Python 3.6 or Later¶
msticpy requires Python 3.6 or later. If you are running in hosted environment such as Azure Notebooks, Python is already installed. Please ensure that the Python 3.6 (or later) kernel is selected for your notebooks.
If you are running the notebooks locally, you will need to install Python 3.6 or later. The Ananconda distribution is a good starting point since it comes with many of packages required by msticpy pre-installed.
Creating a virtual environment¶
This is an optional step. You will most likely want to do this if you are installing msticpy in a local Python installation. If you are using a cloud notebook environment such as Azure ML you will usually not need to create a virtual environment.
msticpy has a significant number of dependencies. To avoid conflicts with packages in your existing Python environment you may want to create a Python virtual environment or a conda environment and install the package there.
For standard python use the
venv command to do this
(there are also several alternatives to venv available).
~$ python -m venv my_env ~$ ./my_env/scripts/activate (my_env) ~$
For Conda use the conda
create command from a conda shell.
(base) c:\users\ian> conda create -n my_env (base) c:\users\ian> conda activate my_env (my_env) c:\users\ian>
You should see the name of the environment that you’ve just created and activated in the prompt.
Run the following command to install the base configuation of msticpy.
pip install msticpy
or for the latest dev build
pip install git+https://github.com/microsoft/msticpy
Selective Installation - using “extras”¶
pip supports specification of an additional parameter sequence known as extras. The syntax for this is:
pip install package_name[extra1, extra2...]
As of version 0.9.0 msticpy has its dependencies split into extras. This allows you to install only the packages that you need and avoid the overhead of time and diskspace of dependencies that you do not need.
extras do not affect the which code from msticpy is installed - only the external libraries on which certain functions inside msticpy need to work.
Extras in msticpy¶
The extras available in msticpy are described in the following table:
|extra||Functionality||Install time (increment)||Install time (full)|
|default install (no extra)||
The installation times here are meant to be indicative of comparative times for installation - they will vary depending on the performance of your computer and network.
The Install time (increment) column shows times relative to
the base install (i.e. assuming you’ve already run
pip install msticpy).
The Install time (full) column shows the time to install the base
plus extra. Both columns assume that the following packages are already
installed: jupyter, pandas and matplotlib.
If you do not specify an “extra” in your pip install command, the base dependencies for msticpy will be installed. This has a lot of functionality such as networking, pivoting, visualization but excludes dependencies that are specific to a particular data environment like Azure Sentinel or Splunk.
Some of the extras, like “all” and “azuresentinel” are combinations of other options collected together as a convenience. You can also specify multiple extras during install, separating them with commas.
pip install msticpy[azure, kql]
If you try to use functionality for a component that needs a dependency that you have not installed you will usually get an informative exception message telling you which “extra” option you need to use to enable that feature.
To fix this simply run pip install with the “extra” option shown in the exception message:
pip install msticpy[ml]
In some cases you many not get an informative error. (We’ve tried to trap all of the cases but if experience a problem with some msticpy functionality (especially an ImportError exception, make sure that you have installed the extra that corresponds to the functionality you are trying to use.