How do I implement rank function for nearest values for a column in dataframe?

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• Python File Tagging System does not retrieve nested dictionaries in dictionary

  I am building a file tagging system using Python. The idea is simple. Given a directory of files (and files within subdirectories), I want to filter them out using a filter input and tag those files with a word or a phrase.

  If I got the following contents in my current directory:

  data/
     budget.xls
     world_building_budget.txt
  a.txt
  b.exe
  hello_world.dat
  world_builder.spec
  

  and I execute the following command in the shell:

  py -3 tag_tool.py -filter=world -tag="World-Building Tool"
  

  My output will be:

  These files were tagged with "World-Building Tool":
  data/ world_building_budget.txt
  hello_world.dat
  world_builder.spec
  

  My current output isn't exactly like this but basically, I am converting all files and files within subdirectories into a single dictionary like this:

  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  

  Right now, my dictionary looks like this: key:''.

  In the following function, I am turning the empty values '' into empty lists (to hold my tags):

  def empty_str_to_list(d):
      for k,v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  

  When I run my entire code, this is my output:

  hello_world.dat ['World-Building Tool']
  world_builder.spec ['World-Building Tool']
  

  But it does not see data/world_building_budget.txt. This is the full dictionary:

  {'data': {'world_building_budget.txt': []},  
  'a.txt': [],
  'hello_world.dat': [], 
  'b.exe': [], 
  'world_builder.spec': []}
  
  

  This is my full code:

  import os, argparse
  
  
  def fs_tree_to_dict(path_):
      file_token = ''
      for root, dirs, files in os.walk(path_):
          tree = {d: fs_tree_to_dict(os.path.join(root, d)) for d in dirs}
          tree.update({f: file_token for f in files})
          return tree
  
  
  def empty_str_to_list(d):
      for k, v in d.items():
          if v == '':
              d[k] = []
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
  
  parser = argparse.ArgumentParser(description="Just an example", formatter_class=argparse.ArgumentDefaultsHelpFormatter)
  
  parser.add_argument("--filter", action="store", help="keyword to filter files")
  parser.add_argument("--tag", action="store", help="a tag phrase to attach to a file")
  parser.add_argument("--get_tagged", action="store", help="retrieve files matching an existing tag")
  
  args = parser.parse_args()
  
  filter = args.filter
  tag = args.tag
  get_tagged = args.get_tagged
  current_dir = os.getcwd()
  
  files_dict = fs_tree_to_dict(current_dir)
  empty_str_to_list(files_dict)
  
  for k, v in files_dict.items():
      if filter in k:
          if v == []:
              v.append(tag)
              print(k, v)
  
          elif isinstance(v, dict):
              empty_str_to_list(v)
  
      if get_tagged in v:
          print(k, v)
  
• Actaully i am working on a project and in it, it is showing no module name pip_internal plz help me for the same. I am using pycharm(conda interpreter

   File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 196, in _run_module_as_main
      return _run_code(code, main_globals, None,
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\runpy.py", line 86, in _run_code
      exec(code, run_globals)
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\Scripts\pip.exe\__main__.py", line 4, in <module>
    File "C:\Users\pjain\AppData\Local\Programs\Python\Python310\lib\site-packages\pip\_internal\__init__.py", line 4, in <module>
      from pip_internal.utils import _log
  

  I am using pycharm with conda interpreter.

  
• Looping the function if the input is not string

  I'm new to python (first of all) I have a homework to do a function about checking if an item exists in a dictionary or not.

  inventory = {"apple" : 50, "orange" : 50, "pineapple" : 70, "strawberry" : 30}
  
  def check_item():
      x = input("Enter the fruit's name: ")
      if not x.isalpha():
          print("Error! You need to type the name of the fruit")
      elif x in inventory:
          print("Fruit found:", x)
          print("Inventory available:", inventory[x],"KG")
      else:
          print("Fruit not found")
  check_item()
  

  I want the function to loop again only if the input written is not string. I've tried to type return Under print("Error! You need to type the name of the fruit") but didn't work. Help
• Any efficient way to compare two dataframes and append new entries in pandas?

  I have new files which I want to add them to historical table, before that, I need to check new file with historical table by comparing its two column in particular, one is state and another one is date column. First, I need to check max (state, date), then check those entries with max(state, date) in historical table; if they are not historical table, then append them, otherwise do nothing. I tried to do this in pandas by group-by on new file and historical table and do comparison, if any new entries from new file that not in historical data, then add them. Now I have issues to append new values to historical table correctly in pandas. Does anyone have quick thoughts?

