Overview

Dataset statistics

Number of variables7
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory66.6 B

Variable types

Numeric5
Text1
Categorical1

Dataset

Description인천광역시 서구 의료급여 수급자에 대한 데이터로 미추홀구 21개동명 1종 가구수, 1종 수급자, 2종 가구수, 2종 수급자 등을 제공합니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15090898/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
(1종) 가구수 is highly overall correlated with (1종) 수급권자수 and 2 other fieldsHigh correlation
(1종) 수급권자수 is highly overall correlated with (1종) 가구수 and 2 other fieldsHigh correlation
(2종) 가구수 is highly overall correlated with (1종) 가구수 and 2 other fieldsHigh correlation
(2종) 수급권자수 is highly overall correlated with (1종) 가구수 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
동 has unique valuesUnique
(1종) 가구수 has unique valuesUnique
(1종) 수급권자수 has unique valuesUnique

Reproduction

Analysis started2024-03-14 16:18:15.617789
Analysis finished2024-03-14 16:18:22.852779
Duration7.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T01:18:23.035962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2024-03-15T01:18:23.635333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-15T01:18:24.387006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9130435
Min length3

Characters and Unicode

Total characters90
Distinct characters36
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

Unique23 ?
Unique (%)100.0%

Sample

1st row검암경서동
2nd row연희동
3rd row청라1동
4th row청라2동
5th row청라3동
ValueCountFrequency (%)
검암경서동 1
 
4.3%
가좌1동 1
 
4.3%
마전동 1
 
4.3%
오류왕길동 1
 
4.3%
당하동 1
 
4.3%
원당동 1
 
4.3%
불로대곡동 1
 
4.3%
검단동 1
 
4.3%
가좌4동 1
 
4.3%
가좌3동 1
 
4.3%
Other values (13) 13
56.5%
2024-03-15T01:18:25.647719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
25.6%
가 7
 
7.8%
좌 4
 
4.4%
라 4
 
4.4%
1 4
 
4.4%
2 4
 
4.4%
3 4
 
4.4%
석 3
 
3.3%
정 3
 
3.3%
남 3
 
3.3%
Other values (26) 31
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
85.6%
Decimal Number 13
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
Decimal Number
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
85.6%
Common 13
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
Common
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
85.6%
ASCII 13
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
석 3
 
3.9%
정 3
 
3.9%
남 3
 
3.9%
청 3
 
3.9%
당 2
 
2.6%
원 2
 
2.6%
Other values (22) 23
29.9%
ASCII
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

(1종) 가구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325
Minimum43
Maximum811
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T01:18:26.021736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54.8
Q1181.5
median293
Q3459
95-th percentile672.7
Maximum811
Range768
Interquartile range (IQR)277.5

Descriptive statistics

Standard deviation205.95145
Coefficient of variation (CV)0.63369677
Kurtosis-0.081044971
Mean325
Median Absolute Deviation (MAD)140
Skewness0.62058904
Sum7475
Variance42416
MonotonicityNot monotonic
2024-03-15T01:18:26.362578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
482 1
 
4.3%
811 1
 
4.3%
249 1
 
4.3%
71 1
 
4.3%
293 1
 
4.3%
334 1
 
4.3%
233 1
 
4.3%
192 1
 
4.3%
487 1
 
4.3%
222 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
43 1
4.3%
53 1
4.3%
71 1
4.3%
81 1
4.3%
160 1
4.3%
171 1
4.3%
192 1
4.3%
208 1
4.3%
222 1
4.3%
233 1
4.3%
ValueCountFrequency (%)
811 1
4.3%
680 1
4.3%
607 1
4.3%
487 1
4.3%
482 1
4.3%
471 1
4.3%
447 1
4.3%
433 1
4.3%
406 1
4.3%
341 1
4.3%

(1종) 수급권자수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.78261
Minimum49
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T01:18:26.586273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile65.9
Q1220
median347
Q3551
95-th percentile780.9
Maximum990
Range941
Interquartile range (IQR)331

Descriptive statistics

Standard deviation247.05243
Coefficient of variation (CV)0.64373014
Kurtosis0.078052638
Mean383.78261
Median Absolute Deviation (MAD)165
Skewness0.67416285
Sum8827
Variance61034.905
MonotonicityNot monotonic
2024-03-15T01:18:26.918015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
585 1
 
4.3%
990 1
 
4.3%
277 1
 
4.3%
83 1
 
4.3%
347 1
 
4.3%
399 1
 
4.3%
273 1
 
4.3%
226 1
 
4.3%
574 1
 
4.3%
240 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
49 1
4.3%
64 1
4.3%
83 1
4.3%
94 1
4.3%
182 1
4.3%
214 1
4.3%
226 1
4.3%
240 1
4.3%
249 1
4.3%
273 1
4.3%
ValueCountFrequency (%)
990 1
4.3%
789 1
4.3%
708 1
4.3%
585 1
4.3%
574 1
4.3%
571 1
4.3%
531 1
4.3%
506 1
4.3%
485 1
4.3%
399 1
4.3%

(2종) 가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.82609
Minimum25
Maximum315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T01:18:27.103732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile33.5
Q154
median82
Q3177
95-th percentile212.8
Maximum315
Range290
Interquartile range (IQR)123

