Contributing Guide

Contributing to danfojs

Table of contents:

• Where to start?

• Working with the code

  • Version control, Git, and GitHub

  • Getting started with Git

  • Forking

  • Creating a development environment

• Documentation Guidelines

• Writing tests

  • Using mocha

  • Running the test suite

• Contributing your changes to danfojs

  • Committing your code

  • Pushing your changes

  • Review your code and finally, make the pull request

• Danfojs internal (Brief)

Where to start?

All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.

For first time contributors, you can find pending issues on the GitHub “issues” page. There are a number of issues listed and "good first issue" where you could start out. Once you’ve found an interesting issue, and have an improvement in mind, next thing is to set up your development environment.

Working with the code

Now that you have an issue you want to fix, enhancement to add, or documentation to improve, you need to learn how to work with GitHub and the danfojs code base.

Version control, Git, and GitHub

The danfojs code is hosted on GitHub. To contribute you will need to sign up for a free GitHub account. We use Git for version control to allow many people to work together on this project.

Some great resources for learning Git:

• Official GitHub pages.

Getting started with Git¶

Find Instructions for installing git, setting up your SSH key, and configuring git. These steps need to be completed before you can work seamlessly between your local repository and GitHub.

Forking the danfojs repo

You will need your own fork to work on the code. Go to the danfojs project page and hit the Fork button.

Next, you will clone your fork to your local machine:

git clone https://github.com/opensource9ja/danfojs.git

This creates the directory danfojs and connects your repository to the upstream (main project) repository.

Creating a development environment

To test out code changes, you’ll need to build danfojs from source, which requires a Nodejs environment.

1) Install Nodejs:

install Nodejs using NPM ​

2) Open the danfojs directory you git clone with terminal or vscode

3) If using Vscode, click on Terminal ---> click New Terminal

4) In the terminal type in

npm install

Documentation Guidelines

Documentation helps clarify what a function or a method is doing. It also gives insight to users of the function or methods on what parameters to pass in and know what the function will return.

Sample documentation:

 /**
 * Add two series of the same length
 * @param {series1} series1 [Series]
 * @param {series2} series2 [Series]
 * @returns Series
 */
function add_series(series1, series2){
​
        ...................
​
        return new Series()
}
​

And for functions that contains more than two argument, keyword argument should be used. Parsing of keyword argument is also applicable to most of the methods in a class

/**
 * Join two or more dataframe together along an axis
 * @param {kwargs} kwargs --> {
 *                      df_list: [Array of DataFrame],
 *                      axis : int {0 or 1},
 *                      by_column : String {name of a column},
 *                    }
 * @returns DataFrame 
 */
function join_df(kwargs){
        ........
​
        return DataFrame
}

Writing tests

We strongly encourages contributors to write test for their code. Like many packages, danfojs uses mocha

All tests should go into the tests subdirectory and placed in the corresponding module. The tests folder contains some current examples of tests, and we suggest looking to these for inspiration.

Below is the general Framework to write test for each module.

import { assert } from "chai"
import { DataFrame } from '../../src/core/frame'
​
describe("Name of the class|module", function(){
 
  it("name of the methods| expected result",function(){
    
       //write your test code here
       //use assert.{proprty} to test your code
   })
​
});

For class with lots of method.

import { assert } from "chai"
import { DataFrame } from '../../src/core/frame'
​
describe("Name of the class|module", function(){
 
 describe("method name 1", function(){
 
   it("expected result",function(){
     
        //write your test code here
        //use assert.{proprty} to test your code
    })
  })
  
  describe("method name 2", function(){
 
   it("expected result",function(){
     
        //write your test code here
        //use assert.{proprty} to test your code
    })
  })
  .......
});

Example: Let write a test, to test if the values in a dataframe are off a certain length. Assuming the method to obtain length is values_len()

import { assert } from "chai"
import { DataFrame } from '../../src/core/frame'
​
describe("DataFrame", function(){
    
  describe("value_len", function(){
 
   it("check dataframe length",function(){
     
       let data = [[1,2],[4,5]]
       let columns = ["A","B"]
       let df = new DataFrame(data,{columns: columns})
       
       let expected_result = 2
       
       assert.deepEqual(sf.value_len(), expected_result))
       
       
    })
  })
​
});

Running the test case

To run the test for the module you created,

1) Open the package.json

2) change the name of the test file to the file name you want. and don't forget the file is in the test folder

"scripts": {
    "test": "....... danfojs/tests/sub_directory_name/filename",

For now the test directory is splitted into Four sub directory; config, core,io, preprocessing.

3) run the test, in the danfojs directory terminal

npm run test

Learn more about mocha here​

Contributing your changes to danfojs

Committing your code

Once you’ve made changes, you can see them by typing:

git status

Next, you can track your changes using

git add .

Next, you commit changes using:

git commit -m "Enter any commit message here"

Pushing your changes

When you want your changes to appear publicly on your GitHub page, you can push to your forked repo with:

git push

Review your code and finally, make the pull request

If everything looks good, you are ready to make a pull request. A pull request is how code from a local repository becomes available to the GitHub community and can be reviewed and eventually merged into the master version. To submit a pull request:

1. Navigate to your repository on GitHub

2. Click on the Pull Request button

3. Write a description of your changes in the Preview Discussion tab

4. Click Send Pull Request.

This request then goes to the repository maintainers, and they will review the code and everything looks good, merge it with the master.

Hooray! Youre now a contributor to danfojs. Now go bask in the euphoria!

​

Danfojs Internals

In other to contribute to the code base of danfojs, there are some functions and properties provided to make implementation easy.

The main exposed modules are the Frame and Series module. This module inherit from the Generic module.

The Generic module consist of the following methods and properties

• .dtypes [source] is used to obtain the dtype for each column

• .index [source] to obtain the index for Dataframe or Series

• .__set_index(label) [source] to set the index value

• .__reset_index() [source] to reset the index in DataFrame and Series

• .values Obtain the values in DataFrame and Series per rows

• .col_data Obtain the values in DataFrame and Series per columns

• .column_names [source] Obtain the list of column names

• .__set_col_types [source] set the dtype for a column or infer the dtype from it

• .columns to access the column names directly

• row_data_tensor store the tensor representation of the data in DataFrame and Series

The Frame module consist of the following methods and properties to aid implementation.

• __frame_is_compactible_for_operation [source]: check if all the values in a DataFrame are numerical. This is helps to check if numerical operation can be done using the dataframe.

• .__get_ops_tensors(tensors, axis) [source] : obtain tensors from dataframes along axis 0 or 1.

• .__get_df_from_tensor(val, col_names) [source]: Obtain dataframe from tensor.

• .__get_tensor_and_idx(df, axis) [source]: Obtain tensors , their index value and their axis from dataframe.

The Series module contains mostly of Generic properties and less special internal properties.

• __check_series_op_compactibility(other) [source] check if two series are compatible for numerical operation

Lastly the Utils module contains goto function for implementations.