Modify a loop based on key and value in dictionary

Question

Im new to python I wrote the code below, to search in a dictionary, do something, clear old items in dictionary and update dictionary with new key and values and break while there is noting to add to dictionary (it is empty), how can I modify my code to do this process?

#since_id - Returns results with an ID greater than #(that is, more recent than) the specified ID. There are limits to the #number of Tweets which can be accessed through the API. # If the limit of Tweets has occured since the since_id, # the since_id will be forced to the oldest ID available. # max_id - Returns results with an ID less than (that is, older than) #or equal to the specified ID. Dict2 = dict({'@TweeetLorraine':1392217841680764931}) d2 = {} rep=[] from tqdm import tqdm for key, value in tqdm(Dict2.items()): for i in tweepy.Cursor(api.search, q='to:{} -filter:retweets"'.format(key),lang="en" ,since_id=value,tweet_mode='extended', wait_on_rate_limit=True, wait_on_rate_limit_notify=True).items(50): if (i.in_reply_to_status_id == value): rep.append(i) from pandas.io.json import json_normalize dfflat = pd.DataFrame() for tweet in rep: df_for_tweet = json_normalize(tweet._json) dfflat=dfflat.append(df_for_tweet,ignore_index=True,sort=True) d2.update(zip(dfflat["user.screen_name"].tolist(), dfflat["id"].tolist())) d2 ```

Wind56 · Accepted Answer · 2021-05-13 22:15:10Z

You can use a while loop for that :

#since_id - Returns results with an ID greater than #(that is, more recent than) the specified ID. There are limits to the #number of Tweets which can be accessed through the API. # If the limit of Tweets has occured since the since_id, # the since_id will be forced to the oldest ID available. # max_id - Returns results with an ID less than (that is, older than) #or equal to the specified ID. Dict2 = dict({'@TweeetLorraine':1392217841680764931}) d2 = {} rep=[] from tqdm import tqdm for key, value in tqdm(Dict2.items()): for i in tweepy.Cursor(api.search, q='to:{} -filter:retweets"'.format(key),lang="en" ,since_id=value,tweet_mode='extended', wait_on_rate_limit=True, wait_on_rate_limit_notify=True).items(50): if (i.in_reply_to_status_id == value): rep.append(i) from pandas.io.json import json_normalize dfflat = pd.DataFrame() for tweet in rep: df_for_tweet = json_normalize(tweet._json) dfflat=dfflat.append(df_for_tweet,ignore_index=True,sort=True) d2.update(zip(dfflat["user.screen_name"].tolist(), dfflat["id"].tolist())) d2

For your use case, here is roughly the code that does what you describe, there is better ways to do that using map, I let you search for it if you want to know more.

Also, I'm not sure whether you want to completely clear the dict or only clear the current "i", but I think you can modify the following snippet to your true needs

mydict = initial_dict # while there is something in the dictionary while mydict: value_searched = None for key, value in mydict.items(): for i in tweepy.Cursor(api.search, q='to:{} -filter:retweets"'.format(key),lang="en" ,since_id=value,tweet_mode='extended', wait_on_rate_limit=True, wait_on_rate_limit_notify=True).items(50): if (i.in_reply_to_status_id == value): replies3.append(i) value_searched = i break break # create new dict from value retrieved mydict = {"@" +value_searched.user.screen_name : value_searched.id_str}

Edit2 : Using recursivity

def tweepy_stub(key, value): if key == "TweeetLorraine" and value == 1392217841680764931: return [ ("AlexBC997", 1392385334155956226), ("ChapinDolores", 1392432099945238529), ] elif key == "AlexBC997" and value == 1392385334155956226: return [("test", 139238533415595852)] elif ("ChapinDolores", 1392432099945238529): return [] def recursive(list_values, nb_recursion): mydict = {} if list_values == None or nb_recursion == 0: return mydict else: for name_user, tweet_id in list_values: mydict[(name_user, tweet_id)] = recursive( retrieve_direct_reply_stub(name_user, tweet_id), nb_recursion - 1 ) return mydict class stub_tweepy_answer: def __init__(self, status_id) -> None: self.in_reply_to_status_id = status_id def retrieve_direct_reply_stub(name_user, tweepy_id): rep = [] d2 = [] return tweepy_stub(name_user, tweepy_id) def retrieve_direct_reply(name_user, tweet_id): rep = [] d2 = [] for i in tweepy_stub(name_user, tweet_id): val = i if (i.in_reply_to_status_id == tweet_id): rep.append(i) from pandas.io.json import json_normalize dfflat = pd.DataFrame() for tweet in rep: df_for_tweet = json_normalize(tweet._json) dfflat=dfflat.append(df_for_tweet,ignore_index=True,sort=True) d2.append(zip(dfflat["user.screen_name"].tolist(), dfflat["id"].tolist())) return d2 #print(retrieve_direct_reply_stub("TweeetLorraine", 1392217841680764931)) elem = [("TweeetLorraine", 1392217841680764931)] print(recursive(elem, 3))

Thank you so much , where should I put my main block of code? can you please update the answer based on my code?
This is simply an example to show you how it's done, you have to write create_new_dict and condition_met for your use case. I'm editing and filling up the blanks with your specific case, but I won't verify that the code works.
The updated code unfortunately doesn't give me a correct answer, I want to extract replies to each tweet, in each loop of my code, it extract just replies to tweet, but I want all chain of replies such as replies to replies ..each tweet or reply has its own tweet id and user name.. my code is bale to bring to just replies to tweets, but what If I want replies to replies? that's why I wanted to clear the dict and fill it again with extracted replies to tweets ( each has tweet id and user name)... in this way I could be able to extract all the chain of replies.. maybe now you can help?

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