Overview

Dataset statistics

Number of variables13
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.5 KiB
Average record size in memory110.4 B

Variable types

Text4
Numeric3
Categorical6

Dataset

Description인천광역시 부평구에서 관리,운영하는 현수막게시대의 현황 (소재지 위치, 게시대명 등)에 관한 데이터를 제공합니다.
Author인천광역시부평구시설관리공단
URLhttps://www.data.go.kr/data/15003015/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
규격 is highly overall correlated with 설치연월High correlation
설치연월 is highly overall correlated with 면수 and 3 other fieldsHigh correlation
면수 is highly overall correlated with 행정면수 and 3 other fieldsHigh correlation
행정면수 is highly overall correlated with 면수 and 2 other fieldsHigh correlation
수수료 is highly overall correlated with 면수 and 1 other fieldsHigh correlation
특징 is highly overall correlated with 면수 and 1 other fieldsHigh correlation
규격 is highly imbalanced (85.5%)Imbalance
관리번호 has unique valuesUnique
설치장소 has unique valuesUnique
면수 has 18 (18.6%) zerosZeros

Reproduction

Analysis started2023-12-12 23:04:47.537345
Analysis finished2023-12-12 23:04:49.444287
Duration1.91 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T08:04:49.693432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3505155
Min length3

Characters and Unicode

Total characters325
Distinct characters23
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

Unique97 ?
Unique (%)100.0%

Sample

1st row갈산1
2nd row갈산2
3rd row갈산3
4th row갈산4
5th row갈산5
ValueCountFrequency (%)
갈산1 1
 
1.0%
산곡6 1
 
1.0%
십정1 1
 
1.0%
삼산17 1
 
1.0%
삼산16 1
 
1.0%
삼산15 1
 
1.0%
삼산14 1
 
1.0%
삼산13 1
 
1.0%
삼산12 1
 
1.0%
삼산11 1
 
1.0%
Other values (87) 87
89.7%
2023-12-13T08:04:50.139396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 43
13.2%
부 37
 
11.4%
산 34
 
10.5%
평 24
 
7.4%
2 19
 
5.8%
삼 17
 
5.2%
개 13
 
4.0%
정 12
 
3.7%
청 12
 
3.7%
천 12
 
3.7%
Other values (13) 102
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 194
59.7%
Decimal Number 131
40.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 37
19.1%
산 34
17.5%
평 24
12.4%
삼 17
8.8%
개 13
 
6.7%
정 12
 
6.2%
청 12
 
6.2%
천 12
 
6.2%
십 12
 
6.2%
곡 10
 
5.2%
Other values (3) 11
 
5.7%
Decimal Number
ValueCountFrequency (%)
1 43
32.8%
2 19
14.5%
3 11
 
8.4%
5 10
 
7.6%
6 9
 
6.9%
4 9
 
6.9%
7 9
 
6.9%
0 7
 
5.3%
9 7
 
5.3%
8 7
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 194
59.7%
Common 131
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 37
19.1%
산 34
17.5%
평 24
12.4%
삼 17
8.8%
개 13
 
6.7%
정 12
 
6.2%
청 12
 
6.2%
천 12
 
6.2%
십 12
 
6.2%
곡 10
 
5.2%
Other values (3) 11
 
5.7%
Common
ValueCountFrequency (%)
1 43
32.8%
2 19
14.5%
3 11
 
8.4%
5 10
 
7.6%
6 9
 
6.9%
4 9
 
6.9%
7 9
 
6.9%
0 7
 
5.3%
9 7
 
5.3%
8 7
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 194
59.7%
ASCII 131
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 43
32.8%
2 19
14.5%
3 11
 
8.4%
5 10
 
7.6%
6 9
 
6.9%
4 9
 
6.9%
7 9
 
6.9%
0 7
 
5.3%
9 7
 
5.3%
8 7
 
5.3%
Hangul
ValueCountFrequency (%)
부 37
19.1%
산 34
17.5%
평 24
12.4%
삼 17
8.8%
개 13
 
