diff --git a/load_data.py b/load_data.py
new file mode 100644
index 0000000..ca93a45
--- /dev/null
+++ b/load_data.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+import os
+import pandas as pd
+import numpy as np
+
+def load_german_data():
+    """
+    Load German Parliament data
+    return : Data with columns [Member, Party, vote_0, vote_1 etc]
+    """  
+    title_file = "filename_to_titles.csv"
+    vote_counter = -1
+    #data = pd.DataFrame()
+    data = {}
+    
+    period_column_g = 'Wahlperiode'
+    name_column_g = 'Bezeichnung'
+    party_column_g = 'Fraktion/Gruppe'
+    name_column = 'Member'
+    party_column = 'Party'
+    
+    vote_column_to_title = {}
+    
+    voting_features = ['ja', 'nein', 'Enthaltung', 'ungültig']
+    for dirname, _, filenames in os.walk('./de/csv'):
+        for filename in filenames:
+            if filename != title_file:
+                
+                print(filename)
+                
+                vote_counter += 1
+                df = pd.read_csv(os.path.join(dirname, filename))
+                
+                # Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
+                for i, feature in enumerate(voting_features):
+                    df[feature] *= i
+                vote_column_name = f'vote_{vote_counter}'
+                
+                # Map column name of vote to filename -> allows retrieving what the vote was about
+                vote_column_to_title[vote_column_name] = filename
+                
+                # add feature for the vote
+                df[vote_column_name] = df[voting_features].sum(axis=1)
+                
+                df=df.rename(columns={name_column_g:name_column,party_column_g:party_column})
+                
+                period = df.iloc[0][period_column_g]
+                                    
+                if period in data:
+                    # merge data with already loaded data 
+                    data[period] = data[period].merge(df[[name_column, vote_column_name]], on=name_column)                    
+                else:
+                    # if first file that is loaded set data equal to data from first file
+                    data[period] = df[[name_column, party_column, vote_column_name]]
+                    
+    print(data)
+    return data
+
+
+def load_uk_data(path):
+    """
+    Load German Parliament data
+    return : Data with columns [Member, Party, vote_0, vote_1 etc]
+    """  
+    #print directory path
+    print(path) 
+    # Preprocess data
+    vote_counter = -1
+    data = pd.DataFrame()
+    
+    name_column = 'Member'
+    party_column = 'Party'
+    vote_column = 'Vote'
+    
+    column_to_filename = {}
+    
+    voting_features = {'Aye':0, 'Teller - Ayes':0, 'No':1, 'Teller - Noes':1, 'No Vote Recorded':2}
+    for dirname, _, filenames in os.walk(path):
+        for filename in filenames:
+            vote_counter += 1
+            
+            # Read title rows
+            # sep is set to new line so it never splits up the title cells
+            title_df = pd.read_csv(os.path.join(dirname, filename), sep='\n',nrows=(3),skip_blank_lines=True,header=None)
+        
+            # Read data rows
+            df = pd.read_csv(os.path.join(dirname, filename),skiprows=(10))
+            
+            # Give each voting behaviour type an identifier from 0 to len(voting_features) - 1
+            df[vote_column].replace(voting_features, inplace=True)
+            
+            #Replace the vote column name
+            vote_column_name = f'vote_{vote_counter}'
+            df=df.rename(columns={vote_column:vote_column_name})
+             
+            # Map column name of vote to title -> allows retrieving what the vote was about
+            column_to_filename[vote_column_name] = title_df.iat[2,0]
+                    
+            if data.empty:
+                # if first file that is loaded set data equal to data from first file
+                data = df[[name_column, party_column, vote_column_name]]
+            else:
+                # merge data with already loaded data 
+                data = data.merge(df[[name_column, vote_column_name]], on=name_column)
+    
+    print(data)
+    return data
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