Getting started¶
Nullval a state of the art package for treatment of null values and outliers. Below is the installation and the usage guide for the package.
Details about the package. 1
Python, you can install nullval with pip
package manager
Creating a python virtual environment¶
Before the installation of the package create a virtual environment with the supported python version
we recommend using python version == 3.9.19
replace myenv with the desired name of your virtual environment
after te virtual environment is created activate the virtual environment using the command
if you are using colab or other online based jupter notebooks run the following command below
!wget https://repo.anaconda.com/miniconda/Miniconda3-py39_4.11.0-Linux-x86_64.sh
!chmod +x Miniconda3-py39_4.11.0-Linux-x86_64.sh
!bash ./Miniconda3-py39_4.11.0-Linux-x86_64.sh -b -f -p /usr/local
!rm Miniconda3-py39_4.11.0-Linux-x86_64.sh
!conda update conda
!conda create -n myenv python==3.9.19
%%shell
eval "$(conda shell.bash hook)"
conda activate myenv
Installation Guide¶
with pip recommended¶
Nullval is published as a python package and can be installed using Python package and can be installed with
pip
, ideally by using a virtual environment. Open up a terminal and install
Material for MkDocs with:
This will automatically install compatible versions of all dependencies:
Material for MkDocs always strives to support the latest versions, so there's no need to install those packages separately.
for more information please connect with me on linkedin in github
Connect with me¶
→ linkedin
Tip
If you don't have prior experience with Python, we recommend reading Using Python's pip to Manage Your Projects' Dependencies, which is a really good introduction on the mechanics of Python package management and helps you troubleshoot if you run into errors.
with git¶
Material for nullval can be directly used from GitHub by cloning the repository into a subfolder of your project root which might be useful if you want to use the very latest version:
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This package is useful financial and numerical data and for basic quant stuff. It can used as a wrapper around your api calls to type check if there is any bad data coming. ↩
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Note that improvements of existing features are sometimes released as new packages and version are being updated constantly based on the dependencies patch releases of the package maintainers ↩