6.7%
정 12
 
6.2%
청 12
 
6.2%
천 12
 
6.2%
십 12
 
6.2%
곡 10
 
5.2%
Other values (3) 11
 
5.7%

소재지주소
Text

Distinct81
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T08:04:50.427701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length17.989691
Min length15

Characters and Unicode

Total characters1745
Distinct characters72
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)67.0%

Sample

1st row인천광역시 부평구 평천로 334
2nd row인천광역시 부평구 부평북로 273
3rd row인천광역시 부평구 부평북로 273
4th row인천광역시 부평구 굴포로 81
5th row인천광역시 부평구 주부토로 254
ValueCountFrequency (%)
인천광역시 97
24.7%
부평구 97
24.7%
굴포로 6
 
1.5%
부영로 6
 
1.5%
충선로 6
 
1.5%
경원대로 6
 
1.5%
평천로 4
 
1.0%
주부토로 4
 
1.0%
부평대로 4
 
1.0%
열우물로 3
 
0.8%
Other values (106) 159
40.6%
2023-12-13T08:04:50.832729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
297
17.0%
부 122
 
7.0%
평 110
 
6.3%
천 105
 
6.0%
인 100
 
5.7%
광 98
 
5.6%
구 98
 
5.6%
역 97
 
5.6%
시 97
 
5.6%
로 91
 
5.2%
Other values (62) 530
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1133
64.9%
Decimal Number 299
 
17.1%
Space Separator 297
 
17.0%
Dash Punctuation 9
 
0.5%
Control 7
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 122
10.8%
평 110
9.7%
천 105
9.3%
인 100
8.8%
광 98
8.6%
구 98
8.6%
역 97
8.6%
시 97
8.6%
로 91
8.0%
길 18
 
1.6%
Other values (49) 197
17.4%
Decimal Number
ValueCountFrequency (%)
1 79
26.4%
2 43
14.4%
3 39
13.0%
5 29
 
9.7%
7 23
 
7.7%
4 22
 
7.4%
6 22
 
7.4%
8 15
 
5.0%
9 15
 
5.0%
0 12
 
4.0%
Space Separator
ValueCountFrequency (%)
297
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Control
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1133
64.9%
Common 612
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 122
10.8%
평 110
9.7%
천 105
9.3%
인 100
8.8%
광 98
8.6%
구 98
8.6%
역 97
8.6%
시 97
8.6%
로 91
8.0%
길 18
 
1.6%
Other values (49) 197
17.4%
Common
ValueCountFrequency (%)
297
48.5%
1 79
 
12.9%
2 43
 
7.0%
3 39
 
6.4%
5 29
 
4.7%
7 23
 
3.8%
4 22
 
3.6%
6 22
 
3.6%
8 15
 
2.5%
9 15
 
2.5%
Other values (3) 28
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1133
64.9%
ASCII 612
35.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
297
48.5%
1 79
 
12.9%
2 43
 
7.0%
3 39
 
6.4%
5 29
 
4.7%
7 23
 
3.8%
4 22
 
3.6%
6 22
 
3.6%
8 15
 
2.5%
9 15
 
2.5%
Other values (3) 28
 
4.6%
Hangul
ValueCountFrequency (%)
부 122
10.8%
평 110
9.7%
천 105
9.3%
인 100
8.8%
광 98
8.6%
구 98
8.6%
역 97
8.6%
시 97
8.6%
로 91
8.0%
길 18
 
1.6%
Other values (49) 197
17.4%

설치장소
Text

UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T08:04:51.112279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length21
Mean length12.876289
Min length5

Characters and Unicode

Total characters1249
Distinct characters164
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)100.0%

Sample

1st row상꾸지공원 앞
2nd row부평IC(우)
3rd row부평IC(좌)
4th row[2단]갈산사거리 갈산타운 109동 앞
5th row갈산1동 주민센터 주차장
ValueCountFrequency (%)
학교 22
 
