Overview

Dataset statistics

Number of variables27
Number of observations26
Missing cells513
Missing cells (%)73.1%
Duplicate rows1
Duplicate rows (%)3.8%
Total size in memory6.0 KiB
Average record size in memory237.1 B

Variable types

Text11
Numeric16

Dataset

Description한국주택금융공사에서 발행한 주택연금 연령별 가입현황에 대한 데이터 입니다. 공공데이터 개방 정책에 따라 등록되었습니다.
Author한국주택금융공사
URLhttps://www.data.go.kr/data/15073691/fileData.do

Alerts

Dataset has 1 (3.8%) duplicate rowsDuplicates
구 분 has 19 (73.1%) missing valuesMissing
’08 건수 has 19 (73.1%) missing valuesMissing
’08 비율 has 19 (73.1%) missing valuesMissing
’09 건수 has 19 (73.1%) missing valuesMissing
’09 비율 has 19 (73.1%) missing valuesMissing
’10 건수 has 19 (73.1%) missing valuesMissing
’10 비율 has 19 (73.1%) missing valuesMissing
’11 건수 has 19 (73.1%) missing valuesMissing
’11 비율 has 19 (73.1%) missing valuesMissing
’12 건수 has 19 (73.1%) missing valuesMissing
’12 비율 has 19 (73.1%) missing valuesMissing
’13 건수 has 19 (73.1%) missing valuesMissing
’13 비율 has 19 (73.1%) missing valuesMissing
’14 건수 has 19 (73.1%) missing valuesMissing
’14 비율 has 19 (73.1%) missing valuesMissing
’15 건수 has 19 (73.1%) missing valuesMissing
’15 비율 has 19 (73.1%) missing valuesMissing
’16 건수 has 19 (73.1%) missing valuesMissing
’16 비율 has 19 (73.1%) missing valuesMissing
’17 건수 has 19 (73.1%) missing valuesMissing
’17 비율 has 19 (73.1%) missing valuesMissing
’18 건수 has 19 (73.1%) missing valuesMissing
’18 비율 has 19 (73.1%) missing valuesMissing
’19 건수 has 19 (73.1%) missing valuesMissing
’19 비율 has 19 (73.1%) missing valuesMissing
’20 건수 has 19 (73.1%) missing valuesMissing
’20 비율 has 19 (73.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 23:24:39.580478
Analysis finished2023-12-12 23:24:39.885707
Duration0.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구 분
Text

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:39.963182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters35
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)100.0%

Sample

1st row55세미만
2nd row55∼59
3rd row60∼64
4th row65∼69
5th row70∼74
ValueCountFrequency (%)
55세미만 1
14.3%
55∼59 1
14.3%
60∼64 1
14.3%
65∼69 1
14.3%
70∼74 1
14.3%
75∼79 1
14.3%
80세이상 1
14.3%
2023-12-13T08:24:40.233121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 7
20.0%
∼ 5
14.3%
6 4
11.4%
7 4
11.4%
9 3
8.6%
0 3
8.6%
세 2
 
5.7%
4 2
 
5.7%
미 1
 
2.9%
만 1
 
2.9%
Other values (3) 3
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
68.6%
Other Letter 6
 
17.1%
Math Symbol 5
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 7
29.2%
6 4
16.7%
7 4
16.7%
9 3
12.5%
0 3
12.5%
4 2
 
8.3%
8 1
 
4.2%
Other Letter
ValueCountFrequency (%)
세 2
33.3%
미 1
16.7%
만 1
16.7%
이 1
16.7%
상 1
16.7%
Math Symbol
ValueCountFrequency (%)
∼ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29
82.9%
Hangul 6
 
17.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 7
24.1%
∼ 5
17.2%
6 4
13.8%
7 4
13.8%
9 3
10.3%
0 3
10.3%
4 2
 
6.9%
8 1
 
3.4%
Hangul
ValueCountFrequency (%)
세 2
33.3%
미 1
16.7%
만 1
16.7%
이 1
16.7%
상 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24
68.6%
Hangul 6
 
17.1%
Math Operators 5
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 7
29.2%
6 4
16.7%
7 4
16.7%
9 3
12.5%
0 3
12.5%
4 2
 
8.3%
8 1
 
4.2%
Math Operators
ValueCountFrequency (%)
∼ 5
100.0%
Hangul
ValueCountFrequency (%)
세 2
33.3%
미 1
16.7%
만 1
16.7%
이 1
16.7%
상 1
16.7%

’08 건수
Text

MISSING 

Distinct5
Distinct (%)71.4%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:40.368409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.1428571
Min length1

