node-pandas_working

node-pandas

An npm package that incorporates minimal features of python pandas. Check it on npm at https://www.npmjs.com/package/node-pandas.

You can also have a look at this colorful documentation at https://hygull.github.io/node-pandas/.

Note: Currently, this package is in development. More methods/functions/attributes will be added with time.

For now, you can

• create Series(using 1D array), DataFrame(using 2D array or file readCsv())
• access Series object using exactly an array like syntax (indexing, looping etc.)
• view columns, index
• save DataFrame in a CSV file toCsv()
• access elements using indices/column names
• view contents in pretty tabular form on console
• access DataFrame's columns using column names

Installation

Installation type	command
Local	npm install node-pandas --save
Local as dev dependency	npm install node-pandas --save-dev
Global	npm install node-pandas

Table of contents

Series

1. Example 1 - Creating Series using 1D array/list

DataFrame

1. Example 1 - Creating DataFrame using 2D array/list

2. Example 2 - Creating DataFrame using a CSV file

3. Example 3 - Saving DataFrame in a CSV file

4. Example 4 - Accessing columns (Retrieving columns using column name) - df.fullName -> ["R A", "B R", "P K"]

---

Getting started

Series

Example 1 - Creating Series using 1D array/list

> const pd = require("node-pandas")
undefined
> 
> s = pd.Series([1, 9, 2, 6, 7, -8, 4, -3, 0, 5]) 
NodeSeries [
  1,
  9,
  2,
  6,
  7,
  -8,
  4,
  -3,
  0,
  5,
]
> 
> s.show
┌─────────┬────────┐
│ (index) │ Values │
├─────────┼────────┤
│    0    │   1    │
│    1    │   9    │
│    2    │   2    │
│    3    │   6    │
│    4    │   7    │
│    5    │   -8   │
│    6    │   4    │
│    7    │   -3   │
│    8    │   0    │
│    9    │   5    │
└─────────┴────────┘
undefined
> 
> s[0]  // First element in Series
1
> s.length // Total number of elements 
10
> 

---

DataFrame

Example 1 - Creating DataFrame using 2D array/list

> const pd = require("node-pandas")
undefined
> 
> columns = ['full_name', 'user_id', 'technology']
[ 'full_name', 'user_id', 'technology' ]
> 
> df = pd.DataFrame([
...     ['Guido Van Rossum', 6, 'Python'],
...     ['Ryan Dahl', 5, 'Node.js'],
...     ['Anders Hezlsberg', 7, 'TypeScript'],
...     ['Wes McKinney', 3, 'Pandas'],
...     ['Ken Thompson', 1, 'B language']
... ], columns)
NodeDataFrame [
  [ 'Guido Van Rossum', 6, 'Python' ],
  [ 'Ryan Dahl', 5, 'Node.js' ],
  [ 'Anders Hezlsberg', 7, 'TypeScript' ],
  [ 'Wes McKinney', 3, 'Pandas' ],
  [ 'Ken Thompson', 1, 'B language' ],
  columns: [ 'full_name', 'user_id', 'technology' ],
  index: [ 0, 1, 2, 3, 4 ],
  rows: 5,
  cols: 3,
  out: true
]
> 
> df.show
┌─────────┬────────────────────┬─────────┬──────────────┐
│ (index) │     full_name      │ user_id │  technology  │
├─────────┼────────────────────┼─────────┼──────────────┤
│    0    │ 'Guido Van Rossum' │    6    │   'Python'   │
│    1    │    'Ryan Dahl'     │    5    │  'Node.js'   │
│    2    │ 'Anders Hezlsberg' │    7    │ 'TypeScript' │
│    3    │   'Wes McKinney'   │    3    │   'Pandas'   │
│    4    │   'Ken Thompson'   │    1    │ 'B language' │
└─────────┴────────────────────┴─────────┴──────────────┘
undefined
> 
> df.index
[ 0, 1, 2, 3, 4 ]
> 
> df.columns
[ 'full_name', 'user_id', 'technology' ]
> 

Example 2 - Creating DataFrame using a CSV file

Note: If CSV will have multiple newlines b/w 2 consecutive rows, no problem, it takes care of it and considers as single newline.

df = pd.readCsv(csvPath) where CsvPath is absolute/relative path of the CSV file.

