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

Text1
Numeric5
Categorical1

Dataset

Description인천광역시 서구 구청장 주민소환 투표 청구권자 총수 및 최소서명인수 데이터 로 행정동명 주민수,등록외국인수, 청구권자 총계, 최소서명인수 등으로 구성 되어 있습니다.
Author인천광역시 서구
URLhttps://www.data.go.kr/data/15091236/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
주민수(19세이상) is highly overall correlated with 등록외국인수(19세이상) and 2 other fieldsHigh correlation
등록외국인수(19세이상) is highly overall correlated with 주민수(19세이상) and 2 other fieldsHigh correlation
청구권자 총수 is highly overall correlated with 주민수(19세이상) and 2 other fieldsHigh correlation
서명인수 is highly overall correlated with 주민수(19세이상) and 2 other fieldsHigh correlation
행정동명 has unique valuesUnique
주민수(19세이상) has unique valuesUnique
청구권자 총수 has unique valuesUnique
서명인수 has unique valuesUnique

Reproduction

Analysis started2024-03-14 10:45:14.859415
Analysis finished2024-03-14 10:45:21.907169
Duration7.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-14T19:45:22.444221image/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%
2024-03-14T19:45:23.429536image/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%

주민수(19세이상)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22277.304
Minimum7985
Maximum47878
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-14T19:45:23.633509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7985
5-th percentile10423.4
Q114038.5
median19389
Q327041.5
95-th percentile43904.2
Maximum47878
Range39893
Interquartile range (IQR)13003

Descriptive statistics

Standard deviation10878.071
Coefficient of variation (CV)0.48830283
Kurtosis0.26211796
Mean22277.304
Median Absolute Deviation (MAD)7261
Skewness0.9102179
Sum512378
Variance1.1833242 × 108
MonotonicityNot monotonic
2024-03-14T19:45:23.843376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
44772 1
 
4.3%
33761 1
 
4.3%
47878 1
 
4.3%
17678 1
 
4.3%
20220 1
 
4.3%
23717 1
 
4.3%
18376 1
 
4.3%
19389 1
 
4.3%
27433 1
 
4.3%
10368 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
7985 1
4.3%
10368 1
4.3%
10922 1
4.3%
11343 1
4.3%
11430 1
4.3%
13905 1
4.3%
14172 1
4.3%
16130 1
4.3%
17678 1
4.3%
18376 1
4.3%
ValueCountFrequency (%)
47878 1
4.3%
44772 1
4.3%
36094 1
4.3%
33761 1
4.3%
32952 1
4.3%
27433 1
4.3%
26650 1
4.3%
25199 1
4.3%
23717 1
4.3%
23328 1
4.3%

등록외국인수(19세이상)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.521739
Minimum13
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-14T19:45:24.141220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile17.4
Q133
median58
Q384.5
95-th percentile133.5
Maximum143
Range130
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation35.806131
Coefficient of variation (CV)0.5726989
Kurtosis0.044335671
Mean62.521739
Median Absolute Deviation (MAD)27
Skewness0.69854276
Sum1438
Variance1282.0791
MonotonicityNot monotonic
2024-03-14T19:45:24.355192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
68 2
 
8.7%
136 1
 
4.3%
17 1
 
4.3%
87 1
 
4.3%
37 1
 
4.3%
86 1
 
4.3%
77 1
 
4.3%
29 1
 
4.3%
35 1
 
4.3%
143 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
13 1
4.3%
17 1
4.3%
21 1
4.3%
26 1
4.3%
29 1
4.3%
31 1
4.3%
35 1
4.3%
37 1
4.3%
50 1
4.3%
53 1
4.3%
ValueCountFrequency (%)
143 1
4.3%
136 1
4.3%
111 1
4.3%
89 1
4.3%
87 1
4.3%
86 1
4.3%
83 1
4.3%
77 1
4.3%
68 2
8.7%
65 1
4.3%

청구권자 총수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22339.826
Minimum8006
Maximum47965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-14T19:45:24.567667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8006
5-th percentile10456.6
Q114070
median19424
Q327145.5
95-th percentile44033.4
Maximum47965
Range39959
Interquartile range (IQR)13075.5

Descriptive statistics

Standard deviation10899.641
Coefficient of variation (CV)0.48790176
Kurtosis0.2592839
Mean22339.826
Median Absolute Deviation (MAD)7291
Skewness0.90919922
Sum513816
Variance1.1880216 × 108
MonotonicityNot monotonic
2024-03-14T19:45:24.776556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
44908 1
 
4.3%
33844 1
 
4.3%
47965 1
 
4.3%
17715 1
 
4.3%
20306 1
 
4.3%
23794 1
 
4.3%
18405 1
 
4.3%
19424 1
 
4.3%
27576 1
 
4.3%
10399 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
8006 1
4.3%
10399 1
4.3%
10975 1
4.3%
11432 1
4.3%
11456 1
4.3%
13918 1
4.3%
14222 1
4.3%
16147 1
4.3%
17715 1
4.3%
18405 1
4.3%
ValueCountFrequency (%)
47965 1
4.3%
44908 1
4.3%
36162 1
4.3%
33844 1
4.3%
33020 1
4.3%
27576 1
4.3%
26715 1
4.3%
25254 1
4.3%
23794 1
4.3%
23386 1
4.3%

