15 Python tips and tricks to master Data Science and Machine Learning

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It has never been easier to step into a technical domain, especially the ones that hold the potential to make a huge impact on technological advances like Artificial Intelligence, Machine Learning, Deep Learning, etc. since they tackle the growing needs of humankind. One variable that remains common among all these technologies is the use and implementation of Python language. We, luckily, live in a fairly advanced age that offers a plethora of knowledge which often becomes hard to segregate, leading us into an information paralysis. There is an abundance of python tricks we can implement to improve the quality of our code, speed up our data science tasks, and write code with efficiency. To help sort this clutter, we bring you 15 python tips and tricks to grasp the concepts of Data Science more clearly and gain useful insights into the fuzzy world of Machine Learning.

1. Find resources you resonate with

It is important to keep learning when you embark on a labyrinth of a journey that is data science. It becomes essential to seek guidance and help, and for that, there must be reliable resources at hand to help you out. Find a good youtube channel, a podcast station or a few good books that you feel resonant with. Listening to experts talk about data science, machine learning, robotics and deep learning will enthral you to become more and more interested.

2. The zip function

We all have found ourselves writing gritty for loops to combine multiple lists. No more. The zip function allows you to create an iterator that combines several elements from each list. Here’s a brief guide to help you with Python zip Function

3. Using R and Python together

Yes, it is possible. Not just possible you can even pass variables between them. These open-source programming languages together pave way for a strong data science foundation. R combines the statistical analysis part and Python provides the easy interface to visualize math into code. Both of them can be run in a single Jupyter notebook. Here’s how.

4. Finding the best approach that takes the least time – %%time command

A problem can be solved in many ways and more often than not the computational timing affects matters a lot. To see which solution takes what time, add the %%time command to check the runtime of a code block. Here’s a guide.

5. Plotting coordinates in your data set to Google maps

It would seem best to plot longitudinal and latitudinal coordinates present in your data set on a real map so that one can easily visualize and solve a particular problem, especially when dealing with route optimization problems. Here’s a definitive guide.

6. Lambda functions can help you shorten code

Lambda – A function without being a function. They can take multiple arguments but can have only a single expression. This makes them extremely potent in terms of code readability and processing as well.

7. Track your time spent in Data Science problems

Monitoring the time you spend on tasks such as cleaning your datasets and segregating knowledge from waste is important because its study can show you where you can improve your abilities. Nobody wants to spend days and days cleaning their data sets and delaying other steps. This is where progress_apply function comes in to help. Here’s a detailed guide.

8. Studying your data sets in detail

Rushing straight to model building in data science is a foolish mistake because it is important to know what your data set is all about and what it has to offer you. We also know that it takes enormous effort to go through data sets and understanding them. This is where a python package comes into play. pandas_profiling package generates a detailed report of your data sets, making it much easier to understand and analyze them. Here is the official documentation to help you through the installation.

9. Thoroughly explore Pandas library

For data manipulation and analysis in Python language, a special software library was created – Pandas. It offers a multitude of features. Primarily it offers data structures and operations to manipulate numerical tables and time-series data. A guide to installation is always helpful along with its applications in data science code.

10. Grouper function in Python

Yet another feature of Pandas library is the less renowned grouper function. It is an extremely important function for time series data analysis. A definitive guide will help you sort your data into groups so that knowledge can be segregated through simple query and grouping technique.

11. itertools in Python

itertools in Python language offers a multitude of features that allow you to manipulate and analyze an otherwise cluttery dataset easily. It is used to handle the iterators you use in a for loop and makes them manageable.

12. Regressions techniques in Python

 Machine Learning specifically requires you to analyze data sets and make models based on that. Data processing can often become a pain in the neck if you do not know the right regression analysis techniques and where to apply them. Here are the 7 important techniques you should expertise in to master data science.

• Linear Regression
• Stepwise Regression
• ElasticNet Regression
• Ridge Regression
• Logistic Regression
• Polynomial Regression
• Lasso Regression

It is also essential to choose the right type of technique. Here is how you can achieve that.

13. Interactive plots using Matplotlib

The matplotlib library is the most common data visualization library, and we use it to generate a plethora of schemes in Jupyter notebooks. One of the most significant benefits of visualization is that it allows us visual access to vast amounts of data in easily digestible visuals. Matplotlib constitutes of various plots like line plot, bar plot, scatter plot, histogram plot etc. Here is a guide to get you started.

14. Using sorted() to solve your problems

Using an inbuilt function for sorting any sequence has proven to one of the most beneficial features of using Python. It takes in a tuple or a list and sorts it. Then it returns a single sorted string.

15. Using useful blogs and papers to stay updated

Python gets new updates regularly, and it is vital to keep track of the features added or deprecated. That is because you may be using a multitude of packages which are developed and maintained individually. It then becomes essential to read software release versions and related blogs to help you with keeping track of the updates.

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