name, value=other)
else:
raise NotImplementedError(other, type(other))
@property
def ndim(self):
"""
Returns 1 by definition of a Series
Returns
-------
int
By definition 1
See Also
--------
:pandas_api_docs:`pandas.Series.ndim`
"""
return 1
def info_es(self):
buf = StringIO()
super()._info_es(buf)
return buf.getvalue()
def __add__(self, right):
"""
Return addition of series and right, element-wise (binary operator add).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.taxful_total_price + 1
0 37.980000
1 54.980000
2 200.979996
3 175.979996
4 81.980003
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price + df.total_quantity
0 38.980000
1 55.980000
2 201.979996
3 176.979996
4 82.980003
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __truediv__(self, right):
"""
Return floating division of series and right, element-wise (binary operator truediv).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price / df.total_quantity
0 18.490000
1 26.990000
2 99.989998
3 87.489998
4 40.490002
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __floordiv__(self, right):
"""
Return integer division of series and right, element-wise (binary operator floordiv //).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price // df.total_quantity
0 18.0
1 26.0
2 99.0
3 87.0
4 40.0
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __mod__(self, right):
"""
Return modulo of series and right, element-wise (binary operator mod %).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price % df.total_quantity
0 0.980000
1 1.980000
2 1.979996
3 0.979996
4 0.980003
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __mul__(self, right):
"""
Return multiplication of series and right, element-wise (binary operator mul).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price * df.total_quantity
0 73.959999
1 107.959999
2 399.959991
3 349.959991
4 161.960007
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __sub__(self, right):
"""
Return subtraction of series and right, element-wise (binary operator sub).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price - df.total_quantity
0 34.980000
1 51.980000
2 197.979996
3 172.979996
4 78.980003
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __pow__(self, right):
"""
Return exponential power of series and right, element-wise (binary operator pow).
Parameters
----------
right: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> df.taxful_total_price ** df.total_quantity
0 1367.520366
1 2913.840351
2 39991.998691
3 30617.998905
4 6557.760944
dtype: float64
"""
return self._numeric_op(right, _get_method_name())
def __radd__(self, left):
"""
Return addition of series and left, element-wise (binary operator add).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 1 + df.taxful_total_price
0 37.980000
1 54.980000
2 200.979996
3 175.979996
4 81.980003
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rtruediv__(self, left):
"""
Return division of series and left, element-wise (binary operator div).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 1.0 / df.taxful_total_price
0 0.027042
1 0.018525
2 0.005001
3 0.005715
4 0.012349
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rfloordiv__(self, left):
"""
Return integer division of series and left, element-wise (binary operator floordiv //).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 500.0 // df.taxful_total_price
0 13.0
1 9.0
2 2.0
3 2.0
4 6.0
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rmod__(self, left):
"""
Return modulo of series and left, element-wise (binary operator mod %).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 500.0 % df.taxful_total_price
0 19.260006
1 14.180004
2 100.040009
3 150.040009
4 14.119980
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rmul__(self, left):
"""
Return multiplication of series and left, element-wise (binary operator mul).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 10.0 * df.taxful_total_price
0 369.799995
1 539.799995
2 1999.799957
3 1749.799957
4 809.800034
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rpow__(self, left):
"""
Return exponential power of series and left, element-wise (binary operator pow).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.total_quantity
0 2
1 2
2 2
3 2
4 2
Name: total_quantity, dtype: int64
>>> np.int(2) ** df.total_quantity
0 4.0
1 4.0
2 4.0
3 4.0
4 4.0
Name: total_quantity, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
def __rsub__(self, left):
"""
Return subtraction of series and left, element-wise (binary operator sub).
Parameters
----------
left: eland.Series
Returns
-------
eland.Series
Examples
--------
>>> df = ed.DataFrame('localhost', 'ecommerce').head(5)
>>> df.taxful_total_price
0 36.98
1 53.98
2 199.98
3 174.98
4 80.98
Name: taxful_total_price, dtype: float64
>>> 1.0 - df.taxful_total_price
0 -35.980000
1 -52.980000
2 -198.979996
3 -173.979996
4 -79.980003
Name: taxful_total_price, dtype: float64
"""
return self._numeric_rop(left, _get_method_name())
add = __add__
div = __truediv__
divide = __truediv__
floordiv = __floordiv__
mod = __mod__
mul = __mul__
multiply = __mul__
pow = __pow__
sub = __sub__
subtract = __sub__
truediv = __truediv__
radd = __radd__
rdiv = __rtruediv__
rdivide = __rtruediv__
rfloordiv = __rfloordiv__
rmod = __rmod__
rmul = __rmul__
rmultiply = __rmul__
rpow = __rpow__
rsub = __rsub__
rsubtract = __rsub__
rtruediv = __rtruediv__
def _numeric_op(self, right, method_name):
"""
return a op b
a & b == Series
a & b must share same eland.Client, index_pattern and index_field
a == Series, b == numeric
"""
if isinstance(right, Series):
# Check compatibility of Elasticsearch cluster
self._query_compiler.check_arithmetics(right._query_compiler)
