What does it take to design and build multi-purpose and multi-tasking devices? Report on “multitasking nanomachine” by https://scitechdaily.com: A multitasking nanomachine that can act as a heat engine and a refrigerator at the same time has been created by RIKEN engineers. The device is one of the first to test how quantum effects, which govern the behavior of particles on the smallest scale, might one … Continue reading Multitasking Devices!
Summary of a few trends: 1. GAMECHANGER: BRING IN THE CONTEXT Excerpt from “Will Google Now make apps obsolete one day?” The next evolution of that is to obtain desired information that isn’t contained in a single app. “Individual app developers can’t think far enough ahead and their apps aren’t nuanced enough to know what I want to do in my current situation at my … Continue reading How & When will Mobile Apps become Obsolete?
Checking out Python Numpy Tutorial by Justin Johnson. Quick Review of Python Data Types: Booleans t = True f = False print(type(t)) # Prints “<class ‘bool’>” print(t and f) # Logical AND; prints “False” Strings s = “hello” print(s.capitalize()) # Capitalize a string; prints “Hello” print(s.upper()) # Convert a string to uppercase; prints “HELLO” print(s.rjust(7)) # Right-justify a string, padding with spaces; prints ” hello” … Continue reading Day 20: Exploring Python for Data Analysis – The Numpy Library – Part 2
From “Quickstart tutorial”: NumPy’s main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy dimensions are called axes. NumPy’s array class is called ndarray – some important attributes listed: ndarray.ndim (the number of axes (dimensions) of the array.) ndarray.shape (the dimensions of the array – a … Continue reading Day 19: Exploring Python for Data Analysis – The Numpy Library
As advice from “Step by step approach to perform data analysis using Python”: First, start learning NumPy as it is the fundamental package for scientific computing with Python. A good understanding of Numpy will help you use tools like Pandas effectively. Next let us check out “A Complete Python Tutorial to Learn Data Science from Scratch”. It is provided a long list of libraries for … Continue reading Day 18: Exploring Python for Data Analysis
Today I am checking out “How to get started with Machine Learning in about 10 minutes”. Few excerpts: IMPORT import numpy as np import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsimport warningswarnings.filterwarnings(‘ignore’)%matplotlib inline LOAD train_df=pd.read_csv(“train.csv”)train_df.head() CHECK MISSING def missingdata(data): total = data.isnull().sum().sort_values(ascending = False) percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False) ms=pd.concat([total, percent], axis=1, keys=[‘Total’, ‘Percent’]) ms= ms[ms[“Percent”] > 0] f,ax =plt.subplots(figsize=(8,6)) plt.xticks(rotation=’90’) fig=sns.barplot(ms.index, ms[“Percent”],color=”green”,alpha=0.8) … Continue reading Day 17: Explore Machine Learning with Python-2
SciPy is a Python library for scientific / technical computing. Some of its core packages: 1) NumPy: Base N-dimensional array package 2) SciPy: Fundamental library for scientific computing 3) Matplotlib: Comprehensive 2D Plotting 4) IPython: Enhanced Interactive Console 5) Sympy: Symbolic mathematics 6) pandas: Data structures & analysis Let us directly jump in and look at some of the steps listed at Your First Machine … Continue reading Day 16: Exploring Machine Learning with Python
A summary from the enticing post “How to Web Scrape with Python in 4 Minutes”: LIBRARIES: import requests import urllib.request import time from bs4 import BeautifulSoup Accessing the target URL: url = ‘http://web.mta.info/developers/turnstile.html’ response = requests.get(url) Nest the data using BeautifulSoup data structure (See the BeatifulSoup documentation). soup = BeautifulSoup(response.text, “html.parser”) Search for links: soup.findAll(‘a’) Extract the link: one_a_tag = soup.findAll(‘a’) link = one_a_tag[‘href’] Another … Continue reading Day 15: Python – Notes on Webscraping
Django is good for faster building of full web applications (because lot of stuff is pre-packaged). But, then, that makes it heavier and you may have less control and you may have lot of unnecessary packaged code shipped. Considering speed as a factor, from a business (though maybe short-term) perspective, Django could be seen as attractive. Twilio tech team shares how they a using Flask on … Continue reading Day 14 Exploring Python – Flask vs Django
We start Day 13 with a new resource – a book available for reading online – Invent Your Own Computer Games with Python (4th Edition) by Al Sweigart. 1. Introduction – after installation – Start IDLE (Interactive DeveLopment Environment), on Windows, click the Start menu in the lower-left corner of the screen, type IDLE, and select IDLE (Python GUI). 2. Chapter 2: Hello World print(‘Hello … Continue reading Day 13: Exploring Python – Creating Games