# 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

pandas.Series

## 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). 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_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) Detect missing values. 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). Detect existing (non-missing) values. Detect existing (non-missing) values. 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]) 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). 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 es_field_name: str Return the Elasticsearch field name for this series `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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