Overview

Dataset statistics

Number of variables10
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory84.1 B

Variable types

Numeric1
Text2
Categorical5
Boolean1
DateTime1

Dataset

Description인천광역시 중구 급경사지 및 붕괴위험지역에 관한 정보입니다.
Author인천광역시 중구
URLhttps://www.data.go.kr/data/15038703/fileData.do

Alerts

붕괴위험지역지정여부 has constant value ""Constant
데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 읍면동 and 1 other fieldsHigh correlation
읍면동 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
구조 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
유형 is highly overall correlated with 읍면동High correlation
유형 is highly imbalanced (72.9%)Imbalance
연번 has unique valuesUnique
명칭 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:51:16.295143
Analysis finished2023-12-12 11:51:17.076984
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T20:51:17.158685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2023-12-12T20:51:17.359406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

명칭
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T20:51:17.546619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length4.9767442
Min length4

Characters and Unicode

Total characters214
Distinct characters37
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

Unique43 ?
Unique (%)100.0%

Sample

1st row송학동 1
2nd row송학동 2
3rd row신흥동 1
4th row신흥동 2
5th row신흥동 3
ValueCountFrequency (%)
1 13
15.7%
2 9
10.8%
3 7
 
8.4%
도원동 6
 
7.2%
4 6
 
7.2%
송월동 5
 
6.0%
운북동 4
 
4.8%
전동 4
 
4.8%
운남동 4
 
4.8%
신흥동 3
 
3.6%
Other values (17) 22
26.5%
2023-12-12T20:51:17.926282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 40
18.7%
40
18.7%
1 13
 
6.1%
2 10
 
4.7%
북 10
 
4.7%
운 9
 
4.2%
3 7
 
3.3%
송 7
 
3.3%
원 7
 
3.3%
4 6
 
2.8%
Other values (27) 65
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
62.6%
Space Separator 40
 
18.7%
Decimal Number 40
 
18.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 40
29.9%
북 10
 
7.5%
운 9
 
6.7%
송 7
 
5.2%
원 7
 
5.2%
도 6
 
4.5%
남 6
 
4.5%
월 5
 
3.7%
성 5
 
3.7%
신 4
 
3.0%
Other values (20) 35
26.1%
Decimal Number
ValueCountFrequency (%)
1 13
32.5%
2 10
25.0%
3 7
17.5%
4 6
15.0%
5 3
 
7.5%
6 1
 
2.5%
Space Separator
ValueCountFrequency (%)
40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
62.6%
Common 80
37.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 40
29.9%
북 10
 
7.5%
운 9
 
6.7%
송 7
 
5.2%
원 7
 
5.2%
도 6
 
4.5%
남 6
 
4.5%
월 5
 
3.7%
성 5
 
3.7%
신 4
 
3.0%
Other values (20) 35
26.1%
Common
ValueCountFrequency (%)
40
50.0%
1 13
 
16.2%
2 10
 
12.5%
3 7
 
8.8%
4 6
 
7.5%
5 3
 
3.8%
6 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
62.6%
ASCII 80
37.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 40
29.9%
북 10
 
7.5%
운 9
 
6.7%
송 7
 
5.2%
원 7
 
5.2%
도 6
 
4.5%
남 6
 
4.5%
월 5
 
3.7%
성 5
 
3.7%
신 4
 
3.0%
Other values (20) 35
26.1%
ASCII
ValueCountFrequency (%)
40
50.0%
1 13
 
16.2%
2 10
 
12.5%
3 7
 
8.8%
4 6
 
7.5%
5 3
 
3.8%
6 1
 
1.2%

등급
Categorical

Distinct3
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
B
25 
C
16 
A
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC
2nd rowC
3rd rowB
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
B 25
58.1%
C 16
37.2%
A 2
 
4.7%

Length

2023-12-12T20:51:18.105789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:18.233621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 25
58.1%
c 16
37.2%
a 2
 
4.7%

읍면동
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Memory size476.0 B
도원동
7 
송월동
5 
운북동
5 
전동
4 
운남동
4 
Other values (10)
18 

