Overview

Dataset statistics

Number of variables14
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 KiB
Average record size in memory116.9 B

Variable types

Numeric4
Text10

Dataset

Description한국주택금융공사에서 발행한 주택담보노후연금보증 현황에 대한 데이터 입니다. 공공데이터 개방 정책에 따라 등록되었습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073703/fileData.do

Alerts

연도 is highly overall correlated with 증가형2)건수 and 1 other fieldsHigh correlation
증가형2)건수 is highly overall correlated with 연도 and 1 other fieldsHigh correlation
증가형2)연금지급액5) is highly overall correlated with 연도 and 1 other fieldsHigh correlation
정액형1)건수 has unique valuesUnique
정액형1)연금지급액5) has unique valuesUnique
정액형1)보증공급액6) has unique valuesUnique
증가형2)건수 has 5 (3.3%) zerosZeros
증가형2)연금지급액5) has 6 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-12 05:43:16.266676
Analysis finished2023-12-12 05:43:19.387133
Duration3.12 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
2023-12-12T14:43:19.448780image/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
2023-12-12T14:43:19.572112image/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
2023-12-12T14:43:19.691373image/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
2023-12-12T14:43:19.822195image/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

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:20.246174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.4466667
Min length3

Characters and Unicode

Total characters817
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

Unique150 ?
Unique (%)100.0%

Sample

1st row547
2nd row567
3rd row614
4th row661
5th row724
ValueCountFrequency (%)
547 1
 
0.7%
17,797 1
 
0.7%
18,716 1
 
0.7%
19,177 1
 
0.7%
19,713 1
 
0.7%
20,209 1
 
0.7%
20,602 1
 
0.7%
21,520 1
 
0.7%
22,124 1
 
0.7%
22,847 1
 
0.7%
Other values (140) 140
93.3%
2023-12-12T14:43:20.852123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 141
17.3%
1 103
12.6%
2 85
10.4%
3 70
8.6%
7 66
8.1%
8 65
8.0%
4 62
7.6%
5 59
7.2%
0 58
7.1%
9 57
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 676
82.7%
Other Punctuation 141
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 103
15.2%
2 85
12.6%
3 70
10.4%
7 66
9.8%
8 65
9.6%
4 62
9.2%
5 59
8.7%
0 58
8.6%
9 57
8.4%
6 51
7.5%
Other Punctuation
ValueCountFrequency (%)
, 141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 817
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 141
17.3%
1 103
12.6%
2 85
10.4%
3 70
8.6%
7 66
8.1%
8 65
8.0%
4 62
7.6%
5 59
7.2%
0 58
7.1%
9 57
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 817
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 141
17.3%
1 103
12.6%
2 85
10.4%
3 70
8.6%
7 66
8.1%
8 65
8.0%
4 62
7.6%
5 59
7.2%
0 58
7.1%
9 57
7.0%

정액형1)연금지급액5)
Text

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:21.277732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9666667
Min length2