9.0%
앞 13
 
5.3%
주민센터 9
 
3.7%
사거리 7
 
2.9%
옆 6
 
2.4%
맞은편 5
 
2.0%
주차장 5
 
2.0%
입구 4
 
1.6%
우 4
 
1.6%
앞(우 3
 
1.2%
Other values (136) 167
68.2%
2023-12-13T08:04:51.485641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
 
11.8%
) 78
 
6.2%
( 51
 
4.1%
리 42
 
3.4%
교 40
 
3.2%
학 36
 
2.9%
거 36
 
2.9%
사 30
 
2.4%
우 28
 
2.2%
산 28
 
2.2%
Other values (154) 732
58.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 903
72.3%
Space Separator 148
 
11.8%
Close Punctuation 85
 
6.8%
Open Punctuation 58
 
4.6%
Decimal Number 43
 
3.4%
Uppercase Letter 10
 
0.8%
Control 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 42
 
4.7%
교 40
 
4.4%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
우 28
 
3.1%
산 28
 
3.1%
동 24
 
2.7%
주 23
 
2.5%
부 22
 
2.4%
Other values (134) 594
65.8%
Decimal Number
ValueCountFrequency (%)
1 17
39.5%
2 14
32.6%
3 3
 
7.0%
0 3
 
7.0%
7 3
 
7.0%
5 1
 
2.3%
9 1
 
2.3%
4 1
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
G 2
20.0%
M 2
20.0%
I 2
20.0%
C 2
20.0%
L 1
10.0%
H 1
10.0%
Close Punctuation
ValueCountFrequency (%)
) 78
91.8%
] 7
 
8.2%
Open Punctuation
ValueCountFrequency (%)
( 51
87.9%
[ 7
 
12.1%
Space Separator
ValueCountFrequency (%)
148
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 903
72.3%
Common 336
 
26.9%
Latin 10
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 42
 
4.7%
교 40
 
4.4%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
우 28
 
3.1%
산 28
 
3.1%
동 24
 
2.7%
주 23
 
2.5%
부 22
 
2.4%
Other values (134) 594
65.8%
Common
ValueCountFrequency (%)
148
44.0%
) 78
23.2%
( 51
 
15.2%
1 17
 
5.1%
2 14
 
4.2%
[ 7
 
2.1%
] 7
 
2.1%
3 3
 
0.9%
0 3
 
0.9%
7 3
 
0.9%
Other values (4) 5
 
1.5%
Latin
ValueCountFrequency (%)
G 2
20.0%
M 2
20.0%
I 2
20.0%
C 2
20.0%
L 1
10.0%
H 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 903
72.3%
ASCII 346
 
27.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
42.8%
) 78
22.5%
( 51
 
14.7%
1 17
 
4.9%
2 14
 
4.0%
[ 7
 
2.0%
] 7
 
2.0%
3 3
 
0.9%
0 3
 
0.9%
7 3
 
0.9%
Other values (10) 15
 
4.3%
Hangul
ValueCountFrequency (%)
리 42
 
4.7%
교 40
 
4.4%
학 36
 
4.0%
거 36
 
4.0%
사 30
 
3.3%
우 28
 
3.1%
산 28
 
3.1%
동 24
 
2.7%
주 23
 
2.5%
부 22
 
2.4%
Other values (134) 594
65.8%

행정동
Text

Distinct58
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-12-13T08:04:51.636156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.8350515
Min length5

Characters and Unicode

Total characters857
Distinct characters21
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

Unique35 ?
Unique (%)36.1%

Sample

1st row 갈산1동
2nd row 갈산1동
3rd row 갈산1동
4th row 갈산동
5th row 갈산1동
ValueCountFrequency (%)
삼산동 12
 
12.4%
청천2동 9
 
9.3%
십정1동 8
 
8.2%
부평3동 7
 
7.2%
부개3동 7
 
7.2%
산곡4동 6
 
6.2%
부평4동 5
 
5.2%
부평2동 5
 
5.2%
삼산2동 4
 
4.1%
부평6동 4
 
4.1%
Other values (17) 30
30.9%
2023-12-13T08:04:51.941970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
484
56.5%
동 97
 
