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What is the appeal of dynamically-typed languages?

Kris Nuttycombe asks:

I genuinely wish I understood the appeal of unityped languages better. Can someone who really knows both well-typed and unityped explain?

I think the terms well-typed and unityped are a bit of question-begging here (you might as well say good-typed versus bad-typed), so instead I will say statically-typed and dynamically-typed.

I'm going to approach this article using Scala to stand-in for static typing and Python for dynamic typing. I feel like I am credibly proficient both languages: I don't currently write a lot of Python, but I still have affection for the language, and have probably written hundreds of thousands of lines of Python code over the years.

ideasman42

/* Originally from: http://stackoverflow.com/a/27663998/432509
 *
 * With following modifications: 
 * - Use a pointer to the tail (remove 2x conditional checks, reduces code-size).
 * - Avoid re-assigning empty values the size doesn't change.
 * - Corrected comments.
 */

static void *listbase_sort_impl(struct Link *head, int (*cmp)(const void *, const void *))
{

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

yusugomori

# -*- coding: utf-8 -*-

import sys
import numpy

numpy.seterr(all='ignore')


'''

hasnainv

#How to load the model
def build_model(img_width=224, img_height=224):
  from keras.models import Sequential
  from keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D, Activation
  
  model = Sequential()
  model = Sequential()
  model.add(ZeroPadding2D((1,1),input_shape=(3,img_width,img_height)))
  model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
  model.add(Activation('relu'))

zachbrowne

#!/bin/bash
iatest=$(expr index "$-" i)

#######################################################
# SOURCED ALIAS'S AND SCRIPTS BY zachbrowne.me
#######################################################

# Source global definitions
if [ -f /etc/bashrc ]; then
	 . /etc/bashrc

joel-wright

#!/bin/sh
path=/sys/class/backlight/intel_backlight

luminance() {
    read -r level < "$path"/actual_brightness
    factor=$((max / 100))
    ret=`printf '%d\n' "$((level / factor))"`
    if [ $ret -gt 100 ]; then
       ret=100
    fi

edz-o

def DepthConversion(PointDepth, f):
    H = PointDepth.shape[0]
    W = PointDepth.shape[1]
    i_c = np.float(H) / 2 - 1
    j_c = np.float(W) / 2 - 1
    columns, rows = np.meshgrid(np.linspace(0, W-1, num=W), np.linspace(0, H-1, num=H))
    DistanceFromCenter = ((rows - i_c)**2 + (columns - j_c)**2)**(0.5)
    PlaneDepth = PointDepth / (1 + (DistanceFromCenter / f)**2)**(0.5)
    return PlaneDepth

kevinzakka

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

muety

# Inspired by https://keon.io/deep-q-learning/

import random
import gym
import math
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam