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| from wordle_functions import * | |
| import pandas as pd | |
| import streamlit as st | |
| import plotly.express as px | |
| import operator | |
| ### Official wordlist | |
| official_words = [] | |
| with open("data/official_words_processed.txt", "r", encoding = "utf-8") as f: | |
| for word in f.read().split("\n"): | |
| if word.isalpha(): | |
| official_words.append(word) | |
| f.close() # closes connection to file | |
| english_alphabet = "abcdefghijklmnopqrstuvwxyz" | |
| def count_plot(): | |
| letter_counts = get_letter_counts(word_list = official_words, letters = english_alphabet, sort = "descending", unique = True) | |
| letter_counts_dict = {} # {letter : count} | |
| letter_counts_dict["Letter"] = [] | |
| letter_counts_dict["Count"] = [] | |
| letter_counts_dict["Type"] = [] | |
| for tup in letter_counts: | |
| letter_counts_dict["Letter"].append(tup[0].upper()) | |
| letter_counts_dict["Count"].append(tup[1]) | |
| if tup[0] in "aeiouy": | |
| letter_counts_dict["Type"].append("Vowel") | |
| else: | |
| letter_counts_dict["Type"].append("Consonant") | |
| letters_dist_df = pd.DataFrame(letter_counts_dict) | |
| counts_plot = px.bar(letters_dist_df, x = "Letter", y = "Count", title = "Distribution of Letters in Official Wordle List", | |
| color = "Type", color_discrete_map = {"Vowel": "#6ca965", "Consonant": "#c8b653"}) | |
| counts_plot.update_layout(xaxis = {'categoryorder' : 'total descending'}, title_font_size = 25, font = dict(size = 17)) | |
| # counts_plot.show() | |
| st.plotly_chart(counts_plot, use_container_width = True) | |
| def words_plot(): | |
| letter_counts = get_letter_counts(word_list = official_words, letters = english_alphabet, sort = "descending", unique = True) | |
| total_letters_sum = sum(count for letter, count in letter_counts) | |
| word_counts = [] | |
| for word in official_words: | |
| # get set of all letters in the word (this intentionally doesn't count duplicate letters) | |
| word_letters = set() | |
| for letter in word: | |
| word_letters.add(letter) | |
| # get the sum of all counts of each letter in the word | |
| word_sum = 0 | |
| for letter in word_letters: | |
| word_sum += dict(letter_counts)[letter] | |
| # finally, add the word and its letter count sum to the list | |
| word_counts.append((word, round(word_sum / total_letters_sum * 100, 2))) | |
| # word_counts | |
| ### Best and worst x words | |
| words_counts_top_10 = sorted(word_counts, key = operator.itemgetter(1), reverse = True)[:5] # top 10 words | |
| words_counts_middle_10 = sorted(word_counts, key = operator.itemgetter(1), reverse = True)[(len(word_counts) // 2) - 10 : (len(word_counts) // 2) - 5] # top 10 words | |
| words_counts_bottom_10 = sorted(word_counts, key = operator.itemgetter(1), reverse = False)[:6] # bottom 10 words | |
| words_counts_x_dict = {} | |
| words_counts_x_dict["Word"] = [] | |
| words_counts_x_dict["Rating"] = [] | |
| for word, rating in words_counts_top_10: | |
| words_counts_x_dict["Word"].append(word) | |
| words_counts_x_dict["Rating"].append(rating) | |
| for word, rating in words_counts_middle_10: | |
| words_counts_x_dict["Word"].append(word) | |
| words_counts_x_dict["Rating"].append(rating) | |
| for word, rating in words_counts_bottom_10: | |
| words_counts_x_dict["Word"].append(word) | |
| words_counts_x_dict["Rating"].append(rating) | |
| words_counts_x_df = pd.DataFrame(words_counts_x_dict) | |
| words_counts_x_plot = px.bar(words_counts_x_df, x = "Word", y = "Rating", title = "A Selection of Wordle Words and Their Ratings") | |
| words_counts_x_plot.update_layout(xaxis = {'categoryorder' : 'total descending'}, title_font_size = 25, font = dict(size = 17)) | |
| words_counts_x_plot.update_traces(marker_color = "#6ca965") | |
| # words_counts_x_plot.show() | |
| st.plotly_chart(words_counts_x_plot, use_container_width = True) |