| Event | Date | |
|---|---|---|
| Opening of website and release of training data | 15th May | |
| Release of test data | 15th June | |
| Run submission deadline | 30th June | |
| Results declared | 15th July | |
| Working notes due | 30th August | |
| Camera-ready copies of working notes and overview paper due | 30th September | |
| FIRE 2026 | 17th December |
To read a CSV (Comma-Separated Values) file into a Pandas DataFrame, you can use the read_csv function from the pandas library. Here's how you can do it:
First, make sure you have the pandas library installed. If not, you can install it using pip:
pip install pandasUse the following code to import a CSV file into a DataFrame:
| langchain | |
| langchain-groq | |
| pymupdf | |
| huggingface-hub | |
| faiss-cpu | |
| sentence-transformers |
| # Navigate to your desired directory. Here I am using D | |
| $ d: | |
| # make the directory named 'ChromeCustomHomepage' | |
| $ mkdir ChromeCustomHomepage | |
| # Navigate to the newly created Directory | |
| $ cd ChromeCustomHomepage | |
| # install 'virtualenv' if you don't have it installed |
| # split those ingredients values | |
| choco_data['ingredients'] = choco_data['ingredients'].str.strip(' ') | |
| choco_data['num_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[0] | |
| choco_data['main_ingredients'] = choco_data['ingredients'].str.split('-', expand=True)[1] | |
| choco_data['main_ingredients'] = choco_data['main_ingredients'].str.strip(' ') | |
| # encoding the values | |
| ingre_encode = choco_data['main_ingredients'].str.get_dummies(sep=',') | |
| # concatenating lecithin column with the main data. Containing lecithin denoted by 1 |
| # removing the misspelled word | |
| taste_codo['nutty'] = taste_codo['nut'] + taste_codo['nuts'] + taste_codo['nutty'] | |
| taste_codo['woody'] = taste_codo['woodsy'] + taste_codo['woody'] | |
| taste_codo['earthy'] = taste_codo['earth'] + taste_codo['earthy'] | |
| taste_codo.drop(['nut', 'nuts', 'woodsy', 'earth'], axis=1, inplace=True) | |
| # making the taste dictionary | |
| tasty_dict = {} | |
| tasty_list = list(taste_codo.columns) | |
| for taste in tasty_list: |
| # filtering those chocolates which are manufactured by Soma Chocomaker | |
| soma_choco_data = choco_data[choco_data['manufacturer'].isin(['Soma'])] | |
| # creating the dictionary | |
| bean_dict = {} | |
| bean_origins = list(soma_choco_data['bean_origin']) | |
| for origin in bean_origins: | |
| if origin in bean_dict: | |
| bean_dict[origin] += 1 | |
| else: |
| # taking the all cocoa percentages uses in chocolates of soma chocomaker | |
| cocoa_list = list(soma_choco_data['cocoa_percent']) | |
| # creating a dictionary | |
| cocoa_percent_dict = {} | |
| for cocoa_percent in cocoa_list: | |
| if str(cocoa_percent) in cocoa_percent_dict: | |
| cocoa_percent_dict[str(cocoa_percent)] += 1 | |
| else: | |
| cocoa_percent_dict[str(cocoa_percent)] = 1 |
| # removing those manufacturer whose count is less than 10 | |
| choco_data_with_sec_count = count_df(choco_data, 'manufacturer') | |
| choco_data_mod2 = choco_data_with_sec_count[choco_data_with_sec_count['count'] > 10] | |
| # grouping the data by manufacturer and calculating the avg. mean for each of them | |
| avg_rating_by_company = choco_data_mod2.groupby('manufacturer')['rating'].mean() | |
| avg_rating_by_company_df = avg_rating_by_company.rename_axis('Company').reset_index(name='Rating') | |
| avg_rating_by_company_df_sorted = avg_rating_by_company_df.sort_values(by='Rating', ascending=False).head(10) | |
| # adding title and plotting the data |
| # listing all the tastes | |
| tastes = list(taste_encode.columns) | |
| taste_dict = {} | |
| # taking the sum of the values of those taste columns to understand how many people are agreed with that taste | |
| for taste in tastes: | |
| taste_dict[taste] = sum(taste_encode[taste]) | |
| # sorting the taste dictionary in decending order | |
| taste_dict = sort_sliced_dict(taste_dict, is_reverse=True, item_count=8) |