data analysis and visualization using python
TRANSCRIPT
A guide to mind your data.
Data Analysis AndVisualization using Python LibrariesSept 2017
Chariza PladinData Analyst - Accenture [email protected]
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 :)