Series

Constructor

Series([es_client, es_index_pattern, name, …])

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

Attributes and underlying data

Axes

Series.index	Return eland index referencing Elasticsearch field to index a DataFrame/Series
Series.shape	Return a tuple representing the dimensionality of the Series.
Series.name
Series.empty	Determines if the Series is empty.

Indexing, iteration

Series.head(self[, n])
Series.tail(self[, n])
Series.sample(self, n, frac, random_state)

Binary operator functions

Series.add(self, right)	Return addition of series and right, element-wise (binary operator add).
Series.sub(self, right)	Return subtraction of series and right, element-wise (binary operator sub).
Series.subtract(self, right)	Return subtraction of series and right, element-wise (binary operator sub).
Series.mul(self, right)	Return multiplication of series and right, element-wise (binary operator mul).
Series.multiply(self, right)	Return multiplication of series and right, element-wise (binary operator mul).
Series.div(self, right)	Return floating division of series and right, element-wise (binary operator truediv).
Series.divide(self, right)	Return floating division of series and right, element-wise (binary operator truediv).
Series.truediv(self, right)	Return floating division of series and right, element-wise (binary operator truediv).
Series.floordiv(self, right)	Return integer division of series and right, element-wise (binary operator floordiv //).
Series.mod(self, right)	Return modulo of series and right, element-wise (binary operator mod %).
Series.pow(self, right)	Return exponential power of series and right, element-wise (binary operator pow).
Series.radd(self, left)	Return addition of series and left, element-wise (binary operator add).
Series.rsub(self, left)	Return subtraction of series and left, element-wise (binary operator sub).
Series.rsubtract(self, left)	Return subtraction of series and left, element-wise (binary operator sub).
Series.rmul(self, left)	Return multiplication of series and left, element-wise (binary operator mul).
Series.rmultiply(self, left)	Return multiplication of series and left, element-wise (binary operator mul).
Series.rdiv(self, left)	Return division of series and left, element-wise (binary operator div).
Series.rdivide(self, left)	Return division of series and left, element-wise (binary operator div).
Series.rtruediv(self, left)	Return division of series and left, element-wise (binary operator div).
Series.rfloordiv(self, left)	Return integer division of series and left, element-wise (binary operator floordiv //).
Series.rmod(self, left)	Return modulo of series and left, element-wise (binary operator mod %).
Series.rpow(self, left)	Return exponential power of series and left, element-wise (binary operator pow).

Computations / descriptive stats

Series.describe(self)	Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
Series.max(self[, numeric_only])	Return the maximum of the Series values
Series.mean(self[, numeric_only])	Return the mean of the Series values
Series.min(self[, numeric_only])	Return the minimum of the Series values
Series.sum(self[, numeric_only])	Return the sum of the Series values
Series.median(self[, numeric_only])	Return the median value for each numeric column
Series.mad(self[, numeric_only])	Return median absolute deviation for a Series
Series.std(self[, numeric_only])	Return standard deviation for a Series
Series.var(self[, numeric_only])	Return variance for a Series
Series.nunique(self)	Return the number of unique values in a Series
Series.value_counts(self[, es_size])	Return the value counts for the specified field.

Reindexing / selection / label manipulation

Series.rename(self, new_name)	Rename name of series.
Series.isna(self)	Detect missing values.
Series.notna(self)	Detect existing (non-missing) values.
Series.isnull(self)	Detect missing values.
Series.notnull(self)	Detect existing (non-missing) values.
Series.isin(self, other)
Series.filter(self, items, NoneType] = None, …)	Subset the dataframe rows or columns according to the specified index labels.

Plotting

Series.hist(self[, by, ax, grid, …])

Draw histogram of the input series using matplotlib.

Serialization / IO / conversion

Series.to_string(self[, buf, na_rep, …])	Render a string representation of the Series.
Series.to_numpy(self)	Not implemented.
Series.to_pandas(self[, show_progress])

Elasticsearch Functions

Series.es_info(self)

eland.DataFrame.to_pandas eland.Series