Characters and Unicode

Total characters15
Distinct characters7
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

Unique4 ?
Unique (%)57.1%

Sample

1st row-
2nd row-
3rd row-
4th row177
5th row224
ValueCountFrequency (%)
3
42.9%
177 1
 
14.3%
224 1
 
14.3%
176 1
 
14.3%
118 1
 
14.3%
2023-12-13T08:24:40.580519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
26.7%
- 3
20.0%
7 3
20.0%
2 2
13.3%
4 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12
80.0%
Dash Punctuation 3
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
33.3%
7 3
25.0%
2 2
16.7%
4 1
 
8.3%
6 1
 
8.3%
8 1
 
8.3%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
26.7%
- 3
20.0%
7 3
20.0%
2 2
13.3%
4 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
26.7%
- 3
20.0%
7 3
20.0%
2 2
13.3%
4 1
 
6.7%
6 1
 
6.7%
8 1
 
6.7%

’08 비율
Text

MISSING 

Distinct5
Distinct (%)71.4%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:40.675972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.4285714
Min length1

Characters and Unicode

Total characters17
Distinct characters7
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)57.1%

Sample

1st row-
2nd row-
3rd row-
4th row25.5
5th row32.2
ValueCountFrequency (%)
3
42.9%
25.5 1
 
14.3%
32.2 1
 
14.3%
25.3 1
 
14.3%
17 1
 
14.3%
2023-12-13T08:24:40.896776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 4
23.5%
- 3
17.6%
5 3
17.6%
. 3
17.6%
3 2
11.8%
1 1
 
5.9%
7 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11
64.7%
Dash Punctuation 3
 
17.6%
Other Punctuation 3
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4
36.4%
5 3
27.3%
3 2
18.2%
1 1
 
9.1%
7 1
 
9.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4
23.5%
- 3
17.6%
5 3
17.6%
. 3
17.6%
3 2
11.8%
1 1
 
5.9%
7 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 4
23.5%
- 3
17.6%
5 3
17.6%
. 3
17.6%
3 2
11.8%
1 1
 
5.9%
7 1
 
5.9%

’09 건수
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:41.005770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.4285714
Min length1

Characters and Unicode

Total characters17
Distinct characters7
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

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row141
4th row262
5th row314
ValueCountFrequency (%)
2
28.6%
141 1
14.3%
262 1
14.3%
314 1
14.3%
233 1
14.3%
174 1
14.3%
2023-12-13T08:24:41.239730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
23.5%
4 3
17.6%
2 3
17.6%
3 3
17.6%
- 2
11.8%
6 1
 
5.9%
7 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
88.2%
Dash Punctuation 2
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
26.7%
4 3
20.0%
2 3
20.0%
3 3
20.0%
6 1
 
6.7%
7 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
23.5%
4 3
17.6%
2 3
17.6%
3 3
17.6%
- 2
11.8%
6 1
 
5.9%
7 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
23.5%
4 3
17.6%
2 3
17.6%
3 3
17.6%
- 2
11.8%
6 1
 
5.9%
7 1
 
5.9%

’09 비율
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:41.375938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.1428571
Min length1

Characters and Unicode

Total characters22
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row12.6
4th row23.3
5th row27.9
ValueCountFrequency (%)
2
28.6%
12.6 1
14.3%
23.3 1
14.3%
27.9 1
14.3%
20.7 1
14.3%
15.5 1
14.3%
2023-12-13T08:24:41.626605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 5
22.7%
2 4
18.2%
- 2
 
9.1%
1 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
5 2
 
9.1%
6 1
 
4.5%
9 1
 
4.5%
0 1
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
68.2%
Other Punctuation 5
 
22.7%
Dash Punctuation 2
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4
26.7%
1 2
13.3%
3 2
13.3%
7 2
13.3%
5 2
13.3%
6 1
 
6.7%
9 1
 
6.7%
0 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
22.7%
2 4
18.2%
- 2
 
9.1%
1 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
5 2
 
9.1%
6 1
 
4.5%
9 1
 
4.5%
0 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
22.7%
2 4
18.2%
- 2
 
9.1%
1 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
5 2
 
9.1%
6 1
 
4.5%
9 1
 
4.5%
0 1
 
4.5%

’10 건수
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:41.734360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.4285714
Min length1

Characters and Unicode

Total characters17
Distinct characters9
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

