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Python Plotting for EDA

Python Plotting for Exploratory Analysis

Code:


 (mpg
 .plot
 .scatter(x='displ', y='hwy')
 .set(title='Engine Displacement in Liters vs Highway MPG',
      xlabel='Engine Displacement in Liters',
      ylabel='Highway MPG'))

Code:


 (ggplot(mpg) +
    aes(x = 'displ', y = 'hwy') +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG'))

Code:


 px.scatter(
    mpg, x='displ', y='hwy', 
    title='Engine Displacement in Liters vs Highway MPG',
    labels=dict(
       displ='Engine Displacement in Liters', 
       hwy='Highway MPG')
)

Code:


 alt.Chart(mpg).mark_circle().encode(
    alt.X(
        'displ',
        title='Engine Displacement in Liters',
    ),
    alt.Y(
        'hwy',
        title='Highway Miles per Gallon',
    ),
).properties(
    title='Engine Displacement in Liters'
)

Code:


 ggplot(data = mpg) +
    aes(x = displ, y = hwy) +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG')

Code:


 fig, ax = pyplot.subplots()
for c, df in mpg.groupby('class'):
    ax.scatter(df['displ'], df['hwy'], label=c)
ax.legend()
ax.set_title('Engine Displacement in Liters vs Highway MPG')
ax.set_xlabel('Engine Displacement in Liters')
ax.set_ylabel('Highway MPG')

Code:


 (ggplot(mpg) + 
    aes(x = 'displ', y = 'hwy', color = 'class') +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG'))

Code:


 px.scatter(
    mpg, x='displ', y='hwy', color='class', 
    title='Engine Displacement in Liters vs Highway MPG',
    labels=dict(
       displ='Engine Displacement in Liters', 
       hwy='Highway MPG')
)

Code:


 (
    alt.Chart(
        mpg,
        title='Engine Displacement in Liters vs Highway MPG',
    )
    .mark_circle()
    .encode(
        alt.X(
            'displ',
            title='Engine Displacament in Liters',
        ),
        alt.Y('hwy', title='Highway MPG'),
        color='class',
    )
)

Code:


 ggplot(data = mpg) + 
    aes(x = displ, y = hwy, color = class) +
    geom_point() + 
    ggtitle('Engine Displacement in Liters vs Highway MPG') +
    xlab('Engine Displacement in Liters') +
    ylab('Highway MPG')

Code:


 ax = (mpg
    .plot
    .scatter(x='cty', 
             y='hwy', 
             s=10*mpg['cyl'],
             alpha=.5))
ax.set_title('City MPG vs Highway MPG')
ax.set_xlabel('City MPG')
ax.set_ylabel('Highway MPG')

Code:


 (ggplot(mpg) +
    aes(x='cty', y='hwy', size='cyl') +
    geom_point(alpha=.5))

Code:


 px.scatter(
    mpg, x='cty', y='hwy', 
    size='cyl', size_max=10,
    title='City MPG vs Highway MPG',
    labels=dict(cty='City MPG', hwy='Highway MPG')
)

Code:


 (
    alt.Chart(
        mpg,
        title='City MPG vs Highway MPG',
    )
    .mark_circle(opacity=0.3)
    .encode(
        x=alt.X(
            'cty',
            axis=alt.Axis(title='City MPG'),
        ),
        y=alt.Y(
            'hwy',
            axis=alt.Axis(
                title='Highway MPG'
            ),
        ),
        size='cyl',
    )
)

Code:


 ggplot(data = mpg) +
    aes(x = cty, y = hwy, size = cyl) +
    geom_point(alpha=.5)

Code:


 (mpg
 .pipe(sns.FacetGrid, 
       col='class', 
       col_wrap=4, 
       aspect=.5, 
       size=6)
 .map(pyplot.scatter, 'displ', 'hwy', s=20)
 .fig.subplots_adjust(wspace=.2, hspace=.2)
)

Code:


 (ggplot(mpg.assign(c=mpg['class'])) + 
  aes(x='displ', y='hwy') +
  geom_point() +
  facet_wrap(' ~ c', nrow = 2))

Code:


 px.scatter(
    mpg, x='displ', y='hwy', 
    facet_col='class', facet_col_wrap=4
)

