Overview

Dataset statistics

Number of variables14
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 KiB
Average record size in memory122.9 B

Variable types

Numeric10
Text4

Dataset

Description주택담보노후연금보증에 대한 데이터로 월별 총보증공급액, 종신형 건수, 종신형 보증공급액 등의 항목을 제공합니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073702/fileData.do

Alerts

연도 is highly overall correlated with 정액형1)연금지급액5) and 3 other fieldsHigh correlation
정액형1)연금지급액5) is highly overall correlated with 연도 and 3 other fieldsHigh correlation
증가형2)건수 is highly overall correlated with 증가형2)보증공급액6) and 1 other fieldsHigh correlation
증가형2)연금지급액5) is highly overall correlated with 감소형3)연금지급액5)High correlation
증가형2)보증공급액6) is highly overall correlated with 증가형2)건수 and 3 other fieldsHigh correlation
감소형3)건수 is highly overall correlated with 연도 and 5 other fieldsHigh correlation
감소형3)연금지급액5) is highly overall correlated with 증가형2)연금지급액5)High correlation
전후후박형4)건수 is highly overall correlated with 연도 and 4 other fieldsHigh correlation
전후후박형4)연금지급액5) is highly overall correlated with 연도 and 4 other fieldsHigh correlation
증가형2)건수 has 78 (52.0%) zerosZeros
증가형2)연금지급액5) has 18 (12.0%) zerosZeros
증가형2)보증공급액6) has 77 (51.3%) zerosZeros
감소형3)건수 has 60 (40.0%) zerosZeros
감소형3)연금지급액5) has 9 (6.0%) zerosZeros
전후후박형4)건수 has 55 (36.7%) zerosZeros
전후후박형4)연금지급액5) has 55 (36.7%) zerosZeros

Reproduction

Analysis started2023-12-12 17:33:13.615833
Analysis finished2023-12-12 17:33:24.497885
Duration10.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.76
Minimum2008
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:24.571846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12011
median2014
Q32017
95-th percentile2019
Maximum2020
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6263021
Coefficient of variation (CV)0.0018007618
Kurtosis-1.1922372
Mean2013.76
Median Absolute Deviation (MAD)3
Skewness0.016662561
Sum302064
Variance13.150067
MonotonicityIncreasing
2023-12-13T02:33:24.722949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2008 12
 
8.0%
2009 12
 
8.0%
2010 12
 
8.0%
2011 12
 
8.0%
2012 12
 
8.0%
2013 12
 
8.0%
2014 12
 
8.0%
2015 12
 
8.0%
2016 12
 
8.0%
2017 12
 
8.0%
Other values (3) 30
20.0%
ValueCountFrequency (%)
2008 12
8.0%
2009 12
8.0%
2010 12
8.0%
2011 12
8.0%
2012 12
8.0%
2013 12
8.0%
2014 12
8.0%
2015 12
8.0%
2016 12
8.0%
2017 12
8.0%
ValueCountFrequency (%)
2020 6
4.0%
2019 12
8.0%
2018 12
8.0%
2017 12
8.0%
2016 12
8.0%
2015 12
8.0%
2014 12
8.0%
2013 12
8.0%
2012 12
8.0%
2011 12
8.0%

월
Real number (ℝ)

Distinct12
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.38
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:24.860656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4615239
Coefficient of variation (CV)0.5425586
Kurtosis-1.2127331
Mean6.38
Median Absolute Deviation (MAD)3
Skewness0.053050668
Sum957
Variance11.982148
MonotonicityNot monotonic
2023-12-13T02:33:24.996171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 13
8.7%
2 13
8.7%
3 13
8.7%
4 13
8.7%
5 13
8.7%
6 13
8.7%
7 12
8.0%
8 12
8.0%
9 12
8.0%
10 12
8.0%
Other values (2) 24
16.0%
ValueCountFrequency (%)
1 13
8.7%
2 13
8.7%
3 13
8.7%
4 13
8.7%
5 13
8.7%
6 13
8.7%
7 12
8.0%
8 12
8.0%
9 12
8.0%
10 12
8.0%
ValueCountFrequency (%)
12 12
8.0%
11 12
8.0%
10 12
8.0%
9 12
8.0%
8 12
8.0%
7 12
8.0%
6 13
8.7%
5 13
8.7%
4 13
8.7%
3 13
8.7%

정액형1)건수
Text

Distinct137
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T02:33:25.360052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.86
Min length2

