Python Libraries for Data Science

This article is part of my “ML Roadmap Series” in which I cover all the topics required to master in order to become a good Machine learning Engineer.

Python has a vast variety of libraries that help in achieving complex tasks very easily. In this blog, we cover the three most important libraries.

I have used google collab for writing code. You will find the comments written very clearly for all the codes since these are straight out of my practice.

Numpy

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and much more.

Google collab link for Numpy tutorial:https://colab.research.google.com/drive/1veBb8gTHL7G8d0lebIx5Xi0su7KCmvkK?usp=sharing

Topics Covered

1. Installing NumPy and Introduction to Numpy.
2. Basic Array operation.
3. Slicing arrays and Indexing arrays.
4. Iterating through Numpy array using nditer.

Pandas

Pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.

Google Collab Link for Pandas Tutorial:https://colab.research.google.com/drive/1y1xFhTyX-LKVQXs-U-iHft_6Np1vgPkQ?usp=sharing

Topics Covered

1. Introduction and Installation of Pandas.
2. Basics of Data frame and different ways of creating Data Frame using Pandas.
3. Reading and Writing CSV files using Pandas.
4. Handling missing data in the data frame using Pandas.

MatplotLib

Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack.

Google Collab Link for MatplotLib Tutorial:https://colab.research.google.com/drive/15Kqy-vkghkG3YYLpejoVGDLIBYvfKZ5O?usp=sharing

Topics Covered

1. Introduction and Installation.
2. Playing with plot() function.
3. Working with Axes, Label, Legend, Grid.
4. Barchart using matplotlib.
5. Histogram using matplotlib.
6. Piechart using matplotlib.
7. Save chart to a file using save fig.

These are just the basics. Use this as a starting point and refer to the documentation to play with these libraries.

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Happy Learning.Thank you.