Overview

Dataset statistics

Number of variables14
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 KiB
Average record size in memory118.9 B

Variable types

Categorical1
Numeric6
Text7

Dataset

Description한국주택금융공사에서 발행한 지급방식별 주택담보노후연금보증 현황에 대한 데이터로 공공데이터 개방을 위해 등록된 자료입니다. 년도,월,합계 건수,합계 연금지급액,합계 보증공급액,종신지급방식 건수,종신지급방식 연금지급액 ,종신지급방식 보증공급액,종신혼합방식 건수,종신혼합방식 연금지급액,종신혼합방식 보증공급액,기타 건수,기타 연금지급액,기타 보증공급액 관련 값이 포함되어있습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073700/fileData.do

Alerts

종신지급방식2)연금지급액5) is highly overall correlated with 종신혼합방식3)건수 and 4 other fieldsHigh correlation
종신혼합방식3)건수 is highly overall correlated with 종신지급방식2)연금지급액5) and 3 other fieldsHigh correlation
종신혼합방식3)연금지급액5) is highly overall correlated with 종신지급방식2)연금지급액5) and 4 other fieldsHigh correlation
기타4)건수 is highly overall correlated with 종신지급방식2)연금지급액5) and 3 other fieldsHigh correlation
기타4)연금지급액5) is highly overall correlated with 종신지급방식2)연금지급액5) and 3 other fieldsHigh correlation
During is highly overall correlated with 종신지급방식2)연금지급액5) and 1 other fieldsHigh correlation
기타4)건수 has 65 (43.3%) zerosZeros
기타4)연금지급액5) has 65 (43.3%) zerosZeros

Reproduction

Analysis started2023-12-12 05:15:58.281159
Analysis finished2023-12-12 05:16:03.385710
Duration5.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

During
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2008
12 
2009
12 
2010
12 
2011
12 
2012
12 
Other values (8)
90 

Length

Max length6
Median length4
Mean length4.16
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008
2nd row2008
3rd row2008
4th row2008
5th row2008

Common Values

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%
20177) 12
 
8.0%
Other values (3) 30
20.0%

Length

2023-12-12T14:16:03.462060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
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%
20177 12
 
8.0%
Other values (3) 30
20.0%

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
2023-12-12T14:16:03.611794image/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
2023-12-12T14:16:03.749816image/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%

합계 건수
Text

Distinct142
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:04.116001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0333333
Min length2

Characters and Unicode

Total characters455
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

Unique134 ?
Unique (%)89.3%

Sample

1st row39
2nd row22
3rd row49
4th row56
5th row71
ValueCountFrequency (%)
876 2
 
1.3%
284 2
 
1.3%
79 2
 
1.3%
245 2
 
1.3%
157 2
 
1.3%
478 2
 
1.3%
488 2
 
1.3%
477 2
 
1.3%
39 1
 
0.7%
630 1
 
0.7%
Other values (132) 132
88.0%
2023-12-12T14:16:04.629688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 63
13.8%
8 56
12.3%
7 50
11.0%
5 48
10.5%
3 44
9.7%
4 40
8.8%
2 39
8.6%
0 36
7.9%
9 34
7.5%
6 32
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 442
97.1%
Other Punctuation 13
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 63
14.3%
8 56
12.7%
7 50
11.3%
5 48
10.9%
3 44
10.0%
4 40
9.0%
2 39
8.8%
0 36
8.1%
9 34
7.7%
6 32
7.2%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 63
13.8%
8 56
12.3%
7 50
11.0%
5 48
10.5%
3 44
9.7%
4 40
8.8%
2 39
8.6%
0 36
7.9%
9 34
7.5%
6 32
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 63
13.8%
8 56
12.3%
7 50
11.0%
5 48
10.5%
3 44
9.7%
4 40
8.8%
2 39
8.6%
0 36
7.9%
9 34
7.5%
6 32
7.0%

합계 연금지급액5)
Text

Distinct129
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:04.994576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.78
Min length1