  My current attempt:

  import pandas as pd 
    
  src_df=pd.read_csv("https://raw.githubusercontent.com/adamFlyn/test_rl/main/src_df.csv")
  hist_df=pd.read_csv("https://raw.githubusercontent.com/adamFlyn/test_rl/main/historical_df.csv")
  picked_rows = src_df.loc[src_df.groupby('state')['yyyy_mm'].idxmax()]
  

  I want to check picked_rows in hist_df where I need to check by state and yyyy_mm columns, so only add entries from picked_rows where state has max value or recent dates. I created desired output below. I tried inner join or pandas.concat but it is not giving me correct out. Does anyone have any ideas on this?

  Here is my desired output that I want to get:

  import pandas as pd
  desired_output=pd.read_csv("https://raw.githubusercontent.com/adamFlyn/test_rl/main/output_df.csv")
  

  
• How to bring data frame into single column from multiple columns in python

  I have data format in these multiple columns. So I want to bring all 4 columns of data into a single column.

  YEAR Month pcp1 pcp2 pcp3 pcp4              
  1984    1   0   0   0   0
  1984    2   1.2 0   0   0
  1984    3   0   0   0   0
  1984    4   0   0   0   0
  1984    5   0   0   0   0
  1984    6   0   0   0   1.6
  1984    7   3   3   9.2 3.2
  1984    8   6.2 27.1    5.4 0
  1984    9   0   0   0   0
  1984    10  0   0   0   0
  1984    11  0   0   0   0
  1984    12  0   0   0   0
  
• Exclude Japanese Stopwords from File

  I am trying to remove Japanese stopwords from a text corpus from twitter. Unfortunately the frequently used nltk does not contain Japanese, so I had to figure out a different way.

  This is my MWE:

  import urllib
  from urllib.request import urlopen
  import MeCab
  import re
  
  # slothlib
  slothlib_path = "http://svn.sourceforge.jp/svnroot/slothlib/CSharp/Version1/SlothLib/NLP/Filter/StopWord/word/Japanese.txt"
  sloth_file = urllib.request.urlopen(slothlib_path)
  
  # stopwordsiso
  iso_path = "https://raw.githubusercontent.com/stopwords-iso/stopwords-ja/master/stopwords-ja.txt"
  iso_file = urllib.request.urlopen(iso_path)
  stopwords = [line.decode("utf-8").strip() for line in iso_file]
  
  stopwords = [ss for ss in stopwords if not ss==u'']
  stopwords = list(set(stopwords))
  
  text = '日本語の自然言語処理は本当にしんどい、と彼は十回言った。'
  tagger = MeCab.Tagger("-Owakati")
  tok_text = tagger.parse(text)
  
  ws = re.compile(" ")
  words = [word for word in ws.split(tok_text)]
  if words[-1] == u"\n":
      words = words[:-1]
  ws = [w for w in words if w not in stopwords]
  
  print(words)
  print(ws)
  

  Successfully Completed: It does give out the original tokenized text as well as the one without stopwords

  ['日本語', 'の', '自然', '言語', '処理', 'は', '本当に', 'しんどい', '、', 'と', '彼', 'は', '十', '回', '言っ', 'た', '。']
  ['日本語', '自然', '言語', '処理', '本当に', 'しんどい', '、', '十', '回', '言っ', '。']
  

  ---

  There is still 2 issues I am facing though:

  a) Is it possible to have 2 stopword lists regarded? namely iso_file and sloth_file ? so if the word is either a stopword from iso_file or sloth_file it will be removed? (I tried to use line 14 as stopwords = [line.decode("utf-8").strip() for line in zip('iso_file','sloth_file')] but received an error as tuple attributes may not be decoded

  b) The ultimate goal would be to generate a new text file in which all stopwords are removed.

