Python is a powerful programming language used by engineers across multiple industries. Data mining, processing, modeling, and machine learning are all common applications of Python used by high-level data scientists/analysts due to its ease of use, flexibility, and abundant selection of libraries/resources. Python had even consistently held a top 5 position as the most commonly used and loved programming language by Stack Overflow Developer Survey 2020.

So, let's explore the many Python Data Analysis and Visualization tools!

# Seaborn

Seaborn is a widely used Python library that serves to help visualize data. Seaborn is utilized to plot data and present a graphical representation of the data. This library uses matplotlib.

Installation: pip install seaborn

Import: import seaborn as sns

# Matplotlib

Matplotlib is an extremely popular Python library for data visualization, it is a simple and common method used to plot data in Python. In fact, matplotlib is one of the most powerful libraries in python as it provides users with the ability to visualize their data using a wide range of plots (including bar charts, scatterplots, histograms, errorcharts, and boxplots).

Installation: pip install matplotlib
import: matplotlib.pyplot as plt

# NumPy

NumPy is a library for Python, it is widely used by data scientists/analysts. NumPy provides multidimensional array objects and functions to manipulate them. NumPy is often used for a wide variety of array-based mathematical operations due to its fast process, unlike that of Python’s lists. In short, NumPy is ultimately used for working with arrays.

Installation: pip install pandas

import: import numpy as np

# Pandas

Pandas is ultimately a Python library for data analysis and manipulation. It is often used for machine learning, data science, and/or data analysis tasks. Pandas is widely used as it provides high-performance data analysis and data manipulation tools using powerful data structures.

Installation: pip install pandas

Import: import pandas

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