Overview

Dataset statistics

Number of variables5
Number of observations99
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory46.3 B

Variable types

Categorical2
Numeric3

Dataset

Description샘플 데이터
Author코리아크레딧뷰로 / 장윤상
URLhttps://www.bigdata-transportation.kr/frn/prdt/detail?prdtId=PRDTNUM_000000020203

Alerts

BS_YR_MON has constant value ""Constant

Reproduction

Analysis started2023-12-11 22:38:02.853955
Analysis finished2023-12-11 22:38:03.799033
Duration0.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

BS_YR_MON
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
202112
99 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202112
2nd row202112
3rd row202112
4th row202112
5th row202112

Common Values

ValueCountFrequency (%)
202112 99
100.0%

Length

2023-12-12T07:38:03.843431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:38:03.915559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202112 99
100.0%

ADM_CD
Real number (ℝ)

Distinct95
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27973086
Minimum26170530
Maximum31710253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:38:03.998508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26170530
5-th percentile26230676
Q126530630
median27260652
Q329148184
95-th percentile31140570
Maximum31710253
Range5539723
Interquartile range (IQR)2617554.5

Descriptive statistics

Standard deviation1598070.2
Coefficient of variation (CV)0.05712885
Kurtosis-0.67881946
Mean27973086
Median Absolute Deviation (MAD)970052
Skewness0.7497151
Sum2.7693355 × 109
Variance2.5538285 × 1012
MonotonicityNot monotonic
2023-12-12T07:38:04.141695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26230640 2
 
2.0%
26230610 2
 
2.0%
31140570 2
 
2.0%
27290624 2
 
2.0%
28260740 1
 
1.0%
26320543 1
 
1.0%
26290600 1
 
1.0%
29110685 1
 
1.0%
30200548 1
 
1.0%
28185820 1
 
1.0%
Other values (85) 85
85.9%
ValueCountFrequency (%)
26170530 1
1.0%
26230610 2
2.0%
26230640 2
2.0%
26230680 1
1.0%
26230710 1
1.0%
26260560 1
1.0%
26260580 1
1.0%
26260761 1
1.0%
26290510 1
1.0%
26290600 1
1.0%
ValueCountFrequency (%)
31710253 1
1.0%
31200560 1
1.0%
31200510 1
1.0%
31170510 1
1.0%
31140570 2
2.0%
31140520 1
1.0%
31110520 1
1.0%
30230543 1
1.0%
30200600 1
1.0%
30200550 1
1.0%

GENDER
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
50 
1
49 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 50
50.5%
1 49
49.5%

Length

2023-12-12T07:38:04.251312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:38:04.317766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 50
50.5%
1 49
49.5%

AGE_CD
Real number (ℝ)

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.929293
Minimum25
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:38:04.385796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile30
Q140
median50
Q360
95-th percentile70
Maximum71
Range46
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.360448
Coefficient of variation (CV)0.24755903
Kurtosis-1.0739594
Mean49.929293
Median Absolute Deviation (MAD)10
Skewness0.045271633
Sum4943
Variance152.78066
MonotonicityNot monotonic
2023-12-12T07:38:04.487768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
40 16
16.2%
55 15
15.2%
45 14
14.1%
65 11
11.1%
60 10
10.1%
35 9
9.1%
50 7
7.1%
30 7
7.1%
70 6
 
6.1%
71 3
 
3.0%
ValueCountFrequency (%)
25 1
 
1.0%
30 7
7.1%
35 9
9.1%
40 16
16.2%
45 14
14.1%
50 7
7.1%
55 15
15.2%
60 10
10.1%
65 11
11.1%
70 6
 
6.1%
ValueCountFrequency (%)
71 3
 
3.0%
70 6
 
6.1%
65 11
11.1%
60 10
10.1%
55 15
15.2%
50 7
7.1%
45 14
14.1%
40 16
16.2%
35 9
9.1%
30 7
7.1%

POP_CNT
Real number (ℝ)

Distinct29
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.323232
Minimum4
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:38:04.586516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16
median10
Q318
95-th percentile30.3
Maximum51
Range47
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1949723
Coefficient of variation (CV)0.69014576
Kurtosis2.5562682
Mean13.323232
Median Absolute Deviation (MAD)5
Skewness1.4613272
Sum1319
Variance84.547516
MonotonicityNot monotonic
2023-12-12T07:38:04.672650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 13
13.1%
5 8
 
8.1%
8 8
 
8.1%
6 7
 
7.1%
10 7
 
7.1%
23 5
 
5.1%
17 5
 
5.1%
12 5
 
5.1%
19 5
 
5.1%
9 5
 
5.1%
Other values (19) 31
31.3%
ValueCountFrequency (%)
4 13
13.1%
5 8
8.1%
6 7
7.1%
7 2
 
2.0%
8 8
8.1%
9 5
 
5.1%
10 7
7.1%
11 3
 
3.0%
12 5
 
5.1%
13 2
 
2.0%
ValueCountFrequency (%)
51 1
1.0%
39 1
1.0%
38 1
1.0%
36 1
1.0%
33 1
1.0%
30 1
1.0%
29 2
2.0%
26 1
1.0%
25 1
1.0%
24 1
1.0%

Interactions

2023-12-12T07:38:03.514763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:02.980160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.200255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.576656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.060332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.259359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.631489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.133197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:38:03.312869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:38:04.731363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDGENDERAGE_CDPOP_CNT
ADM_CD1.0000.3180.0000.000
GENDER0.3181.0000.0000.000
AGE_CD0.0000.0001.0000.117
POP_CNT0.0000.0000.1171.000
2023-12-12T07:38:04.801358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDAGE_CDPOP_CNTGENDER
ADM_CD1.0000.0380.1750.242
AGE_CD0.0381.000-0.1910.000
POP_CNT0.175-0.1911.0000.000
GENDER0.2420.0000.0001.000

Missing values

2023-12-12T07:38:03.703825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:38:03.772340image/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

BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
02021122826074024515
12021122726058024019
2202112282005602405
32021123017051015523
4202112311105201555
52021122726059024517
62021123020054026022
72021122823758025021
82021122729062417010
92021123011074014536
BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
89202112282005222656
90202112263505522308
91202112263506101305
92202112262306101658
932021122623064016511
942021122623068014017
952021122632057216513
962021123020055025512
97202112282456111714
98202112281106202704