Characters and Unicode

Total characters745
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

Unique150 ?
Unique (%)100.0%

Sample

1st row53
2nd row60
3rd row67
4th row86
5th row106
ValueCountFrequency (%)
53 1
 
0.7%
10,936 1
 
0.7%
11,415 1
 
0.7%
11,653 1
 
0.7%
11,944 1
 
0.7%
12,218 1
 
0.7%
12,507 1
 
0.7%
12,936 1
 
0.7%
13,338 1
 
0.7%
13,691 1
 
0.7%
Other values (140) 140
93.3%
2023-12-12T14:43:21.872004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 119
16.0%
1 93
12.5%
2 77
10.3%
3 65
8.7%
0 64
8.6%
4 61
8.2%
9 54
7.2%
7 54
7.2%
5 53
7.1%
8 53
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 626
84.0%
Other Punctuation 119
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 93
14.9%
2 77
12.3%
3 65
10.4%
0 64
10.2%
4 61
9.7%
9 54
8.6%
7 54
8.6%
5 53
8.5%
8 53
8.5%
6 52
8.3%
Other Punctuation
ValueCountFrequency (%)
, 119
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 745
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 119
16.0%
1 93
12.5%
2 77
10.3%
3 65
8.7%
0 64
8.6%
4 61
8.2%
9 54
7.2%
7 54
7.2%
5 53
7.1%
8 53
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 745
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 119
16.0%
1 93
12.5%
2 77
10.3%
3 65
8.7%
0 64
8.6%
4 61
8.2%
9 54
7.2%
7 54
7.2%
5 53
7.1%
8 53
7.1%

정액형1)보증공급액6)
Text

UNIQUE 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:22.282721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.6066667
Min length5

Characters and Unicode

Total characters991
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

Unique150 ?
Unique (%)100.0%

Sample

1st row6,348
2nd row6,592
3rd row7,038
4th row7,546
5th row8,333
ValueCountFrequency (%)
6,348 1
 
0.7%
240,963 1
 
0.7%
251,718 1
 
0.7%
257,911 1
 
0.7%
264,526 1
 
0.7%
270,223 1
 
0.7%
274,873 1
 
0.7%
282,939 1
 
0.7%
288,440 1
 
0.7%
295,441 1
 
0.7%
Other values (140) 140
93.3%
2023-12-12T14:43:22.831923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 150
15.1%
4 105
10.6%
1 104
10.5%
2 94
9.5%
3 85
8.6%
6 84
8.5%
8 77
7.8%
5 77
7.8%
9 76
7.7%
0 71
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 841
84.9%
Other Punctuation 150
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 105
12.5%
1 104
12.4%
2 94
11.2%
3 85
10.1%
6 84
10.0%
8 77
9.2%
5 77
9.2%
9 76
9.0%
0 71
8.4%
7 68
8.1%
Other Punctuation
ValueCountFrequency (%)
, 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 991
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 150
15.1%
4 105
10.6%
1 104
10.5%
2 94
9.5%
3 85
8.6%
6 84
8.5%
8 77
7.8%
5 77
7.8%
9 76
7.7%
0 71
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 991
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 150
15.1%
4 105
10.6%
1 104
10.5%
2 94
9.5%
3 85
8.6%
6 84
8.5%
8 77
7.8%
5 77
7.8%
9 76
7.7%
0 71
7.2%

증가형2)건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.9
Minimum0
Maximum158
Zeros5
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:43:23.030173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q143.75
median128
Q3139
95-th percentile151.55
Maximum158
Range158
Interquartile range (IQR)95.25

Descriptive statistics

Standard deviation52.083406
Coefficient of variation (CV)0.53200619
Kurtosis-1.1232148
Mean97.9
Median Absolute Deviation (MAD)19
Skewness-0.69588535
Sum14685
Variance2712.6812
MonotonicityNot monotonic
2023-12-12T14:43:23.197423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128 10
 
6.7%
145 8
 
5.3%
0 5
 
3.3%
131 5
 
3.3%
141 5
 
3.3%
134 4
 
2.7%
137 4
 
2.7%
147 4
 
2.7%
43 3
 
2.0%
129 3
 
2.0%
Other values (67) 99
66.0%
ValueCountFrequency (%)
0 5
3.3%
2 2
 
1.3%
4 2
 
1.3%
5 3
2.0%
8 1
 
0.7%
9 2
 
1.3%
12 1
 
0.7%
15 1
 
0.7%
20 1
 
0.7%
21 1
 
0.7%
ValueCountFrequency (%)
158 1
 
0.7%
157 2
1.3%
155 1
 
0.7%
154 2
1.3%
152 2
1.3%
151 1
 
0.7%
150 1
 
0.7%
149 1
 
0.7%
148 1
 
0.7%
147 4
2.7%

증가형2)연금지급액5)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct110
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.64
Minimum0
Maximum191
Zeros6
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T14:43:23.356854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q131.25
median112
Q3153
95-th percentile183.55
Maximum191
Range191
Interquartile range (IQR)121.75

Descriptive statistics

Standard deviation63.592271
Coefficient of variation (CV)0.65129323
Kurtosis-1.4252954
Mean97.64
Median Absolute Deviation (MAD)53.5
Skewness-0.22088439
Sum14646
Variance4043.9769
MonotonicityNot monotonic
2023-12-12T14:43:23.602875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
4.0%
143 6
 
4.0%
1 5
 
3.3%
2 4
 
2.7%
165 3
 
2.0%
168 3
 
2.0%
137 3
 
2.0%
190 3
 
2.0%
13 2
 
1.3%
28 2
 
1.3%
Other values (100) 113
75.3%
ValueCountFrequency (%)
0 6
4.0%
1 5
3.3%
2 4
2.7%
4 1
 
0.7%
5 1
 
0.7%
8 2
 
1.3%
9 1
 
0.7%
13 2
 
1.3%
14 1
 
0.7%
15 2
 
1.3%
ValueCountFrequency (%)
191 2
1.3%
190 3
2.0%
188 1
 
0.7%
186 1
 
0.7%
184 1
 
0.7%
183 1
 
0.7%
181 1
 
0.7%
179 2
1.3%
177 2
1.3%
176 1
 
0.7%

증가형2)보증공급액6)
Text

Distinct100
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:23.927327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.3533333
Min length1

Characters and Unicode

Total characters653
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

Unique71 ?
Unique (%)47.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
2,319 11
 
7.3%
0 5
 
3.3%
2,555 4
 
2.7%
2,503 4
 
2.7%
2,341 3
 
2.0%
2,348 3
 
2.0%
2,667 3
 
2.0%
2,559 3
 
2.0%
85 3
 
2.0%
2,458 2
 
1.3%
Other values (90) 109
72.7%
2023-12-12T14:43:24.468300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 130
19.9%
, 110
16.8%
5 65
10.0%
1 63
9.6%
3 52
 
8.0%
9 44
 
6.7%
8 40
 
6.1%
4 39
 
6.0%
0 38
 
5.8%
7 38
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 543
83.2%
Other Punctuation 110
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 130
23.9%
5 65
12.0%
1 63
11.6%
3 52
 
9.6%
9 44
 
8.1%
8 40
 
7.4%
4 39
 
7.2%
0 38
 
7.0%
7 38
 
7.0%
6 34
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 653
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 130
19.9%
, 110
16.8%
5 65
10.0%
1 63
9.6%
3 52
 
8.0%
9 44
 
6.7%
8 40
 
6.1%
4 39
 
6.0%
0 38
 
5.8%
7 38
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 130
19.9%
, 110
16.8%
5 65
10.0%
1 63
9.6%
3 52
 
8.0%
9 44
 
6.7%
8 40
 
6.1%
4 39
 
6.0%
0 38
 
5.8%
7 38
 
5.8%

감소형3)건수
Text

Distinct139
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:24.915468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.3533333
Min length1

Characters and Unicode

Total characters653
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

Unique135 ?
Unique (%)90.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9
 
6.0%
3,124 2
 
1.3%
3,152 2
 
1.3%
3,181 2
 
1.3%
2,961 1
 
0.7%
3,161 1
 
0.7%
2,945 1
 
0.7%
3,137 1
 
0.7%
3,110 1
 
0.7%
3,095 1
 
0.7%
Other values (129) 129
86.0%
2023-12-12T14:43:25.492097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 113
17.3%
2 100
15.3%
1 77
11.8%
3 69
10.6%
0 51
7.8%
9 47
7.2%
8 42
 
6.4%
5 40
 
6.1%
7 40
 
6.1%
4 38
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 540
82.7%
Other Punctuation 113
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 100
18.5%
1 77
14.3%
3 69
12.8%
0 51
9.4%
9 47
8.7%
8 42
7.8%
5 40
 
