Overview

Dataset statistics

Number of variables8
Number of observations2000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory136.8 KiB
Average record size in memory70.1 B

Variable types

Numeric6
Text1
DateTime1

Dataset

Description샘플 데이터
Author(주)모토브 / 신재훈
URLhttps://www.bigdata-transportation.kr/frn/prdt/detail?prdtId=PRDTNUM_000000020256

Alerts

register_at has constant value ""Constant
accel_sensor_value_id has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:34:51.705123
Analysis finished2023-12-11 22:34:56.284471
Duration4.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

accel_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
2023-12-12T07:34:56.340939image/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
2023-12-12T07:34:56.445170image/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

Distinct156
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2023-12-12T07:34:56.675967image/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_48230939 14
 
0.7%
t_44056049 13
 
0.7%
t_96875931 13
 
0.7%
t_17939084 13
 
0.7%
t_48743645 13
 
0.7%
t_98047829 13
 
0.7%
t_93872940 13
 
0.7%
t_98267560 13
 
0.7%
t_48816888 13
 
0.7%
t_17133403 13
 
0.7%
Other values (146) 1869
93.5%
2023-12-12T07:34:56.996251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 2133
10.7%
T 2000
10.0%
_ 2000
10.0%
9 1791
9.0%
8 1731
8.7%
7 1687
8.4%
3 1647
8.2%
6 1578
7.9%
2 1503
7.5%
1 1457
7.3%
Other values (2) 2473
12.4%

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 2133
13.3%
9 1791
11.2%
8 1731
10.8%
7 1687
10.5%
3 1647
10.3%
6 1578
9.9%
2 1503
9.4%
1 1457
9.1%
5 1243
7.8%
0 1230
7.7%
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 2133
11.8%
_ 2000
11.1%
9 1791
10.0%
8 1731
9.6%
7 1687
9.4%
3 1647
9.2%
6 1578
8.8%
2 1503
8.3%
1 1457
8.1%
5 1243
6.9%
Latin
ValueCountFrequency (%)
T 2000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 2133
10.7%
T 2000
10.0%
_ 2000
10.0%
9 1791
9.0%
8 1731
8.7%
7 1687
8.4%
3 1647
8.2%
6 1578
7.9%
2 1503
7.5%
1 1457
7.3%
Other values (2) 2473
12.4%

latitude
Real number (ℝ)

Distinct1311
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.496865
Minimum36.941452
Maximum37.778934
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T07:34:57.123696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.941452
5-th percentile37.407019
Q137.461149
median37.495274
Q337.529408
95-th percentile37.602837
Maximum37.778934
Range0.837482
Interquartile range (IQR)0.068259

Descriptive statistics

Standard deviation0.075858641
Coefficient of variation (CV)0.0020230662
Kurtosis18.023049
Mean37.496865
Median Absolute Deviation (MAD)0.034134
Skewness-2.1109858
Sum74993.73
Variance0.0057545334
MonotonicityNot monotonic
2023-12-12T07:34:57.228042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.479614 14
 
0.7%
37.41697 13
 
0.7%
37.443947 13
 
0.7%
37.46929 13
 
0.7%
37.60681 13
 
0.7%
37.473686 13
 
0.7%
37.523636 13
 
0.7%
37.48688 13
 
0.7%
37.5649 13
 
0.7%
37.43951 13
 
0.7%
Other values (1301) 1869
93.5%
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)

Distinct1008
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.82134
Minimum126.48862
Maximum127.46536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T07:34:57.336437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.48862
5-th percentile126.63768
Q1126.69009
median126.72744
Q3126.97359
95-th percentile127.12796
Maximum127.46536
Range0.976735
Interquartile range (IQR)0.2835

Descriptive statistics

Standard deviation0.17404787
Coefficient of variation (CV)0.0013723863
Kurtosis-0.1438309
Mean126.82134
Median Absolute Deviation (MAD)0.072755
Skewness0.77860072
Sum253642.68
Variance0.030292662
MonotonicityNot monotonic
2023-12-12T07:34:57.437880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.69009 17
 
