Overview

Dataset statistics

Number of variables7
Number of observations3845
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory221.7 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Categorical4

Dataset

Description대전광역시 동구 관내 복지대상자 정보로서, 읍면동명, 읍면동코드, 복지구분, 성별, 연령(5세단위) 및 인구수 정보를 포함하고 있습니다.
Author대전광역시 동구
URLhttps://www.data.go.kr/data/15111108/fileData.do

Alerts

순번 is highly overall correlated with 법정읍면동명High correlation
법정동코드 is highly overall correlated with 법정읍면동명High correlation
법정읍면동명 is highly overall correlated with 순번 and 1 other fieldsHigh correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:45:55.694964
Analysis finished2023-12-12 23:45:56.976316
Duration1.28 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3845
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1922
Minimum0
Maximum3844
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-12-13T08:45:57.038909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192.2
Q1961
median1922
Q32883
95-th percentile3651.8
Maximum3844
Range3844
Interquartile range (IQR)1922

Descriptive statistics

Standard deviation1110.1002
Coefficient of variation (CV)0.57757556
Kurtosis-1.2
Mean1922
Median Absolute Deviation (MAD)961
Skewness0
Sum7390090
Variance1232322.5
MonotonicityStrictly increasing
2023-12-13T08:45:57.177736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
2404 1
 
< 0.1%
2556 1
 
< 0.1%
2557 1
 
< 0.1%
2558 1
 
< 0.1%
2559 1
 
< 0.1%
2560 1
 
< 0.1%
2561 1
 
< 0.1%
2562 1
 
< 0.1%
2563 1
 
< 0.1%
Other values (3835) 3835
99.7%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
3844 1
< 0.1%
3843 1
< 0.1%
3842 1
< 0.1%
3841 1
< 0.1%
3840 1
< 0.1%
3839 1
< 0.1%
3838 1
< 0.1%
3837 1
< 0.1%
3836 1
< 0.1%
3835 1
< 0.1%

법정읍면동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
산내동
275 
효동
265 
가양1동
256 
판암1동
256 
용전동
254 
Other values (11)
2539 

Length

Max length4
Median length3
Mean length3.1222367
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가양1동
2nd row가양1동
3rd row가양1동
4th row가양1동
5th row가양1동

Common Values

ValueCountFrequency (%)
산내동 275
 
7.2%
효동 265
 
6.9%
가양1동 256
 
6.7%
판암1동 256
 
6.7%
용전동 254
 
6.6%
삼성동 251
 
6.5%
용운동 249
 
6.5%
신인동 245
 
6.4%
대동 242
 
6.3%
성남동 241
 
6.3%
Other values (6) 1311
34.1%

Length

2023-12-13T08:45:57.293712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산내동 275
 
7.2%
효동 265
 
6.9%
가양1동 256
 
6.7%
판암1동 256
 
6.7%
용전동 254
 
6.6%
삼성동 251
 
6.5%
용운동 249
 
6.5%
신인동 245
 
6.4%
대동 242
 
6.3%
성남동 241
 
6.3%
Other values (6) 1311
34.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0110119 × 109
Minimum3.0110101 × 109
Maximum3.0110149 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-12-13T08:45:57.417744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0110101 × 109
5-th percentile3.0110103 × 109
Q13.0110109 × 109
median3.0110114 × 109
Q33.0110118 × 109
95-th percentile3.0110149 × 109
Maximum3.0110149 × 109
Range4800
Interquartile range (IQR)900

Descriptive statistics

Standard deviation1525.3065
Coefficient of variation (CV)5.0657604 × 10-7
Kurtosis-0.3409501
Mean3.0110119 × 109
Median Absolute Deviation (MAD)400
Skewness1.1221624
Sum1.1577341 × 1013
Variance2326559.9
MonotonicityNot monotonic
2023-12-13T08:45:57.778018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3011011400 494
12.8%
3011010700 482
12.5%
3011011500 254
 