Characters and Unicode

Total characters429
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)84.0%

Sample

1st row39
2nd row22
3rd row49
4th row56
5th row71
ValueCountFrequency (%)
352 3
 
2.0%
56 3
 
2.0%
572 2
 
1.3%
209 2
 
1.3%
404 2
 
1.3%
596 2
 
1.3%
526 2
 
1.3%
382 2
 
1.3%
39 2
 
1.3%
49 2
 
1.3%
Other values (127) 128
85.3%
2023-12-13T02:33:25.878171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 56
13.1%
2 54
12.6%
3 46
10.7%
6 46
10.7%
1 44
10.3%
7 43
10.0%
4 42
9.8%
8 34
7.9%
0 32
7.5%
9 29
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 426
99.3%
Other Punctuation 3
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 56
13.1%
2 54
12.7%
3 46
10.8%
6 46
10.8%
1 44
10.3%
7 43
10.1%
4 42
9.9%
8 34
8.0%
0 32
7.5%
9 29
6.8%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 429
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 56
13.1%
2 54
12.6%
3 46
10.7%
6 46
10.7%
1 44
10.3%
7 43
10.0%
4 42
9.8%
8 34
7.9%
0 32
7.5%
9 29
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 56
13.1%
2 54
12.6%
3 46
10.7%
6 46
10.7%
1 44
10.3%
7 43
10.0%
4 42
9.8%
8 34
7.9%
0 32
7.5%
9 29
6.8%

정액형1)연금지급액5)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.22667
Minimum7
Maximum727
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:26.053802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20
Q168.5
median222.5
Q3435.75
95-th percentile634.95
Maximum727
Range720
Interquartile range (IQR)367.25

Descriptive statistics

Standard deviation207.88824
Coefficient of variation (CV)0.78086932
Kurtosis-0.89109144
Mean266.22667
Median Absolute Deviation (MAD)169
Skewness0.5539706
Sum39934
Variance43217.519
MonotonicityNot monotonic
2023-12-13T02:33:26.195786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 4
 
2.7%
216 4
 
2.7%
79 3
 
2.0%
40 3
 
2.0%
21 2
 
1.3%
25 2
 
1.3%
19 2
 
1.3%
173 2
 
1.3%
257 2
 
1.3%
31 2
 
1.3%
Other values (118) 124
82.7%
ValueCountFrequency (%)
7 1
 
0.7%
8 1
 
0.7%
9 1
 
0.7%
19 2
1.3%
20 4
2.7%
21 2
1.3%
23 1
 
0.7%
25 2
1.3%
28 1
 
0.7%
30 1
 
0.7%
ValueCountFrequency (%)
727 1
0.7%
718 1
0.7%
710 1
0.7%
688 1
0.7%
687 1
0.7%
659 1
0.7%
651 1
0.7%
639 1
0.7%
630 1
0.7%
628 1
0.7%

정액형1)보증공급액6)
Text

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T02:33:26.677406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.7933333
Min length3

Characters and Unicode

Total characters719
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)98.7%

Sample

1st row401
2nd row255
3rd row478
4th row577
5th row854
ValueCountFrequency (%)
1,715 2
 
1.3%
6,657 1
 
0.7%
6,137 1
 
0.7%
7,308 1
 
0.7%
6,556 1
 
0.7%
5,521 1
 
0.7%
9,078 1
 
0.7%
6,514 1
 
0.7%
8,082 1
 
0.7%
5,224 1
 
0.7%
Other values (139) 139
92.7%
2023-12-13T02:33:27.199830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 133
18.5%
1 73
10.2%
6 71
9.9%
4 69
9.6%
7 62
8.6%
2 62
8.6%
3 60
8.3%
5 56
7.8%
0 50
 
7.0%
8 47
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 586
81.5%
Other Punctuation 133
 
18.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 73
12.5%
6 71
12.1%
4 69
11.8%
7 62
10.6%
2 62
10.6%
3 60
10.2%
5 56
9.6%
0 50
8.5%
8 47
8.0%
9 36
6.1%
Other Punctuation
ValueCountFrequency (%)
, 133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 133
18.5%
1 73
10.2%
6 71
9.9%
4 69
9.6%
7 62
8.6%
2 62
8.6%
3 60
8.3%
5 56
7.8%
0 50
 
7.0%
8 47
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 133
18.5%
1 73
10.2%
6 71
9.9%
4 69
9.6%
7 62
8.6%
2 62
8.6%
3 60
8.3%
5 56
7.8%
0 50
 
