## 19

April 2022# Top 6 Python Libraries for Data Science

* This blog covers the 6 famous Python libraries for data science that are easy to use, have extensive documentation, and can perform computations faster.*

Data scientist is the sexiest job of the 21st century, but

what is a data scientist without data? Harvard Business Review labels data as

the new oil. There is a massive dearth of people qualified for data-related

jobs. As a beginner, you can be tempted to wet your feet in the ever-evolving

field of data science. But fear not, because Python is a programming language

that can be easily learned. Sometimes, your pseudocode can directly be

converted into Python code.

Python is increasingly used in data science-related tasks

and is becoming the de-facto standard

because it is easy to learn, easy to debug, has a rich userbase, is object-oriented,

and is easy to interpret. However, you can get lost in the intricacies and

subtleties of the many available specialized packages. Fret not, because we

have you covered!

You might be tempted

to learn about many of these libraries, but there are some libraries that are frequently used in

the domain of data science given their versatility and ease of use. In this

blog, we will be going over the six most commonly used libraries.

**NumPy**:

Be it the

creation of vectors and arrays, performing some matrix multiplication, or

performing singular value decomposition, NumPy is a linear algebra-based

library that provides a vast repertoire of mathematical routines at your

disposal. NumPy is a library that deals with vectors, and matrices and offers fast

operations. It provides various functions such as array indexing and

broadcasting, consumes less memory, and is convenient. Behind the hood, it uses

multiple optimization algorithms to accelerate typically slow operations such

as matrix multiplication. The automatic broadcasting takes care of different

array sizes and makes life very convenient ultimately making it one of the most famous Python libraries for data science.

**Pandas**:

null values, merging and joining datasets, Pandas is a python library that is

both easy and intuitive. Since it is built on top of NumPy, it can perform

tasks that would otherwise take a lot of time. Usually, by using native Python

functionality, it becomes tough to iterate over thousands of tuples to perform

some pre-processing, but by using Pandas’ wrappers, these tasks can be done in

significantly less time. Moreover, Pandas is widely used for data analysis and

looking into the summary statistics, and inferring some patterns from data,

which can help answer or validate our assumptions and hypothesis.

**SciKit-Learn:**

If you want

to train complex machine learning models or have an ensemble of different

machine learning models with an intuitive and easy-to-use interface, Scikit-learn

is your friend. The beauty of Scikit-learn is that it provides a similar

interface for every machine learning algorithm, which makes the library very

intuitive to use and can easily extend the current learning algorithms by using

custom cost functions and optimization algorithms. The library also offers

various optimization algorithms to tune the model's hyperparameters. Therefore,

Scikit-learn stays one of the most popular machine learning libraries for

Python.

**Keras:**

Machine

learning and deep learning have become immensely popular in recent days due to

ever-increasing computing power and that is why you see complicated models

being developed, and Keras is a Python library for data science to do that. Keras is a static

graph-based machine learning library. One of the distinguishing features is

that the computational graph of a network, once formed, will be fixed, and will

not be changed on the run-time, which means that the variables will be locked

at the run time, making the models very efficient. Moreover, the Keras

application programming interface is highly abstracted, which makes Keras very

easy to use once you have a good grasp of Python. It is used to build custom

machine learning models and is widely used in the machine learning community

for research and deployment purposes.

### SciPy:

Testing whether your assumption is valid or not to make a fundamental decision about a product's life cycle is an important task. As SciPy is written in various low-level languages such as C, C++, and Fortran, the speed gains are tremendous compared to a library written in a high-level language. Moreover, Scipy extends the functionality of NumPy by providing access to structures that can be used to store sparse data in a highly optimized fashion and perform computations on it. The open-source nature of Scipy allows anyone to look at the source code, find bugs or optimize the numerical algorithms further. Hence, SciPy remains one of the most popular libraries for statistical tasks.

**PyTorch**:

PyTorch is a dynamic graph-based machine learning library developed by Facebook to aid

in their model development and deployment purposes. The variables, including

layers, can be changed during the iterations, making the neural networks easier

to debug and providing more flexibility. Moreover, for people having access to

GPUs, this library offers a remarkably simple flag to switch between GPU and

CPU, which makes the life of programmers extremely easy by making the code

portable.