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row259
4th row426
5th row521
ValueCountFrequency (%)
2
28.6%
259 1
14.3%
426 1
14.3%
521 1
14.3%
463 1
14.3%
347 1
14.3%
2023-12-13T08:24:42.019237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3
17.6%
4 3
17.6%
- 2
11.8%
5 2
11.8%
6 2
11.8%
3 2
11.8%
9 1
 
5.9%
1 1
 
5.9%
7 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
88.2%
Dash Punctuation 2
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3
20.0%
4 3
20.0%
5 2
13.3%
6 2
13.3%
3 2
13.3%
9 1
 
6.7%
1 1
 
6.7%
7 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3
17.6%
4 3
17.6%
- 2
11.8%
5 2
11.8%
6 2
11.8%
3 2
11.8%
9 1
 
5.9%
1 1
 
5.9%
7 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3
17.6%
4 3
17.6%
- 2
11.8%
5 2
11.8%
6 2
11.8%
3 2
11.8%
9 1
 
5.9%
1 1
 
5.9%
7 1
 
5.9%

’10 비율
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:42.120532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length2.8571429
Min length1

Characters and Unicode

Total characters20
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row12.8
4th row21.1
5th row25.9
ValueCountFrequency (%)
2
28.6%
12.8 1
14.3%
21.1 1
14.3%
25.9 1
14.3%
23 1
14.3%
17.2 1
14.3%
2023-12-13T08:24:42.373611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 5
25.0%
1 4
20.0%
. 4
20.0%
- 2
 
10.0%
8 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%
7 1
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14
70.0%
Other Punctuation 4
 
20.0%
Dash Punctuation 2
 
10.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 5
35.7%
1 4
28.6%
8 1
 
7.1%
5 1
 
7.1%
9 1
 
7.1%
3 1
 
7.1%
7 1
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 5
25.0%
1 4
20.0%
. 4
20.0%
- 2
 
10.0%
8 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%
7 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 5
25.0%
1 4
20.0%
. 4
20.0%
- 2
 
10.0%
8 1
 
5.0%
5 1
 
5.0%
9 1
 
5.0%
3 1
 
5.0%
7 1
 
5.0%

’11 건수
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:42.474720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.4285714
Min length1

Characters and Unicode

Total characters17
Distinct characters9
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

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row326
4th row573
5th row842
ValueCountFrequency (%)
2
28.6%
326 1
14.3%
573 1
14.3%
842 1
14.3%
708 1
14.3%
487 1
14.3%
2023-12-13T08:24:42.722247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3
17.6%
8 3
17.6%
- 2
11.8%
3 2
11.8%
2 2
11.8%
4 2
11.8%
6 1
 
5.9%
5 1
 
5.9%
0 1
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
88.2%
Dash Punctuation 2
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 3
20.0%
8 3
20.0%
3 2
13.3%
2 2
13.3%
4 2
13.3%
6 1
 
6.7%
5 1
 
6.7%
0 1
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 3
17.6%
8 3
17.6%
- 2
11.8%
3 2
11.8%
2 2
11.8%
4 2
11.8%
6 1
 
5.9%
5 1
 
5.9%
0 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 3
17.6%
8 3
17.6%
- 2
11.8%
3 2
11.8%
2 2
11.8%
4 2
11.8%
6 1
 
5.9%
5 1
 
5.9%
0 1
 
5.9%

’11 비율
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:42.868701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.1428571
Min length1

Characters and Unicode

Total characters22
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row11.1
4th row19.5
5th row28.7
ValueCountFrequency (%)
2
28.6%
11.1 1
14.3%
19.5 1
14.3%
28.7 1
14.3%
24.1 1
14.3%
16.6 1
14.3%
2023-12-13T08:24:43.093271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6
27.3%
. 5
22.7%
- 2
 
9.1%
2 2
 
9.1%
6 2
 
9.1%
9 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
4 1
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
68.2%
Other Punctuation 5
 
22.7%
Dash Punctuation 2
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6
40.0%
2 2
 
13.3%
6 2
 
13.3%
9 1
 
6.7%
5 1
 
6.7%
8 1
 
6.7%
7 1
 
6.7%
4 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6
27.3%
. 5
22.7%
- 2
 
9.1%
2 2
 
9.1%
6 2
 
9.1%
9 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
4 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6
27.3%
. 5
22.7%
- 2
 
9.1%
2 2
 
9.1%
6 2
 
9.1%
9 1
 
4.5%
5 1
 
4.5%
8 1
 
4.5%
7 1
 
4.5%
4 1
 
4.5%

’12 건수
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:43.199426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.7142857
Min length1

Characters and Unicode

Total characters19
Distinct characters9
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