6.6%
3011011800 251
 
6.5%
3011010900 249
 
6.5%
3011011000 242
 
6.3%
3011011600 241
 
6.3%
3011010300 237
 
6.2%
3011011700 237
 
6.2%
3011014700 231
 
6.0%
Other values (20) 927
24.1%
ValueCountFrequency (%)
3011010100 2
 
0.1%
3011010200 20
 
0.5%
3011010300 237
6.2%
3011010400 13
 
0.3%
3011010500 15
 
0.4%
3011010600 14
 
0.4%
3011010700 482
12.5%
3011010800 1
 
< 0.1%
3011010900 249
6.5%
3011011000 242
6.3%
ValueCountFrequency (%)
3011014900 201
5.2%
3011014800 211
5.5%
3011014700 231
6.0%
3011014600 156
4.1%
3011014500 6
 
0.2%
3011014300 3
 
0.1%
3011014100 1
 
< 0.1%
3011013900 10
 
0.3%
3011013700 1
 
< 0.1%
3011013600 23
 
0.6%

복지구분
Categorical

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
의료급여
621 
기초수급자
614 
장애인
603 
차상위계층
580 
한부모가족
358 
Other values (5)
1069 

Length

Max length6
Median length5
Mean length4.4637191
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의료급여 621
16.2%
기초수급자 614
16.0%
장애인 603
15.7%
차상위계층 580
15.1%
한부모가족 358
9.3%
서비스이용권 352
9.2%
기초연금 248
 
6.4%
긴급복지 206
 
5.4%
자활지원 198
 
5.1%
영유아 보육 65
 
1.7%

Length

2023-12-13T08:45:57.882243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:58.007315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의료급여 621
15.9%
기초수급자 614
15.7%
장애인 603
15.4%
차상위계층 580
14.8%
한부모가족 358
9.2%
서비스이용권 352
9.0%
기초연금 248
 
6.3%
긴급복지 206
 
5.3%
자활지원 198
 
5.1%
영유아 65
 
1.7%

성별
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
여성
1975 
남성
1870 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여성 1975
51.4%
남성 1870
48.6%

Length

2023-12-13T08:45:58.124063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:45:58.197433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 1975
51.4%
남성 1870
48.6%

연령
Categorical

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size30.2 KiB
65~69세
 
235
5~9세
 
221
50~54세
 
218
60~64세
 
218
55~59세
 
215
Other values (16)
2738 

Length

Max length7
Median length6
Mean length5.7979194
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0~4세
2nd row10~14세
3rd row15~19세
4th row20~24세
5th row25~29세

Common Values

ValueCountFrequency (%)
65~69세 235
 
6.1%
5~9세 221
 
5.7%
50~54세 218
 
5.7%
60~64세 218
 
5.7%
55~59세 215
 
5.6%
40~44세 214
 
5.6%
45~49세 214
 
5.6%
70~74세 198
 
5.1%
35~39세 195
 
5.1%
75~79세 193
 
5.0%
Other values (11) 1724
44.8%

Length

2023-12-13T08:45:58.281037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
65~69세 235
 
6.1%
5~9세 221
 
5.7%
50~54세 218
 
5.6%
60~64세 218
 
5.6%
55~59세 215
 
5.5%
40~44세 214
 
5.5%
45~49세 214
 
5.5%
70~74세 198
 
5.1%
35~39세 195
 
5.0%
75~79세 193
 
5.0%
Other values (12) 1761
45.4%

인구수
Real number (ℝ)

Distinct226
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.36697
Minimum1
Maximum488
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size33.9 KiB
2023-12-13T08:45:58.386115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median8
Q321
95-th percentile84.6
Maximum488
Range487
Interquartile range (IQR)18

