Overview

Dataset statistics

Number of variables15
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory130.9 B

Variable types

Numeric6
Categorical2
DateTime2
Text5

Dataset

Description대전광역시 서구 어린이노인 보호구역 현황(보호구역구분 정보, 보호구역명칭 정보, 소재지 정보, 위도, 경도) 등을 제공합니다.
Author대전광역시 서구
URLhttps://www.data.go.kr/data/15075609/fileData.do

Alerts

자치구명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
순번 is highly overall correlated with 추진연도High correlation
행정동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
추진연도 is highly overall correlated with 순번High correlation
위도 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 행정동코드 and 2 other fieldsHigh correlation
구분명 is highly overall correlated with 위도High correlation
구분명 is highly imbalanced (61.9%)Imbalance
순번 has unique valuesUnique
시설명 has unique valuesUnique
지번주소 has unique valuesUnique
도로명주소 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-01-14 13:48:11.702047
Analysis finished2024-01-14 13:48:17.698159
Duration6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:17.774286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.3
Q17.5
median14
Q320.5
95-th percentile25.7
Maximum27
Range26
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9372539
Coefficient of variation (CV)0.56694671
Kurtosis-1.2
Mean14
Median Absolute Deviation (MAD)7
Skewness0
Sum378
Variance63
MonotonicityStrictly increasing
2024-01-14T22:48:17.966118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1 1
 
3.7%
2 1
 
3.7%
27 1
 
3.7%
26 1
 
3.7%
25 1
 
3.7%
24 1
 
3.7%
23 1
 
3.7%
22 1
 
3.7%
21 1
 
3.7%
20 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
1 1
3.7%
2 1
3.7%
3 1
3.7%
4 1
3.7%
5 1
3.7%
6 1
3.7%
7 1
3.7%
8 1
3.7%
9 1
3.7%
10 1
3.7%
ValueCountFrequency (%)
27 1
3.7%
26 1
3.7%
25 1
3.7%
24 1
3.7%
23 1
3.7%
22 1
3.7%
21 1
3.7%
20 1
3.7%
19 1
3.7%
18 1
3.7%

자치구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
서구
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 27
100.0%

Length

2024-01-14T22:48:18.230357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:48:18.388513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 27
100.0%

지정연월
Date

Distinct8
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2008-12-01 00:00:00
Maximum2020-04-01 00:00:00
2024-01-14T22:48:18.508840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:18.692213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)

시설명
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-14T22:48:19.002185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.0740741
Min length5

Characters and Unicode

Total characters164
Distinct characters62
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

Unique27 ?
Unique (%)100.0%

Sample

1st row서구노인복지관
2nd row건능골경로당
3rd row신선암경로당
4th row남산경로당
5th row변동갓골경로당
ValueCountFrequency (%)
서구노인복지관 1
 
3.7%
유등노인복지관 1
 
3.7%
삼천경로당 1
 
3.7%
도마1경로당 1
 
3.7%
한민경로당 1
 
3.7%
변정경로당 1
 
3.7%
명암경로당 1
 
3.7%
우명2경로당 1
 
3.7%
오동경로당 1
 
3.7%
평촌1경로당 1
 
3.7%
Other values (17) 17
63.0%
2024-01-14T22:48:19.526895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 24
 
14.6%
경 23
 
14.0%
당 23
 
14.0%
1 7
 
4.3%
골 4
 
2.4%
마 4
 
2.4%
산 3
 
1.8%
평 3
 
1.8%
정 3
 
1.8%
동 3
 
1.8%
Other values (52) 67
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
93.9%
Decimal Number 9
 
5.5%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 24
 
15.6%
경 23
 
14.9%
당 23
 
14.9%
골 4
 
2.6%
마 4
 
2.6%
산 3
 
1.9%
평 3
 
1.9%
정 3
 
1.9%
동 3
 
1.9%
명 2
 
1.3%
Other values (49) 62
40.3%
Decimal Number
ValueCountFrequency (%)
1 7
77.8%
2 2
 
