diff --git a/cds1011-ls1/Auswertung_DataScience_cons.xlsm b/cds1011-ls1/Auswertung_DataScience_cons.xlsm
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diff --git a/cds1011-ls1/heatmap.py b/cds1011-ls1/heatmap.py
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+++ b/cds1011-ls1/heatmap.py
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+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+# loading the seperate excel sheets for 2023-2025
+
+def load_dataframe25():
+    df2025 = pd.read_excel("Auswertung_DataScience_cons.xlsm", sheet_name=0, header=2, index_col=2)
+    return df2025
+
+def load_dataframe24():
+    df2024 = pd.read_excel("Auswertung_DataScience_cons.xlsm", sheet_name=1, header=2, index_col=2)
+    return df2024
+    
+def load_dataframe23():
+    df2023 = pd.read_excel("Auswertung_DataScience_cons.xlsm", sheet_name=2, header=2, index_col=2)
+    return df2023
+
+# function to create a suitable pivot-table from the xlsm file
+def generate_hm_df(df):
+    # create a column "count" that contains the nr of (coordinate) duplicates
+    cols = ['Aveg X', 'Aveg Y']
+    df['count'] = df.groupby(cols)['Aveg X'].transform('size')
+    print(df.columns)
+    print(df)
+    
+    # drop coordinate duplicates
+    df_nd = df.drop_duplicates(subset=cols)
+    print(df_nd)
+    
+    # create a pivot table with the x-coordinates as columns and y-coordinates as rows, containing the nr. of duplicates = nr of shots
+    pivot = df_nd.pivot_table(values='count', index='Aveg Y', columns='Aveg X', fill_value=0)
+    print(pivot)
+    
+    # create a list with the 41 coordinates
+    y=-10
+    coordinates = []
+    for x in range(41):
+        coordinates.append(y)
+        y = y + 0.5
+    else:
+        print(coordinates)
+    
+    
+    # check if columns contain all coordinates from -10 to 10, create columns if necessary
+    y=-10.0
+    while (y <= 10) == True:
+        if y in pivot.columns:
+            print("no column added for", y)
+            y = y + 0.5
+        else:
+            pivot.insert(29, y, 0.0)
+            print("column added for", y)
+            y = y + 0.5
+    
+    # check if rows contain all coordinates from -10 to 10, create rows if necessary
+    y=-10.0
+    while (y<=10) == True:
+        if y in pivot.index:
+            print("no row added for", y)
+            y = y + 0.5
+        else:
+            pivot = pivot._append(pd.Series(name=y))
+            pivot = pivot.fillna(value=0)
+            print("row added for", y)
+            y = y + 0.5
+    
+    # sort the columns and rows
+    pivot = pivot.sort_index(axis=1)
+    pivot = pivot.sort_index(axis=0, ascending=False)
+    
+    print(pivot.index)
+    print(pivot.columns)
+    
+    # remove columns and rows <-10 and >10
+    for x in pivot.columns:
+        if (x < -10) or (x > 10):
+            pivot.drop(labels=x, axis=1, inplace=True)
+            print("COLUMN", x, "HAS BEEN DELETED.")
+            continue
+        else:
+            print("Row", x, "has not been deleted.")
+            continue
+    
+    for x in pivot.index:
+        if (x < -10) or (x > 10):
+            pivot.drop(labels=x, axis=0, inplace=True)
+            print("ROW", x, "HAS BEEN DELETED.")
+            continue
+        else:
+            print("Row", x, "has not been deleted.")
+            continue
+    
+    print(pivot)
+    
+    return pivot
+
+def main():
+    
+    # change the load_datframe23() to whatever year you want the heatmap from
+    df = load_dataframe25()
+    df_hm = generate_hm_df(df)
+    
+    # generate the heatmap, rename both axis
+    pal = sns.cubehelix_palette(start=2, rot=0.3, dark=0, light=1, reverse=False, as_cmap=True)
+    hm = sns.heatmap(df_hm, cmap=pal, annot=False, annot_kws={'size': 9}, square=True)
+    hm.set(xlabel="X-Koordinaten", ylabel="Y-Koordinaten")
+    plt.show()
+
+if __name__ == "__main__":
+    main()
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diff --git a/cds1011-ls1/lathex_raw.zip b/cds1011-ls1/lathex_raw.zip
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