# Check compatibility of dtypes
# either not a number?
if not (np.issubdtype(self._dtype, np.number) and np.issubdtype(right._dtype, np.number)):
# TODO - support limited ops on strings https://github.com/elastic/eland/issues/65
raise TypeError("Unsupported operation: '{}' {} '{}'".format(self._dtype, method_name, right._dtype))
new_field_name = "{0}_{1}_{2}".format(self.name, method_name, right.name)
# Compatible, so create new Series
series = Series(query_compiler=self._query_compiler.arithmetic_op_fields(
new_field_name, method_name, self.name, right.name))
series.name = None
return series
elif np.issubdtype(np.dtype(type(right)), np.number) and np.issubdtype(self._dtype, np.number):
new_field_name = "{0}_{1}_{2}".format(self.name, method_name, str(right).replace('.', '_'))
# Compatible, so create new Series
series = Series(query_compiler=self._query_compiler.arithmetic_op_fields(
new_field_name, method_name, self.name, right))
# name of Series remains original name
series.name = self.name
return series
else:
# TODO - support limited ops on strings https://github.com/elastic/eland/issues/65
raise TypeError(
"unsupported operand type(s) for '{}' {} '{}'".format(type(self), method_name, type(right))
)
def _numeric_rop(self, left, method_name):
"""
e.g. 1 + ed.Series
"""
op_method_name = str(method_name).replace('__r', '__')
if isinstance(left, Series):
# if both are Series, revese args and call normal op method and remove 'r' from radd etc.
return left._numeric_op(self, op_method_name)
elif np.issubdtype(np.dtype(type(left)), np.number) and np.issubdtype(self._dtype, np.number):
# Prefix new field name with 'f_' so it's a valid ES field name
new_field_name = "f_{0}_{1}_{2}".format(str(left).replace('.', '_'), op_method_name, self.name)
# Compatible, so create new Series
series = Series(query_compiler=self._query_compiler.arithmetic_op_fields(
new_field_name, op_method_name, left, self.name))
# name of Series pinned to valid series (like pandas)
series.name = self.name
return series
else:
# TODO - support limited ops on strings https://github.com/elastic/eland/issues/65
raise TypeError(
"unsupported operand type(s) for '{}' {} '{}'".format(type(self), method_name, type(left))
)
def max(self):
"""
Return the maximum of the Series values
TODO - implement remainder of pandas arguments, currently non-numerics are not supported
Returns
-------
float
max value
See Also
--------
:pandas_api_docs:`pandas.Series.max`
Examples
--------
>>> s = ed.Series('localhost', 'flights', name='AvgTicketPrice')
>>> int(s.max())
1199
"""
results = super().max()
return results.squeeze()
def mean(self):
"""
Return the mean of the Series values
TODO - implement remainder of pandas arguments, currently non-numerics are not supported
Returns
-------
float
max value
See Also
--------
:pandas_api_docs:`pandas.Series.mean`
Examples
--------
>>> s = ed.Series('localhost', 'flights', name='AvgTicketPrice')
>>> int(s.mean())
628
"""
results = super().mean()
return results.squeeze()
def min(self):
"""
Return the minimum of the Series values
TODO - implement remainder of pandas arguments, currently non-numerics are not supported
Returns
-------
float
max value
See Also
--------
:pandas_api_docs:`pandas.Series.min`
Examples
--------
>>> s = ed.Series('localhost', 'flights', name='AvgTicketPrice')
>>> int(s.min())
100
"""
results = super().min()
return results.squeeze()
def sum(self):
"""
Return the sum of the Series values
TODO - implement remainder of pandas arguments, currently non-numerics are not supported
Returns
-------
float
max value
See Also
--------
:pandas_api_docs:`pandas.Series.sum`
Examples
--------
>>> s = ed.Series('localhost', 'flights', name='AvgTicketPrice')
>>> int(s.sum())
8204364
"""
results = super().sum()
return results.squeeze()
def nunique(self):
"""
Return the sum of the Series values
Returns
-------
float
max value
See Also
--------
:pandas_api_docs:`pandas.Series.sum`
Examples
--------
>>> s = ed.Series('localhost', 'flights', name='Carrier')
>>> s.nunique()
4
"""
results = super().nunique()
return results.squeeze()
# def values TODO - not implemented as causes current implementation of query to fail
def to_numpy(self):
"""
Not implemented.
In pandas this returns a Numpy representation of the Series. This would involve scan/scrolling the
entire index.
If this is required, call ``ed.eland_to_pandas(ed_series).values``, *but beware this will scan/scroll the entire
Elasticsearch index(s) into memory.*
See Also
--------
:pandas_api_docs:`pandas.DataFrame.to_numpy`
eland_to_pandas
Examples
--------
>>> ed_s = ed.Series('localhost', 'flights', name='Carrier').head(5)
>>> pd_s = ed.eland_to_pandas(ed_s)
>>> print("type(ed_s)={0}\\ntype(pd_s)={1}".format(type(ed_s), type(pd_s)))
type(ed_s)=<class 'eland.series.Series'>
type(pd_s)=<class 'pandas.core.series.Series'>
>>> ed_s
0 Kibana Airlines
1 Logstash Airways
2 Logstash Airways
3 Kibana Airlines
4 Kibana Airlines
Name: Carrier, dtype: object
>>> pd_s.to_numpy()
array(['Kibana Airlines', 'Logstash Airways', 'Logstash Airways',
'Kibana Airlines', 'Kibana Airlines'], dtype=object)
"""
raise NotImplementedError(
"This method would scan/scroll the entire Elasticsearch index(s) into memory."
"If this is explicitly required and there is sufficient memory, call `ed.eland_to_pandas(ed_df).values`"
)
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