Introduction to Plotly Express

The plotly.express module (usually imported as px) contains functions that can create entire figures at once, and is referred to as Plotly Express or PX. Plotly Express is a built-in part of the plotly library, and is the recommended starting point for creating most common figures. Every Plotly Express function uses graph objects internally and returns a plotly.graph_objects.Figure instance. Throughout the plotly documentation, you will find the Plotly Express way of building figures at the top of any applicable page, followed by a section on how to use graph objects to build similar figures. Any figure created in a single function call with Plotly Express could be created using graph objects alone, but with between 5 and 100 times more code.

import plotly.express as px

df = px.data.gapminder().query("year==2007")

df.columns

Index(['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
       'iso_alpha', 'iso_num'],
      dtype='object')

df.describe()

	year	lifeExp	pop	gdpPercap	iso_num
count	142.0	142.000000	1.420000e+02	142.000000	142.000000
mean	2007.0	67.007423	4.402122e+07	11680.071820	425.880282
std	0.0	12.073021	1.476214e+08	12859.937337	249.111541
min	2007.0	39.613000	1.995790e+05	277.551859	4.000000
25%	2007.0	57.160250	4.508034e+06	1624.842248	209.500000
50%	2007.0	71.935500	1.051753e+07	6124.371109	410.000000
75%	2007.0	76.413250	3.121004e+07	18008.835640	636.000000
max	2007.0	82.603000	1.318683e+09	49357.190170	894.000000

px.strip(df, x='lifeExp', hover_name="country")

px.strip(df, x='lifeExp', color="continent", hover_name="country")

px.histogram(df, x='lifeExp', color="continent", hover_name="country")

px.histogram(df, x='lifeExp', color="continent", hover_name="country", marginal="rug")

px.histogram(df, x='lifeExp', y="pop", color="continent", hover_name="country", marginal="rug")

px.histogram(df, x='lifeExp', y="pop", color="continent", hover_name="country", marginal="rug", facet_col="continent")

px.bar(df, color='lifeExp', x="pop", y="continent", hover_name="country")

px.sunburst(df, color='lifeExp', values="pop", path=["continent", "country"], hover_name="country", height=500)

px.treemap(df, color='lifeExp', values="pop", path=["continent", "country"], hover_name="country", height=500)

px.choropleth(df, color='lifeExp', locations="iso_alpha", hover_name="country", height=500)

px.scatter(df, x="gdpPercap", y='lifeExp', hover_name="country", height=500)

px.scatter(df, x="gdpPercap", y='lifeExp', hover_name="country", color="continent",size="pop", height=500)

We can see that the curve follows a logarithmic path, so make log_x=True to straighten out the line to view the relationships in an easier manner. In the graph below we can view the monotic and nonmonotonic relationships in the dataset.

px.scatter(df, x="gdpPercap", y='lifeExp', hover_name="country", color="continent",size="pop", size_max=60, log_x=True, height=500)

fig = px.scatter(df, x="gdpPercap", y='lifeExp', hover_name="country", color="continent",size="pop", size_max=60, log_x=True, height=500)

This will allow you to inspect the values for each of these cells, unfortunately this is a great deal easier to see in JupyterLab.

fig.show("json")

Unable to display output for mime type(s): application/json

import plotly.express as px

df = px.data.gapminder().query("year == 2007")

fig = px.scatter(df, x="gdpPercap",y="lifeExp", color="continent", log_x=True, size="pop", size_max=60,
                 hover_name="country", height=600, width=1000, template="simple_white", 
                 color_discrete_sequence=px.colors.qualitative.G10,
                 title="Health vs Wealth 2007",
                 labels=dict(
                     continent="Continent", pop="Population",
                     gdpPercap="GDP per Capita (US$, price-adjusted)", 
                     lifeExp="Life Expectancy (years)"))

fig.update_layout(font_family="Rockwell",
                  legend=dict(orientation="h", title="", y=1.1, x=1, xanchor="right", yanchor="bottom"))
fig.update_xaxes(tickprefix="$", range=[2,5], dtick=1)
fig.update_yaxes(range=[30,90])
fig.add_hline((df["lifeExp"]*df["pop"]).sum()/df["pop"].sum(), line_width=1, line_dash="dot")
fig.add_vline((df["gdpPercap"]*df["pop"]).sum()/df["pop"].sum(), line_width=1, line_dash="dot")
fig.show()

# fig.write_image("gapminder_2007.svg") # static export
# fig.write_html("gapminder_2007.html") # interactive export
# fig.write_json("gapminder_2007.json") # serialized export

Animations in Plotly Express

df_animation = px.data.gapminder()

anim_fig = px.scatter(df_animation, x="gdpPercap", y="lifeExp",
                      title="Health vs Wealth from 1952 to 2007",
                      labels=dict(continent="Continent", pop="Population", gdpPercap="GDP per Capita (US$, price-adjusted)", lifeExp="Life Expectancy (years)"),
                      animation_frame="year", animation_group="country",
                      size="pop",
                      color="continent",
                      hover_name="country",
                      height=600,width=1000,
                      template="simple_white",
                      color_discrete_sequence=px.colors.qualitative.G10,
                      log_x=True,
                      size_max=60,
                      range_x=[100,100000],
                      range_y=[25,90])

anim_fig.update_layout(font_family="Rockwell",
                  legend=dict(orientation="h", title="", y=1.1, x=1, xanchor="right", yanchor="bottom"))
anim_fig.update_xaxes(tickprefix="$", range=[2,5], dtick=1)

anim_fig.write_html("gapminder_animation.html", auto_play=False) # You're able to export this animation.

px.defaults.height=600

import plotly.express as px

z = [[.1, .3, .5, .7, .9],
     [1, .8, .6, .4, .2],
     [.2, 0, .5, .7, .9],
     [.9, .8, .4, .2, 0],
     [.3, .4, .5, .7, 1]]

fig = px.imshow(z, text_auto=True)
fig.show()

import plotly.express as px
df = px.data.wind()
fig = px.bar_polar(df, r="frequency", theta="direction", height=600,
                   color="strength", template="plotly_dark",
                   color_discrete_sequence= px.colors.sequential.Plasma_r)
fig.show()

df = px.data.election()
fig = px.scatter_ternary(df, a="Joly", b="Coderre", c="Bergeron", color="winner", size="total", hover_name="district",
                   size_max=15, color_discrete_map = {"Joly": "blue", "Bergeron": "green", "Coderre":"red"} )
fig.show()

df = px.data.election()
fig = px.scatter_3d(df, x="Joly", y="Coderre", z="Bergeron", color="winner", size="total", hover_name="district",
                  symbol="result", color_discrete_map = {"Joly": "blue", "Bergeron": "green", "Coderre":"red"})
fig.show()