In [ ]:
from dsc106_utils import *
In [ ]:
mpg = sns.load_dataset('mpg').dropna()
mpg
In [ ]:
mpg['origin'].value_counts().reset_index()

Nimble design moves¶

In [ ]:
px.bar(
    mpg['origin'].value_counts().reset_index(),
    x='origin',
    y='count',
)
In [ ]:
In [ ]:
In [ ]:

1 Nominal, 1 Quantitative¶

In [ ]:
px.bar(
    mpg['origin'].value_counts().reset_index(),
    x='origin',
    y='count',
)
In [ ]:
In [ ]:

Encoding 3 variables¶

In [ ]:
px.scatter(
    mpg,
    x='horsepower',
    y='mpg',
)
In [ ]:
In [ ]:
In [ ]:
occ = mpg.groupby(['origin', 'cylinders']).size().rename('count').reset_index()
occ
In [ ]:
px.bar(
    occ,
    x='count',
    y='origin',
    color='cylinders',
)
In [ ]:
omc = mpg.groupby(['origin', 'model_year']).size().rename('count').reset_index()
omc
In [ ]:
px.line(
    omc,
    x='model_year',
    y='count',
    color='origin'
)

What about 4 channels?¶

In [ ]:
mpg
In [ ]:
means = (mpg
 .groupby(['origin', 'model_year'])
 ['mpg']
 .max()
 .reset_index()
)
means
In [ ]:
px.scatter(
    means,
    x='model_year',
    y='origin',
    color='origin', # redundant encoding!
    size='mpg',
)