Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory517.6 KiB
Average record size in memory53.0 B

Variable types

Numeric4
Categorical1

Dataset

DescriptionKCB(코리아크레딧뷰로)에서 제공한 데이터의 통계 자료로 월별, 시군별(행정동코드), 연령구간(만18세이상 ~ 104세이하를 5세단위로 구분), 월소득현황(단위 : 천원)을 확인할 수 있습니다. <성별> 1 남성, 2 여성 <연령구간> 10 : 20세미만, 21 : 25세미만, 22 : 30세미만, 31 : 35세미만, 32 : 40세미만, 41 : 45세미만, 42 : 50세미만, 51 : 55세미만, 52 : 60세미만, 61 : 65세미만, 62 : 70세미만, 70 : 70세이상 본 자료는 상업적 이용금지 및 재배포할 수 없습니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2159

Alerts

월소득평균 is highly overall correlated with 성별High correlation
성별 is highly overall correlated with 월소득평균High correlation

Reproduction

Analysis started2024-01-09 21:34:36.988780
Analysis finished2024-01-09 21:34:38.807855
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201637.52
Minimum201601
Maximum201712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:34:38.851250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201601
5-th percentile201601
Q1201605
median201609
Q3201702
95-th percentile201712
Maximum201712
Range111
Interquartile range (IQR)97

Descriptive statistics

Standard deviation46.61986
Coefficient of variation (CV)0.00023120627
Kurtosis-1.293221
Mean201637.52
Median Absolute Deviation (MAD)5
Skewness0.82198677
Sum2.0163752 × 109
Variance2173.4114
MonotonicityNot monotonic
2024-01-10T06:34:38.936367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
201710 609
 
6.1%
201604 600
 
6.0%
201607 600
 
6.0%
201606 592
 
5.9%
201603 591
 
5.9%
201609 590
 
5.9%
201601 587
 
5.9%
201602 585
 
5.9%
201701 574
 
5.7%
201612 570
 
5.7%
Other values (8) 4102
41.0%
ValueCountFrequency (%)
201601 587
5.9%
201602 585
5.9%
201603 591
5.9%
201604 600
6.0%
201605 559
5.6%
201606 592
5.9%
201607 600
6.0%
201608 559
5.6%
201609 590
5.9%
201610 560
5.6%
ValueCountFrequency (%)
201712 559
5.6%
201711 564
5.6%
201710 609
6.1%
201703 254
2.5%
201702 535
5.3%
201701 574
5.7%
201612 570
5.7%
201611 512
5.1%
201610 560
5.6%
201609 590
5.9%

행정동코드
Real number (ℝ)

Distinct208
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44418236
Minimum44131250
Maximum44825360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:34:39.034957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44131250
5-th percentile44131540
Q144180360
median44230400
Q344770253
95-th percentile44810380
Maximum44825360
Range694110
Interquartile range (IQR)589893

Descriptive statistics

Standard deviation289639.74
Coefficient of variation (CV)0.0065207393
Kurtosis-1.7340672
Mean44418236
Median Absolute Deviation (MAD)96850
Skewness0.4340776
Sum4.4418236 × 1011
Variance8.3891178 × 1010
MonotonicityNot monotonic
2024-01-10T06:34:39.148004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44760390 68
 
0.7%
44800320 64
 
0.6%
44200600 64
 
0.6%
44180350 63
 
0.6%
44760320 63
 
0.6%
44230400 62
 
0.6%
44790370 62
 
0.6%
44760250 62
 
0.6%
44770310 61
 
0.6%
44200380 60
 
0.6%
Other values (198) 9371
93.7%
ValueCountFrequency (%)
44131250 45
0.4%
44131310 46
0.5%
44131320 38
0.4%
44131330 52
0.5%
44131340 48
0.5%
44131350 50
0.5%
44131360 46
0.5%
44131370 45
0.4%
44131510 39
0.4%
44131520 39
0.4%
ValueCountFrequency (%)
44825360 52
0.5%
44825350 42
0.4%
44825340 45
0.4%
44825330 59
0.6%
44825320 51
0.5%
44825310 42
0.4%
44825253 47
0.5%
44825250 38
0.4%
44810400 52
0.5%
44810390 42
0.4%

성별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5008 
2
4992 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 5008
50.1%
2 4992
49.9%

Length

2024-01-10T06:34:39.247773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:34:39.318027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5008
50.1%
2 4992
49.9%