Characters and Unicode

Total characters417
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

Unique108 ?
Unique (%)72.0%

Sample

1st row9
2nd row7
3rd row8
4th row20
5th row21
ValueCountFrequency (%)
717 2
 
1.3%
42 2
 
1.3%
108 2
 
1.3%
316 2
 
1.3%
245 2
 
1.3%
289 2
 
1.3%
333 2
 
1.3%
354 2
 
1.3%
281 2
 
1.3%
565 2
 
1.3%
Other values (119) 130
86.7%
2023-12-12T14:16:05.548968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 53
12.7%
2 53
12.7%
3 51
12.2%
9 41
9.8%
6 40
9.6%
8 39
9.4%
4 39
9.4%
5 37
8.9%
0 31
7.4%
7 29
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 413
99.0%
Other Punctuation 4
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 53
12.8%
2 53
12.8%
3 51
12.3%
9 41
9.9%
6 40
9.7%
8 39
9.4%
4 39
9.4%
5 37
9.0%
0 31
7.5%
7 29
7.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 417
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 53
12.7%
2 53
12.7%
3 51
12.2%
9 41
9.8%
6 40
9.6%
8 39
9.4%
4 39
9.4%
5 37
8.9%
0 31
7.4%
7 29
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 417
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 53
12.7%
2 53
12.7%
3 51
12.2%
9 41
9.8%
6 40
9.6%
8 39
9.4%
4 39
9.4%
5 37
8.9%
0 31
7.4%
7 29
7.0%

합계 보증공급액6)
Text

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:05.870422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9266667
Min length3

Characters and Unicode

Total characters739
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 (%)
1,002 2
 
1.3%
9,834 1
 
0.7%
7,119 1
 
0.7%
9,451 1
 
0.7%
9,447 1
 
0.7%
7,947 1
 
0.7%
12,097 1
 
0.7%
8,849 1
 
0.7%
10,803 1
 
0.7%
8,646 1
 
0.7%
Other values (139) 139
92.7%
2023-12-12T14:16:06.353553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 138
18.7%
1 71
9.6%
8 68
9.2%
5 65
8.8%
4 65
8.8%
2 61
8.3%
9 60
8.1%
3 57
7.7%
0 54
 
7.3%
7 51
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 601
81.3%
Other Punctuation 138
 
18.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 71
11.8%
8 68
11.3%
5 65
10.8%
4 65
10.8%
2 61
10.1%
9 60
10.0%
3 57
9.5%
0 54
9.0%
7 51
8.5%
6 49
8.2%
Other Punctuation
ValueCountFrequency (%)
, 138
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 739
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 138
18.7%
1 71
9.6%
8 68
9.2%
5 65
8.8%
4 65
8.8%
2 61
8.3%
9 60
8.1%
3 57
7.7%
0 54
 
7.3%
7 51
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 138
18.7%
1 71
9.6%
8 68
9.2%
5 65
8.8%
4 65
8.8%
2 61
8.3%
9 60
8.1%
3 57
7.7%
0 54
 
7.3%
7 51
 
6.9%

종신지급방식2) 건수
Text

Distinct130
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:06.713661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.82
Min length2

Characters and Unicode

Total characters423
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

Unique113 ?
Unique (%)75.3%

Sample

1st row35
2nd row18
3rd row29
4th row23
5th row33
ValueCountFrequency (%)
516 3
 
2.0%
43 3
 
2.0%
38 3
 
2.0%
24 2
 
1.3%
511 2
 
1.3%
271 2
 
1.3%
589 2
 
1.3%
214 2
 
1.3%
35 2
 
1.3%
608 2
 
1.3%
Other values (120) 127
84.7%
2023-12-12T14:16:07.176008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 57
13.5%
5 54
12.8%
1 52
12.3%
4 51
12.1%
2 51
12.1%
6 36
8.5%
9 36
8.5%
7 31
7.3%
8 30
7.1%
0 23
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 421
99.5%
Other Punctuation 2
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 57
13.5%
5 54
12.8%
1 52
12.4%
4 51
12.1%
2 51
12.1%
6 36
8.6%
9 36
8.6%
7 31
7.4%
8 30
7.1%
0 23
5.5%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 423
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 57
13.5%
5 54
12.8%
1 52
12.3%
4 51
12.1%
2 51
12.1%
6 36
8.5%
9 36
8.5%
7 31
7.3%
8 30
7.1%
0 23
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 423
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 57
13.5%
5 54
12.8%
1 52
12.3%
4 51
12.1%
2 51
12.1%
6 36
8.5%
9 36
8.5%
7 31
7.3%
8 30
7.1%
0 23
5.4%