7.4%
7 40
 
7.4%
4 38
 
7.0%
6 36
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 653
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 113
17.3%
2 100
15.3%
1 77
11.8%
3 69
10.6%
0 51
7.8%
9 47
7.2%
8 42
 
6.4%
5 40
 
6.1%
7 40
 
6.1%
4 38
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 653
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 113
17.3%
2 100
15.3%
1 77
11.8%
3 69
10.6%
0 51
7.8%
9 47
7.2%
8 42
 
6.4%
5 40
 
6.1%
7 40
 
6.1%
4 38
 
5.8%

감소형3)연금지급액5)
Text

Distinct141
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:25.883929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.0333333
Min length1

Characters and Unicode

Total characters605
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

Unique139 ?
Unique (%)92.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9
 
6.0%
3,216 2
 
1.3%
3,421 1
 
0.7%
2,776 1
 
0.7%
2,480 1
 
0.7%
2,543 1
 
0.7%
2,877 1
 
0.7%
2,855 1
 
0.7%
2,827 1
 
0.7%
2,799 1
 
0.7%
Other values (131) 131
87.3%
2023-12-12T14:43:26.403794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 93
15.4%
2 82
13.6%
3 77
12.7%
1 70
11.6%
0 47
7.8%
4 44
7.3%
6 43
7.1%
7 39
6.4%
5 39
6.4%
9 36
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 512
84.6%
Other Punctuation 93
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 82
16.0%
3 77
15.0%
1 70
13.7%
0 47
9.2%
4 44
8.6%
6 43
8.4%
7 39
7.6%
5 39
7.6%
9 36
7.0%
8 35
6.8%
Other Punctuation
ValueCountFrequency (%)
, 93
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 605
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 93
15.4%
2 82
13.6%
3 77
12.7%
1 70
11.6%
0 47
7.8%
4 44
7.3%
6 43
7.1%
7 39
6.4%
5 39
6.4%
9 36
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 605
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 93
15.4%
2 82
13.6%
3 77
12.7%
1 70
11.6%
0 47
7.8%
4 44
7.3%
6 43
7.1%
7 39
6.4%
5 39
6.4%
9 36
 
6.0%

감소형3)보증공급액6)
Text

Distinct141
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:26.765795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.48
Min length1

Characters and Unicode

Total characters822
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

Unique139 ?
Unique (%)92.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 9
 
6.0%
34,992 2
 
1.3%
29,559 1
 
0.7%
33,908 1
 
0.7%
35,325 1
 
0.7%
33,182 1
 
0.7%
33,323 1
 
0.7%
33,527 1
 
0.7%
33,634 1
 
0.7%
33,784 1
 
0.7%
Other values (131) 131
87.3%
2023-12-12T14:43:27.220345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 136
16.5%
3 129
15.7%
2 93
11.3%
1 71
8.6%
4 69
8.4%
0 63
7.7%
9 61
7.4%
5 57
6.9%
8 53
 
6.4%
7 52
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 686
83.5%
Other Punctuation 136
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 129
18.8%
2 93
13.6%
1 71
10.3%
4 69
10.1%
0 63
9.2%
9 61
8.9%
5 57
8.3%
8 53
7.7%
7 52
7.6%
6 38
 
5.5%
Other Punctuation
ValueCountFrequency (%)
, 136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 822
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 136
16.5%
3 129
15.7%
2 93
11.3%
1 71
8.6%
4 69
8.4%
0 63
7.7%
9 61
7.4%
5 57
6.9%
8 53
 
6.4%
7 52
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 136
16.5%
3 129
15.7%
2 93
11.3%
1 71
8.6%
4 69
8.4%
0 63
7.7%
9 61
7.4%
5 57
6.9%
8 53
 
