Python is all about its powerful libraries and packages. A library is a prewritten code that contains many modules that you can import in your program to enhance and bring the extra functionality. A package could be defined as a distributed folder which contains many libraries and executable code; in simple words, we can say that a container is a collection of libraries. In over five years, technologies like Data-Science, Machine Learning, Artificial Intelligence, and Deep Learning gained too much popularity over the world, and python stands as the core for all these technologies; this is because python has many powerful packages and libraries for the development of these technologies.

Here in this article, we have provided the top 10 Python Packages which you can use for the Data Science projects and models. It’s not like that all the packages we have mentioned here are limited to the data science, for some reasons Data Science, Machine Learning and Artificial Intelligence are related to each other so these packages can also be used for AI and Machine learning projects.

## 10 Python Data Science Packages and Libraries

### 1. Pandas

URL**:** https://pypi.org/project/pandas/

pip install Command: pip install pandas

Open-Source Package

- It is an Open source python package frequently used for data analysis and machine learning algorithms.
- It provides you with fast, flexible, and expressive data structures.
- The main objective of this package is to build a high-level block for practical and real-world data analysis using python.
- It is one of the most important and flexible tools for data analysis and manipulation.
- It can work with different kinds of data sets, such as Tabular data with heterogeneously-typed columns (SQL or Excel spreadsheets), Arbitrary matrix data, Ordered and unordered (not necessarily fixed-frequency) time-series data, and any other form of statistical data.
- It is built on the NumPy package, which is specially designed for mathematical tools that are why pandas are also capable of handling mathematical tool for statistics.
- It uses two primary data structures which are Series(1-D) and Data Frames(2-D), and projects like finance, statistics, social science, and many areas of engineering, often use these data structures.

**Features of Pandas**

- It makes the process of data manipulation and analysis easier.
- It’s easy to insert and delete a large number of data sets from the Data Frame(2-D) data structures.
- It provides you with intuitive techniques for merging and joining data sets.
- With pandas, you can efficiently deal with numeric data types such as floating and non-floating data sets.
- It has powerful tools for loading data from different data formats, such as Excel files and databases.

### 2. NumPy

**URL:** https://numpy.org/

pip install Command: pip install NumPy

Open-Source Package

- NumPy is the most famous python open-source package for scientific computation.
- As we know that python does not have any standard support for Array Data structure, but NumPy resolves this problem with its NumPy array module.
- It contains all the mathematical concepts and support for multi-dimensional Arrays.
- For a python developer it becomes necessary to know the NumPy package whether he/she is in web development or Data Science.
- Many other popular libraries of Pandas and TensorFlow itself use NumPy for many operations.
- It also contains a tool for integrating C++ and Fortran code.

**Features of NumPy**

- It is easy to use this package.
- Open Source, so everyone is free to install it.
- It provides us with sophisticated mathematical methods.
- It has modules for powerful multidimensional array.
- Sophisticated functions
- It also provides tools for integrating C/C++ and Fortran Code.

### 3. TensorFlow

URL**:** https://www.tensorflow.org/

pip install Command: pip install TensorFlow

Open-Source Package

- TensorFlow is a python library developed by Google for Machine learning, but it is also used in data science for numerical computation using data flow graphs.
- It is an open-source symbolic math library which has various mathematical operations and its data flow graph represented by graph nodes.
- This library is only supported by python.
- All the libraries of TensorFlow are written in C and C++
- Google used this technology in their products such as Google Photos and Google Voice Search

**Features of TensorFlow**

- It gives support for face recognition libraries.
- Video Detection
- It can visualize graph better than Numpy or Scikit Library
- A vast community of developer support TensorFlow
- Used for Neural network models

### 4. SciPy

pip install Command: pip install scipy

Open-Source Package

- This package is pronounced as “Sigh Pie,” and it is mostly used for Data Science and Machine learning projects.
- It is an open-source python package which focuses on software related to mathematics, science, and engineering
- It includes many mathematical computation tools such as numerical integration, interpolation, optimization, linear algebra, and statistics.

**Features of SciPy**

- It can easily handle various mathematical concepts.
- Its function built on the NumPy extension
- It makes python powerful to build sophisticated programs and specialized applications.

