Data types define the kind of data that can be stored in a column, such as text, numbers, dates, or Boolean values. Converting a column's data type may be necessary to ensure that the data is in the appropriate format for analysis or modeling. This conversion is an important aspect of data cleaning that can improve the accuracy, consistency, and usability of the dataset. It also helps avoid errors and obtain more meaningful insights from the data.
For example, if a column contains dates stored as text, it may be necessary to convert the data type to a date format to perform date-based calculations or comparisons. Similarly, if a column contains numerical data stored as text, converting the data type to a numeric format can enable mathematical operations and aggregations.
Converting a column's data type can also help to detect and handle data inconsistencies or errors. For instance, if a column is expected to contain only numerical data, but some rows contain text values, converting the data type can identify these inconsistencies and either replace or remove the problematic values.
Conversion of data types may also allow for significant reduction in space taken up by the table (for example, an int column that contains 0 or 1 values, when converted to a byteint will save 15 bytes per row of storage space).