종신지급방식2)연금지급액5)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.02667
Minimum6
Maximum569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:16:07.313232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q136.25
median142.5
Q3318
95-th percentile496.65
Maximum569
Range563
Interquartile range (IQR)281.75

Descriptive statistics

Standard deviation167.23515
Coefficient of variation (CV)0.87089547
Kurtosis-0.92287929
Mean192.02667
Median Absolute Deviation (MAD)121.5
Skewness0.62178669
Sum28804
Variance27967.597
MonotonicityNot monotonic
2023-12-12T14:16:07.453197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 4
 
2.7%
8 4
 
2.7%
9 4
 
2.7%
13 4
 
2.7%
15 3
 
2.0%
74 2
 
1.3%
201 2
 
1.3%
6 2
 
1.3%
137 2
 
1.3%
110 2
 
1.3%
Other values (118) 121
80.7%
ValueCountFrequency (%)
6 2
1.3%
7 4
2.7%
8 4
2.7%
9 4
2.7%
11 1
 
0.7%
13 4
2.7%
14 2
1.3%
15 3
2.0%
17 1
 
0.7%
18 1
 
0.7%
ValueCountFrequency (%)
569 1
0.7%
537 1
0.7%
519 1
0.7%
512 1
0.7%
511 1
0.7%
508 1
0.7%
505 1
0.7%
498 1
0.7%
495 1
0.7%
494 1
0.7%

종신지급방식2)보증공급액6)
Text

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:07.790879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.7133333
Min length3

Characters and Unicode

Total characters707
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 row369
2nd row230
3rd row245
4th row234
5th row341
ValueCountFrequency (%)
6,252 2
 
1.3%
3,293 1
 
0.7%
5,249 1
 
0.7%
7,528 1
 
0.7%
7,473 1
 
0.7%
7,345 1
 
0.7%
6,877 1
 
0.7%
5,518 1
 
0.7%
7,237 1
 
0.7%
5,665 1
 
0.7%
Other values (139) 139
92.7%
2023-12-12T14:16:08.264843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 127
18.0%
2 79
11.2%
4 77
10.9%
5 70
9.9%
3 70
9.9%
1 69
9.8%
6 53
7.5%
7 48
 
6.8%
9 44
 
6.2%
8 41
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 580
82.0%
Other Punctuation 127
 
18.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 79
13.6%
4 77
13.3%
5 70
12.1%
3 70
12.1%
1 69
11.9%
6 53
9.1%
7 48
8.3%
9 44
7.6%
8 41
7.1%
0 29
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 127
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 127
18.0%
2 79
11.2%
4 77
10.9%
5 70
9.9%
3 70
9.9%
1 69
9.8%
6 53
7.5%
7 48
 
6.8%
9 44
 
6.2%
8 41
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 127
18.0%
2 79
11.2%
4 77
10.9%
5 70
9.9%
3 70
9.9%
1 69
9.8%
6 53
7.5%
7 48
 
6.8%
9 44
 
6.2%
8 41
 
5.8%

종신혼합방식3)건수
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.58
Minimum4
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:16:08.397853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile31.35
Q173.5
median102
Q3153.75
95-th percentile207.75
Maximum284
Range280
Interquartile range (IQR)80.25

Descriptive statistics

Standard deviation56.607594
Coefficient of variation (CV)0.50282105
Kurtosis-0.29945804
Mean112.58
Median Absolute Deviation (MAD)41
Skewness0.44035834
Sum16887
Variance3204.4197
MonotonicityNot monotonic
2023-12-12T14:16:08.511103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 5
 
