From d202fea352a7fcb3e528a2e605df4082d4c8e3af Mon Sep 17 00:00:00 2001
From: Crista Lopes <crista@tagide.com>
Date: Mon, 12 Aug 2019 20:15:33 -0700
Subject: [PATCH] Format to fit in 1 page

---
 03-arrays/tf-03.py | 23 +++++++++++------------
 1 file changed, 11 insertions(+), 12 deletions(-)

diff --git a/03-arrays/tf-03.py b/03-arrays/tf-03.py
index 130f216..e220035 100644
--- a/03-arrays/tf-03.py
+++ b/03-arrays/tf-03.py
@@ -1,25 +1,24 @@
 import sys, string
 import numpy as np
 
-# Get an array of characters from the file, make sure it starts and ends with a space
-# Example input: "Hello  World! " 
-characters = np.array([' '] + list(open(sys.argv[1]).read()) + [' '])
+# Example input: "Hello  World!!" 
+characters = np.array([' ']+list(open(sys.argv[1]).read())+[' '])
 # Result: array([' ', 'H', 'e', 'l', 'l', 'o', ' ', ' ', 
-#                'W', 'o', 'r', 'l', 'd', '!', ' ', ' '], dtype='<U1')
+#           'W', 'o', 'r', 'l', 'd', '!', '!', ' '], dtype='<U1')
 
 # Normalize
 characters[~np.char.isalpha(characters)] = ' '
 characters = np.char.lower(characters)
 # Result: array([' ', 'h', 'e', 'l', 'l', 'o', ' ', ' ', 
-#                'w', 'o', 'r', 'l', 'd', ' ', ' '], dtype='<U1')
+#           'w', 'o', 'r', 'l', 'd', ' ', ' '], dtype='<U1')
 
 ### Split the words, by finding the indices of spaces
 sp = np.where(characters == ' ')
-# Result: (array([ 0,  6,  7, 13, 14], dtype=int64),)
+# Result: (array([ 0, 6, 7, 13, 14], dtype=int64),)
 
 # A little trick: let's double each index, and then take pairs
 sp2 = np.repeat(sp, 2)
-# Result: array([ 0,  0,  6,  6,  7,  7, 13, 13, 14, 14], dtype=int64)
+# Result: array([ 0, 0, 6, 6, 7, 7, 13, 13, 14, 14], dtype=int64)
 
 # Get the pairs as a 2D matrix, skip the first and the last
 w_ranges = np.reshape(sp2[1:-1], (-1, 2))
@@ -29,12 +28,12 @@ w_ranges = np.reshape(sp2[1:-1], (-1, 2))
 #                [13, 14]], dtype=int64)
 
 # Voila! Words are in between spaces, given as pairs of indices
-# But remember to skip contiguous spaces (the conditional at the end)
+# But skip contiguous spaces (the conditional at the end)
 words = [characters[w_ranges[i][0] : w_ranges[i][1]] for i in range(len(w_ranges)) if w_ranges[i][1]-w_ranges[i][0] > 1]
 # Result: [array([' ', 'h', 'e', 'l', 'l', 'o'], dtype='<U1'), 
 #          array([' ', 'w', 'o', 'r', 'l', 'd'], dtype='<U1')]
 
-# But this is too much! Let's recode the characters as strings
+# Let's recode the characters as strings
 swords = np.array([''.join(row).strip() for row in words])
 # Result: array(['hello', 'world'], dtype='<U5')
 
@@ -42,11 +41,11 @@ swords = np.array([''.join(row).strip() for row in words])
 stop_words = open('../stop_words.txt').read().split(',')
 stop_words.extend(list(string.ascii_lowercase))
 stop_words = np.array(list(set(stop_words)))
-non_stop_words = swords[~np.isin(swords, stop_words)]
+ns_words = swords[~np.isin(swords, stop_words)]
 
 ### Finally, count the word occurrences
-uniq, counts = np.unique(non_stop_words, axis=0, return_counts=True)
-wf_sorted = sorted(zip(uniq, counts), key = lambda t: t[1], reverse=True)
+uniq, counts = np.unique(ns_words, axis=0, return_counts=True)
+wf_sorted = sorted(zip(uniq, counts), key=lambda t: t[1], reverse=True)
 
 for w, c in wf_sorted[:25]:
     print(w, '-', c)