Pandas: ValueError: cannot convert float NaN to integer

Question

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) 

Answer 1

For identifying NaN values use boolean indexing:

print(df[df['x'].isnull()]) 

Then for removing all non-numeric values use to_numeric with parameter errors='coerce' - to replace non-numeric values to NaNs:

df['x'] = pd.to_numeric(df['x'], errors='coerce') 

And for remove all rows with NaNs in column x use dropna:

df = df.dropna(subset=['x']) 

Last convert values to ints:

df['x'] = df['x'].astype(int)