A data frame is a table-like data structure available in languages like Python and R. Statisticians, scientists, and programmers use them in data analysis code. For Row64, dataframes are like a imported data sheet in Excel but with all kinds of super powers.
The data frame is based on the idea that many datasets in statistics can be arranged as rectangular arrays, with the rows representing individual data units and the columns representing designated variables.
Dataframes are similar to many data structures you might be familiar with. Here’s a chart explaining the similarities and differences:
The dataframe manipulates an entire sheet. Rather than a grid of formulas that run cell by cell, it can do sheet wide transformation.
So with Row64’s Dataframe Formulas - a single formula will apply to the entire sheet.
This leads to doing data operations quickly, and makes it fast to experiment and explore datasets.
Row64 provides an interface for dataframes that are simpler and more powerful than Python & R alone. Beyond all the GPU acceleration capabilities the basic data workflow has many unique capabilities.
Dataframes in Row64 are simpler because you don’t have to learn Python to use them.
Instead you can have simple spreadsheet formulas to transform entire dataframes
Dataframe Formulas are as easy as spreadsheet formulas - the major feature is they don’t use variables. By not having variables and spreadsheet syntax it lets people with no interest in programming take advantage of these groundbreaking new features.
For those who do know programming, Dataframe Formulas are an incredible time-saver. This is because the dataframe automatically generate Python code, and you can solve data science problems quickly by roughing things into place with these “Data Science Presets”.
The “super powers” of the dataframe for example, are the Headings in the Presets:
Each function under the blue categories are essentially neatly packaged Python code that create and modify Dataframes. Because they are formulas, it’s easy to tune and tinker to fit your exact data manipulation needs.
Introductory Datacamp Reference: Pandas Tutorial: DataFrames in Python
Another Python Pandas Dataframe Reference: Python | Pandas DataFrame
Credits to Washington EDU’s online dataframe notes: Data frames in SPlus