  I had created this MWE

  ### first clean twitter csv
  import pandas as pd
  import re
  import emoji
  
  df = pd.read_csv("input.csv")
  
  def cleaner(tweet):
      tweet = re.sub(r"@[^\s]+","",tweet) #Remove @username 
      tweet = re.sub(r"(?:\@|http?\://|https?\://|www)\S+|\\n","", tweet) #Remove http links & \n
      tweet = " ".join(tweet.split())
      tweet = ''.join(c for c in tweet if c not in emoji.UNICODE_EMOJI) #Remove Emojis
      tweet = tweet.replace("#", "").replace("_", " ") #Remove hashtag sign but keep the text
      return tweet
  df['text'] = df['text'].map(lambda x: cleaner(x))
  df['text'].to_csv(r'cleaned.txt', header=None, index=None, sep='\t', mode='a')
  
  ### remove stopwords
  
  import urllib
  from urllib.request import urlopen
  import MeCab
  import re
  
  # slothlib
  slothlib_path = "http://svn.sourceforge.jp/svnroot/slothlib/CSharp/Version1/SlothLib/NLP/Filter/StopWord/word/Japanese.txt"
  sloth_file = urllib.request.urlopen(slothlib_path)
  
  #stopwordsiso
  iso_path = "https://raw.githubusercontent.com/stopwords-iso/stopwords-ja/master/stopwords-ja.txt"
  iso_file = urllib.request.urlopen(iso_path)
  stopwords = [line.decode("utf-8").strip() for line in iso_file]
  
  stopwords = [ss for ss in stopwords if not ss==u'']
  stopwords = list(set(stopwords))
  
  with open("cleaned.txt",encoding='utf8') as f:
      cleanedlist = f.readlines()
      cleanedlist = list(set(cleanedlist))
  
  tagger = MeCab.Tagger("-Owakati")
  tok_text = tagger.parse(cleanedlist)
  
  ws = re.compile(" ")
  words = [word for word in ws.split(tok_text)]
  if words[-1] == u"\n":
      words = words[:-1]
  ws = [w for w in words if w not in stopwords]
  
  print(words)
  print(ws)
  

  While it works for the simple input text in the first MWE, for the MWE I just stated I get the error

  in method 'Tagger_parse', argument 2 of type 'char const *'
  Additional information:
  Wrong number or type of arguments for overloaded function 'Tagger_parse'.
    Possible C/C++ prototypes are:
      MeCab::Tagger::parse(MeCab::Lattice *) const
      MeCab::Tagger::parse(char const *)
  

  for this line: tok_text = tagger.parse(cleanedlist) So I assume I will need to make amendments to the cleanedlist?

  I have uploaded the cleaned.txt on github for reproducing the issue: [txt on github][1]

  Also: How would I be able to get the tokenized list that excludes stopwords back to a text format like cleaned.txt? Would it be possible to for this purpose create a df of ws? Or might there even be a more simple way?

  Sorry for the long request, I tried a lot and tried to make it as easy as possible to understand what I'm driving at :-)

  Thank you very much! [1]: https://gist.github.com/yin-ori/1756f6236944e458fdbc4a4aa8f85a2c
• separate datetime column in R while keeping time accurate

  4/12/2016 12:00:00 AM I have dates in the format above and have tried to use separate() to create two columns in the data frame where the data is present. When I do the columns are created but AM/PM so the times just become numbers or worse appear as "12H 0M 0S". Can anyone help me out, pretty new to data analysis as a whole and would be much appreciated!
• XGBOOST RANKER not smooth

  I have the following toy problem centered around Baseball.

  In this baseball dataset, I have a lot of data about tournament results over many many years where I am able to collect the following information per result:

  1. Final ranking of each Team that participated
  2. The Batting average of that team over the entire tournament.

  Let us say in this tourny 2005 World Cup, 8 teams participated, I can generate the following dataframe:

  Tourny, Team, Rank, Batting Avg,
  2005-WC, Jays, 1, 0.45,
  2005-WC, Cards, 2, 0.25,
  2005-WC, Ravens, 3, 0.85,
  2005-WC, Crows, 4, 0.23,
  2005-WC, Jays, 5, 0.11,
  ...
  
  

  Then if I have multiple Tournys from different years, I can then extend my list and get lots of data.

  I can then ask the question, is Batting average useful in predicting the final rank of my Team?

  This seems like the question that XGBRanker should be able to answer.

  We can then plug in this data with some vanilla XGBRanker via the following:

  model = xgb.XGBRanker( 
      max_depth = 10,
      learning_rate = 0.01,
      n_estimators = 100, 
      objective='rank:pairwise',
      booster = 'gbtree',
      gamma = 5,
      min_child_weight=1,
      subsample=0.1,
      colsample_bytree = 1,
      reg_alpha = 0.5,
      reg_lambda = 0.5,
      base_score = 0.5,
      seed = 42,
      )
  
  X_train = df['Batting Avg']
  Y_train = df['Rank]
  groups = [number of entries per tourney]
  
  model.fit(X_train.values, Y_train.values, group=groups)
  
  

  After the appropriate training (very fast) We do the following plot to view how Rank changes with the team's batting average.

  x = np.arange(0, 1, 0.001) 
  y = model.predict(x)
  plot(x,y)
  

  As a general statement, our normalized X values, (which is why the graph isn't 0-1) shows that roughly, better batting average, better rank! Exactly what we expect:

  

  However, if you start zooming in, you start finding extremely undesirable traits. Namely, a very small change in batting averages can DRASTICALLY change your rank, and while we do not expect a fully monotonic result, we do expect to be way more SMOOTH, given there is only 1 parameter being used.

