how to compare two data frames of different lengths without getting identically-labelled series error

See also questions close to this topic

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

  df.head():
  
                        run_time match_datetime         country                league             home_team                  away_team
  0   2021-08-07 00:04:36.326391     2021-08-06          Russia       FNL 2 - Group 2             Yenisey 2          Lokomotiv-Kazanka
  1   2021-08-07 00:04:36.326391     2021-08-07          Russia          Youth League              Ural U19  Krylya Sovetov Samara U19
  2   2021-08-07 00:04:36.326391     2021-08-08           World         Club Friendly                Alaves                    Al Nasr
  3   2021-08-07 00:04:36.326391     2021-08-09           China            Jia League     Chengdu Rongcheng          Shenyang Urban FC
  4   2021-08-06 00:04:36.326391     2021-08-06           China          Super League              Wuhan FC       Tianjin Jinmen Tiger
  5   2021-08-06 00:04:36.326391     2021-08-07  Czech Republic            U19 League     Sigma Olomouc U19                Karvina U19
  6   2021-08-06 00:04:36.326391     2021-08-08          Russia          Youth League  Konoplev Academy U19            Rubin Kazan U19
  7   2021-08-06 00:04:36.326391     2021-08-09           World         Club Friendly         Real Sociedad                      Eibar
  

  desired df

                        run_time match_datetime         country                league             home_team                  away_team
  0   2021-08-07 00:04:36.326391     2021-08-06          Russia       FNL 2 - Group 2             Yenisey 2          Lokomotiv-Kazanka
  1   2021-08-07 00:04:36.326391     2021-08-07          Russia          Youth League              Ural U19  Krylya Sovetov Samara U19
  4   2021-08-06 00:04:36.326391     2021-08-06           China          Super League              Wuhan FC       Tianjin Jinmen Tiger
  5   2021-08-06 00:04:36.326391     2021-08-07  Czech Republic            U19 League     Sigma Olomouc U19                Karvina U19
  

  How do i use rank function to filter only the 2 nearest match_datetime dates for every run_time value. i.e. desired dataframe will be a filtered dataframe that will have all the nearest 2 match_datetime values for every run_time