22.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
93.9%
Common 10
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 24
 
15.6%
경 23
 
14.9%
당 23
 
14.9%
골 4
 
2.6%
마 4
 
2.6%
산 3
 
1.9%
평 3
 
1.9%
정 3
 
1.9%
동 3
 
1.9%
명 2
 
1.3%
Other values (49) 62
40.3%
Common
ValueCountFrequency (%)
1 7
70.0%
2 2
 
20.0%
1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
93.9%
ASCII 10
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 24
 
15.6%
경 23
 
14.9%
당 23
 
14.9%
골 4
 
2.6%
마 4
 
2.6%
산 3
 
1.9%
평 3
 
1.9%
정 3
 
1.9%
동 3
 
1.9%
명 2
 
1.3%
Other values (49) 62
40.3%
ASCII
ValueCountFrequency (%)
1 7
70.0%
2 2
 
20.0%
1
 
10.0%

지번주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-14T22:48:19.826645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17.555556
Min length16

Characters and Unicode

Total characters474
Distinct characters48
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

Unique27 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 탄방동 1084
2nd row대전광역시 서구 갈마동 402-1
3rd row대전광역시 서구 관저동 1063
4th row대전광역시 서구 탄방동 1388
5th row대전광역시 서구 변동 11-23
ValueCountFrequency (%)
대전광역시 27
25.0%
서구 27
25.0%
도마동 4
 
3.7%
변동 2
 
1.9%
정림동 2
 
1.9%
평촌동 2
 
1.9%
오동 2
 
1.9%
탄방동 2
 
1.9%
갈마동 2
 
1.9%
1063 1
 
0.9%
Other values (37) 37
34.3%
2024-01-14T22:48:20.294549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
81
17.1%
대 27
 
5.7%
광 27
 
5.7%
역 27
 
5.7%
시 27
 
5.7%
서 27
 
5.7%
구 27
 
5.7%
전 27
 
5.7%
동 27
 
5.7%
1 22
 
4.6%
Other values (38) 155
32.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 266
56.1%
Decimal Number 107
22.6%
Space Separator 81
 
17.1%
Dash Punctuation 20
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 27
10.2%
광 27
10.2%
역 27
10.2%
시 27
10.2%
서 27
10.2%
구 27
10.2%
전 27
10.2%
동 27
10.2%
마 6
 
2.3%
정 4
 
1.5%
Other values (26) 40
15.0%
Decimal Number
ValueCountFrequency (%)
1 22
20.6%
3 18
16.8%
4 14
13.1%
8 12
11.2%
2 11
10.3%
0 8
 
7.5%
6 8
 
7.5%
5 7
 
6.5%
7 4
 
3.7%
9 3
 
2.8%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 266
56.1%
Common 208
43.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 27
10.2%
광 27
10.2%
역 27
10.2%
시 27
10.2%
서 27
10.2%
구 27
10.2%
전 27
10.2%
동 27
10.2%
마 6
 
2.3%
정 4
 
1.5%
Other values (26) 40
15.0%
Common
ValueCountFrequency (%)
81
38.9%
1 22
 
10.6%
- 20
 
9.6%
3 18
 
8.7%
4 14
 
6.7%
8 12
 
5.8%
2 11
 
5.3%
0 8
 
3.8%
6 8
 
3.8%
5 7
 
3.4%
Other values (2) 7
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 266
56.1%
ASCII 208
43.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
81
38.9%
1 22
 
10.6%
- 20
 
9.6%
3 18
 
8.7%
4 14
 
6.7%
8 12
 
5.8%
2 11
 
5.3%
0 8
 
3.8%
6 8
 
3.8%
5 7
 
3.4%
Other values (2) 7
 
3.4%
Hangul
ValueCountFrequency (%)
대 27
10.2%
광 27
10.2%
역 27
10.2%
시 27
10.2%
서 27
10.2%
구 27
10.2%
전 27
10.2%
동 27
10.2%
마 6
 
