Overview

Dataset statistics

Number of variables4
Number of observations2490
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory80.4 KiB
Average record size in memory33.1 B

Variable types

Categorical2
Text1
Numeric1

Dataset

Description지역별 성별 연금수급자 인원수를 나타낸 자료입니다. 2022년 말 연금수급자를 대상으로 하였으며 우편번호 3자리를 통해 시군구를 구분하였습니다.
URLhttps://www.data.go.kr/data/15106707/fileData.do

Reproduction

Analysis started2023-12-12 23:41:13.258334
Analysis finished2023-12-12 23:41:13.662430
Duration0.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역명
Categorical

Distinct17
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
서울
418 
경기
415 
경북
179 
경남
159 
부산
142 
Other values (12)
1177 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울 418
16.8%
경기 415
16.7%
경북 179
 
7.2%
경남 159
 
6.4%
부산 142
 
5.7%
강원 142
 
5.7%
충남 133
 
5.3%
전남 128
 
5.1%
인천 125
 
5.0%
충북 109
 
4.4%
Other values (7) 540
21.7%

Length

2023-12-13T08:41:13.717413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 418
16.8%
경기 415
16.7%
경북 179
 
7.2%
경남 159
 
6.4%
부산 142
 
5.7%
강원 142
 
5.7%
충남 133
 
5.3%
전남 128
 
5.1%
인천 125
 
5.0%
충북 109
 
4.4%
Other values (7) 540
21.7%

우편번호(3자리)
Text

Distinct473
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
2023-12-13T08:41:14.064899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length2.9297189
Min length1

Characters and Unicode

Total characters7295
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.8%

Sample

1st row13
2nd row21
3rd row27
4th row28
5th row33
ValueCountFrequency (%)
우편번호부재/불일치 33
 
1.3%
301 22
 
0.9%
300 21
 
0.8%
169 19
 
0.8%
311 15
 
0.6%
80 15
 
0.6%
63 14
 
0.6%
165 14
 
0.6%
145 13
 
0.5%
635 13
 
0.5%
Other values (463) 2311
92.8%
2023-12-13T08:41:14.536678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1130
15.5%
2 926
12.7%
3 908
12.4%
5 809
11.1%
4 801
11.0%
6 632
8.7%
0 527
7.2%
7 470
6.4%
8 458
6.3%
9 304
 
4.2%
Other values (10) 330
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6965
95.5%
Other Letter 297
 
4.1%
Other Punctuation 33
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1130
16.2%
2 926
13.3%
3 908
13.0%
5 809
11.6%
4 801
11.5%
6 632
9.1%
0 527
7.6%
7 470
6.7%
8 458
6.6%
9 304
 
4.4%
Other Letter
ValueCountFrequency (%)
일 33
11.1%
치 33
11.1%
편 33
11.1%
불 33
11.1%
재 33
11.1%
부 33
11.1%
호 33
11.1%
번 33
11.1%
우 33
11.1%
Other Punctuation
ValueCountFrequency (%)
/ 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6998
95.9%
Hangul 297
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1130
16.1%
2 926
13.2%
3 908
13.0%
5 809
11.6%
4 801
11.4%
6 632
9.0%
0 527
7.5%
7 470
6.7%
8 458
6.5%
9 304
 
4.3%
Hangul
ValueCountFrequency (%)
일 33
11.1%
치 33
11.1%
편 33
11.1%
불 33
11.1%
재 33
11.1%
부 33
11.1%
호 33
11.1%
번 33
11.1%
우 33
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6998
95.9%
Hangul 297
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1130
16.1%
2 926
13.2%
3 908
13.0%
5 809
11.6%
4 801
11.4%
6 632
9.0%
0 527
7.5%
7 470
6.7%
8 458
6.5%
9 304
 
4.3%
Hangul
ValueCountFrequency (%)
일 33
11.1%
치 33
11.1%
편 33
11.1%
불 33
11.1%
재 33
11.1%
부 33
11.1%
호 33
11.1%
번 33
11.1%
우 33
11.1%

성별
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.6 KiB
남
1587 
여
903 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여
2nd row남
3rd row남
4th row남
5th row남

Common Values

ValueCountFrequency (%)
남 1587
63.7%
여 903
36.3%

Length

2023-12-13T08:41:14.669207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:41:14.763645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 1587
63.7%
여 903
36.3%

인원수
Real number (ℝ)

Distinct661
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.28112
Minimum1
Maximum4265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.0 KiB
2023-12-13T08:41:14.865293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q3199.25
95-th percentile1234.85
Maximum4265
Range4264
Interquartile range (IQR)198.25

Descriptive statistics

Standard deviation468.3373
Coefficient of variation (CV)2.1357848
Kurtosis12.642091
Mean219.28112
Median Absolute Deviation (MAD)1
Skewness3.1643094
Sum546010
Variance219339.82
MonotonicityNot monotonic
2023-12-13T08:41:14.985051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1142
45.9%
2 230
 
9.2%
3 76
 
3.1%
4 35
 
1.4%
5 30
 
1.2%
6 11
 
0.4%
8 11
 
0.4%
7 11
 
0.4%
43 6
 
0.2%
81 5
 
0.2%
Other values (651) 933
37.5%
ValueCountFrequency (%)
1 1142
45.9%
2 230
 
9.2%
3 76
 
3.1%
4 35
 
1.4%
5 30
 
1.2%
6 11
 
0.4%
7 11
 
0.4%
8 11
 
0.4%
9 4
 
0.2%
10 5
 
0.2%
ValueCountFrequency (%)
4265 1
< 0.1%
3885 1
< 0.1%
3809 1
< 0.1%
3655 1
< 0.1%
3000 1
< 0.1%
2969 1
< 0.1%
2815 1
< 0.1%
2789 1
< 0.1%
2782 1
< 0.1%
2732 1
< 0.1%

Interactions

2023-12-13T08:41:13.414607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:41:15.070059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명성별인원수
지역명1.0000.0390.071
성별0.0391.0000.230
인원수0.0710.2301.000
2023-12-13T08:41:15.146547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명성별
지역명1.0000.035
성별0.0351.000
2023-12-13T08:41:15.220251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인원수지역명성별
인원수1.0000.0280.176
지역명0.0281.0000.035
성별0.1760.0351.000

Missing values

2023-12-13T08:41:13.533075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:41:13.632010image/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

지역명우편번호(3자리)성별인원수
0강원13여1
1강원21남1
2강원27남2
3강원28남1
4강원33남1
5강원34여1
6강원40여1
7강원49여1
8강원53남1
9강원56남2
지역명우편번호(3자리)성별인원수
2480충북396남2
2481충북414남1
2482충북462남1
2483충북485남1
2484충북549남1
2485충북611여1
2486충북623남1
2487충북635여1
2488충북우편번호부재/불일치남467
2489충북우편번호부재/불일치여37