Overview

Dataset statistics

Number of variables6
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory58.3 B

Variable types

Numeric4
Categorical1
Text1

Dataset

Description인천광역시 미추홀구의 행정구역 현황에 대한 데이터로 연번, 법정동, 행정동, 면적(제곱킬로미터), 통반의 수 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15081982&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 법정동High correlation
면적(제곱킬로미터) is highly overall correlated with 통 and 1 other fieldsHigh correlation
통 is highly overall correlated with 면적(제곱킬로미터) and 1 other fieldsHigh correlation
반 is highly overall correlated with 면적(제곱킬로미터) and 1 other fieldsHigh correlation
법정동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
행정동 has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:47:34.484353
Analysis finished2024-03-18 04:47:38.027084
Duration3.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:47:38.091043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2024-03-18T13:47:38.187750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

법정동
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
주안동
8 
용현동
4 
숭의동
3 
학익동
2 
도화동
2 
Other values (2)
2 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row숭의동
2nd row숭의동
3rd row숭의동
4th row용현동
5th row용현동

Common Values

ValueCountFrequency (%)
주안동 8
38.1%
용현동 4
19.0%
숭의동 3
 
14.3%
학익동 2
 
9.5%
도화동 2
 
9.5%
관교동 1
 
4.8%
문학동 1
 
4.8%

Length

2024-03-18T13:47:38.360257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:47:38.549565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주안동 8
38.1%
용현동 4
19.0%
숭의동 3
 
14.3%
학익동 2
 
9.5%
도화동 2
 
9.5%
관교동 1
 
4.8%
문학동 1
 
4.8%

행정동
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-18T13:47:38.718629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.1904762
Min length3

Characters and Unicode

Total characters88
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row숭의1·3동
2nd row숭의2동
3rd row숭의4동
4th row용현1·4동
5th row용현2동
ValueCountFrequency (%)
숭의1·3동 1
 
4.8%
주안1동 1
 
4.8%
관교동 1
 
4.8%
주안8동 1
 
4.8%
주안7동 1
 
4.8%
주안6동 1
 
4.8%
주안5동 1
 
4.8%
주안4동 1
 
4.8%
주안3동 1
 
4.8%
주안2동 1
 
4.8%
Other values (11) 11
52.4%
2024-03-18T13:47:38.975379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
23.9%
안 8
 
9.1%
주 8
 
9.1%
1 5
 
5.7%
2 5
 
5.7%
3 4
 
4.5%
용 4
 
4.5%
현 4
 
4.5%
숭 3
 
3.4%
의 3
 
3.4%
Other values (13) 23
26.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63
71.6%
Decimal Number 22
 
25.0%
Other Punctuation 3
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Decimal Number
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
· 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63
71.6%
Common 25
 
28.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
Common
ValueCountFrequency (%)
1 5
20.0%
2 5
20.0%
3 4
16.0%
4 3
12.0%
· 3
12.0%
5 2
 
8.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63
71.6%
ASCII 22
 
25.0%
None 3
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
33.3%
안 8
 
12.7%
주 8
 
12.7%
용 4
 
6.3%
현 4
 
6.3%
숭 3
 
4.8%
의 3
 
4.8%
학 3
 
4.8%
익 2
 
3.2%
도 2
 
3.2%
Other values (4) 5
 
7.9%
ASCII
ValueCountFrequency (%)
1 5
22.7%
2 5
22.7%
3 4
18.2%
4 3
13.6%
5 2
 
9.1%
6 1
 
4.5%
7 1
 
4.5%
8 1
 
4.5%
None
ValueCountFrequency (%)
· 3
100.0%

면적(제곱킬로미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1804762
Minimum0.45
Maximum5.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:47:39.082715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.45
5-th percentile0.46
Q10.62
median0.84
Q31.18
95-th percentile2.91
Maximum5.07
Range4.62
Interquartile range (IQR)0.56

Descriptive statistics

Standard deviation1.0648403
Coefficient of variation (CV)0.90204297
Kurtosis9.0743867
Mean1.1804762
Median Absolute Deviation (MAD)0.23
Skewness2.8538435
Sum24.79
Variance1.1338848
MonotonicityNot monotonic
2024-03-18T13:47:39.175563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.9 2
 
9.5%
0.77 1
 
4.8%
0.61 1
 
4.8%
1.73 1
 
4.8%
0.84 1
 
4.8%
0.52 1
 
4.8%
0.62 1
 
4.8%
1.18 1
 
4.8%
0.96 1
 
4.8%
0.46 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
0.45 1
4.8%
0.46 1
4.8%
0.5 1
4.8%
0.52 1
4.8%
0.61 1
4.8%
0.62 1
4.8%
0.65 1
4.8%
0.74 1
4.8%
0.77 1
4.8%
0.8 1
4.8%
ValueCountFrequency (%)
5.07 1
4.8%
2.91 1
4.8%
1.89 1
4.8%
1.73 1
4.8%
1.3 1
4.8%
1.18 1
4.8%
0.99 1
4.8%
0.96 1
4.8%
0.9 2
9.5%
0.84 1
4.8%

