Polars dataframes

In this notebook we make sure that our test Polars dataframes are displayed nicely with the default itables settings.

import polars as pl

import itables

dict_of_test_dfs = itables.sample_polars_dfs.get_dict_of_test_dfs()
itables.init_notebook_mode()

empty

itables.show(dict_of_test_dfs["empty"])

ⓘ

No rows

itables.show(dict_of_test_dfs["no_rows"])

ⓘa
f64

bool

itables.show(dict_of_test_dfs["bool"])

ⓘa	b	c	d
bool	bool	bool	bool
True	True	False	False
True	False	True	False

Nullable boolean

itables.show(dict_of_test_dfs["nullable_boolean"])

ⓘa	b	c	d
bool	bool	bool	bool
True	True	False
True	False		False
	False	True	False

int

itables.show(dict_of_test_dfs["int"])

ⓘa	b	c	d	e
i64	i64	i64	i64	i64
-1	2	-3	4	-5
6	-7	8	-9	10

Nullable integer

itables.show(dict_of_test_dfs["nullable_int"])

ⓘa	b	c
i64	i64	i64
-1	2	-3
4	-5	6
	7

float

itables.show(dict_of_test_dfs["float"])

ⓘint	inf	nan	math	unsorted
f64	f64	f64	f64	f64
0.0	inf	NaN	3.141593	100.0
1.0	-inf	NaN	2.718282	NaN
2.0	inf	NaN	3.141593	NaN
3.0	-inf	NaN	2.718282	20.0
4.0	inf	NaN	3.141593	3.141593
5.0	-inf	NaN	2.718282	2.718282
6.0	inf	NaN	3.141593	0.0
7.0	-inf	NaN	2.718282	-0.0
8.0	inf	NaN	3.141593	inf
9.0	-inf	NaN	2.718282	-inf

float_types

itables.show(dict_of_test_dfs["float_types"])

ⓘfloat16	float32	float64	decimal
f16	f32	f64	decimal[10,6]
1.0	1.0	1.0	1.000000
0.0	0.0	0.0	0.000000
3.140625	3.141593	3.141593	3.000000
NaN	NaN	NaN	null
inf	inf	inf	null
-inf	-inf	-inf	null

Ordered floats

itables.show(
    pl.DataFrame(
        {"float": [float("nan"), float("inf")] + [float(x) for x in range(18)]}
    ),
    order=[[0, "asc"]],
)

ⓘfloat
f64
NaN
inf
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
(10 more rows not shown)

str

itables.show(dict_of_test_dfs["str"])

ⓘtext_column	very_long_text_column
str	str
some	a very very very very very very very very very very very very long text
text	a very very very very very very very very very very very very long text

time

itables.show(dict_of_test_dfs["time"])

ⓘdatetime	timestamp	timedelta
datetime[μs]	datetime[μs]	duration[μs]
2000-01-01 00:00:00	null	2d
2001-01-01 00:00:00	2000-01-01 18:55:33	50s
null	2001-01-01 18:55:55.456654	null

ordered_categories

itables.show(dict_of_test_dfs["ordered_categories"])

ⓘint	category
i64	enum
0	first
1	second
2	third
3	fourth

countries

itables.show(dict_of_test_dfs["countries"])

ⓘiso2Code	region	country	capital	longitude	latitude
str	str	str	str	f64	f64
AW	Latin America & Caribbean	Aruba	Oranjestad	-70.0167	12.5167
AF	South Asia	Afghanistan	Kabul	69.1761	34.5228
AO	Sub-Saharan Africa	Angola	Luanda	13.242	-8.81155
AL	Europe & Central Asia	Albania	Tirane	19.8172	41.3317
AD	Europe & Central Asia	Andorra	Andorra la Vella	1.5218	42.5075
AE	Middle East & North Africa	United Arab Emirates	Abu Dhabi	54.3705	24.4764
AR	Latin America & Caribbean	Argentina	Buenos Aires	-58.4173	-34.6118
AM	Europe & Central Asia	Armenia	Yerevan	44.509	40.1596
AS	East Asia & Pacific	American Samoa	Pago Pago	-170.691	-14.2846
AG	Latin America & Caribbean	Antigua and Barbuda	Saint John's	-61.8456	17.1175
(198 more rows not shown)

int_float_str

itables.show(dict_of_test_dfs["int_float_str"])

ⓘint	float	str
i64	f64	str
0	0.5	a
1	1.5	b
2	2.5	c
3	3.5	d
4	4.5	e
5	5.5	f
6	6.5	g
7	7.5	h
8	8.5	i
9	9.5	j
(90 more rows not shown)

wide

itables.show(dict_of_test_dfs["wide"], maxBytes=100000, maxColumns=100)