11.3%
부 37
 
4.3%
산 34
 
4.0%
2 27
 
3.2%
평 24
 
2.8%
1 20
 
2.3%
삼 17
 
2.0%
3 15
 
1.8%
개 13
 
1.5%
Other values (11) 89
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Control 484
56.5%
Other Letter 291
34.0%
Decimal Number 77
 
9.0%
Space Separator 5
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 97
33.3%
부 37
 
12.7%
산 34
 
11.7%
평 24
 
8.2%
삼 17
 
5.8%
개 13
 
4.5%
천 12
 
4.1%
십 12
 
4.1%
정 12
 
4.1%
청 12
 
4.1%
Other values (4) 21
 
7.2%
Decimal Number
ValueCountFrequency (%)
2 27
35.1%
1 20
26.0%
3 15
19.5%
4 11
14.3%
6 4
 
5.2%
Control
ValueCountFrequency (%)
484
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 566
66.0%
Hangul 291
34.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 97
33.3%
부 37
 
12.7%
산 34
 
11.7%
평 24
 
8.2%
삼 17
 
5.8%
개 13
 
4.5%
천 12
 
4.1%
십 12
 
4.1%
정 12
 
4.1%
청 12
 
4.1%
Other values (4) 21
 
7.2%
Common
ValueCountFrequency (%)
484
85.5%
2 27
 
4.8%
1 20
 
3.5%
3 15
 
2.7%
4 11
 
1.9%
5
 
0.9%
6 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 566
66.0%
Hangul 291
34.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
484
85.5%
2 27
 
4.8%
1 20
 
3.5%
3 15
 
2.7%
4 11
 
1.9%
5
 
0.9%
6 4
 
0.7%
Hangul
ValueCountFrequency (%)
동 97
33.3%
부 37
 
12.7%
산 34
 
11.7%
평 24
 
8.2%
삼 17
 
5.8%
개 13
 
4.5%
천 12
 
4.1%
십 12
 
4.1%
정 12
 
4.1%
청 12
 
4.1%
Other values (4) 21
 
7.2%

면수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1030928
Minimum0
Maximum6
Zeros18
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T08:04:52.032859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.3160875
Coefficient of variation (CV)0.56447358
Kurtosis-0.84531695
Mean4.1030928
Median Absolute Deviation (MAD)1
Skewness-0.9086171
Sum398
Variance5.3642612
MonotonicityNot monotonic
2023-12-13T08:04:52.147366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 39
40.2%
5 27
27.8%
0 18
18.6%
2 11
 
11.3%
4 1
 
1.0%
3 1
 
1.0%
ValueCountFrequency (%)
0 18
18.6%
2 11
 
11.3%
3 1
 
1.0%
4 1
 
1.0%
5 27
27.8%
6 39
40.2%
ValueCountFrequency (%)
6 39
40.2%
5 27
27.8%
4 1
 
1.0%
3 1
 
1.0%
2 11
 
11.3%
0 18
18.6%

행정면수
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
0
51 
1
28 
3
17 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row3

Common Values

ValueCountFrequency (%)
0 51
52.6%
1 28
28.9%
3 17
 
17.5%
2 1
 
1.0%

Length

2023-12-13T08:04:52.256021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:52.356340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51
52.6%
1 28
28.9%
3 17
 
17.5%
2 1
 
1.0%

수수료
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
28660
58 
8800
18 
28860
15 
27860
6 

Length

Max length5
Median length5
Mean length4.814433
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28660
2nd row28660
3rd row28660
4th row28660
5th row8800

Common Values

ValueCountFrequency (%)
28660 58
59.8%
8800 18
 
18.6%
28860 15
 
15.5%
27860 6
 
6.2%

Length

2023-12-13T08:04:52.470999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:52.567194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28660 58
59.8%
8800 18
 
18.6%
28860 15
 
15.5%
27860 6
 
6.2%

위도
Real number (ℝ)

