Data Visualization (UCSD)

Question 1

difference between explorative and declarative data viz

Answer 1

explorative - doesn’t need to be polished, just for exploring; declarative - presenting findings

Question 2

goals for data viz

Answer 2

trustworthy, accessible, elegant

Question 3

Plot a basic bar chart using column ‘year’ for X axis and ‘values’ for y-axis, from df ‘stage”

Answer 3

plt. bar( stage[‘year’], stage[‘values’] )

plt. show()

Question 4

Plot a basic line chart using column ‘year’ for x axis and ‘values’ for y-axis, from df ‘stage”

Answer 4

plt. line( stage[‘year’], stage[‘values’])

plt. show()

Question 5

Label the axes on a plot

Answer 5

plt. xlabel(‘Year’)

plt. ylabel(stage[‘IndicatorName’].iloc[0]) ## gets value from df in that col

Question 6

label the figure

Answer 6

plt.title(‘CO2 Emissions in USA’)

Question 7

start they y axis at 0

Answer 7

plt.axis([1959, 2011,0,25])

Question 8

plot a histogram

Answer 8

hist_data = stage[‘Value’].values

plt.hist(hist_data, 10, density=False, facecolor=’green’)

Question 9

What is the folium package

Answer 9

Allows you to do geographic overlays of data

Question 10

When to use seaborn package?

Answer 10

Specialized statistical plots, like automatically fitting a linear regression with confidence interval or like scatter plots color-coded by category

Question 11

packages with easier interfaces for plotting?

Answer 11

ggplot or altair

Question 12

packages for interactive plots that can be exported as javascript

Answer 12

bokeh, plotly

Question 13

package for realtime plots that update with streaming data

Answer 13

bokeh plot server

Question 14

package for 3d plots

Answer 14

mplot3d