2.3%
정 4
 
1.5%
Other values (26) 40
15.0%

도로명주소
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-14T22:48:20.650050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length30
Mean length25.148148
Min length20

Characters and Unicode

Total characters679
Distinct characters84
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 남선로 66 (탄방동, 남선공원)
2nd row대전광역시 서구 갈마로103번길 41 (갈마동)
3rd row대전광역시 서구 관저로129번길 26 (관저동, 노인정)
4th row대전광역시 서구 도솔로483번길 81 (탄방동)
5th row대전광역시 서구 중반4길 23 (변동)
ValueCountFrequency (%)
대전광역시 27
 
19.1%
서구 27
 
19.1%
도마동 4
 
2.8%
변동 2
 
1.4%
1 2
 
1.4%
18 2
 
1.4%
평촌동 2
 
1.4%
49 2
 
1.4%
오동 2
 
1.4%
탄방동 2
 
1.4%
Other values (68) 69
48.9%
2024-01-14T22:48:21.223454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
 
16.8%
대 28
 
4.1%
동 28
 
4.1%
서 28
 
4.1%
전 28
 
4.1%
시 28
 
4.1%
광 28
 
4.1%
구 27
 
4.0%
역 27
 
4.0%
( 26
 
3.8%
Other values (74) 317
46.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
59.8%
Space Separator 114
 
16.8%
Decimal Number 98
 
14.4%
Open Punctuation 26
 
3.8%
Close Punctuation 26
 
3.8%
Other Punctuation 6
 
0.9%
Dash Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 28
 
6.9%
동 28
 
6.9%
서 28
 
6.9%
전 28
 
6.9%
시 28
 
6.9%
광 28
 
6.9%
구 27
 
6.7%
역 27
 
6.7%
길 25
 
6.2%
로 16
 
3.9%
Other values (59) 143
35.2%
Decimal Number
ValueCountFrequency (%)
1 22
22.4%
2 12
12.2%
6 11
11.2%
4 9
9.2%
8 9
9.2%
0 8
 
8.2%
3 8
 
8.2%
5 7
 
7.1%
9 6
 
6.1%
7 6
 
6.1%
Space Separator
ValueCountFrequency (%)
114
100.0%
Open Punctuation
ValueCountFrequency (%)
( 26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
59.8%
Common 273
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 28
 
6.9%
동 28
 
6.9%
서 28
 
6.9%
전 28
 
6.9%
시 28
 
6.9%
광 28
 
6.9%
구 27
 
6.7%
역 27
 
6.7%
길 25
 
6.2%
로 16
 
3.9%
Other values (59) 143
35.2%
Common
ValueCountFrequency (%)
114
41.8%
( 26
 
9.5%
) 26
 
9.5%
1 22
 
8.1%
2 12
 
4.4%
6 11
 
4.0%
4 9
 
3.3%
8 9
 
3.3%
0 8
 
2.9%
3 8
 
2.9%
Other values (5) 28
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
59.8%
ASCII 273
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114
41.8%
( 26
 
9.5%
) 26
 
9.5%
1 22
 
8.1%
2 12
 
4.4%
6 11
 
4.0%
4 9
 
3.3%
8 9
 
3.3%
0 8
 
2.9%
3 8
 
2.9%
Other values (5) 28
 
10.3%
Hangul
ValueCountFrequency (%)
대 28
 
6.9%
동 28
 
6.9%
서 28
 
6.9%
전 28
 
6.9%
시 28
 
6.9%
광 28
 
6.9%
구 27
 
6.7%
역 27
 
6.7%
길 25
 
6.2%
로 16
 
3.9%
Other values (59) 143
35.2%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170573 × 109
Minimum3.017052 × 109
Maximum3.017066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:21.399427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.017052 × 109
5-th percentile3.017052 × 109
Q13.017054 × 109
median3.0170582 × 109
Q33.01706 × 109
95-th percentile3.01706 × 109
Maximum3.017066 × 109
Range14000
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation3478.9641
Coefficient of variation (CV)1.1530984 × 10-6
Kurtosis-0.19673453
Mean3.0170573 × 109
Median Absolute Deviation (MAD)1800
Skewness0.099883785
Sum8.1460548 × 1010
Variance12103191
MonotonicityNot monotonic
2024-01-14T22:48:21.571192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3017060000 9
33.3%
3017052000 3
 