Length

Max length3
Median length3
Mean length2.8837209
Min length2

Unique

Unique5 ?
Unique (%)11.6%

Sample

1st row송학동
2nd row송학동
3rd row신흥동
4th row신흥동
5th row신흥동

Common Values

ValueCountFrequency (%)
도원동 7
16.3%
송월동 5
11.6%
운북동 5
11.6%
전동 4
9.3%
운남동 4
9.3%
송학동 3
7.0%
신흥동 3
7.0%
북성동 3
7.0%
남북동 2
 
4.7%
무의동 2
 
4.7%
Other values (5) 5
11.6%

Length

2023-12-12T20:51:18.387076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
도원동 7
16.3%
송월동 5
11.6%
운북동 5
11.6%
전동 4
9.3%
운남동 4
9.3%
송학동 3
7.0%
신흥동 3
7.0%
북성동 3
7.0%
남북동 2
 
4.7%
무의동 2
 
4.7%
Other values (5) 5
11.6%

지번
Text

Distinct39
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T20:51:18.688234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.069767
Min length14

Characters and Unicode

Total characters777
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)86.0%

Sample

1st row인천광역시 중구 송학동1가 3
2nd row인천광역시 중구 송학동2가 16-6
3rd row인천광역시 중구 신흥동1가 4-2
4th row인천광역시 중구 신흥동1가 20-2
5th row인천광역시 중구 신흥동2가 23
ValueCountFrequency (%)
인천광역시 43
24.9%
중구 43
24.9%
도원동 7
 
4.0%
운북동 5
 
2.9%
12-73 4
 
2.3%
전동 4
 
2.3%
운남동 4
 
2.3%
송월동3가 3
 
1.7%
송월동1가 2
 
1.2%
26 2
 
1.2%
Other values (52) 56
32.4%
2023-12-12T20:51:19.144738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
130
16.7%
중 44
 
5.7%
인 43
 
5.5%
동 43
 
5.5%
구 43
 
5.5%
천 43
 
5.5%
시 43
 
5.5%
역 43
 
5.5%
광 43
 
5.5%
1 41
 
5.3%
Other values (33) 261
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 443
57.0%
Decimal Number 169
 
21.8%
Space Separator 130
 
16.7%
Dash Punctuation 35
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 44
9.9%
인 43
9.7%
동 43
9.7%
구 43
9.7%
천 43
9.7%
시 43
9.7%
역 43
9.7%
광 43
9.7%
가 14
 
3.2%
북 10
 
2.3%
Other values (21) 74
16.7%
Decimal Number
ValueCountFrequency (%)
1 41
24.3%
2 28
16.6%
3 27
16.0%
5 15
 
8.9%
6 12
 
7.1%
0 11
 
6.5%
4 11
 
6.5%
7 11
 
6.5%
8 9
 
5.3%
9 4
 
2.4%
Space Separator
ValueCountFrequency (%)
130
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 443
57.0%
Common 334
43.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 44
9.9%
인 43
9.7%
동 43
9.7%
구 43
9.7%
천 43
9.7%
시 43
9.7%
역 43
9.7%
광 43
9.7%
가 14
 
3.2%
북 10
 
2.3%
Other values (21) 74
16.7%
Common
ValueCountFrequency (%)
130
38.9%
1 41
 
12.3%
- 35
 
10.5%
2 28
 
8.4%
3 27
 
8.1%
5 15
 
4.5%
6 12
 
3.6%
0 11
 
3.3%
4 11
 
3.3%
7 11
 
3.3%
Other values (2) 13
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 443
57.0%
ASCII 334
43.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
130
38.9%
1 41
 
12.3%
- 35
 
10.5%
2 28
 
8.4%
3 27
 
8.1%
5 15
 
4.5%
6 12
 
3.6%
0 11
 
3.3%
4 11
 
3.3%
7 11
 
3.3%
Other values (2) 13
 
3.9%
Hangul
ValueCountFrequency (%)
중 44
9.9%
인 43
9.7%
동 43
9.7%
구 43
9.7%
천 43
9.7%
시 43
9.7%
역 43
9.7%
광 43
9.7%
가 14
 