Descriptive statistics

Standard deviation50.279301
Coefficient of variation (CV)2.1517253
Kurtosis27.120018
Mean23.36697
Median Absolute Deviation (MAD)6
Skewness4.8390119
Sum89846
Variance2528.0081
MonotonicityNot monotonic
2023-12-13T08:45:58.518602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 490
 
12.7%
2 364
 
9.5%
3 272
 
7.1%
4 205
 
5.3%
6 182
 
4.7%
5 181
 
4.7%
7 152
 
4.0%
8 128
 
3.3%
10 106
 
2.8%
9 101
 
2.6%
Other values (216) 1664
43.3%
ValueCountFrequency (%)
1 490
12.7%
2 364
9.5%
3 272
7.1%
4 205
5.3%
5 181
 
4.7%
6 182
 
4.7%
7 152
 
4.0%
8 128
 
3.3%
9 101
 
2.6%
10 106
 
2.8%
ValueCountFrequency (%)
488 1
< 0.1%
468 1
< 0.1%
452 1
< 0.1%
439 1
< 0.1%
427 1
< 0.1%
424 1
< 0.1%
406 1
< 0.1%
397 1
< 0.1%
394 1
< 0.1%
393 1
< 0.1%

Interactions

2023-12-13T08:45:56.584776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.073568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.326588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.671671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.164243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.412959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.746540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.242798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:45:56.510314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:45:58.596448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정읍면동명법정동코드복지구분성별연령인구수
순번1.0000.9700.8280.3450.0000.0000.145
법정읍면동명0.9701.0000.9420.0570.0000.0000.150
법정동코드0.8280.9421.0000.2680.0000.0000.029
복지구분0.3450.0570.2681.0000.0430.5030.634
성별0.0000.0000.0000.0431.0000.0570.082
연령0.0000.0000.0000.5030.0571.0000.365
인구수0.1450.1500.0290.6340.0820.3651.000
2023-12-13T08:45:58.683948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
복지구분연령법정읍면동명성별
복지구분1.0000.2100.0220.033
연령0.2101.0000.0000.049
법정읍면동명0.0220.0001.0000.000
성별0.0330.0490.0001.000
2023-12-13T08:45:58.775345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번법정동코드인구수법정읍면동명복지구분성별연령
순번1.000-0.3110.0170.8600.1120.0000.000
법정동코드-0.3111.000-0.0570.8080.1390.0000.000
인구수0.017-0.0571.0000.0590.2410.0630.143
법정읍면동명0.8600.8080.0591.0000.0220.0000.000
복지구분0.1120.1390.2410.0221.0000.0330.210
성별0.0000.0000.0630.0000.0331.0000.049
연령0.0000.0000.1430.0000.2100.0491.000

Missing values

2023-12-13T08:45:56.843283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:45:56.936947image/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

순번법정읍면동명법정동코드복지구분성별연령인구수
00가양1동3011011400기초수급자남성0~4세2
11가양1동3011011400기초수급자남성10~14세7
22가양1동3011011400기초수급자남성15~19세12
33가양1동3011011400기초수급자남성20~24세10
44가양1동3011011400기초수급자남성25~29세4
55가양1동3011011400기초수급자남성30~34세6
66가양1동3011011400기초수급자남성35~39세5
77가양1동3011011400기초수급자남성40~44세7
88가양1동3011011400기초수급자남성45~49세14
99가양1동3011011400기초수급자남성50~54세28
순번법정읍면동명법정동코드복지구분성별연령인구수
38353835효동3011010300한부모가족여성25~29세2
38363836효동3011010300한부모가족여성30~34세7
38373837효동3011010300한부모가족여성35~39세14
38383838효동3011010300한부모가족여성40~44세33
38393839효동3011010300한부모가족여성45~49세37
38403840효동3011010300한부모가족여성50~54세18
38413841효동3011010300한부모가족여성55~59세4
38423842효동3011010300한부모가족여성5~9세11
38433843효동3011010300한부모가족여성60~64세3
38443844효동3011010300한부모가족여성85~89세1