11.1%
3017055500 2
 
7.4%
3017054000 2
 
7.4%
3017053500 2
 
7.4%
3017058200 1
 
3.7%
3017059700 1
 
3.7%
3017058100 1
 
3.7%
3017053000 1
 
3.7%
3017057500 1
 
3.7%
Other values (4) 4
14.8%
ValueCountFrequency (%)
3017052000 3
11.1%
3017053000 1
 
3.7%
3017053500 2
7.4%
3017054000 2
7.4%
3017055500 2
7.4%
3017056000 1
 
3.7%
3017057500 1
 
3.7%
3017058100 1
 
3.7%
3017058200 1
 
3.7%
3017058600 1
 
3.7%
ValueCountFrequency (%)
3017066000 1
 
3.7%
3017060000 9
33.3%
3017059700 1
 
3.7%
3017059000 1
 
3.7%
3017058600 1
 
3.7%
3017058200 1
 
3.7%
3017058100 1
 
3.7%
3017057500 1
 
3.7%
3017056000 1
 
3.7%
3017055500 2
 
7.4%

행정동명
Text

Distinct14
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-14T22:48:21.771027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2592593
Min length2

Characters and Unicode

Total characters88
Distinct characters25
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

Unique9 ?
Unique (%)33.3%

Sample

1st row탄방동
2nd row갈마2동
3rd row관저2동
4th row탄방동
5th row변동
ValueCountFrequency (%)
기성동 9
33.3%
도마1동 3
 
11.1%
탄방동 2
 
7.4%
변동 2
 
7.4%
정림동 2
 
7.4%
갈마2동 1
 
3.7%
관저2동 1
 
3.7%
갈마1동 1
 
3.7%
도마2동 1
 
3.7%
내동 1
 
3.7%
Other values (4) 4
14.8%
2024-01-14T22:48:22.284765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
30.7%
기 9
 
10.2%
성 9
 
10.2%
마 6
 
6.8%
1 5
 
5.7%
도 4
 
4.5%
정 3
 
3.4%
2 3
 
3.4%
변 2
 
2.3%
림 2
 
2.3%
Other values (15) 18
20.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
89.8%
Decimal Number 9
 
10.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
34.2%
기 9
 
11.4%
성 9
 
11.4%
마 6
 
7.6%
도 4
 
5.1%
정 3
 
3.8%
변 2
 
2.5%
림 2
 
2.5%
갈 2
 
2.5%
탄 2
 
2.5%
Other values (12) 13
16.5%
Decimal Number
ValueCountFrequency (%)
1 5
55.6%
2 3
33.3%
3 1
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
89.8%
Common 9
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
34.2%
기 9
 
11.4%
성 9
 
11.4%
마 6
 
7.6%
도 4
 
5.1%
정 3
 
3.8%
변 2
 
2.5%
림 2
 
2.5%
갈 2
 
2.5%
탄 2
 
2.5%
Other values (12) 13
16.5%
Common
ValueCountFrequency (%)
1 5
55.6%
2 3
33.3%
3 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
89.8%
ASCII 9
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
34.2%
기 9
 
11.4%
성 9
 
11.4%
마 6
 
7.6%
도 4
 
5.1%
정 3
 
3.8%
변 2
 
2.5%
림 2
 
2.5%
갈 2
 
2.5%
탄 2
 
2.5%
Other values (12) 13
16.5%
ASCII
ValueCountFrequency (%)
1 5
55.6%
2 3
33.3%
3 1
 
