Overview

Dataset statistics

Number of variables5
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory46.1 B

Variable types

Text4
Numeric1

Dataset

Description서울특별시 자치구별 65세 이상 노인인구, 기초연금 수급자 수, 미수급자 수, 수급율에 대한 데이터입니다.
Author서울특별시
URLhttps://www.data.go.kr/data/15086010/fileData.do

Alerts

자치구 has unique valuesUnique
65세이상 노인 인구 has unique valuesUnique
기초연금 수급자 수 has unique valuesUnique
미수급자 수 has unique valuesUnique
수급률 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:01:47.264111
Analysis finished2023-12-12 16:01:48.095195
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

자치구
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T01:01:48.255997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length2

Characters and Unicode

Total characters104
Distinct characters39
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row합계
2nd row종로구
3rd row중구
4th row용산구
5th row성동구
ValueCountFrequency (%)
합계 1
 
3.8%
종로구 1
 
3.8%
송파구 1
 
3.8%
강남구 1
 
3.8%
서초구 1
 
3.8%
관악구 1
 
3.8%
동작구 1
 
3.8%
영등포구 1
 
3.8%
금천구 1
 
3.8%
구로구 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T01:01:48.676951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 26
25.0%
25
24.0%
강 4
 
3.8%
동 4
 
3.8%
서 3
 
2.9%
문 2
 
1.9%
대 2
 
1.9%
성 2
 
1.9%
천 2
 
1.9%
중 2
 
1.9%
Other values (29) 32
30.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
76.0%
Space Separator 25
 
24.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 26
32.9%
강 4
 
5.1%
동 4
 
5.1%
서 3
 
3.8%
문 2
 
2.5%
대 2
 
2.5%
성 2
 
2.5%
천 2
 
2.5%
중 2
 
2.5%
로 2
 
2.5%
Other values (28) 30
38.0%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
76.0%
Common 25
 
24.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 26
32.9%
강 4
 
5.1%
동 4
 
5.1%
서 3
 
3.8%
문 2
 
2.5%
대 2
 
2.5%
성 2
 
2.5%
천 2
 
2.5%
중 2
 
2.5%
로 2
 
2.5%
Other values (28) 30
38.0%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
76.0%
ASCII 25
 
24.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 26
32.9%
강 4
 
5.1%
동 4
 
5.1%
서 3
 
3.8%
문 2
 
2.5%
대 2
 
2.5%
성 2
 
2.5%
천 2
 
2.5%
중 2
 
2.5%
로 2
 
2.5%
Other values (28) 30
38.0%
ASCII
ValueCountFrequency (%)
25
100.0%

65세이상 노인 인구
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T01:01:48.909314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.1153846
Min length7

Characters and Unicode

Total characters185
Distinct characters12
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

Unique26 ?
Unique (%)100.0%

Sample

1st row1,562,652
2nd row27,181
3rd row23,478
4th row37,975
5th row45,210
ValueCountFrequency (%)
1,562,652 1
 
3.8%
27,181 1
 
3.8%
94,475 1
 
3.8%
75,254 1
 
3.8%
59,734 1
 
3.8%
77,976 1
 
3.8%
65,200 1
 
3.8%
60,064 1
 
3.8%
39,514 1
 
3.8%
69,850 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T01:01:49.344072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 27
14.6%
26
14.1%
5 17
9.2%
6 16
8.6%
2 15
8.1%
7 15
8.1%
9 15
8.1%
4 13
7.0%
3 12
6.5%
0 12
6.5%
Other values (2) 17
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 132
71.4%
Other Punctuation 27
 
14.6%
Space Separator 26
 
14.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 17
12.9%
6 16
12.1%
2 15
11.4%
7 15
11.4%
9 15
11.4%
4 13
9.8%
3 12
9.1%
0 12
9.1%
1 9
6.8%
8 8
6.1%
Other Punctuation
ValueCountFrequency (%)
, 27
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 185
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 27
14.6%
26
14.1%
5 17
9.2%
6 16
8.6%
2 15
8.1%
7 15
8.1%
9 15
8.1%
4 13
7.0%
3 12
6.5%
0 12
6.5%
Other values (2) 17
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 27
14.6%
26
14.1%
5 17
9.2%
6 16
8.6%
2 15
8.1%
7 15
8.1%
9 15
8.1%
4 13
7.0%
3 12
6.5%
0 12
6.5%
Other values (2) 17
9.2%

기초연금 수급자 수
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T01:01:49.584039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0384615
Min length7

Characters and Unicode

Total characters183
Distinct characters12
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

Unique26 ?
Unique (%)100.0%

Sample

1st row862,713
2nd row13,538
3rd row13,520
4th row16,070
5th row23,025
ValueCountFrequency (%)
862,713 1
 
3.8%
13,538 1
 
3.8%
35,223 1
 
3.8%
20,248 1
 
3.8%
15,354 1
 
3.8%
46,731 1
 
3.8%
32,245 1
 
3.8%
28,566 1
 
3.8%
25,684 1
 
3.8%
42,576 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T01:01:50.027497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
14.2%
26
14.2%
2 19
10.4%
3 18
9.8%
4 16
8.7%
5 15
8.2%
6 14
7.7%
8 13
7.1%
7 12
6.6%
1 10
 
5.5%
Other values (2) 14
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 131
71.6%
Other Punctuation 26
 
