Learn Data Visualization using Python

1Data Visualization - Introduction...
2Python Libraries for Data Visuali...
3Visualizing Data in Matplotlib
4Visualizing Data in Pandas
5Visualizing Data in Seaborn
6Data Visualization Practice

1. Data Visualization - Introduction and Data Import

Data visualization is a graphical form to represent the information and data. To see and understand the trends, outliers, and patterns in data, data visualization tools uses visual elements like charts, graphs, and maps to provide an accessible way.

Experts and Artists in Data visualization topics are creating amazing things in the world of data design every single day.

Data visualization is not a new phenomenon. Even before it was “cool,” data visualization techniques was an important tool for visualizing data in many different fields.

Data visualization projects encompass all things data art, infographics, and data dashboards.

What has happened over the last few years is that data and data analysis has taken on a new quality. Data and information is now a tool for creating beautiful visuals.

Why it is important?

According to the World Economic Forum, the world produces 2.5 quintillion bytes of data every day, and 90% of all data has been created in the last two years. With so much data, it’s become increasingly difficult to manage and make sense of it all. It would be impossible for any single person to wade through data line-by-line and see distinct patterns and make observations. Data proliferation can be managed as part of the data science process, which includes data visualization best practices.

Python offers multiple libraries for Data visualization tools that come packed with a lot of different features. Python allows you to create interactive, live or highly customized plots by using different libraries like Matplotlib, Pandas, and Seaborn.

To get a little overview here are a few popular plotting libraries:

  • Matplotlib: low level, provides lots of freedom
  • Pandas Visualization: easy to use interface, built on Matplotlib
  • Seaborn: high-level interface, great default styles

Data visualization datasets can be imported by using any of these libraries. Here, we are using Pandas Library to import our Data set.

Importing Dataset

The first step is to read the data. The data is stored as a comma-separated value, or CSV file, where each row is separated by a new line, and each column by a comma (,). In order to be able to work with the data in Python, it is needed to read the CSV file into a Pandas DataFrame. A DataFrame is a way to represent and work with tabular data. Tabular data has rows and columns, just like this CSV file(Click Download).

#Import the pandas library, renamed as pd
import pandas as pd

#Read IND_data.csv into a DataFrame, assigned yo df
df=pd.read_csv("IND_data.csv")

#Prints the first 5 rows of a DataFrame as default
df.head()

#Prints no. of rows and columns of a DataFrame
df.shape()

 

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