Overview

Dataset statistics

Number of variables10
Number of observations119
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory90.1 B

Variable types

Numeric9
Categorical1

Dataset

DescriptionSample
Author고려대학교 세종산학협력단
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=KRUINCOME00000000001

Alerts

STDYY is highly overall correlated with ORIN_INCOME and 1 other fieldsHigh correlation
ATPT_CODE is highly overall correlated with ATPT_NMHigh correlation
HSHLD_DISTRB is highly overall correlated with ATPT_NMHigh correlation
MBHS_CO is highly overall correlated with HSHLDR_AGE and 1 other fieldsHigh correlation
HSHLDR_AGE is highly overall correlated with MBHS_CO and 1 other fieldsHigh correlation
ORIN_INCOME is highly overall correlated with STDYY and 3 other fieldsHigh correlation
DEBT is highly overall correlated with STDYY and 3 other fieldsHigh correlation
ASSETS is highly overall correlated with ORIN_INCOME and 2 other fieldsHigh correlation
PURE_ASSETS_AMOUNT is highly overall correlated with ORIN_INCOME and 3 other fieldsHigh correlation
ATPT_NM is highly overall correlated with ATPT_CODE and 4 other fieldsHigh correlation
HSHLD_DISTRB has 6 (5.0%) zerosZeros
MBHS_CO has 6 (5.0%) zerosZeros
HSHLDR_AGE has 6 (5.0%) zerosZeros
ORIN_INCOME has 7 (5.9%) zerosZeros
DEBT has 6 (5.0%) zerosZeros
ASSETS has 6 (5.0%) zerosZeros
PURE_ASSETS_AMOUNT has 6 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 06:31:54.881528
Analysis finished2023-12-10 06:32:10.499450
Duration15.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDYY
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015
Minimum2012
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:10.588677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12013
median2015
Q32017
95-th percentile2018
Maximum2018
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0084567
Coefficient of variation (CV)0.0009967527
Kurtosis-1.2519894
Mean2015
Median Absolute Deviation (MAD)2
Skewness0
Sum239785
Variance4.0338983
MonotonicityIncreasing
2023-12-10T15:32:10.792765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2012 17
14.3%
2013 17
14.3%
2014 17
14.3%
2015 17
14.3%
2016 17
14.3%
2017 17
14.3%
2018 17
14.3%
ValueCountFrequency (%)
2012 17
14.3%
2013 17
14.3%
2014 17
14.3%
2015 17
14.3%
2016 17
14.3%
2017 17
14.3%
2018 17
14.3%
ValueCountFrequency (%)
2018 17
14.3%
2017 17
14.3%
2016 17
14.3%
2015 17
14.3%
2014 17
14.3%
2013 17
14.3%
2012 17
14.3%

ATPT_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.705882
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:11.010089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q129
median41
Q345
95-th percentile50
Maximum50
Range39
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.297567
Coefficient of variation (CV)0.28054268
Kurtosis-0.038891863
Mean36.705882
Median Absolute Deviation (MAD)7
Skewness-0.75220781
Sum4368
Variance106.03988
MonotonicityNot monotonic
2023-12-10T15:32:11.216184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
11 7
 
5.9%
26 7
 
5.9%
50 7
 
5.9%
48 7
 
5.9%
47 7
 
5.9%
46 7
 
5.9%
45 7
 
5.9%
44 7
 
5.9%
43 7
 
5.9%
42 7
 
5.9%
Other values (7) 49
41.2%
ValueCountFrequency (%)
11 7
5.9%
26 7
5.9%
27 7
5.9%
28 7
5.9%
29 7
5.9%
30 7
5.9%
31 7
5.9%
36 7
5.9%
41 7
5.9%
42 7
5.9%
ValueCountFrequency (%)
50 7
5.9%
48 7
5.9%
47 7
5.9%
46 7
5.9%
45 7
5.9%
44 7
5.9%
43 7
5.9%
42 7
5.9%
41 7
5.9%
36 7
5.9%