7.0%
8 47
 
6.5%

증가형2)건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.26
Minimum0
Maximum9
Zeros78
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:27.364129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8076274
Coefficient of variation (CV)1.4346249
Kurtosis3.4004654
Mean1.26
Median Absolute Deviation (MAD)0
Skewness1.79496
Sum189
Variance3.2675168
MonotonicityNot monotonic
2023-12-13T02:33:27.466315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 78
52.0%
1 23
 
15.3%
2 19
 
12.7%
3 14
 
9.3%
4 7
 
4.7%
6 4
 
2.7%
5 2
 
1.3%
8 1
 
0.7%
9 1
 
0.7%
7 1
 
0.7%
ValueCountFrequency (%)
0 78
52.0%
1 23
 
15.3%
2 19
 
12.7%
3 14
 
9.3%
4 7
 
4.7%
5 2
 
1.3%
6 4
 
2.7%
7 1
 
0.7%
8 1
 
0.7%
9 1
 
0.7%
ValueCountFrequency (%)
9 1
 
0.7%
8 1
 
0.7%
7 1
 
0.7%
6 4
 
2.7%
5 2
 
1.3%
4 7
 
4.7%
3 14
 
9.3%
2 19
 
12.7%
1 23
 
15.3%
0 78
52.0%

증가형2)연금지급액5)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8866667
Minimum0
Maximum6
Zeros18
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:27.565120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q32
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0962821
Coefficient of variation (CV)0.58106825
Kurtosis1.9755899
Mean1.8866667
Median Absolute Deviation (MAD)0
Skewness0.66135795
Sum283
Variance1.2018345
MonotonicityNot monotonic
2023-12-13T02:33:27.689269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 88
58.7%
1 21
 
14.0%
0 18
 
12.0%
3 12
 
8.0%
4 6
 
4.0%
5 4
 
2.7%
6 1
 
0.7%
ValueCountFrequency (%)
0 18
 
12.0%
1 21
 
14.0%
2 88
58.7%
3 12
 
8.0%
4 6
 
4.0%
5 4
 
2.7%
6 1
 
0.7%
ValueCountFrequency (%)
6 1
 
0.7%
5 4
 
2.7%
4 6
 
4.0%
3 12
 
8.0%
2 88
58.7%
1 21
 
14.0%
0 18
 
12.0%

증가형2)보증공급액6)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.9
Minimum-4
Maximum180
Zeros77
Zeros (%)51.3%
Negative1
Negative (%)0.7%
Memory size1.4 KiB
2023-12-13T02:33:27.832139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile0
Q10
median0
Q331.75
95-th percentile81.75
Maximum180
Range184
Interquartile range (IQR)31.75

Descriptive statistics

Standard deviation34.288697
Coefficient of variation (CV)1.6406075
Kurtosis6.2718352
Mean20.9
Median Absolute Deviation (MAD)0
Skewness2.3518235
Sum3135
Variance1175.7148
MonotonicityNot monotonic
2023-12-13T02:33:27.982561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
51.3%
18 4
 
2.7%
42 4
 
2.7%
67 3
 
2.0%
8 3
 
2.0%
15 3
 
2.0%
19 3
 
2.0%
43 2
 
1.3%
6 2
 
1.3%
30 2
 
1.3%
Other values (40) 47
31.3%
ValueCountFrequency (%)
-4 1
 
0.7%
0 77
51.3%
4 1
 
0.7%
5 1
 
0.7%
6 2
 
1.3%
7 1
 
0.7%
8 3
 
2.0%
9 1
 
0.7%
10 1
 
0.7%
11 1
 
0.7%
ValueCountFrequency (%)
180 1
0.7%
164 1
0.7%
151 1
0.7%
140 1
0.7%
126 1
0.7%
108 1
0.7%
84 2
1.3%
79 1
0.7%
77 1
0.7%
74 2
1.3%

감소형3)건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.153333
Minimum0
Maximum130
Zeros60
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:28.120283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q347
95-th percentile79.1
Maximum130
Range130
Interquartile range (IQR)47

Descriptive statistics

Standard deviation28.434957
Coefficient of variation (CV)1.0872403
Kurtosis0.42364717
Mean26.153333
Median Absolute Deviation (MAD)21
Skewness0.97636734
Sum3923
Variance808.5468
MonotonicityNot monotonic
2023-12-13T02:33:28.265489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 60
40.0%
21 4
 