11.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0170112 × 109
Minimum3.0170102 × 109
Maximum3.0170124 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:22.516055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0170102 × 109
5-th percentile3.0170102 × 109
Q13.0170104 × 109
median3.0170111 × 109
Q33.017012 × 109
95-th percentile3.0170123 × 109
Maximum3.0170124 × 109
Range2200
Interquartile range (IQR)1550

Descriptive statistics

Standard deviation767.46521
Coefficient of variation (CV)2.5437931 × 10-7
Kurtosis-1.5072756
Mean3.0170112 × 109
Median Absolute Deviation (MAD)800
Skewness0.16583742
Sum8.1459302 × 1010
Variance589002.85
MonotonicityNot monotonic
2024-01-14T22:48:22.709950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3017010300 4
14.8%
3017010600 2
 
7.4%
3017011100 2
 
7.4%
3017010400 2
 
7.4%
3017012100 2
 
7.4%
3017012200 2
 
7.4%
3017010200 2
 
7.4%
3017012400 1
 
3.7%
3017011000 1
 
3.7%
3017011600 1
 
3.7%
Other values (8) 8
29.6%
ValueCountFrequency (%)
3017010200 2
7.4%
3017010300 4
14.8%
3017010400 2
7.4%
3017010600 2
7.4%
3017010800 1
 
3.7%
3017011000 1
 
3.7%
3017011100 2
7.4%
3017011200 1
 
3.7%
3017011300 1
 
3.7%
3017011400 1
 
3.7%
ValueCountFrequency (%)
3017012400 1
3.7%
3017012300 1
3.7%
3017012200 2
7.4%
3017012100 2
7.4%
3017012000 1
3.7%
3017011900 1
3.7%
3017011700 1
3.7%
3017011600 1
3.7%
3017011400 1
3.7%
3017011300 1
3.7%

법정동명
Text

Distinct18
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-01-14T22:48:22.911304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8518519
Min length2

Characters and Unicode

Total characters77
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)40.7%

Sample

1st row탄방동
2nd row갈마동
3rd row관저동
4th row탄방동
5th row변동
ValueCountFrequency (%)
도마동 4
14.8%
정림동 2
 
7.4%
평촌동 2
 
7.4%
오동 2
 
7.4%
변동 2
 
7.4%
탄방동 2
 
7.4%
갈마동 2
 
7.4%
관저동 1
 
3.7%
원정동 1
 
3.7%
내동 1
 
3.7%
Other values (8) 8
29.6%
2024-01-14T22:48:23.402863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
35.1%
마 6
 
7.8%
도 4
 
5.2%
정 4
 
5.2%
평 3
 
3.9%
변 2
 
2.6%
원 2
 
2.6%
산 2
 
2.6%
방 2
 
2.6%
탄 2
 
2.6%
Other values (19) 23
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
35.1%
마 6
 
7.8%
도 4
 
5.2%
정 4
 
5.2%
평 3
 
3.9%
변 2
 
2.6%
원 2
 
2.6%
산 2
 
2.6%
방 2
 
2.6%
탄 2
 
2.6%
Other values (19) 23
29.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
35.1%
마 6
 
7.8%
도 4
 
5.2%
정 4
 
5.2%
평 3
 
3.9%
변 2
 
2.6%
원 2
 
2.6%
산 2
 
2.6%
방 2
 
2.6%
탄 2
 
2.6%
Other values (19) 23
29.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
35.1%
마 6
 
7.8%
도 4
 
5.2%
정 4
 
5.2%
평 3
 
3.9%
변 2
 
2.6%
원 2
 
2.6%
산 2
 
2.6%
방 2
 
2.6%
탄 2
 
2.6%
Other values (19) 23
29.9%

구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
여가복지
25 
주거복지
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여가복지 25
92.6%
주거복지 2
 
7.4%

Length

2024-01-14T22:48:23.564423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:48:23.672706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여가복지 25
92.6%
주거복지 2
 
