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Evolving Data Scientist

A Beginner Guide for Identifying and Handling Outliers

Have you ever noticed, while going through the rain, that one person walks casually without an umbrella and does not worry about anything while everyone is running away from the rain? Exactly that person is the outlier 😅. Fun apart, Let’s talk technically… Outliers are values within a dataset but unusual to other typical values…
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These 3 books may change your way of visualizing the data!

Data visualization is a method to visualize statistical data to communicate the insights and patterns behind the data. However, plotting the various charts with statistical plots only makes sense sometimes. Good data analysts/data scientists used to open the secret behind the data with their expertise. However, beginners also gain that expertise through reading well-written books…
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These 7 categorical plots forced me to love Seaborn Library.

Plotting categorical variables is always a challenging task for data scientists. However, there are dozens of libraries that do the magic of plotting. I am gonna explain the categorical plots which forced me to love Seaborn Library. Click 👉🏻 here to download the Early stage diabetes risk prediction dataset from Kaggle to reproduce this code. Import Libraries…
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Become a Pro in Scatter Plot Visualization

A Matplotlib Guide for Creating Scatter Plot The primary purpose of a scatter plot is to determine the relationship between two variables (bivariate). Scatterplots play a very important role in regression algorithms to find the relationship. Also, In classification problems, scatter plot help to identify the correlation between the features. Here I am gonna explain…
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Let Lambda do Feature Engineering

We all know a lambda function can have any number of arguments but can have only one expression. It is one of the famous one-liner functions in Python programming. By using this lambda function with Pandas, we can do many feature engineering operations. In this article, I am gonna explain some of the use cases.…
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Stunning Python Libraries you should know to work on Data Science projects.

As a data scientist, data analysis and manipulation is a day-to-day job. Knowing the right library for the right tasks tremendously reduces working hours. So, I thought to share the 3 primary libraries I used to work for my regular tasks for various purposes. 1. Pandas Library pandas were released on 11 January 2008 pandas…
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Hard to imagine 🥹 data analysis 📊without the aggregate functions.

Aggregation is one of the most useful descriptive analysis functions to perform complex statistical steps more efficiently. The primary purpose of the aggregate is to summarize the group of data to analyze the statistical summaries such as mean, standard deviation, variance, etc. I quickly demonstrate various methods of using an aggregate function in Pandas in…
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You’ll never forget Shallow Copy and Deep Copy in Python after reading this…

Indeed, one of the most common interview questions I’ve been asked in the Python interviews is the difference between Shallow copy and Deep Copy. Copying the compound objects, such as those containing lists, class instances or nested lists in Python/NumPy, is too dangerous. To understand why it is too dangerous, we should know the mechanism…
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Pandas filtering methods to solve most of the data analysis tasks

Introduction: Filtering is one of the key 🔑 operations of filtering datas from raw data set for the data analyst.In this article I am explaining different filtering methods available in pandas. Here I’ve used most famous Titanic dataset for filtering operations, so that you can easily reproduce the code. Please download the data file from here.…
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Make Your Own Python Libraries in 5 Minutes

A Python Library is a pack of interconnected modules. It encompasses a bunch of code, It can be used for a different program. Whenever We need this bunch of code can be used in our programs or projects. Python Libraries be involved in vital responsibility in the domain of Data Science, Data Visualization, Machine Learning,…
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