Pandas

You may find yourself in a situation where you’d like to generate mock data, like when writing a blog post series on taking a pipeline and model to production. Luckily numpy and pandas makes this task incredibly easy. For my use case, I wanted to generate a Pandas DataFrame with one independent column temperature_celsius and one dependent column ice_cream_sales_euro. The goal was to make a data set where temperature_celsius would affect ice_cream_sales_euro....

I find myself using Pandas with almost ever python data-esque project. Additionally, the databases I most often interact with are relational, specifically SQL. These basic instructions will allow you to query a SQL database and get the results as a Pandas data frame. $ sudo pip3 install pandas sqlalchemy mysqlclient I’ve also used flask_sqlalchemy when writing an API. $ sudo pip install sqlalchemy For ubuntu, install the g++, sqlclient and make sure you get python3....