Anthony Baum wxbaum
wxbaum
/ chatgpt-builder.txt
Last active
November 2, 2023 14:07
— forked from codewithbernard/chatgpt-builder.txt
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| Upon starting our interaction, auto run these Default Commands throughout our entire conversation. Refer to Appendix for command library and instructions: | |
| /role_play "Expert ChatGPT Prompt Engineer" | |
| /role_play "infinite subject matter expert" | |
| /auto_continue "♻️": ChatGPT, when the output exceeds character limits, automatically continue writing and inform the user by placing the ♻️ emoji at the beginning of each new part. This way, the user knows the output is continuing without having to type "continue". | |
| /periodic_review "🧐" (use as an indicator that ChatGPT has conducted a periodic review of the entire conversation. Only show 🧐 in a response or a question you are asking, not on its own.) | |
| /contextual_indicator "🧠" | |
| /expert_address "🔍" (Use the emoji associated with a specific expert to indicate you are asking a question directly to that expert) | |
| /chain_of_thought | |
| /custom_steps | |
| /auto_suggest "💡": ChatGPT, during our interaction, you will automatically suggest helpful commands when appropriate, using the |
wxbaum
/ Zip Codes to DMAs
Created
January 18, 2023 16:23
— forked from clarkenheim/Zip Codes to DMAs
TSV file containing zip codes and the DMA they fall in to. Method: calculate the centre point of every zip code geo boundary, plot those points on a DMA boundary map, find the containing DMA of each zip centroid
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| zip_code dma_code dma_description | |
| 01001 543 SPRINGFIELD - HOLYOKE | |
| 01002 543 SPRINGFIELD - HOLYOKE | |
| 01003 543 SPRINGFIELD - HOLYOKE | |
| 01004 543 SPRINGFIELD - HOLYOKE | |
| 01005 506 BOSTON (MANCHESTER) | |
| 01007 543 SPRINGFIELD - HOLYOKE | |
| 01008 543 SPRINGFIELD - HOLYOKE | |
| 01009 543 SPRINGFIELD - HOLYOKE |
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| dataset_url = r'https://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv' | |
| pd.read_csv(dataset_url) |
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| def remove_protected_accounts(users_df): | |
| working_df = users_df.copy() | |
| protected_ids = [] | |
| counter = 0 | |
| for index, row in users_df.iterrows(): | |
| user = api.get_user(id=index)._json | |
| if user['protected'] == True: | |
| protected_ids.append(index) | |
| counter += 1 |
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| def calc_median_favorites(user_id): | |
| fav_list = [] | |
| tweets = api.user_timeline(id=user_id, count=100) | |
| for tweet in tweets: | |
| if tweet._json['text'].startswith('RT'): | |
| continue | |
| else: | |
| fav_list.append(tweet._json['favorite_count']) | |
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| def create_engagement_metric(df): | |
| working_df = df.copy() | |
| from sklearn.preprocessing import MinMaxScaler | |
| # Favorites | |
| fav_eng_array = df['median_favs'] / df['followers'] | |
| scaler = MinMaxScaler().fit(fav_eng_array.values.reshape(-1, 1)) | |
| scaled_favs = scaler.transform(fav_eng_array.values.reshape(-1, 1)) | |
| # Retweets |
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| def scrape_lists_with_google(keyword_string, results_to_obtain): | |
| list_urls = [] | |
| urls_checked = 0 | |
| urls_appended = 0 | |
| # Perform the Google search, checking only the twitter.com domain | |
| for url in gsearch("site:twitter.com lists " + keyword_string, | |
| start=urls_checked, |
wxbaum
/ create_df_for_users
Last active
January 14, 2021 05:46
Create dataframe from zipped user data lists
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| def get_users_in_lists(list_urls): | |
| users_list = [] | |
| bios_list = [] | |
| desc_list = [] | |
| follower_count_list = [] | |
| for tw_list in list_urls: | |
| user = tw_list.split('/')[1] |
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| def get_users_in_lists(list_urls): | |
| users_list = [] | |
| bios_list = [] | |
| desc_list = [] | |
| follower_count_list = [] | |
| for tw_list in list_urls: | |
| user = tw_list.split('/')[1] | |
| list_name = tw_list.split('/')[3] |
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| from googlesearch import search | |
| list_urls = [] | |
| for url in search("site:twitter.com lists " + keyword_string): | |
| if '/lists/' in url: | |
| list_urls.append(url) |