3.3%
102 5
 
3.3%
82 4
 
2.7%
81 3
 
2.0%
37 3
 
2.0%
108 3
 
2.0%
134 3
 
2.0%
109 3
 
2.0%
99 3
 
2.0%
4 2
 
1.3%
Other values (97) 116
77.3%
ValueCountFrequency (%)
4 2
1.3%
20 1
 
0.7%
23 1
 
0.7%
26 1
 
0.7%
28 1
 
0.7%
29 1
 
0.7%
30 1
 
0.7%
33 2
1.3%
37 3
2.0%
38 1
 
0.7%
ValueCountFrequency (%)
284 1
0.7%
255 1
0.7%
244 1
0.7%
227 1
0.7%
218 1
0.7%
214 1
0.7%
213 1
0.7%
210 1
0.7%
205 1
0.7%
198 2
1.3%

종신혼합방식3)연금지급액5)
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.37333
Minimum1
Maximum372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:16:08.630228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q163
median126
Q3212.25
95-th percentile321.6
Maximum372
Range371
Interquartile range (IQR)149.25

Descriptive statistics

Standard deviation97.750507
Coefficient of variation (CV)0.67241017
Kurtosis-0.76192012
Mean145.37333
Median Absolute Deviation (MAD)73.5
Skewness0.48997335
Sum21806
Variance9555.1617
MonotonicityNot monotonic
2023-12-12T14:16:08.755562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123 4
 
2.7%
126 4
 
2.7%
63 3
 
2.0%
315 3
 
2.0%
2 2
 
1.3%
283 2
 
1.3%
264 2
 
1.3%
207 2
 
1.3%
223 2
 
1.3%
130 2
 
1.3%
Other values (113) 124
82.7%
ValueCountFrequency (%)
1 1
0.7%
2 2
1.3%
11 1
0.7%
12 1
0.7%
13 2
1.3%
14 2
1.3%
15 1
0.7%
16 1
0.7%
17 1
0.7%
18 1
0.7%
ValueCountFrequency (%)
372 1
 
0.7%
356 1
 
0.7%
345 1
 
0.7%
340 1
 
0.7%
339 1
 
0.7%
333 1
 
0.7%
330 1
 
0.7%
327 1
 
0.7%
315 3
2.0%
312 1
 
0.7%

종신혼합방식3)보증공급액6)
Text

Distinct147
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:09.090101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.2933333
Min length2

Characters and Unicode

Total characters644
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

Unique144 ?
Unique (%)96.0%

Sample

1st row33
2nd row26
3rd row233
4th row343
5th row513
ValueCountFrequency (%)
1,104 2
 
1.3%
1,297 2
 
1.3%
1,902 2
 
1.3%
2,074 1
 
0.7%
1,812 1
 
0.7%
1,659 1
 
0.7%
2,338 1
 
0.7%
33 1
 
0.7%
2,010 1
 
0.7%
1,098 1
 
0.7%
Other values (137) 137
91.3%
2023-12-12T14:16:09.522503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 113
17.5%
, 98
15.2%
2 70
10.9%
0 53
8.2%
3 52
8.1%
8 49
7.6%
9 48
7.5%
7 47
7.3%
4 42
 
6.5%
6 39
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 546
84.8%
Other Punctuation 98
 
15.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 113
20.7%
2 70
12.8%
0 53
9.7%
3 52
9.5%
8 49
9.0%
9 48
8.8%
7 47
8.6%
4 42
 
7.7%
6 39
 
7.1%
5 33
 
6.0%
Other Punctuation
ValueCountFrequency (%)
, 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 644
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 113
17.5%
, 98
15.2%
2 70
10.9%
0 53
8.2%
3 52
8.1%
8 49
7.6%
9 48
7.5%
7 47
7.3%
4 42
 
6.5%
6 39
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 113
17.5%
, 98
15.2%
2 70
10.9%
0 53
8.2%
3 52
8.1%
8 49
7.6%
9 48
7.5%
7 47
7.3%
4 42
 
