text: import pandas as pd
  import sys, datetime
  
  """
      This script will calculate job applications by month/year over time
      based on an input csv called "Job Applications.csv" that you can get
      from the LinkedIn Data Privacy page
  """
  default_csv_filename = "csv/Job Applications.csv"
  app_date_column_name = "Application Date"
  
  # the fields we care about
  fields = [app_date_column_name]
  
  filePth = None
  
  try:
      filePth = sys.argv[1]
  except:
      print("No cmd args found! Running with default %s file instead.\n" % default_csv_filename)
  
  jobsAppsDF = None
  
  # NOTE: skiprows=3 is to ignore the stupid notes that LinkedIn adds to the csv file!
  try:
      if filePth:
          print(filePth)
          jobsAppsDF = pd.read_csv(filePth, usecols=fields, skiprows=3)
      else:
          jobsAppsDF = pd.read_csv(default_csv_filename, usecols=fields)
  except Exception as e:
      print("\n\n!!!Error: you need to provide me with a %s file from LinkedIn for me to parse!!\n\n" %
            default_csv_filename)
      print(e)
  
      # Ye this is apparently not recommended but it doesn't quit the Idle shell on my Windows box so...
      raise SystemExit
  
  # Dictionary for counting, printing & saving data off to CSV later on
  applicationDates = {}
  
  # Pandas dataframe for exporting back to csv
  exportedjobsAppsDF = pd.DataFrame(columns = ["MONTH_YEAR", "COUNT"])
  
  # Group it ourselves based on year and month.
  for index, row in jobsAppsDF.iterrows():
  
      # This looks like "6/11/17, 10:19 PM" in the CSV file
      dateSplit = str(row[app_date_column_name]).split(",")
      dateSplit = dateSplit[0]            # disregard the time stuff
      dateSplit = dateSplit.split("/")    # Split again to get month/year
  
      month = dateSplit[0]
      year = dateSplit[2]
  
      datePart = month + "/01/" + year
      if datePart in applicationDates:
          applicationDates[datePart] += 1
  
      else:
          applicationDates[datePart] = 1
  
  # Print Month/Year: Count
  # Also builds up the exportedConnectionsDF at the same time
  for date in sorted(applicationDates):
      print(date + ": " + str(applicationDates[date]))
      exportedjobsAppsDF = exportedjobsAppsDF.append({"MONTH_YEAR": date, "COUNT": applicationDates[date]},
                                                       ignore_index=True)
  
  # Should be good to export exportedConnectionsDF to csv now
  try:
      exportedjobsAppsDF.to_csv("analyzed/job_apps_analyzed.csv", sep=",")
  except:
      print("\n\n¡¡¡ERROR: Close the damn file you dummy!!!\n\n")
  