Plotly graphics#
One of the major players in interactive graphs is Plotly.
Some alternatives are Bokeh and Altair.
Interfacing it comes in two main flavours:
graph_objects: low-level graphics handling
plotly.express: high-level graphics handling
In addition plotly is integrated in the dash environment with its dialect.
Figures are dictionaries, which we will leverage.
# The following renders plotly graphs in Jupyter Notebook, Jupyter Lab and VS Code formats
import plotly.io as pio
pio.renderers.default = "notebook+plotly_mimetype"
Plotting with AI assistance#
Many plot commands can be obtained by describing plots to AIs.
AIs can also translate from one plotting framework to another.
Sketching a set of plot and adding sufficient descriptions, may result in usable code.
Basic plotting#
# Gapminder dataset of health and wealth stats for different countries
import plotly.express as px
df = px.data.gapminder()
df.head()
| country | continent | year | lifeExp | pop | gdpPercap | iso_alpha | iso_num | |
|---|---|---|---|---|---|---|---|---|
| 0 | Afghanistan | Asia | 1952 | 28.801 | 8425333 | 779.445314 | AFG | 4 |
| 1 | Afghanistan | Asia | 1957 | 30.332 | 9240934 | 820.853030 | AFG | 4 |
| 2 | Afghanistan | Asia | 1962 | 31.997 | 10267083 | 853.100710 | AFG | 4 |
| 3 | Afghanistan | Asia | 1967 | 34.020 | 11537966 | 836.197138 | AFG | 4 |
| 4 | Afghanistan | Asia | 1972 | 36.088 | 13079460 | 739.981106 | AFG | 4 |
Line plot#
# Create a line plot of life expectancy over time for Norway.
# Let the figure be 400 pixels high and 700 pixels wide.
# Set the title to 'Life Expectancy in Norway'.
# Set the x-axis label to 'Year'.
# Set the y-axis label to 'Life Expectancy (years)'.
fig = px.line(df[df['country'] == 'Norway'], x='year', y='lifeExp', title='Life Expectancy in Norway', width=700, height=400)
fig.update_xaxes(title='Year')
fig.update_yaxes(title='Life Expectancy (years)')
fig