-
Notifications
You must be signed in to change notification settings - Fork 4
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
DataFrame/DataTable related functionality #29
Labels
TODO
A list of some features openGPMP aims to implement
Comments
Closed
assigning to @eeddgg and myself |
update 04/12/2023DataTable class is in development (header / src) with 3 specific types // alias for the pair type of strings
typedef std::pair<std::vector<std::string>,
std::vector<std::vector<std::string>>>
DataTableStr;
// alias for pair type of 64 bit integers
typedef std::pair<std::vector<int64_t>, std::vector<std::vector<int64_t>>>
DataTableInt;
// alias for pair type of long doubles
typedef std::pair<std::vector<long double>,
std::vector<std::vector<long double>>>
DataTableDouble; with 4 semi-working functions // similar to pandas.read_csv, parses CSV files
DataTableStr csv_read(std::string filename, std::vector<std::string> columns = {});
// converts DataTableStr -> DataTableInt
DataTableInt str_to_int(DataTableStr src);
// converts DataTableStr -> DataTableDouble
DataTableDouble str_to_double(DataTableStr src);
// function to display the DataTable neatly
template <typename T>
void display(std::pair<std::vector<T>, std::vector<std::vector<T>>> data, bool display_all = false); By default CSVs are read in and elements stored as Early stage implementation of this will suffice for PR + merge |
akielaries
added a commit
that referenced
this issue
Apr 15, 2023
akielaries
added a commit
that referenced
this issue
Apr 29, 2023
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
A data structure similar to that of numpy arrays and pandas DataFrame is the ultimate goal of the
DataTable structure. This allows data to be parsed and visualized in tabular format and is often how data comes in. It is common to pair the pandas.read_csv or pandas.read_json functions to Machine Learning related code, and the DataFrame object allows for easy specification on sub-data in our sets. For example specifying columns and rows and perhaps more. The Matrix/Vector portion of the Linear Algebra module offers similar implementation of tabular data but the current development is focused in
/modules/structs/
directory with several attempts at this type of data structure.The text was updated successfully, but these errors were encountered: