Here's an example of how lambda functions are defined and used: (lambda x, y, z: (x+y) ** z)(3,2,2)
So, instead of formally defining the small function with a specific name, body, and return statement, you can write everything in one short line of code using a lambda function. They're very helpful when you need to define a function that's very short and consists of only one expression.
Lambda functions are anonymous functions in Python. Notice how the value 50 is accessed via the corresponding key age.
As you can see, the corresponding values can be of different data types, including numbers, strings, and lists.
Here, the keys include name, age, and films. A dictionary is created with curly braces and indexed using the square bracket notation. Notably, dictionaries are mutable, which means they can be modified. Dictionaries are indexed by keys, and the values can be any valid Python data type (even a user-defined class). It defines an unordered mapping of unique keys to values.
Of course, Python requirements for data scientists are different from those for software engineers and developers. Moreover, Python has plenty of data analysis libraries that make your work easier. Python's readability and simple syntax make it relatively easy to learn, even for non-programmers. Thus, if you work with big data and need to perform complex computations or create aesthetically pleasing and interactive plots, Python is one of the most efficient solutions out there. But how are they going to test this? What are they going to ask? Let's prepare you for some interview questions! Why Do Data Scientists Need Python?ĭata science goes beyond simple data analysis and requires that you be able to work with more advanced tools. Looking for a data science job? Then you've probably noticed that most positions require applicants to have some level of Python programming skills.