![]() Mahesh Sudhakaran filed a complaint with the Karnataka Real Estate Regulatory Authority (RERA), seeking a refund of the amount paid with interest. ![]() Filing of Complaint and RERA's Decision:ĭue to the developer's failure to complete the project within the stipulated timeframe, Mr. As per the agreement between the appellant and the developer, possession of the apartment was to be delivered within 46 months, with an additional grace period of six months. 1104, 11th Floor, C Block of the project. Mahesh Sudhakaran, the appellant, had booked Apartment No. The Glen Classic Project, situated in the House of Hiranandani, Hebbal, Bangalore, was being developed by Antevorta Developers Pvt Ltd. This article provides an in-depth analysis of the case, the legal proceedings, and the ultimate settlement that secured the substantial refund for the aggrieved homebuyer. The dispute arose when the developer, Antevorta Developers Pvt Ltd, failed to deliver possession of the apartment within the agreed timeframe. 42 lakhs in the Glen Classic Project case. Mahesh Sudhakaran, is set to receive a refund of Rs. In a significant ruling by the Karnataka Real Estate Appellate Tribunal, a homebuyer, Mr. 42 Lakhs Refund in Glen Classic Project Case #RealEstate #HomebuyerRights #KarnatakaTribunal Karnataka Homebuyer to Receive Rs. 42 lakhs to homebuyer in the Glen Classic Project case. This polars project isĬompiled without avx target features.Breaking News! Karnataka Real Estate Tribunal orders Antevorta Developers Pvt Ltd to refund Rs. 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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