import pandas as pd
import altair as alt
# we use sample data from this package
from vega_datasets import data
df = data.gapminder_health_income()
df.head()
alt.Chart(df).mark_point().encode(
x='income',
y='health'
)
df2 = data.iowa_electricity()
# while head displays the first rows, sample gives as a random selection:
df2.sample(5)
renewables = df2[df2["source"] == "Renewables"]
alt.Chart(renewables).mark_line().encode(
x="year",
y="net_generation",
)
alt.Chart(df2[df2["year"]=="2017"]).mark_arc().encode(
theta="net_generation",
color="source"
)
alt.Chart(df2).mark_area().encode(
x="year",
y="net_generation",
color="source"
)
print(df2["year"].dtype)
df2["year_only"] = df2["year"].dt.year
alt.Chart(df2).mark_bar().encode(
x="year_only:O",
y="net_generation",
color="source",
xOffset="source"
)
alt.Chart(df).mark_point().encode(
alt.X('income').scale(type='log'),
alt.Y('health').scale(zero=False),
)
df.head()
alt.Chart(df).mark_point().encode(
alt.X('income').scale(type='log'),
alt.Y('health').scale(zero=False),
size='population'
)
alt.Chart(df2).mark_area().encode(
alt.Color("source").scale(
domain=["Renewables", "Nuclear Energy", "Fossil Fuels"],
range=['green', 'red', 'purple']
),
x="year",
y="net_generation",
)
alt.Chart(df2).mark_line().encode(
alt.Color("source").scale(
domain=["Renewables", "Nuclear Energy", "Fossil Fuels"],
range=['green', 'red', 'purple']
),
x="year",
y="net_generation"
)
alt.Chart(df).mark_point().encode(
alt.X('income').scale(type='log'),
alt.Y('health').scale(zero=False),
alt.Opacity('population')
)
alt.Chart(df).mark_point(shape="square").encode(
alt.X('income').scale(type='log'),
alt.Y('health').scale(zero=False),
alt.Opacity('population').scale(zero=False),
)