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As famous author Wayne W.Dyer puts it,

Change the way you look at things and the things you look at change.

When the new version of Python came out, many were worried about backward compatibility issues and other things. But if you love Python, you are sure to be excited by the cool features that have been released in the new update.

The latest version of Python is out on Monday, October 5th, 2020. This article presents you with a list of Python 3.9 features that you can try right now.

Updating Python

Let’s begin by updating to the new version of python. If you’re unsure of the version you are currently using, then use the code below to check your current version. …

Understanding Pandas Time Series data structures

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Photo by Nathan Dumlao on Unsplash

DATA! Not just data science but every other field in the software industry handles data. From system software to application software — handling and storing data in an efficient manner is always a challenge. A common and most effective combat strategy is the utilization of data structures.

As the computer scientist Fred Brooks puts it,

The programmer’s primary weapon in the never-ending battle against slow system is to change the intramodular structure. Our first response should be to reorganize the modules’ data structures.

One can’t stress data structures highly enough — you can have the perfect code, perfect logic, zero errors yet storing data in a clumsy manner can be the downfall of the application. …

Some flexible approaches to combine multiple filters

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Photo by Nathan Dumlao on Unsplash

Looking for some good book recommendations in Goodreads — I found this quote,

In the Information Age, the first step to sanity is FILTERING. Filter the information: extract for knowledge. — Marc Stiegler

I wondered how it applies to Data Science! This quote fits perfectly to the most significant and the most underrated step in the entire Data Science process — Data Preprocessing! Data Scientists enjoy building models so much that they overlook this process. In actual essence, this process can be intriguing. As mentioned in the quote above, filtering is knowledgable. Filtering data can really guarantee some sanity when you are stumbled upon which variables to fit on the model. …

Why Multi-level Indexing is not as daunting as it seems

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Photo by Ozgu Ozden on Unsplash

There’s this beautiful quote I hear oftentimes,

“Sometimes we don’t see what is in front of us”

That’s so true with everything — be it the cause of your bug or the bug itself, misspelled variable names, not setting inplace = True and wondering why my DataFrame doesn’t change and quite a lot. Not just bugs or typos, even functionalities. We don’t see the basic functionalities as the building blocks of a product. One such thing which is overlooked most of the time but plays a crucial role in Data Analysis and Manipulation is Index in Pandas.

Indexes, Series and DataFrames are the core Pandas data structures. Indexes are the identifiers or the address to a location in a DataFrame. While manipulating one-dimensional and two-dimensional indexes are the most common practice. Learning to work with an arbitrary number of dimensions can be very handy at times. Luckily, Pandas enables the use and storage of higher-dimensional data into one-dimensional data structures like series and two-dimensional data structures like DataFrames. This is very well known as the Hierarchical indexing, Advanced Indexing, or Multi-level indexing. …

Consistency breeds consistency

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GitHub’s Contribution Graph Clone

I’m a productivity freak. I used to monitor my tasks each day with pen and paper. Then, as the tasks got more complicated, I started logging them in a spreadsheet. That worked fine for four weeks, but then I lost motivation. I needed a push to follow my routine. That’s where the contribution graph comes in.

If you use GitHub, you’ve seen the heat map in your profile that shows your contributions (commits, pull requests, etc) for every day of the year — as shown in the image above. I worked on a project every day to keep up my GitHub streak — all the time loathing that gray square in my profile! I found this tutorial in Node.js. …

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Photo by Vidar Nordli-Mathisen on Unsplash

Data Science is one of the most sought after fields of this century. If you Google the skills required to become a Data Scientist, there are a ton of websites listing “n” number of skills you need to master in order to become a Data Scientist based on your current skills and experience. Data Science is such a vast field that it encompasses so many different fields within itself. There are no predefined set of skills that you can master to become a Data Scientist as this field is evolving every day even as you read this article. …

A simple guide on Machine Learning Classification Algorithms

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Photo by Juan Rumimpunu on Unsplash

Well, if you’re someone who’s like that monkey in the picture (in a state of doubt) when it comes to Classification modeling in Machine Learning and evaluating their accuracy, this story is for you. Let’s get started.

Classification is a supervised learning (where you know what kind of output to expect from your data) approach and as the name implies, is a means of categorizing or classifying some unknown data into a distinct or discrete set of groups or classes. It learns from the input variable(s) X to predict the output variable y which is categorical.

There are many types of classification algorithms, but in this post, we’ll discuss four of the most popular…


Padhma Sahithya

I write about Data tricks | I believe learning lasts until life lasts! |

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