Our great sponsors
-
There are various techniques and modules/APIs that I already know/that I have used already that can be used (this talk is quite to the point and very enlightening: - NumPy’s own functions / Pandas’ own functions that are row-wise/column-wise - vectorization - Numba (although it has some limitations with “special functions”, and with the implementation with Pandas, as far as I know) - Cython - Dask - Apache Spark (with a focus in PySpark )
-
There are various techniques and modules/APIs that I already know/that I have used already that can be used (this talk is quite to the point and very enlightening: - NumPy’s own functions / Pandas’ own functions that are row-wise/column-wise - vectorization - Numba (although it has some limitations with “special functions”, and with the implementation with Pandas, as far as I know) - Cython - Dask - Apache Spark (with a focus in PySpark )
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
I'd like to add Polars as a library (https://github.com/pola-rs/polars).
-
You can also compare benchmarks: https://h2oai.github.io/db-benchmark