ATPT_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
서울
 
7
부산
 
7
대구
 
7
인천
 
7
광주
 
7
Other values (12)
84 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주

Common Values

ValueCountFrequency (%)
서울 7
 
5.9%
부산 7
 
5.9%
대구 7
 
5.9%
인천 7
 
5.9%
광주 7
 
5.9%
대전 7
 
5.9%
울산 7
 
5.9%
세종 7
 
5.9%
경기 7
 
5.9%
강원 7
 
5.9%
Other values (7) 49
41.2%

Length

2023-12-10T15:32:11.425416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 7
 
5.9%
강원 7
 
5.9%
경남 7
 
5.9%
경북 7
 
5.9%
전남 7
 
5.9%
전북 7
 
5.9%
충남 7
 
5.9%
충북 7
 
5.9%
경기 7
 
5.9%
부산 7
 
5.9%
Other values (7) 49
41.2%

HSHLD_DISTRB
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8789916
Minimum0
Maximum23.4
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:11.696217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.54
Q13.05
median3.8
Q35.8
95-th percentile22.42
Maximum23.4
Range23.4
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation5.9111052
Coefficient of variation (CV)1.0054624
Kurtosis3.0841251
Mean5.8789916
Median Absolute Deviation (MAD)1.6
Skewness2.044632
Sum699.6
Variance34.941165
MonotonicityNot monotonic
2023-12-10T15:32:11.967108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3.2 10
 
8.4%
6.6 7
 
5.9%
3.8 7
 
5.9%
3.0 7
 
5.9%
0.0 6
 
5.0%
2.2 6
 
5.0%
1.1 5
 
4.2%
3.7 5
 
4.2%
3.1 5
 
4.2%
5.4 5
 
4.2%
Other values (32) 56
47.1%
ValueCountFrequency (%)
0.0 6
5.0%
0.6 1
 
0.8%
1.1 5
4.2%
1.2 2
 
1.7%
2.1 1
 
0.8%
2.2 6
5.0%
2.9 2
 
1.7%
3.0 7
5.9%
3.1 5
4.2%
3.2 10
8.4%
ValueCountFrequency (%)
23.4 1
0.8%
23.2 1
0.8%
23.0 1
0.8%
22.9 1
0.8%
22.8 1
0.8%
22.6 1
0.8%
22.4 1
0.8%
19.8 1
0.8%
19.6 1
0.8%
19.5 2
1.7%

MBHS_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7151261
Minimum0
Maximum3.1
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:12.146096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.34
Q12.8
median2.9
Q32.9
95-th percentile3.1
Maximum3.1
Range3.1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.64079382
Coefficient of variation (CV)0.23600887
Kurtosis14.184191
Mean2.7151261
Median Absolute Deviation (MAD)0.1
Skewness-3.8975129
Sum323.1
Variance0.41061672
MonotonicityNot monotonic
2023-12-10T15:32:12.342510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2.9 32
26.9%
2.8 29
24.4%
3.0 22
18.5%
2.7 16
13.4%
3.1 7
 
5.9%
2.6 7
 
5.9%
0.0 6
 
5.0%
ValueCountFrequency (%)
0.0 6
 
5.0%
2.6 7
 
5.9%
2.7 16
13.4%
2.8 29
24.4%
2.9 32
26.9%
3.0 22
18.5%
3.1 7
 
5.9%
ValueCountFrequency (%)
3.1 7
 
5.9%
3.0 22
18.5%
2.9 32
26.9%
2.8 29
24.4%
2.7 16
13.4%
2.6 7
 
5.9%
0.0 6
 
5.0%

HSHLDR_AGE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.884034
Minimum0
Maximum58
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:12.577760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44.46
Q151.3
median53.3
Q355.2
95-th percentile57.2
Maximum58
Range58
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation11.990724
Coefficient of variation (CV)0.23564806
Kurtosis14.288812
Mean50.884034
Median Absolute Deviation (MAD)2
Skewness-3.916781
Sum6055.2
Variance143.77745
MonotonicityNot monotonic
2023-12-10T15:32:12.803499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
5.0%
52.9 4
 
