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‎chapter01_basic/01_notebook.ipynb‎

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‎chapter01_basic/02_pandas.ipynb‎

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‎chapter01_basic/03_numpy.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Introducing the multidimensional array in NumPy for fast array computations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import random\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"n = 1000000\n",
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"x = [random.random() for _ in range(n)]\n",
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"y = [random.random() for _ in range(n)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"([0.926, 0.722, 0.962], [0.291, 0.339, 0.819])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"x[:3], y[:3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[1.217, 1.061, 1.781]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"z = [x[i] + y[i] for i in range(n)]\n",
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"z[:3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"101 ms ± 5.12 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit [x[i] + y[i] for i in range(n)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"xa = np.array(x)\n",
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"ya = np.array(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 0.926, 0.722, 0.962])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"xa[:3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([ 1.217, 1.061, 1.781])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"za = xa + ya\n",
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"za[:3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1.09 ms ± 37.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit xa + ya"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.94 ms ± 4.44 µs per loop (mean ± std. dev. of 7 runs\n",
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" 100 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit sum(x) # pure Python"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"298 µs ± 4.62 µs per loop (mean ± std. dev. of 7 runs,\n",
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" 1000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit np.sum(xa) # NumPy"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [],
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"source": [
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"d = [abs(x[i] - y[j])\n",
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" for i in range(1000)\n",
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" for j in range(1000)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.635, 0.587, 0.106]"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"d[:3]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [],
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"source": [
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"da = np.abs(xa[:1000, np.newaxis] - ya[:1000])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0.635, 0.587, ..., 0.849, 0.046],\n",
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" [ 0.431, 0.383, ..., 0.646, 0.158],\n",
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" ...,\n",
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" [ 0.024, 0.024, ..., 0.238, 0.566],\n",
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" [ 0.081, 0.033, ..., 0.295, 0.509]])"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"da"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"134 ms ± 1.79 ms per loop (mean ± std. dev. of 7 runs,\n",
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" 10 loops each)\n",
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" 1000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit [abs(x[i] - y[j]) \\\n",
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" for i in range(1000) \\\n",
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" for j in range(1000)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" 10 loops each)\n",
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"1.54 ms ± 48.9 µs per loop (mean ± std. dev. of 7 runs\n",
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" 1000 loops each)\n"
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]
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}
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],
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"source": [
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"%timeit np.abs(xa[:1000, np.newaxis] - ya[:1000])"
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]
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}
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],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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