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row817
4th row1127
5th row1385
ValueCountFrequency (%)
2
28.6%
817 1
14.3%
1127 1
14.3%
1385 1
14.3%
987 1
14.3%
697 1
14.3%
2023-12-13T08:24:43.428052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 4
21.1%
7 4
21.1%
8 3
15.8%
- 2
10.5%
9 2
10.5%
2 1
 
5.3%
3 1
 
5.3%
5 1
 
5.3%
6 1
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17
89.5%
Dash Punctuation 2
 
10.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4
23.5%
7 4
23.5%
8 3
17.6%
9 2
11.8%
2 1
 
5.9%
3 1
 
5.9%
5 1
 
5.9%
6 1
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4
21.1%
7 4
21.1%
8 3
15.8%
- 2
10.5%
9 2
10.5%
2 1
 
5.3%
3 1
 
5.3%
5 1
 
5.3%
6 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4
21.1%
7 4
21.1%
8 3
15.8%
- 2
10.5%
9 2
10.5%
2 1
 
5.3%
3 1
 
5.3%
5 1
 
5.3%
6 1
 
5.3%

’12 비율
Text

MISSING 

Distinct6
Distinct (%)85.7%
Missing19
Missing (%)73.1%
Memory size340.0 B
2023-12-13T08:24:43.563507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.1428571
Min length1

Characters and Unicode

Total characters22
Distinct characters9
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)71.4%

Sample

1st row-
2nd row-
3rd row16.3
4th row22.5
5th row27.6
ValueCountFrequency (%)
2
28.6%
16.3 1
14.3%
22.5 1
14.3%
27.6 1
14.3%
19.7 1
14.3%
13.9 1
14.3%
2023-12-13T08:24:43.795792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 5
22.7%
1 3
13.6%
2 3
13.6%
- 2
 
9.1%
6 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
9 2
 
9.1%
5 1
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15
68.2%
Other Punctuation 5
 
22.7%
Dash Punctuation 2
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3
20.0%
2 3
20.0%
6 2
13.3%
3 2
13.3%
7 2
13.3%
9 2
13.3%
5 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5
22.7%
1 3
13.6%
2 3
13.6%
- 2
 
9.1%
6 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
9 2
 
9.1%
5 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5
22.7%
1 3
13.6%
2 3
13.6%
- 2
 
9.1%
6 2
 
9.1%
3 2
 
9.1%
7 2
 
9.1%
9 2
 
9.1%
5 1
 
4.5%

’13 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean756.57143
Minimum23
Maximum1338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:43.904111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile63.2
Q1455.5
median833
Q31095.5
95-th percentile1272.3
Maximum1338
Range1315
Interquartile range (IQR)640

Descriptive statistics

Standard deviation495.37355
Coefficient of variation (CV)0.65476111
Kurtosis-1.0580743
Mean756.57143
Median Absolute Deviation (MAD)286
Skewness-0.64167089
Sum5296
Variance245394.95
MonotonicityNot monotonic
2023-12-13T08:24:44.022784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
23 1
 
3.8%
157 1
 
3.8%
833 1
 
3.8%
1072 1
 
3.8%
1338 1
 
3.8%
1119 1
 
3.8%
754 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
23 1
3.8%
157 1
3.8%
754 1
3.8%
833 1
3.8%
1072 1
3.8%
1119 1
3.8%
1338 1
3.8%
ValueCountFrequency (%)
1338 1
3.8%
1119 1
3.8%
1072 1
3.8%
833 1
3.8%
754 1
3.8%
157 1
3.8%
23 1
3.8%

’13 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.285714
Minimum0.4
Maximum25.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:44.487654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile1.18
Q18.6
median15.7
Q320.7
95-th percentile24.04
Maximum25.3
Range24.9
Interquartile range (IQR)12.1

Descriptive statistics

Standard deviation9.3640142
Coefficient of variation (CV)0.65548099
Kurtosis-1.0550879
Mean14.285714
Median Absolute Deviation (MAD)5.4
Skewness-0.63706859
Sum100
Variance87.684762
MonotonicityNot monotonic
2023-12-13T08:24:44.590666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.4 1
 
3.8%
3.0 1
 
3.8%
15.7 1
 
3.8%
20.3 1
 
3.8%
25.3 1
 
3.8%
21.1 1
 
3.8%
14.2 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.4 1
3.8%
3.0 1
3.8%
14.2 1
3.8%
15.7 1
3.8%
20.3 1
3.8%
21.1 1
3.8%
25.3 1
3.8%
ValueCountFrequency (%)
25.3 1
3.8%
21.1 1
3.8%
20.3 1
3.8%
15.7 1
3.8%
14.2 1
3.8%
3.0 1
3.8%
0.4 1
3.8%