7.4%

추진연도
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.2222
Minimum2011
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:23.778314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2012
Q12015
median2016
Q32019
95-th percentile2020
Maximum2020
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8867513
Coefficient of variation (CV)0.0014317625
Kurtosis-1.1777871
Mean2016.2222
Median Absolute Deviation (MAD)3
Skewness-0.16704431
Sum54438
Variance8.3333333
MonotonicityNot monotonic
2024-01-14T22:48:23.910913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 7
25.9%
2020 5
18.5%
2012 3
11.1%
2018 3
11.1%
2019 3
11.1%
2013 2
 
7.4%
2016 2
 
7.4%
2011 1
 
3.7%
2017 1
 
3.7%
ValueCountFrequency (%)
2011 1
 
3.7%
2012 3
11.1%
2013 2
 
7.4%
2015 7
25.9%
2016 2
 
7.4%
2017 1
 
3.7%
2018 3
11.1%
2019 3
11.1%
2020 5
18.5%
ValueCountFrequency (%)
2020 5
18.5%
2019 3
11.1%
2018 3
11.1%
2017 1
 
3.7%
2016 2
 
7.4%
2015 7
25.9%
2013 2
 
7.4%
2012 3
11.1%
2011 1
 
3.7%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.297294
Minimum36.213692
Maximum36.356823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:24.045572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.213692
5-th percentile36.224676
Q136.245993
median36.315307
Q336.335148
95-th percentile36.349575
Maximum36.356823
Range0.14313035
Interquartile range (IQR)0.089155505

Descriptive statistics

Standard deviation0.047570406
Coefficient of variation (CV)0.0013105772
Kurtosis-1.319159
Mean36.297294
Median Absolute Deviation (MAD)0.02967009
Skewness-0.54047023
Sum980.02693
Variance0.0022629435
MonotonicityNot monotonic
2024-01-14T22:48:24.578643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
36.34539716 1
 
3.7%
36.34474302 1
 
3.7%
36.35682278 1
 
3.7%
36.34992739 1
 
3.7%
36.31530706 1
 
3.7%
36.33472731 1
 
3.7%
36.32344079 1
 
3.7%
36.31139081 1
 
3.7%
36.22424653 1
 
3.7%
36.22973901 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
36.21369243 1
3.7%
36.22424653 1
3.7%
36.22567664 1
3.7%
36.22973901 1
3.7%
36.23542646 1
3.7%
36.2389963 1
3.7%
36.23940184 1
3.7%
36.25258376 1
3.7%
36.25808158 1
3.7%
36.29679848 1
3.7%
ValueCountFrequency (%)
36.35682278 1
3.7%
36.34992739 1
3.7%
36.34875278 1
3.7%
36.34539716 1
3.7%
36.34497715 1
3.7%
36.34474302 1
3.7%
36.3355693 1
3.7%
36.33472731 1
3.7%
36.32740741 1
3.7%
36.3261624 1
3.7%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.35678
Minimum127.2949
Maximum127.39968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-01-14T22:48:24.812863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.2949
5-th percentile127.30514
Q1127.33674
median127.3671
Q3127.38139
95-th percentile127.39884
Maximum127.39968
Range0.1047809
Interquartile range (IQR)0.044643

Descriptive statistics

Standard deviation0.030943982
Coefficient of variation (CV)0.00024297083
Kurtosis-0.86567696
Mean127.35678
Median Absolute Deviation (MAD)0.0168095
Skewness-0.50630406
Sum3438.6331
Variance0.00095753005
MonotonicityNot monotonic
2024-01-14T22:48:25.009676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.3982541 1
 
