eland.Series

class eland.Series(es_client: Optional[Elasticsearch] = None, es_index_pattern: Optional[str] = None, name: Optional[str] = None, es_index_field: Optional[str] = None, _query_compiler: Optional[QueryCompiler] = None)

pandas.Series like API that proxies into Elasticsearch index(es).

Parameters

es_clientelasticsearch.Elasticsearch

A reference to a Elasticsearch python client

es_index_patternstr

An Elasticsearch index pattern. This can contain wildcards.

es_index_fieldstr

The field to base the series on

See also

pandas.Series

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.

Examples

>>> ed.Series(es_client='http://localhost:9200', es_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

__init__(es_client: Optional[Elasticsearch] = None, es_index_pattern: Optional[str] = None, name: Optional[str] = None, es_index_field: Optional[str] = None, _query_compiler: Optional[QueryCompiler] = None) → None

pandas.DataFrame/Series like API that proxies into Elasticsearch index(es).

Parameters

clientelasticsearch.Elasticsearch

A reference to a Elasticsearch python client

Methods

__init__([es_client, es_index_pattern, ...])	pandas.DataFrame/Series like API that proxies into Elasticsearch index(es).
add(right)	Return addition of series and right, element-wise (binary operator add).
describe()	Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
div(right)	Return floating division of series and right, element-wise (binary operator truediv).
divide(right)	Return floating division of series and right, element-wise (binary operator truediv).
es_info()
es_match(text, *[, match_phrase, ...])	Filters data with an Elasticsearch match or match_phrase query depending on the given parameters.
filter([items, like, regex, axis])	Subset the dataframe rows or columns according to the specified index labels.
floordiv(right)	Return integer division of series and right, element-wise (binary operator floordiv //).
head([n])
hist([by, ax, grid, xlabelsize, xrot, ...])	Draw histogram of the input series using matplotlib.
isin(other)
isna()	Detect missing values.
isnull()	Detect missing values.
mad([numeric_only])	Return median absolute deviation for a Series
max([numeric_only])	Return the maximum of the Series values
mean([numeric_only])	Return the mean of the Series values
median([numeric_only])	Return the median of the Series values
min([numeric_only])	Return the minimum of the Series values
mod(right)	Return modulo of series and right, element-wise (binary operator mod %).
mode([es_size])	Calculate mode of a series
mul(right)	Return multiplication of series and right, element-wise (binary operator mul).
multiply(right)	Return multiplication of series and right, element-wise (binary operator mul).
notna()	Detect existing (non-missing) values.
notnull()	Detect existing (non-missing) values.
nunique()	Return the number of unique values in a Series
pow(right)	Return exponential power of series and right, element-wise (binary operator pow).
quantile([q])	Used to calculate quantile for a given Series.
radd(left)	Return addition of series and left, element-wise (binary operator add).
rdiv(left)	Return division of series and left, element-wise (binary operator div).
rdivide(left)	Return division of series and left, element-wise (binary operator div).
rename(new_name)	Rename name of series.
rfloordiv(left)	Return integer division of series and left, element-wise (binary operator floordiv //).
rmod(left)	Return modulo of series and left, element-wise (binary operator mod %).
rmul(left)	Return multiplication of series and left, element-wise (binary operator mul).
rmultiply(left)	Return multiplication of series and left, element-wise (binary operator mul).
rpow(left)	Return exponential power of series and left, element-wise (binary operator pow).
rsub(left)	Return subtraction of series and left, element-wise (binary operator sub).
rsubtract(left)	Return subtraction of series and left, element-wise (binary operator sub).
rtruediv(left)	Return division of series and left, element-wise (binary operator div).
sample([n, frac, random_state])
std([numeric_only])	Return standard deviation for a Series
sub(right)	Return subtraction of series and right, element-wise (binary operator sub).
subtract(right)	Return subtraction of series and right, element-wise (binary operator sub).
sum([numeric_only])	Return the sum of the Series values
tail([n])
to_numpy()	Not implemented.
to_pandas([show_progress])
to_string([buf, na_rep, float_format, ...])	Render a string representation of the Series.
truediv(right)	Return floating division of series and right, element-wise (binary operator truediv).
unique()	Returns all unique values within a Series.
value_counts([es_size])	Return the value counts for the specified field.
var([numeric_only])	Return variance for a Series

Attributes

dtype	Return the dtype object of the underlying data.
dtypes	Return the pandas dtypes in the DataFrame.
empty	Determines if the Series is empty.
es_dtype	Return the Elasticsearch type of the underlying data.
es_dtypes	Return the Elasticsearch dtypes in the index
es_field_name	Returns
index	Return eland index referencing Elasticsearch field to index a DataFrame/Series
name
ndim	Returns 1 by definition of a Series
shape	Return a tuple representing the dimensionality of the Series.
size	Return an int representing the number of elements in this object.

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