Pythonic power: Mastering Data Science with Python and its Libraries

Do you want to learn about the best Python libraries? Which are the top Python libraries? What about the relationship between the Python programming language and data science? This article will answer all these questions and more!

As of 2022, Python remains the most popular programming language. Python packs a powerful punch in speeding up software development.

Due to the simplicity of Python’s syntax, and libraries, it has become a favorite amongst data science enthusiasts. In this article, we’ll delve into a gripping exploration of the top Python libraries.

Best Python libraries in data science

“Python Libraries are collections of functions and methods that allow data scientists to perform many actions without writing code.”

Giuliano Liguori , technology expert on LinkedIn.

Pandas

The data cleaning and analysis process can often be time-consuming. However, with the emergence of Pandas, data manipulation is now simpler and more efficient. The Panda library’s diverse functionalities make it the ultimate toolkit for handling complex data operations

Pandas library has a unique high-level interface for data structures like series and data frames. These data structures serve as the backbone for organizing, manipulating, and analyzing data. With Pandas, data scientists can tackle large, unwieldy datasets and transform them into meaningful insights.

“The massive expansion of Pandas flexibility concerning data structuring, data clearing, and manipulation is the key reason for its progressive usage and popularity in the field of AI and ML.” Kavya Agarwal, data science enthusiast on LinkedIn

The library’s capacity to deal with missing values and perform group-wise operations makes it useful for tasks such as wrangling, preprocessing, and analyzing data. Pandas libraries are gradually becoming a valuable asset for every data scientist.

Below is an example to get you started!

NumPy

What name comes next in this section for data science? Yes, you guessed it! NumPy — better known as the jack-of-all-trades is an indispensable library for scientific computing in top Python librariesPython.

Do you know the name of the man behind the creation of NumPy? In addition to NumPy, Travis Oliphant is the principal author of SciPy.

As we already know, NumPy is written in Python. However, a huge chunk of the parts that require fast computation is written in C++ or C.

NumPy is a powerhouse that offers outstanding support for multi-dimensional arrays and matrices. It also has a broad array of mathematical functions for precise manipulations.

NumPy supports large, multi-dimensional arrays, and matrices while providing a wide range of mathematical functions. With its impressive features, NumPy empowers data scientists to explore the exciting world of scientific computing.

One of the most common functions is the array() function, mainly used in creating arrays.

Let us take a look at this example import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr)

In just a few lines of code, we create an array of values; 1, 2, 3, 4, and 5, enabling swift and straightforward transactions.

Scikit-learn

In the world of data science, machine learning has become the beating heart that fuels innovations from self-driving cars to computer-aided medical diagnosis. Scikit-learn is the shining star of machine learning in Python!

Scikit-learn provides critical tools for model selection, evaluation, and preprocessing, further enhancing its usability. Therefore, its most common uses include predictive modeling, classification, and clustering, making it a go-to resource for creating robust machine-learning models.

“Rather than focusing on loading, manipulating and summarising data, Scikit-learn library is focused on modeling the data.”

Scikit-learn provides a simple and consistent interface, enabling beginner-level data scientists to navigate the world of machine learning with ease. Thus, Scikit-learn democratizes machine learning, making it accessible to a more extensive range of data enthusiasts.

That’s why Scikit-learn deserves a mention in our article for the best Python libraries

To use Scikit-learn, you’ll need to:

Install it on your system with the command pip install scikit-learn. Import it in your Python script with the command import sklearn.

Utilize its functions and methods for building machine learning models;

Split the data into training and testing sets using the train_test_split() method;

Fit the model with the training data;

Finally, evaluate its performance on the testing data

Matplotlib

What does Matplotlib do? It can create stunning visuals for exploring and presenting data in Python. In addition, it has a broad range of plot types including line plots, scatter plots, bar charts histograms, and many more.

Whether you’re a beginner or an experienced programmer, Matplotlib can help you create high-quality, customizable visualizations of your data.

Suraj Kumar Soni, data analyst on LinkedIn

Here’s your guide to creating line plots with Matplotlib.

Import the library in your Python script using import matplotlib.pyplot as plt

Use the plt.plot() function for creating a line plot with x on the x-axis and y on the y-axis, e.g. plt.plot(x, y)

Seaborn

Built on Matplotlib, it offers a higher-level interface while providing an array of tools for creating heatmaps, violin plots, and other visualizations used in Data Science.

With just a few lines of code, Seaborn can help you generate complex plots for uncovering patterns and trends quickly and easily. Unlock the potential of data visualization today with Seaborn!

