Data Wrangling with DataFrames
SQL Frames has built-in intelligence to automatically detect the data types while processing the input from sources such as CSV files and JSON. However, it is not always possible to pick the right data type. Sometimes data comes in a format that is not suitable for analysis. It requires processing it further and extract and standardize the input. These are some of the reasons to make use of the
Raw data may be in a format that is not even parsable as a CSV file. Dedicated Data Wrangling applications can offer tools to start from such completely unstructured data and bring structure to it. SQL Frames current scope of data wrangling is limited to munging data after it can be parsed from a CSV file or loaded from a database. This is still useful especially if it is not easy to fix the data at the source as it is used by other applications or fixing the existing data models might not be easy or not possible due to limitations of the SAAS applications.