df=data.iris() # Itto is pretty smart df
fig, ax = plt.subplots() colors=['#1f77b4', '#ff7f0e', '#2ca02c'] for color, species in zip(colors, df.species.unique()): tmp = df[df.species == species] ax.scatter(tmp.petalLength, tmp.petalWidth, label=species, color=color) ax.set(xlabel='Petal Length', ylabel='Petal Width', title='Petal Width v. Length -- by Species') ax.legend(loc=2);
alt.Chart(df).mark_circle().encode( x='petalLength', y='petalWidth', color='species' )
alt.Chart(df).mark_circle().encode( x='petalLength', y='petalWidth' )
alt.Chart(fake_data).mark_image(width=50).encode( x='Beauty', y='Number of letters:N', url='img' ).properties(title="Notebook logo metrics")
alt.Chart(df).mark_point().encode( x='petalWidth', y='petalLength', shape='species', color='species' #color='species' # shape, opacity, color, tooltips, size )
cars=data.cars() alt.Chart(cars).mark_circle().encode( x='Horsepower', y='Miles_per_Gallon', color='Cylinders:O' #O, N, Q )
alt.Chart(df).mark_bar().encode( x='mean(petalWidth)', #sum, max, mean, variance, ci0, etc... y='species', color='species' )
stocks=data.stocks() stocks
alt.Chart(stocks).mark_line().encode( x='quarter(date)', #month, year, quarter, yearmonth y='mean(price)', color='symbol' )
bar=alt.Chart(df).mark_bar().encode( x='mean(petalWidth)', y='species', color='species' ) point=alt.Chart(df).mark_point().encode( x='petalWidth', y='petalLength', color='species' ) point | bar
bar=alt.Chart(df).mark_bar().encode( x='mean(petalWidth)', y='species', color='species' ) mean=alt.Chart(df).mark_rule().encode( x='mean(petalWidth)', ) bar + mean
circle=alt.Chart(df).mark_circle(size=400).encode( x='mean(petalWidth)', y='mean(petalLength)', color='species' ) point=alt.Chart(df).mark_point().encode( x='petalWidth', y='petalLength', color='species' ) point + circle
alt.Chart(df).mark_circle().encode( x=alt.X('petalLength'), y=alt.Y('petalWidth') )
alt.Chart(df).mark_circle().encode( x=alt.X('petalLength', scale=alt.Scale(domain=[-4,10])), y=alt.Y('petalWidth', axis=alt.Axis(labelColor='red')), color=alt.Color('species', legend=alt.Legend(labelColor='blue')) )
alt.Chart(df).mark_point(filled=True, color='green', size=300).encode( x=alt.X('petalLength', axis=alt.Axis(labelColor='red')), y=alt.Y('petalWidth', axis=alt.Axis(labelColor='blue')), shape=alt.Shape('species', legend=alt.Legend(labelColor='grey')) ).properties(title='Long form of API & adjusted properties', width=300, height=300)
alt.Chart(df).mark_bar().encode( x=alt.X('sepalLength', bin=True), #alt.Bin(maxmins=25) y='count()' )
alt.Chart(df).mark_rect().encode( x=alt.X('sepalLength', bin=True), #alt.Bin(maxmins=25) y=alt.X('sepalWidth', bin=True), #alt.Bin(maxmins=25) color='count()' )
iowa_1=data.iowa_electricity() iowa_1
iowa_2=data.iowa_electricity() iowa_2
cars_1=data.cars() cars_1
cars_2=data.cars() cars_2
cars_3=data.cars() cars_3
cars_4=data.cars() cars_4
barley_1=data.barley() barley_1
iowa_3=data.iowa_electricity() iowa_3
birds_1=data.birdstrikes().sample(500, random_state=42) birds_1
crimea_1=data.crimea() crimea_1
weather_1=data.seattle_temps().sample(1000, random_state=42) weather_1
alt.Chart(weather_1).mark_line().encode( x='month(date):T', y='mean(temp)', )
alt.Chart(weather_1).mark_line().encode( y='mean(temp)', x='month(date)' )
cars_5 = data.cars() cars_5
birds_2= data.birdstrikes().sample(1000, random_state=42) birds_2
birds_3= data.birdstrikes().sample(1000, random_state=42) birds_3
ansc_1=data.anscombe() ansc_1
barley_2=data.barley() barley_2
barley_3=data.barley() barley_3
birds_4=data.birdstrikes().sample(500, random_state=42) birds_4
barley_4=data.barley() barley_4
weather_2=data.seattle_weather() weather_2
iris_1=data.iris() iris_1
stocks_1=data.stocks() stocks_1
movies_1=data.movies() movies_1
unemp_1=data.unemployment_across_industries() unemp_1.loc[unemp_1['series'] == 'Government']
barley_5=data.barley() barley_5
unemp_2=data.unemployment_across_industries() unemp_2
imdb_2= data.movies() imdb_2
unemp_3=data.unemployment_across_industries() unemp_3
cars_6 = data.cars() cars_6
barley_6 = data.barley() barley_6
url = data.population.url pop_1 = pd.read_json(url) pop_1