Python itself is rather slow indeed, but more often than not, it doesn't matter: for many data science projects, python is only used as a glue to control the processing and/or pass data between low-level C++ implementation that can chew through data at great speed, (e.g using parallelism).

Examples that immediately come to mind include : numpy, tensorflow, etc ...

Even heavy-duty I/O can be made to crank with python if you do it properly.