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3 Python Operations for Solving Specific Data Processing Tasks Efficiently
Leverage the flexibility of Pandas and Python
Raw data that comes to you is almost always different from the preferred or required format. Your workflow starts with getting the raw data into the specified format of choice, which takes up a substantial amount of your time.
Thankfully, there are lots of tools made available to us that expedite this process. As these tools evolve, they get better at solving even specific tasks very efficiently. Pandas has been around quite a long time and it has become one of the most widely-used data analysis and cleaning tools.
The built-in functionalities of Python also make it easy to deal with data operations. It’s no surprise that Python is the dominant language in the data science ecosystem.
In this article, we’ll go over three specific cases and learn how to leverage the flexibility of Python and Pandas to solve them.
1. Expand date ranges
We’re likely to encounter this task when working with time series data. Consider we have a dataset that shows the lifecycle of products at different stores as shown below:
