QuickDataScience | Quick & Easy Data Science

Introduction  ExploriPy is yet another Python library used for Exploratory Data Analysis. This library pulled our attention because it is Quick & Easy to implement also simple to grasp the basics. Moreover, the visuals provided by this library are self-explanatory and are graspable by any new user.  The most interesting part that we can't resist mentioning  is the easy grouping of the variables in different sections. This makes it more straightforward to understand and analyze our data. The Four Major sections presented are:-  Null Values Categorical VS Target Continuous VS Target Continuous VS Continuous  

Exploring the dataset is both fun and tedious but an inevitable step for the Machine Learning journey. The challenge always stands for correctness, completeness and timely analysis of the data.  To overcome these issues lot of libraries are present, having their advantages and disadvantages. We have already discussed a few of them( Pandas profiling , dtale , autoviz , lux , sweetviz ) in previous articles. Today, we would like to present a new library for Exploratory Data Analysis --- Dora.  Saying only an EDA library would not be justified as it does not help explore the dataset but also helps to adjust data for the modelling purpose.

We have discussed Exploratory Data Analysis, known as EDA & have also seen few powerful libraries that we can use extensively for EDA. EDA is a key step in Machine Learning, as it provides the start point for our Machine Learning task. But, there are a lot of issues related to traditional Data Analysis techniques. There are too many new libraries coming up in the market to rectify these issues. One such API is AutoViz, which provides Quick and Easy visualization with some insights about the data.

Another day, another beautiful library for Exploratory Data Analysis(EDA) . Having studied some great libraries like Lux , D-tale , pandas profiling of EDA , we are back with another great API, 'SWEETVIZ', which you can use for your Data Science Project. Introduction It is an open-source Library of Python & is still in the development phase. It already has some great features to offer, & makes it our choice to bring it for you. Its sole purpose is to visualise & analyse data Quickly. The best feature of this API is it provides an option to compare two datasets, i.e. we can compare & analyse the test vs training data together. That's not all it's, just the starting. Let's dive deeper and see what it has more to offer us. 

Source: Google Images Pandas Profiling is an Open-Source Library of Python. It focuses on easing out the process of initial data analysis, by providing a tool to perform the analysis of our data Quick & Easy. It's also considered a major EDA library, creating visuals, graphs, data profiling reports, pandas reports within seconds, in just a line of code. It saves a lot of time, which is usually lost in visualizing & understanding the data. It extends the pandas data frame to create a report for Quick & Easy Data Analysis.