CSV files are received from third-party sources, most of the time it has null values for blanks/empty. By usingpandas.read_csv()we can load the CSV file into DataFrame and pandas converts all null values into NaN in DataFrame. Either you candrop rows with NaN valuesusingpandas.DataFrame....
After we find any missing data in any row that runs the code that is previously mentioned, we can remove those rows since those rows don’t have much useful data. We can even guess those missing values and fill in the blanks with educated guesses by estimating the time-based data based ...