Python Libraries for Data Science

Data Science is a combination of Data Analysis and Machine Learning (ML). This involves the extraction of data, cleansing, transforming, and modeling data to help us discover useful information from the data, answer questions and even foretell the future.

Python is one of the preferred programming languages for Data Science because of its simplicity and readability, standard pre-built libraries, and clean syntax, hence, it takes less time to write.

A library is a group of functions that let’s carry out lots of actions without writing any code. They contain built-in modules which provide different functionalities that you can use directly.

Listed below are some of the most important Python libraries for Data Science tasks, covering areas such as Data Computing, Data Visualization, and Machine Learning algorithms.

Computing Libraries for Data Analysis

  • Pandas: This is a software library written for Python programming language for Data Manipulation and Analysis. It is the most popular and widely used Python library for Data Science and Analysis, along with NumPy and Matplotlib. Pandas provides data structures and tools for productive Data Analysis and Manipulation, and it gives a fast axis to structured data. The major instrument of Pandas, is a 2-D table containing column and row labels which are called a data frame. It is created to give easy indexing and functionality.
  • NumPy: This is an acronym for Numerical Python. It is a library consisting of multidimensional array objects and a collection of tools for processing those arrays. NumPy uses arrays for its inputs and outputs. It can be extended to objects for matrices, and with little coding changes, developers can perform fast array processing.
  • SciPy: This is an abbreviation for Scientific Python is a computation library that includes functions for some advanced math, engineering, scientific and technical problems as well as data visualization.

Data Visualization Libraries

In order to communicate with others, Data Visualization comes handy. It is actually the best way to communicate, showing them meaningful results of the analysis. These libraries allow you to create maps, graphs, and charts.

  • Matplotlib: This is a well-known Python library for Data Visualization. It can be used to generate two-dimensional diagrams and graphs such as histograms, scatterplots, non-Cartesian coordinates graphs.
  • Seaborn: This is a library in Python that is based on Matplotlib. It is used to create various types of plots such as heat maps, time series, joint plots, and violin diagrams.

Algorithmic Libraries

With Machine Learning algorithms, you can develop a model using your data sets and obtain predictions. These libraries, tackle some Machine Learning tasks from basic to complex.

  • Scikit-Learn: This library is built on NumPy,  SciPy,  and Matplotlib. It contains tools for statistical modeling including regression, classification, clustering, model selection, and dimensionality reduction.
  • StatsModels: This is also a Python library that allows users to explore data, estimate statistical models and carry out statistical tests.

Conclusion

There are lots of Python libraries out there. Python has many other tools that can be useful for Data Science. Data scientists make use of these tools because they are needed for building high-performance Machine Learning models in Python.

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