**Python** and **R** are preferred languages for **data science** due to their rich ecosystem of libraries, extensive community support, and powerful tools for statistical analysis, data manipulation, machine learning, and visualization. You can choose either Python or R for Data Science. So, if you want to know about all the libraries used in Python and R for Data Science, this article is for you. In this article, I will take you through all the Python and R libraries for Data Science.

## Why Python and R are Preferred Languages for Data Science?

Python and R are preferred languages for data science due to their rich ecosystem of libraries, extensive community support, and powerful tools for statistical analysis, data manipulation, machine learning, and visualization. Python’s syntax and readability make it beginner-friendly and conducive to collaborative coding. R’s ability to produce publication-quality graphs is advantageous for data exploration and presentation.

Both languages have strong communities that actively contribute to the development and maintenance of libraries, making it easier to find solutions to data science problems. They also have vast resources, including tutorials, documentation, and online forums, enabling individuals to quickly learn and apply Data Science concepts.

So, Python and R’s popularity in Data Science stems from their comprehensive libraries, ease of use, and robust community support, making them versatile tools for data scientists to analyze, manipulate, and visualize data effectively.

## Python and R Libraries for Data Science

In this section, I’ll take you through all the Python and R libraries used for Data Science in the industry.

#### Python and R Libraries for Data Manipulation and Transformation

Data manipulation and transformation refer to modifying and reshaping datasets to specific analysis needs. It involves performing operations such as filtering, sorting, merging, aggregating, and restructuring data to derive meaningful insights and prepare it for further analysis.

In Python, one of the primary libraries for data manipulation is **Pandas**. Pandas provides a rich set of data structures, such as DataFrames, which allow analysts to organize and manipulate data efficiently. Another library commonly used in Python for data manipulation is **NumPy**. NumPy provides powerful numerical computing capabilities and supports multi-dimensional arrays and matrices.

In R, one of the primary libraries for data manipulation is **dplyr**. Dplyr offers a collection of functions specifically designed for efficient data manipulation tasks. And the **tidyr** package in R complements dplyr by providing functions for data tidying and reshaping.

So below are Python and R libraries for data manipulation and transformation with their learning resources:

#### Python and R Libraries for Statistical Analysis

Statistical analysis is a branch of data analysis that focuses on drawing meaningful insights and conclusions from data using statistical techniques. It involves exploring data, identifying patterns, testing hypotheses, estimating parameters, and making inferences about populations.

In Python, one of the prominent libraries for statistical analysis is **SciPy**. SciPy is built upon the functionality of NumPy and provides additional modules for scientific computing and statistical analysis. It offers functions for common statistical tests, including t-tests, ANOVA, correlation, and regression. Another library commonly used in Python for statistical analysis is **Pandas**. It offers data aggregation, grouping, and summary statistics capabilities, making it convenient for EDA and descriptive statistics.

In R, statistical analysis is in the language’s core functionality. R provides a comprehensive set of built-in functions and packages for statistical analysis. The base R package offers functions for basic statistical computations, probability distributions, hypothesis testing, and more. It provides functionalities for linear models, non-linear models, time series analysis, and multivariate analysis.

So below are some of the resources to learn Python libraries and R’s way of statistical analysis:

#### Python and R Libraries for Data Visualization

Data visualization is a crucial aspect of data analysis that involves representing data in visual formats such as charts, graphs, and plots. It allows analysts to effectively communicate complex information, patterns, and relationships present in the data to both technical and non-technical stakeholders.

In Python, one of the libraries for data visualization is **Matplotlib**. Matplotlib provides a comprehensive collection of functions and classes for generating various visualizations, including line plots, bar charts, scatter plots, histograms, and heat maps.

For data visualization in R, the prominent library is **ggplot2**. Based on the grammar of the graphics concept, ggplot2 offers a powerful and flexible framework for creating sophisticated visualizations.

Both Python and R also offer interactive data visualization libraries. **Plotly**, as a Python library, allows analysts to create interactive and web-based visualizations which can be embedded in web applications or notebooks. Plotly is also available in R, along with the “shiny” package, which allows analysts to build interactive dashboards and web applications with live visualizations.

So below are Python and R libraries for data visualizations with their learning resources:

**Matplotlib**(Python)**ggplot2**(R)**Plotly for Python**and**Plotly for R**

#### Python and R Libraries for Modelling and Evaluation

Modelling and evaluation are integral components of the Data Science workflow, where you build statistical or machine learning models to gain insights, make predictions, or classify data. The process involves selecting an appropriate model, training it on the available data, evaluating its performance, and fine-tuning it for optimal results.

In Python, one of the prominent libraries for modelling and evaluation is **Scikit-learn**. Scikit-learn offers a comprehensive collection of machine learning algorithms and tools for classification, regression, clustering, and dimensionality reduction. Another library commonly used in Python for modelling and evaluation is **StatsModels**. StatsModels focuses on statistical modelling, providing a wide range of statistical techniques for hypothesis testing, linear regression, time series analysis, and more.

In R, the **caret** package is used for modelling and evaluation. Caret (Classification And REgression Training) provides a unified interface for a diverse set of machine learning algorithms to compare and train models. It supports tasks such as classification, regression, and feature selection. Caret provides functions for data preprocessing, model training, performance evaluation, and tuning hyperparameters.

So below are Python and R libraries for modelling and evaluation with their learning resources:

**Scikit-learn**and**StatsModels**(Python)**caret**(R)

#### Python and R Libraries for Web Scraping

Web scraping means extracting data from websites by automatically navigating web pages, retrieving their content, and parsing the desired information. It enables analysts to collect large amounts of data from diverse online sources efficiently and automate the extraction process.

In Python, one of the primary libraries for web scraping is **BeautifulSoup**. BeautifulSoup enables you to parse HTML and XML documents, making it easier to extract specific elements and data from web pages.

In R, the primary library for web scraping is **rvest**. Rvest provides similar functionalities to BeautifulSoup in Python, allowing you to parse and extract data from HTML documents.

So below are Python and R libraries for web scraping with their learning resources:

**BeautifulSoup**(Python)**rvest**(R)

#### Python and R Libraries for Neural Networks and Deep Learning

Neural networks and deep learning are advanced techniques in Machine Learning that mimic the structure and functioning of the human brain. These techniques involve the construction of intricate networks of interconnected artificial neurons, enabling machines to learn and make predictions from complex patterns and data.

In Python, one of the prominent libraries for neural networks and deep learning is **TensorFlow**. TensorFlow provides a comprehensive ecosystem for building and training neural networks, including deep learning models. It offers a high-level API called **Keras**, which simplifies the process of constructing neural networks by providing a user-friendly interface. Another popular library for deep learning in Python is **PyTorch**. PyTorch is widely known for its dynamic computational graph, which allows flexible and intuitive model design and customization.

In R, the primary library for neural networks and deep learning is **TensorFlow**.

So below are Python and R libraries for Neural Networks and Deep Learning with their learning resources:

**TensorFlow**and**PyTorch**(Python)**TensorFlow**for R

### Summary

So below are all the necessary Python libraries for Data Science:

- NumPy
- Pandas
- SciPy
- Matplotlib
- Plotly
- Scikit-learn
- StatsModels
- BeautifulSoup
- TensorFlow
- PyTorch

And below are all the necessary R libraries for Data Science:

- dplyr
- tidyr
- ggplot2
- Plotly
- Caret
- Rvest
- TensorFlow

I hope you liked this article on all Python and R libraries for Data Science. Feel free to ask valuable questions in the comments section below.