Distinct62
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3863678.4
Minimum37.469441
Maximum3.747732 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T08:04:52.936640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.469441
5-th percentile37.474855
Q137.497075
median37.506434
Q337.512726
95-th percentile37.52116
Maximum3.747732 × 108
Range3.7477317 × 108
Interquartile range (IQR)0.015651

Descriptive statistics

Standard deviation38052450
Coefficient of variation (CV)9.8487622
Kurtosis97
Mean3863678.4
Median Absolute Deviation (MAD)0.006508
Skewness9.8488578
Sum3.7477681 × 108
Variance1.4479889 × 1015
MonotonicityNot monotonic
2023-12-13T08:04:53.110778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.506434 14
 
14.4%
37.517224 6
 
6.2%
37.472779 4
 
4.1%
37.505941 3
 
3.1%
37.512942 3
 
3.1%
37.507932 3
 
3.1%
37.498905 3
 
3.1%
37.507099 3
 
3.1%
37.507196 3
 
3.1%
37.507186 3
 
3.1%
Other values (52) 52
53.6%
ValueCountFrequency (%)
37.469441 1
 
1.0%
37.472779 4
4.1%
37.4753741 1
 
1.0%
37.47722936 1
 
1.0%
37.477299 1
 
1.0%
37.477309 1
 
1.0%
37.4811389 1
 
1.0%
37.482585 1
 
1.0%
37.483383 1
 
1.0%
37.4852849 1
 
1.0%
ValueCountFrequency (%)
374773205.0 1
 
1.0%
37.5225683 1
 
1.0%
37.522502 1
 
1.0%
37.522465 1
 
1.0%
37.522414 1
 
1.0%
37.520846 1
 
1.0%
37.517309 1
 
1.0%
37.517224 6
6.2%
37.516776 1
 
1.0%
37.516548 1
 
1.0%

경도
Real number (ℝ)

Distinct62
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1302947.4
Minimum123.74207
Maximum1.2637374 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1005.0 B
2023-12-13T08:04:53.236228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123.74207
5-th percentile126.69275
Q1126.70942
median126.72552
Q3126.74039
95-th percentile126.74565
Maximum1.2637374 × 108
Range1.2637361 × 108
Interquartile range (IQR)0.030971

Descriptive statistics

Standard deviation12831296
Coefficient of variation (CV)9.8479002
Kurtosis97
Mean1302947.4
Median Absolute Deviation (MAD)0.014909
Skewness9.8488578
Sum1.263859 × 108
Variance1.6464216 × 1014
MonotonicityNot monotonic
2023-12-13T08:04:53.362727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.740433 14
 
14.4%
126.71013 6
 
6.2%
126.692749 4
 
4.1%
126.725524 3
 
3.1%
126.730361 3
 
3.1%
126.737327 3
 
3.1%
126.716272 3
 
3.1%
126.725673 3
 
3.1%
126.725556 3
 
3.1%
126.725871 3
 
3.1%
Other values (52) 52
53.6%
ValueCountFrequency (%)
123.742066 1
 
1.0%
126.37875 1
 
1.0%
126.692749 4
4.1%
126.692985 1
 
1.0%
126.692997 1
 
1.0%
126.69324 1
 
1.0%
126.693258 1
 
1.0%
126.69331 1
 
1.0%
126.693824 1
 
1.0%
126.69573 1
 
1.0%
ValueCountFrequency (%)
126373737.0 1
1.0%
126.75283 1
1.0%
126.749828 1
1.0%
126.746872 1
1.0%
126.745678 1
1.0%
126.745649 1
1.0%
126.743117 1
1.0%
126.742066 1
1.0%
126.741405 1
1.0%
126.740461 1
1.0%

규격
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
600X70
95 
600X70
 
2

Length

Max length8
Median length8
Mean length7.9793814
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 600X70
2nd row 600X70
3rd row 600X70
4th row 600X70
5th row 600X70

Common Values

ValueCountFrequency (%)
600X70 95
97.9%
600X70 2
 
2.1%

Length

2023-12-13T08:04:53.493228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:53.609866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
600x70 97
100.0%

