read_csv using dtypes but there is na value in columns

clean_pdf_type=pd.read_csv('table_updated.csv',usecols=col_names)
clean_pdf_type = (clean_pdf_type.fillna(0)).astype(col_types)

As said in the comments, don't specify the type, remove the NA and then cast to a certain type

Pandas v0.24+

See NumPy or Pandas: Keeping array type as integer while having a NaN value

Pandas pre-v0.24

You cannot have NaN values in an int dtype series. This is non-avoidable, because NaN values are considered float:

import numpy as np
type(np.nan)  # float

Your best bet is to read in these columns as float instead. If you are then able to replace NaN values by a filler value such as 0 or -1, you can process accordingly and convert to int:

int_cols = ['col1', 'col2', 'col3']
df[int_cols] = df[int_cols].fillna(-1)
df[int_cols] = df[int_cols].apply(pd.to_numeric, downcast='integer')

The alternative of having mixed int and float values will result in a series of dtype object. It is not recommended.