Fill in the table using variables from the health and wealth gapminder plot.
| Variable | Type | Visual Cue |
|---|---|---|
| gdpPercap | ||
| lifeExp | ||
| continent | ||
| pop | ||
| country | ||
| year |
Is date continuous or discrete?
x and y?)Concisely describe the components of a graphic
e.g., px.scatter(data, ...):
x, y, color, symbol, size, animation_frame, textlog_x, log_y, range_x, range_ylabelsfacet_row, facet_col, facet_col_wraptitle, hover_name (tooltip), etc.Other details can be customized with update_layout, update_traces, etc.
(
px.scatter(votes, x='year', y='percent_yes', color='country',
1 facet_col='issue', facet_col_wrap=3, facet_col_spacing=.1,
2 trendline='lowess',
3 labels={"percent_yes": "% Yes Votes", "year": "Year", "country": "Country"},
4 title="Percentage of 'Yes' votes in the UN General Assembly",
5 ).update_traces(marker_size=2)
)(See notes for details.)
penguins = pd.read_csv("https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/inst/extdata/penguins.csv")
penguins.info()<class 'pandas.core.frame.DataFrame'>
RangeIndex: 344 entries, 0 to 343
Data columns (total 8 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 species 344 non-null object
1 island 344 non-null object
2 bill_length_mm 342 non-null float64
3 bill_depth_mm 342 non-null float64
4 flipper_length_mm 342 non-null float64
5 body_mass_g 342 non-null float64
6 sex 333 non-null object
7 year 344 non-null int64
dtypes: float64(4), int64(1), object(3)
memory usage: 21.6+ KBpx.scatter())px.bar: stack one mark for each row (notice the little gaps between the bars)px.histogram: count how many rows have each species, make a single mark for each speciesBut not useful for comparing categories.
Just use a bar chart.