Gaurav Malhotra discusses how you can operationalize Jars and Python scripts running on Azure Databricks as an activity step in a Data Factory pipeline.
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Spend some time in Python and you’ll likely encounter its bytecode files — those ‘.pyc’ files Python likes to leave behind after it runs.
Have you ever wondered what’s really going on in those files? Watch this video from PyCon 2018 to learn more about these files and what’s in them.
The pandas library is a powerful tool for multiple phases of the data science workflow, including data cleaning, visualization, and exploratory data analysis. However, proper data science requires careful coding, and pandas will not stop you from creating misleading plots, drawing incorrect conclusions, ignoring relevant data, including misleading data, or executing incorrect calculations.
In this tutorial session from PyCon Cleveland 2018, you’ll perform a variety of data science tasks on a handful of real-world datasets using pandas.
Brandon Rohrer explains autocorrelation and partial autocorrelation, complete with pictures and Python code.
Daniel Chen presents an introduction to Pandas at the PyData Carolinas conference last year.
Here’s a great overview of one of the key data structures you will encounter in Python: the DataFrame.
You can imagine my enthusiasm when I first heard about Beautiful Soup, a web scraping library for Python.
Fortunately, Data Science Dojo has posted a video on how to get started with it.
In this talk from SciPy 2017, Daniil Pakhomov goes through the theory of the recent state-of-the-art methods for image segmentation based on FCNs and presents his library which aims to provide a simplified way for users to apply these methods for their own problems.