data analysis and visualization using python

A guide to mind your data.

Data Analysis AndVisualization using Python LibrariesSept 2017

Chariza PladinData Analyst - Accenture Inc.chariza.b.pladin@accenture.com

AGENDA● Mind the Data

● Data Analysis: 5 Steps to better decision making

● Why Visualize my Data?

● Introduction to Python3 and Jupyter Notebook

● Python libraries for Visualization

● Q/A

Mind the Data

Data Analysis and Visualization using Python Sept 2017

Data is

EVERYWHERE.and it never sleeps.

Data Analysis and Visualization using Python Sept 2017

More than 90% of all the data in the globe was generated over the course of the past two years. Resource: (Business2Community)

Data Analysis and Visualization using Python Sept 2017

Resource: (Bernard Marr)

Every 1 second = 40,000

Search queries (Google) which makes it 3.5 searches per day and 1.2 trillion searches per year.

Data Analysis and Visualization using Python Sept 2017

And this just happened.(While I’m busy talking…)

Data Analysis and Visualization using Python Sept 2017

20XX

we will have over 6.1 billion smartphone users globally.

2020

Within five years there will be over 50 billion smart connected devices

in the world, all developed to collect, analyze and share data

2017

nearly 80% of photos will be taken on smart phones.

2015

1 trillion photos taken and billions of them were shared online.

Data Analysis and Visualization using Python Sept 2017

That’s a lot of DATA.

Data Analysis and Visualization using Python Sept 2017

Data Analysis

Resource: Doing Data Science", Cathy O'Neil and Rachel Schutt, 2013

Data Analysis and Visualization using Python Sept 2017

The Way to a Better Decision Making

5

Inte

rpre

t Resu

lts

4

Analy

ze D

ata

3

Collect D

ata

Set Cle

ar Measu

rem

ent Prio

ritie

s

21

Define Y

our Quest

ions

Data Analysis and Visualization using Python Sept 2017

Why visualize?

Visualizeto Analyze

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

Patterns Correlation

Trends

Data Analysis and Visualization using Python Sept 2017

Make decision based on a massive dataset

IN ONELOOK.

Data Analysis and Visualization using Python Sept 2017

Visualizeto Discover

Data Analysis and Visualization using Python Sept 2017

Interactive data visualizations let you mine data to discover information.

Data Analysis and Visualization using Python Sept 2017

Visualizeto Support a Story

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

Show off your CV using visuals.

Data Analysis and Visualization using Python Sept 2017

Visualizeto tell aStory By itself

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

I thought Hillary will be the 45th President...

Data Analysis and Visualization using Python Sept 2017

VisualizeToTeach

Data Analysis and Visualization using Python Sept 2017

Introduction to Python3 and Jupyter Notebook

Data Analysis and Visualization using Python Sept 2017

beautiful notebook that lets you write and execute code, analyze data, embed content, and share reproducible work.

Jupyter

Data Analysis and Visualization using Python Sept 2017

Install Jupyter

Use $ pip install jupyter.

Windows users can install with setuptools.

Anaconda and Enthought allow you to download a desktop version of Jupyter Notebook.

Microsoft Azure provides hosted access to Jupyter Notebooks.

Data Analysis and Visualization using Python Sept 2017

Power Python Libraries for Data Visualization

Data Analysis and Visualization using Python Sept 2017

matplotlib

- Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.

- Python forerunner library for data visualization.

- “is extremely powerful but with that power comes complexity.”

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

seaborn

- harnesses the power of matplotlib to create beautiful charts in a few lines of code. The key difference is Seaborn’s default styles and color palettes, which are designed to be more aesthetically pleasing and modern.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

ggplot

- plotting system for Python based on R's ggplot2 and the Grammar of Graphics.

- layer components to create a complete plot.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

Bokeh

- is also based on The Grammar of Graphics, but unlike ggplot, it’s native to Python, not ported over from R.

- supports streaming and real-time data.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

pygal

- offers interactive plots that can be embedded in the web browser. Its prime differentiator is the ability to output charts as SVGs.

- Since each chart type is packaged into a method and the built-in styles are pretty, it’s easy to create a nice-looking chart in a few lines of code.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

plotly

making interactive plots, but it offers some charts you won’t find in most libraries, like contour plots, dendrograms, and 3D charts.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

geoplotlib

toolbox for creating maps and plotting geographical data. You can use it to create a variety of map-types, like choropleths, heatmaps, and dot density maps.

Data Analysis and Visualization using Python Sept 2017

Data Analysis and Visualization using Python Sept 2017

Q/A

A guide to mind your data.

Thank you :)