from wordle_assistant_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)