colab is impossible

Pill_Ideologies-Post_Titles.py

@@ -114,7 +114,6 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
                     "ups": datasets.Value("int32"),
                     "downs": datasets.Value("int32"),
                     "upvote_ratio": datasets.Value("float32"),
-                    "num_reports": datasets.Value("string"),
                     "is_video": datasets.Value("bool"),
                     # These are the features of your dataset like images, labels ...
                 }
@@ -135,7 +134,6 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
                     "ups": datasets.Value("int32"),
                     "downs": datasets.Value("int32"),
                     "upvote_ratio": datasets.Value("float32"),
-                    "num_reports": datasets.Value("string"),
                     "is_video": datasets.Value("bool"),
                     # These are the features of your dataset like images, labels ...
                 }
@@ -167,23 +165,23 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
         data_dir = dl_manager.download_and_extract(urls)
         data = pd.read_csv(data_dir)
 
-        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("NAN -Nothing found", inplace=True)
-                elif df[col].dtype in ["int64", "float64", "int32", "float32"]:
-                    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 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("NAN -Nothing found", inplace=True)
+        #         elif df[col].dtype in ["int64", "float64", "int32", "float32"]:
+        #             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)
+        # 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
@@ -246,7 +244,6 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
                     "ups": row.get("ups"),
                     "downs": row.get("downs"),
                     "upvote_ratio": row.get("upvote_ratio"),
-                    "num_reports": row.get("num_reports"),
                     "is_video": row.get("is_video"),
                 }
 
@@ -265,6 +262,5 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
                     "ups": row.get("ups"),
                     "downs": row.get("downs"),
                     "upvote_ratio": row.get("upvote_ratio"),
-                    "num_reports": row.get("num_reports"),
                     "is_video": row.get("is_video"),
                 }

README.md

@@ -169,7 +169,7 @@ The main usage of this dataset is to study linguistic patterns. Running models a
 
 Here is an example analysis notebook showing what can be done with this type of data.
 
-  Example : []
+  Example : [https://colab.research.google.com/drive/15PL9RsmB6HLBvbI6KInq_Az7QhkrXxqi?usp=sharing]
 
 ### Direct Use
 

reddit_posts_fm.csv

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:73f24e2254fad46d35088c9a214c1b2378e20470846d0ea75ef70627f00b4d6e
-size 11252730
+oid sha256:5fdfbc4cc7c4d85fb76eeca459c0f05b8b64911e03df2c9d53a204ab6fb0e41f
+size 11241004

redditscraper_fm.py

@@ -163,4 +163,25 @@ df = pd.DataFrame(
     ],
 )
 
+# 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": "",
+    "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)
+
 df.to_csv("reddit_posts_fm.csv", index=False)