• Data Science
  • 5 min read

2021's Top 10 Python Libraries For Data Scientists To Use

2021's top 10 python libraries for data scientists to use
Python is the most widespread programming language in the data science world. For solving data science tasks & challenges, Python never stops surprising its users. All data scientists 2021 are already reaping the benefits of Python programming daily. Python is a user-friendly, extensively used, open-source, object-oriented, high-performing, easy-to-debug language, and there are several benefits to Python programming. Python language has been built with incredible Python libraries for data science that programmers use daily to resolve issues.
Top 10 Python Libraries for Data Scientists to Use
- TensorFlow
- SciPy 
- NumPy
- Pandas
- Matplotlib 
- PyTorch
- BeautifulSoup
- Keras
- SciKit-Learn
- Scrapy
1. TensorFlow
TensorFlow is a library for high-performance numerical calculations with approx. 35k comments & a vibrant community of around 1500 contributors. It is used across various scientific fields. This library is a framework for defining and running calculations that include tensors, which are partially defined arithmetic objects that ultimately generate a value.
Key Features:
- Improved arithmetic graph visualizations
- Prevents error by 50-60% in neural machine learning
- Parallel computation to perform complex models
- Smooth library management supported by Google
- Faster updates & frequent new releases to render you recent features
TensorFlow is specifically helpful for the following applications:
- Speech & photo recognition 
- Text-based applications 
- Time-series analysis
- Video detection
2. SciPy
Scientific Python is another free & open-source Python library for data science that is widely used for high-level calculations. SciPy has approximately 19k comments on GitHub & an active community of around 600 contributors. It's widely used for technical and scientific measures as it extends NumPy and offers several user-friendly and efficient routines for scientific computations.
Key Features:
- Collection of algorithms & functions established on the NumPy extension of Python
- High-level commands for data visualization and manipulation
- It includes in-built functions for solving differential equations
- Multidimensional photo operations
- Linear algebra
- Optimization algorithms
- Solving differential equations & the Fourier transform
3. NumPy
Numerical Python is the basic package for numerical calculators in Python as it includes a robust N-dimensional array object. It has about 18k comments on Github & an active community of 700 contributors. It is an all-around array-processing package that renders high-performance multidimensional objects known as arrays & tools for working with them.
Key Features:
- It renders quick, precompiled functions for numerical routines
- Supports an object-oriented approach
- Array-oriented calculations for enhanced efficiency
- Compact & quicker computations with vectorization
- Widely used in data analysis 
- Forms the foundation of other libraries, like SciPy & scikit-learn
- Creates robust N-dimensional array
4. Keras
Keras is another famous library used widely for neural network modules and deep learning. This library supports Theano backends and TensorFlow; hence it is a good option if you don't wish to dive into TensorFlow's details.
Key Features:
- Keras offers broad prelabeled datasets which can be directly used to import & load.
- It includes various implemented layers & parameters that can be used for the construction, training, configuration, & evaluation of neural networks.
One of Kera's major applications are the deep learning models that are available with their instructed weights. You can utilize these models directly to forecast or extract their features without creating/training your new model.
5. Pandas
Also referred to as Python data analysis, Pandas is a must in the data science life cycle. It is the most trending and extensively used library for data science, alongside NumPy in matplotlib. It has around 17k comments on GitHub & an active community of 1200 contributors and is massively used for data cleaning and analysis. This library offers quick and flexible data structures, like data frame CDs, designed to work with structured data effortlessly and intuitively.
Key Features:
- Fluent syntax & rich functionalities that offers you the freedom to manage the missing data
- High-level abstraction
- Allows you to create your function & run it across a series of data
- It includes high-level data structures & manipulation tools
General Data Cleaning & Wrangling
- Time-series-specific functionality, like date shifting, date range generation, moving window, and linear regression
- Used in various academic & commercial areas, including statistics, finance, & neuroscience 
6. Matplotlib
This library has robust yet beautiful visualization. It is a plotting library for Python with approx. 26k comments on GitHub & an active community of around 700 contributors. Due to the graphs & plots that it generates, it is widely used for data visualization. It also renders an object-oriented API, which can be used to infuse those plots into apps.
Key Features:
- It can be used as a MATLAB replacement, with the benefit of being free & open source 
- Low memory consumption & improved runtime behavior
- Correlation analysis of variables
- Outlier detection using a scatter plot etc.
- Visualize the distribution of data to get prompt insights
- Visualize 95% confidence intervals of the models
7. Scikit-learn
Scikit-learn is next on the list of the top Python libraries for data science. This is a machine learning library that offers all the machine learning algorithms you might need. It is created to be interpolated into NumPy & SciPy.
- Clustering
- Regression
- Classification
- Model selection
- Dimensionality reduction
8. PyTorch
PyTorch is a Python-based scientific arithmetic package that uses the power of graphics processing units. This is one of the most widely preferred deep learning research platforms developed to render maximum flexibility & speed.
- PyTorch is popular for offering two of the most high-level features
- Developing deep neural networks on a tape-based system
9. Scrapy
Scrapy is one of the most famous, quick, open-source web crawling frameworks in Python. It is widely used to pull the data from the web page via XPath-based selectors.
- Scrapy helps in creating crawling programs that can recover structured data from the web
- This library is even used to collect data from APIs and abides by a 'Don't Repeat Yourself' principle in the design interface.
10. BeautifulSoup
BeautifulSoup is another famous python library most widely known for data scraping and web crawling. Users can gather data on some websites without an appropriate API or CSV, and this library can help them scrape it & organize it into the format needed.
Apart from these top 10 Python libraries for data science, several other helpful python libraries are available that deserve to be looked at. Unlock your career as a data scientist by using these top Python libraries best-suited to your needs.

Harnil Oza is a CEO of HData Systems - Data Science Company & Hyperlink InfoSystem a top mobile app development company in Canada, USA, UK, and India having a team of best app developers who deliver best mobile solutions mainly on Android and iOS platform and also listed as one of the top app development companies by leading research platform.

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