Python is an excellent language for developers. When it comes to data science projects, it is even better and reliable. There are a lot of people working on data science projects, but not all will have expertise in Python.
It is one of the simplest languages to learn and implement, and a pool of libraries it comes with helps you complete any task much faster. You need to have some level of programming knowledge to execute data science projects. The good news is you don’t need to have expertise in Python to do so.
Creating a machine learning model at a large scale requires a data scientist and a machine working simultaneously. Python programming’s power shines in this scenario. There are very few languages as versatile as Python. Python libraries are available to help data scientists quickly execute these tasks – that’s just an added bonus.
In this article, we will talk about some Python hacks and tricks that will help you with data science projects.
Best Python Hacks and Tips for Data Science Projects
How do you feel on Saturday evening after you have messed the house completely? You feel terrified to clean everything on Sunday, right? How would you feel if on a Sunday morning everything cleans on its own – all the mess you created is gone? Does it sound too good to be true?
Well, it is not when you use black. Black is known as the uncompromising code formatter. You can write code as per your style and the way you want to write. Black being a code formatter, will format it into a consistently formatted code.
As a developer, you can focus on the logic and not the structure of the code. It will make coding really faster for you.
Encode categorical variables using encoding schemes
When you start with a data science project – like every other developer, you will face issues with categorical variables. Dealing with categories is a common problem and a big one. Some machine learning algorithms handle these variables on their own.
However, you still need to convert them into numerical variables. The solution to this problem is the use of category_encoders that comes with 15 different encoding schemes. You can install category_encoders and access encoding methods like Hashing Encoding, Ordinal Encoding, Target Encoding, and many more.
Mix Python and R
It is a great combination as it makes it possible for you to pass variables between them. Both of these are open-source programming languages and help you get started with data science projects. On one hand, Python provides an easy interface to visualize math into code, and on the other hand, R combines the statistical analysis part.
Plot coordinate in data set to Google maps with ease
Google Maps is one of the most data-rich applications you will come across. If you want to find a relationship between two variables, you have an option to use Scatterplots. However, you will not use them when you are dealing with latitude and longitude. The best thing to do would be to plot these points on a real map. It will help you easily visualize and solve a particular problem.
To combine multiple lists, you must have written gritty for loops. Once you know the zipper function, there is no need to do so. The zip function allows you to create an iterator. Using this iterator, you can combine several elements from each list.
Know how much time you spend on your data science projects
One of the important and time-consuming tasks in a data science project is cleaning and pre-processing data. Typically, a data scientist spends 60-70% of their time cleaning data. You would not want to spend days cleaning the data, and hence you must track the time.
To know how much time you are spending and track your progress you can use the ‘progress_apply’ function. It makes your life a lot easier.
When you start a data science project, you should not rush to model building. The first thing you need to do is know your data set – what it has to offer and what it is about. It is not an easy task to go through all the datasets and understand them.
For data analysis and manipulation in Python, there is a special library known as Pandas. You will find hundreds of features inside this library. Pandas library offers you data operations and structures to manipulate time series data and numerical tables. Pandas library also comes with a less known grouper function. If you are working on the time series data analysis function, it will be extremely useful for you.
When you work on a data science project, you will have to first analyze data sets and then make models based on your analysis. If you don’t know the right regression analysis technique, data processing can become a real challenge for you.
Some of the regression techniques you should know to master your data science projects are Linear regression, stepwise regression, logistic regression, lasso regression, etc. If you can choose the right regression technique for your data science project, you will save a lot of time.
Running time of block of Python code
As a data scientist, you know you can solve a particular problem in multiple ways. If you are part of a small or mid-sized organization, you have to take care of the computational cost of your code. Hence, you should look for a solution by which you can accomplish your goal (solve your problem) in a minimum amount of time.
The best practice is to check the run time of your block of code before you make it live. All you need to do is add the ‘%%time’ command to check the run time of a particular cell. You will see two returns – Wall time and CPU time. The CPU time tells you the total execution time for which the CPU was dedicated. The Wall time is the time that a normal clock would have measured – clock time between the start and stop of the process.
Above, we talked about how grouper function can help you. The next challenge for you would be to see the name column as the column of your data frame. When your requirement is such, you can get to unstack function and make your life easy.
You have now learned some good tricks to use in your data science projects using Python language. Any Trusted Python companies always keeping an eye on Python-related blogs and papers to stay updated with the changes. Python gets updated regularly, so following what is added and what is deprecated is vital.
The reason is that you might be using a variety of packages that are developed and maintained separately. Once you understand the updates better and start using them in your day-to-day work, you will see your productivity increasing, and using Python will be fun for you.