ⓘcolumn_0	column_1	column_2	column_3	column_4	column_5	column_6	column_7	column_8	column_9	column_10	column_11	column_12	column_13	column_14	column_15	column_16	column_17	column_18	column_19	column_20	column_21	column_22	column_23	column_24	column_25	column_26	column_27	column_28	column_29	column_30	column_31	column_32	column_33	column_34	column_35	column_36	column_37	column_38	column_39	column_40	column_41	column_42	column_43	column_44	column_45	column_46	column_47	column_48	column_49	column_50	column_51	column_52	column_53	column_54	column_55	column_56	column_57	column_58	column_59	column_60	column_61	column_62	column_63	column_64	column_65	column_66	column_67	column_68	column_69	column_70	column_71	column_72	column_73	column_74	column_75	column_76	column_77	column_78	column_79	column_80	column_81	column_82	column_83	column_84	column_85	column_86	column_87	column_88	column_89	column_90	column_91	column_92	column_93	column_94	column_95	column_96	column_97	column_98	column_99
f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64	f64
0.0	0.01	0.02	0.03	0.04	0.05	0.06	0.07	0.08	0.09	0.1	0.11	0.12	0.13	0.14	0.15	0.16	0.17	0.18	0.19	0.2	0.21	0.22	0.23	0.24	0.25	0.26	0.27	0.28	0.29	0.3	0.31	0.32	0.33	0.34	0.35	0.36	0.37	0.38	0.39	0.4	0.41	0.42	0.43	0.44	0.45	0.46	0.47	0.48	0.49	0.5	0.51	0.52	0.53	0.54	0.55	0.56	0.57	0.58	0.59	0.6	0.61	0.62	0.63	0.64	0.65	0.66	0.67	0.68	0.69	0.7	0.71	0.72	0.73	0.74	0.75	0.76	0.77	0.78	0.79	0.8	0.81	0.82	0.83	0.84	0.85	0.86	0.87	0.88	0.89	0.9	0.91	0.92	0.93	0.94	0.95	0.96	0.97	0.98	0.99
0.0001	0.0101	0.0201	0.0301	0.0401	0.0501	0.0601	0.0701	0.0801	0.0901	0.1001	0.1101	0.1201	0.1301	0.1401	0.1501	0.1601	0.1701	0.1801	0.1901	0.2001	0.2101	0.2201	0.2301	0.2401	0.2501	0.2601	0.2701	0.2801	0.2901	0.3001	0.3101	0.3201	0.3301	0.3401	0.3501	0.3601	0.3701	0.3801	0.3901	0.4001	0.4101	0.4201	0.4301	0.4401	0.4501	0.4601	0.4701	0.4801	0.4901	0.5001	0.5101	0.5201	0.5301	0.5401	0.5501	0.5601	0.5701	0.5801	0.5901	0.6001	0.6101	0.6201	0.6301	0.6401	0.6501	0.6601	0.6701	0.6801	0.6901	0.7001	0.7101	0.7201	0.7301	0.7401	0.7501	0.7601	0.7701	0.7801	0.7901	0.8001	0.8101	0.8201	0.8301	0.8401	0.8501	0.8601	0.8701	0.8801	0.8901	0.9001	0.9101	0.9201	0.9301	0.9401	0.9501	0.9601	0.9701	0.9801	0.9901
0.0002	0.0102	0.0202	0.0302	0.0402	0.0502	0.0602	0.0702	0.0802	0.0902	0.1002	0.1102	0.1202	0.1302	0.1402	0.1502	0.1602	0.1702	0.1802	0.1902	0.2002	0.2102	0.2202	0.2302	0.2402	0.2502	0.2602	0.2702	0.2802	0.2902	0.3002	0.3102	0.3202	0.3302	0.3402	0.3502	0.3602	0.3702	0.3802	0.3902	0.4002	0.4102	0.4202	0.4302	0.4402	0.4502	0.4602	0.4702	0.4802	0.4902	0.5002	0.5102	0.5202	0.5302	0.5402	0.5502	0.5602	0.5702	0.5802	0.5902	0.6002	0.6102	0.6202	0.6302	0.6402	0.6502	0.6602	0.6702	0.6802	0.6902	0.7002	0.7102	0.7202	0.7302	0.7402	0.7502	0.7602	0.7702	0.7802	0.7902	0.8002	0.8102	0.8202	0.8302	0.8402	0.8502	0.8602	0.8702	0.8802	0.8902	0.9002	0.9102	0.9202	0.9302	0.9402	0.9502	0.9602	0.9702	0.9802	0.9902