### 5. Matplotlib

pip install Command: pip install matplotlib

Open-Source Package

- This python library is used to create 2D and 3D graphs, so the data formed by the different data structures can be visualized.
- It is always used along with pandas and NumPy libraries, so the output of their methods can be put in a graphical interface.
- The main objective of the matplotlib library is to visualize the data so it could be interpreted by the user easily.
- Apart from python shell or python script and IPython, matplotlib can also be used in Jupyter Notebook, Web application, and Graphical user Interface.

**Features of Matplotlib**

- Open Source library.
- It is easy to learn and implement.
- It contains all types of graphs.
- Give a proper visual representation of data.

### 6. Scikit-Learn

URL https://scikit-learn.org/stable/

pip install Command: pip install sci-kit-learn

Open-Source Package

- Scikit-Learn is a Machine Learning Library which contains scientific operations of NumPy, and SciPy which make it an appropriate tool for data analysis.
- It was built on SciPy, NumPy, and Matplotlib and introduced as a Google Summer of Code project.
- It is highly used to supervised and unsupervised learning algorithms.
- For a beginner-level Machine Learning and Data Science learner Scikit-Learn is an ideal library.

**Features of Scikit-Learn**

- One of the best python tools for predictive data analysis
- Built on NumPy, SciPy, and matplotlib, which help it to access the various modules of all three libraries.
- It can extract features from images and text
- It comprises a wide range of algorithms such as clustering, factor analysis, principal component analysis to unsupervised neural networks

### 7. Keras

URL: https://keras.io/

pip install Command: pip install sci-kit-learn

Open-Source Package

- It is a high-level neural networks API, which can run on top of TensorFlow, CNTK, or Theano.
- It’s considered as one of the slowest machine learning python libraries because it creates a computational graph using the backend infrastructure first and then uses the same to perform operations
- It gives support for expressing neural networks.
- It comprises many utilities such as compiling models, processing datasets, visualizing graphs, etc.

**Features of Keras**

- It can run smoothly on CPU as well as GPU.
- It is a more human-friendly API tool.
- Because it focuses more on the user interface. As a result, it processes very slow.

### 8. Statsmodels

URL: http://www.statsmodels.org/stable/index.html

pip install Command: pip install statsmodels

Open-Source Package

- It is a python module for various statistical models.
- Statsmodels is also capable of conducting statistical tests and analytical data exploration.
- This package provides a complement to tips for statistical computations, including descriptive statistics and estimation and inference for statistical models.

**Features of Statsmodels**

- It has support for Linear Regression Models.
- RLM: Robust linear models with support for several M-estimators.
- Time Series Analysis: models for time series analysis
- It supports a wide range of statistical tests.

### 9. Seaborn

URL: https://seaborn.pydata.org/

pip install Command: pip install seaborn

Open-Source Package

- It is a python data visualization library.
- It is built on the top of the matplotlib library.
- It can be integrated with the pandas’ library data structures.
- The main objective of Seaborn to visualize the data.
- It provides a high-level interface for drawing attractive and informative statistical graphics.

**Features of Seaborn**

- Give support for categorical variables to show observations or aggregate statistics
- Automatic estimation and plotting of linear regression models for different kinds of dependent variables
- Convenient views onto the overall structure of complex datasets
- High-level abstractions for structuring multi-plot grids that let you quickly build complex visualizations
- Concise control over matplotlib figure styling with several built-in themes

### 10. Gensim

URL: https://pypi.org/project/gensim/

pip install Command: pip install gensim

Open-Source Package

- It is used for natural language processing and unsupervised topic modeling tasks.
- It only works with the natural language processing (NLP) and information retrieval (IR) community.

**Features of Gensim:**

- All algorithms are memory-independent concerning the corpus size.
- Intuitive interface

**Conclusion**

Data Science is not only about mathematical concepts but also the data representation, analysis, and manipulation. Python is well known for its libraries, and all the libraries we have provided here are related to Data Science. Here some of the libraries are used for machine learning and natural language processing, but Data Science intercepts both machine learning and Natural language, so for a data science engineer, it is necessary to know both the technologies.