diff --git a/src/hdf5_lib_part2.py b/src/hdf5_lib_part2.py
index 663f33f..29c528c 100644
--- a/src/hdf5_lib_part2.py
+++ b/src/hdf5_lib_part2.py
@@ -12,65 +12,7 @@ import utils.g5505_utils as utils
 import instruments.readers.filereader_registry as filereader_registry
 import src.hdf5_ops as hdf5_ops
 
-def read_mtable_as_dataframe(filename):
-
-    """ Reconstruct a Matlab Table encoded in a .h5 file as a Pandas DataFrame. The input .h5 file
-    contains as many groups as rows in the Matlab Table, and each group stores dataset-like variables in the Table as
-    Datasets while categorical and numerical variables in the table are represented as attributes of each group.
-
-    Note: DataFrame is constructed columnwise to ensure homogenous data columns.
-
-    Parameters:
-
-    filename (str): .h5 file's name. It may include location-path information.
-
-    Returns:
-
-    output_dataframe (pd.DataFrame): Matlab's Table as a Pandas DataFrame
-
-    """ 
-    
-    #contructs dataframe by filling out entries columnwise. This way we can ensure homogenous data columns"""
-
-    with h5py.File(filename,'r') as file:
-     
-        # Define group's attributes and datasets. This should hold
-        # for all groups. TODO: implement verification and noncompliance error if needed.
-        group_list = list(file.keys())
-        group_attrs = list(file[group_list[0]].attrs.keys())
-        # 
-        column_attr_names = [item[item.find('_')+1::] for item in group_attrs]
-        column_attr_names_idx = [int(item[4:(item.find('_'))]) for item in group_attrs]
-    
-        group_datasets = list(file[group_list[0]].keys()) if not 'DS_EMPTY' in file[group_list[0]].keys() else []
-        #
-        column_dataset_names = [file[group_list[0]][item].attrs['column_name'] for item in group_datasets]
-        column_dataset_names_idx = [int(item[2:]) for item in group_datasets]
-
-
-        # Define data_frame as group_attrs + group_datasets
-        #pd_series_index = group_attrs + group_datasets
-        pd_series_index = column_attr_names + column_dataset_names
-
-        output_dataframe = pd.DataFrame(columns=pd_series_index,index=group_list)
-
-        tmp_col = []
-
-        for meas_prop in group_attrs + group_datasets:
-            if meas_prop in group_attrs:
-                column_label = meas_prop[meas_prop.find('_')+1:]
-                # Create numerical or categorical column from group's attributes
-                tmp_col = [file[group_key].attrs[meas_prop][()][0] for group_key in group_list]
-            else:
-                # Create dataset column from group's datasets
-                column_label = file[group_list[0] + '/' + meas_prop].attrs['column_name']
-                #tmp_col = [file[group_key + '/' + meas_prop][()][0] for group_key in group_list]
-                tmp_col = [file[group_key + '/' + meas_prop][()] for group_key in group_list]
-            
-            output_dataframe.loc[:,column_label] = tmp_col
-
-    return output_dataframe
-  
+ 
 def create_group_hierarchy(obj, df, columns):
 
     """