split column data frame into multiple columns

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• is there a method to transform this function to python

  i develop this function in R but i want to have a python version of this function and im not expert in python with statistical treatement, please help: the input is a dataframe with columns like

  uci_class   doc_id  sentence_id token   lemma   pos token_id
  

  spacy_token_df$is_punct <- 0
  spacy_token_df$is_punct[round(as.integer(spacy_token_df$n_tokens) / long_sentence_threshold) >= 2 & spacy_token_df$pos == 'PUNCT'] <- 1
  ## the trick is to add to sentence_id the cumulative sum of is punct. this will offset sentence_id and generate new ids for sub segments
  spacy_token_df <- spacy_token_df %>%
      dplyr::group_by(uci_class, doc_id) %>% 
      dplyr::mutate(sentence_id = cumsum(is_punct) + sentence_id) %>%
      dplyr::group_by(uci_class, doc_id, sentence_id) %>% 
      mutate(n_tokens = n(), token_id = row_number())
  
• rtweet package in R, finding the tweet location and user location

  I am collecting some tweets using rtweet package in R using below chunk of codes:

  df <- search_tweets(
    "#some_random_tweets",
    n = 10,
    type = "mixed"
  )
  

  It returns several columns from which the location can be extracted such as df$location, df$place_full_name, df$country, df$retweet_location, etc.

  Does anyone know how to distinguish between the tweet location (when user shares location at time of Tweet) and the user location (home location provided by user in their public profile); which one returns the tweet location and which one returns the user location?

  Does it store the latitude and longitude information as well?

  Thank you in advance!
• How to use R to create baseball Splits from Game Logs

  I'm trying to use R to recreate Baseball Splits as found on MLB.com. The splits are created from Game Logs and provide different cuts of the data. For example, home games vs. away games, day games vs. night games, August vs. September and many more all in one convenient table. I believe the ratios (AVG, OBP SLG) can all be added via mutate once the basic splits have been totaled.

  My Question is, what's the best and most efficient way to create these splits and how should the data be shaped. The game log obviously has additional (hidden) column(s) that contain the Split topics. The nature of the problem leads me to believe purrr might be a tool to employ but I can't quite wrap my mind around how to approach this one.

  Here is how I believe the data should be shaped and a link to a sample game log. I would appreciate any thoughts, ideas or solutions to this problem.

  

  Links and images of Game Logs and Splits for National outfielder Juan Soto are set forth below.

  Game Logs: Juan Soto Game Log

  Splits: Juan Soto Game Splits

  

  Splits