![]() ![]() For instance, the mixed_col has a and missing_col has NaN. However, astype() won’t work for a column of mixed types. use it to downcast to a smaller or upcast to a larger byte size. ![]() convert it to another numeric data type (int to float, float to int, etc.).If you already have a numeric data type ( int8, int16, int32, int64, float16, float32, float64, float128, and boolean) you can also use astype() to: ![]() The method is supported by both Pandas DataFrame and Series. The simplest way to convert data type from one to the other is to use astype() method. Difference between astype() and to_numeric() When data is a bit complex to convert, we can create a custom function and apply it to each value to convert to the appropriate data type.įor instance, the money_col column, here is a simple function we can use: > def convert_money(value): value = value.replace('£','').replace(',', '') return float(value) > df. Creating a custom function to convert data to numbers The difference between this and above is that this method does the converting during the reading process and can be time-saving and more memory efficient. The dtype argument takes a dictionary with the key representing the column and the value representing the data type.
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