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jrjames83/euler42.ipynb

Created February 3, 2018 21:31
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Project Euler 42 in Python / Pandas (at the end of the notebook)
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euler42.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = [1,2,3,-9,5,7,-99,2,8] # Should be 12\n",
"# What are the indices "
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def max_subarr_brute(input_array):\n",
" global_max = 0\n",
" indices = []\n",
" for x in range(len(input_array)+1):\n",
" for j in range(len(input_array)+1):\n",
" if data[x:j]:\n",
" current_max = sum(input_array[x:j])\n",
" # print(current_max, data[x:j])\n",
" if current_max > global_max:\n",
" global_max = current_max\n",
" indices.append((x, j-1))\n",
" return global_max, indices.pop()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(12, (4, 5))"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max_subarr_brute(data)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"840"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# O n ^2 n log n, n\n",
"132 + 226 + 197 + 13 + 272"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-101 132 226 197 13 272 -56 -152 -54 287]\n",
"CPU times: user 151 µs, sys: 118 µs, total: 269 µs\n",
"Wall time: 163 µs\n"
]
},
{
"data": {
"text/plain": [
"(865, (1, 9))"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"\n",
"val = np.random.randint(-200, 300, size=10)\n",
"print(val)\n",
"%time max_subarr_brute(val)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6.57 ms, sys: 690 µs, total: 7.26 ms\n",
"Wall time: 6.7 ms\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jeff/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: This function is deprecated. Please call randint(-200, 300 + 1) instead\n",
" if __name__ == '__main__':\n"
]
},
{
"data": {
"text/plain": [
"(6435, (0, 90))"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time max_subarr_brute(np.random.random_integers(-200, 300, size=100))"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jeff/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: This function is deprecated. Please call randint(-200, 300 + 1) instead\n",
" if __name__ == '__main__':\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 511 ms, sys: 8.3 ms, total: 520 ms\n",
"Wall time: 566 ms\n"
]
},
{
"data": {
"text/plain": [
"(46106, (2, 999))"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time max_subarr_brute(np.random.random_integers(-200, 300, size=1000))"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jeff/anaconda/envs/3point6/lib/python3.6/site-packages/ipykernel/__main__.py:1: DeprecationWarning: This function is deprecated. Please call randint(-200, 300 + 1) instead\n",
" if __name__ == '__main__':\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 48.2 s, sys: 223 ms, total: 48.4 s\n",
"Wall time: 48.8 s\n"
]
},
{
"data": {
"text/plain": [
"(508677, (0, 9997))"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%time max_subarr_brute(np.random.random_integers(-200, 300, size=10000))"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3.98696989446\n"
]
},
{
"data": {
"image/png": 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N3Ulv5tPF9G5EP9PqDwGPtZvX79Gb7SRJGqJBZit9dYryQ6dZfzuwfYr6KLBqivqHwK3T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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1140e8748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Law of large numbers\n",
"\n",
"# Take samples from any random distribution\n",
"import numpy as np\n",
"import random\n",
"\n",
"shape, scale = 2., 2. # mean=4, std=2*sqrt(2)\n",
"s = np.random.gamma(shape, scale, 100000)\n",
"print(np.mean(s))\n",
"import matplotlib.pyplot as plt\n",
"import scipy.special as sps\n",
"plt.hist(s)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x113716908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"samples = [ np.mean(random.choices(s, k=20)) for _ in range(1000) ]\n",
"plt.hist(samples)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"np.random.gamma?"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.43 s, sys: 34.3 ms, total: 1.47 s\n",
"Wall time: 1.53 s\n"
]
}
],
"source": [
"# Finding the area of a circle through simulation?\n",
"import math\n",
"import random\n",
"\n",
"def dart_throw_simulator(radius, runs):\n",
" hits = 0\n",
" for run in range(runs):\n",
" rand_x = random.uniform(-2,2)\n",
" rand_y = random.uniform(-2,2)\n",
" c_squared = (rand_x ** 2) + (rand_y ** 2)\n",
" outcome = math.sqrt(c_squared)\n",
" if outcome < radius:\n",
" hits += 1\n",
" hit_ratio = hits / runs\n",
" total_area_square = (2*radius)**2\n",
" return hit_ratio * total_area_square\n",
"\n",
"%time assert(dart_throw_simulator(2, 100000) > 12 and dart_throw_simulator(2, 1000000) < 13)"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['A',\n",
" 'ABILITY',\n",
" 'ABLE',\n",
" 'ABOUT',\n",
" 'ABOVE',\n",
" 'ABSENCE',\n",
" 'ABSOLUTELY',\n",
