Libraries in Python for Data Science

First of all, why using Python for Data Science ? According to recent surveys by KDNugget, Python is the preferred programming language for data scientists. Python has long been known as a easiest programming language to learn, from a syntax point of view, anyway. Python also has an active community with a vast selection of libraries and resources. As i mention befire, huge success of Python in Data Science is its extensive library support for data science and analytics. There are many Python libraries that contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data mining, neural networks, data visualization, and so on. In this post, i will tell you about the libraries in Python you must have.

1 . Pandas

PANDAS referred as Python Data Analysis Library, is a perfect tool for data wrangling or munging. It is designed for quick and easy data manipulation, reading, aggregation, and visualization. Using DataFrame in Pandas, you can store and manage data from tables by performing manipulation over rows and columns. Methods like square bracket notations reducing effort in data analysis tasks like square bracket notations.

Pandas also has multiple tools for reading and writing data between in-memory data structures and different file formats. Here, you will get tools for accessing data in-memory data structures performing read and write tasks even if they are in multiple formats such as CSV, SQL, HDFS or excel etc. In short, it is perfect for quick and easy data manipulation, data aggregation, reading, and writing the data as well as data visualization

2. Numpy

NumPy (Numerical Python) is a free Python software library for numerical computing on data that can be in the form of large arrays and multi-dimensional matrices. NumPy’s main object is the homogeneous multidimensional array. It is a table of elements or numbers of the same datatype, indexed by a tuple of positive integers.

NumPy also provides various tools to work with these arrays and high-level mathematical functions to manipulate this data with linear algebra, Fourier transforms, random number analysis, etc. Some of the basic array operations that can be performed using NumPy include adding, slicing, multiplying, flattening, reshaping, and indexing the arrays. Other advanced functions include stacking the arrays, splitting them into sections, broadcasting arrays, etc.

3. Scipy

SciPy (Science Python) provides statistics, optimizations, integration and linear algebra packages for computation. SciPy uses arrays as its basic data structure. SciPy allows for various scientific computing tasks that handle data optimization, data integration, data interpolation, and data modification using linear algebra, Fourier transforms, random number generation, special functions, etc.

4. Matplotlib

Matplotlib (Mathematical Plot Library) is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs. thanks to this library that Python can compete with scientific tools like MatLab or Mathematica. Histogram, bar plots, scatter plots, area plot to pie plot, Matplotlib can depict a wide range of visualizations. With a bit of effort and tint of visualization capabilities, with Matplotlib, you can create just any visualizations:

1. Line plots
2. Scatter plots
3. Area plots
4. Bar charts and Histograms
5. Pie charts
6. Stem plots
7. Contour plots
8. Quiver plots
9. Spectrograms

Matplotlib also facilitates labels, grids, legends, and some more formatting entities with Matplotlib.

5. Scikit-Learn

Scikit-Learn (Scientific Kit for Machine Learning) is a robust tool for data analysis and mining-related tasks. Scikit-learn provides a range of supervised and unsupervised learning algorithms. It features ML algorithms like SVMs, random forests, k-means clustering, spectral clustering, mean shift, cross-validation and more.

The more amazing thing about Scikit-Learn is NumPy, SciPy and related scientific operations are supported by Scikit Learn. Scikit-Learn also provides dummy dataset (for practicing or testing) and neatly written documentation.

Summary

Here are the most important libraries if you want to start practicing with Python. But where is popular libraries such as NLTK, Tensorflow, Keras, OpenCV, Scrapy, Seaborn, etc ? Well because personally i think those libraries is not as important as the 5 library i explain before. Those popular libraries that i am not mentioned is really depending of your specialization in Data Science. The world of Data Science is so vast. You can’t expect me to cover all of those things. But do not be worried, because in the future i will explain it to you directly or indirectly.

I almost forgot. Here are the documentation of those 5 Libraries if you want to learn more about them

1. Pandas : https://pandas.pydata.org/docs/
2. NumPy : https://numpy.org/doc/
3. SciPy : https://docs.scipy.org/doc/
4. Matplotlib : https://matplotlib.org/3.3.1/contents.html
5. Scikit-Learn : https://scikit-learn.org/stable/index.html