Python Data Basics - Applying Functions
In the 4th class of the free Python Data Basics Data Simple program. In this free Python class, we discuss the use of user-defined functions. An incredibly important part of programming and more importantly writing clean Python code. Any time we notice we start to copy lines of code only making a small change we ask ourselves if could we do this in a loop or a function. In data science, we commonly use functions to write code in one place but use it many times later on in our workflow. This is valuable because we will often want to later make changes to our code, if we've been copying and pasting our code we will have to make the change in many places if we've used a Python user-defined function we will only have to make that change in our function and it will take effect every time we use the function in the following code. This makes writing our own function a very practical skill. In data science writing our own function and then using them with the Pandas function apply, we apply our user-defined function on our DataFrame. Using Pandas' apply with our custom functions will give the same flexibility we have to manipulate data in Pandas with Python as we do as using a mouse in Excel.
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Python Data Basics - Applying Functions
If we know we are going to perform the same operation, data manipulation, or data analysis we can put that code in a function. This will be easier the copy and pasting code when a change is need one once versus multiple times. Writing in data science then allows us to keep our code more organized. As we write code to complete a task we can re use that code to complete a similar task on different data. This comes in handy when we want to make a change to our code and will then only have to make the change in one place.
In Python we have access to two types of custom functions. We can create a user defined function that we are able to give a name to. The other type of Python function is a Lambda funtion or an anomonys function. Although we do complex things in a lambda function we generally use them to do simple functions that are used only once. In data science using Python we tend to use a user defined function the most as they offer a lot of flexibility in how we are completing task and user defined function allow for the complexity needed in data science.
In computer science 1,000s or 10,000s of functions will make up a simple computer application. The reality is in data science we don't build applications but we do use both Python lambda and Python user defined function is any data analysis or machine learning project. As data scientist we tend to use Python functions slightly diffrently than a computer sicence. Alhtough we do stack many functions together, we just as offer use each function as a stand a long task solver.
In data science the way our functions are commonly used is also different than a computer scientist would be applying their functions in their appilcation. In data science we use a Pandas function called apply. With apply we take our function a apply with apply the function on one or many columns in our Pandas DataFrame. Getting good writing our own functions and applying them to our DattaFrame allows us to acheive the ability to manipulate data that is akin to using a mouse and Excel spreadsheets.
Python Why do we need functions?
Learn for free why as Data Scientist we need to use functions. Python is a very flexible language and your purpose for using Python will change how you work the Python code. Computer scientist will focus on stacking thousand of functions together to create a function. As data scientist we quite often use functions as stand alone task solvers. Where each function is to complete the same task many times. As we get to advanced deep learning techniques we will start stacking functions together like computer scientists but we can start the easy way and learn functions in Python an easier way.
Python Types of Functions
In the free Python Data Basics class easily learn how to use Python functions. There are two types of functions we have access to in Python. User-defined functions, UDFs, are functions that we can name and easily access and re-use later on in our workflow. Lambda functions are more of a one time use function that is simple and doesn't have a name. This can be used to quickly insert a function's operations into your code. In data science we tend to use named functions so that we can reuse them and because in data science we often need to complete complex task in a function that won't fit into a l
How we use Python Functions in Data Science
In the educational independent learning data material in Python, we discuss when and how we use functions in data science. Traditionally Python has been taught to computer scientists but the way we use Python in data science is different that how computer science would use it. A computer scientist is concerned with building an application and functions will be stacked together to create this. Often in data science, we create functions that are used to aid in our data analysis or data preprocessing and are meant to only perform a specific task over and over again.
Python Applying User Defined Function
Learn for free how we most commonly use user-defined functions in our data science projects. After writing our user-defined function in Python we often use a Pandas function to apply our custom function on the Pandas DataFrame. Getting good with this technique will allow to to work with data in Python as if we were working with data in a spreadsheet using a mouse. Getting good with creating our own functions and applying those to a DataFrame will give you a lot of flexibility in your analysis and how you are able to manipulate data.
Explore Python Functions with Free Python Code
In the last section of this free Python Data Basics class we go through the easy Python code in the colab. In the Python data basics class, we cover how to how to write our own custom functions. We teach how to use functions while working with data and how Python functions can be used to to manipulate or preprocess. In this free Python coding walkthrough we show how a the pandas function apply can we use with a Python user defined function to highlight anomalies and but this concept and easily be expanded to many other use cases.