ih2502mk

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

FJR2

import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from scipy.optimize import minimize
TOLERANCE = 1e-10


def _allocation_risk(weights, covariances):

villares

'''From Coding Train
https://youtu.be/BAejnwN4Ccw 
3/2/2017
Added Genetic Algorithm
4/27/2017
'''

import random

cities = [];

juanplopes

def next_digit(value, base):
    return value + str(sum(int(a)*b for a,b in zip(value, base))%11%10)

def make_valid(value, ap2, base):
    return next_digit(next_digit(value, base), ap2+base)

def is_valid_cpf(cpf):
    return make_valid(cpf[:9], [0], [1,2,3,4,5,6,7,8,9]) == cpf

def is_valid_cnpj(cnpj):

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

yanofsky

This is free and unencumbered software released into the public domain.

Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any
means.

In jurisdictions that recognize copyright laws, the author or authors
of this software dedicate any and all copyright interest in the
software to the public domain. We make this dedication for the benefit

hellerbarde

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

purcell

function cached_completer(completer) {
  var cache = {};
  return function(request, response) {
    if (request.term in cache) {
      response(cache[request.term]);
    } else {
      completer(request, function(resp) {
          cache[request.term] = resp;
          response(resp);
      });

KirkWylie

##
 # Copyright (C) 2011 - present by OpenGamma Inc. and the OpenGamma group of companies
 #
 # Please see distribution for license.
 ##

# Loads the time-series for yield curve data points to construct a 3D "curve over time" graph.

# Curve tickers
tickers <- c ("US00O/N Index", "US0001W Index", "US0002W Index", "US0001M Index", "US0002M Index", "US0003M Index", "USSW2 Curncy", "USSW3 Curncy", "USSW4 Curncy", "USSW5 Curncy", "USSW6 Curncy", "USSW7 Curncy", "USSW8 Curncy", "USSW9 Curncy", "USSW10 Curncy", "USSW15 Curncy", "USSW20 Curncy", "USSW25 Curncy", "USSW30 Curncy")

alexbowe

import nltk

text = """The Buddha, the Godhead, resides quite as comfortably in the circuits of a digital
computer or the gears of a cycle transmission as he does at the top of a mountain
or in the petals of a flower. To think otherwise is to demean the Buddha...which is
to demean oneself."""

# Used when tokenizing words
sentence_re = r'''(?x)      # set flag to allow verbose regexps
      ([A-Z])(\.[A-Z])+\.?  # abbreviations, e.g. U.S.A.