설치연월
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size908.0 B
2017-12-01
20 
2015-05-01
18 
2015-11-01
14 
2018-12-01
11 
2014-03-01
10 
Other values (5)
24 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row 2015-05-01
2nd row 2013-07-01
3rd row 2014-03-01
4th row 2016-10-01
5th row 2017-12-01

Common Values

ValueCountFrequency (%)
2017-12-01 20
20.6%
2015-05-01 18
18.6%
2015-11-01 14
14.4%
2018-12-01 11
11.3%
2014-03-01 10
10.3%
2013-07-01 9
9.3%
2016-10-01 7
 
7.2%
2012-12-01 5
 
5.2%
2020-11-01 2
 
2.1%
2019-07-09 1
 
1.0%

Length

2023-12-13T08:04:53.702482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:53.833832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017-12-01 20
20.6%
2015-05-01 18
18.6%
2015-11-01 14
14.4%
2018-12-01 11
11.3%
2014-03-01 10
10.3%
2013-07-01 9
9.3%
2016-10-01 7
 
7.2%
2012-12-01 5
 
5.2%
2020-11-01 2
 
2.1%
2019-07-09 1
 
1.0%

특징
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size908.0 B
정보없음
63 
학교
27 
2단
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정보없음
2nd row정보없음
3rd row정보없음
4th row 2단
5th row정보없음

Common Values

ValueCountFrequency (%)
정보없음 63
64.9%
학교 27
27.8%
2단 7
 
7.2%

Length

2023-12-13T08:04:53.971435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:54.073424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정보없음 63
64.9%
학교 27
27.8%
2단 7
 
7.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size908.0 B
2023-10-31
97 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-31
2nd row2023-10-31
3rd row2023-10-31
4th row2023-10-31
5th row2023-10-31

Common Values

ValueCountFrequency (%)
2023-10-31 97
100.0%

Length

2023-12-13T08:04:54.183562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:04:54.270489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-31 97
100.0%

Interactions

2023-12-13T08:04:48.871003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.279705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.571671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.962202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.363151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.670901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:49.046906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.481528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:04:48.774333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:04:54.342166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호소재지주소설치장소행정동면수행정면수수수료위도경도규격설치연월특징
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지주소1.0001.0001.0000.9970.8460.9790.9700.0000.0001.0000.9221.000
설치장소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동1.0000.9971.0001.0000.0000.9150.9701.0000.0000.7520.8730.911
면수1.0000.8461.0000.0001.0000.9170.7730.0000.0000.2880.7430.868
행정면수1.0000.9791.0000.9150.9171.0000.9040.0000.0000.3950.7170.143
수수료1.0000.9701.0000.9700.7730.9041.0000.0000.0000.3760.6890.154
위도1.0000.0001.0001.0000.0000.0000.0001.0000.0000.0000.0000.000
경도1.0000.0001.0000.0000.0000.0000.0000.0001.0000.0000.4300.000
규격1.0001.0001.0000.7520.2880.3950.3760.0000.0001.0001.0000.000
설치연월1.0000.9221.0000.8730.7430.7170.6890.0000.4301.0001.0000.821
특징1.0001.0001.0000.9110.8680.1430.1540.0000.0000.0000.8211.000
2023-12-13T08:04:54.493042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특징수수료규격행정면수설치연월
특징1.0000.1440.0000.1330.694
수수료0.1441.0000.2490.5930.473
규격0.0000.2491.0000.2620.957
행정면수0.1330.5930.2621.0000.503
설치연월0.6940.4730.9570.5031.000
2023-12-13T08:04:54.593629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면수위도경도행정면수수수료규격설치연월특징
면수1.0000.1970.0120.7960.6080.2020.5000.560
위도0.1971.0000.2110.0000.0000.0000.0000.000
경도0.0120.2111.0000.0000.0000.0000.3150.000
행정면수0.7960.0000.0001.0000.5930.2620.5030.133
수수료0.6080.0000.0000.5931.0000.2490.4730.144
규격0.2020.0000.0000.2620.2491.0000.9570.000
설치연월0.5000.0000.3150.5030.4730.9571.0000.694
특징0.5600.0000.0000.1330.1440.0000.6941.000