0.9%
127.12796 14
 
0.7%
126.67689 14
 
0.7%
126.63667 13
 
0.7%
126.70612 13
 
0.7%
126.659004 13
 
0.7%
126.97359 13
 
0.7%
126.6923 13
 
0.7%
126.71236 13
 
0.7%
126.70156 13
 
0.7%
Other values (998) 1864
93.2%
ValueCountFrequency (%)
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%
126.48992 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%

accel_x
Real number (ℝ)

Distinct723
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41457.549
Minimum0
Maximum65534
Zeros3
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T07:34:57.536298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.9
Q1284
median65013
Q365346
95-th percentile65502
Maximum65534
Range65534
Interquartile range (IQR)65062

Descriptive statistics

Standard deviation31264.564
Coefficient of variation (CV)0.7541344
Kurtosis-1.6888169
Mean41457.549
Median Absolute Deviation (MAD)456
Skewness-0.5588776
Sum82915098
Variance9.7747295 × 108
MonotonicityNot monotonic
2023-12-12T07:34:57.640647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65332 13
 
0.7%
65456 11
 
0.5%
65384 11
 
0.5%
200 11
 
0.5%
65488 10
 
0.5%
65316 10
 
0.5%
82 10
 
0.5%
65510 10
 
0.5%
65364 9
 
0.4%
65416 9
 
0.4%
Other values (713) 1896
94.8%
ValueCountFrequency (%)
0 3
 
0.1%
2 5
0.2%
4 3
 
0.1%
6 4
0.2%
8 7
0.4%
10 4
0.2%
12 7
0.4%
14 6
0.3%
16 9
0.4%
18 5
0.2%
ValueCountFrequency (%)
65534 9
0.4%
65532 6
0.3%
65530 5
0.2%
65528 4
0.2%
65526 7
0.4%
65524 7
0.4%
65522 4
0.2%
65520 5
0.2%
65518 2
 
0.1%
65516 6
0.3%

accel_y
Real number (ℝ)

Distinct914
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57260.166
Minimum0
Maximum65534
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T07:34:57.742762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile350
Q164348
median64757
Q365028
95-th percentile65350.1
Maximum65534
Range65534
Interquartile range (IQR)680

Descriptive statistics

Standard deviation20620.378
Coefficient of variation (CV)0.36011733
Kurtosis3.7235832
Mean57260.166
Median Absolute Deviation (MAD)312
Skewness-2.3905999
Sum1.1452033 × 108
Variance4.2519998 × 108
MonotonicityNot monotonic
2023-12-12T07:34:58.032123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65012 10
 
0.5%
64544 9
 
0.4%
64848 9
 
0.4%
65094 9
 
0.4%
64834 8
 
0.4%
64984 8
 
0.4%
64516 8
 
0.4%
64606 8
 
0.4%
64936 8
 
0.4%
64792 7
 
0.4%
Other values (904) 1916
95.8%
ValueCountFrequency (%)
0 1
 
0.1%
2 1
 
0.1%
4 2
0.1%
6 3
0.1%
8 2
0.1%
10 1
 
0.1%
12 2
0.1%
16 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
ValueCountFrequency (%)
65534 1
0.1%
65528 1
0.1%
65518 1
0.1%
65516 1
0.1%
65514 1
0.1%
65512 1
0.1%
65508 1
0.1%
65506 2
0.1%
65504 2
0.1%
65500 2
0.1%

accel_z
Real number (ℝ)

Distinct752
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8246.941
Minimum5070
Maximum12044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.7 KiB
2023-12-12T07:34:58.152005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5070
5-th percentile7543.8
Q18022
median8237
Q38450
95-th percentile8968.1
Maximum12044
Range6974
Interquartile range (IQR)428

Descriptive statistics

Standard deviation503.73011
Coefficient of variation (CV)0.061080843
Kurtosis10.546437
Mean8246.941
Median Absolute Deviation (MAD)213
Skewness0.91966934
Sum16493882
Variance253744.02
MonotonicityNot monotonic
2023-12-12T07:34:58.255593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8128 12
 
