Python Key Packages for Data Science

Hi! Young Data Science enthusiast, Let’s understand key packages for Data Science implementation. This is really very simple to understand and apply on your data set.

Especially Python libraries for Data Science, Machine Learning models are very interesting, easy to understand and absolutely you can apply straight away and you can feel the insight of the data and realize/visualize the nature of the data set.

Even the complex algorithms can be implemented in two or three lines of code, all major mathematical concepts are embedded inside of the packages for the implementation point of view.

Of Course, this is something different and interesting than other programming libraries I have seen so far, that is the main reason Python playing a vital role in the AI space with this simplicity and robustness! I believe, Yes! I realized, understood thoroughly and enjoyed it.

Let’s Discuss in short and sweet way! (For detail you can refer my YouTube Channel)

What is package in Python? A package is a collection of Python modules and assembled in single pack. Once you import in your note book cells, you can start using class, methods, attributes and etc., But before that you should necessity and usage of the package and import into your file/package.

Let’s discuss key packages in Python for Data Science and Machine Learning.

1.Pandas
2.Numpy
3.Scikit Learn
4.Matplotlib
5.Seaborn

Mainly used for structured data operations and manipulations. Pandas offers powerful data processing capabilities, I ever seen such a wonderful features in my IT journey. It provides high-performance, easy-to-use and applied on data structures and to analysis the data.

import pandas as pd

Then, your Note Book is ready to extract all features in-side the pandas.

Pandas has below capabilities

Will explain detail Pandas Story and interesting demos in my YouTube Channel !

Numpy is considered as one of the most popular machine learning library in Python, The best and the most important feature of Numpy is Array interface and manipulations.

Are you scared about mathematics while implementing your Data Science/ML model ? No worries, Numpy makes complex mathematical implementations very simple functions. But remember understand the requirements and use the pack accordingly.

If you have started play with data using Numpy…. Certainly you need more and more time…to understand the concepts, all are extremely organized in this package. believe me!

Scikit Learn library is one of the richest library in Python family, it contains huge number of machine learning algorithms and other key performance related libraries. Python Scikit-learn allows users to perform various Machine Learning specific tasks. To perform, it needs to work along with SciPy and NumPy libraries, this is something internal matters, Anyway keep it in your mind.

Regression
Classification
Clustering
Model Selection
Dimensionality Reduction

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

Visualization Packages from Python

Matplotlib & Seaborn Libraries

Python providing 2D graphics features with Matplotlib library. this is very simple and easy to understand. you can accomplish by 1 or 2 lines. Even 3D visualization also there.

import matplotlib.pyplot as plt
import seaborn as sns

Hope you have worked on multiple charts in excel worksheet and other BI tools. But in Python in-house visualization packages are providing extremely high quality graphs and charts.

Matplotlib is one of the major and basic visualization package, which provides Histograms (Frequency Level), Bar charts (Univariate and Bivariate Plotting), Scatter Plots(Clustering) and etc.,

Rich and Luxury data visualization library from Seaborn. It provides a high-level interface for drawing attractive and informative statistical graphics. Box Plots (Data Distribution with differentquartiles),Violin Plots (Data Distribution and Probability density),Bar Plots (Comparisons among categorical features),Heat map (Correlation of features in terms of Matrix representation), Word Cloud (Visual representation of Text Data)

So, All these libraries are helping us to build nice model and playing with Data! But remember always, before the usage of the induvial packages, you should understand the necessity and requirements of the package and then import into your file/package. Hope now you got the feel and certain level of details on Python packages. Will see more in depth concepts in up coming days! and in my YouTube channel as well.

Until then bye for now — Cheers ! From — Shanthababu!