" 'ACADEMIC',\n",
" 'ACCEPT',\n",
" 'ACCESS']"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"\n",
"url = 'https://projecteuler.net/project/resources/p042_words.txt'\n",
"r = requests.get(url)\n",
"data = r.text.split(\",\")\n",
"data = [x.strip().replace('\"', \"\") for x in data]\n",
"data[:10]"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1786, 1)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ABILITY</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ABLE</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ABOUT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ABOVE</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" word\n",
"0 A\n",
"1 ABILITY\n",
"2 ABLE\n",
"3 ABOUT\n",
"4 ABOVE"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(data)\n",
"df.columns = ['word']\n",
"print(df.shape)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" <th>word_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ABILITY</td>\n",
" <td>78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ABLE</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ABOUT</td>\n",
" <td>59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ABOVE</td>\n",
" <td>45</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" word word_score\n",
"0 A 1\n",
"1 ABILITY 78\n",
"2 ABLE 20\n",
"3 ABOUT 59\n",
"4 ABOVE 45"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import string\n",
"\n",
"def convert_word_to_indices(row):\n",
" score = 0\n",
" for char in row:\n",
" score += string.ascii_lowercase.index(char.lower()) + 1\n",
" return score\n",
"\n",
"df['word_score'] = df['word'].map(convert_word_to_indices)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1786.000000\n",
"mean 72.318029\n",
"std 31.237586\n",
"min 1.000000\n",
"25% 49.000000\n",
"50% 68.000000\n",
"75% 91.000000\n",
"max 192.000000\n",
"Name: word_score, dtype: float64"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"convert_word_to_indices(\"SKY\")\n",
"df['word_score'].describe()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get our triangle numbers\n",
"# tn = ½n(n+1);\n",
"# 1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ..."
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tris = []\n",
"for x in range(200):\n",
" tris.append(.50 * x * (x + 1))"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df['has_match'] = df['word_score'].map(lambda x: x in tris)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>word</th>\n",
" <th>word_score</th>\n",
" <th>has_match</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ABILITY</td>\n",
" <td>78</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ABLE</td>\n",
" <td>20</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ABOUT</td>\n",
" <td>59</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ABOVE</td>\n",
" <td>45</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>ABSENCE</td>\n",
" <td>49</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>ABSOLUTELY</td>\n",
" <td>132</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ACADEMIC</td>\n",
" <td>39</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ACCEPT</td>\n",
" <td>48</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ACCESS</td>\n",
" <td>50</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ACCIDENT</td>\n",
" <td>59</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ACCOMPANY</td>\n",
" <td>91</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ACCORDING</td>\n",
" <td>74</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ACCOUNT</td>\n",
" <td>77</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>ACHIEVE</td>\n",
" <td>53</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>ACHIEVEMENT</td>\n",
" <td>105</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>ACID</td>\n",
" <td>17</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>ACQUIRE</td>\n",
" <td>74</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>ACROSS</td>\n",
" <td>75</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>ACT</td>\n",
" <td>24</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>ACTION</td>\n",
" <td>62</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>ACTIVE</td>\n",
" <td>60</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>ACTIVITY</td>\n",
" <td>109</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>ACTUAL</td>\n",
" <td>58</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>ACTUALLY</td>\n",
" <td>95</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>ADD</td>\n",
" <td>9</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>ADDITION</td>\n",
" <td>76</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>ADDITIONAL</td>\n",
" <td>89</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>ADDRESS</td>\n",
" <td>70</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>ADMINISTRATION</td>\n",
" <td>166</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1756</th>\n",
" <td>WITHIN</td>\n",
" <td>83</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1757</th>\n",
