data

Pill_Ideologies-Post_Titles.py

@@ -164,24 +164,33 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
         urls = _URLS[self.config.name]
         data_dir = dl_manager.download_and_extract(urls)
         data = pd.read_csv(data_dir)
+        data = self.process_data(data)
+        print("PAssed the processing")
 
-        def clean_data_nans(df):
-            """This function takes a dataframe and fills all NaNs with a value
-            This is to appease google colab, because my local machine did not raise errors
-            ... and it's a windows. That should tell you a lot."""
-            for col in data.columns:
-                print(f"Cleaning NaNs in {col}")
-                if df[col].dtype == "object":
-                    df[col].fillna("No-NAN-Nothing found", inplace=True)
-                elif df[col].dtype in ["int64", "float64", "int32", "float32", "int"]:
-                    df[col].fillna(0, inplace=True)
-                elif df[col].dtype == "bool":
-                    df[col].fillna(False, inplace=True)
-                else:
-                    df[col].fillna("NAN - problematic {col} found", inplace=True)
-            return None
-
-        clean_data_nans(data)
+        # def clean_data_nans(df):
+        #     """This function takes a dataframe and fills all NaNs with a value
+        #     This is to appease google colab, because my local machine did not raise errors
+        #     ... and it's a windows. That should tell you a lot."""
+        #     for col in data.columns:
+        #         print(f"Cleaning NaNs in {col}")
+        #         if df[col].dtype == "object":
+        #             df[col].fillna("No-NAN-Nothing found", inplace=True)
+        #         elif df[col].dtype in ["int64", "float64", "int32", "float32", "int"]:
+        #             df[col].fillna(0, inplace=True)
+        #         elif df[col].dtype == "bool":
+        #             df[col].fillna(False, inplace=True)
+        #         else:
+        #             df[col].fillna("NAN - problematic {col} found", inplace=True)
+        #     return None
+
+        def show_nans_profile(df):
+
+            for col in df.columns:
+
+                print(col, df[col].isna().sum())
+
+        show_nans_profile(data)
+        # clean_data_nans(data)
         print("PAssed the cleaning")
         # commented out the splits, due to google colab being uncooperative
         # raised too many errors that my local machine did not
@@ -264,3 +273,28 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
                     "upvote_ratio": row.get("upvote_ratio"),
                     "is_video": row.get("is_video"),
                 }
+
+    def process_data(df):
+        """This function takes a dataframe and processes it to remove any unwanted columns and rows"""
+        # columns that cannot be empty,so drop rows
+        df = df.dropna(subset=["subreddit"])
+        df = df.dropna(subset=["title"])
+
+        # cleaning to make colab importing the dataset through huggingface work
+        values = {
+            "id": "",
+            "text": "",
+            "url": "",
+            "score": 0,
+            "date": 0.0,
+            "subreddit_subscribers": 0,
+            "num_comments": 0,
+            "ups": 0,
+            "downs": 0,
+            "upvote_ratio": 0.0,
+            "is_video": False,
+        }
+
+        df.fillna(value=values, inplace=True)
+
+        return df

reddit_posts_fm.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2517556a2189140bf673ff9ef8a36a21e6e613feb21a3524b83cfbc3cd819e12
-size 11273908
+oid sha256:c1c32caf7adba027c63be0a0bc8263bad86ab30049290b53b458eb235a69d115
+size 11259261

redditscraper_fm.py

@@ -121,7 +121,6 @@ def pull_info_from_reddit_dict(
         "ups",
         "downs",
         "upvote_ratio",
-        "num_reports",
         "is_video",
     ],
 ):
@@ -151,39 +150,15 @@ df = pd.DataFrame(
         "text",
         "url",
         "score",
-        "author",
+        # "author", author is not useful for the analysis
         "date",
         "subreddit_subscribers",
         "num_comments",
         "ups",
         "downs",
         "upvote_ratio",
-        "num_reports",
         "is_video",
     ],
 )
 
-# columns that cannot be empty,so drop rows
-df = df.dropna(subset=["subreddit"])
-df = df.dropna(subset=["title"])
-df = df.drop(columns=["num_reports"])  # drop num_reports, always empty
-
-# cleaning to make colab importing the dataset through huggingface work
-values = {
-    "id": "NOTEXT",
-    "text": "NOTEXT",
-    "url": "NOTEXT",
-    "score": 0,
-    "date": 0.0,
-    "subreddit_subscribers": 0,
-    "num_comments": 0,
-    "ups": 0,
-    "downs": 0,
-    "upvote_ratio": 0.0,
-    "is_video": "False",
-}
-df.fillna(value=values, inplace=True)
-
-df = df[df["subreddit"].isin(subreddit_list)]
-
 df.to_csv("reddit_posts_fm.csv", index=False)