6.5%
6 39
 
6.1%

기타4)건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.433333
Minimum0
Maximum484
Zeros65
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:16:09.646295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3145.75
95-th percentile230.1
Maximum484
Range484
Interquartile range (IQR)145.75

Descriptive statistics

Standard deviation92.394138
Coefficient of variation (CV)1.3701553
Kurtosis1.894518
Mean67.433333
Median Absolute Deviation (MAD)8
Skewness1.381894
Sum10115
Variance8536.6767
MonotonicityNot monotonic
2023-12-12T14:16:10.018202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
43.3%
6 5
 
3.3%
11 3
 
2.0%
9 3
 
2.0%
7 3
 
2.0%
162 2
 
1.3%
142 2
 
1.3%
155 2
 
1.3%
8 2
 
1.3%
231 2
 
1.3%
Other values (59) 61
40.7%
ValueCountFrequency (%)
0 65
43.3%
4 1
 
0.7%
6 5
 
3.3%
7 3
 
2.0%
8 2
 
1.3%
9 3
 
2.0%
10 1
 
0.7%
11 3
 
2.0%
13 1
 
0.7%
14 1
 
0.7%
ValueCountFrequency (%)
484 1
0.7%
349 1
0.7%
276 1
0.7%
270 1
0.7%
263 1
0.7%
233 1
0.7%
231 2
1.3%
229 1
0.7%
222 1
0.7%
217 1
0.7%

기타4)연금지급액5)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.92
Minimum0
Maximum219
Zeros65
Zeros (%)43.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:16:10.135657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q368
95-th percentile126.3
Maximum219
Range219
Interquartile range (IQR)68

Descriptive statistics

Standard deviation47.516937
Coefficient of variation (CV)1.2870243
Kurtosis0.55849928
Mean36.92
Median Absolute Deviation (MAD)5.5
Skewness1.1225383
Sum5538
Variance2257.8593
MonotonicityNot monotonic
2023-12-12T14:16:10.261282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
43.3%
5 10
 
6.7%
6 9
 
6.0%
61 3
 
2.0%
90 3
 
2.0%
62 2
 
1.3%
108 2
 
1.3%
85 2
 
1.3%
40 2
 
1.3%
74 2
 
1.3%
Other values (44) 50
33.3%
ValueCountFrequency (%)
0 65
43.3%
5 10
 
6.7%
6 9
 
6.0%
8 1
 
0.7%
9 1
 
0.7%
12 1
 
0.7%
30 1
 
0.7%
37 1
 
0.7%
40 2
 
1.3%
48 1
 
0.7%
ValueCountFrequency (%)
219 1
0.7%
155 1
0.7%
151 1
0.7%
147 1
0.7%
140 1
0.7%
139 1
0.7%
138 1
0.7%
129 1
0.7%
123 2
1.3%
120 1
0.7%

기타4)보증공급액6)
Text

Distinct83
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:16:10.467892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.3466667
Min length1

Characters and Unicode

Total characters352
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

Unique79 ?
Unique (%)52.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 65
43.3%
89 2
 
1.3%
82 2
 
1.3%
160 2
 
1.3%
951 1
 
0.7%
757 1
 
0.7%
892 1
 
0.7%
1,117 1
 
0.7%
1,145 1
 
0.7%
1,376 1
 
0.7%
Other values (73) 73
48.7%
2023-12-12T14:16:10.926464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 83
23.6%
1 46
13.1%
5 30
 
8.5%
8 29
 
8.2%
6 26
 
7.4%
7 26
 
7.4%
9 25
 
7.1%
2 25
 
7.1%
, 24
 
6.8%
4 19
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328
93.2%
Other Punctuation 24
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83
25.3%
1 46
14.0%
5 30
 
9.1%
8 29
 
8.8%
6 26
 
7.9%
7 26
 
7.9%
9 25
 
7.6%
2 25
 
7.6%
4 19
 
5.8%
3 19
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 352
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 83
23.6%
1 46
13.1%
5 30
 
8.5%
8 29
 
8.2%
6 26
 
7.4%
7 26
 
7.4%
9 25
 
7.1%
2 25
 
7.1%
, 24
 
6.8%
4 19
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 83
23.6%
1 46
13.1%
5 30
 
