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SIYAKS-ARESSIYAKS-ARES
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Lots of Progress
1 parent bb433ed commit b673405

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lines changed

‎lst.ipynb renamed to ‎1.lst_org copy 2.ipynb

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@@ -2086,9 +2086,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/bn/fsn7y0ds1vl73pjmxwqvqdxw0000gn/T/ipykernel_10456/3895599987.py:5: DtypeWarning: Columns (0,1,2) have mixed types. Specify dtype option on import or set low_memory=False.\n",
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" book_tags = pd.read_csv('/Users/siyaksares/Developer/GitHub/ReadersRadarProject/book_tags.csv')\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
@@ -2104,28 +2112,59 @@
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"22753 owned-books\n",
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"30521 to-buy\n",
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"30574 to-read\n",
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"39461 books-i-own\n",
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"42971 currently-reading\n",
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"45811 favorites\n",
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"45997 fiction\n",
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"51467 kindle\n",
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"52299 library\n",
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"56997 owned\n",
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"57007 owned-books\n",
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"64775 to-buy\n",
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"64828 to-read\n",
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"Name: tag_name, dtype: object\n",
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"Selected tag: fiction\n",
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"Selected Book:\n",
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" title\n",
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"1978 Dead Ever After (Sookie Stackhouse, #13)\n"
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"Empty DataFrame\n",
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"Columns: [title]\n",
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"Index: []\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"\n",
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"tags = pd.read_csv('tags.csv')\n",
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"book_tags = pd.read_csv('book_tags.csv')\n",
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"# Read tags and book tags data\n",
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"tags = pd.read_csv('/Users/siyaksares/Developer/GitHub/ReadersRadarProject/tags.csv')\n",
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"book_tags = pd.read_csv('/Users/siyaksares/Developer/GitHub/ReadersRadarProject/book_tags.csv')\n",
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"\n",
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"# Convert tag_id column to integer type in both DataFrames\n",
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"tags['tag_id'] = pd.to_numeric(tags['tag_id'], errors='coerce')\n",
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"book_tags['tag_id'] = pd.to_numeric(book_tags['tag_id'], errors='coerce')\n",
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"\n",
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"# Drop rows with NaN values in the tag_id column\n",
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"tags.dropna(subset=['tag_id'], inplace=True)\n",
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"book_tags.dropna(subset=['tag_id'], inplace=True)\n",
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"\n",
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"# Identify the top 10 most used tags\n",
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"top_tags = book_tags.groupby('tag_id').count().sort_values(by='count', ascending=False).head(10)\n",
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"top_tag_names = tags[tags['tag_id'].isin(top_tags.index)]['tag_name']\n",
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"\n",
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"# Display top tag names\n",
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"print(\"Top 10 Most Used Tags:\")\n",
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"print(top_tag_names)\n",
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"selected_tag = input(\"Please select a tag: \")\n",
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"selected_tag_id = tags[tags['tag_name'] == selected_tag]['tag_id'].values\n",
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"if not selected_tag_id:\n",
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" print(\"Invalid label. Please select an existing tag.\")\n",
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"\n",
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"# Request user input to select a tag\n",
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"selected_tag = input(\"Please select a tag: \").strip() # Strip whitespace from the input\n",
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"print(\"Selected tag:\", selected_tag)\n",
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"\n",
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"# Check if the selected tag is empty\n",
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"if not selected_tag:\n",
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" print(\"No tag selected. Please enter a tag.\")\n",
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"elif selected_tag not in top_tag_names.values:\n",
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" print(\"Invalid label. Please select a tag from the top 10 most used tags.\")\n",
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"else:\n",
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" selected_tag_id = selected_tag_id[0]\n",
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" selected_tag_id = tags[tags['tag_name'] == selected_tag]['tag_id'].values[0]\n",
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" filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]\n",
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" if not filtered_books.empty:\n",
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" random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]\n",
@@ -2134,7 +2173,7 @@
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" print(\"Selected Book:\")\n",
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" print(selected_book)\n",
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" else:\n",
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" print(\"No books were found associated with the selected tag.\")"
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" print(\"No books were found associated with the selected tag.\")\n"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.0"
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"version": "3.12.2"
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}
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},
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"nbformat": 4,