6.4%
7 52
 
6.3%

전후후박형4)건수
Text

Distinct96
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:27.482246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.5066667
Min length1

Characters and Unicode

Total characters526
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

Unique95 ?
Unique (%)63.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 55
36.7%
55 1
 
0.7%
9,905 1
 
0.7%
9,606 1
 
0.7%
9,362 1
 
0.7%
9,120 1
 
0.7%
9,003 1
 
0.7%
8,833 1
 
0.7%
8,589 1
 
0.7%
8,325 1
 
0.7%
Other values (86) 86
57.3%
2023-12-12T14:43:27.879870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 88
16.7%
, 81
15.4%
1 80
15.2%
5 44
8.4%
2 42
8.0%
3 39
7.4%
9 38
7.2%
6 31
 
5.9%
8 29
 
5.5%
7 27
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 445
84.6%
Other Punctuation 81
 
15.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88
19.8%
1 80
18.0%
5 44
9.9%
2 42
9.4%
3 39
8.8%
9 38
8.5%
6 31
 
7.0%
8 29
 
6.5%
7 27
 
6.1%
4 27
 
6.1%
Other Punctuation
ValueCountFrequency (%)
, 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 526
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88
16.7%
, 81
15.4%
1 80
15.2%
5 44
8.4%
2 42
8.0%
3 39
7.4%
9 38
7.2%
6 31
 
5.9%
8 29
 
5.5%
7 27
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88
16.7%
, 81
15.4%
1 80
15.2%
5 44
8.4%
2 42
8.0%
3 39
7.4%
9 38
7.2%
6 31
 
5.9%
8 29
 
5.5%
7 27
 
5.1%

전후후박형4)연금지급액5)
Text

Distinct96
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:28.226249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3
Min length1

Characters and Unicode

Total characters450
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

Unique95 ?
Unique (%)63.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 55
36.7%
8 1
 
0.7%
4,715 1
 
0.7%
4,533 1
 
0.7%
4,358 1
 
0.7%
4,184 1
 
0.7%
4,051 1
 
0.7%
3,904 1
 
0.7%
3,754 1
 
0.7%
3,589 1
 
0.7%
Other values (86) 86
57.3%
2023-12-12T14:43:28.639058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 84
18.7%
, 58
12.9%
1 42
9.3%
8 38
8.4%
2 37
8.2%
3 35
7.8%
4 34
7.6%
5 34
7.6%
7 32
 
7.1%
9 29
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 392
87.1%
Other Punctuation 58
 
12.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84
21.4%
1 42
10.7%
8 38
9.7%
2 37
9.4%
3 35
8.9%
4 34
8.7%
5 34
8.7%
7 32
 
8.2%
9 29
 
7.4%
6 27
 
6.9%
Other Punctuation
ValueCountFrequency (%)
, 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 450
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 84
18.7%
, 58
12.9%
1 42
9.3%
8 38
8.4%
2 37
8.2%
3 35
7.8%
4 34
7.6%
5 34
7.6%
7 32
 
7.1%
9 29
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 450
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 84
18.7%
, 58
12.9%
1 42
9.3%
8 38
8.4%
2 37
8.2%
3 35
7.8%
4 34
7.6%
5 34
7.6%
7 32
 
7.1%
9 29
 
6.4%

전후후박형4)보증공급액6)
Text

Distinct96
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-12T14:43:28.949586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.2333333
Min length1

Characters and Unicode

Total characters635
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

Unique95 ?
Unique (%)63.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 55
36.7%
801 1
 
0.7%
99,947 1
 
0.7%
96,833 1
 
0.7%
94,545 1
 
0.7%
91,975 1
 
0.7%
90,703 1
 
0.7%
88,947 1
 
0.7%
86,569 1
 
0.7%
84,037 1
 
0.7%
Other values (86) 86
57.3%
2023-12-12T14:43:29.378215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 106
16.7%
, 94
14.8%
1 73
11.5%
3 57
9.0%
4 51
8.0%
6 45
7.1%
5 45
7.1%
9 43
6.8%
2 42
 
