DataFrame

Constructor

DataFrame([es_client, es_index_pattern, …])

Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns) referencing data stored in Elasticsearch indices.

Attributes and underlying data

DataFrame.index	Return eland index referencing Elasticsearch field to index a DataFrame/Series
DataFrame.columns	The column labels of the DataFrame.
DataFrame.dtypes	Return the pandas dtypes in the DataFrame.
DataFrame.select_dtypes([include, exclude])	Return a subset of the DataFrame’s columns based on the column dtypes.
DataFrame.values	Not implemented.
DataFrame.empty	Determines if the DataFrame is empty.
DataFrame.shape	Return a tuple representing the dimensionality of the DataFrame.
DataFrame.ndim	Returns 2 by definition of a DataFrame
DataFrame.size	Return an int representing the number of elements in this object.

Indexing, iteration

DataFrame.head(n)	Return the first n rows.
DataFrame.keys()	Return columns
DataFrame.tail(n)	Return the last n rows.
DataFrame.get(key[, default])	Get item from object for given key (ex: DataFrame column).
DataFrame.query(expr)	Query the columns of a DataFrame with a boolean expression.
DataFrame.sample(n, frac, random_state)	Return n randomly sample rows or the specify fraction of rows

Function application, GroupBy & window

DataFrame.agg(func[, axis])	Aggregate using one or more operations over the specified axis.
DataFrame.aggregate(func[, axis])	Aggregate using one or more operations over the specified axis.

Computations / descriptive stats

DataFrame.count()	Count non-NA cells for each column.
DataFrame.describe()	Generate descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.
DataFrame.info([verbose, buf, max_cols, …])	Print a concise summary of a DataFrame.
DataFrame.max(numeric_only)	Return the maximum value for each numeric column
DataFrame.mean(numeric_only)	Return mean value for each numeric column
DataFrame.min(numeric_only)	Return the minimum value for each numeric column
DataFrame.median(numeric_only)	Return the median value for each numeric column
DataFrame.mad(numeric_only)	Return standard deviation for each numeric column
DataFrame.std(numeric_only)	Return standard deviation for each numeric column
DataFrame.var(numeric_only)	Return variance for each numeric column
DataFrame.sum(numeric_only)	Return sum for each numeric column
DataFrame.nunique()	Return cardinality of each field.

Reindexing / selection / label manipulation

DataFrame.drop([labels, axis, index, …])	Return new object with labels in requested axis removed.
DataFrame.filter(items, like, regex, axis, …)	Subset the dataframe rows or columns according to the specified index labels.

Plotting

DataFrame.hist([column, by, grid, …])

Make a histogram of the DataFrame’s.

Elasticsearch Functions

DataFrame.es_info()	A debug summary of an eland DataFrame internals.
DataFrame.es_query(query)	Applies an Elasticsearch DSL query to the current DataFrame.

Serialization / IO / conversion

DataFrame.info([verbose, buf, max_cols, …])	Print a concise summary of a DataFrame.
DataFrame.to_numpy()	Not implemented.
DataFrame.to_csv([path_or_buf, sep, na_rep, …])	Write Elasticsearch data to a comma-separated values (csv) file.
DataFrame.to_html([buf, columns, col_space, …])	Render a Elasticsearch data as an HTML table.
DataFrame.to_string([buf, columns, …])	Render a DataFrame to a console-friendly tabular output.
DataFrame.to_pandas(show_progress)	Utility method to convert eland.Dataframe to pandas.Dataframe

Supported Pandas APIs eland.DataFrame