3.4%
52.7 4
 
3.4%
54.1 4
 
3.4%
50.0 3
 
2.5%
53.7 3
 
2.5%
56.5 3
 
2.5%
51.2 3
 
2.5%
55.1 3
 
2.5%
55.0 3
 
2.5%
Other values (59) 83
69.7%
ValueCountFrequency (%)
0.0 6
5.0%
49.4 2
 
1.7%
49.5 1
 
0.8%
49.6 2
 
1.7%
49.7 1
 
0.8%
49.8 1
 
0.8%
50.0 3
2.5%
50.1 1
 
0.8%
50.2 1
 
0.8%
50.3 1
 
0.8%
ValueCountFrequency (%)
58.0 1
0.8%
57.9 1
0.8%
57.8 1
0.8%
57.6 1
0.8%
57.5 1
0.8%
57.2 2
1.7%
57.1 2
1.7%
57.0 1
0.8%
56.8 1
0.8%
56.7 1
0.8%

ORIN_INCOME
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct109
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4386.7479
Minimum0
Maximum6580
Zeros7
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:13.222119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14073
median4629
Q35059.5
95-th percentile5977.8
Maximum6580
Range6580
Interquartile range (IQR)986.5

Descriptive statistics

Standard deviation1283.27
Coefficient of variation (CV)0.29253333
Kurtosis5.8458623
Mean4386.7479
Median Absolute Deviation (MAD)490
Skewness-2.1433423
Sum522023
Variance1646781.8
MonotonicityNot monotonic
2023-12-10T15:32:13.529545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
5.9%
4777 2
 
1.7%
3791 2
 
1.7%
3896 2
 
1.7%
4378 2
 
1.7%
4633 1
 
0.8%
4117 1
 
0.8%
4181 1
 
0.8%
4517 1
 
0.8%
4855 1
 
0.8%
Other values (99) 99
83.2%
ValueCountFrequency (%)
0 7
5.9%
3404 1
 
0.8%
3405 1
 
0.8%
3571 1
 
0.8%
3598 1
 
0.8%
3617 1
 
0.8%
3683 1
 
0.8%
3791 2
 
1.7%
3798 1
 
0.8%
3804 1
 
0.8%
ValueCountFrequency (%)
6580 1
0.8%
6493 1
0.8%
6341 1
0.8%
6319 1
0.8%
6172 1
0.8%
6039 1
0.8%
5971 1
0.8%
5913 1
0.8%
5638 1
0.8%
5535 1
0.8%

DEBT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29864.731
Minimum0
Maximum60220
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:13.852903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16239.6
Q126163.5
median28713
Q333196.5
95-th percentile47223.1
Maximum60220
Range60220
Interquartile range (IQR)7033

Descriptive statistics

Standard deviation10376.66
Coefficient of variation (CV)0.34745531
Kurtosis2.5439088
Mean29864.731
Median Absolute Deviation (MAD)3460
Skewness-0.41684321
Sum3553903
Variance1.0767506 × 108
MonotonicityNot monotonic
2023-12-10T15:32:14.161012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.0%
47114 1
 
0.8%
39735 1
 
0.8%
30464 1
 
0.8%
39500 1
 
0.8%
31482 1
 
0.8%
54431 1
 
0.8%
35011 1
 
0.8%
32118 1
 
0.8%
30746 1
 
0.8%
Other values (104) 104
87.4%
ValueCountFrequency (%)
0 6
5.0%
18044 1
 
0.8%
20342 1
 
0.8%
20735 1
 
0.8%
21793 1
 
0.8%
21814 1
 
0.8%
21859 1
 
0.8%
22277 1
 
0.8%
22390 1
 
0.8%
23331 1
 
0.8%
ValueCountFrequency (%)
60220 1
0.8%
54431 1
0.8%
53879 1
0.8%
50681 1
0.8%
48687 1
0.8%
48205 1
0.8%
47114 1
0.8%
46785 1
0.8%
45578 1
0.8%
45567 1
0.8%

