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_000000020202

Alerts

BS_YR_MON has constant value ""Constant

Reproduction

Analysis started2023-12-11 22:43:50.216699
Analysis finished2023-12-11 22:43:51.299288
Duration1.08 second
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

2023-12-12T07:43:51.348391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:43:51.424258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202112 99
100.0%

ADM_CD
Real number (ℝ)

Distinct93
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27187494
Minimum11170555
Maximum41830370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:51.506932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11170555
5-th percentile11200569
Q111440630
median28260575
Q341286570
95-th percentile41632510
Maximum41830370
Range30659815
Interquartile range (IQR)29845940

Descriptive statistics

Standard deviation14188981
Coefficient of variation (CV)0.52189366
Kurtosis-1.896992
Mean27187494
Median Absolute Deviation (MAD)13349935
Skewness-0.12556854
Sum2.6915619 × 109
Variance2.0132717 × 1014
MonotonicityNot monotonic
2023-12-12T07:43:51.618783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11470520 3
 
3.0%
11440630 3
 
3.0%
11170640 2
 
2.0%
11470580 2
 
2.0%
41220610 1
 
1.0%
11200570 1
 
1.0%
11350570 1
 
1.0%
41463535 1
 
1.0%
41117540 1
 
1.0%
11545670 1
 
1.0%
Other values (83) 83
83.8%
ValueCountFrequency (%)
11170555 1
1.0%
11170640 2
2.0%
11200535 1
1.0%
11200560 1
1.0%
11200570 1
1.0%
11215830 1
1.0%
11230536 1
1.0%
11260520 1
1.0%
11260540 1
1.0%
11260610 1
1.0%
ValueCountFrequency (%)
41830370 1
1.0%
41820325 1
1.0%
41670340 1
1.0%
41650360 1
1.0%
41650330 1
1.0%
41630530 1
1.0%
41610560 1
1.0%
41610510 1
1.0%
41610259 1
1.0%
41570560 1
1.0%

GENDER
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
55 
1
44 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 55
55.6%
1 44
44.4%

Length

2023-12-12T07:43:51.954165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:43:52.065323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 55
55.6%
1 44
44.4%

AGE_CD
Real number (ℝ)

Distinct9
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.191919
Minimum35
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:52.136909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile35
Q145
median55
Q370
95-th percentile71
Maximum71
Range36
Interquartile range (IQR)25

Descriptive statistics

Standard deviation12.031995
Coefficient of variation (CV)0.21412323
Kurtosis-1.1359921
Mean56.191919
Median Absolute Deviation (MAD)10
Skewness-0.32226608
Sum5563
Variance144.76891
MonotonicityNot monotonic
2023-12-12T07:43:52.232849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
55 14
14.1%
70 13
13.1%
71 13
13.1%
45 11
11.1%
60 11
11.1%
65 11
11.1%
50 10
10.1%
35 10
10.1%
40 6
6.1%
ValueCountFrequency (%)
35 10
10.1%
40 6
6.1%
45 11
11.1%
50 10
10.1%
55 14
14.1%
60 11
11.1%
65 11
11.1%
70 13
13.1%
71 13
13.1%
ValueCountFrequency (%)
71 13
13.1%
70 13
13.1%
65 11
11.1%
60 11
11.1%
55 14
14.1%
50 10
10.1%
45 11
11.1%
40 6
6.1%
35 10
10.1%

POP_CNT
Real number (ℝ)

Distinct50
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.393939
Minimum5
Maximum111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:43:52.339521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q112.5
median27
Q342.5
95-th percentile71.3
Maximum111
Range106
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.951954
Coefficient of variation (CV)0.73109507
Kurtosis1.2523797
Mean31.393939
Median Absolute Deviation (MAD)15
Skewness1.0639629
Sum3108
Variance526.79221
MonotonicityNot monotonic
2023-12-12T07:43:52.457001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 8
 
8.1%
41 5
 
5.1%
14 5
 
5.1%
39 4
 
4.0%
7 4
 
4.0%
16 4
 
4.0%
40 4
 
4.0%
9 4
 
4.0%
11 3
 
3.0%
37 3
 
3.0%
Other values (40) 55
55.6%
ValueCountFrequency (%)
5 8
8.1%
6 2
 
2.0%
7 4
4.0%
8 2
 
2.0%
9 4
4.0%
10 1
 
1.0%
11 3
 
3.0%
12 1
 
1.0%
13 2
 
2.0%
14 5
5.1%
ValueCountFrequency (%)
111 1
1.0%
102 1
1.0%
96 1
1.0%
79 1
1.0%
74 1
1.0%
71 1
1.0%
69 1
1.0%
65 1
1.0%
64 2
2.0%
58 2
2.0%

Interactions

2023-12-12T07:43:50.930861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.314105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.712635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:51.032268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.561935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.791401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:51.108258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.644272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:43:50.864115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:43:52.535311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDGENDERAGE_CDPOP_CNT
ADM_CD1.0000.0000.0000.202
GENDER0.0001.0000.3650.181
AGE_CD0.0000.3651.0000.466
POP_CNT0.2020.1810.4661.000
2023-12-12T07:43:52.627414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDAGE_CDPOP_CNTGENDER
ADM_CD1.000-0.114-0.1600.032
AGE_CD-0.1141.0000.2860.264
POP_CNT-0.1600.2861.0000.171
GENDER0.0320.2640.1711.000

Missing values

2023-12-12T07:43:51.195570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:43:51.269514image/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
02021121135062525569
12021121117064015018
2202112114105852359
32021121123053625043
42021124148025025564
52021121132067015018
6202112411335302455
7202112416102592407
82021124145053025058
92021122826057513514
BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
89202112416703402505
902021121126052014514
91202112281105302559
922021122823764617038
932021121132068115541
942021124128553017039
952021121141064025526
962021124161056024027
972021121135059527050
982021124111354026040