Tuesday, 2 October 2018

Top 10 Python Libraries

1. Pandas

Pandas is a software library written for the python programming language for data manipulation and analysis.

Pandas is well suited for many different kinds of data:
  • Tabular data with heterogeneously-types columns.
  • Ordered and unordered time series data.
  • Arbitrary matrix data with row and column labels.
  • Any other form of observational / statistical data sets.
 The data actually need not be labeled at all to be placed into a pandas data structure.

2. NumPy

Numpy is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

3. Matplotlib

Matplotlib is a Python package used for 2D graphics.
  • Bar graph
  • Histograms
  • Scatter Plot
  • Pie Plot
  • Hexagonal Bin Plot
  • Area Plot

4. Selenium

The selenium package is used to automate web browser interaction from Python.

5. OpenCV

OpenCV- Python is a library of Python designed to solve computer vision problems.

6. SciPy

Scipy is a free and open-source Python library used for scientific computing and technical computing.

7. Scikit-Learn

Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms.

8.  PySpark

The Spark Python API (PySpark) exposes the Spark programming model to Python.

9. Django

Diango is a Python web framework. A framework provides a structure and common methods to make the life of a web application developer much easier for building flexible, scalable and maintainable web applications
  • Django is a high-level and has a MVC-MVT styled architecture.
  • Django web framework is written on quick and powerful Python language.
  • Django has a open-source collection of libraries for building a fully functioning web application.

10. Tensor Flow

TensorFlow is a Python library used to implement deep networks. In TensorFlow, computation is approached as a dataflow graph.


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