" <td>WITHOUT</td>\n",
" <td>116</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1758</th>\n",
" <td>WOMAN</td>\n",
" <td>66</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1759</th>\n",
" <td>WONDER</td>\n",
" <td>79</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1760</th>\n",
" <td>WONDERFUL</td>\n",
" <td>118</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1761</th>\n",
" <td>WOOD</td>\n",
" <td>57</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1762</th>\n",
" <td>WORD</td>\n",
" <td>60</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1763</th>\n",
" <td>WORK</td>\n",
" <td>67</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1764</th>\n",
" <td>WORKER</td>\n",
" <td>90</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1765</th>\n",
" <td>WORKING</td>\n",
" <td>97</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1766</th>\n",
" <td>WORKS</td>\n",
" <td>86</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1767</th>\n",
" <td>WORLD</td>\n",
" <td>72</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1768</th>\n",
" <td>WORRY</td>\n",
" <td>99</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1769</th>\n",
" <td>WORTH</td>\n",
" <td>84</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1770</th>\n",
" <td>WOULD</td>\n",
" <td>75</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1771</th>\n",
" <td>WRITE</td>\n",
" <td>75</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1772</th>\n",
" <td>WRITER</td>\n",
" <td>93</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1773</th>\n",
" <td>WRITING</td>\n",
" <td>100</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1774</th>\n",
" <td>WRONG</td>\n",
" <td>77</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1775</th>\n",
" <td>YARD</td>\n",
" <td>48</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1776</th>\n",
" <td>YEAH</td>\n",
" <td>39</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1777</th>\n",
" <td>YEAR</td>\n",
" <td>49</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1778</th>\n",
" <td>YES</td>\n",
" <td>49</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1779</th>\n",
" <td>YESTERDAY</td>\n",
" <td>122</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1780</th>\n",
" <td>YET</td>\n",
" <td>50</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1781</th>\n",
" <td>YOU</td>\n",
" <td>61</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1782</th>\n",
" <td>YOUNG</td>\n",
" <td>82</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1783</th>\n",
" <td>YOUR</td>\n",
" <td>79</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1784</th>\n",
" <td>YOURSELF</td>\n",
" <td>121</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1785</th>\n",
" <td>YOUTH</td>\n",
" <td>89</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1786 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" word word_score has_match\n",
"0 A 1 True\n",
"1 ABILITY 78 True\n",
"2 ABLE 20 False\n",
"3 ABOUT 59 False\n",
"4 ABOVE 45 True\n",
"5 ABSENCE 49 False\n",
"6 ABSOLUTELY 132 False\n",
"7 ACADEMIC 39 False\n",
"8 ACCEPT 48 False\n",
"9 ACCESS 50 False\n",
"10 ACCIDENT 59 False\n",
"11 ACCOMPANY 91 True\n",
"12 ACCORDING 74 False\n",
"13 ACCOUNT 77 False\n",
"14 ACHIEVE 53 False\n",
"15 ACHIEVEMENT 105 True\n",
"16 ACID 17 False\n",
"17 ACQUIRE 74 False\n",
"18 ACROSS 75 False\n",
"19 ACT 24 False\n",
"20 ACTION 62 False\n",
"21 ACTIVE 60 False\n",
"22 ACTIVITY 109 False\n",
"23 ACTUAL 58 False\n",
"24 ACTUALLY 95 False\n",
"25 ADD 9 False\n",
"26 ADDITION 76 False\n",
"27 ADDITIONAL 89 False\n",
"28 ADDRESS 70 False\n",
"29 ADMINISTRATION 166 False\n",
"... ... ... ...\n",
"1756 WITHIN 83 False\n",
"1757 WITHOUT 116 False\n",
"1758 WOMAN 66 True\n",
"1759 WONDER 79 False\n",
"1760 WONDERFUL 118 False\n",
"1761 WOOD 57 False\n",
"1762 WORD 60 False\n",
"1763 WORK 67 False\n",
"1764 WORKER 90 False\n",
"1765 WORKING 97 False\n",
"1766 WORKS 86 False\n",
"1767 WORLD 72 False\n",
"1768 WORRY 99 False\n",
"1769 WORTH 84 False\n",
"1770 WOULD 75 False\n",
"1771 WRITE 75 False\n",
"1772 WRITER 93 False\n",
"1773 WRITING 100 False\n",
"1774 WRONG 77 False\n",
"1775 YARD 48 False\n",
"1776 YEAH 39 False\n",
"1777 YEAR 49 False\n",
"1778 YES 49 False\n",
"1779 YESTERDAY 122 False\n",
"1780 YET 50 False\n",
"1781 YOU 61 False\n",
"1782 YOUNG 82 False\n",
"1783 YOUR 79 False\n",
"1784 YOURSELF 121 False\n",
"1785 YOUTH 89 False\n",
"\n",
"[1786 rows x 3 columns]"
]
},
"execution_count": 207,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False 1624\n",
"True 162\n",
"Name: has_match, dtype: int64"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['has_match'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 162"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:3point6]",
"language": "python",
"name": "conda-env-3point6-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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