Code:


 alt.Chart(mpg).mark_circle().encode(
    x=alt.X('displ'),
    y=alt.Y('hwy'),
    facet=alt.Facet('class:O', columns=4),
).properties(width=200, height=300)

Code:


 ggplot(data = mpg) + 
  aes(x=displ, y=hwy) +
  geom_point() + 
  facet_wrap(~ class, nrow = 2)

Code:


 (mpg
 .pipe(sns.FacetGrid, 
       col='cyl', 
       row='drv', 
       aspect=.9, 
       size=4)
 .map(pyplot.scatter, 'displ', 'hwy', s=20)
 .fig.subplots_adjust(wspace=.02, hspace=.02)
)

Code:


 (ggplot(mpg) + 
  aes(x='displ', y='hwy') +
  geom_point() + 
  facet_grid('drv ~ cyl'))

Code:


 px.scatter(
    mpg, x='displ', y='hwy', 
    facet_col='cyl', facet_row='drv',
    category_orders=dict(cyl=[4,5,6,8])
)

Code:


 (alt
 .Chart(mpg)
 .mark_circle()
 .encode(x='displ', y='hwy',)
 .properties(
    width=100, height=150
  )
 .facet(column='cyl', row='drv')
)

Code:


 ggplot(data = mpg) + 
  aes(x = displ, y = hwy) +
  geom_point() + 
  facet_grid(drv ~ cyl)

Code:


 sns.lmplot(x='displ', y='hwy', 
           data=mpg, size=12)

Code:


 (ggplot(mpg) +
    aes('displ', 'hwy') +
    geom_point() +
    geom_smooth(method='lm'))

Code:


 import statsmodels.api as sm
from statsmodels.stats.outliers_influence import summary_table

y=mpg.hwy
x=mpg.displ
X = sm.add_constant(x)
res = sm.OLS(y, X).fit()

st, data, ss2 = summary_table(res, alpha=0.05)
preds = pd.DataFrame.from_records(data, columns=[s.replace('\n', ' ') for s in ss2])
preds['displ'] = mpg.displ
preds = preds.sort_values(by='displ')

fig = graph_objects.Figure(layout={
    'title' : 'Engine Displacement in Liters vs Highway MPG',
    'xaxis' : {
        'title' : 'Engine Displacement in Liters'
    },
    'yaxis' : {
        'title' : 'Highway MPG'
    }
})
p1 = graph_objects.Scatter(**{
    'mode' : 'markers',
    'x' : mpg.displ,
    'y' : mpg.hwy,
    'name' : 'Points'
})
p2 = graph_objects.Scatter({
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Predicted Value'],
    'name' : 'Regression',
})
#Add a lower bound for the confidence interval, white
p3 = graph_objects.Scatter({
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Mean ci 95% low'],
    'name' : 'Lower 95% CI',
    'showlegend' : False,
    'line' : {
        'color' : 'white'
    }
})
# Upper bound for the confidence band, transparent but with fill
p4 = graph_objects.Scatter( {
    'type' : 'scatter',
    'mode' : 'lines',
    'x' : preds['displ'],
    'y' : preds['Mean ci 95% upp'],
    'name' : '95% CI',
    'fill' : 'tonexty',
    'line' : {
        'color' : 'white'
    },
    'fillcolor' : 'rgba(255, 127, 14, 0.3)'
})
fig.add_trace(p1)
fig.add_trace(p2)
fig.add_trace(p3)
fig.add_trace(p4)

Code:


 ggplot(data = mpg) +
    aes(x = displ, y = hwy) +
    geom_point() +
    geom_smooth(method=lm)

Code:


 (ggplot(data=mpg, 
        mapping=aes(x='displ', y='hwy')) + 
  geom_point(mapping=aes(color = 'class')) + 
  geom_smooth(data=mpg[mpg['class'] == 'subcompact'], 
              se=False,
              method = 'loess'
             ))

Code:


 traces = []
for cls in mpg['class'].unique():
    traces.append(graph_objects.Scatter({
        'mode' : 'markers',
        'x' : mpg.displ[mpg['class'] == cls],
        'y' : mpg.hwy[mpg['class'] == cls],
        'name' : cls
    }))

    
subcompact = mpg[mpg['class'] == 'subcompact'].sort_values(by='displ')

traces.append(graph_objects.Scatter({
    'mode' : 'lines',
    'x' : subcompact.displ,
    'y' : subcompact.hwy,
    'name' : 'smoothing',
    'line' : {
        'shape' : 'spline',
        'smoothing' : 1.3
    }
}))
    
fig = graph_objects.Figure(**{
    'data' : traces,
    'layout' : {
        'title' : 'Engine Displacement in Liters vs Highway MPG',
        'xaxis' : {
            'title' : 'Engine Displacement in Liters',
        },
        'yaxis' : {
            'title' : 'Highway MPG'
        }
    }
})

