Critiquing DataVisualization

Visualization is an interactive representation of (abstract, complex) data that can help human to perform the task more effectively. It helps us see patterns in broader contexts that specific statistical questions do not reveal. Also, it helps us drive insights and questions that even predefined analytical queries do not elicit.

In this blog post, I will critique one good and one bad visualization.

Good Visualization:

In the below visualization, the three maps accurately show the life expectancy in the years 1800, 1950 and 2015. We can easily interpret how life expectancy has changed over the last three centuries. In the year 1800, people could expect a life span of only 25–40 years, irrespective of the location of their birth. As the new century (1950) began, newborns had the chance of longer life (over 60 years) but it is highly dependent on the location of their birth. People in continents like North America have a higher life expectancy as compared to people born in Asia. In recent decades every country has made very substantial progress in health and many other aspects.

Life Expectancy in 1800, 1950 and 2015 [source]

Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of the progress, we can expect to live even twice as long as our ancestors.

#visualization #data-visualization #visual studio code #visual studio

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Critiquing DataVisualization

Visualization is an interactive representation of (abstract, complex) data that can help human to perform the task more effectively. It helps us see patterns in broader contexts that specific statistical questions do not reveal. Also, it helps us drive insights and questions that even predefined analytical queries do not elicit.

In this blog post, I will critique one good and one bad visualization.

Good Visualization:

In the below visualization, the three maps accurately show the life expectancy in the years 1800, 1950 and 2015. We can easily interpret how life expectancy has changed over the last three centuries. In the year 1800, people could expect a life span of only 25–40 years, irrespective of the location of their birth. As the new century (1950) began, newborns had the chance of longer life (over 60 years) but it is highly dependent on the location of their birth. People in continents like North America have a higher life expectancy as compared to people born in Asia. In recent decades every country has made very substantial progress in health and many other aspects.

Life Expectancy in 1800, 1950 and 2015 [source]

Globally the life expectancy increased from less than 30 years to over 72 years; after two centuries of the progress, we can expect to live even twice as long as our ancestors.

#visualization #data-visualization #visual studio code #visual studio

Everything You Need to Know About R Programming

R Programming Full Course for 2022 | R Programming For Beginners | R Tutorial 

In this R Programming Full Course In 7 Hours video, we'll learn about What is R, variables, and data types in R. This R Programming for Beginners is the ideal video for anyone starting with R Programming and Data Analysis. We'll Understand Data Handling, Manipulation, and Visualization in R. So, let's get started with this R Tutorial!

Dataset Link - https://drive.google.com/drive/folders/1Wn2TRSbM2CHzxEk-qclzGJcyZT4LHeRV 

This R Programming Full Course Video Covers the following Topics:

• What is R Programming
• Variables and Data Types in R
• Logical Operators
• Vectors
• List
• Matrix
• Data Frame
• Flow Control
• Functions in R
• Data Manipulation in R- dplyr
• Data Manipulation in R- tidyr
• Data Visualization In R
• Time Series Analysis in R

What Is R Programming?

R is an open-source programming language used for statistical computing. It is one of the most popular programming languages today. R was inspired by S+, which is similar to the S programming language. R has various data structures and operators. It can be integrated with other programming languages like C, C++, Java, and Python.

This Data Analyst Master’s Program in collaboration with IBM will make you an expert in data analytics. In this Data Analytics course, you'll learn analytics tools and techniques, how to work with SQL databases, the languages of R and Python, how to create data visualizations, and how to apply statistics and predictive analytics in a business environment.

#r #programming #datasciene #dataanalysis #datavisualization

Getting Started with Data Visualization for Beginners

In this article, we have curated a list of data visualization courses. Learn data visualization with the best courses from the best platforms.

#data #datavisualizations 

Joyplots in Python with Matplotlib & Pandas

JoyPy

JoyPy is a one-function Python package based on matplotlib + pandas with a single purpose: drawing joyplots (a.k.a. ridgeline plots).

The code for JoyPy borrows from the code for kdes in pandas.plotting, and uses a couple of utility functions therein.

What are joyplots?

Joyplots are stacked, partially overlapping density plots, simple as that. They are a nice way to plot data to visually compare distributions, especially those that change across one dimension (e.g., over time). Though hardly a new technique, they have become very popular lately thanks to the R package ggjoy (which is much better developed/maintained than this one -- and I strongly suggest you use that if you can use R and ggplot.) Update: the ggjoy package has now been renamed ggridges.

