Overview

Dataset statistics

Number of variables6
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.7 KiB
Average record size in memory52.1 B

Variable types

Numeric4
Text1
Categorical1

Dataset

Description샘플 데이터
Author(주)모토브 / 신재훈
URLhttps://www.bigdata-transportation.kr/frn/prdt/detail?prdtId=PRDTNUM_000000020252

Alerts

register_at has constant value ""Constant
light_sensor_value_id has unique valuesUnique

Reproduction

Analysis started2024-04-22 00:31:16.959637
Analysis finished2024-04-22 00:31:18.768365
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

light_sensor_value_id
Real number (ℝ)

UNIQUE 

Distinct2000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000.5
Minimum1
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-04-22T09:31:18.846450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile100.95
Q1500.75
median1000.5
Q31500.25
95-th percentile1900.05
Maximum2000
Range1999
Interquartile range (IQR)999.5

Descriptive statistics

Standard deviation577.49459
Coefficient of variation (CV)0.57720599
Kurtosis-1.2
Mean1000.5
Median Absolute Deviation (MAD)500
Skewness0
Sum2001000
Variance333500
MonotonicityStrictly increasing
2024-04-22T09:31:18.988662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
1331 1
 
0.1%
1344 1
 
0.1%
1343 1
 
0.1%
1342 1
 
0.1%
1341 1
 
0.1%
1340 1
 
0.1%
1339 1
 
0.1%
1338 1
 
0.1%
1337 1
 
0.1%
Other values (1990) 1990
99.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
2000 1
0.1%
1999 1
0.1%
1998 1
0.1%
1997 1
0.1%
1996 1
0.1%
1995 1
0.1%
1994 1
0.1%
1993 1
0.1%
1992 1
0.1%
1991 1
0.1%

taxi_id
Text

Distinct152
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2024-04-22T09:31:19.286478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20000
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowT_96289981
2nd rowT_73322493
3rd rowT_45081461
4th rowT_44934973
5th rowT_47791477
ValueCountFrequency (%)
t_41712252 14
 
0.7%
t_95044839 14
 
0.7%
t_72223838 14
 
0.7%
t_94898351 14
 
0.7%
t_49402838 14
 
0.7%
t_47644990 14
 
0.7%
t_94605377 14
 
0.7%
t_19037739 14
 
0.7%
t_97974586 14
 
0.7%
t_95704032 14
 
0.7%
Other values (142) 1860
93.0%
2024-04-22T09:31:19.673649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2111
10.6%
T 2000
10.0%
_ 2000
10.0%
9 1800
9.0%
7 1700
8.5%
8 1695
8.5%
3 1652
8.3%
6 1582
7.9%
2 1522
7.6%
1 1471
7.4%
Other values (2) 2467
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16000
80.0%
Uppercase Letter 2000
 
10.0%
Connector Punctuation 2000
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 2111
13.2%
9 1800
11.2%
7 1700
10.6%
8 1695
10.6%
3 1652
10.3%
6 1582
9.9%
2 1522
9.5%
1 1471
9.2%
5 1247
7.8%
0 1220
7.6%
Uppercase Letter
ValueCountFrequency (%)
T 2000
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18000
90.0%
Latin 2000
 
10.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 2111
11.7%
_ 2000
11.1%
9 1800
10.0%
7 1700
9.4%
8 1695
9.4%
3 1652
9.2%
6 1582
8.8%
2 1522
8.5%
1 1471
8.2%
5 1247
6.9%
Latin
ValueCountFrequency (%)
T 2000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2111
10.6%
T 2000
10.0%
_ 2000
10.0%
9 1800
9.0%
7 1700
8.5%
8 1695
8.5%
3 1652
8.3%
6 1582
7.9%
2 1522
7.6%
1 1471
7.4%
Other values (2) 2467
12.3%

latitude
Real number (ℝ)

Distinct1332
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.497015
Minimum36.941452
Maximum37.778934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-04-22T09:31:19.839630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.941452
5-th percentile37.406297
Q137.460313
median37.497256
Q337.530572
95-th percentile37.60284
Maximum37.778934
Range0.837482
Interquartile range (IQR)0.07025875

Descriptive statistics

Standard deviation0.076530507
Coefficient of variation (CV)0.002040976
Kurtosis17.339603
Mean37.497015
Median Absolute Deviation (MAD)0.0349585
Skewness-2.0596037
Sum74994.03
Variance0.0058569185
MonotonicityNot monotonic
2024-04-22T09:31:19.976459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.479614 15
 
0.8%
37.473686 14
 
0.7%
37.5649 14
 
0.7%
37.60681 14
 
0.7%
37.41697 14
 
0.7%
37.523636 14
 
0.7%
37.443947 13
 
0.7%
37.43951 13
 
0.7%
37.48688 13
 
0.7%
37.46929 13
 
0.7%
Other values (1322) 1863
93.2%
ValueCountFrequency (%)
36.941452 1
0.1%
36.941525 1
0.1%
36.941605 1
0.1%
36.941692 1
0.1%
36.941776 1
0.1%
36.94187 1
0.1%
36.941967 1
0.1%
36.94207 1
0.1%
36.942173 1
0.1%
36.942276 1
0.1%
ValueCountFrequency (%)
37.778934 1
0.1%
37.778854 1
0.1%
37.778793 1
0.1%
37.77873 1
0.1%
37.778675 1
0.1%
37.778606 1
0.1%
37.77855 1
0.1%
37.778515 1
0.1%
37.7785 1
0.1%
37.778496 2
0.1%

longitude
Real number (ℝ)

