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