diff --git a/gseapy/base.py b/gseapy/base.py
index 9e749f4..c2efa60 100644
--- a/gseapy/base.py
+++ b/gseapy/base.py
@@ -202,6 +202,8 @@ def _check_data(self, exprs: pd.DataFrame) -> pd.DataFrame:
         """
         check NAs, duplicates.
         exprs: dataframe, the frist column must be gene identifiers
+
+        return: dataframe, index is gene ids
         """
         ## if gene names contain NA, drop them
         if exprs.iloc[:, 0].isnull().any():
@@ -212,30 +214,43 @@ def _check_data(self, exprs: pd.DataFrame) -> pd.DataFrame:
             exprs.dropna(how="all", inplace=True)  # drop rows with all NAs
             exprs = exprs.fillna(0)
         ## check duplicated IDs
-        # gene_id = exprs.columns[0]
-        # if exprs.duplicated(subset=gene_id).sum() > 0:
-        #     self._logger.info("Found duplicated gene names, make unique")
-        #     mask = exprs.duplicated(subset=gene_id, keep=False) #
-        #     dups = exprs.loc[mask, gene_id].groupby(gene_id).cumcount().map(lambda c: "_" + str(c) if c else "")
-        #     exprs.loc[mask, gene_id] = exprs.loc[mask, gene_id] + dups
-        # check whether contains infinity values
-
         # set gene name as index
         exprs.set_index(keys=exprs.columns[0], inplace=True)
         # select numberic columns
         df = exprs.select_dtypes(include=[np.number])
         # microarray data may contained multiple probs of same gene, average them
-        if exprs.index.duplicated().sum() > 0:
+        if df.index.duplicated().sum() > 0:
             self._logger.warning(
                 "Found duplicated gene names, values averaged by gene names!"
             )
             df = df.groupby(level=0).mean()
-
+        # check whether contains infinity values
         if np.isinf(df).values.sum() > 0:
             self._logger.warning("Input gene rankings contains inf values!")
-            df = df.apply()
+            col_min_max = {
+                np.inf: df[np.isfinite(df)].max(),  # column-wise max
+                -np.inf: df[np.isfinite(df)].min(),  # column-wise min
+            }
+            df = df.replace({col: col_min_max for col in df.columns})
         return df
 
+    def make_unique(self, rank_metric: pd.DataFrame, col_idx: int) -> pd.DataFrame:
+        """
+        make gene id column unique
+        """
+        id_col = rank_metric.columns[col_idx]
+        if rank_metric.duplicated(subset=id_col).sum() > 0:
+            self._logger.info("Input gene rankings contains duplicated IDs")
+            mask = rank_metric.duplicated(subset=id_col, keep=False)
+            dups = (
+                rank_metric.loc[mask, id_col]
+                .groupby(id_col)
+                .cumcount()
+                .map(lambda c: "_" + str(c) if c else "")
+            )
+            rank_metric.loc[mask, id_col] = rank_metric.loc[mask, id_col] + dups
+        return rank_metric
+
     def load_gmt_only(
         self, gmt: Union[List[str], str, Dict[str, str]]
     ) -> Dict[str, List[str]]:
diff --git a/gseapy/gsea.py b/gseapy/gsea.py
index e623a4f..7fea1e0 100644
--- a/gseapy/gsea.py
+++ b/gseapy/gsea.py
@@ -361,23 +361,6 @@ def __init__(
         self._noplot = no_plot
         self.permutation_type = "gene_set"
 
-    def make_unique(self, rank_metric: pd.DataFrame, col_idx: int) -> pd.DataFrame:
-        """
-        make gene id column unique
-        """
-        id_col = rank_metric.columns[col_idx]
-        if rank_metric.duplicated(subset=id_col).sum() > 0:
-            self._logger.info("Input gene rankings contains duplicated IDs")
-            mask = rank_metric.duplicated(subset=id_col, keep=False)
-            dups = (
-                rank_metric.loc[mask, id_col]
-                .groupby(id_col)
-                .cumcount()
-                .map(lambda c: "_" + str(c) if c else "")
-            )
-            rank_metric.loc[mask, id_col] = rank_metric.loc[mask, id_col] + dups
-        return rank_metric
-
     def _load_ranking(self, rank_metric: pd.DataFrame) -> pd.Series:
         """Parse ranking
         rank_metric: two column dataframe. first column is gene ids
@@ -439,23 +422,7 @@ def load_ranking(self):
         # make unique
         rank_metric = self.make_unique(rank_metric, col_idx=0)
         # set index
-        rank_metric.set_index(keys=rank_metric.columns[0], inplace=True)
-        if rank_metric.isnull().any().sum() > 0:
-            self._logger.warning("Input rankings contains NA values!")
-            # fill na
-            rank_metric.dropna(how="all", inplace=True)
-            rank_metric.fillna(0, inplace=True)
-
-        # check whether contains infinity values
-        if np.isinf(rank_metric).values.sum() > 0:
-            self._logger.warning("Input gene rankings contains inf values!")
-            col_min_max = {
-                np.inf: rank_metric[np.isfinite(rank_metric)].max(),  # column-wise max
-                -np.inf: rank_metric[np.isfinite(rank_metric)].min(),  # column-wise min
-            }
-            rank_metric = rank_metric.replace(
-                {col: col_min_max for col in rank_metric.columns}
-            )
+        rank_metric = self._check_data(rank_metric)
         # check ties in prerank stats
         dups = rank_metric.apply(lambda df: df.duplicated().sum() / df.size)
         if (dups > 0).sum() > 0: