"In pandas 3.0, string data is now stored using a dedicated str dtype instead of the previous object dtype from NumPy. This change aims to provide a consistent method for handling string data. The new string dtype only accepts string values and allows for missing values, simplifying the management of missing data. Code that checks for the object dtype or handles missing values in the old way may need to be updated to align with these new standards."
"Another change is the formal adoption of Copy-on-Write semantics. Indexing and subsetting operations now behave as if they return copies from the user's perspective, eliminating longstanding ambiguity between views and copies. As a result, chained assignment no longer works, SettingWithCopyWarning has been removed, and defensive .copy() calls are no longer necessary to silence warnings. Internally, pandas may still use views for performance, but the API guarantees predictable copy-like behavior."
"The release also introduces early support for a new expression syntax using pd.col(), allowing column-based transformations to be written declaratively instead of via lambda functions. For example, df.assign(c = pd.col("a") + pd.col("b")) replaces the need for inline callables. The feature is expected to expand in future versions. Datetime handling has changed as well. Instead of defaulting to nanosecond precision, pandas now infers the most appropriate resolution when parsing input."
Pandas 3.0 replaces object-backed string storage with a dedicated str dtype that only accepts strings and supports missing values, requiring updates to code that assumed object dtype. The release formally adopts Copy-on-Write semantics so indexing and subsetting behave like copies from the API perspective, removes SettingWithCopyWarning, and disables chained assignment while preserving internal view optimizations. Early support for a pd.col() expression syntax enables declarative column transformations. Datetime parsing now infers an appropriate resolution instead of defaulting to nanoseconds, and Arrow PyCapsule support enables zero-copy data exchange, alongside removal of many deprecated features.
Read at InfoQ
Unable to calculate read time
Collection
[
|
...
]