The Tour de Montaigne (Montaigne's tower), where Montaigne's library was located, remains mostly unchanged since the sixteenth centur

Book Reading – Deep Work – by Cal Newport Page 3

Moving on to the 3rd page of the book “Deep Work” – Rules for Focused Success in a Distracted World by Cal Newport. Few topics mentioned: Current intellectual capacity Psychology Neuroscience Going away from immediate pursuits Not shy of taking time off (from routine work) Michel De Montaigne Let us look at the wiki entry of the gentleman mentioned- Michel De Montaigne: Michel Eyquem de Montaigne, … Continue reading Book Reading – Deep Work – by Cal Newport Page 3

Book Reading – Deep Work – by Cal Newport Page 2

Moving on to the 2nd page of the book “Deep Work” – Rules for Focused Success in a Distracted World by Cal Newport. Few mentions: Psychological Types by Carl Jung Differences between Carl Jung and Sigmund Freud Checking up on first item above, the wikipedia entry says the original German language edition, Psychologische Typen, was first published by Rascher Verlag, Zurich in 1921. In the book Jung … Continue reading Book Reading – Deep Work – by Cal Newport Page 2

Bollingen Tower as seen from Lake Zürich.

Book Reading – Deep Work – by Cal Newport Page 1

Just began reading the book “Deep Work” – Rules for Focused Success in a Distracted World by Cal Newport. If I have to take the premise of “Deep Work” into reading this book as well, then the first page itself can take a few days to ruminate. For example, people and places mentioned on first page are: Swiss Village Bollingen Carl Jung India Mason Currey for … Continue reading Book Reading – Deep Work – by Cal Newport Page 1

How & When will Mobile Apps become Obsolete?

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?

Day 20: Exploring Python for Data Analysis – The Numpy Library – Part 2

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

Day 19: Exploring Python for Data Analysis – The Numpy Library

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

Day 18: Exploring Python for Data Analysis

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

Day 17: Explore Machine Learning with Python-2

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

Day 16: Exploring Machine Learning with Python

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

Day 15: Python – Notes on Webscraping

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’)[36] link = one_a_tag[‘href’] Another … Continue reading Day 15: Python – Notes on Webscraping