Datasets#

The following example tables come with the Plotly package.

import plotly.express as px

carshare#

px.data.carshare().tail(10)

	centroid_lat	centroid_lon	car_hours	peak_hour
239	45.570923	-73.577155	898.000000	10
240	45.533611	-73.601066	701.666667	11
241	45.522280	-73.568434	873.166667	21
242	45.570443	-73.525182	235.416667	7
243	45.486260	-73.638602	998.666667	18
244	45.547171	-73.556258	951.416667	3
245	45.546482	-73.574939	795.416667	2
246	45.495523	-73.627725	425.750000	8
247	45.521199	-73.581789	1044.833333	17
248	45.532564	-73.567535	694.916667	5

px.data.election().tail(10)

	district	Coderre	Bergeron	Joly	total	winner	result	district_id
48	71-Tétreaultville	3694	2589	3454	9737	Coderre	plurality	71
49	72-MaisonneuveLongue-Pointe	2746	3250	3139	9135	Bergeron	plurality	72
50	73-Hochelaga	1546	3679	2675	7900	Bergeron	plurality	73
51	74-Louis-Riel	3509	2178	2338	8025	Coderre	plurality	74
52	81-Marie-Clarac	6591	1085	1435	9111	Coderre	majority	81
53	82-Ovide-Clermont	6229	780	1051	8060	Coderre	majority	82
54	91-Claude-Ryan	996	643	423	2062	Coderre	plurality	91
55	92-Joseph-Beaubien	540	833	592	1965	Bergeron	plurality	92
56	93-Robert-Bourassa	446	465	419	1330	Bergeron	plurality	93
57	94-Jeanne-Sauvé	491	698	489	1678	Bergeron	plurality	94

px.data.experiment().tail(10)

	experiment_1	experiment_2	experiment_3	gender	group
90	100.177493	104.673700	73.250996	male	treatment
91	108.151672	137.994359	105.054792	female	treatment
92	101.320525	105.711291	73.730844	male	control
93	99.177558	88.570729	78.655041	female	control
94	102.770625	129.002274	52.111122	male	treatment
95	108.156964	105.971541	64.524029	female	treatment
96	91.739992	111.125377	64.260993	male	control
97	95.410347	84.448322	75.505991	female	control
98	106.362406	115.522382	123.469689	male	treatment
99	94.269237	104.651064	92.387490	female	treatment

px.data.gapminder().tail(10)

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
1694	Zimbabwe	Africa	1962	52.358	4277736	527.272182	ZWE	716
1695	Zimbabwe	Africa	1967	53.995	4995432	569.795071	ZWE	716
1696	Zimbabwe	Africa	1972	55.635	5861135	799.362176	ZWE	716
1697	Zimbabwe	Africa	1977	57.674	6642107	685.587682	ZWE	716
1698	Zimbabwe	Africa	1982	60.363	7636524	788.855041	ZWE	716
1699	Zimbabwe	Africa	1987	62.351	9216418	706.157306	ZWE	716
1700	Zimbabwe	Africa	1992	60.377	10704340	693.420786	ZWE	716
1701	Zimbabwe	Africa	1997	46.809	11404948	792.449960	ZWE	716
1702	Zimbabwe	Africa	2002	39.989	11926563	672.038623	ZWE	716
1703	Zimbabwe	Africa	2007	43.487	12311143	469.709298	ZWE	716

px.data.iris().tail(10)

	sepal_length	sepal_width	petal_length	petal_width	species	species_id
140	6.7	3.1	5.6	2.4	virginica	3
141	6.9	3.1	5.1	2.3	virginica	3
142	5.8	2.7	5.1	1.9	virginica	3
143	6.8	3.2	5.9	2.3	virginica	3
144	6.7	3.3	5.7	2.5	virginica	3
145	6.7	3.0	5.2	2.3	virginica	3
146	6.3	2.5	5.0	1.9	virginica	3
147	6.5	3.0	5.2	2.0	virginica	3
148	6.2	3.4	5.4	2.3	virginica	3
149	5.9	3.0	5.1	1.8	virginica	3

px.data.medals_long()

px.data.medals_wide()

	nation	gold	silver	bronze
0	South Korea	24	13	11
1	China	10	15	8
2	Canada	9	12	12

px.data.stocks().tail(10)

px.data.tips().tail(10)

	total_bill	tip	sex	smoker	day	time	size
234	15.53	3.00	Male	Yes	Sat	Dinner	2
235	10.07	1.25	Male	No	Sat	Dinner	2
236	12.60	1.00	Male	Yes	Sat	Dinner	2
237	32.83	1.17	Male	Yes	Sat	Dinner	2
238	35.83	4.67	Female	No	Sat	Dinner	3
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

px.data.wind().tail(10)