ASSETS
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct112
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5002.3025
Minimum0
Maximum9800
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:14.451838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1850.4
Q13965
median4645
Q36049
95-th percentile9067.7
Maximum9800
Range9800
Interquartile range (IQR)2084

Descriptive statistics

Standard deviation2072.535
Coefficient of variation (CV)0.4143162
Kurtosis0.9063441
Mean5002.3025
Median Absolute Deviation (MAD)884
Skewness0.15629925
Sum595274
Variance4295401.3
MonotonicityNot monotonic
2023-12-10T15:32:14.716143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.0%
4164 2
 
1.7%
3761 2
 
1.7%
8263 1
 
0.8%
4827 1
 
0.8%
4312 1
 
0.8%
6333 1
 
0.8%
6424 1
 
0.8%
5181 1
 
0.8%
9690 1
 
0.8%
Other values (102) 102
85.7%
ValueCountFrequency (%)
0 6
5.0%
2056 1
 
0.8%
2488 1
 
0.8%
2903 1
 
0.8%
2981 1
 
0.8%
3106 1
 
0.8%
3125 1
 
0.8%
3258 1
 
0.8%
3376 1
 
0.8%
3529 1
 
0.8%
ValueCountFrequency (%)
9800 1
0.8%
9780 1
0.8%
9756 1
0.8%
9736 1
0.8%
9690 1
0.8%
9425 1
0.8%
9028 1
0.8%
8924 1
0.8%
8596 1
0.8%
8263 1
0.8%

PURE_ASSETS_AMOUNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24862.445
Minimum0
Maximum50420
Zeros6
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-10T15:32:14.967508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14389.2
Q121692
median24355
Q327403
95-th percentile38892.1
Maximum50420
Range50420
Interquartile range (IQR)5711

Descriptive statistics

Standard deviation8470.9026
Coefficient of variation (CV)0.34071076
Kurtosis2.8505355
Mean24862.445
Median Absolute Deviation (MAD)2793
Skewness-0.4972162
Sum2958631
Variance71756191
MonotonicityNot monotonic
2023-12-10T15:32:15.219299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
5.0%
38851 1
 
0.8%
31527 1
 
0.8%
24131 1
 
0.8%
33076 1
 
0.8%
26301 1
 
0.8%
44741 1
 
0.8%
29665 1
 
0.8%
26771 1
 
0.8%
26101 1
 
0.8%
Other values (104) 104
87.4%
ValueCountFrequency (%)
0 6
5.0%
15988 1
 
0.8%
17754 1
 
0.8%
17854 1
 
0.8%
18285 1
 
0.8%
18687 1
 
0.8%
18691 1
 
0.8%
18734 1
 
0.8%
18782 1
 
0.8%
19452 1
 
0.8%
ValueCountFrequency (%)
50420 1
0.8%
44741 1
0.8%
44098 1
0.8%
41796 1
0.8%
40944 1
0.8%
39262 1
0.8%
38851 1
0.8%
37861 1
0.8%
37126 1
0.8%
36982 1
0.8%

