--- a
+++ b/Excel_Processing.py
@@ -0,0 +1,33 @@
+import pandas as pd
+
+def ProcessSpreadsheets(dataset_info_path, scan_info_path) -> pd.DataFrame:
+    r"""
+    Method for processing ImageCHD's two Excel spreadsheets.
+    SPREADSHEETS NEED TO BE PRE-PROCESSED THEMSELVES BEFORE CALLING THIS METHOD!
+
+    Arguments:
+      dataset_info_path (string): path to the imageCHD_dataset_info.xlsx file
+      scan_info_path (string): path to the imageCHD_dataset_image_info.xlsx file
+    """
+    dataset_info = pd.read_excel(io = dataset_info_path, sheet_name = 'classification dataset')
+    scan_info = pd.read_excel(io = scan_info_path, sheet_name = 'Sheet1')
+    dataset_info = pd.concat([dataset_info, scan_info], axis = 1)
+
+    ignore = dataset_info[dataset_info["IGNORED"] > 0].index
+    drop_cols = ["DORV", "CAT", "APVC", "DSVC", "AAH", "DAA", "IAA", "PAS",
+                "NORMAL", "ONLYFIRST8", "FIRST8+MORE", "NORMAL.1", "IGNORED",
+                "idx", "PatientBirthDate1", "AcquisitionDate1",
+                "PixelSpacing1", "PixelSpacing2", "calculate_z_thick",
+                "ManufacturerModelName", "AGE", "UNKNOWN"]
+
+    dataset_info = dataset_info.drop(drop_cols, axis = 1) \
+                            .drop(ignore) \
+                            .reset_index() \
+                            .drop("level_0", axis = 1)
+
+    nan_cols = ["ASD", "VSD", "AVSD", "ToF", "TGA", "CA", "PA", "PDA"]
+    for col in nan_cols:
+        mask = dataset_info[dataset_info[col] != 1].index
+        dataset_info.loc[mask, col] = 0
+    
+    return dataset_info
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