Great work. Very handy for identifying problems
thanks
On Tuesday 21 May 2024 at 18:12:15 BST, Mich Talebzadeh
<mich.talebza...@gmail.com> wrote:
A colleague kindly pointed out about giving an example of output which wll be
added to README
Doing analysis for column Postcode
Json formatted output
{ "Postcode": { "exists": true, "num_rows": 93348,
"data_type": "string", "null_count": 21921, "null_percentage":
23.48, "distinct_count": 38726, "distinct_percentage": 41.49 }}
Mich Talebzadeh,
Technologist | Architect | Data Engineer | Generative AI | FinCrime
London
United Kingdom
view my Linkedin profile
https://en.everybodywiki.com/Mich_Talebzadeh
Disclaimer: The information provided is correct to the best of my knowledge but
of course cannot be guaranteed . It is essential to note that, as with any
advice, quote "one test result is worth one-thousand expert opinions (Werner
Von Braun)".
On Tue, 21 May 2024 at 16:21, Mich Talebzadeh <mich.talebza...@gmail.com> wrote:
I just wanted to share a tool I built called spark-column-analyzer. It's a
Python package that helps you dig into your Spark DataFrames with ease.
Ever spend ages figuring out what's going on in your columns? Like, how many
null values are there, or how many unique entries? Built with data preparation
for Generative AI in mind, it aids in data imputation and augmentation – key
steps for creating realistic synthetic data.
Basics
- Effortless Column Analysis: It calculates all the important stats you need
for each column, like null counts, distinct values, percentages, and more. No
more manual counting or head scratching!
- Simple to Use: Just toss in your DataFrame and call the analyze_column
function. Bam! Insights galore.
- Makes Data Cleaning easier: Knowing your data's quality helps you clean it
up way faster. This package helps you figure out where the missing values are
hiding and how much variety you've got in each column.
- Detecting skewed columns
- Open Source and Friendly: Feel free to tinker, suggest improvements, or
even contribute some code yourself! We love collaboration in the Spark
community.
Installation:
Using pip from the link: https://pypi.org/project/spark-column-analyzer/
pip install spark-column-analyzer
Also you can clone the project from gitHub
git clone https://github.com/michTalebzadeh/spark_column_analyzer.git
The details are in the attached RENAME file
Let me know what you think! Feedback is always welcome.
HTH
Mich Talebzadeh,
Technologist | Architect | Data Engineer | Generative AI | FinCrime
London
United Kingdom
view my Linkedin profile
https://en.everybodywiki.com/Mich_Talebzadeh
Disclaimer: The information provided is correct to the best of my knowledge but
of course cannot be guaranteed . It is essential to note that, as with any
advice, quote "one test result is worth one-thousand expert opinions (Werner
Von Braun)".