통
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.809524
Minimum18
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:47:39.267947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q125
median27
Q339
95-th percentile49
Maximum56
Range38
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.29378
Coefficient of variation (CV)0.32360685
Kurtosis-0.029842483
Mean31.809524
Median Absolute Deviation (MAD)6
Skewness0.76854973
Sum668
Variance105.9619
MonotonicityNot monotonic
2024-03-18T13:47:39.358894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
27 3
14.3%
18 2
 
9.5%
41 2
 
9.5%
26 2
 
9.5%
24 2
 
9.5%
25 1
 
4.8%
56 1
 
4.8%
45 1
 
4.8%
32 1
 
4.8%
31 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
18 2
9.5%
21 1
 
4.8%
24 2
9.5%
25 1
 
4.8%
26 2
9.5%
27 3
14.3%
31 1
 
4.8%
32 1
 
4.8%
33 1
 
4.8%
38 1
 
4.8%
ValueCountFrequency (%)
56 1
 
4.8%
49 1
 
4.8%
45 1
 
4.8%
41 2
9.5%
39 1
 
4.8%
38 1
 
4.8%
33 1
 
4.8%
32 1
 
4.8%
31 1
 
4.8%
27 3
14.3%

반
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.04762
Minimum82
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-18T13:47:39.448134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile96
Q1122
median142
Q3184
95-th percentile262
Maximum271
Range189
Interquartile range (IQR)62

Descriptive statistics

Standard deviation52.537107
Coefficient of variation (CV)0.33884498
Kurtosis0.075706053
Mean155.04762
Median Absolute Deviation (MAD)25
Skewness0.92890428
Sum3256
Variance2760.1476
MonotonicityNot monotonic
2024-03-18T13:47:39.542036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
125 2
 
9.5%
142 1
 
4.8%
113 1
 
4.8%
146 1
 
4.8%
120 1
 
4.8%
123 1
 
4.8%
163 1
 
4.8%
204 1
 
4.8%
107 1
 
4.8%
209 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
82 1
4.8%
96 1
4.8%
107 1
4.8%
113 1
4.8%
120 1
4.8%
122 1
4.8%
123 1
4.8%
125 2
9.5%
127 1
4.8%
142 1
4.8%
ValueCountFrequency (%)
271 1
4.8%
262 1
4.8%
224 1
4.8%
209 1
4.8%
204 1
4.8%
184 1
4.8%
167 1
4.8%
163 1
4.8%
146 1
4.8%
144 1
4.8%

Interactions

2024-03-18T13:47:37.552568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.473149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.921718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.237336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.638792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.611933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.992969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.315871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.705492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.686305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.060753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.381239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.778844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:36.814456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.152047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:47:37.468433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:47:39.622788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번법정동행정동면적(제곱킬로미터)통반
연번1.0000.6421.0000.5250.7810.268
법정동0.6421.0001.0000.6820.3430.306
행정동1.0001.0001.0001.0001.0001.000
면적(제곱킬로미터)0.5250.6821.0001.0000.9780.819
통0.7810.3431.0000.9781.0000.929
반0.2680.3061.0000.8190.9291.000
2024-03-18T13:47:39.712839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(제곱킬로미터)통반법정동
연번1.0000.134-0.001-0.0100.740
면적(제곱킬로미터)0.1341.0000.7660.8280.461
통-0.0010.7661.0000.9390.066
반-0.0100.8280.9391.0000.000
법정동0.7400.4610.0660.0001.000

Missing values

2024-03-18T13:47:37.886055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:47:37.981339image/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

연번법정동행정동면적(제곱킬로미터)통반
01숭의동숭의1·3동0.7725125
12숭의동숭의2동0.51882
23숭의동숭의4동0.7427122
34용현동용현1·4동1.341184
45용현동용현2동0.6526127
56용현동용현3동0.451896
67용현동용현5동1.8956271
78학익동학익1동5.0745224
89학익동학익2동0.832144
910도화동도화1동0.931167
연번법정동행정동면적(제곱킬로미터)통반
1112주안동주안1동0.6127142
1213주안동주안2동0.9941209
1314주안동주안3동0.4621107
1415주안동주안4동0.9638204
1516주안동주안5동1.1839163
1617주안동주안6동0.6226123
1718주안동주안7동0.5227120
1819주안동주안8동0.8433146
1920관교동관교동0.924113
2021문학동문학동1.7324125