![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1905996%2F6ab80df99cdb5b4f256e616042363e02%2FScreenshot%202020-02-15%20at%201.10.34%20PM.png?generation=1581752469694930&alt=media\")
In MPlayers.csv, there are 4 columns, but at line number 80, there are 5 values. So pd.read_csv() gives following error:
ParserError: Error tokenizing data. C error: Expected 4 fields in line 80, saw 5
Here's the actual data:
\n\n
To ignore such erroneous lines while reading, use error_bad_lines=False in pd.read_csv() as following:
pd.read_csv('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MPlayers.csv', error_bad_lines=False)
Thanks!
", "messages": [ { "id": "746559", "postDate": "02/15/2020 07:36:59", "content": "In MPlayers.csv, there are 4 columns, but at line number 80, there are 5 values. So pd.read_csv() gives following error:
ParserError: Error tokenizing data. C error: Expected 4 fields in line 80, saw 5
Here's the actual data:
\n\n
To ignore such erroneous lines while reading, use error_bad_lines=False in pd.read_csv() as following:
pd.read_csv('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MPlayers.csv', error_bad_lines=False)
Thanks!
", "rawMarkdown": "In MPlayers.csv, there are 4 columns, but at line number 80, there are 5 values. So `pd.read_csv()` gives following error: \n\n`ParserError: Error tokenizing data. C error: Expected 4 fields in line 80, saw 5`\n\nHere's the actual data:\n\n\n\n\nTo ignore such erroneous lines while reading, use `error_bad_lines=False` in `pd.read_csv()` as following:\n\n`pd.read_csv('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MPlayers.csv', error_bad_lines=False)`\n\nThanks!", "votes": null }, { "id": "746929", "postDate": "02/15/2020 18:45:31", "content": "Ironically, there are no errors like this in the women's WPlayers file, because there aren't any women players with a suffix like \"Jr\" or \"III\" in their name that gets a comma in it. I thought that values with commas would be enclosed with quotes but it looks like I was wrong. I will figure out how file corrections work and update the file. Fortunately it is a relatively unimportant file since it just provides text labels to the player ID's.
", "rawMarkdown": "Ironically, there are no errors like this in the women's WPlayers file, because there aren't any women players with a suffix like \"Jr\" or \"III\" in their name that gets a comma in it. I thought that values with commas would be enclosed with quotes but it looks like I was wrong. I will figure out how file corrections work and update the file. Fortunately it is a relatively unimportant file since it just provides text labels to the player ID's.", "votes": null }, { "id": "747058", "postDate": "02/15/2020 22:36:07", "content": "I wrote a small script to remove the unwanted commas. Maybe, it can be useful to someone. To run it, launch :\npython clean_player.py MPlayers.csv MPlayers_correction.csv.\nThen, you should be able to read the file without issues.
Updated the file! Thanks for letting us know.
", "rawMarkdown": "Updated the file! Thanks for letting us know.", "votes": null } ], "comments": [ { "id": 746929, "author_name": "jeffsonas", "author_url": "", "post_date": "02/15/2020 18:45:31", "content": "Ironically, there are no errors like this in the women's WPlayers file, because there aren't any women players with a suffix like \"Jr\" or \"III\" in their name that gets a comma in it. I thought that values with commas would be enclosed with quotes but it looks like I was wrong. I will figure out how file corrections work and update the file. Fortunately it is a relatively unimportant file since it just provides text labels to the player ID's.
", "votes": null, "replies": [] }, { "id": 747058, "author_name": "stevengolo", "author_url": "", "post_date": "02/15/2020 22:36:07", "content": "I wrote a small script to remove the unwanted commas. Maybe, it can be useful to someone. To run it, launch :\npython clean_player.py MPlayers.csv MPlayers_correction.csv.\nThen, you should be able to read the file without issues.
Updated the file! Thanks for letting us know.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "746559": "In MPlayers.csv, there are 4 columns, but at line number 80, there are 5 values. So `pd.read_csv()` gives following error: \n\n`ParserError: Error tokenizing data. C error: Expected 4 fields in line 80, saw 5`\n\nHere's the actual data:\n\n\n\n\nTo ignore such erroneous lines while reading, use `error_bad_lines=False` in `pd.read_csv()` as following:\n\n`pd.read_csv('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MPlayers.csv', error_bad_lines=False)`\n\nThanks!", "746929": "Ironically, there are no errors like this in the women's WPlayers file, because there aren't any women players with a suffix like \"Jr\" or \"III\" in their name that gets a comma in it. I thought that values with commas would be enclosed with quotes but it looks like I was wrong. I will figure out how file corrections work and update the file. Fortunately it is a relatively unimportant file since it just provides text labels to the player ID's.", "747058": "I wrote a small script to remove the unwanted commas. Maybe, it can be useful to someone. To run it, launch :\n`python clean_player.py MPlayers.csv MPlayers_correction.csv`.\nThen, you should be able to read the file without issues.", "747121": "Updated the file! Thanks for letting us know." }, "source": "meta" }