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‎week_2/your_turn_solutions.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Problem 1\n",
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"\n",
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"Write a function that takes a list of 0s and 1s and produces the corresponding integer. The equation for converting a list $L = [l_1, l_2, ..., l_n]$ of 0's and 1's to binary is $\\sum_i l_i*2^i$. What is the integer representation of `[1, 0, 0, 0, 1, 1, 0, 1]`?"
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]
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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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"# Problem 2\n",
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"- Read `data/alice_in_wonderland.txt` into memory. How many characters does it contain? How does this compare to its size on disk?\n",
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"- Print out the unique non-ASCII characters in Alice in Wonderland (hint: non-ASCII means that the number of bytes used is greater than 1).\n",
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"- Write the first 10,000 characters of Alice in Wonderland as text and as a pickle. What are the sizes of each file on disk?"
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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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"collapsed": false
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},
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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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"number of characters is 163817\n",
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"number of bytes on disk is 173595\n"
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]
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}
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],
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"source": [
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"import os\n",
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"\n",
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"with open('data/alice_in_wonderland.txt', 'r') as file:\n",
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" alice = file.read()\n",
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"\n",
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"# how many characters are in Alice?\n",
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"print('number of characters is {}'.format(len(alice)))\n",
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"\n",
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"# how large is the file on disk?\n",
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"print('number of bytes on disk is {}'.format(os.path.getsize('data/alice_in_wonderland.txt')))"
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]
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},
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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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"So this tells us that there are non-ASCII characters (characters that use more than 1 byte) in the file"
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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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"collapsed": false
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},
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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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"unique non-ASCII characters: {'‘', '’', '\\ufeff', '“', '”'}\n"
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]
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}
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],
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"source": [
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"# non-ASCI characters are characters that use more\n",
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"# than 1 byte to represent the character\n",
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"non_ascii = []\n",
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"for character in alice:\n",
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" # convert character to Unicode bytes and check how many bytes there are\n",
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" if len(bytes(character, 'UTF-8')) > 1:\n",
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" non_ascii.append(character)\n",
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"\n",
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"# convert list to set to get only the unique characters\n",
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"print('unique non-ASCII characters:', set(non_ascii))"
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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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"collapsed": false
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},
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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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"size of plain text file: 10182\n",
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"size of pickled file: 10192\n"
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]
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}
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],
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"source": [
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"import pickle\n",
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"\n",
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"# open a file in write mode ('w') to write plain text\n",
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"with open('data/alice_partial.txt', 'w') as file:\n",
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" file.write(alice[:10000])\n",
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"\n",
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"# open a file in write-binary ('wb') mode to write pickle protocol\n",
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"with open('data/alice_partial.pickle', 'wb') as file:\n",
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" pickle.dump(alice[:10000], file)\n",
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"\n",
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"print('size of plain text file: {}'.format(os.path.getsize('data/alice_partial.txt')))\n",
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"print('size of pickled file: {}'.format(os.path.getsize('data/alice_partial.pickle')))"
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]
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},
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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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"# Problem 3\n",
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"\n",
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"- Iterating over `good_movies`, print the name of the movies that Ben Affleck stars in.\n",
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"- Find the total number of Oscar nominations for 2016 movies in the dataset."
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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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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import json\n",
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"\n",
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"# use the `json` library to read json-structured plain text into Python objects\n",
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"with open('data/good_movies.json', 'r') as file:\n",
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" good_movies = json.loads(file.read())"
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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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"collapsed": false
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},
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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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"Argo\n",
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"Gone Girl\n"
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]
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}
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],
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"source": [
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"# iterate over the movies, checking the list of stars for each\n",
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"for movie in good_movies:\n",
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" if 'Ben Affleck' in movie['stars']:\n",
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" print(movie['title'])"
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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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"collapsed": false
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},
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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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"22\n"
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]
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}
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],
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"source": [
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"# iterate over the movies, tallying the Oscars for movies in 2016\n",
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"nominations_2016 = 0\n",
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"for movie in good_movies:\n",
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" if movie['year'] == 2016:\n",
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" nominations_2016 += movie['oscar_nominations']\n",
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"\n",
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"print(nominations_2016)"
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]
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},
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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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"# Problem 4\n",
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"\n",
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"Create a NumPy array with 100,000 random integers between 1 and 100. Then, write two functions (in pure Python, not using built-in NumPy functions):\n",
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"\n",
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"- Compute the average\n",
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"- Compute the standard deviation\n",
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"- Create *weight vector* of 100,000 elements (the sum of the elements is 1). Compute the weighted average of your first vector with these weights."
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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": 18,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"rand_array = np.random.randint(1, high=100, size=100000)"
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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": 19,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def my_average(x):\n",
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" the_sum = 0\n",
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" for el in x:\n",
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" the_sum += el\n",
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" \n",
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" return the_sum / len(x)\n",
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"\n",
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"def my_stdev(x):\n",
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" the_sum = 0\n",
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" the_avg = my_average(x)\n",
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" for xi in x:\n",
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" the_sum += (xi - the_avg) ** 2\n",
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" return np.sqrt(the_sum / len(x))\n",
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"\n",
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"def my_weighted_average(x, weights):\n",
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" the_sum = 0\n",
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" for el, weight in zip(x, weights):\n",
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" the_sum += el * weight\n",
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" \n",
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" return the_sum"
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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": 20,
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"metadata": {
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"collapsed": false
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},
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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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"average: 49.9322\n",
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"standard deviation: 28.5287448578\n"
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]
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}
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],
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"source": [
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"print('average:', my_average(rand_array))\n",
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"print('standard deviation:', my_stdev(rand_array))"
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]
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},
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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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"A weight vector needs to sum to 1. So we'll create a vector of random numbers between 0 and 1 and normalize it (divide by its sum) so that it sums to 1."
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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": 23,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"rand_weights = np.random.random(size=100000)\n",
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"rand_weights /= np.sum(rand_weights)"
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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": 25,
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"metadata": {
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"collapsed": false
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},
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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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"weighted average: 49.9482673521\n"
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]
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}
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],
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"source": [
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"print('weighted average:', my_weighted_average(rand_array, rand_weights))"
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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": null,
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},
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"moveMenuLeft": true,
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"nav_menu": {
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"height": "12px",
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"height": "30px",
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"width": "252px"
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},
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"navigate_menu": true,

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