8.5%
8 29
 
8.2%
6 26
 
7.4%
7 26
 
7.4%
9 25
 
7.1%
2 25
 
7.1%
, 24
 
6.8%
4 19
 
5.4%

Interactions

2023-12-12T14:16:02.386745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:58.832680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.431567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.354692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.935276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.695018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.487411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:58.926493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.524502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.447789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.043108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.797971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.577456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.032534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.612111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.537799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.143832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.890676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.675050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.123298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.709414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.656078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.248662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.018725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.790810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.227889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.159415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.759309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.406126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.170148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.880921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:15:59.325396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.263363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:00.848149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:01.569968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:16:02.294713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:16:11.066773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
During월종신지급방식2)연금지급액5)종신혼합방식3)건수종신혼합방식3)연금지급액5)기타4)건수기타4)연금지급액5)기타4)보증공급액6)
During1.0000.0000.9240.6860.8660.6940.7580.829
월0.0001.0000.0000.0000.0000.0000.0000.298
종신지급방식2)연금지급액5)0.9240.0001.0000.7920.9310.7190.7760.986
종신혼합방식3)건수0.6860.0000.7921.0000.8900.8180.7360.898
종신혼합방식3)연금지급액5)0.8660.0000.9310.8901.0000.6820.7320.933
기타4)건수0.6940.0000.7190.8180.6821.0000.8111.000
기타4)연금지급액5)0.7580.0000.7760.7360.7320.8111.0001.000
기타4)보증공급액6)0.8290.2980.9860.8980.9331.0001.0001.000
2023-12-12T14:16:11.241578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월종신지급방식2)연금지급액5)종신혼합방식3)건수종신혼합방식3)연금지급액5)기타4)건수기타4)연금지급액5)During
월1.0000.0160.0650.0360.0180.0180.000
종신지급방식2)연금지급액5)0.0161.0000.8390.9810.9040.8980.722
종신혼합방식3)건수0.0650.8391.0000.9020.7770.7380.363
종신혼합방식3)연금지급액5)0.0360.9810.9021.0000.8850.8740.594
기타4)건수0.0180.9040.7770.8851.0000.9680.398
기타4)연금지급액5)0.0180.8980.7380.8740.9681.0000.448
During0.0000.7220.3630.5940.3980.4481.000

Missing values

2023-12-12T14:16:03.057806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:16:03.310431image/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

During월합계 건수합계 연금지급액5)합계 보증공급액6)종신지급방식2) 건수종신지급방식2)연금지급액5)종신지급방식2)보증공급액6)종신혼합방식3)건수종신혼합방식3)연금지급액5)종신혼합방식3)보증공급액6)기타4)건수기타4)연금지급액5)기타4)보증공급액6)
0200813994013573694233000
1200822272551862304126000
220083498478296245202233000
32008456205772372343313343000
42008571218543373413814513000
52008679259842973615018623000
62008775221,0023885713714431000
72008847205901982512812339000
82008955196892683092911380000
920081078261,0513594194317632000
During월합계 건수합계 연금지급액5)합계 보증공급액6)종신지급방식2) 건수종신지급방식2)연금지급액5)종신지급방식2)보증공급액6)종신혼합방식3)건수종신혼합방식3)연금지급액5)종신혼합방식3)보증공급액6)기타4)건수기타4)연금지급액5)기타4)보증공급액6)
140201996598666,9123864784,4721312801,669142108771
1412019108519228,7895114985,7721853152,105155109911
1422019117589508,3274865125,4461543152,024118123857
1432019121,08593210,5526085087,0482143152,2482631101,257
144202012698642,5781415111,504622687996685275
145202021,05692910,4996595057,1291983102,2351991141,135
146202031,0621,04210,4865895196,3892443722,7252291511,372
147202048831,0409,8975285696,5691703332,0741851391,254
148202059149619,4855164945,7851933272,3242051401,376
149202069401,0319,2095325375,7511933562,2152151381,243