Integration of Python for data science, graph processing for NoSQL-like functionality, and it runs on Linux as well as Windows. At almost 30 years of age, Microsoft's flagship database has learned ...

The best parallel processing libraries for Python. Ray: Parallelizes and distributes AI and machine learning workloads across CPUs, machines, and GPUs.; Dask: Parallelizes Python data science ...

The final step in our process is to export our log data and pivots. For ease of analysis, it makes sense to export this to an Excel file (XLSX) rather than a CSV.

Python developers should be the same -- if a library helps do X better, then use it. The application-specific logic is still done in Python and easy for noncomputer scientists to understand. Using a C ...

Another tweak simplifies the process of configuring the debugger for individual workspaces. The new release also features improvements to the Python Language Server, which implements the Language ...

Writing programs that access LDAP servers is easy to do using Python and python-ldap.The python-ldap package contains a module that wraps the OpenLDAP C API and provides an object-oriented client API ...

Its appeal for data science is how quickly developers can get started with Python. "For data science experts looking to start writing application code, this is the most straightforward route," said ...