Missing values

2023-12-13T08:04:49.178429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:04:49.369680image/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

관리번호소재지주소설치장소행정동면수행정면수수수료위도경도규격설치연월특징데이터기준일자
0갈산1인천광역시 부평구 평천로 334상꾸지공원 앞갈산1동602866037.514331126.725715600X702015-05-01정보없음2023-10-31
1갈산2인천광역시 부평구 부평북로 273부평IC(우)갈산1동512866037.522465126.722064600X702013-07-01정보없음2023-10-31
2갈산3인천광역시 부평구 부평북로 273부평IC(좌)갈산1동512866037.522502126.72207600X702014-03-01정보없음2023-10-31
3갈산4인천광역시 부평구 굴포로 81[2단]갈산사거리 갈산타운 109동 앞갈산동202866037.512942126.730361600X702016-10-012단2023-10-31
4갈산5인천광역시 부평구 주부토로 254갈산1동 주민센터 주차장갈산1동03880037.512942126.730361600X702017-12-01정보없음2023-10-31
5갈산6인천광역시 부평구 굴포로 5부평세관 앞 한국지엠사거리갈산2동602866037.512942126.730361600X702019-07-09정보없음2023-10-31
6갈산7인천광역시 부평구 주부토로173갈산2동 행정복지센터갈산2동03880037.510527126.725524600X702020-11-01정보없음2023-10-31
7부개1인천광역시 부평구 장제로 234신복사거리부개3동512866037.506699126.733867600X702013-07-01정보없음2023-10-31
8부개2인천광역시 부평구 길주남로 143부개주공 1단지 앞(삼산체육관 맞은편)(우)부개3동602866037.5065126373737.0600X702012-12-01정보없음2023-10-31
9부개3인천광역시 부평구 길주남로 143부개주공 1단지 앞(삼산체육관 맞은편)(좌)부개3동602866037.506566126.737508600X702015-11-01정보없음2023-10-31
관리번호소재지주소설치장소행정동면수행정면수수수료위도경도규격설치연월특징데이터기준일자
87청천3인천광역시 부평구 부평대로 233GM대우 정문 옆청천2동512866037.517224126.71013600X702014-03-01정보없음2023-10-31
88청천4인천광역시 부평구 안남로 274학교) 산곡사거리(금호아파트 앞)청천2동602866037.517224126.71013600X702014-03-01학교2023-10-31
89청천5인천광역시 부평구 부평대로 233새마을금고 청천본점 옆청천2동602866037.512758126.705916600X702012-12-01정보없음2023-10-31
90청천6인천광역시 부평구 세월천로 16GM대우 서문 맞은편(푸르지오 아파트앞)청천2동602866037.517224126.71013600X702014-03-01정보없음2023-10-31
91청천7인천광역시 부평구 평천로 187수출공단 오거리청천2동602866037.517224126.71013600X702014-03-01정보없음2023-10-31
92청천8인천광역시 부평구 청천동 125-1나비공원 공영주차장 (좌)(1)청천1동03880037.517224126.71013600X702017-12-01정보없음2023-10-31
93청천9인천광역시 부평구 청천동 125-1나비공원 공영주차장 (좌)(2)청천2동602866037.517224126.71013600X702017-12-01정보없음2023-10-31
94청천10인천광역시 부평구 청안로 8인향아파트 101동 맞은편청천1동602866037.520846126.69573600X702017-12-01정보없음2023-10-31
95청천11인천광역시 부평구 청천178-17청천2동 주민센터 주차장청천2동03880037.514686126.704838600X702017-12-01정보없음2023-10-31
96청천12인천광역시 부평구 부평북로 99새벼리사거리청천1동602866037.522568126.704645600X702017-12-01정보없음2023-10-31