Descriptive statistics

Standard deviation78.056886
Coefficient of variation (CV)0.67391456
Kurtosis-0.033360117
Mean115.82609
Median Absolute Deviation (MAD)44
Skewness0.80593169
Sum2664
Variance6092.8775
MonotonicityNot monotonic
2024-03-15T01:18:27.299121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
54 2
 
8.7%
64 2
 
8.7%
199 2
 
8.7%
214 1
 
4.3%
41 1
 
4.3%
82 1
 
4.3%
111 1
 
4.3%
63 1
 
4.3%
49 1
 
4.3%
202 1
 
4.3%
Other values (10) 10
43.5%
ValueCountFrequency (%)
25 1
4.3%
33 1
4.3%
38 1
4.3%
41 1
4.3%
49 1
4.3%
54 2
8.7%
58 1
4.3%
63 1
4.3%
64 2
8.7%
82 1
4.3%
ValueCountFrequency (%)
315 1
4.3%
214 1
4.3%
202 1
4.3%
199 2
8.7%
189 1
4.3%
165 1
4.3%
164 1
4.3%
158 1
4.3%
123 1
4.3%
111 1
4.3%

(2종) 수급권자수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean209
Minimum47
Maximum542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T01:18:27.589836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile61.6
Q190
median143
Q3320
95-th percentile400.8
Maximum542
Range495
Interquartile range (IQR)230

Descriptive statistics

Standard deviation140.79676
Coefficient of variation (CV)0.6736687
Kurtosis-0.51342079
Mean209
Median Absolute Deviation (MAD)79
Skewness0.71178757
Sum4807
Variance19823.727
MonotonicityNot monotonic
2024-03-15T01:18:27.979327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
320 2
 
8.7%
86 2
 
8.7%
384 1
 
4.3%
103 1
 
4.3%
67 1
 
4.3%
143 1
 
4.3%
222 1
 
4.3%
121 1
 
4.3%
401 1
 
4.3%
94 1
 
4.3%
Other values (11) 11
47.8%
ValueCountFrequency (%)
47 1
4.3%
61 1
4.3%
67 1
4.3%
78 1
4.3%
86 2
8.7%
94 1
4.3%
103 1
4.3%
113 1
4.3%
114 1
4.3%
121 1
4.3%
ValueCountFrequency (%)
542 1
4.3%
401 1
4.3%
399 1
4.3%
384 1
4.3%
331 1
4.3%
320 2
8.7%
293 1
4.3%
275 1
4.3%
222 1
4.3%
207 1
4.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
2023-12-31
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-12-31
2nd row2023-12-31
3rd row2023-12-31
4th row2023-12-31
5th row2023-12-31

Common Values

ValueCountFrequency (%)
2023-12-31 23
100.0%

Length

2024-03-15T01:18:28.396097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:18:28.585532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-12-31 23
100.0%

Interactions

2024-03-15T01:18:21.140638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:15.872527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:17.191889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:18.661864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:20.005518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:21.370788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:16.099855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:17.445894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:18.906369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:20.260786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:21.634560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:16.346333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:17.781416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:19.230460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:20.575504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:21.763522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:16.647894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:18.143142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:19.440512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:20.713449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:21.973586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:16.947446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:18.408199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:19.733475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:18:20.905768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:18:28.745414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동(1종) 가구수(1종) 수급권자수(2종) 가구수(2종) 수급권자수
연번1.0001.0000.0000.0000.2780.000
동1.0001.0001.0001.0001.0001.000
(1종) 가구수0.0001.0001.0000.9770.8280.805
(1종) 수급권자수0.0001.0000.9771.0000.9580.963
(2종) 가구수0.2781.0000.8280.9581.0000.970
(2종) 수급권자수0.0001.0000.8050.9630.9701.000
2024-03-15T01:18:29.032269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번(1종) 가구수(1종) 수급권자수(2종) 가구수(2종) 수급권자수
연번1.000-0.119-0.133-0.201-0.192
(1종) 가구수-0.1191.0000.9970.9610.947
(1종) 수급권자수-0.1330.9971.0000.9610.949
(2종) 가구수-0.2010.9610.9611.0000.983
(2종) 수급권자수-0.1920.9470.9490.9831.000

Missing values

2024-03-15T01:18:22.297253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:18:22.698852image/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

연번동(1종) 가구수(1종) 수급권자수(2종) 가구수(2종) 수급권자수데이터기준일자
01검암경서동4825852143842023-12-31
12연희동8119903155422023-12-31
23청라1동536438782023-12-31
34청라2동171214581132023-12-31
45청라3동434925472023-12-31
56가정1동6807891893202023-12-31
67가정2동819433612023-12-31
78가정3동208249641142023-12-31
89신현원창동4715711993992023-12-31
910석남1동6077081993312023-12-31
연번동(1종) 가구수(1종) 수급권자수(2종) 가구수(2종) 수급권자수데이터기준일자
1314가좌2동160182541032023-12-31
1415가좌3동4335061582932023-12-31
1516가좌4동22224064942023-12-31
1617검단동4875742024012023-12-31
1718불로대곡동19222649862023-12-31
1819원당동233273631212023-12-31
1920당하동3343991112222023-12-31
2021오류왕길동293347821432023-12-31
2122마전동718341672023-12-31
2223아라동24927754862023-12-31