’14 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean719.85714
Minimum27
Maximum1368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:44.683028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile67.8
Q1425
median745
Q31024.5
95-th percentile1277.1
Maximum1368
Range1341
Interquartile range (IQR)599.5

Descriptive statistics

Standard deviation483.19024
Coefficient of variation (CV)0.67123074
Kurtosis-0.92986281
Mean719.85714
Median Absolute Deviation (MAD)320
Skewness-0.35881603
Sum5039
Variance233472.81
MonotonicityNot monotonic
2023-12-13T08:24:44.792550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
27 1
 
3.8%
163 1
 
3.8%
687 1
 
3.8%
984 1
 
3.8%
1368 1
 
3.8%
1065 1
 
3.8%
745 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
27 1
3.8%
163 1
3.8%
687 1
3.8%
745 1
3.8%
984 1
3.8%
1065 1
3.8%
1368 1
3.8%
ValueCountFrequency (%)
1368 1
3.8%
1065 1
3.8%
984 1
3.8%
745 1
3.8%
687 1
3.8%
163 1
3.8%
27 1
3.8%

’14 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.257143
Minimum0.5
Maximum27.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:44.902564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.31
Q18.4
median14.8
Q320.3
95-th percentile25.3
Maximum27.1
Range26.6
Interquartile range (IQR)11.9

Descriptive statistics

Standard deviation9.5872932
Coefficient of variation (CV)0.67245543
Kurtosis-0.93030704
Mean14.257143
Median Absolute Deviation (MAD)6.3
Skewness-0.36349872
Sum99.8
Variance91.91619
MonotonicityNot monotonic
2023-12-13T08:24:45.029182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.5 1
 
3.8%
3.2 1
 
3.8%
13.6 1
 
3.8%
19.5 1
 
3.8%
27.1 1
 
3.8%
21.1 1
 
3.8%
14.8 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.5 1
3.8%
3.2 1
3.8%
13.6 1
3.8%
14.8 1
3.8%
19.5 1
3.8%
21.1 1
3.8%
27.1 1
3.8%
ValueCountFrequency (%)
27.1 1
3.8%
21.1 1
3.8%
19.5 1
3.8%
14.8 1
3.8%
13.6 1
3.8%
3.2 1
3.8%
0.5 1
3.8%

’15 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean926.57143
Minimum10
Maximum1677
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:45.138468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile42.7
Q1512
median1001
Q31387
95-th percentile1602.3
Maximum1677
Range1667
Interquartile range (IQR)875

Descriptive statistics

Standard deviation644.04629
Coefficient of variation (CV)0.69508542
Kurtosis-1.2164669
Mean926.57143
Median Absolute Deviation (MAD)427
Skewness-0.58853841
Sum6486
Variance414795.62
MonotonicityNot monotonic
2023-12-13T08:24:45.249823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 1
 
3.8%
119 1
 
3.8%
1001 1
 
3.8%
1428 1
 
3.8%
1677 1
 
3.8%
1346 1
 
3.8%
905 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
10 1
3.8%
119 1
3.8%
905 1
3.8%
1001 1
3.8%
1346 1
3.8%
1428 1
3.8%
1677 1
3.8%
ValueCountFrequency (%)
1677 1
3.8%
1428 1
3.8%
1346 1
3.8%
1001 1
3.8%
905 1
3.8%
119 1
3.8%
10 1
3.8%

’15 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.3
Minimum0.2
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:45.359654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.68
Q17.9
median15.4
Q321.4
95-th percentile24.73
Maximum25.9
Range25.7
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.936968
Coefficient of variation (CV)0.69489287
Kurtosis-1.2193603
Mean14.3
Median Absolute Deviation (MAD)6.6
Skewness-0.58570294
Sum100.1
Variance98.743333
MonotonicityNot monotonic
2023-12-13T08:24:45.477722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.2 1
 
3.8%
1.8 1
 
3.8%
15.4 1
 
3.8%
22.0 1
 
3.8%
25.9 1
 
3.8%
20.8 1
 
3.8%
14.0 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.2 1
3.8%
1.8 1
3.8%
14.0 1
3.8%
15.4 1
3.8%
20.8 1
3.8%
22.0 1
3.8%
25.9 1
3.8%
ValueCountFrequency (%)
25.9 1
3.8%
22.0 1
3.8%
20.8 1
3.8%
15.4 1
3.8%
14.0 1
3.8%
1.8 1
3.8%
0.2 1
3.8%