‎lst_org.ipynb renamed to ‎1.lst_org copy.ipynb

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‎2.lst-to-solve 2.ipynb

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‎3.lst-to-solve 3.ipynb

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‎README.md

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# ReadersRadarProject
2-
Python Book Recomandation Project
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Python Book Recomandation Project

‎Start.py

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‎book_tags.csv

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<<<<<<< HEAD
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goodreads_book_id,tag_id,count
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1,30574,167697
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1,11305,37174

‎books.csv

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<<<<<<< HEAD
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id,book_id,best_book_id,work_id,books_count,isbn,isbn13,authors,original_publication_year,original_title,title,language_code,average_rating,ratings_count,work_ratings_count,work_text_reviews_count,ratings_1,ratings_2,ratings_3,ratings_4,ratings_5,image_url,small_image_url
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1,2767052,2767052,2792775,272,439023483,9.78043902348e+12,Suzanne Collins,2008.0,The Hunger Games,"The Hunger Games (The Hunger Games, #1)",eng,4.34,4780653,4942365,155254,66715,127936,560092,1481305,2706317,https://images.gr-assets.com/books/1447303603m/2767052.jpg,https://images.gr-assets.com/books/1447303603s/2767052.jpg
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2,3,3,4640799,491,439554934,9.78043955493e+12,"J.K. Rowling, Mary GrandPré",1997.0,Harry Potter and the Philosopher's Stone,"Harry Potter and the Sorcerer's Stone (Harry Potter, #1)",eng,4.44,4602479,4800065,75867,75504,101676,455024,1156318,3011543,https://images.gr-assets.com/books/1474154022m/3.jpg,https://images.gr-assets.com/books/1474154022s/3.jpg