2.7%
50 4
 
2.7%
24 4
 
2.7%
27 4
 
2.7%
20 3
 
2.0%
47 3
 
2.0%
31 3
 
2.0%
60 2
 
1.3%
58 2
 
1.3%
Other values (48) 61
40.7%
ValueCountFrequency (%)
0 60
40.0%
2 1
 
0.7%
11 1
 
0.7%
12 1
 
0.7%
13 1
 
0.7%
15 2
 
1.3%
17 1
 
0.7%
18 1
 
0.7%
19 1
 
0.7%
20 3
 
2.0%
ValueCountFrequency (%)
130 1
0.7%
101 1
0.7%
98 1
0.7%
93 1
0.7%
91 1
0.7%
87 1
0.7%
81 1
0.7%
80 1
0.7%
78 1
0.7%
75 1
0.7%

감소형3)연금지급액5)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.5
Minimum0
Maximum50
Zeros9
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:28.392747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127
median36
Q341
95-th percentile46.55
Maximum50
Range50
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.841747
Coefficient of variation (CV)0.43942054
Kurtosis-0.0091036545
Mean31.5
Median Absolute Deviation (MAD)7
Skewness-1.0300821
Sum4725
Variance191.59396
MonotonicityNot monotonic
2023-12-13T02:33:28.526938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
36 10
 
6.7%
44 10
 
6.7%
0 9
 
6.0%
40 9
 
6.0%
38 7
 
4.7%
39 7
 
4.7%
45 7
 
4.7%
37 7
 
4.7%
33 7
 
4.7%
43 6
 
4.0%
Other values (31) 71
47.3%
ValueCountFrequency (%)
0 9
6.0%
2 1
 
0.7%
3 1
 
0.7%
4 1
 
0.7%
5 2
 
1.3%
7 1
 
0.7%
9 4
2.7%
10 1
 
0.7%
11 3
 
2.0%
12 1
 
0.7%
ValueCountFrequency (%)
50 2
 
1.3%
49 3
 
2.0%
48 2
 
1.3%
47 1
 
0.7%
46 3
 
2.0%
45 7
4.7%
44 10
6.7%
43 6
4.0%
42 2
 
1.3%
41 4
 
2.7%

감소형3)보증공급액6)
Text

Distinct89
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T02:33:28.767816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.2333333
Min length1

Characters and Unicode

Total characters335
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)56.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 58
38.7%
265 3
 
2.0%
229 2
 
1.3%
303 2
 
1.3%
1,123 1
 
0.7%
783 1
 
0.7%
222 1
 
0.7%
132 1
 
0.7%
275 1
 
0.7%
650 1
 
0.7%
Other values (79) 79
52.7%
2023-12-13T02:33:29.178693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 74
22.1%
2 46
13.7%
3 34
10.1%
1 33
9.9%
4 27
 
8.1%
7 25
 
7.5%
8 25
 
7.5%
5 23
 
6.9%
9 23
 
6.9%
6 22
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 332
99.1%
Other Punctuation 3
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 74
22.3%
2 46
13.9%
3 34
10.2%
1 33
9.9%
4 27
 
8.1%
7 25
 
7.5%
8 25
 
7.5%
5 23
 
6.9%
9 23
 
6.9%
6 22
 
6.6%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 335
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 74
22.1%
2 46
13.7%
3 34
10.1%
1 33
9.9%
4 27
 
8.1%
7 25
 
7.5%
8 25
 
7.5%
5 23
 
6.9%
9 23
 
6.9%
6 22
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 74
22.1%
2 46
13.7%
3 34
10.1%
1 33
9.9%
4 27
 
8.1%
7 25
 
7.5%
8 25
 
7.5%
5 23
 
6.9%
9 23
 
6.9%
6 22
 
6.6%

전후후박형4)건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.91333
Minimum0
Maximum549
Zeros55
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:29.660253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median84.5
Q3226.5
95-th percentile324.3
Maximum549
Range549
Interquartile range (IQR)226.5

Descriptive statistics

Standard deviation123.27352
Coefficient of variation (CV)1.0280218
Kurtosis-0.19719297
Mean119.91333
Median Absolute Deviation (MAD)84.5
Skewness0.76387529
Sum17987
Variance15196.362
MonotonicityNot monotonic
2023-12-13T02:33:29.816782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
36.7%
66 3
 