Code:


 scatter = (
    alt.Chart(
        mpg,
        title='Engine Displacement in Liters vs Highway MPG',
    )
    .mark_circle()
    .encode(
        x=alt.X(
            'displ',
            axis=alt.Axis(
                title='Engine Displacament in Liters'
            ),
        ),
        y=alt.Y(
            'hwy',
            axis=alt.Axis(
                title='Highway MPG'
            ),
        ),
        color='class',
    )
)

line = (
    alt.Chart(
        mpg[mpg['class'] == 'subcompact']
    )
    .transform_loess('displ', 'hwy')
    .mark_line()
    .encode(x=alt.X('displ'), y=alt.Y('hwy'))
)

scatter + line

Code:


 subcompact = mpg[mpg$`class` == 'subcompact', ]
ggplot(data = mpg, 
       mapping = aes(x = displ, y = hwy)) + 
  geom_point(mapping = aes(color = class)) + 
  geom_smooth(data = subcompact, 
              se = FALSE,
              method = 'loess')

Code:


 (diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .plot.bar(stacked=True)
)

Code:


 (ggplot(diamonds) + 
  aes(x='cut', fill='clarity') +
  geom_bar())

Code:


 px.histogram(
    diamonds, x='cut', color='clarity',
    category_orders=dict(cut=[
     'Fair', 'Good',  'Very Good', 
     'Premium', 'Ideal'])
)

Code:


 alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).mark_bar().encode(
    x='cut', y='count(cut)', color='clarity'
).properties(width=300)

Code:


 ggplot(data = diamonds) + 
  aes(x = cut, fill = clarity) +
  geom_bar()

Code:


 (diamonds
 .groupby(['cut', 'clarity'])
 .size()
 .unstack()
 .plot.bar()
)

Code:


 (ggplot(diamonds) + 
  aes(x='cut', fill='clarity') +
  geom_bar(position = 'dodge'))

Code:


 px.histogram(
    diamonds, x='cut', color='clarity', barmode='group',
    category_orders=dict(cut=[
     'Fair', 'Good',  'Very Good', 
     'Premium', 'Ideal'])
)

Code:


 alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).mark_bar().encode(
    x='clarity',
    y='count(cut)',
    color='clarity',
    column='cut',
).properties(width=100)

Code:


 ggplot(data = diamonds) + 
  aes(x = cut, fill = clarity) +
  geom_bar(position = 'dodge')

Code:


 fig, ax = pyplot.subplots()
ax.set_xlim(55, 70)
for cut in diamonds['cut'].unique():
    s = diamonds[diamonds['cut'] == cut]['depth']
    s.plot.kde(ax=ax, label=cut)
ax.legend()

Code:


 (sns
  .FacetGrid(diamonds, 
             hue='cut', 
             size=10, 
             xlim=(55, 70))
  .map(sns.kdeplot, 'depth', shade=True)
 .add_legend()
)

Code:


 (ggplot(diamonds) +
  aes('depth', fill='cut', color='cut') +
  geom_density(alpha=0.1))

Code:


 fig = figure_factory.create_distplot(
    [diamonds['depth'][diamonds['cut'] == c].values 
     for c in diamonds.cut.unique()
    ],
    diamonds.cut.unique(),
    show_hist=False,
    show_rug=False,
)
for d in fig['data']:
    d.update({'fill': 'tozeroy'})

Code:


 alt.data_transformers.disable_max_rows()
alt.Chart(diamonds).transform_density(
    'depth',
    as_=['depth', 'density'],
    groupby=['cut'],
    extent=[55, 70],
).mark_area(fillOpacity=0.3,).encode(
    x='depth',
    y='density:Q',
    color='cut',
    stroke='cut',
)

Code:


 ggplot(diamonds) +
  aes(depth, fill = cut, colour = cut) +
  geom_density(alpha = 0.1) +
  xlim(55, 70)

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