![]() ![]() Allow for modest density increase and expanded range of housing forms within existing single-detached neighbourhoods.Create improved compatibility with new construction and existing housing stock.The aim of this zoning review includes the following desired outcomes: The City is exploring these changes to the RS-1 zone to both regulate the construction of new homes and permit a broader range of uses that contribute to housing choice, which aligns with the City’s Housing Action Plan (see inset). Setting a maximum buildable area for brand new home.Accelerated approval process for 371 m 2/4,000 ft 2 lots.Accelerated approval process for duplexes. ![]() Through this review, the Planning Department is exploring three changes to the RS-1 Single Detached Residential Zone: What is the Purpose of the Infill Development Options Work? Today, the RS-1 zone minimum lot size is considered quite sizable for a single detached housing form and is one of the largest urban single detached zones in the City’s Zoning Bylaw. The RS-1 zone has a minimum lot size of 668 m 2 (7,190 ft 2) and was a widely used zone after World War II through to the early 2000’s for development of urban single detached houses. The RS-1 zone is a residential zone that allows a primary use of a single detached home and a secondary use, such as a detached garden suite or secondary suite, and workshop/shed/detached garage. This polars project isĬompiled without avx target features.All land has a zoning classification, which corresponds with the uses and building regulations permitted on the property. dating from before 2011)? Install pip install polars-lts-cpu. Legacyĭo you want polars to run on an old CPU (e.g. Or for python users install pip install polars-u64-idx.ĭon't use this unless you hit the row boundary as the default polars is faster and consumes less memory. Going big.ĭo you expect more than 2^32 ~4,2 billion rows? Compile polars with the bigidx feature flag. We expose pyo3 extensions for DataFrame and Seriesĭata structures. Use custom Rust function in python?Įxtending polars with UDFs compiled in Rust is easy. However, both the Python package and the Python module are named polars, so youĬan pip install polars and import polars. Note that the Rust crate implementing the Python bindings is called py-polars to distinguish from the wrapped $ cd py-polars & maturin develop -release -C codegen-units=16 -C lto=thin -C target-cpu=native You can take latest release from crates.io, or if you want to use the latest features / performance improvements Releases happen quite often (weekly / every few days) at the moment, so updating polars regularly to get the latest bugfixes / features might not be a bad idea. Timezone support, only needed if are on Python<3.9 or you are on Windows Support for reading from Delta Lake Tables Support for reading from remote file systems Install with numpy for converting data to and from numpy arrays Install with Pandas for converting data to and from Pandas Dataframes/Series Install all optional dependencies (all of the following) You can also install the dependencies directly. Pip install 'polars ' # install a subset of all optional dependencies Collect with collect(streaming=True) to run the query streaming. Streaming fashion, this drastically reduces memory requirements so you might be able to process your 250GB dataset on your If you have data that does not fit into memory, polars lazy is able to process your query (or parts of your query) in a It comes with zero required dependencies, and this shows in the import times: In the TPCH benchmarks polars is orders of magnitudes faster than pandas, dask, modin and vaex See the results in DuckDB's db-benchmark. In fact, it is one of the best performing solutions available. Refer to polars-cli for more information. > SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc( 'file.arrow ') WHERE id1 = 'id016 ' LIMIT 10 # run an inline sql query > polars -c "SELECT sum(v1) as sum_v1, min(v2) as min_v2 FROM read_ipc('file.arrow') WHERE id1 = 'id016' LIMIT 10 " # run interactively > polars │ - ┆ - ┆ ng_fruits ┆ - ┆ rs ┆ uits ┆ uits ┆ _by_fruits │ │ fruits ┆ cars ┆ literal_stri ┆ B ┆ sum_A_by_ca ┆ sum_A_by_fr ┆ rev_A_by_fr ┆ sort_A_by_B │ # embarrassingly parallel execution & very expressive query language > df. ![]()
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