2.0%
98 3
 
2.0%
58 2
 
1.3%
269 2
 
1.3%
214 2
 
1.3%
83 2
 
1.3%
180 2
 
1.3%
285 2
 
1.3%
327 2
 
1.3%
Other values (69) 75
50.0%
ValueCountFrequency (%)
0 55
36.7%
46 1
 
0.7%
54 1
 
0.7%
55 1
 
0.7%
56 1
 
0.7%
58 2
 
1.3%
65 1
 
0.7%
66 3
 
2.0%
67 1
 
0.7%
69 2
 
1.3%
ValueCountFrequency (%)
549 1
0.7%
477 1
0.7%
355 1
0.7%
343 1
0.7%
340 1
0.7%
331 1
0.7%
327 2
1.3%
321 1
0.7%
311 1
0.7%
306 1
0.7%

전후후박형4)연금지급액5)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.906667
Minimum0
Maximum312
Zeros55
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:33:30.041694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26.5
Q3129.75
95-th percentile265
Maximum312
Range312
Interquartile range (IQR)129.75

Descriptive statistics

Standard deviation93.608376
Coefficient of variation (CV)1.2496668
Kurtosis-0.27831782
Mean74.906667
Median Absolute Deviation (MAD)26.5
Skewness1.0669821
Sum11236
Variance8762.5281
MonotonicityNot monotonic
2023-12-13T02:33:30.257369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 55
36.7%
15 4
 
2.7%
26 3
 
2.0%
265 3
 
2.0%
27 3
 
2.0%
24 2
 
1.3%
23 2
 
1.3%
22 2
 
1.3%
20 2
 
1.3%
51 2
 
1.3%
Other values (66) 72
48.0%
ValueCountFrequency (%)
0 55
36.7%
8 1
 
0.7%
15 4
 
2.7%
16 1
 
0.7%
19 2
 
1.3%
20 2
 
1.3%
21 1
 
0.7%
22 2
 
1.3%
23 2
 
1.3%
24 2
 
1.3%
ValueCountFrequency (%)
312 1
 
0.7%
296 1
 
0.7%
292 1
 
0.7%
276 1
 
0.7%
272 1
 
0.7%
271 1
 
0.7%
269 1
 
0.7%
265 3
2.0%
260 1
 
0.7%
258 1
 
0.7%

전후후박형4)보증공급액6)
Text

Distinct95
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T02:33:30.581002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.1466667
Min length1

Characters and Unicode

Total characters472
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)62.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 55
36.7%
3,161 2
 
1.3%
2,557 1
 
0.7%
2,116 1
 
0.7%
2,924 1
 
0.7%
1,613 1
 
0.7%
2,026 1
 
0.7%
2,664 1
 
0.7%
2,676 1
 
0.7%
1,781 1
 
0.7%
Other values (85) 85
56.7%
2023-12-13T02:33:31.098395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85
18.0%
, 66
14.0%
1 51
10.8%
2 51
10.8%
6 39
8.3%
3 36
7.6%
8 32
 
6.8%
7 32
 
6.8%
4 30
 
6.4%
5 29
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 406
86.0%
Other Punctuation 66
 
14.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
20.9%
1 51
12.6%
2 51
12.6%
6 39
9.6%
3 36
8.9%
8 32
 
7.9%
7 32
 
7.9%
4 30
 
7.4%
5 29
 
7.1%
9 21
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 472
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85
18.0%
, 66
14.0%
1 51
10.8%
2 51
10.8%
6 39
8.3%
3 36
7.6%
8 32
 
6.8%
7 32
 
6.8%
4 30
 
6.4%
5 29
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85
18.0%
, 66
14.0%
1 51
10.8%
2 51
10.8%
6 39
8.3%
3 36
7.6%
8 32
 
