Introduction

The last decade has seen an exponential growth in all data analysis and data science areas. In fact, the demand for data analyst and data scientists is continually growing and increasing as it is now a requirement that developers incorporate the sphere of data analysis and data science into their code.

Python is undoubtedly one of the best languages to use for various data analysis project and applications. It is also the most popular language among data scientists. And one of the main reasons for its popularity is the large number of open source packages which have been developed by thousands of contributors collaborating to provide free usable resources. Many packages are top rated because they are efficient and provide outstanding functionality. In no particular order of preference, these packages include:

• Scikit-Learn
• Pandas
• Numpy
• Matplotlib
• Scipy

Scikit-Learn

Scikit-Learn is used for predictive learning and its built on top of other popular packages. It consists of various supervised and unsupervised machine learning algorithm for classification, regression and SVM’s. The primary focus of this library is “modelling data” and it provides popular modules such as clustering, feature extraction and collection, validation and dimensionality reduction.

Pandas

Pandas is an acronym for “Python Data Analysis”. It is a data analysis and manipulation tool used primarily for working with data sets and provides functions for cleaning, analysing and manipulating data. Using it, you can compare different columns and find the arithmetic mean, max and min values.

The primary data structures used in pandas are “series” and “data frames”. Pandas most common applications are reading CSV files and JSON objects and you use them within python code for faster retrieval. Pandas are known to bring speed and flexibility to data analysis. Pandas library are normally imported by the following code:

import pandas as pd

Numpy

Numpy stands for “Numerical Python” and its a powerful library forming the base for libraries such as Scikit-Learn, Scipy, Plotly and Matplotlib. Python scientists use the ability of Numpy especially when working in scientific domains such as image processing, signal, statistical computing and quantum computing. It also carries out the calculation needed for algebraic areas such as fourier transforms and matrices.

The backbone data structure in Numpy is called ND array or N-dimensional array which acts a substitute for the conventional use of lists in python and is a much faster solution than lists. The dimension in Numpy are called “axes” and the Numpy of such axes is called a “rank”. To import Numpy into your code, you do the following:

import numpy as np

Matplotlib

Matplotlib is the visualisation library used in python. It can be used to create static, interactive and animated visualisation. Many third party tools such as “ggplot” and “seaborn” extends the function of matplotlib. These functions are located inside the pyplot stub package. To import this package, do the following:

import matplotlib.pyplot as plt

Conclusion

In this article, we covered the various python packages used in data analysis and data science and how they aid higher efficiency and flexibility for both data analysts and data scientists.