Examples:

df = pd.readCsv("../node-pandas/docs/csvs/devs.csv")

df = pd.readCsv("/Users/hygull/Projects/NodeJS/node-pandas/docs/csvs/devs.csv")

devs.csv » cat /Users/hygull/Projects/NodeJS/node-pandas/docs/csvs/devs.csv

fullName,Profession,Language,DevId
Ken Thompson,C developer,C,1122
Ron Wilson,Ruby developer,Ruby,4433
Jeff Thomas,Java developer,Java,8899


Rishikesh Agrawani,Python developer,Python,6677
Kylie Dwine,C++,C++ Developer,0011

Briella Brown,JavaScript developer,JavaScript,8844

Now have a look the below statements executed on Node REPL.

> const pd = require("node-pandas")
undefined
> 
> df = pd.readCsv("/Users/hygull/Projects/NodeJS/node-pandas/docs/csvs/devs.csv")
NodeDataFrame [
  {
    fullName: 'Ken Thompson',
    Profession: 'C developer',
    Language: 'C',
    DevId: 1122
  },
  {
    fullName: 'Ron Wilson',
    Profession: 'Ruby developer',
    Language: 'Ruby',
    DevId: 4433
  },
  {
    fullName: 'Jeff Thomas',
    Profession: 'Java developer',
    Language: 'Java',
    DevId: 8899
  },
  {
    fullName: 'Rishikesh Agrawani',
    Profession: 'Python developer',
    Language: 'Python',
    DevId: 6677
  },
  {
    fullName: 'Kylie Dwine',
    Profession: 'C++',
    Language: 'C++ Developer',
    DevId: 11
  },
  {
    fullName: 'Briella Brown',
    Profession: 'JavaScirpt developer',
    Language: 'JavaScript',
    DevId: 8844
  },
  columns: [ 'fullName', 'Profession', 'Language', 'DevId' ],
  index: [ 0, 1, 2, 3, 4, 5 ],
  rows: 6,
  cols: 4,
  out: true
]
> 
> df.index
[ 0, 1, 2, 3, 4, 5 ]
> 
> df.columns
[ 'fullName', 'Profession', 'Language', 'DevId' ]
> 
> df.show
┌─────────┬──────────────────────┬────────────────────────┬─────────────────┬───────┐
│ (index) │       fullName       │       Profession       │    Language     │ DevId │
├─────────┼──────────────────────┼────────────────────────┼─────────────────┼───────┤
│    0    │    'Ken Thompson'    │     'C developer'      │       'C'       │ 1122  │
│    1    │     'Ron Wilson'     │    'Ruby developer'    │     'Ruby'      │ 4433  │
│    2    │    'Jeff Thomas'     │    'Java developer'    │     'Java'      │ 8899  │
│    3    │ 'Rishikesh Agrawani' │   'Python developer'   │    'Python'     │ 6677  │
│    4    │    'Kylie Dwine'     │         'C++'          │ 'C++ Developer' │  11   │
│    5    │   'Briella Brown'    │ 'JavaScript developer' │  'JavaScript'   │ 8844  │
└─────────┴──────────────────────┴────────────────────────┴─────────────────┴───────┘
undefined
> 

> df[0]['fullName']
'Ken Thompson'
> 
> df[3]['Profession']
'Python developer'
> 
> df[5]['Language']
'JavaScript'
> 

Example 3 - Saving DataFrame in a CSV file

Note: Here we will save DataFrame in /Users/hygull/Desktop/newDevs.csv (in this case) which can be different in your case.