0.0003	0.0103	0.0203	0.0303	0.0403	0.0503	0.0603	0.0703	0.0803	0.0903	0.1003	0.1103	0.1203	0.1303	0.1403	0.1503	0.1603	0.1703	0.1803	0.1903	0.2003	0.2103	0.2203	0.2303	0.2403	0.2503	0.2603	0.2703	0.2803	0.2903	0.3003	0.3103	0.3203	0.3303	0.3403	0.3503	0.3603	0.3703	0.3803	0.3903	0.4003	0.4103	0.4203	0.4303	0.4403	0.4503	0.4603	0.4703	0.4803	0.4903	0.5003	0.5103	0.5203	0.5303	0.5403	0.5503	0.5603	0.5703	0.5803	0.5903	0.6003	0.6103	0.6203	0.6303	0.6403	0.6503	0.6603	0.6703	0.6803	0.6903	0.7003	0.7103	0.7203	0.7303	0.7403	0.7503	0.7603	0.7703	0.7803	0.7903	0.8003	0.8103	0.8203	0.8303	0.8403	0.8503	0.8603	0.8703	0.8803	0.8903	0.9003	0.9103	0.9203	0.9303	0.9403	0.9503	0.9603	0.9703	0.9803	0.9903
0.0004	0.0104	0.0204	0.0304	0.0404	0.0504	0.0604	0.0704	0.0804	0.0904	0.1004	0.1104	0.1204	0.1304	0.1404	0.1504	0.1604	0.1704	0.1804	0.1904	0.2004	0.2104	0.2204	0.2304	0.2404	0.2504	0.2604	0.2704	0.2804	0.2904	0.3004	0.3104	0.3204	0.3304	0.3404	0.3504	0.3604	0.3704	0.3804	0.3904	0.4004	0.4104	0.4204	0.4304	0.4404	0.4504	0.4604	0.4704	0.4804	0.4904	0.5004	0.5104	0.5204	0.5304	0.5404	0.5504	0.5604	0.5704	0.5804	0.5904	0.6004	0.6104	0.6204	0.6304	0.6404	0.6504	0.6604	0.6704	0.6804	0.6904	0.7004	0.7104	0.7204	0.7304	0.7404	0.7504	0.7604	0.7704	0.7804	0.7904	0.8004	0.8104	0.8204	0.8304	0.8404	0.8504	0.8604	0.8704	0.8804	0.8904	0.9004	0.9104	0.9204	0.9304	0.9404	0.9504	0.9604	0.9704	0.9804	0.9904
0.0005	0.0105	0.0205	0.0305	0.0405	0.0505	0.0605	0.0705	0.0805	0.0905	0.1005	0.1105	0.1205	0.1305	0.1405	0.1505	0.1605	0.1705	0.1805	0.1905	0.2005	0.2105	0.2205	0.2305	0.2405	0.2505	0.2605	0.2705	0.2805	0.2905	0.3005	0.3105	0.3205	0.3305	0.3405	0.3505	0.3605	0.3705	0.3805	0.3905	0.4005	0.4105	0.4205	0.4305	0.4405	0.4505	0.4605	0.4705	0.4805	0.4905	0.5005	0.5105	0.5205	0.5305	0.5405	0.5505	0.5605	0.5705	0.5805	0.5905	0.6005	0.6105	0.6205	0.6305	0.6405	0.6505	0.6605	0.6705	0.6805	0.6905	0.7005	0.7105	0.7205	0.7305	0.7405	0.7505	0.7605	0.7705	0.7805	0.7905	0.8005	0.8105	0.8205	0.8305	0.8405	0.8505	0.8605	0.8705	0.8805	0.8905	0.9005	0.9105	0.9205	0.9305	0.9405	0.9505	0.9605	0.9705	0.9805	0.9905
0.0006	0.0106	0.0206	0.0306	0.0406	0.0506	0.0606	0.0706	0.0806	0.0906	0.1006	0.1106	0.1206	0.1306	0.1406	0.1506	0.1606	0.1706	0.1806	0.1906	0.2006	0.2106	0.2206	0.2306	0.2406	0.2506	0.2606	0.2706	0.2806	0.2906	0.3006	0.3106	0.3206	0.3306	0.3406	0.3506	0.3606	0.3706	0.3806	0.3906	0.4006	0.4106	0.4206	0.4306	0.4406	0.4506	0.4606	0.4706	0.4806	0.4906	0.5006	0.5106	0.5206	0.5306	0.5406	0.5506	0.5606	0.5706	0.5806	0.5906	0.6006	0.6106	0.6206	0.6306	0.6406	0.6506	0.6606	0.6706	0.6806	0.6906	0.7006	0.7106	0.7206	0.7306	0.7406	0.7506	0.7606	0.7706	0.7806	0.7906	0.8006	0.8106	0.8206	0.8306	0.8406	0.8506	0.8606	0.8706	0.8806	0.8906	0.9006	0.9106	0.9206	0.9306	0.9406	0.9506	0.9606	0.9706	0.9806	0.9906