6.8%
7 32
 
6.8%
4 30
 
6.4%
5 29
 
6.1%

Interactions

2023-12-13T02:33:23.180159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.098938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.079721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.419274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.550710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.609737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.641181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.653727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.377644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.377331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.263798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.190661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.174720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.524436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.656172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.724978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.759000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.727307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.446112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.445940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.342244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.271523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.264935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.624565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.746714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.833728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.874093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.801845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.517199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.522271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.429969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.366658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.376979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.744641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.873717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.957232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.003566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.876781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.605801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.610364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.507018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.448325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.462143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.841697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.962629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.045507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.102731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.940223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.676171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.684684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.605348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.552215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.563698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.954481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.056171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.142100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.201470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.015453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.746873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.761882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.689317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.674999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.682681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.050425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.177472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.244131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.293133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.096079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.824822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.853783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.784244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.783735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:15.818781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.147598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.278154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.342190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.395423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.164473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.921075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.933712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.875723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.883070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.224103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.248022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.386977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.453082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.484634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.234096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.004790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.010522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.967257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:14.985397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:16.308022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:17.405340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:18.494927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:19.539848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:20.569164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:21.303548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:22.083805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:33:23.082682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:33:31.238992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월정액형1)연금지급액5)증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)감소형3)건수감소형3)연금지급액5)감소형3)보증공급액6)전후후박형4)건수전후후박형4)연금지급액5)전후후박형4)보증공급액6)
연도1.0000.0000.9480.6550.5050.3970.7220.8780.8030.7810.9440.654
월0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.204
정액형1)연금지급액5)0.9480.0001.0000.5440.5390.3230.5980.8370.0000.7870.9470.978
증가형2)건수0.6550.0000.5441.0000.5920.8820.7650.4630.9940.4090.3770.221
증가형2)연금지급액5)0.5050.0000.5390.5921.0000.6480.4950.5750.9690.4280.2760.000
증가형2)보증공급액6)0.3970.0000.3230.8820.6481.0000.8730.3220.9980.2650.0000.000
감소형3)건수0.7220.0000.5980.7650.4950.8731.0000.5960.9990.6050.5210.000
감소형3)연금지급액5)0.8780.0000.8370.4630.5750.3220.5961.0000.9440.5910.6580.000
감소형3)보증공급액6)0.8030.0000.0000.9940.9690.9980.9990.9441.0000.0000.0000.000
전후후박형4)건수0.7810.0000.7870.4090.4280.2650.6050.5910.0001.0000.7671.000
전후후박형4)연금지급액5)0.9440.0000.9470.3770.2760.0000.5210.6580.0000.7671.0000.999
전후후박형4)보증공급액6)0.6540.2040.9780.2210.0000.0000.0000.0000.0001.0000.9991.000
2023-12-13T02:33:31.442946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월정액형1)연금지급액5)증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)감소형3)건수감소형3)연금지급액5)전후후박형4)건수전후후박형4)연금지급액5)
연도1.000-0.0580.984-0.4410.445-0.463-0.5610.4120.9330.971
월-0.0581.0000.029-0.011-0.013-0.0000.0440.0700.0070.006
정액형1)연금지급액5)0.9840.0291.000-0.4310.465-0.453-0.5400.4350.9400.966
증가형2)건수-0.441-0.011-0.4311.0000.2930.9730.6930.190-0.483-0.494
증가형2)연금지급액5)0.445-0.0130.4650.2931.0000.3050.1020.6000.3820.377
증가형2)보증공급액6)-0.463-0.000-0.4530.9730.3051.0000.6930.155-0.511-0.517
감소형3)건수-0.5610.044-0.5400.6930.1020.6931.0000.227-0.651-0.677
감소형3)연금지급액5)0.4120.0700.4350.1900.6000.1550.2271.0000.3870.349
전후후박형4)건수0.9330.0070.940-0.4830.382-0.511-0.6510.3871.0000.967
전후후박형4)연금지급액5)0.9710.0060.966-0.4940.377-0.517-0.6770.3490.9671.000

Missing values

2023-12-13T02:33:24.135078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:33:24.406355image/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

연도월정액형1)건수정액형1)연금지급액5)정액형1)보증공급액6)증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)감소형3)건수감소형3)연금지급액5)감소형3)보증공급액6)전후후박형4)건수전후후박형4)연금지급액5)전후후박형4)보증공급액6)
020081399401000000000
120082227255000000000
220083498478000000000
3200845620577000000000
4200857121854000000000
52008677259542030000000
62008775211,002010000000
72008845205392050000000
8200895519689000000000
92008106523923104122124000
연도월정액형1)건수정액형1)연금지급액5)정액형1)보증공급액6)증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)감소형3)건수감소형3)연금지급액5)감소형3)보증공급액6)전후후박형4)건수전후후박형4)연금지급액5)전후후박형4)보증공급액6)
140201994585924,80602003202012412,106
1412019105726306,12102003302792582,667
1422019114796515,16602003302792653,161
1432019127456287,00202003203402713,550
144202011795801,622020032090251956
145202027016277,04302003103552693,456
146202037357187,16602003003272923,320
147202045967106,72802003302872963,169
148202056276596,41802002902872723,067
149202066716876,66102003002693122,548