Here in this article, we have provided the top 10 data science packages for Python that you can use for your data science projects and ML models. 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 as well.

Python is all about its powerful libraries and packages. A library is a prewritten code that contains many modules that you can import into your program to enhance and add extra functionality. A package, on the other hand, could be defined as a distributed folder that contains many libraries and executable code. In simple words, we can say that a package is a collection of libraries.

In the last five to ten years, fields like data science, machine learning, artificial intelligence, and deep learning gained too much popularity all over the world, and Python is at the core of all these fields. This is because Python has many powerful packages and libraries for the development of these fields. So, let’s start discussing the best data science packages for Python.

## 10 Python Data Science Packages and Libraries

### 1. Pandas

pip Installation Command: pip install pandas

Pandas is an open-source data science package frequently used for data analysis and machine learning algorithms. It provides developers with fast, flexible, and expressive data structures. The main objective of this data science package in Python 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. Also, Pandas 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.

Pandas is built on the NumPy package, which is specially designed for mathematical and scientific computations. That’s why Pandas is also capable of handling mathematical tools for statistics. It uses two primary data structures that 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.

**Major Features of Pandas**

- It makes the process of data manipulation and analysis easier.
- It is easy to insert and delete a large number of data sets from the Data Frame (2-D) data structures.
- Pandas provides intuitive techniques for merging and joining data sets.
- With Pandas, developers 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

pip Installation Command: pip install NumPy

NumPy is an open-source data science package and the most famous Python package for scientific computation. Python does not have any standard support for arrays. NumPy resolves this problem with its NumPy array module. It is the default scientific computation package for Python. It contains all the mathematical concepts and provides support for multi-dimensional arrays.

For Python developers, it is necessary to know the NumPy package whether they are to work in web development or data science. Many other popular Python data science libraries, including Pandas and TensorFlow, use NumPy for many operations. It also contains a tool for integrating C++ and Fortran code.

**Major Features of NumPy**

- It is easy to use this package.
- Since NumPy is open-source, everyone is free to install it.
- It provides sophisticated mathematical methods.
- It has modules for using powerful multidimensional arrays.
- NumPy supports sophisticated functions.
- It also provides tools for integrating C, C++, and Fortran Code.

### 3. TensorFlow

pip Installation Command: pip install TensorFlow

TensorFlow is among the most popular data science packages for Python. Although it is a Python library developed by Google for machine learning, it is also widely used in data science for numerical computation using data flow graphs.

It is an open-source symbolic math library that has various mathematical operations and has data flow graphs represented by graph nodes. All the libraries of TensorFlow are written in C and C++, making it performant. Google uses it in its products such as Google Photos and Google Voice Search.

**Major Features of TensorFlow**

- It gives support for face recognition libraries.
- TensorFlow provides support for video detection.
- It can visualize graphs better than NumPy and Scikit.
- A vast, global community of developers and professionals supports TensorFlow.
- It is ideal for developing neural networks and ML models.

### 4. SciPy

pip Installation Command: pip install scipy

Pronounced “Sigh Pie,” it is mainly used for data science and machine learning projects. SciPy is an open-source Python package that focuses on mathematics, science, and engineering. It includes many mathematical computation tools, such as numerical integration, interpolation, optimization, linear algebra, and statistics.

**Major Features of SciPy**

- It can easily handle various mathematical operations.
- It helps to build powerful and sophisticated programs and specialized applications using Python.

### 5. Matplotlib

pip Installation Command: pip install matplotlib

This python library helps to create 2D and 3D graphs so that developers can efficiently visualize data in different data structures. 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 to make its interpretation easy. Apart from Python shell, Python script, and IPython, matplotlib can also be used in Jupyter Notebook, web applications, and graphical user interfaces.

**Major Features of Matplotlib**

- It is an open-source library.
- It is easy to learn and implement.
- Matplotlib contains all types of graphs.
- It gives a proper visual representation of data.

### 6. Scikit-Learn

pip Installation Command: pip install sci-kit-learn

The next name on our list of the best data science packages for Python is Scikit-Learn. Technically, it is a machine learning library that contains scientific operations of NumPy and SciPy, which make it an appropriate tool for data analysis.

Introduced as a Google Summer of Code project, it was built on SciPy, NumPy, and Matplotlib. Scikit-Learn helps to develop supervised and unsupervised learning algorithms. Scikit-Learn is an ideal library for beginners in machine learning and data science.

**Major Features of Scikit-Learn**

- It is one of the best Python tools for predictive data analysis.
- Being built on NumPy, SciPy, and matplotlib helps 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, and principal component analysis.

### 7. Keras

pip Installation Command: pip install sci-kit-learn

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

**Major Features of Keras**

- It can run smoothly on CPU as well as GPU.
- It is a more human-friendly Python data science package.
- Keras focuses more on the user interface.

### 8. Statsmodels

pip Installation Command: pip install statsmodels

It is an open-source package and Python module for various statistical models. Statsmodels is also capable of conducting statistical tests and analytical data exploration. This data science package for Python provides support for statistical computations, including descriptive statistics and estimation and inference for statistical models.

**Major Features of Statsmodels**

- It has support for linear regression models.
- Statsmodels offer RLM, which is Robust linear models with support for several M-estimators.
- It also offers Time Series Analysis: models for time series analysis.
- It supports a wide range of statistical tests.

### 9. Seaborn

pip Installation Command: pip install seaborn

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

**Major Features of Seaborn**

- It gives support for categorical variables to show observations or aggregate statistics.
- It offers automatic estimation and plotting of linear regression models for different kinds of dependent variables.
- Seaborn has convenient views of the overall structure of complex datasets.
- It offers high-level abstractions for structuring multi-plot grids that let developers quickly build complex visualizations.
- It offers concise control over matplotlib figure styling with several built-in themes.

### 10. Gensim

pip Installation Command: pip install gensim

Gensim is an open-source data science package for Python that finds use in natural language processing and unsupervised topic modeling tasks. It only works for natural language processing (NLP) and information retrieval (IR).

**Major Features of Gensim**

- All algorithms in Gensim are memory-independent concerning the corpus size.
- It has an intuitive interface.

**Conclusion**

That completes our list of the best data science packages for Python. Data science is not only about mathematical concepts but also representation, analysis, and manipulation of the data. Python is famous for its extensive set of libraries.

Although some of the libraries that we mentioned above are primarily for machine learning and natural language processing, they are also ideal for data science. This is because data science intercepts both machine learning and natural language processing. So for a data science engineer, it is necessary to know about AI, machine learning, and deep learning as well.

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