3.2%
북 10
 
2.3%
Other values (21) 74
16.7%

붕괴위험지역지정여부
Boolean

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size175.0 B
False
43 
ValueCountFrequency (%)
False 43
100.0%
2023-12-12T20:51:19.263711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

용도
Categorical

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
기타
16 
도로
15 
주택
11 
기타, 도로
 
1

Length

Max length6
Median length2
Mean length2.0930233
Min length2

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row기타
2nd row주택
3rd row기타
4th row주택
5th row주택

Common Values

ValueCountFrequency (%)
기타 16
37.2%
도로 15
34.9%
주택 11
25.6%
기타, 도로 1
 
2.3%

Length

2023-12-12T20:51:19.370351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:19.513247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 17
38.6%
도로 16
36.4%
주택 11
25.0%

구조
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
석축
16 
복합
9 
옹벽
6 
암반
6 
토사
6 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
석축 16
37.2%
복합 9
20.9%
옹벽 6
 
14.0%
암반 6
 
14.0%
토사 6
 
14.0%

Length

2023-12-12T20:51:19.658273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:19.772488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
석축 16
37.2%
복합 9
20.9%
옹벽 6
 
14.0%
암반 6
 
14.0%
토사 6
 
14.0%

유형
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
인공
41 
자연
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인공 41
95.3%
자연 2
 
4.7%

Length

2023-12-12T20:51:19.900152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:51:20.008692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인공 41
95.3%
자연 2
 
4.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2021-07-20 00:00:00
Maximum2021-07-20 00:00:00
2023-12-12T20:51:20.095259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:51:20.190048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T20:51:16.723979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:51:20.268672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번명칭등급읍면동지번용도구조유형
연번1.0001.0000.4620.9000.9700.5500.8570.000
명칭1.0001.0001.0001.0001.0001.0001.0001.000
등급0.4621.0001.0000.0000.9650.0000.1590.117
읍면동0.9001.0000.0001.0001.0000.0000.8981.000
지번0.9701.0000.9651.0001.0001.0000.8371.000
용도0.5501.0000.0000.0001.0001.0000.2480.000
구조0.8571.0000.1590.8980.8370.2481.0000.393
유형0.0001.0000.1171.0001.0000.0000.3931.000
2023-12-12T20:51:20.393624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형구조등급읍면동용도
유형1.0000.4590.1890.8260.000
구조0.4591.0000.1070.5170.196
등급0.1890.1071.0000.0000.000
읍면동0.8260.5170.0001.0000.000
용도0.0000.1960.0000.0001.000
2023-12-12T20:51:20.496567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번등급읍면동용도구조유형
연번1.0000.3300.5930.2770.5190.000
등급0.3301.0000.0000.0000.1070.189
읍면동0.5930.0001.0000.0000.5170.826
용도0.2770.0000.0001.0000.1960.000
구조0.5190.1070.5170.1961.0000.459
유형0.0000.1890.8260.0000.4591.000

Missing values

2023-12-12T20:51:16.839471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:51:17.013373image/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

연번명칭등급읍면동지번붕괴위험지역지정여부용도구조유형데이터기준일자
01송학동 1C송학동인천광역시 중구 송학동1가 3N기타석축인공2021-07-20
12송학동 2C송학동인천광역시 중구 송학동2가 16-6N주택석축인공2021-07-20
23신흥동 1B신흥동인천광역시 중구 신흥동1가 4-2N기타석축인공2021-07-20
34신흥동 2C신흥동인천광역시 중구 신흥동1가 20-2N주택석축인공2021-07-20
45신흥동 3C신흥동인천광역시 중구 신흥동2가 23N주택석축인공2021-07-20
56전동 1B전동인천광역시 중구 전동 26N기타복합인공2021-07-20
67전동 2B전동인천광역시 중구 전동 26N기타복합인공2021-07-20
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