Functions, Users, and Comparative Analysis
We decided that Docs should have prime location.
Build AI products you can trust.
We’re super excited to share that Aporia is now the first ML observability offering integration to the Databricks Lakehouse Platform. This partnership means that you can now effortlessly automate your data pipelines, monitor, visualize, and explain your ML models in production. Aporia and Databricks: A Match Made in Data Heaven One key benefit of this […]
Fundamentals of ML observability
Metrics, feature importance and more
We’re excited 😁 to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Shuffling rows in a DataFrame means changing the order of rows. In this short how-to article, we will learn how to do this operation in Pandas DataFrames.
We can use the sample method, which returns a randomly selected sample from a DataFrame. If we make the size of the sample the same as the original DataFrame, the resulting sample will be the shuffled version of the original one.
# with n parameter
df = df.sample(n=len(df))
# with frac parameter
df = df.sample(frac=1)
The frac and n parameters define the size of the sample.