서명인수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4455.4783
Minimum1597
Maximum9576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-14T19:45:24.989450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1597
5-th percentile2085
Q12807.5
median3878
Q35408.5
95-th percentile8780.5
Maximum9576
Range7979
Interquartile range (IQR)2601

Descriptive statistics

Standard deviation2175.6044
Coefficient of variation (CV)0.48829873
Kurtosis0.26228077
Mean4455.4783
Median Absolute Deviation (MAD)1452
Skewness0.91024399
Sum102476
Variance4733254.4
MonotonicityNot monotonic
2024-03-14T19:45:25.289492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8954 1
 
4.3%
6752 1
 
4.3%
9576 1
 
4.3%
3536 1
 
4.3%
4044 1
 
4.3%
4743 1
 
4.3%
3675 1
 
4.3%
3878 1
 
4.3%
5487 1
 
4.3%
2074 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1597 1
4.3%
2074 1
4.3%
2184 1
4.3%
2269 1
4.3%
2286 1
4.3%
2781 1
4.3%
2834 1
4.3%
3226 1
4.3%
3536 1
4.3%
3675 1
4.3%
ValueCountFrequency (%)
9576 1
4.3%
8954 1
4.3%
7219 1
4.3%
6752 1
4.3%
6590 1
4.3%
5487 1
4.3%
5330 1
4.3%
5040 1
4.3%
4743 1
4.3%
4666 1
4.3%

최소서명인수
Real number (ℝ)

Distinct6
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean895.69565
Minimum596
Maximum1134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-14T19:45:25.488776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum596
5-th percentile615.2
Q1802
median886
Q3935
95-th percentile1134
Maximum1134
Range538
Interquartile range (IQR)133

Descriptive statistics

Standard deviation147.13829
Coefficient of variation (CV)0.16427264
Kurtosis0.319205
Mean895.69565
Median Absolute Deviation (MAD)84
Skewness-0.079243725
Sum20601
Variance21649.676
MonotonicityNot monotonic
2024-03-14T19:45:25.674068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
935 7
30.4%
886 4
17.4%
802 4
17.4%
1134 4
17.4%
788 2
 
8.7%
596 2
 
8.7%
ValueCountFrequency (%)
596 2
 
8.7%
788 2
 
8.7%
802 4
17.4%
886 4
17.4%
935 7
30.4%
1134 4
17.4%
ValueCountFrequency (%)
1134 4
17.4%
935 7
30.4%
886 4
17.4%
802 4
17.4%
788 2
 
8.7%
596 2
 
8.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-01-04
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-04
2nd row2024-01-04
3rd row2024-01-04
4th row2024-01-04
5th row2024-01-04

Common Values

ValueCountFrequency (%)
2024-01-04 23
100.0%

Length

2024-03-14T19:45:26.083195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:45:26.234529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-04 23
100.0%

Interactions

2024-03-14T19:45:20.219174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:15.067202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:16.360123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:17.629927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:18.927741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:20.480980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:15.326172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:16.617658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:17.893491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:19.190624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:20.728929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:15.583495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:16.868060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:18.151767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:19.451654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:20.986358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:15.845968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:17.128028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:18.412127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:19.713842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:21.239604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:16.108156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:17.386667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:18.673932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:45:19.972978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:45:26.386625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명주민수(19세이상)등록외국인수(19세이상)청구권자 총수서명인수최소서명인수
행정동명1.0001.0001.0001.0001.0001.000
주민수(19세이상)1.0001.0000.2731.0001.0000.657
등록외국인수(19세이상)1.0000.2731.0000.2730.2730.000
청구권자 총수1.0001.0000.2731.0001.0000.657
서명인수1.0001.0000.2731.0001.0000.657
최소서명인수1.0000.6570.0000.6570.6571.000
2024-03-14T19:45:26.605791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주민수(19세이상)등록외국인수(19세이상)청구권자 총수서명인수최소서명인수
주민수(19세이상)1.0000.6411.0001.000-0.281
등록외국인수(19세이상)0.6411.0000.6410.641-0.054
청구권자 총수1.0000.6411.0001.000-0.281
서명인수1.0000.6411.0001.000-0.281
최소서명인수-0.281-0.054-0.281-0.2811.000

Missing values

2024-03-14T19:45:21.578376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:45:21.823884image/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

행정동명주민수(19세이상)등록외국인수(19세이상)청구권자 총수서명인수최소서명인수데이터기준일자
0검암경서동447721364490889547882024-01-04
1연희동33761833384467527882024-01-04
2청라1동23328582338646665962024-01-04
3청라2동36094683616272195962024-01-04
4청라3동26650652671553308862024-01-04
5가정1동32952683302065908022024-01-04
6가정2동13905131391827818022024-01-04
7가정3동798521800615978022024-01-04
8신현원창동25199552525450408022024-01-04
9석남1동186761111878737359352024-01-04
행정동명주민수(19세이상)등록외국인수(19세이상)청구권자 총수서명인수최소서명인수데이터기준일자
13가좌2동16130171614732269352024-01-04
14가좌3동14172501422228349352024-01-04
15가좌4동10368311039920749352024-01-04
16검단동2743314327576548711342024-01-04
17불로대곡동193893519424387811342024-01-04
18원당동183762918405367511342024-01-04
19당하동23717772379447438862024-01-04
20오류왕길동20220862030640448862024-01-04
21마전동17678371771535368862024-01-04
22아라동478788747965957611342024-01-04