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_000000020206

Alerts

BS_YR_MON has constant value ""Constant

Reproduction

Analysis started2023-12-11 22:40:57.480564
Analysis finished2023-12-11 22:40:58.833594
Duration1.35 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

2023-12-12T07:40:58.890088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:40:58.970532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202112 99
100.0%

ADM_CD
Real number (ℝ)

Distinct90
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28481431
Minimum26140540
Maximum31710262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:40:59.045056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26140540
5-th percentile26287578
Q127260596
median28185780
Q329200637
95-th percentile31170554
Maximum31710262
Range5569722
Interquartile range (IQR)1940041

Descriptive statistics

Standard deviation1548903.6
Coefficient of variation (CV)0.054382929
Kurtosis-0.79851317
Mean28481431
Median Absolute Deviation (MAD)984882
Skewness0.40400552
Sum2.8196617 × 109
Variance2.3991025 × 1012
MonotonicityNot monotonic
2023-12-12T07:40:59.144344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29200637 3
 
3.0%
28177540 2
 
2.0%
31140670 2
 
2.0%
30170575 2
 
2.0%
27260570 2
 
2.0%
28110570 2
 
2.0%
26320571 2
 
2.0%
29200626 2
 
2.0%
27260550 1
 
1.0%
28710410 1
 
1.0%
Other values (80) 80
80.8%
ValueCountFrequency (%)
26140540 1
1.0%
26200530 1
1.0%
26200640 1
1.0%
26230760 1
1.0%
26260740 1
1.0%
26290560 1
1.0%
26290590 1
1.0%
26320571 2
2.0%
26380550 1
1.0%
26380602 1
1.0%
ValueCountFrequency (%)
31710262 1
1.0%
31200590 1
1.0%
31200520 1
1.0%
31200510 1
1.0%
31170590 1
1.0%
31170550 1
1.0%
31140670 2
2.0%
31140600 1
1.0%
31140550 1
1.0%
31140530 1
1.0%

GENDER
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size924.0 B
2
51 
1
48 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 51
51.5%
1 48
48.5%

Length

2023-12-12T07:40:59.242225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:40:59.309491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 51
51.5%
1 48
48.5%

AGE_CD
Real number (ℝ)

Distinct11
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.646465
Minimum25
Maximum71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:40:59.373416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile35
Q145
median55
Q365
95-th percentile71
Maximum71
Range46
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.434574
Coefficient of variation (CV)0.22754581
Kurtosis-1.1212429
Mean54.646465
Median Absolute Deviation (MAD)10
Skewness-0.16174448
Sum5410
Variance154.61864
MonotonicityNot monotonic
2023-12-12T07:40:59.453104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
71 15
15.2%
45 13
13.1%
50 13
13.1%
40 12
12.1%
60 11
11.1%
65 10
10.1%
55 9
9.1%
70 8
8.1%
35 6
 
6.1%
25 1
 
1.0%
ValueCountFrequency (%)
25 1
 
1.0%
30 1
 
1.0%
35 6
6.1%
40 12
12.1%
45 13
13.1%
50 13
13.1%
55 9
9.1%
60 11
11.1%
65 10
10.1%
70 8
8.1%
ValueCountFrequency (%)
71 15
15.2%
70 8
8.1%
65 10
10.1%
60 11
11.1%
55 9
9.1%
50 13
13.1%
45 13
13.1%
40 12
12.1%
35 6
 
6.1%
30 1
 
1.0%

POP_CNT
Real number (ℝ)

Distinct57
Distinct (%)57.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.20202
Minimum4
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1023.0 B
2023-12-12T07:40:59.567024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q113
median24
Q347
95-th percentile132.2
Maximum196
Range192
Interquartile range (IQR)34

Descriptive statistics

Standard deviation40.018613
Coefficient of variation (CV)1.0757107
Kurtosis5.4548983
Mean37.20202
Median Absolute Deviation (MAD)14
Skewness2.3132779
Sum3683
Variance1601.4894
MonotonicityNot monotonic
2023-12-12T07:40:59.709064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 6
 
6.1%
47 4
 
4.0%
18 4
 
4.0%
9 4
 
4.0%
13 4
 
4.0%
11 3
 
3.0%
14 3
 
3.0%
24 3
 
3.0%
5 3
 
3.0%
16 3
 
3.0%
Other values (47) 62
62.6%
ValueCountFrequency (%)
4 6
6.1%
5 3
3.0%
6 1
 
1.0%
7 1
 
1.0%
8 2
 
2.0%
9 4
4.0%
10 2
 
2.0%
11 3
3.0%
12 1
 
1.0%
13 4
4.0%
ValueCountFrequency (%)
196 1
1.0%
178 1
1.0%
171 1
1.0%
169 1
1.0%
152 1
1.0%
130 2
2.0%
101 1
1.0%
73 1
1.0%
71 1
1.0%
69 1
1.0%

Interactions

2023-12-12T07:40:58.450600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:57.945288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.132621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.529540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.009039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.190724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.603952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.065786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:40:58.384666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:40:59.791622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDGENDERAGE_CDPOP_CNT
ADM_CD1.0000.0000.0000.000
GENDER0.0001.0000.4440.000
AGE_CD0.0000.4441.0000.219
POP_CNT0.0000.0000.2191.000
2023-12-12T07:40:59.860194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_CDAGE_CDPOP_CNTGENDER
ADM_CD1.000-0.0580.1270.000
AGE_CD-0.0581.0000.3490.326
POP_CNT0.1270.3491.0000.000
GENDER0.0000.3260.0001.000

Missing values

2023-12-12T07:40:58.713514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:40:58.799865image/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
02021122818564014011
12021122623076024524
22021122917060114547
32021123017058824553
42021122817753026018
5202112311405502509
62021122641067014010
72021122726064027023
82021122823759213515
92021123114067014515
BS_YR_MONADM_CDGENDERAGE_CDPOP_CNT
892021122826071024544
902021122817762024516
912021122911071027173
922021122729062825065
932021123011062026533
942021122614054016016
9520211229200626155130
962021122817754017016
972021122920062617130
982021122814053025011