Pandas: ValueError: cannot convert float NaN to integer

I get ValueError: cannot convert float NaN to integer for following:

df = pandas.read_csv('zoom11.csv')
df[['x']] = df[['x']].astype(int)

• The “x” is obviously a column in the csv file, but I cannot spot any float NaN in the file, and dont get what does it mean by this.
• When I read the column as String, then it has values like -1,0,1,…2000, all look very nice int numbers to me.
• When I read the column as float, then this can be loaded. Then it shows values as -1.0,0.0 etc, still there are no any NaN-s
• I tried with error_bad_lines = False and dtype parameter in read_csv to no avail. It just cancels loading with same exception.
• The file is not small (10+ M rows), so cannot inspect it manually, when I extract a small header part, then there is no error, but it happens with full file. So it is something in the file, but cannot detect what.
• Logically the csv should not have missing values, but even if there is some garbage then I would be ok to skip the rows. Or at least identify them, but I do not see way to scan through file and report conversion errors.

Update: Using the hints in comments/answers I got my data clean with this:

# x contained NaN
df = df[~df['x'].isnull()]

# Y contained some other garbage, so null check was not enough
df = df[df['y'].str.isnumeric()]

# final conversion now worked
df[['x']] = df[['x']].astype(int)
df[['y']] = df[['y']].astype(int)