The most commonly used Seaborn commands include heatmap(), jointplot(), pairplot(), distplot(), kdeplot() and countplot(). Let’s take a look at an example of the command heatmap() in action :

import seaborn as sns import matplotlib.pyplot as plt # read dataset attrition = pd.read_csv(“attrition.csv”) # create correlation matrix corrMatt = attrition.corr() mask = np.array(corrMatt) # creating mask array from matrix mask[np.tril_indices_from(mask)] = False # setting diagonal values to false fig, ax = plt.subplots() # creating plot figure fig.set_size_inches(20,10)# setting figure size sns.heatmap(corrMatt, mask=mask,vmax= .8, annot=True) # plotting heatmap using seaborn

Tensorflow

TensorFlow is a revolution in the world of machine learning and deep learning. It allows developers to create sophisticated models with unparalleled performance and scalability.

Tensorflow supports a multitude of tools, such as transfer learning, image classification, object detection, natural language processing capabilities, etc. These nifty tools give developers the freedom to build advanced deep-learning models.

Furthermore, its visualization and debugging features give users an in-depth understanding of their model’s inner workings. Plus, an abundance of pre-trained models ready for use makes TensorFlow the go-to library for developing effective machine-learning applications.

“With the help of TensorFlow, we can visualize each and every part of the graph which is not an option while using Numpy or SciKit. The best part about Tensorflow is that it is open source so anyone can use it as long as they have internet connectivity.” Aqsa Z., Ph.D. scholar in machine learning on LinkedIn

Here is a simple example of a TensorFlow command:

tf.placeholder(dtype, shape=None, name=None)

This command creates an object class as a way to feed data into the computational graph. It sets up placeholder tensors that can accept external input when the graph is run.

dtype is the type of data used (e.g. float32), shape defines the shape of the tensor and name assigns it an optional label for ease of recognition.

Keras

Keras is a powerful high-level neural network API built on top of TensorFlow. It is an ideal choice for beginners who want to create and train deep learning models without dealing with the complexity of the underlying TensorFlow technology.

“Designed to enable fast experimentation, it focuses on being user-friendly, modular, and extensible.” How to build a simple Neural Network with Keras

Its pre-trained models can assist in transfer learning. Moreover, Keras has a useful set of tools for visualization and debugging. Therefore, it has become ubiquitous in common tasks such as image classification, text classification, and sequence-to-sequence prediction.

The intuitive interface provided by Keras makes it an easy-to-use yet highly efficient library for developing machine-learning applications.

An example of a simple command in Keras is model. compile(). This use of this command is to compile the model, to specify the optimizer and loss function for training. Once the model is compiled, it can then be trained using the model. fit() command.

SciPy

Another interesting fact is that SciPy comes with documentation support. Tutorials, files, and online references prove to be a steady source of information for developers.

The SciPy library works with NumPy arrays. Hence, a big advantage is the user-friendly numerical practices. Plus, you have the freedom to visualize and manipulate data with high-level commands

It even supports a huge number of sub-packages for scientific communications. For cluster, signal, special, integrate, and many more.

To summarize it all, SciPy is a mathematical and scientific problem solver. The most frequently used feature is the stats module!

Following is an example of the help() function. from scipy import cluster

help(cluster) #with parameter

help() #without parameter

Theano

Did you know that the name Theano comes from a Greek mathematician ? With a title like this, one does anticipate Theano to be a god in the world of the best Python libraries.

“Defining, optimizing, and evaluating mathematical statements using complicated multi-dimensional arrays are all possible with Theano.” Intensive Mathematical and Scientific Calculations using Theano (article on LinkedIn).

Theono amplifies the capabilities of deep learning frameworks such as PyTorch by allowing users to set up neural networks. Another feature that stands out is the user accessibility to a range of tools for the development of complex algorithms.

Why is Theano so popular amongst computer scientists today? It offers advanced technology along with extensive unit-testing ability. With this feature, it can diagnose multiple ambiguities in the model. Moreover, it can perform data-intensive computations much faster than a CPU. Think lightning speed!

pip install Theano sudo pip install -upgrade -no-deps theano pip install -upgrade -no-deps git+git://github.com/Theano/Theano.git

The commands below are a method to install Theano with a Python and SciPy environment.

Pytorch

By using its tools and libraries, you can craft the most intricate models. Sounds promising, doesn’t it? According to Github, PyTorch has a unique way of building neural networks : using and replaying a tape recorder.”

