Tutorial by Topics: dataframe

data.frame(..., row.names = NULL, check.rows = FALSE, check.names = TRUE, stringsAsFactors = default.stringsAsFactors()) as.data.frame(x, row.names = NULL, optional = FALSE, ...) # generic function as.data.frame(x, ..., stringsAsFactors = default.stringsAsFactors()) # S3 method for cl...

ParameterDescriptionpath_or_bufstring or file handle, default None File path or object, if None is provided the result is returned as a string.sepcharacter, default ‘,’ Field delimiter for the output file.na_repstring, default ‘’ Missing data representationfloat_formatstring, default None Format st...

DataFrame is a data structure provided by pandas library,apart from Series & Panel. It is a 2-dimensional structure & can be compared to a table of rows and columns. Each row can be identified by an integer index (0..N) or a label explicitly set when creating a DataFrame object. Each colu...

A DataFrame is an abstraction of data organized in rows and typed columns. It is similar to the data found in relational SQL-based databases. Although it has been transformed into just a type alias for Dataset[Row] in Spark 2.0, it is still widely used and useful for complex processing pipelines mak...

This section provides an overview of what spark-dataframe is, and why a developer might want to use it. It should also mention any large subjects within spark-dataframe, and link out to the related topics. Since the Documentation for spark-dataframe is new, you may need to create initial versio...

Accessing rows in a dataframe using the DataFrame indexer objects .ix, .loc, .iloc and how it differentiates itself from using a boolean mask.

Aggregation is one of the most common uses for R. There are several ways to do so in R, which we will illustrate here.