6.6%
7 40
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 541
85.2%
Other Punctuation 94
 
14.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106
19.6%
1 73
13.5%
3 57
10.5%
4 51
9.4%
6 45
8.3%
5 45
8.3%
9 43
7.9%
2 42
 
7.8%
7 40
 
7.4%
8 39
 
7.2%
Other Punctuation
ValueCountFrequency (%)
, 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 635
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106
16.7%
, 94
14.8%
1 73
11.5%
3 57
9.0%
4 51
8.0%
6 45
7.1%
5 45
7.1%
9 43
6.8%
2 42
 
6.6%
7 40
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 635
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106
16.7%
, 94
14.8%
1 73
11.5%
3 57
9.0%
4 51
8.0%
6 45
7.1%
5 45
7.1%
9 43
6.8%
2 42
 
6.6%
7 40
 
6.3%

Interactions

2023-12-12T14:43:18.274952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:16.938158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.381765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.806785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:18.386098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.055162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.513432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.917713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:18.497994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.138225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.606236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:18.047700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:18.629496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.245029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:17.708829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:43:18.160807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:43:29.520422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)전후후박형4)건수전후후박형4)연금지급액5)전후후박형4)보증공급액6)
연도1.0000.0000.9550.9780.9930.5850.5850.585
월0.0001.0000.0000.0000.5110.0000.0000.000
증가형2)건수0.9550.0001.0000.9651.0000.0000.0000.000
증가형2)연금지급액5)0.9780.0000.9651.0000.9980.8250.8250.825
증가형2)보증공급액6)0.9930.5111.0000.9981.0000.0000.0000.000
전후후박형4)건수0.5850.0000.0000.8250.0001.0001.0001.000
전후후박형4)연금지급액5)0.5850.0000.0000.8250.0001.0001.0001.000
전후후박형4)보증공급액6)0.5850.0000.0000.8250.0001.0001.0001.000
2023-12-12T14:43:29.695727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월증가형2)건수증가형2)연금지급액5)
연도1.000-0.0580.7610.997
월-0.0581.0000.0290.016
증가형2)건수0.7610.0291.0000.761
증가형2)연금지급액5)0.9970.0160.7611.000

Missing values

2023-12-12T14:43:19.089636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:43:19.298801image/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)
020081547536,348000000000
120082567606,592000000000
220083614677,038000000000
320084661867,546000000000
4200857241068,333000000000
5200867921309,1812030000000
62008786315110,1582130000000
72008890617010,6784180000000
82008996118811,3674180000000
92008101,02621012,2905185122124000
연도월정액형1)건수정액형1)연금지급액5)정액형1)보증공급액6)증가형2)건수증가형2)연금지급액5)증가형2)보증공급액6)감소형3)건수감소형3)연금지급액5)감소형3)보증공급액6)전후후박형4)건수전후후박형4)연금지급액5)전후후박형4)보증공급액6)
1402019941,10929,549472,3311281812,3192,5763,40529,70114,0038,109142,375
14120191041,50830,023476,5601281832,3192,5663,42129,55914,2248,323144,434
14220191141,83230,531480,1151281842,3192,5523,43129,41814,4538,552147,009
14320191242,38631,004484,8461281862,3192,5353,43929,29614,7268,777149,905
1442020142,40531,433484,4761281882,3192,5213,45429,14914,7588,995150,233
1452020242,90131,886489,1551281902,3192,5143,47029,08115,0479,220153,010
1462020343,44632,445494,1461281912,3192,5023,48628,96015,3139,474155,694
1472020443,86733,022498,9071281912,3192,4903,50228,88015,5379,727158,350
1482020544,30833,563503,2581251902,2892,4763,51628,78115,7689,958160,911
1492020644,75434,083507,2741231902,2372,4643,53028,67415,95310,214162,626