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
DataFrame.iterrows()	Iterate over eland.DataFrame rows as (index, pandas.Series) pairs.
DataFrame.itertuples([index, name])	Iterate over eland.DataFrame rows as namedtuples.

Function Application, GroupBy & Window

Note

Elasticsearch aggregations using cardinality (count) are accurate approximations using the HyperLogLog++ algorithm so may not be exact.

DataFrame.agg(func[, axis, numeric_only])	Aggregate using one or more operations over the specified axis.
DataFrame.aggregate(func[, axis, numeric_only])	Aggregate using one or more operations over the specified axis.
DataFrame.groupby([by, dropna])	Used to perform groupby operations

DataFrameGroupBy(by, query_compiler[, dropna])	This holds all the groupby methods for eland.DataFrame.groupby()
DataFrameGroupBy.agg(func[, numeric_only])	Used to groupby and aggregate
DataFrameGroupBy.aggregate(func[, numeric_only])	Used to groupby and aggregate
DataFrameGroupBy.count()	Compute the count value for each group.
DataFrameGroupBy.mad([numeric_only])	Compute the median absolute deviation value for each group.
DataFrameGroupBy.max([numeric_only])	Compute the max value for each group.
DataFrameGroupBy.mean([numeric_only])	Compute the mean value for each group.
DataFrameGroupBy.median([numeric_only])	Compute the median value for each group.
DataFrameGroupBy.min([numeric_only])	Compute the min value for each group.
DataFrameGroupBy.nunique()	Compute the nunique value for each group.
DataFrameGroupBy.std([numeric_only])	Compute the standard deviation value for each group.
DataFrameGroupBy.sum([numeric_only])	Compute the sum value for each group.
DataFrameGroupBy.var([numeric_only])	Compute the variance value for each group.
DataFrameGroupBy.quantile([q])	Used to groupby and calculate quantile for a given DataFrame.
GroupBy(by, query_compiler[, dropna])	Base class for calls to eland.DataFrame.groupby()

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.
DataFrame.mode([numeric_only, dropna, es_size])	Calculate mode of a DataFrame
DataFrame.quantile([q, numeric_only])	Used to calculate quantile for a given DataFrame.
DataFrame.idxmax([axis])	Return index of first occurrence of maximum over requested axis.
DataFrame.idxmin([axis])	Return index of first occurrence of minimum over requested axis.

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_match(text, *[, columns, ...])	Filters data with an Elasticsearch match, match_phrase, or multi_match query depending on the given parameters and columns.
DataFrame.es_query(query)	Applies an Elasticsearch DSL query to the current DataFrame.
DataFrame.es_dtypes	Return the Elasticsearch dtypes in the index

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

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