While this datatype currently doesn’t provide any explicit reminiscence or pace improvements, the development team behind Pandas has indicated that it will happen in the future. You have a numerical column which is represented as string and also you wish to convert it to Integer kind. To replace the NaN values with other values you could convert to integers. You can solve this error by either dropping the rows with the NaN values or replacing the NaN values with one other worth that you can convert to an integer. Notice I stated "try" - if astype() does not know the way to convert a value within the Series or DataFrame, it will raise an error. For example, when you have a NaN or inf value you may get an error making an attempt to convert it to an integer. Next, you’ll see how to cast all columns to a different sort. The column_1 and Column_2 might be converted to int utilizing the astype(). You’ll convert the column type to datetime utilizing the below snippet. Now, let’s try to convert the column Available_Quantity to drift. Which has the non-numeric characters in one of many cells. You can use np.int64 in type to transform column to int64. If you simply specify int in astype, it converts the column to int32. You can do it through the use of the to_numeric() methodology as proven below. You can convert column to int by specifying int in the technique parameter as proven under. If column accommodates numbers with decimal points, to_numeric() will convert it to float64. Also, the examples to perform several sorts of conversion. Df. If you liked this write-up and you would like to get additional facts concerning How can I get the name of a List kindly stop by our webpage. astype() – Method to invoke the astype funtion within the dataframe.