![]() ![]() The first package we need to install is pandas-gbq via your Anaconda prompt. Python is now your best friend to crunch this enormous dataset. Furthermore, if you run a large query in the Google BigQuery UI you will reach the export limits very easily. This is possible because we can use the hit timestamp and hit number in Google BigQuery. For instance, it can be very interesting to look at the hit data of your users. As I mentioned in my first blog post, Python is extremely helpful to do analysis on large datasets. If you use Google BigQuery you definitely need to choose this way to connect with your data. Ok, enough introduction about how to work with Anaconda and Jupyter Notebooks. Go to: and download the installer package for your operating system.If you already have installed Anaconda you can skip these steps: Some very useful libraries are Pandas to do data cleaning and analysis, Numpy to do your math and Matplotlib to visualize your data. Libraries are collections of functions and methods you can use without writing lines of codes. After installing Anaconda you can start working in notebooks like Jupyter and with some very useful libraries. Install AnacondaĪnaconda is open-source and one of the easiest ways to perform Python on your local machine. ![]() Besides that, I will learn you how to get connected with your Google BigQuery data. Before you can start working with Python you need to install several things on your local machine. This time I would like to share some more practical things about how to get started with your first analysis. ![]() Hopefully I have convinced you to start with Python after my first blog post. ![]()
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