’16 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean1472.7143
Minimum10
Maximum2642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:45.590273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile59.8
Q1806
median1611
Q32217
95-th percentile2530.1
Maximum2642
Range2632
Interquartile range (IQR)1411

Descriptive statistics

Standard deviation1026.4852
Coefficient of variation (CV)0.69700228
Kurtosis-1.2360083
Mean1472.7143
Median Absolute Deviation (MAD)658
Skewness-0.61957665
Sum10309
Variance1053671.9
MonotonicityNot monotonic
2023-12-13T08:24:45.703590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
10 1
 
3.8%
176 1
 
3.8%
1611 1
 
3.8%
2269 1
 
3.8%
2642 1
 
3.8%
2165 1
 
3.8%
1436 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
10 1
3.8%
176 1
3.8%
1436 1
3.8%
1611 1
3.8%
2165 1
3.8%
2269 1
3.8%
2642 1
3.8%
ValueCountFrequency (%)
2642 1
3.8%
2269 1
3.8%
2165 1
3.8%
1611 1
3.8%
1436 1
3.8%
176 1
3.8%
10 1
3.8%

’16 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.271429
Minimum0.1
Maximum25.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:45.816495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.58
Q17.8
median15.6
Q321.5
95-th percentile24.52
Maximum25.6
Range25.5
Interquartile range (IQR)13.7

Descriptive statistics

Standard deviation9.9508315
Coefficient of variation (CV)0.69725546
Kurtosis-1.2401104
Mean14.271429
Median Absolute Deviation (MAD)6.4
Skewness-0.61817013
Sum99.9
Variance99.019048
MonotonicityNot monotonic
2023-12-13T08:24:45.916662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.1 1
 
3.8%
1.7 1
 
3.8%
15.6 1
 
3.8%
22.0 1
 
3.8%
25.6 1
 
3.8%
21.0 1
 
3.8%
13.9 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.1 1
3.8%
1.7 1
3.8%
13.9 1
3.8%
15.6 1
3.8%
21.0 1
3.8%
22.0 1
3.8%
25.6 1
3.8%
ValueCountFrequency (%)
25.6 1
3.8%
22.0 1
3.8%
21.0 1
3.8%
15.6 1
3.8%
13.9 1
3.8%
1.7 1
3.8%
0.1 1
3.8%

’17 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean1483.7143
Minimum12
Maximum2548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:46.024209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile59.1
Q1874.5
median1693
Q32192
95-th percentile2452.3
Maximum2548
Range2536
Interquartile range (IQR)1317.5

Descriptive statistics

Standard deviation1007.1633
Coefficient of variation (CV)0.67881216
Kurtosis-1.102546
Mean1483.7143
Median Absolute Deviation (MAD)536
Skewness-0.80577153
Sum10386
Variance1014377.9
MonotonicityNot monotonic
2023-12-13T08:24:46.128762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 1
 
3.8%
169 1
 
3.8%
1693 1
 
3.8%
2155 1
 
3.8%
2548 1
 
3.8%
2229 1
 
3.8%
1580 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
12 1
3.8%
169 1
3.8%
1580 1
3.8%
1693 1
3.8%
2155 1
3.8%
2229 1
3.8%
2548 1
3.8%
ValueCountFrequency (%)
2548 1
3.8%
2229 1
3.8%
2155 1
3.8%
1693 1
3.8%
1580 1
3.8%
169 1
3.8%
12 1
3.8%

’17 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.271429
Minimum0.1
Maximum24.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:46.238917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.55
Q18.4
median16.3
Q321.1
95-th percentile23.6
Maximum24.5
Range24.4
Interquartile range (IQR)12.7

Descriptive statistics

Standard deviation9.7002945
Coefficient of variation (CV)0.67970032
Kurtosis-1.103571
Mean14.271429
Median Absolute Deviation (MAD)5.2
Skewness-0.80728566
Sum99.9
Variance94.095714
MonotonicityNot monotonic
2023-12-13T08:24:46.361515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.1 1
 
3.8%
1.6 1
 
3.8%
16.3 1
 
3.8%
20.7 1
 
3.8%
24.5 1
 
3.8%
21.5 1
 
3.8%
15.2 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.1 1
3.8%
1.6 1
3.8%
15.2 1
3.8%
16.3 1
3.8%
20.7 1
3.8%
21.5 1
3.8%
24.5 1
3.8%
ValueCountFrequency (%)
24.5 1
3.8%
21.5 1
3.8%
20.7 1
3.8%
16.3 1
3.8%
15.2 1
3.8%
1.6 1
3.8%
0.1 1
3.8%