Why are they called joyplots?

If you don't know Joy Division, you are lucky: you can still listen to them for the first time! Here's a hint: google "Unknown Pleasures". This kind of plot is now also known as ridgeline plot, since the original name is controversial.

Documentation and examples

JoyPy has no real documentation. You're strongly encouraged to take a look at this jupyter notebook with a growing number of examples. Similarly, github issues may contain some wisdom :-)

A minimal example is the following:

import joypy
import pandas as pd

iris = pd.read_csv("data/iris.csv")
fig, axes = joypy.joyplot(iris)

By default, joypy.joyplot() will draw joyplot with a density subplot for each numeric column in the dataframe. The density is obtained with the gaussian_kde function of scipy.

Note: joyplot() returns n+1 axes, where n is the number of visible rows (subplots). Each subplot has its own axis, while the last axis (axes[-1]) is the one that is used for things such as plotting the background or changing xticks, and is the one you might need to play with in case you want to manually tweak something.

Dependencies

Python 3.5+
Compatibility with python 2.7 has been dropped with release 0.2.0.

numpy

scipy >= 0.11

matplotlib

pandas >= 0.20 Warning: compatibility with pandas >= 0.25 requires joypy >= 0.2.1

Not sure what are the oldest supported versions. As long as you have somewhat recent versions, you should be fine.

Installation

It's actually on PyPI, because why not:

pip install joypy

To install from github, run:

git clone git@github.com:leotac/joypy.git
cd joypy
pip install .

Author: Leotac
Source Code: https://github.com/leotac/joypy 
License: MIT License

#python #datavisualizations #matplotlib 

Python Data Visualization: Bokeh Cheat Sheet

A handy cheat sheet for interactive plotting and statistical charts with Bokeh.

Bokeh distinguishes itself from other Python visualization libraries such as Matplotlib or Seaborn in the fact that it is an interactive visualization library that is ideal for anyone who would like to quickly and easily create interactive plots, dashboards, and data applications. 

Bokeh is also known for enabling high-performance visual presentation of large data sets in modern web browsers. 

For data scientists, Bokeh is the ideal tool to build statistical charts quickly and easily; But there are also other advantages, such as the various output options and the fact that you can embed your visualizations in applications. And let's not forget that the wide variety of visualization customization options makes this Python library an indispensable tool for your data science toolbox.

Now, DataCamp has created a Bokeh cheat sheet for those who have already taken the course and that still want a handy one-page reference or for those who need an extra push to get started.

In short, you'll see that this cheat sheet not only presents you with the five steps that you can go through to make beautiful plots but will also introduce you to the basics of statistical charts. 

In no time, this Bokeh cheat sheet will make you familiar with how you can prepare your data, create a new plot, add renderers for your data with custom visualizations, output your plot and save or show it. And the creation of basic statistical charts will hold no secrets for you any longer. 

Boost your Python data visualizations now with the help of Bokeh! :)

---

Plotting With Bokeh

The Python interactive visualization library Bokeh enables high-performance visual presentation of large datasets in modern web browsers.

Bokeh's mid-level general-purpose bokeh. plotting interface is centered around two main components: data and glyphs.

The basic steps to creating plots with the bokeh. plotting interface are:

1. Prepare some data (Python lists, NumPy arrays, Pandas DataFrames and other sequences of values)
2. Create a new plot
3. Add renderers for your data, with visual customizations
4. Specify where to generate the output
5. Show or save the results

>>> from bokeh.plotting import figure
>>> from bokeh.io import output_file, show
>>> x = [1, 2, 3, 4, 5] #Step 1
>>> y = [6, 7, 2, 4, 5]
>>> p = figure(title="simple line example", #Step 2
x_axis_label='x',
y_axis_label='y')
>>> p.line(x, y, legend="Temp.", line_width=2) #Step 3
>>> output_file("lines.html") #Step 4
>>> show(p) #Step 5

1. Data 

Under the hood, your data is converted to Column Data Sources. You can also do this manually:

>>> import numpy as np
>>> import pandas as pd
>>> df = pd.OataFrame(np.array([[33.9,4,65, 'US'], [32.4, 4, 66, 'Asia'], [21.4, 4, 109, 'Europe']]),
                     columns= ['mpg', 'cyl',   'hp',   'origin'],
                      index=['Toyota', 'Fiat', 'Volvo'])


>>> from bokeh.models import ColumnOataSource
>>> cds_df = ColumnOataSource(df)

