I have a very large CSV file with many cells that have "\N" as the value. For example:
| OBJ_1 | OBJ_2 | TCA |
|---|---|---|
| 16908 | 37152 | 2019-07-29 01:13:37 |
| 37152 | 16908 | 2019-07-29 01:13:37 |
| 16908 | 37152 | 2019-07-29 01:13:37 |
| \N | 16908 | 2019-07-29 01:13:37 |
| 19483 | 23132 | \N |
| 22829 | \N | 2019-07-29 01:13:37 |
When I run the function to read the file: pd.read_csv("path")
I get the error: ValueError: could not convert string to float: '\\N'
How can I read a CSV file with "\N" values and have them either ignored or replaced with some default value (like zero or undefined)?
>Solution :
According to the docs, you can use the na_values argument to automatically convert these to NaNs, like this:
df = pd.read_csv("path", na_values="\\N")