Copying a list

Copy a dict.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd\n",
    "import copy\n",
    "\n",
    "X = np.random.standard_normal(5)\n",
    "D = pd.DataFrame({'Y': np.random.standard_normal(10)})\n",
    "\n",
    "original = {'X':X, 'D':D}\n",
    "new = copy.copy(original)\n",
    "notacopy = original\n",
    "notacopy['X'] = np.random.standard_normal(5)\n",
    "new['X'] = np.random.standard_normal(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.49819521 -0.05616186 -0.47594206 -0.18163103  1.07650505] [ 1.63738458 -0.64480777  0.21228982 -1.03785832  0.97276819] [ 1.63738458 -0.64480777  0.21228982 -1.03785832  0.97276819]\n",
      "4792221248 4738445920 4738445920\n"
     ]
    }
   ],
   "source": [
    "print(new['X'], original['X'], notacopy['X'])\n",
    "print(id(new['X']), id(original['X']), id(notacopy['X']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Y\n",
      "0 -0.612042\n",
      "1 -1.125589\n",
      "2 -0.215406\n",
      "3  0.206762\n",
      "4  0.037567\n",
      "5  0.509506\n",
      "6 -0.755514\n",
      "7 -0.781018\n",
      "8 -1.031885\n",
      "9  0.709235\n",
      "          Y\n",
      "0 -0.612042\n",
      "1 -1.125589\n",
      "2 -0.215406\n",
      "3  0.206762\n",
      "4  0.037567\n",
      "5  0.509506\n",
      "6 -0.755514\n",
      "7 -0.781018\n",
      "8 -1.031885\n",
      "9  0.709235\n",
      "          Y\n",
      "0 -0.612042\n",
      "1 -1.125589\n",
      "2 -0.215406\n",
      "3  0.206762\n",
      "4  0.037567\n",
      "5  0.509506\n",
      "6 -0.755514\n",
      "7 -0.781018\n",
      "8 -1.031885\n",
      "9  0.709235\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(4792506464, 4792506464, 4792506464)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(new['D'])\n",
    "print(original['D'])\n",
    "print(notacopy['D'])\n",
    "id(new['D']), id(original['D']), id(notacopy['D'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The rpy2.ipython extension is already loaded. To reload it, use:\n",
      "  %reload_ext rpy2.ipython\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  0.4928976 -0.4469674  0.8852140 -0.7353284 -0.8700456\n",
      "[1] -1.8172759  0.1827414 -0.9300616 -1.6686778  0.7318398\n",
      "[1] -0.09017105 -0.06625104  1.12459441 -0.49675068 -0.16255790\n",
      "[1] -0.09017105 -0.06625104  1.12459441 -0.49675068 -0.16255790\n",
      "[1] \"0x7f8ea14618d8\"\n",
      "[1] \"0x7f8ea14618d8\"\n",
      "[1] \"0x7f8ea1461940\"\n",
      "[1] \"0x7f8ea1461870\"\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(pryr)\n",
    "original = list(X=rnorm(5), Y=rnorm(5))\n",
    "new = original\n",
    "new[['X']] = rnorm(5)\n",
    "print(new[['X']])\n",
    "print(original[['X']])\n",
    "print(new[['Y']])\n",
    "print(original[['Y']])\n",
    "W = new[['Y']]\n",
    "print(pryr::address(W))\n",
    "W = original[['Y']]\n",
    "print(pryr::address(W))\n",
    "W = new[['X']]\n",
    "print(pryr::address(W))\n",
    "W = original[['X']]\n",
    "print(pryr::address(W))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "all,-slideshow"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}