diff --git a/.gitignore b/.gitignore
index f41b9c8..87208dc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,4 +6,5 @@ CIK*
 stockdb.session.sql
 temp.json
 dataset/
-2024q2.zip
\ No newline at end of file
+2024q2.zip
+num.txt
\ No newline at end of file
diff --git a/fs_datasets.py b/fs_datasets.py
index 8a588ec..bf88a09 100644
--- a/fs_datasets.py
+++ b/fs_datasets.py
@@ -1,6 +1,7 @@
 import requests
 import zipfile
 import os
+import pandas as pd
 
 def download_file(url, filename):
     headers = {"User-Agent": "LeonardExcoffier/1.0 (excoffier.leonard@gmail.com)"}
@@ -18,6 +19,44 @@ dataset_dir = "dataset"
 dataset_link = "https://www.sec.gov/files/dera/data/financial-statement-data-sets/2024q2.zip"
 dataset_zip = "2024q2.zip"
 
-os.makedirs(dataset_dir, exist_ok=True)
-download_file(dataset_link, dataset_zip)
-extract_zip(dataset_zip, dataset_dir)
\ No newline at end of file
+#os.makedirs(dataset_dir, exist_ok=True)
+#download_file(dataset_link, dataset_zip)
+#extract_zip(dataset_zip, dataset_dir)
+
+num_file_path = os.path.join(dataset_dir, "num.txt")
+pre_file_path = os.path.join(dataset_dir, "pre.txt")
+sub_file_path = os.path.join(dataset_dir, "sub.txt")
+tag_file_path = os.path.join(dataset_dir, "tag.txt")
+
+# List of file paths and corresponding DataFrames
+file_paths = {
+    "sub": os.path.join(dataset_dir, "sub.txt"),
+    "tag": os.path.join(dataset_dir, "tag.txt"),
+    "num": os.path.join(dataset_dir, "num.txt"),
+    "pre": os.path.join(dataset_dir, "pre.txt"),
+}
+
+# Dictionary to hold DataFrames
+dfs = {}
+
+# Primary keys for each DataFrame
+primary_keys = {
+    "sub": ["adsh"],
+    "tag": ["tag", "version"],
+    "num": ["adsh", "tag", "version", "ddate", "qtrs", "uom"],
+    "pre": ["adsh", "report", "line"],
+}
+
+# Loop to read each file and create a DataFrame
+for key, path in file_paths.items():
+    dfs[key] = pd.read_csv(path, sep='\t')
+
+    # Drop rows where primary key columns are empty
+    dfs[key].dropna(subset=primary_keys[key], inplace=True)
+    
+    # Drop duplicate rows based on primary key columns
+    dfs[key].drop_duplicates(subset=primary_keys[key], inplace=True)
+
+    print(f"{key.upper()} DataFrame after dropping empty primary keys and duplicates:")
+    print(dfs[key].info())
+    print("\n")