  

  Can someone help me understand this? While on the surface, there is nothing ABSOLUTELY wrong, these are not desirable traits to have. I am not asking for monoticity, but having the graph be smoother is far more intuitive.

  What kind of parameters do I need to tune to make it work better?

  for references, I am using python.
• Excel: Rank a subset with duplicated values defined by another column

  I'm trying to create a column with a rank of values based on a control column like this:

  (A)     (B)     (C)
  Value   Control Rank
  100     1       8
  200     1       6
  200     1       7
  300     0       5
  310     0       4
  320     1       3
  350     1       2
  400     1       1
  

  In col C I've used a combination of RANK and COUNTIF in order to get a general rank of all entries that accommodates duplicates nicely:

  

  Now how I can do a rank on col D that only considers the values if the Control col B has the value 1?

  My goal is to have this:

  

  Note that I need it to deal with duplicates in the same way.

  Thank you.
• PowerPivot: Ranking weekly based on date groups

  I have a question for dax in excel PowerPivot. As it is not Power BI, if you have a solution, please avoid using variables.

  I am still pretty new to this but I am working with parent child structures for date grouping. I want to rank the date weekly with best sales based on three scenarios:

  1. With public holidays
  2. Without public Holidays
  3. Only weekdays

  My structure for dates are as following

  Year, -> Months, -> Weeks, -> weekday types (weekday, weekend, public holiday) -> Dates, Weekdays

  I have a measure for the case with public holidays that looks like this

  =rankx( (filter (all ('Dates'[Week.NO],'Dates'[Week.NO]=max('Dates'[Week.NO])), ([Sales])

  Where [sales] is a calculated measure that looks like this: =sum('Sales'[Sales])

  I tried to make filtered calculated measures and substitute them with [Sales] for each scenario but it did not work. How can I go about this?
• Impala compare consecutive rows and insert identical row if there are no values

  I have a table that gives me data every month and I need that range of time. I noticed that sometimes I don't have data for 3/4 month but I need to duplicate the last row available with the missing timestamp.

  Example:

  product_id	total_revenue	yearmonth
  1	50	202201
  2	17	202201
  3	30	202201
  1	67	202202
  2	31	202202
  1	67	202203
  2	31	202203
  3	33	202203

  But I need an output like:

  product_id	total_revenue	yearmonth
  1	50	202201
  2	17	202201
  3	30	202201
  1	67	202202
  2	31	202202
  3	30	202202
  1	67	202203
  2	31	202203
  3	33	202203

  I have a select statement like:

  select 
      product_id, total_revenue, yearmonth 
  from 
      revenue
  

  I found a similar question, (Postgresql compare consecutive rows and insert identical row if there are no values) but in Impala I have not the lateral join, does anybody know how can I do?
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  I want to fetch 1 record from duplicate rows in Df except one column. example :

  index	col1	col2	col3	col4
  0	abc	efg	hij	op
  1	abc	efg	hij	ki
  2	abc	efg	hij	tf
  3	abc	efg	hij	ge
  4	xyz	mmm	qt	aa
  5	xyz	mmm	qt	bb
  6	xyz	mmm	qt	cc

  (order by col4 asc) Thus, desired result could be like

  index	col1	col2	col3	col4	rank
  0	abc	efg	hij	op	1
  1	abc	efg	hij	ki	2
  2	abc	efg	hij	tf	3
  3	abc	efg	hij	ge	4
  4	xyz	mmm	qt	aa	1
  5	xyz	mmm	qt	bb	2
  6	xyz	mmm	qt	cc	3

  The goal is to obtain a rank for each similar result to fetch data as df = df[df['rank'] == 1]
• Given the price, number of ratings and the rating of an online product, how would one go about ranking them considering all other factors to be even?

  

  I think the intent is pretty clear with the question. I have included an image depicting what I want to do basically. How would I go about calculating the final rank for a product to decide which one to buy.

  Not sure which category it falls under so I would gladly provide more input if needed.