corrects some mistakes

collect.py

@@ -188,19 +188,19 @@ print(timeStartScrape.strftime(fTimeFormat))
 print("---")
 
 # Iterate over each Twitter account using multiprocessing
-with concurrent.futures.ThreadPoolExecutor() as executor:
+# Iterate over each Twitter account using multiprocessing
+with concurrent.futures.ProcessPoolExecutor() as executor:
     # List to store the scraping tasks
     tasks = []
-
     for handle in accounts:
         # Iterate over each time slice
         for slice_data in time_slices:
-            # ... code to prepare the slice_data ...
-
+            # ... Code to prepare the slice_data ...
             # Schedule the scraping task
-            task = executor.submit(scrapeTweets, handle, slice_data, keywords, td, tweetDFColumns)
+            task = executor.submit(
+                scrapeTweets, handle, slice_data, keywords, td, tweetDFColumns
+            )
             tasks.append(task)
-
     # Wait for all tasks to complete
     concurrent.futures.wait(tasks)
     
@@ -227,7 +227,7 @@ with open(f"{logfile}missing-"+timeStartScrape.strftime(fTimeFormat)+".txt", "w"
 if file_alltweets in tweetfiles:
     tweetfiles.remove(file_alltweets)
 # Go through all csv files and merge them into file_alltweets
-if len(tweetfiles) > 0:
+if tweetfiles:
     with open(file_alltweets, "wb") as fout:
         # first file (because of the header):
         with open(tweetfiles[0], "rb") as f:

funs/Scrape.py

@@ -1,7 +1,9 @@
+from datetime import datetime, time
+import pandas as pd
+import snscrape.modules.twitter as sntwitter
+
 def scrapeTweets(handle, slice_data, keywords, td, tweetDFColumns, maxTweets = 5000):
-    from datetime import datetime, time
-    import pandas as pd
-    import snscrape.modules.twitter as sntwitter
+    i = 0
     
     currentTime = datetime.now
     ts_beg = slice_data['beg_time']
@@ -18,17 +20,19 @@ def scrapeTweets(handle, slice_data, keywords, td, tweetDFColumns, maxTweets = 5
     # Snscrape query:
     query = f'from:{handle} since:{ts_beg} until:{ts_end}'
     for i,tweet in enumerate(sntwitter.TwitterSearchScraper(query).get_items()):
-        if i>maxTweets:
+        if i >= maxTweets:
             break
         # get tweet vars from tweetDFColumns and append to singleTweetList
         # which will then be appended to TweetList. TweetList contains all tweets of the current slice.
         singleTweetList = [singleTweetList.append(eval(f'tweet.{col}')) for col in tweetDFColumns]
         TweetList.append(singleTweetList)
+    
     # Check if no tweets fetched for the current time slice. If there are no tweets, skip to next time_slices loop iteration
-    if TweetList:
+    if not TweetList:
         open(tweetDataFilePath, 'a').close()
         print(f'return empty in {handle}{suffix} - from {ts_beg} to {ts_end}')
         return        
+    
     print(f'{i:<6} tweets scraped for: {handle:>15}{suffix:<7}')
         
     # convert to dataframe
@@ -36,7 +40,9 @@ def scrapeTweets(handle, slice_data, keywords, td, tweetDFColumns, maxTweets = 5
         
     ## Check if tweet-text contains keyword
     tweet_df['contains_keyword'] = ''
-    tweet_df['contains_keyword'] = (tweet_df['rawContent'].str.findall('|'.join(keywords)).str.join(',').replace('', 'none'))
+    tweet_df['contains_keyword'] = (
+        tweet_df['rawContent'].str.findall('|'.join(keywords)).str.join(',').replace('', 'none')
+    )
     ## Save two versions of the dataset, one with all fields and one without dict fields
     # define filepaths
     csv_path = tweetDataFilePath