‎note.md

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1+
import pandas as pd
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tags = pd.read_csv('/Users/siyaksares/Developer/GitHub/ReadersRadarProject/tags.csv')
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book_tags = pd.read_csv('/Users/siyaksares/Developer/GitHub/ReadersRadarProject/book_tags.csv')
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tags['tag_id'] = pd.to_numeric(tags['tag_id'], errors='coerce')
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book_tags['tag_id'] = pd.to_numeric(book_tags['tag_id'], errors='coerce')
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tags.dropna(subset=['tag_id'], inplace=True)
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book_tags.dropna(subset=['tag_id'], inplace=True)
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12+
top_tags = book_tags.groupby('tag_id').count().sort_values(by='count', ascending=False).head(10)
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top_tag_names = tags[tags['tag_id'].isin(top_tags.index)]['tag_name']
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print("Top 10 Most Used Tags:")
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print(top_tag_names)
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'''
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selected_tag = input("Please select a tag: ") # Strip whitespace from the input
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print("Selected tag:", selected_tag)
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if not selected_tag:
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print("No tag selected. Please enter a tag.")
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elif selected_tag not in top_tag_names.values:
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print("Invalid label. Please select a tag from the top 10 most used tags.")
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else:
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selected_tag_id = tags[tags['tag_name'] == selected_tag]['tag_id'].values[0]
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filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]
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if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
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selected_book = books[books['book_id'] == random_book_id][['title']]
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print("Selected Book:")
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print(selected_book)
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else:
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print("No books were found associated with the selected tag.")'''
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'''
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selected_tag = input("Please select a tag: ") # Strip whitespace from the input
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print("Selected tag:", selected_tag)
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if not selected_tag:
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print("No tag selected. Please enter a tag.")
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elif selected_tag not in top_tag_names.values:
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print("Invalid label. Please select a tag from the top 10 most used tags.")
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else:
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selected_tag_id = tags[tags['tag_name'] == selected_tag]['tag_id'].values[0]
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filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]
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if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
52+
selected_book = books[books['book_id'] == random_book_id][['title']]
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print("Selected Book:")
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print(selected_book)
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else:
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print("No books were found associated with the selected tag.")
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'''
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'''
60+
selected_tag_lower = selected_tag.lower() # Convert selected tag to lowercase
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62+
if not selected_tag_lower:
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print("No tag selected. Please enter a tag.")
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elif selected_tag_lower not in top_tag_names.str.lower().values: # Convert tag names to lowercase for comparison
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print("Invalid label. Please select a tag from the top 10 most used tags.")
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else:
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filtered_books = book_tags[book_tags['tag_name'].str.lower().str.contains(selected_tag_lower)]
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if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
71+
selected_book = books[books['book_id'] == random_book_id][['title']]
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print("Selected Book:")
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print(selected_book)
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else:
75+
print("No books were found associated with the selected tag.")'''
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'''
78+
selected_tag = input("Please select a tag: ") # Strip whitespace from the input
79+
print("Selected tag:", selected_tag)
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81+
if not selected_tag:
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print("No tag selected. Please enter a tag.")
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elif selected_tag_lower not in top_tag_names.str.lower().values: # Convert tag names to lowercase for comparison
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print("Invalid label. Please select a tag from the top 10 most used tags.")
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else:
86+
selected_tag_lower = selected_tag.lower() # Convert selected tag to lowercase
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selected_tag_id = tags[tags['tag_name'].str.lower() == selected_tag_lower]['tag_id'].values[0]
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filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]
89+
if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
92+
selected_book = books[books['book_id'] == random_book_id][['title']]
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print("Selected Book:")
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print(selected_book)
95+
else:
96+
print("No books were found associated with the selected tag.")'''
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'''
99+
selected_tag = input("Please select a tag: ") # Strip whitespace from the input
100+
print("Selected tag:", selected_tag)
101+
102+
if not selected_tag:
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print("No tag selected. Please enter a tag.")
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elif selected_tag_lower not in top_tag_names.str.lower().values: # Convert tag names to lowercase for comparison
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print("Invalid label. Please select a tag from the top 10 most used tags.")
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else:
107+
selected_tag_lower = selected_tag.lower() # Convert selected tag to lowercase
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selected_tag_id_series = tags[tags['tag_name'].str.lower() == selected_tag_lower]['tag_id']
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if not selected_tag_id_series.empty:
110+
selected_tag_id = selected_tag_id_series.values[0]
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filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]
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if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
115+
selected_book = books[books['book_id'] == random_book_id][['title']]
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print("Selected Book:")
117+
print(selected_book)
118+
else:
119+
print("No books were found associated with the selected tag.")
120+
else:
121+
print("Invalid tag. Please select a valid tag.")'''
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123+
selected_tag = input("Please select a tag: ").strip() # Strip whitespace from the input
124+
print("Selected tag:", selected_tag)
125+
126+
if not selected_tag:
127+
print("No tag selected. Please enter a tag.")
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else:
129+
selected_tag_lower = selected_tag.lower() # Convert selected tag to lowercase
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top_tag_names_lower = top_tag_names.str.lower() # Convert top tag names to lowercase for comparison
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if selected_tag_lower not in top_tag_names_lower.values:
132+
print("Invalid label. Please select a tag from the top 10 most used tags.")
133+
else:
134+
selected_tag_id_series = tags[tags['tag_name'].str.lower() == selected_tag_lower]['tag_id']
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if not selected_tag_id_series.empty:
136+
selected_tag_id = selected_tag_id_series.values[0]
137+
filtered_books = book_tags[book_tags['tag_id'] == selected_tag_id]
138+
if not filtered_books.empty:
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random_book_id = filtered_books.sample(1)['goodreads_book_id'].values[0]
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books = pd.read_csv('books.csv')
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selected_book = books[books['book_id'] == random_book_id][['title']]
142+
print("Selected Book:")
143+
print(selected_book)
144+
else:
145+
print("No books were found associated with the selected tag.")
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else:
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print("Invalid tag. Please select a valid tag.")
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