scholarly

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Referensi

1. scholarly, a module that allows you to retrieve author and publication information from Google Scholar in a friendly, Pythonic way, https://pypi.org/project/scholarly/

Silabus Belajar Bareng Python

Silabur belajar ini diambil dari tau-data.id :

Python for Data Science I – Beginner:

1. Pendahuluan (1 Jam):
   * The Zen of Python
   * Python “distro” : Python, Anaconda/miniconda, WinPython, etc.
   * Editor Jupyter & Spyder
   * Python VS (R, Julia, Matlab, Java, C, PHP, etc)
   * beberapa kelemahan & kelebihan Python
   * Google Colab
2. Dasar Python (1 Jam)
   * Syntax Format (indenting, multiline, import, deklarasi/inisialisasi)
   * Code descriptor & Comments
   * integer, float, Bytes, Boolean
   * list, tuple, dictionary
   * (Frozen) Set
   * types : Beginner Pitfall
   * Slicing in Python
3. Python Logic (3 Jam)
   * (Nested – hierarchical) if Logic
   * Looping For (& list comprehension)
   * Iterator VS Iterable
   * Looping while
   * Breaking Loop
   * Python Exception
   * TQDM
4. Penggunaan & Instalasi Modul (1 Jam)
   * Full and Partial Import
   * Import all functions as first level implicitly
   * Personal Library
   * Conda/pip/easy_install
   * Adding repository modul
   * Automatic update all modules
   * Pure Python vs compiled Modules
   * Module’s Wheels
   * Check modules dependency
   * Installing from Source
   * Installing modul from script
5. Going Deeper in Python (2 jam)
   * Deeper with Print Function
   * Reference/pointer to variable(s)
   * Deeper with Python string
   * List/Dictionary comprehension
   * Zipping List
   * List again : Kelebihan List di Python
   * Optimal Python Data Type use case
6. Python Function (2 Jam)
   * Fungsi di Python
   * Global & local variable
   * vars, dirs**
   * Recursive in Python
   * Lamda Function
7. Python as Numerical computing & (simple) Visualizations (2 Jam)
   * Numpy Matrix:
   * List VS Arrays/Matrix: best use scenarios, etc.
   * Linear Algebra Functions
   * Numpy Operations, etc.
   * DataFrame Basics
   * MatplotLib & Seaborn: Visualisasi dasar di Python
   Scatter plot, histogram, barchart, boxplot, etc.

Belajar Best Practices Pengembangan Software dari pgAdmin 4

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Referensi

1. pgAdmin 4, https://github.com/postgres/pgadmin4

Instagram Python

instaloader, dapat digunakan untuk mendownload otomatis seluruh media (foto atau video) beserta captionnya sebuah user instagram.

Referensi

1. instaloader, https://github.com/instaloader/instaloader

Quantum Computing in Python

Status : Draft 

Referensi

1. Tutorial: Getting started with Quantum Computing in Python, http://dataespresso.com/en/2018/07/22/Tutorial-Generating-random-numbers-with-a-quantum-computer-Python/

Machine Learning in Python

Status : Draft

Referensi

1. scikit-learn, http://scikit-learn.org/stable/index.html
2. UC Irvine Machine Learning Repository, https://archive.ics.uci.edu/ml/index.php

Mengekstrak Tabel dari PDF

Status : Draft


Tutorial [1] menggunakan layanan berbayar, dengan trial 50 halaman PDF.

Referensi

1. Extracting Tables From PDFs, http://stanford.edu/~mgorkove/cgi-bin/rpython_tutorials/Using_Python_to_Extract_Tables_From_PDFs.php
2. tabula-py: Extract table from PDF into Python DataFrame, https://blog.chezo.uno/tabula-py-extract-table-from-pdf-into-python-dataframe-6c7acfa5f302
3. Extract / Identify Tables from PDF python, https://stackoverflow.com/questions/28532770/extract-identify-tables-from-pdf-python
4. PDF Data and Table Scraping to Excel, https://stackoverflow.com/questions/29868541/pdf-data-and-table-scraping-to-excel
5. Extract tables from PDF pages., https://github.com/ashima/pdf-table-extract
6. Extracting table contents from a collection of PDF files [closed], https://stackoverflow.com/questions/17217194/extracting-table-contents-from-a-collection-of-pdf-files/26110587