diff --git a/pdf_oralia/extract.py b/pdf_oralia/extract.py
index 0058a10..4435042 100644
--- a/pdf_oralia/extract.py
+++ b/pdf_oralia/extract.py
@@ -32,8 +32,8 @@ def extract_from_pdf(pdf, charge_dest, location_dest):
             df_charge.to_excel(charge_dest, sheet_name="Charges", index=False)
             print(f"{charge_dest} saved")
 
-    df_loc = extract_situation_loc(loc_table)
-    df_loc = df_loc.assign(mois=mois, annee=annee)
+    df_loc = extract_situation_loc(loc_table, mois=mois, annee=annee)
+    df_loc = df_loc.assign()
     df_loc.to_excel(location_dest, sheet_name="Location", index=False)
     print(f"{location_dest} saved")
 
diff --git a/pdf_oralia/extract_charge.py b/pdf_oralia/extract_charge.py
index 4705104..90a0302 100644
--- a/pdf_oralia/extract_charge.py
+++ b/pdf_oralia/extract_charge.py
@@ -8,10 +8,20 @@ def extract_charge(table):
         pd.DataFrame(table[1:], columns=table[0])
         .replace("", np.nan)
         .dropna(subset=["Débits"])
+        .astype(
+            {
+                "Débits": "float64",
+                "Crédits": "float64",
+                "Dont T.V.A.": "float64",
+                "Locatif": "float64",
+                "Déductible": "float64",
+            }
+        )
     )
     drop_index = df[
         df["RECAPITULATIF DES OPERATIONS"].str.contains("TOTAUX", case=False)
         | df["RECAPITULATIF DES OPERATIONS"].str.contains("solde", case=False)
     ].index
     df.drop(drop_index, inplace=True)
+
     return df
diff --git a/pdf_oralia/extract_locataire.py b/pdf_oralia/extract_locataire.py
index 8053d42..b92698f 100644
--- a/pdf_oralia/extract_locataire.py
+++ b/pdf_oralia/extract_locataire.py
@@ -18,7 +18,7 @@ def parse_above_loc(content):
     return pd.Series(row)
 
 
-def extract_situation_loc(table):
+def extract_situation_loc(table, mois, annee):
     """From pdfplumber table extract locataire df"""
     try:
         df = pd.DataFrame(table[1:], columns=table[0])
@@ -37,4 +37,19 @@ def extract_situation_loc(table):
         rows.append(up_row)
     df_cleaned = pd.concat(rows, axis=1).T
     df_cleaned.drop(["Locataires", "", "Période"], axis=1, inplace=True)
+
+    df_cleaned = df_cleaned.astype(
+        {
+            "Loyers": "float64",
+            "Taxes": "float64",
+            "Provisions": "float64",
+            "Divers": "float64",
+            "Total": "float64",
+            "Réglés": "float64",
+            "Impayés": "float64",
+        },
+        errors="ignore",
+    )
+
+    df_cleaned = df_cleaned.assign(mois=mois, annee=annee)
     return df_cleaned