diff --git a/fix_clean_tweets_dataframe.py b/clean_tweets_dataframe.py
similarity index 60%
rename from fix_clean_tweets_dataframe.py
rename to clean_tweets_dataframe.py
index 7b45a35..f5ebcc5 100644
--- a/fix_clean_tweets_dataframe.py
+++ b/clean_tweets_dataframe.py
@@ -15,22 +15,33 @@ def drop_unwanted_column(self, df:pd.DataFrame)->pd.DataFrame:
         df.drop(unwanted_rows , inplace=True)
         df = df[df['polarity'] != 'polarity']
         
-        return df
+        return 
+        
+
+
     def drop_duplicate(self, df:pd.DataFrame)->pd.DataFrame:
         """
-        drop duplicate rows
+        drop duplicate rows from the DataFrame
+
+        Parameters
+        df (pd.DataFrame): Dataframe from which to remove duplicates 
+
+        Returns:
+        pd.DataFrame: A Dataframe with duplicates remofved
         """
         
-        ---
+        df = df.drop_duplicates(keep='first',inplace=False)
+
+        print("Duplicate rows removed from the DataFrame.")
         
         return df
+    
+
     def convert_to_datetime(self, df:pd.DataFrame)->pd.DataFrame:
         """
         convert column to datetime
         """
-        ----
-        
-        ----
+        df['created_at'] = pd.to_datetime(df['created_at'])
         
         df = df[df['created_at'] >= '2020-12-31' ]
         
@@ -41,18 +52,21 @@ def convert_to_numbers(self, df:pd.DataFrame)->pd.DataFrame:
         convert columns like polarity, subjectivity, retweet_count
         favorite_count etc to numbers
         """
-        df['polarity'] = pd.----
-        
-        ----
-        ----
+        df['polarity'] = pd.to_numeric(df['polarity'])
+        df['subjectivity'] = pd.to_numeric(df['subjectivity'])
+        df['retweet_count'] = pd.to_numeric(df['retweet_count'])
+        df['favorite_count'] = pd.to_numeric(df['favorite_count'])
+        df['retweet_count'] = pd.to_numeric(df['retweet_count'])
+        #df['followers_count'] = pd.to_numeric(df['followers_count'])        
         
         return df
-    
+
+
     def remove_non_english_tweets(self, df:pd.DataFrame)->pd.DataFrame:
         """
         remove non english tweets from lang
         """
         
-        df = ----
+        df = pd.drop(df[df['lang'] == 'en'])
         
         return df
\ No newline at end of file
diff --git a/eda.ipynb b/eda.ipynb
new file mode 100644
index 0000000..e69de29
diff --git a/fix_extract_dataframe.py b/extract_dataframe.py
similarity index 93%
rename from fix_extract_dataframe.py
rename to extract_dataframe.py
index 3bd792d..51449bf 100644
--- a/fix_extract_dataframe.py
+++ b/extract_dataframe.py
@@ -39,7 +39,11 @@ def find_statuses_count(self)->list:
         statuses_count 
         
     def find_full_text(self)->list:
-        text = 
+        """
+        This funciton gets the entire text f rom the tweet strings
+        """
+        text = full_text
+        return text 
        
     
     def find_sentiments(self, text)->list:
@@ -47,8 +51,14 @@ def find_sentiments(self, text)->list:
         return polarity, self.subjectivity
 
     def find_created_time(self)->list:
+        created_at = []
+        for items in self.tweets_list:
+            created_at.append(items.get('craeted_at',None))
        
         return created_at
+    
+
+
 
     def find_source(self)->list:
         source =