Removed old class from the tool as it has its own file.

fdcTool.py

@@ -13,87 +13,6 @@ from fdc.fdc import canberra_modified, FDC, Clustering
 
 from fdc.dataSheet import DataSheet
 
-
-
-# class FdcToolbox:
-# 
-#   def __init__(self, file_name, index_col=0):
-#     data = pd.read_csv(file_name, index_col=0)
-#     self.data = data.sample(frac=1)
-# 
-#     self.value_dict = {}
-#     self.value_dict_rev = {}
-#     self.cols_cont = []
-#     self.cols_ord = []
-#     self.cols_nom = []
-# 
-#     for k in self.data.dtypes.keys():
-#       t = str(self.data.dtypes[k])
-#       if t[:3] == "int":
-#         self.cols_ord.append(k)
-#       elif t == "object":
-#         self.cols_nom.append(k)
-#       else:
-#         self.cols_cont.append(k)
-# 
-#     self.has_missing_values = False
-#     self.updateMissingValuesState()
-# 
-#   def updateMissingValuesState(self):
-#     self.has_missing_values = False
-#     for k in self.data.isna().sum():
-#       if k > 0:
-#         self.has_missing_values = True
-#         break
-# 
-#   def showStatistic(self):
-#     print(f"Fratures: {self.data.shape[1]}")
-#     print(f"Points:   {self.data.shape[0]}")
-#     print(f"Columns:")
-# 
-#     for k in self.data.dtypes.keys():
-#       t = str(self.data.dtypes[k])
-#       e = " c"
-#       if k in self.cols_ord:
-#         e = " o"
-#       if k in self.cols_nom:
-#         e = " n"
-#       indentPair(k, t, e)
-#     print()
-#     print(f"Missing values:")
-# 
-#     n = 0
-#     d = self.data.isna().sum()
-#     for k in d.keys():
-#       if d[k] > 0:
-#         indentPair(k, str(d[k]))
-#         n += 1
-#     if n == 0:
-#       print("  none")
-# 
-# 
-#   def fixDatatypes(self):
-#     columnsToFix = []
-#     for k in self.data.dtypes.keys():
-#       if str(self.data.dtypes[k]) == "object":
-#         columnsToFix.append(k)
-# 
-#     self.value_dict = {}
-#     self.value_dict_rev = {}
-#     for c in columnsToFix:
-#       histogram = self.data[c].value_counts()
-#       self.value_dict[c] = { k : n for n, k in enumerate(histogram.keys()) }
-#       self.value_dict_rev[c] = { n : k for n, k in enumerate(histogram.keys()) }
-#     
-#     if len(self.value_dict.keys()) > 0:
-#       self.data.replace(self.value_dict, inplace=True)
-# 
-#   def fix_missing_values(self):
-#     self.data = fix_missing_values(self.data, 4)
-#     self.updateMissingValuesState()
-# 
-
-
 filename='healthcare-dataset-stroke-data.csv'
 np.random.seed(42)
 tb = DataSheet(filename)