’18 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean1462.4286
Minimum9
Maximum2492
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:46.490132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile53.7
Q1721.5
median1841
Q32226
95-th percentile2483.3
Maximum2492
Range2483
Interquartile range (IQR)1504.5

Descriptive statistics

Standard deviation1026.9067
Coefficient of variation (CV)0.70219269
Kurtosis-1.3789454
Mean1462.4286
Median Absolute Deviation (MAD)622
Skewness-0.646853
Sum10237
Variance1054537.3
MonotonicityNot monotonic
2023-12-13T08:24:46.604027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
9 1
 
3.8%
158 1
 
3.8%
1285 1
 
3.8%
1841 1
 
3.8%
2463 1
 
3.8%
2492 1
 
3.8%
1989 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
9 1
3.8%
158 1
3.8%
1285 1
3.8%
1841 1
3.8%
1989 1
3.8%
2463 1
3.8%
2492 1
3.8%
ValueCountFrequency (%)
2492 1
3.8%
2463 1
3.8%
1989 1
3.8%
1841 1
3.8%
1285 1
3.8%
158 1
3.8%
9 1
3.8%

’18 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.285714
Minimum0.1
Maximum24.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:46.697550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.52
Q17.05
median18
Q321.75
95-th percentile24.24
Maximum24.3
Range24.2
Interquartile range (IQR)14.7

Descriptive statistics

Standard deviation10.03418
Coefficient of variation (CV)0.70239258
Kurtosis-1.3749378
Mean14.285714
Median Absolute Deviation (MAD)6.1
Skewness-0.6501363
Sum100
Variance100.68476
MonotonicityNot monotonic
2023-12-13T08:24:46.808693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.1 1
 
3.8%
1.5 1
 
3.8%
12.6 1
 
3.8%
18.0 1
 
3.8%
24.1 1
 
3.8%
24.3 1
 
3.8%
19.4 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.1 1
3.8%
1.5 1
3.8%
12.6 1
3.8%
18.0 1
3.8%
19.4 1
3.8%
24.1 1
3.8%
24.3 1
3.8%
ValueCountFrequency (%)
24.3 1
3.8%
24.1 1
3.8%
19.4 1
3.8%
18.0 1
3.8%
12.6 1
3.8%
1.5 1
3.8%
0.1 1
3.8%

’19 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean1568.8571
Minimum7
Maximum2651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:46.912710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile67
Q1841
median1969
Q32336.5
95-th percentile2620.7
Maximum2651
Range2644
Interquartile range (IQR)1495.5

Descriptive statistics

Standard deviation1072.6699
Coefficient of variation (CV)0.68372697
Kurtosis-1.2376957
Mean1568.8571
Median Absolute Deviation (MAD)581
Skewness-0.73552983
Sum10982
Variance1150620.8
MonotonicityNot monotonic
2023-12-13T08:24:47.003755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 1
 
3.8%
207 1
 
3.8%
1475 1
 
3.8%
1969 1
 
3.8%
2651 1
 
3.8%
2550 1
 
3.8%
2123 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
7 1
3.8%
207 1
3.8%
1475 1
3.8%
1969 1
3.8%
2123 1
3.8%
2550 1
3.8%
2651 1
3.8%
ValueCountFrequency (%)
2651 1
3.8%
2550 1
3.8%
2123 1
3.8%
1969 1
3.8%
1475 1
3.8%
207 1
3.8%
7 1
3.8%

’19 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.271429
Minimum0.1
Maximum24.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:47.101895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.64
Q17.65
median17.9
Q321.25
95-th percentile23.83
Maximum24.1
Range24
Interquartile range (IQR)13.6

Descriptive statistics

Standard deviation9.7417853
Coefficient of variation (CV)0.68260758
Kurtosis-1.2414768
Mean14.271429
Median Absolute Deviation (MAD)5.3
Skewness-0.73292623
Sum99.9
Variance94.902381
MonotonicityNot monotonic
2023-12-13T08:24:47.219862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.1 1
 
3.8%
1.9 1
 
3.8%
13.4 1
 
3.8%
17.9 1
 
3.8%
24.1 1
 
3.8%
23.2 1
 
3.8%
19.3 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.1 1
3.8%
1.9 1
3.8%
13.4 1
3.8%
17.9 1
3.8%
19.3 1
3.8%
23.2 1
3.8%
24.1 1
3.8%
ValueCountFrequency (%)
24.1 1
3.8%
23.2 1
3.8%
19.3 1
3.8%
17.9 1
3.8%
13.4 1
3.8%
1.9 1
3.8%
0.1 1
3.8%