0.0007	0.0107	0.0207	0.0307	0.0407	0.0507	0.0607	0.0707	0.0807	0.0907	0.1007	0.1107	0.1207	0.1307	0.1407	0.1507	0.1607	0.1707	0.1807	0.1907	0.2007	0.2107	0.2207	0.2307	0.2407	0.2507	0.2607	0.2707	0.2807	0.2907	0.3007	0.3107	0.3207	0.3307	0.3407	0.3507	0.3607	0.3707	0.3807	0.3907	0.4007	0.4107	0.4207	0.4307	0.4407	0.4507	0.4607	0.4707	0.4807	0.4907	0.5007	0.5107	0.5207	0.5307	0.5407	0.5507	0.5607	0.5707	0.5807	0.5907	0.6007	0.6107	0.6207	0.6307	0.6407	0.6507	0.6607	0.6707	0.6807	0.6907	0.7007	0.7107	0.7207	0.7307	0.7407	0.7507	0.7607	0.7707	0.7807	0.7907	0.8007	0.8107	0.8207	0.8307	0.8407	0.8507	0.8607	0.8707	0.8807	0.8907	0.9007	0.9107	0.9207	0.9307	0.9407	0.9507	0.9607	0.9707	0.9807	0.9907
0.0008	0.0108	0.0208	0.0308	0.0408	0.0508	0.0608	0.0708	0.0808	0.0908	0.1008	0.1108	0.1208	0.1308	0.1408	0.1508	0.1608	0.1708	0.1808	0.1908	0.2008	0.2108	0.2208	0.2308	0.2408	0.2508	0.2608	0.2708	0.2808	0.2908	0.3008	0.3108	0.3208	0.3308	0.3408	0.3508	0.3608	0.3708	0.3808	0.3908	0.4008	0.4108	0.4208	0.4308	0.4408	0.4508	0.4608	0.4708	0.4808	0.4908	0.5008	0.5108	0.5208	0.5308	0.5408	0.5508	0.5608	0.5708	0.5808	0.5908	0.6008	0.6108	0.6208	0.6308	0.6408	0.6508	0.6608	0.6708	0.6808	0.6908	0.7008	0.7108	0.7208	0.7308	0.7408	0.7508	0.7608	0.7708	0.7808	0.7908	0.8008	0.8108	0.8208	0.8308	0.8408	0.8508	0.8608	0.8708	0.8808	0.8908	0.9008	0.9108	0.9208	0.9308	0.9408	0.9508	0.9608	0.9708	0.9808	0.9908
0.0009	0.0109	0.0209	0.0309	0.0409	0.0509	0.0609	0.0709	0.0809	0.0909	0.1009	0.1109	0.1209	0.1309	0.1409	0.1509	0.1609	0.1709	0.1809	0.1909	0.2009	0.2109	0.2209	0.2309	0.2409	0.2509	0.2609	0.2709	0.2809	0.2909	0.3009	0.3109	0.3209	0.3309	0.3409	0.3509	0.3609	0.3709	0.3809	0.3909	0.4009	0.4109	0.4209	0.4309	0.4409	0.4509	0.4609	0.4709	0.4809	0.4909	0.5009	0.5109	0.5209	0.5309	0.5409	0.5509	0.5609	0.5709	0.5809	0.5909	0.6009	0.6109	0.6209	0.6309	0.6409	0.6509	0.6609	0.6709	0.6809	0.6909	0.7009	0.7109	0.7209	0.7309	0.7409	0.7509	0.7609	0.7709	0.7809	0.7909	0.8009	0.8109	0.8209	0.8309	0.8409	0.8509	0.8609	0.8709	0.8809	0.8909	0.9009	0.9109	0.9209	0.9309	0.9409	0.9509	0.9609	0.9709	0.9809	0.9909
(90 more rows not shown)

long_column_names

itables.show(dict_of_test_dfs["long_column_names"])

ⓘshort name	very very very very very long name	very very very very very very very very very very long name	very very very very very very very very very very very very very very very very very very very very long name	nospaceinveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryverylongname	nospaceinveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryveryverylongname
i64	i64	i64	i64	i64	i64
0	0	1	2	3	3
0	0	1	2	3	3
0	0	1	2	3	3
0	0	1	2	3	3
0	0	1	2	3	3

big_integers

itables.show(dict_of_test_dfs["big_integers"])

ⓘbigint	expected
i64	str
-1234567890123456789	-1234567890123456789
1234567890123456789	1234567890123456789
2345678901234567890	2345678901234567890
3456789012345678901	3456789012345678901