Data Science is getting very much popular in the last 10 years and there are many popular languages for it. The most popular language used in Data Science is Python.

As Python is the most popular language for Data Science among all other languages, here are the top 10 essential Python packages for Data Science.

**1. Numpy**

For scientific Programming in Python, Numpy is the Most Essential package for Data Science. Numpy was designed for scientific calculation and best fits any Data Science need for scientific calculations. Numpy is most popular and very essential for Data Science because it has an array that is very fast and computable.

Firstly Numpy was part of Scipy but the scientist who needs only an array of Numpy and doesn’t want any other functionalities of Scipy has to install Scipy as a full package so Numpy is separated from Scipy for Data Science.

**2. Pandas**

For Data Analysis in Python, Pandas is the most flexible and most used library in Python. While it’s not a special machine learning library though it is used for data analysis of Large Datasets. Pandas are most used for its Dataframes, NumPy arrays, Time series analysis, Numerical data tables, and Series data. Setuptools, Numpy, Python Datautil, pytz are required to use Pandas in Python for Data Science.

**3. Matplotlib**

Matplotlib is a Python 2D plotting library that makes it easy to produce cross-platform charts and figures.

So far in this roundup, we’ve covered plenty of machine learning, deep learning, and even fast computational frameworks. But with data science, you also need to draw graphs and charts. When you talk about data science and Python, It is what comes to mind for plotting and data visualization. It’s ideal for publication-quality charts and figures across platforms.

**4. Scipy**

Scipy is biggest among the data science libraries as it includes mathematics, science, and engineering. If you want any technical and scientific computing, you can use it as it contains all types of mathematical and scientific computations.

Since it builds on top of NumPy, SciPy has the same target audience. It has a wide collection of sub-packages, each focused on niches such as Fourier transforms, signal processing, optimizing algorithms, spatial algorithms, and nearest neighbors.

**5. Scikit-Learn**

Scikit-Learn is the Machine Learning package in python developed on top of Scipy and Numpy. It has such a gentle learning curve, even the people on the business side of an organization can use it. For example, the Scikit-Learn website show you how to analyze real-world data sets. If you’re a beginner and want to pick up a machine learning library, Scikit-Learn is the one to start with.

Scikit-Learn requires

- Python 3.5 or higher
- NumPy 1.11.0 or higher
- SciPy 0.17.0 or higher

**6. Tensorflow**

TensorFlow is one of the most famous machine learning libraries for some very good reasons. It specializes in numerical computation using dataflow graphs.

TensorFlow was originally developed by Google Brains. TensorFlow is an open-source package or library. It processes the large datasets quickly so its most essential Machine Learning library. A recent version of TensorFlow is 1.13.1 and 2.0 is in the beta version.

**7. Keras**

Keras is built for fast experimentation. It’s capable of running on top of other frameworks like TensorFlow, too. Keras is best for easy and fast prototyping as a deep learning library.

Keras is popular amongst deep learning library aficionados for its easy-to-use API. It needs one of three possible backend engines, like TensorFlow, Theano or CNTK.

**8. PyTorch**

PyTorch does two things very well. First, it accelerates tensor computation using a strong GPU. Second, it builds dynamic neural networks on a tape-based autograd system, thus allowing reuse and greater performance.

If you’re an academic or an engineer who wants an easy-to-learn package to perform these two things, PyTorch is for you. Requirements for PyTorch depend on your operating system

**9. Caffe**

Caffe is one of the fastest implementations of a convolutional network, making it ideal for image recognition. It’s best for processing images.

Caffe assumes you have at least a mid-level knowledge of machine learning, although the learning curve is still relatively gentle. As with PyTorch, requirements depend on your operating system

**10. Theano**

Theano is one of the earliest open-source software libraries for deep-learning development. It’s best for high-speed computation.

This made a list of top ten data science packages for Python.

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