2. Plotting 

>>> from bokeh.plotting import figure
>>>p1= figure(plot_width=300, tools='pan,box_zoom')
>>> p2 = figure(plot_width=300, plot_height=300,
x_range=(0, 8), y_range=(0, 8))
>>> p3 = figure()

3. Renderers & Visual Customizations 

Glyphs 

Scatter Markers 

>>> p1.circle(np.array([1,2,3]), np.array([3,2,1]), fill_color='white')
>>> p2.square(np.array([1.5,3.5,5.5]), [1,4,3],
color='blue', size=1)

Line Glyphs 

>>> pl.line([1,2,3,4], [3,4,5,6], line_width=2)
>>> p2.multi_line(pd.DataFrame([[1,2,3],[5,6,7]]),
pd.DataFrame([[3,4,5],[3,2,1]]),
color="blue")

Customized Glyphs

Selection and Non-Selection Glyphs 

>>> p = figure(tools='box_select')
>>> p. circle ('mpg', 'cyl', source=cds_df,
selection_color='red',
nonselection_alpha=0.1)

Hover Glyphs

>>> from bokeh.models import HoverTool
>>>hover= HoverTool(tooltips=None, mode='vline')
>>> p3.add_tools(hover)

Color Mapping 

>>> from bokeh.models import CategoricalColorMapper
>>> color_mapper = CategoricalColorMapper(
             factors= ['US', 'Asia', 'Europe'],
             palette= ['blue', 'red', 'green'])
>>>  p3. circle ('mpg', 'cyl', source=cds_df,
            color=dict(field='origin',
                 transform=color_mapper), legend='Origin')

4. Output & Export 

Notebook

>>> from bokeh.io import output_notebook, show
>>> output_notebook()

HTML 

Standalone HTML 

>>> from bokeh.embed import file_html
>>> from bokeh.resources import CON
>>> html = file_html(p, CON, "my_plot")

>>> from  bokeh.io  import  output_file,  show
>>> output_file('my_bar_chart.html',  mode='cdn')

Components

>>> from bokeh.embed import components
>>> script, div= components(p)

PNG

>>> from bokeh.io import export_png
>>> export_png(p, filename="plot.png")

SVG 

>>> from bokeh.io import export_svgs
>>> p. output_backend = "svg"
>>> export_svgs(p,filename="plot.svg")

Legend Location 

Inside Plot Area 

>>> p.legend.location = 'bottom left'

Outside Plot Area 

>>> from bokeh.models import Legend
>>> r1 = p2.asterisk(np.array([1,2,3]), np.array([3,2,1])
>>> r2 = p2.line([1,2,3,4], [3,4,5,6])
>>> legend = Legend(items=[("One" ,[p1, r1]),("Two",[r2])], location=(0, -30))
>>> p.add_layout(legend, 'right')

Legend Background & Border 

>>> p.legend. border_line_color = "navy"
>>> p.legend.background_fill_color = "white"

Legend Orientation 

>>> p.legend.orientation = "horizontal"
>>> p.legend.orientation = "vertical"

Rows & Columns Layout

Rows

>>> from bokeh.layouts import row
>>>layout= row(p1,p2,p3)

Columns

>>> from bokeh.layouts import columns
>>>layout= column(p1,p2,p3)

Nesting Rows & Columns 

>>>layout= row(column(p1,p2), p3)

Grid Layout 

>>> from bokeh.layouts import gridplot
>>> rowl = [p1,p2]
>>> row2 = [p3]
>>> layout = gridplot([[p1, p2],[p3]])

Tabbed Layout 

>>> from bokeh.models.widgets import Panel, Tabs
>>> tab1 = Panel(child=p1, title="tab1")
>>> tab2 = Panel(child=p2, title="tab2")
>>> layout = Tabs(tabs=[tab1, tab2])

Linked Plots

Linked Axes 

Linked Axes
>>> p2.x_range = p1.x_range
>>> p2.y_range = p1.y_range

Linked Brushing 

>>> p4 = figure(plot_width = 100, tools='box_select,lasso_select')
>>> p4.circle('mpg', 'cyl' , source=cds_df)
>>> p5 = figure(plot_width = 200, tools='box_select,lasso_select')
>>> p5.circle('mpg', 'hp', source=cds df)
>>>layout= row(p4,p5)

5. Show or Save Your Plots  

>>> show(p1)
>>> show(layout)
>>> save(p1)

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#python #datavisualization #bokeh #cheatsheet