Can a date column safely support analysis, or might it contain entries such as “last Tuesday, around noon”? A data type identifies the kind of value a variable or column should represent, while validation checks whether actual values follow that expectation. section 2.5.4
Use data types as part of a practical validation routine:
- Define expected types and other constraints before analysis. section 2.5.4
- Check for values that fall outside those expectations. section 2.5.4
- Investigate deviations instead of automatically treating them as errors; they may indicate collection problems or important business events. section 2.5.4
- Check consistency across tables and correct or handle violations. section 2.5.4
Converting a date column
A dataframe column can be converted explicitly with df_name['column_name'] = df_name.column_name.astype('datetime64[ns]'). Afterward, inspect failures and compare unusual values with the raw records rather than assuming the conversion made the data correct. section 2.5.4
df_name['column_name'] = df_name.column_name.astype('datetime64[ns]')
Inspect the concrete datetime conversion used to enforce an expected date representation. section 2.5.4
