code: # coding: utf-8
  __author__ = 'Antonis'
  
  '''
  Python script for Kaggle Competition
  
  This script focuses on reading the train dataset and getting information for non-numerical values.
  To run the script you need to have the "train.csv" file inside the /dataset folder in the project root.
  '''
  
  import pandas as pd
  import numpy as np
  import warnings
  
  warnings.filterwarnings('ignore', 'numpy not_equal will not check object identity in the future')
  
  # Get the data from reading the training set
  data = pd.read_csv('../dataset/train.csv', sep=',', na_values='.')  # read csv file, seperated by ;, na values exists
  
  # Find and print the names of all non-numerical features.
  print("Export the features with non-numerical data")
  
  non_numerical = []
  
  # data is of pandas's type Dataframe. It is a table that consists columns and rows.
  for column in data:
      # column_series is of pandas type Series ( One-dimensional ndarray with axis labels)
      column_series = data[column]
  
      # dtype is property of a Series. It declares the data type of the values inside it.
      if column_series.dtype not in ['int64', 'float64']:
          first_item = column_series.iloc[0]
          # Detect NaN values(they are calculated as Objects with type "float")
          if type(first_item) is not float:
              non_numerical.append(column)
  print("")
  print(non_numerical)
  
  non_numerical_values = {}
  
  for column in data:
      column_series = data[column]
  
      if column_series.dtype not in ['int64', 'float64']:
          first_item = column_series.iloc[0]
  
          if type(first_item) is not float:
              if column not in non_numerical_values:
                  non_numerical_values[column] = np.unique(column_series.values)
  
  print("")
  for item in non_numerical_values:
      print("")
      print(item)
      print(non_numerical_values[item])
      print("")
  # print(non_numerical_values)
apis: pandas.read_csv numpy.unique