We can create a zoneinfo.ZoneInfo(key) object where the key is of type string indicating the search path of the zone fil

Speaking of a website, I strongly recommend building a data science portfolio in the form of a website as well. HTML and CSS are very simple to learn and it would be a fun project! If you don’t have the time, something like Squarespace would work well too.

If we study the functions, internally they check if the string starts with a prefix (or ends with a suffix) and if it does then they return a string without a prefix (or after a suffix) using str[:] slicing feature.

I’m biased because this has worked well for me, but that doesn’t mean that I can’t recommend blogging! With a platform like Medium, you can write project walkthroughs, like mine on Wine Quality Prediction.

Lastly, take advantage of non-profit data science opportunities. I came across a resourceful article written by Susan Currie Sivek, which provides several organizations where you can get the opportunity to work on real-life data science projects.

This tutorial explains how to preprocess data using the Pandas library. Preprocessing is the process of doing a pre-analysis of data, in order to transform them into a standard and normalised format. Preprocessing involves the following aspects:

We use the datetime library to create a datetime object and specify its timezone by setting the tzinfo propety. However, we can end up creating complex timezone rules when using the datetime.tzinfo baseline.

I hope that this provides some direction and helps you in your data science career. There’s no cookie-cutter way of approaching this, so feel free to take this with a grain of salt. Nonetheless, I wish you the best in your data science endeavors!

Over the past few releases, a number of static typing features have been built incrementally on top of the existing Python runtime. Some of these features were constrained by existing syntax and runtime behavior. As a consequence, there was a duplicated collection hierarchy in the typing module due to generics.

A generic type is usually a container e.g. list. It is a type that can be parameterized. Parameterized generic is an instance of a generic with the expected types for container elements e.g. list[str]

I hope that this provides some direction and helps you in your data science career. There’s no cookie-cutter way of approaching this, so feel free to take this with a grain of salt. Nonetheless, I wish you the best in your data science endeavors!

Although Python is a dynamically typed language, the annotation of the types in the Python program enables introspection of the type. Subsequently, the annotation can be used for API generation of runtime type checking.

IANA time zones are often called tz or zone info. There are a large number of IANA time zones with different search paths to specify the IANA timezone to a date-time object. As an instance, we can pass in the name of the search path as the Continent/City to a datetime object to set its tzinfo.

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woods, and you get familiar with your neighborhood, you’re like, ‘Oh, this is a place where there’s a snake. This is a place where there’s poison ivy. This is a place where I’ll trip over this log every single time and sprain my ankle.’ Eventually, you’re just like: ‘Oh, I can avoid all these things.’”

Remember string is a collection of characters and each character has an index in a string. We can use the indexes along with the colon : to return a subset of the string. This feature is known as slicing a string.

One of the common tasks in a data science application that involves manipulating text is to remove the prefix/suffix of the strings. Two new functions have been added to the str object. These functions can be used to remove unneeded prefix and suffix from a string.