Interactions

2023-12-10T15:32:08.228536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:55.360177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.916561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.292268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:59.623878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.095369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.547311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.286988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.748839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:08.389053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:55.511488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.075847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.425793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:00.141240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.306283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.773033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.460674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.894666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:08.625343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:55.756706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.228693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.567911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:00.320156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.524770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.980230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.643761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.089536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:08.801577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:55.915041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.377456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.690775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:00.605187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.682781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.124887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.768471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.245340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:08.962422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.055861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.551048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.817697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:00.922159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.830021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.331865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.931780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.411168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:09.098929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.224372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.693086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.949610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:01.228828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:02.963485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.546385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.081168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.561543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:09.234482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.388928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.836167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:59.083947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:01.620234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.108229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.732122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.221989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.750590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:09.369425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.535518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:57.990179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:59.313312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:01.821059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.241933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:04.917408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.398201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:07.936302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:09.518014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:56.735710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:58.151051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:31:59.476526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:01.957921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:03.385641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:05.080294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:06.612132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:32:08.102073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:32:15.406557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDYYATPT_CODEATPT_NMHSHLD_DISTRBMBHS_COHSHLDR_AGEORIN_INCOMEDEBTASSETSPURE_ASSETS_AMOUNT
STDYY1.0000.0000.0000.0000.4290.4000.5730.3430.3020.241
ATPT_CODE0.0001.0001.0000.8840.7870.8300.7340.7360.7260.883
ATPT_NM0.0001.0001.0000.9740.8980.9070.7770.8120.8080.838
HSHLD_DISTRB0.0000.8840.9741.0000.6810.7620.6710.7590.7470.720
MBHS_CO0.4290.7870.8980.6811.0000.9530.9160.9430.9400.791
HSHLDR_AGE0.4000.8300.9070.7620.9531.0000.9180.9480.9550.810
ORIN_INCOME0.5730.7340.7770.6710.9160.9181.0000.8160.7940.747
DEBT0.3430.7360.8120.7590.9430.9480.8161.0000.9280.957
ASSETS0.3020.7260.8080.7470.9400.9550.7940.9281.0000.793
PURE_ASSETS_AMOUNT0.2410.8830.8380.7200.7910.8100.7470.9570.7931.000
2023-12-10T15:32:15.635291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDYYATPT_CODEHSHLD_DISTRBMBHS_COHSHLDR_AGEORIN_INCOMEDEBTASSETSPURE_ASSETS_AMOUNTATPT_NM
STDYY1.0000.000-0.020-0.2260.4280.6070.5170.4760.4930.000
ATPT_CODE0.0001.000-0.192-0.2310.494-0.260-0.245-0.339-0.2070.959
HSHLD_DISTRB-0.020-0.1921.0000.0490.0820.2340.2970.3660.2740.858
MBHS_CO-0.226-0.2310.0491.000-0.5590.2830.3020.3670.2860.735
HSHLDR_AGE0.4280.4940.082-0.5591.0000.018-0.046-0.147-0.0150.751
ORIN_INCOME0.607-0.2600.2340.2830.0181.0000.8160.7190.8030.482
DEBT0.517-0.2450.2970.302-0.0460.8161.0000.8920.9880.475
ASSETS0.476-0.3390.3660.367-0.1470.7190.8921.0000.8240.470
PURE_ASSETS_AMOUNT0.493-0.2070.2740.286-0.0150.8030.9880.8241.0000.527
ATPT_NM0.0000.9590.8580.7350.7510.4820.4750.4700.5271.000

Missing values

2023-12-10T15:32:09.713536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:32:10.381859image/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

STDYYATPT_CODEATPT_NMHSHLD_DISTRBMBHS_COHSHLDR_AGEORIN_INCOMEDEBTASSETSPURE_ASSETS_AMOUNT
0201211서울19.82.850.0485547114826338851
1201226부산7.12.852.6379824668375020918
2201227대구5.02.951.8384527099392723173
3201228인천5.43.049.6389626384529821086
4201229광주3.02.950.2411822390360818782
5201230대전3.13.149.4426026712458222130
6201231울산2.23.049.6510630910416426746
7201236세종0.00.00.00000
8201241경기22.43.049.5468239151753631616
9201242강원3.22.754.7340521814352918285
STDYYATPT_CODEATPT_NMHSHLD_DISTRBMBHS_COHSHLDR_AGEORIN_INCOMEDEBTASSETSPURE_ASSETS_AMOUNT
109201836세종0.63.052.7053879978044098
110201841경기23.42.952.4631945567975635811
111201842강원3.22.658.0481631047448726560
112201843충북3.22.657.6482630603497225632
113201844충남4.22.656.5524630807565225154
114201845전북3.72.757.8486027041428622755
115201846전남3.72.657.9477728837452424313
116201847경북5.62.756.7505431641497126670
117201848경남6.62.855.6509532225634325882
118201850제주1.22.755.1512448205640941796