Distinct1025
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.82073
Minimum126.48848
Maximum127.46536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-04-22T09:31:20.119446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.48848
5-th percentile126.63768
Q1126.69009
median126.72744
Q3126.95759
95-th percentile127.12796
Maximum127.46536
Range0.97688
Interquartile range (IQR)0.26749625

Descriptive statistics

Standard deviation0.17486108
Coefficient of variation (CV)0.0013788052
Kurtosis-0.14400696
Mean126.82073
Median Absolute Deviation (MAD)0.0739175
Skewness0.77904006
Sum253641.45
Variance0.030576398
MonotonicityNot monotonic
2024-04-22T09:31:20.522426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.69009 17
 
0.9%
126.74964 14
 
0.7%
126.65602 14
 
0.7%
127.12796 14
 
0.7%
126.71236 14
 
0.7%
126.659004 14
 
0.7%
127.06097 14
 
0.7%
126.67689 14
 
0.7%
126.65866 14
 
0.7%
126.70156 14
 
0.7%
Other values (1015) 1857
92.8%
ValueCountFrequency (%)
126.48848 1
0.1%
126.488625 1
0.1%
126.48877 1
0.1%
126.48893 1
0.1%
126.48908 1
0.1%
126.48923 1
0.1%
126.48937 1
0.1%
126.4895 1
0.1%
126.48965 1
0.1%
126.48978 1
0.1%
ValueCountFrequency (%)
127.46536 1
0.1%
127.46499 1
0.1%
127.464615 1
0.1%
127.46424 1
0.1%
127.46387 1
0.1%
127.46349 1
0.1%
127.46312 1
0.1%
127.462746 1
0.1%
127.46239 1
0.1%
127.46201 1
0.1%

light_level
Real number (ℝ)

Distinct53
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.8255
Minimum1
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2024-04-22T09:31:20.653423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median10
Q317
95-th percentile33
Maximum234
Range233
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.921105
Coefficient of variation (CV)1.0792452
Kurtosis42.231743
Mean13.8255
Median Absolute Deviation (MAD)5
Skewness5.0371618
Sum27651
Variance222.63937
MonotonicityNot monotonic
2024-04-22T09:31:20.785376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 145
 
7.2%
7 141
 
7.0%
6 133
 
6.7%
10 115
 
5.8%
3 103
 
5.1%
8 100
 
5.0%
9 100
 
5.0%
4 95
 
4.8%
2 93
 
4.7%
13 84
 
4.2%
Other values (43) 891
44.5%
ValueCountFrequency (%)
1 25
 
1.2%
2 93
4.7%
3 103
5.1%
4 95
4.8%
5 145
7.2%
6 133
6.7%
7 141
7.0%
8 100
5.0%
9 100
5.0%
10 115
5.8%
ValueCountFrequency (%)
234 1
 
0.1%
122 2
 
0.1%
116 2
 
0.1%
115 2
 
0.1%
114 11
0.5%
93 5
0.2%
61 2
 
0.1%
60 12
0.6%
57 2
 
0.1%
50 1
 
0.1%

register_at
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2020-09-10 22:00
2000 

Length

Max length16
Median length16
Mean length16
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-10 22:00
2nd row2020-09-10 22:00
3rd row2020-09-10 22:00
4th row2020-09-10 22:00
5th row2020-09-10 22:00

Common Values

ValueCountFrequency (%)
2020-09-10 22:00 2000
100.0%

Length

2024-04-22T09:31:20.904192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T09:31:20.997274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-10 2000
50.0%
22:00 2000
50.0%

Interactions

2024-04-22T09:31:18.251962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.162917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.541409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.909152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.350792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.261807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.644985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.001905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.433916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.348893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.733750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.086520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.521267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.451784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:17.818633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-22T09:31:18.170097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-22T09:31:21.057684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
light_sensor_value_idlatitudelongitudelight_level
light_sensor_value_id1.0000.0000.0000.000
latitude0.0001.0000.6860.211
longitude0.0000.6861.0000.260
light_level0.0000.2110.2601.000
2024-04-22T09:31:21.154989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
light_sensor_value_idlatitudelongitudelight_level
light_sensor_value_id1.000-0.002-0.004-0.010
latitude-0.0021.0000.206-0.013
longitude-0.0040.2061.000-0.093
light_level-0.010-0.013-0.0931.000

Missing values

2024-04-22T09:31:18.625387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-22T09:31:18.726966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

light_sensor_value_idtaxi_idlatitudelongitudelight_levelregister_at
01T_9628998137.610664126.72465582020-09-10 22:00
12T_7332249337.621788127.08755132020-09-10 22:00
23T_4508146137.554714126.67306522020-09-10 22:00
34T_4493497337.52825126.67643102020-09-10 22:00
45T_4779147737.502796127.04183252020-09-10 22:00
56T_4368983137.52204126.7964232020-09-10 22:00
67T_9738863637.527405126.90563102020-09-10 22:00
78T_9270104137.462093126.6375562020-09-10 22:00
89T_1837854637.50068126.730644602020-09-10 22:00
910T_7310276337.55852126.859764182020-09-10 22:00
light_sensor_value_idtaxi_idlatitudelongitudelight_levelregister_at
19901991T_9482510837.379314126.6560242020-09-10 22:00
19911992T_9160238637.465008126.7048592020-09-10 22:00
19921993T_4742525937.490837127.055374152020-09-10 22:00
19931994T_6643758837.388325126.7661422020-09-10 22:00
19941995T_2013639437.56815126.6636922020-09-10 22:00
19951996T_4830418337.53475127.136215162020-09-10 22:00
19961997T_4705904037.511803126.88821462020-09-10 22:00
19971998T_4940283837.421562127.1589822020-09-10 22:00
19981999T_4478848637.524105126.7677462020-09-10 22:00
19992000T_4171225237.595737126.8329142020-09-10 22:00