Styling Tables and DataFrames

Getting started - a simple table

df = pd.DataFrame({
    'text': ['one', 'two', 'three', 'four', 'five'],
    'category': ['odd', 'even', 'odd', 'even', 'odd'],
    'heatmap': [10, 20, 30, 40, 70],
    'bar': [18, 17, 12, 10, 5]
})
df

	text	category	heatmap	bar
0	one	odd	10	18
1	two	even	20	17
2	three	odd	30	12
3	four	even	40	10
4	five	odd	70	5

Using simple default options

adviz.style_table(
    df,
    column_types=['text', 'category', 'heatmap', 'bar'])

Modify look and feel

adviz.style_table(
    df,
    column_types=['text', 'category', 'heatmap', 'bar'],
    column_widths=[0.15, 0.25, 0.2, 0.4],
    title='Styling tables with <b>adviz',
    theme='plotly_dark',
    precision=0,
    width=600,
    height=400,
    font_size=14,
    title_font_size=25)

Multiple columns & multiple types

medals = px.data.medals_long()[['medal', 'nation', 'count']]
medals['medal'] = (medals['medal']
                   .astype('category')
                   .cat.reorder_categories(['bronze', 'silver', 'gold']))
medals = medals.sort_values(['medal', 'count'], ascending=False)
medals

	medal	nation	count
0	gold	South Korea	24
1	gold	China	10
2	gold	Canada	9
4	silver	China	15
3	silver	South Korea	13
5	silver	Canada	12
8	bronze	Canada	12
6	bronze	South Korea	11
7	bronze	China	8

Style the table

adviz.style_table(
    medals,
    column_types=['category', 'category', 'bar'],
    title='Olympic Medals',
    theme='none',
    precision=0,
    width=800,
    height=500)

Using heatmap instead of bar

adviz.style_table(
    medals,
    column_types=['category', 'category', 'heatmap'],
    title='Olympic Medals',
    theme='none',
    precision=0,
    width=800,
    height=400)

Styling many columns with multiple chart types

election = px.data.election().iloc[:, :-1]
election.head()

	district	Coderre	Bergeron	Joly	total	winner	result
0	101-Bois-de-Liesse	2481	1829	3024	7334	Joly	plurality
1	102-Cap-Saint-Jacques	2525	1163	2675	6363	Joly	plurality
2	11-Sault-au-Récollet	3348	2770	2532	8650	Coderre	plurality
3	111-Mile-End	1734	4782	2514	9030	Bergeron	majority
4	112-DeLorimier	1770	5933	3044	10747	Bergeron	majority

Visualizing a more complex table

adviz.style_table(
    election.sort_values('total', ascending=False).head(20),
    column_types=['text', 'heatmap', 'heatmap', 'heatmap', 'bar', 'category', 'category'],
    column_widths=[.3, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12 ],
    precision=0,
    theme='plotly_dark',
    title='Election Results – Top 20',
    height=750,
)

Producing more detailed and readable tables

top5_gdp_percap = (px.data
                   .gapminder()
                   .sort_values(['year', 'gdpPercap'],
                                ascending=[True, False])
                   .groupby('year').head().iloc[:, :-2]
                   [['country', 'continent', 'year', 'gdpPercap', 'lifeExp', 'pop']]
                   .rename(columns={
                       'gdpPercap': 'GDP per capita',
                       'lifeExp': 'Life expectancy',
                       'pop': 'population'}))
top5_gdp_percap.head()

	country	continent	year	GDP per capita	Life expectancy	population
852	Kuwait	Asia	1952	108382.35290	55.565	160000
1476	Switzerland	Europe	1952	14734.23275	69.620	4815000
1608	United States	Americas	1952	13990.48208	68.440	157553000
240	Canada	Americas	1952	11367.16112	68.750	14785584
1092	New Zealand	Oceania	1952	10556.57566	69.390	1994794

More complex and customized report

adviz.style_table(
    top5_gdp_percap,
    ['text', 'category', 'category', 'bar', 'heatmap', 'heatmap'],
    column_widths=[.1, .07, .05, .12, .12, .12, ],
    theme='seaborn',
    precision=0,
    title="Top 5 countries per year – GDP per capita",
    height=1300)

Visualizing hierarchical data

See also: the adviz.status_codes chart.

status_codes = pd.read_csv('data/status_codes.csv')
status_codes['category'] = status_codes['status'].round(-2)
status_codes

	status	category
0	200	200
1	200	200
2	200	200
3	200	200
4	200	200
...	...	...
414398	200	200
414399	200	200
414400	200	200
414401	200	200
414402	200	200

414403 rows × 2 columns

Hierarchical table

adviz.style_table(
    status_codes[['category', 'status']].value_counts().reset_index().sort_values(['category', 'count'], ascending=[True, False]),
    column_types=['category', 'text', 'bar'],
    column_widths=[.2, .2, .6],
    precision=0,
    theme='plotly_dark',
    title='Status Codes',
    height=600, width=700)

Get started now:

python3 -m pip install adviz

Explore more advertools data visualizations