In Pandas, one can easily apply operations on all the data using the apply method. However, this method is quite slow and is not useful when scaling up your methods. Is there a way to speed up these operations? And if so, how? Yes, there is! This blog post will explain how you can use Dask to maximize the power of parallelization and to scale out your DataFrame operations.
Starting to learn programming most of the times is overwhelming because of the number of programming languages available to learn. This causes most of us to search for generic terms like “what is the easiest programming language to learn”.
More than 90% of the websites on the internet claims that Python is the easiest programming language to learn. This lands us to another question which is “Should I Learn Python or Not?”. In fact, not just you, I too have faced the same problem when I started to learn programming.
But, over the years of my learning, I have figured out the exact answer to this question. So, today in this post I am going to share everything you need to know in order to finally decide that do you want to add Python to your learning curriculum or NOT?
In this era of Big Data, an issue that has been escalating off- late relates to data fragmentation across organisations. This makes the process of analytics and reporting to become even more complex. This is where data pipeline tools come into play. To define it, a data pipeline denotes a set of actions carried out to extract data from different sources. For a startup, building a data pipeline is an important aspect of data science. They need to gather data points from all users and process it in real- time for developing data products.