연령구간
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.1821
Minimum10
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:34:39.379294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q122
median41
Q352
95-th percentile70
Maximum70
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.6687
Coefficient of variation (CV)0.42903834
Kurtosis-1.069583
Mean41.1821
Median Absolute Deviation (MAD)11
Skewness-0.065144469
Sum411821
Variance312.18296
MonotonicityNot monotonic
2024-01-10T06:34:39.463501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
51 878
8.8%
22 865
8.6%
21 857
8.6%
52 841
8.4%
42 841
8.4%
31 836
8.4%
41 832
8.3%
61 828
8.3%
32 827
8.3%
70 819
8.2%
Other values (2) 1576
15.8%
ValueCountFrequency (%)
10 790
7.9%
21 857
8.6%
22 865
8.6%
31 836
8.4%
32 827
8.3%
41 832
8.3%
42 841
8.4%
51 878
8.8%
52 841
8.4%
61 828
8.3%
ValueCountFrequency (%)
70 819
8.2%
62 786
7.9%
61 828
8.3%
52 841
8.4%
51 878
8.8%
42 841
8.4%
41 832
8.3%
32 827
8.3%
31 836
8.4%
22 865
8.6%

월소득평균
Real number (ℝ)

HIGH CORRELATION 

Distinct2407
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2146.9082
Minimum1020
Maximum5899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T06:34:39.566170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1020
5-th percentile1153
Q11695
median2081
Q32461.25
95-th percentile3324.05
Maximum5899
Range4879
Interquartile range (IQR)766.25

Descriptive statistics

Standard deviation655.95122
Coefficient of variation (CV)0.30553296
Kurtosis1.9827202
Mean2146.9082
Median Absolute Deviation (MAD)384
Skewness0.96533705
Sum21469082
Variance430272
MonotonicityNot monotonic
2024-01-10T06:34:39.679690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2063 22
 
0.2%
1162 20
 
0.2%
2080 19
 
0.2%
1155 19
 
0.2%
1666 18
 
0.2%
1158 18
 
0.2%
2139 17
 
0.2%
2095 17
 
0.2%
1143 17
 
0.2%
1138 17
 
0.2%
Other values (2397) 9816
98.2%
ValueCountFrequency (%)
1020 1
 
< 0.1%
1037 1
 
< 0.1%
1055 2
 
< 0.1%
1062 3
< 0.1%
1066 1
 
< 0.1%
1069 1
 
< 0.1%
1070 2
 
< 0.1%
1075 2
 
< 0.1%
1081 1
 
< 0.1%
1082 6
0.1%
ValueCountFrequency (%)
5899 1
< 0.1%
5854 1
< 0.1%
5851 1
< 0.1%
5840 1
< 0.1%
5742 1
< 0.1%
5714 1
< 0.1%
5645 1
< 0.1%
5640 1
< 0.1%
5619 1
< 0.1%
5598 1
< 0.1%

Interactions

2024-01-10T06:34:38.315886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.366157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.674385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.999509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.417388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.439658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.754683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.076481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.523364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.520563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.835766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.155437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.611906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.602542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:37.919367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:34:38.229988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:34:39.749072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월행정동코드성별연령구간월소득평균
기준연월1.0000.0180.0250.0000.013
행정동코드0.0181.0000.0000.0000.139
성별0.0250.0001.0000.0140.674
연령구간0.0000.0000.0141.0000.718
월소득평균0.0130.1390.6740.7181.000
2024-01-10T06:34:39.823165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월행정동코드연령구간월소득평균성별
기준연월1.0000.005-0.0020.0150.015
행정동코드0.0051.0000.004-0.0130.000
연령구간-0.0020.0041.0000.3690.014
월소득평균0.015-0.0130.3691.0000.525
성별0.0150.0000.0140.5251.000

Missing values

2024-01-10T06:34:38.699958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:34:38.775173image/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

기준연월행정동코드성별연령구간월소득평균
3790201601442702531101186
54098201608447602502611861
30550201604447103902522111
52353201608442103602621810
9753201610442303502422426
23675201602441315901612848
16030201612447903601211467
64601201710447603202322040
31144201604448253101412494
16253201612442103701211464
기준연월행정동코드성별연령구간월소득평균
15548201612441332531101152
36227201605442104002311962
61759201701448103801412909
24396201602441505902612092
42109201606441315801513803
85670201703447903201423185
27617201603442303902101101
72773201711441803102101132
32373201604442303502611771
23403201602448003101423767