14.2%
Space Separator 26
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 19
14.5%
3 18
13.7%
4 16
12.2%
5 15
11.5%
6 14
10.7%
8 13
9.9%
7 12
9.2%
1 10
7.6%
0 9
6.9%
9 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 26
14.2%
26
14.2%
2 19
10.4%
3 18
9.8%
4 16
8.7%
5 15
8.2%
6 14
7.7%
8 13
7.1%
7 12
6.6%
1 10
 
5.5%
Other values (2) 14
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 26
14.2%
26
14.2%
2 19
10.4%
3 18
9.8%
4 16
8.7%
5 15
8.2%
6 14
7.7%
8 13
7.1%
7 12
6.6%
1 10
 
5.5%
Other values (2) 14
7.7%

미수급자 수
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T01:01:50.282140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7
Min length6

Characters and Unicode

Total characters182
Distinct characters12
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

Unique26 ?
Unique (%)100.0%

Sample

1st row699,939
2nd row13,643
3rd row9,958
4th row21,905
5th row22,185
ValueCountFrequency (%)
699,939 1
 
3.8%
13,643 1
 
3.8%
59,252 1
 
3.8%
55,006 1
 
3.8%
44,380 1
 
3.8%
31,245 1
 
3.8%
32,955 1
 
3.8%
31,498 1
 
3.8%
13,830 1
 
3.8%
27,274 1
 
3.8%
Other values (16) 16
61.5%
2023-12-13T01:01:50.712008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 26
14.3%
26
14.3%
2 22
12.1%
5 17
9.3%
3 16
8.8%
4 16
8.8%
9 15
8.2%
1 11
6.0%
0 11
6.0%
6 9
 
4.9%
Other values (2) 13
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130
71.4%
Other Punctuation 26
 
14.3%
Space Separator 26
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 22
16.9%
5 17
13.1%
3 16
12.3%
4 16
12.3%
9 15
11.5%
1 11
8.5%
0 11
8.5%
6 9
6.9%
8 8
 
6.2%
7 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 26
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 182
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 26
14.3%
26
14.3%
2 22
12.1%
5 17
9.3%
3 16
8.8%
4 16
8.8%
9 15
8.2%
1 11
6.0%
0 11
6.0%
6 9
 
4.9%
Other values (2) 13
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 26
14.3%
26
14.3%
2 22
12.1%
5 17
9.3%
3 16
8.8%
4 16
8.8%
9 15
8.2%
1 11
6.0%
0 11
6.0%
6 9
 
4.9%
Other values (2) 13
7.1%

수급률
Real number (ℝ)

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.956923
Minimum25.7
Maximum73.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T01:01:50.896872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.7
5-th percentile29.5025
Q150.09
median58.05
Q362.245
95-th percentile68.5775
Maximum73.02
Range47.32
Interquartile range (IQR)12.155

Descriptive statistics

Standard deviation11.919033
Coefficient of variation (CV)0.21687956
Kurtosis0.89348629
Mean54.956923
Median Absolute Deviation (MAD)7.035
Skewness-1.0123469
Sum1428.88
Variance142.06336
MonotonicityNot monotonic
2023-12-13T01:01:51.073803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
55.21 1
 
3.8%
52.45 1
 
3.8%
51.13 1
 
3.8%
37.28 1
 
3.8%
26.91 1
 
3.8%
25.7 1
 
3.8%
59.93 1
 
3.8%
49.46 1
 
3.8%
47.56 1
 
3.8%
65.0 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
25.7 1
3.8%
26.91 1
3.8%
37.28 1
3.8%
42.32 1
3.8%
47.56 1
3.8%
49.46 1
3.8%
49.81 1
3.8%
50.93 1
3.8%
51.13 1
3.8%
51.41 1
3.8%
ValueCountFrequency (%)
73.02 1
3.8%
69.06 1
3.8%
67.13 1
3.8%
67.1 1
3.8%
65.73 1
3.8%
65.0 1
3.8%
62.56 1
3.8%
61.3 1
3.8%
60.95 1
3.8%
60.57 1
3.8%

Interactions

2023-12-13T01:01:47.460212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:01:51.180988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구65세이상 노인 인구기초연금 수급자 수미수급자 수수급률
자치구1.0001.0001.0001.0001.000
65세이상 노인 인구1.0001.0001.0001.0001.000
기초연금 수급자 수1.0001.0001.0001.0001.000
미수급자 수1.0001.0001.0001.0001.000
수급률1.0001.0001.0001.0001.000

Missing values

2023-12-13T01:01:47.913908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:01:48.045419image/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

자치구65세이상 노인 인구기초연금 수급자 수미수급자 수수급률
0합계1,562,652862,713699,93955.21
1종로구27,18113,53813,64349.81
2중구23,47813,5209,95857.59
3용산구37,97516,07021,90542.32
4성동구45,21023,02522,18550.93
5광진구50,34825,88324,46551.41
6동대문구60,90536,89324,01260.57
7중랑구69,94548,30721,63869.06
8성북구73,13044,06729,06360.26
9강북구63,18346,13917,04473.02
자치구65세이상 노인 인구기초연금 수급자 수미수급자 수수급률
16강서구89,90256,24733,65562.56
17구로구69,85042,57627,27460.95
18금천구39,51425,68413,83065.0
19영등포구60,06428,56631,49847.56
20동작구65,20032,24532,95549.46
21관악구77,97646,73131,24559.93
22서초구59,73415,35444,38025.7
23강남구75,25420,24855,00626.91
24송파구94,47535,22359,25237.28
25강동구71,90936,76435,14551.13