Neil
Neil
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Data Visualizing with Python

Hi, in this post you will learn about Data Visualizing with Python

Introduction

Analytical web applications were a task for seasoned developers that required knowledge of multiple programming languages and frameworks. Unfortunately, that’s no longer the case. Nowadays, you can make data visualization interfaces using pure Python.

Python provides various libraries that come with different features for data visualization. In addition, all these libraries come with additional features and can support multiple graphs.

We will discuss these libraries individually and plot some most commonly used graphs. 

Note: If you want to learn in-depth information about these libraries, you can follow their complete tutorial.

Install using your Linux distribution’s package manager.

The commands in this table will install pandas for Python 3 from your distribution.

Distribution

Status

Download / Repository Link

Install method

Debian

stable

official Debian repository

sudo apt-get install python3-pandas

Debian & Ubuntu

unstable (latest packages)

NeuroDebian

sudo apt-get install python3-pandas

Ubuntu

stable

official Ubuntu repository

sudo apt-get install python3-pandas

OpenSuse

stable

OpenSuse Repository

zypper in python3-pandas

Fedora

stable

official Fedora repository

dnf install python3-pandas

Centos/RHEL

stable

EPEL repository

yum install python3-pandas

However, the packages in the Linux package managers are often a few versions behind, so to get the newest version of pandas, it’s recommended to install using the pip or conda methods described above.

Visualization

Dependency Minimum Version

matplotlib 3.3.2 Plotting library

Jinja2 2.11 Conditional formatting with DataFrame.style

tabulate 0.8.7 Printing in Markdown-friendly format (see tabulate)

Python

By default, the read_csv() method uses the first row of the CSV file as the column headers. Sometimes, these headers might have odd names, and you might want to use your titles. You can set headers after reading the file simply by assigning the columns field of the DataFrame instance to another list, or you can select the headers while reading the CSV in the first place.

import pandas as pd


# reading the database
data = pd.read_csv("unixcop.csv")

# printing the top 10 rows
print(data.head())

Download your test database unixcop.csv

Pandas will search for this file in the script’s directory, and we supply the file path to the file we’d like to parse as the only required argument of this method. Let’s take a look at the head() of this dataset to make sure it’s imported correctly:

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