0.6%
8358 10
 
0.5%
8166 10
 
0.5%
8152 9
 
0.4%
8420 9
 
0.4%
8146 9
 
0.4%
8240 9
 
0.4%
8326 9
 
0.4%
8264 9
 
0.4%
8246 9
 
0.4%
Other values (742) 1905
95.2%
ValueCountFrequency (%)
5070 1
0.1%
5296 1
0.1%
6020 1
0.1%
6032 1
0.1%
6060 1
0.1%
6094 1
0.1%
6118 1
0.1%
6252 1
0.1%
6424 1
0.1%
6448 1
0.1%
ValueCountFrequency (%)
12044 1
0.1%
12024 1
0.1%
11566 1
0.1%
11542 1
0.1%
11464 1
0.1%
11152 1
0.1%
11006 1
0.1%
10992 1
0.1%
10750 1
0.1%
10566 1
0.1%

register_at
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Minimum2020-09-10 22:00:00
Maximum2020-09-10 22:00:00
2023-12-12T07:34:58.347033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:58.413596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T07:34:55.554770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.235852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.150312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.937307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.465928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.004253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.640339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.315874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.334259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.034873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.552595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.111984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.716729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.390973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.619154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.117225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.654802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.204741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.804022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.506912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.714579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.188243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.735695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.300769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.898827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.721438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.789395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.261763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.814961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.398152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:56.002523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:52.963367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:53.862453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.386271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:54.907786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:34:55.475611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:34:58.464387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
accel_sensor_value_idlatitudelongitudeaccel_xaccel_yaccel_z
accel_sensor_value_id1.0000.0000.0000.0000.0140.000
latitude0.0001.0000.6870.1220.1780.126
longitude0.0000.6871.0000.2350.0860.143
accel_x0.0000.1220.2351.0000.0000.066
accel_y0.0140.1780.0860.0001.0000.196
accel_z0.0000.1260.1430.0660.1961.000
2023-12-12T07:34:58.542262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
accel_sensor_value_idlatitudelongitudeaccel_xaccel_yaccel_z
accel_sensor_value_id1.000-0.0000.0110.026-0.0190.031
latitude-0.0001.0000.196-0.092-0.0690.010
longitude0.0110.1961.000-0.106-0.0350.088
accel_x0.026-0.092-0.1061.0000.109-0.044
accel_y-0.019-0.069-0.0350.1091.0000.009
accel_z0.0310.0100.088-0.0440.0091.000

Missing values

2023-12-12T07:34:56.115267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:34:56.244041image/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

accel_sensor_value_idtaxi_idlatitudelongitudeaccel_xaccel_yaccel_zregister_at
01T_9628998137.610664126.72465506479481582020-09-10 22:00
12T_7332249337.621788127.08755655266454682582020-09-10 22:00
23T_4508146137.554714126.6730656544222465522020-09-10 22:00
34T_4493497337.52825126.676431466505473362020-09-10 22:00
45T_4779147737.502796127.04183506488483602020-09-10 22:00
56T_4368983137.52204126.796426445426873762020-09-10 22:00
67T_9738863637.527405126.90563652366480284122020-09-10 22:00
78T_9270104137.462093126.6375565214283002020-09-10 22:00
89T_1837854637.50068126.730644653226480082782020-09-10 22:00
910T_7310276337.55852126.859764654746456481422020-09-10 22:00
accel_sensor_value_idtaxi_idlatitudelongitudeaccel_xaccel_yaccel_zregister_at
19901991T_6907436037.458294126.68937654066446280822020-09-10 22:00
19911992T_4229820137.47085126.63768653806483680822020-09-10 22:00
19921993T_4808445237.53413126.90669653766513687642020-09-10 22:00
19931994T_7083220837.443947126.7028651326482683902020-09-10 22:00
19941995T_9921972837.58152126.8875654486454281042020-09-10 22:00
19951996T_9826756037.516994126.94112826464478262020-09-10 22:00
19961997T_4881688837.48363127.01179655166420282322020-09-10 22:00
19971998T_6885462937.450733126.6963351206446286182020-09-10 22:00
19981999T_9387294037.46588126.68919546458483662020-09-10 22:00
19992000T_7449439237.569748126.81935346492682362020-09-10 22:00