“As of September 2022, PyTorch is the machine learning framework used for 64% of machine learning research teams who publish their code.” Daniel Burke, machine learning instructor on LinkedIn

You can easily integrate customized components into existing architectures with its APIs. The Pytorch framework supports more than 200 mathematical operations. Isn’t it a powerhouse of a framework?

This a specimen of a code layout for Pytorch. data/ experiments/ model/ net.py data_loader.py train.py evaluate.py search_hyperparams.py synthesize_results.py evaluate.py utils.py

In addition, Pytorch is one of the most widely used technologies for machine learning. What makes it even better is the ease of use along with its strength in dealing with big data projects.

CNTK

Let’s begin with breaking down the definition of CNTK. The Microsoft Cognitive Toolkit is an open-source tool kit for deep learning. Formerly known as the Computational network toolkit, CNTK can assist in speeding up projects.

Besides being a huge time-saver, this toolkit allows developers to create unique models with customizable APIs. Do you want to solve difficult problems without a lot of hassle? CNTK is a trustworthy tool.

“CNTK makes Deep learning fast & scalable. It is used in a large number of production loads in the cloud environment. This Toolkit is tested in the production setting for accuracy, efficiency & scalability in the multi GPU, multi-server environment.” Microsoft CNTK (Cognitive Toolkit) on E2E’s GPU Cloud pip install cntk

It has a set of components to feed data into your neural network. Plus, you can keep a check on the performance of neural networks.

This is the most common way to install CNTK package .

“Data scientist is now called the “Sexiest Job of the 21st century” when nobody expected geeky jobs to ever be sexy! But Data Science is sexy now and that is because of the immense value of data. And Python is one of the best programming languages to extract value from this data because of its capacity.” Akshay Gangshettiwar, data scientist and business analyst on LinkedIn

Conclusion

Which Python libraries are used for data science?

Python is the go-to language for data science due to its simplicity, flexibility, and the availability of advantageous libraries. Due to these reasons, Python still remains immensely popular.

There is no doubt that Python is a highly popular programming language in the world of coding. The coding language has quite a lot of significance in data science, from beneficial tools to libraries and frameworks.

NumPy: It is an open-source library for scientific computing and data analysis.

Pandas: Pandas is a software library, data analysis, and manipulation tool.

Matplotlib: It is an open-source plotting library for Python.

SciPy: SciPy is a scientific computation library that uses NumPy.

How to learn Python libraries for data science?

Sci-kit-learn: It is a free software machine learning library.

To kickstart your data science journey, it is crucial to know the fundamentals. Programmers, software engineers as well as data scientists are now using the Python programming language for problem-solving.

Online courses have become more popular than ever. Everyone knows there are options to introduce beginners to the basics. However, there are intermediate and expert-level courses to take the knowledge of data scientists to the next level.

How many libraries are used for data science in Python?

Another way is learning through online coding tutorials. These act as a step-by-step guide if you want to know about a specific Python library such as CNTK or even Pytorch.

The internet is enough to tell that there is an abundance of Python libraries to use. However, it’s better to start practicing any of the top Python libraries. The names of the best Python libraries are as follows:

Sci-kit-learn

Matplotlib

TensorFlow

Statsmodels

How do I master Python for data science?

Beautifulsoup

Don’t miss out on the basics

Having a degree in computer science might be of some value. However, knowledge lies in experience and constant practice. To master Python, you need to create a set of milestones.

What about data structures?

Practice makes perfect! In this case, the more you practice, the better. Plus, doing projects on your own adds to your portfolio. Essential basics of Python programming including variables, functions, loops, etc.

Python libraries

This is a pivotal part of the phase of learning Python. You need to fully understand the meaning of data structure as well its examples. Knowing their manipulation is also a must.

Practice and work

How can an individual skip this step? The process of mastering data science with Python is incomplete without these Python libraries. There has to be a deep level of interest and hunger to evolve if you want to become an expert.

We cannot emphasize enough the importance of practicing. Look for data sets, and begin to analyze them. Sources to find data sets include Earth Data, CERN Open Data Portal, Kaggle, etc.

Again, the project size doesn’t matter as long as you are getting practical experience.

How many days it will take to learn Python for data science?

Last but not least, always keep research and stay up-to-date with the latest trends. Follow social media pages, blogs, Github communities, Linkedin pages, etc.

Generally, it takes a week or two to develop an understanding of Python basics for data science. It also depends on the experience of the individual and their learning approach.

If you have no experience at all, it may take three to six months to learn Python and its libraries. The more time you invest, the more you’ll learn. It is a continuous process that demands consistency, patience, and a whole lot of practice.

Originally published at https://gaper.io on September 5, 2023.