’20 건수
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean1164.7143
Minimum25
Maximum1884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:47.318618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile100.9
Q1607
median1488
Q31771
95-th percentile1881.9
Maximum1884
Range1859
Interquartile range (IQR)1164

Descriptive statistics

Standard deviation765.61995
Coefficient of variation (CV)0.65734572
Kurtosis-1.4519968
Mean1164.7143
Median Absolute Deviation (MAD)396
Skewness-0.67437094
Sum8153
Variance586173.9
MonotonicityNot monotonic
2023-12-13T08:24:47.437256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
25 1
 
3.8%
278 1
 
3.8%
936 1
 
3.8%
1488 1
 
3.8%
1877 1
 
3.8%
1884 1
 
3.8%
1665 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
25 1
3.8%
278 1
3.8%
936 1
3.8%
1488 1
3.8%
1665 1
3.8%
1877 1
3.8%
1884 1
3.8%
ValueCountFrequency (%)
1884 1
3.8%
1877 1
3.8%
1665 1
3.8%
1488 1
3.8%
936 1
3.8%
278 1
3.8%
25 1
3.8%

’20 비율
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)100.0%
Missing19
Missing (%)73.1%
Infinite0
Infinite (%)0.0%
Mean14.285714
Minimum0.3
Maximum23.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T08:24:47.538316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile1.23
Q17.45
median18.3
Q321.7
95-th percentile23.07
Maximum23.1
Range22.8
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation9.3896093
Coefficient of variation (CV)0.65727265
Kurtosis-1.4482903
Mean14.285714
Median Absolute Deviation (MAD)4.8
Skewness-0.67853803
Sum100
Variance88.164762
MonotonicityNot monotonic
2023-12-13T08:24:47.649722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.3 1
 
3.8%
3.4 1
 
3.8%
11.5 1
 
3.8%
18.3 1
 
3.8%
23.0 1
 
3.8%
23.1 1
 
3.8%
20.4 1
 
3.8%
(Missing) 19
73.1%
ValueCountFrequency (%)
0.3 1
3.8%
3.4 1
3.8%
11.5 1
3.8%
18.3 1
3.8%
20.4 1
3.8%
23.0 1
3.8%
23.1 1
3.8%
ValueCountFrequency (%)
23.1 1
3.8%
23.0 1
3.8%
20.4 1
3.8%
18.3 1
3.8%
11.5 1
3.8%
3.4 1
3.8%
0.3 1
3.8%

Sample

구 분’08 건수’08 비율’09 건수’09 비율’10 건수’10 비율’11 건수’11 비율’12 건수’12 비율’13 건수’13 비율’14 건수’14 비율’15 건수’15 비율’16 건수’16 비율’17 건수’17 비율’18 건수’18 비율’19 건수’19 비율’20 건수’20 비율
055세미만----------230.4270.5100.2100.1120.190.170.1250.3
155∼59----------1573.01633.21191.81761.71691.61581.52071.92783.4
260∼64--14112.625912.832611.181716.383315.768713.6100115.4161115.6169316.3128512.6147513.493611.5
365∼6917725.526223.342621.157319.5112722.5107220.398419.5142822.0226922.0215520.7184118.0196917.9148818.3
470∼7422432.231427.952125.984228.7138527.6133825.3136827.1167725.9264225.6254824.5246324.1265124.1187723.0
575∼7917625.323320.74632370824.198719.7111921.1106521.1134620.8216521.0222921.5249224.3255023.2188423.1
680세이상1181717415.534717.248716.669713.975414.274514.890514.0143613.9158015.2198919.4212319.3166520.4
7<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
구 분’08 건수’08 비율’09 건수’09 비율’10 건수’10 비율’11 건수’11 비율’12 건수’12 비율’13 건수’13 비율’14 건수’14 비율’15 건수’15 비율’16 건수’16 비율’17 건수’17 비율’18 건수’18 비율’19 건수’19 비율’20 건수’20 비율
16<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구 분’08 건수’08 비율’09 건수’09 비율’10 건수’10 비율’11 건수’11 비율’12 건수’12 비율’13 건수’13 비율’14 건수’14 비율’15 건수’15 비율’16 건수’16 비율’17 건수’17 비율’18 건수’18 비율’19 건수’19 비율’20 건수’20 비율# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>19