DataFrame([es_client, es_index_pattern, …])
DataFrame
Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns) referencing data stored in Elasticsearch indices.
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])
DataFrame.select_dtypes
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.
DataFrame.head(n)
DataFrame.head
Return the first n rows.
DataFrame.keys()
DataFrame.keys
Return columns
DataFrame.tail(n)
DataFrame.tail
Return the last n rows.
DataFrame.get(key[, default])
DataFrame.get
Get item from object for given key (ex: DataFrame column).
DataFrame.query(expr)
DataFrame.query
Query the columns of a DataFrame with a boolean expression.
DataFrame.sample(n, frac, random_state)
DataFrame.sample
Return n randomly sample rows or the specify fraction of rows
DataFrame.agg(func, List[str]], axis, …)
DataFrame.agg
Aggregate using one or more operations over the specified axis.
DataFrame.aggregate(func, List[str]], axis, …)
DataFrame.aggregate
DataFrame.count()
DataFrame.count
Count non-NA cells for each column.
DataFrame.describe()
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, …])
DataFrame.info
Print a concise summary of a DataFrame.
DataFrame.max(numeric_only)
DataFrame.max
Return the maximum value for each numeric column
DataFrame.mean(numeric_only)
DataFrame.mean
Return mean value for each numeric column
DataFrame.min(numeric_only)
DataFrame.min
Return the minimum value for each numeric column
DataFrame.median(numeric_only)
DataFrame.median
Return the median value for each numeric column
DataFrame.mad(numeric_only)
DataFrame.mad
Return standard deviation for each numeric column
DataFrame.std(numeric_only)
DataFrame.std
DataFrame.var(numeric_only)
DataFrame.var
Return variance for each numeric column
DataFrame.sum(numeric_only)
DataFrame.sum
Return sum for each numeric column
DataFrame.nunique()
DataFrame.nunique
Return cardinality of each field.
DataFrame.drop([labels, axis, index, …])
DataFrame.drop
Return new object with labels in requested axis removed.
DataFrame.filter(items, like, regex, axis, …)
DataFrame.filter
Subset the dataframe rows or columns according to the specified index labels.
DataFrame.hist([column, by, grid, …])
DataFrame.hist
Make a histogram of the DataFrame’s.
DataFrame.es_info()
DataFrame.es_info
A debug summary of an eland DataFrame internals.
DataFrame.es_query(query)
DataFrame.es_query
Applies an Elasticsearch DSL query to the current DataFrame.
DataFrame.to_numpy()
DataFrame.to_numpy
DataFrame.to_csv([path_or_buf, sep, na_rep, …])
DataFrame.to_csv
Write Elasticsearch data to a comma-separated values (csv) file.
DataFrame.to_html([buf, columns, col_space, …])
DataFrame.to_html
Render a Elasticsearch data as an HTML table.
DataFrame.to_string([buf, columns, …])
DataFrame.to_string
Render a DataFrame to a console-friendly tabular output.
DataFrame.to_pandas(show_progress)
DataFrame.to_pandas
Utility method to convert eland.Dataframe to pandas.Dataframe