The most advanced ML Observability platform
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 […]
Start integrating our products and tools.
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 […]
In a column with categorical or distinct values, it is important to know the number of occurrences of each value. In this short how-to article, we will learn how to perform this task in Pandas and PySpark DataFrames.
The value_counts function returns the distinct values in a column along with their number of occurrences.
df["Color"].value_counts()
Missing values are ignored by default. If we know that there are missing values in a column, it is best to count them as well. The dropna parameter is set to False to include the missing values.
df["Color"].value_counts(dropna=False)
To count the number of occurrences of distinct values in a column, we use the groupby and count functions. The rows are grouped by the column of interest and then the count function is applied.
df.groupby("name").count().show()
The missing values are included in this calculation.