3.7%
127.3714106 1
 
3.7%
127.3600305 1
 
3.7%
127.3990857 1
 
3.7%
127.3812172 1
 
3.7%
127.383912 1
 
3.7%
127.382502 1
 
3.7%
127.3583442 1
 
3.7%
127.2948974 1
 
3.7%
127.3171102 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
127.2948974 1
3.7%
127.3014062 1
3.7%
127.313851 1
3.7%
127.315629 1
3.7%
127.3171102 1
3.7%
127.3184524 1
3.7%
127.3366254 1
3.7%
127.3368601 1
3.7%
127.3409181 1
3.7%
127.3424795 1
3.7%
ValueCountFrequency (%)
127.3996783 1
3.7%
127.3990857 1
3.7%
127.3982541 1
3.7%
127.383912 1
3.7%
127.3827015 1
3.7%
127.382502 1
3.7%
127.3815543 1
3.7%
127.3812172 1
3.7%
127.3768837 1
3.7%
127.3758371 1
3.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
Minimum2023-12-28 00:00:00
Maximum2023-12-28 00:00:00
2024-01-14T22:48:25.154374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:25.319588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-14T22:48:16.517839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.358889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.110874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.232852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.881233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.704534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.615763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.481444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.231464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.319565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.008054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.840733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.749337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.626708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.368581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.430449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.164602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.996739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.864215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.759930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.487621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.539709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.282678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.136943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.989975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.879861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.625806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.675685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.437611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.284563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:17.132016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:12.997158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:13.790088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:14.790962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:15.586913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-14T22:48:16.417807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:48:25.448645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번지정연월시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명구분명추진연도위도경도
순번1.0000.8321.0001.0001.0000.4850.0000.5930.0000.3060.8760.4280.400
지정연월0.8321.0001.0001.0001.0000.4840.7060.2600.0000.0000.8680.2240.415
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동코드0.4850.4841.0001.0001.0001.0001.0000.8611.0000.0000.0000.6230.596
행정동명0.0000.7061.0001.0001.0001.0001.0000.9010.9590.0000.0000.8170.839
법정동코드0.5930.2601.0001.0001.0000.8610.9011.0001.0000.0000.5490.8630.869
법정동명0.0000.0001.0001.0001.0001.0000.9591.0001.0000.6510.5860.9990.925
구분명0.3060.0001.0001.0001.0000.0000.0000.0000.6511.0000.7810.6890.306
추진연도0.8760.8681.0001.0001.0000.0000.0000.5490.5860.7811.0000.5610.207
위도0.4280.2241.0001.0001.0000.6230.8170.8630.9990.6890.5611.0000.816
경도0.4000.4151.0001.0001.0000.5960.8390.8690.9250.3060.2070.8161.000
2024-01-14T22:48:25.638377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번행정동코드법정동코드추진연도위도경도구분명
순번1.0000.1490.0730.965-0.153-0.0600.157
행정동코드0.1491.0000.9020.222-0.456-0.6090.000
법정동코드0.0730.9021.0000.162-0.607-0.8190.000
추진연도0.9650.2220.1621.000-0.242-0.1280.488
위도-0.153-0.456-0.607-0.2421.0000.7970.588
경도-0.060-0.609-0.819-0.1280.7971.0000.157
구분명0.1570.0000.0000.4880.5880.1571.000

Missing values

2024-01-14T22:48:17.337910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:48:17.612741image/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