> const pd = require("node-pandas")
undefined
> 
> df = pd.readCsv("./docs/csvs/devs.csv")
NodeDataFrame [
  {
    fullName: 'Ken Thompson',
    Profession: 'C developer',
    Language: 'C',
    DevId: 1122
  },
  {
    fullName: 'Ron Wilson',
    Profession: 'Ruby developer',
    Language: 'Ruby',
    DevId: 4433
  },
  {
    fullName: 'Jeff Thomas',
    Profession: 'Java developer',
    Language: 'Java',
    DevId: 8899
  },
  {
    fullName: 'Rishikesh Agrawani',
    Profession: 'Python developer',
    Language: 'Python',
    DevId: 6677
  },
  {
    fullName: 'Kylie Dwine',
    Profession: 'C++',
    Language: 'C++ Developer',
    DevId: 11
  },
  {
    fullName: 'Briella Brown',
    Profession: 'JavaScirpt developer',
    Language: 'JavaScript',
    DevId: 8844
  },
  columns: [ 'fullName', 'Profession', 'Language', 'DevId' ],
  index: [ 0, 1, 2, 3, 4, 5 ],
  rows: 6,
  cols: 4,
  out: true
]
> 
> df.cols
4
> df.rows
6
> df.columns
[ 'fullName', 'Profession', 'Language', 'DevId' ]
> df.index
[ 0, 1, 2, 3, 4, 5 ]
> 
> df.toCsv("/Users/hygull/Desktop/newDevs.csv")
undefined
> CSV file is successfully created at /Users/hygull/Desktop/newDevs.csv

> 

Let's see content of /Users/hygull/Desktop/newDevs.csv

cat /Users/hygull/Desktop/newDevs.csv

fullName,Profession,Language,DevId
Ken Thompson,C developer,C,1122
Ron Wilson,Ruby developer,Ruby,4433
Jeff Thomas,Java developer,Java,8899
Rishikesh Agrawani,Python developer,Python,6677
Kylie Dwine,C++,C++ Developer,11
Briella Brown,JavaScript developer,JavaScript,8844

---

Example 4 - Accessing columns (Retrieving columns using column name)

CSV file (devs.csv): ./docs/csvs/devs.csv

const pd = require("node-pandas")
df = pd.readCsv("./docs/csvs/devs.csv") // Node DataFrame object

df.show // View DataFrame in tabular form
/*
┌─────────┬──────────────────────┬────────────────────────┬─────────────────┬───────┐
│ (index) │       fullName       │       Profession       │    Language     │ DevId │
├─────────┼──────────────────────┼────────────────────────┼─────────────────┼───────┤
│    0    │    'Ken Thompson'    │     'C developer'      │       'C'       │ 1122  │
│    1    │     'Ron Wilson'     │    'Ruby developer'    │     'Ruby'      │ 4433  │
│    2    │    'Jeff Thomas'     │    'Java developer'    │     'Java'      │ 8899  │
│    3    │ 'Rishikesh Agrawani' │   'Python developer'   │    'Python'     │ 6677  │
│    4    │    'Kylie Dwine'     │         'C++'          │ 'C++ Developer' │  11   │
│    5    │   'Briella Brown'    │ 'JavaScirpt developer' │  'JavaScript'   │ 8844  │
└─────────┴──────────────────────┴────────────────────────┴─────────────────┴───────┘
*/

console.log(df['fullName'])
/*
    NodeSeries [
      'Ken Thompson',
      'Ron Wilson',
      'Jeff Thomas',
      'Rishikesh Agrawani',
      'Kylie Dwine',
      'Briella Brown'
    ]
*/

console.log(df.DevId)
/* 
    NodeSeries [ 1122, 4433, 8899, 6677, 11, 8844 ]
*/

let languages = df.Language
console.log(languages) 
/*
    NodeSeries [
      'C',
      'Ruby',
      'Java',
      'Python',
      'C++ Developer',
      'JavaScript'
    ]
*/

console.log(languages[0], '&', languages[1]) // C & Ruby


let professions = df.Profession
console.log(professions) 
/*
    NodeSeries [
      'C developer',
      'Ruby developer',
      'Java developer',
      'Python developer',
      'C++',
      'JavaScirpt developer'
    ]
*/

// Iterate like arrays
for(let profession of professions) {
    console.log(profession)
}
/*
    C developer
    Ruby developer
    Java developer
    Python developer
    C++
    JavaScirpt developer
*/

---

References

• Node's util

• JavaScript Arrays - w3schools

• How to test your new NPM module without publishing it every 5 minutes

• Node's path

• Node's fs - file system

• 9 Ways to Remove Elements From A JavaScript Array - Plus How to Safely Clear JavaScript Arrays

• JS - isNaN()

• Check synchronously if file/directory exists in Node.js

• Node's require() returns an empty object (circular refs -> {})

• Javascript - Mixins (Adding methods to classes)

• JavaScript Object accessors(setters & getters)

• JavaScript setter

• JavaScript getter

• JavaScript (enumerable, writable, configurable)