순번자치구명지정연월시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명구분명추진연도위도경도데이터기준일자
01서구2008-12-01서구노인복지관대전광역시 서구 탄방동 1084대전광역시 서구 남선로 66 (탄방동, 남선공원)3017055500탄방동3017010600탄방동여가복지201136.345397127.3982542023-12-28
12서구2011-12-01건능골경로당대전광역시 서구 갈마동 402-1대전광역시 서구 갈마로103번길 41 (갈마동)3017058200갈마2동3017011100갈마동여가복지201236.344743127.3714112023-12-28
23서구2011-12-01신선암경로당대전광역시 서구 관저동 1063대전광역시 서구 관저로129번길 26 (관저동, 노인정)3017059700관저2동3017011600관저동여가복지201236.299873127.3366252023-12-28
34서구2011-12-01남산경로당대전광역시 서구 탄방동 1388대전광역시 서구 도솔로483번길 81 (탄방동)3017055500탄방동3017010600탄방동여가복지201236.344977127.3996782023-12-28
45서구2011-12-01변동갓골경로당대전광역시 서구 변동 11-23대전광역시 서구 중반4길 23 (변동)3017054000변동3017010200변동여가복지201336.327407127.3827012023-12-28
56서구2012-12-01갈마제1경로당대전광역시 서구 갈마동 336-23대전광역시 서구 신갈마로167번길 20 (갈마동)3017058100갈마1동3017011100갈마동여가복지201336.348753127.3671032023-12-28
67서구2012-12-01원정1경로당대전광역시 서구 원정동 31-7대전광역시 서구 노적골길 2 (원정동)3017060000기성동3017012400원정동여가복지201536.258082127.3014062023-12-28
78서구2013-12-01산적골경로당대전광역시 서구 도마동 124-13대전광역시 서구 사마4길 25 (도마동)3017053000도마2동3017010300도마동여가복지201536.31696127.3758372023-12-28
89서구2013-12-01평촌2경로당대전광역시 서구 평촌동 356대전광역시 서구 길마루길 49 (평촌동)3017060000기성동3017012100평촌동여가복지201536.239402127.3156292023-12-28
910서구2014-12-01가장골경로당대전광역시 서구 내동 38-16대전광역시 서구 동서대로1005번길 78 (내동)3017057500내동3017011000내동여가복지201536.335569127.3768842023-12-28
순번자치구명지정연월시설명지번주소도로명주소행정동코드행정동명법정동코드법정동명구분명추진연도위도경도데이터기준일자
1718서구2017-12-01장안1경로당대전광역시 서구 장안동 338-2대전광역시 서구 장안로 560 (장안동)3017060000기성동3017012000장안동여가복지202036.213692127.336862023-12-28
1819서구2017-12-01평촌1경로당대전광역시 서구 평촌동 171-4대전광역시 서구 길평길 5 (평촌동)3017060000기성동3017012100평촌동여가복지201836.238996127.3184522023-12-28
1920서구2017-12-01오동경로당대전광역시 서구 오동 190-1대전광역시 서구 장전길 49 (오동, 오동경로당)3017060000기성동3017012200오동여가복지201936.229739127.317112023-12-28
2021서구2017-12-01우명2경로당대전광역시 서구 우명동 268-1대전광역시 서구 우명길 46 (우명동, 우명2경로당)3017060000기성동3017012300우명동여가복지201936.224247127.2948972023-12-28
2122서구2017-12-01명암경로당대전광역시 서구 정림동 250-1대전광역시 서구 계백로1249번안길 36-363017053500정림동3017010400정림동여가복지201936.311391127.3583442023-12-28
2223서구2017-12-01변정경로당대전광역시 서구 변동 45-1대전광역시 서구 변정3길 18 (변동)3017054000변동3017010200변동여가복지201836.323441127.3825022023-12-28
2324서구2020-04-01한민경로당대전광역시 서구 괴정동 88-32대전광역시 서구 도솔로308번길 25-1 (괴정동)3017056000괴정동3017010800괴정동여가복지202036.334727127.3839122023-12-28
2425서구2020-04-01도마1경로당대전광역시 서구 도마동 134-94대전광역시 서구 도마시장1길 88 (도마동)3017052000도마1동3017010300도마동여가복지202036.315307127.3812172023-12-28
2526서구2020-04-01삼천경로당대전광역시 서구 둔산동 2006대전광역시 서구 둔산남로191번길 24 (둔산동)3017066000둔산3동3017011200둔산동여가복지202036.349927127.3990862023-12-28
2627서구2020-04-01월평경로당대전광역시 서구 월평동 405대전광역시 서구 월평로27번길 1 (월평동)3017058600월평1동3017011300월평동여가복지202036.356823127.360032023-12-28