Overview

Dataset statistics

Number of variables10
Number of observations37
Missing cells16
Missing cells (%)4.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory87.5 B

Variable types

Numeric4
Text5
DateTime1

Dataset

Description전북특별자치도 전주시 내 녹색아파트지정현황을 제공하며 지정연도, 단지명, 사무실전화번호, 팩스, 도로명주소 등을 제공합니다.항목 : 지정연도, 단지명, 사무실 전화번호, 팩스, 도로명주소, 지번주소 등제공부서 : 기후변화대응과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15060777/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
사무실 전화번호 has 1 (2.7%) missing valuesMissing
팩스 has 15 (40.5%) missing valuesMissing

Reproduction

Analysis started2024-03-15 01:04:08.392484
Analysis finished2024-03-15 01:04:13.514586
Duration5.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지정연도
Real number (ℝ)

Distinct8
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.1351
Minimum2013
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size461.0 B
2024-03-15T10:04:13.691885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12013
median2014
Q32015
95-th percentile2020.2
Maximum2022
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6579249
Coefficient of variation (CV)0.001318981
Kurtosis0.32179268
Mean2015.1351
Median Absolute Deviation (MAD)1
Skewness1.2539246
Sum74560
Variance7.0645646
MonotonicityIncreasing
2024-03-15T10:04:14.040530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2013 13
35.1%
2014 9
24.3%
2015 6
16.2%
2018 3
 
8.1%
2019 2
 
5.4%
2020 2
 
5.4%
2021 1
 
2.7%
2022 1
 
2.7%
ValueCountFrequency (%)
2013 13
35.1%
2014 9
24.3%
2015 6
16.2%
2018 3
 
8.1%
2019 2
 
5.4%
2020 2
 
5.4%
2021 1
 
2.7%
2022 1
 
2.7%
ValueCountFrequency (%)
2022 1
 
2.7%
2021 1
 
2.7%
2020 2
 
5.4%
2019 2
 
5.4%
2018 3
 
8.1%
2015 6
16.2%
2014 9
24.3%
2013 13
35.1%

단지명
Text

Distinct28
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Memory size424.0 B
2024-03-15T10:04:14.991686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length7.6216216
Min length3

Characters and Unicode

Total characters282
Distinct characters89
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

Unique21 ?
Unique (%)56.8%

Sample

1st row삼천호반리젠시빌
2nd row평화동 동도미소드림
3rd row우미린
4th row삼천 주공 4,5단지
5th row서신대우대창
ValueCountFrequency (%)
삼천 3
 
5.5%
4,5단지 3
 
5.5%
광진선수촌 3
 
5.5%
효자휴먼시아 3
 
5.5%
주공 3
 
5.5%
삼천호반리젠시빌 2
 
3.6%
서신대우대창 2
 
3.6%
5단지 2
 
3.6%
팔학골 2
 
3.6%
영창 2
 
3.6%
Other values (29) 30
54.5%
2024-03-15T10:04:16.512937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
6.4%
지 10
 
3.5%
단 10
 
3.5%
화 9
 
3.2%
1 8
 
2.8%
시 7
 
2.5%
천 7
 
2.5%
신 7
 
2.5%
평 7
 
2.5%
대 7
 
2.5%
Other values (79) 192
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
81.9%
Decimal Number 25
 
8.9%
Space Separator 18
 
6.4%
Other Punctuation 5
 
1.8%
Dash Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 10
 
4.3%
단 10
 
4.3%
화 9
 
3.9%
시 7
 
3.0%
천 7
 
3.0%
신 7
 
3.0%
평 7
 
3.0%
대 7
 
3.0%
공 7
 
3.0%
주 7
 
3.0%
Other values (68) 153
66.2%
Decimal Number
ValueCountFrequency (%)
1 8
32.0%
4 6
24.0%
5 5
20.0%
2 4
16.0%
6 1
 
4.0%
3 1
 
4.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 231
81.9%
Common 51
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 10
 
4.3%
단 10
 
4.3%
화 9
 
3.9%
시 7
 
3.0%
천 7
 
3.0%
신 7
 
3.0%
평 7
 
3.0%
대 7
 
3.0%
공 7
 
3.0%
주 7
 
3.0%
Other values (68) 153
66.2%
Common
ValueCountFrequency (%)
18
35.3%
1 8
15.7%
4 6
 
11.8%
5 5
 
9.8%
, 5
 
9.8%
2 4
 
7.8%
6 1
 
2.0%
- 1
 
2.0%
3 1
 
2.0%
( 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 231
81.9%
ASCII 51
 
18.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
35.3%
1 8
15.7%
4 6
 
11.8%
5 5
 
9.8%
, 5
 
9.8%
2 4
 
7.8%
6 1
 
2.0%
- 1
 
2.0%
3 1
 
2.0%
( 1
 
2.0%
Hangul
ValueCountFrequency (%)
지 10
 
4.3%
단 10
 
4.3%
화 9
 
3.9%
시 7
 
3.0%
천 7
 
3.0%
신 7
 
3.0%
평 7
 
3.0%
대 7
 
3.0%
공 7
 
3.0%
주 7
 
3.0%
Other values (68) 153
66.2%

사무실 전화번호
Text

MISSING 

Distinct28
Distinct (%)77.8%
Missing1
Missing (%)2.7%
Memory size424.0 B
2024-03-15T10:04:17.840951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters432
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)61.1%

Sample

1st row063-903-3337
2nd row063-236-0017
3rd row063-228-4664
4th row063-228-3582
5th row063-252-6923
ValueCountFrequency (%)
063-228-3582 3
 
8.3%
063-253-5604 3
 
8.3%
063-252-6923 2
 
5.6%
063-229-9490 2
 
5.6%
063-285-6946 2
 
5.6%
063-224-7840 2
 
5.6%
063-908-5335 1
 
2.8%
063-903-3337 1
 
2.8%
063-236-4190 1
 
2.8%
063-251-9331 1
 
2.8%
Other values (18) 18
50.0%
2024-03-15T10:04:19.136078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 72
16.7%
3 62
14.4%
0 59
13.7%
2 58
13.4%
6 55
12.7%
5 31
7.2%
4 24
 
5.6%
8 22
 
5.1%
9 19
 
4.4%
1 17
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 360
83.3%
Dash Punctuation 72
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 62
17.2%
0 59
16.4%
2 58
16.1%
6 55
15.3%
5 31
8.6%
4 24
 
6.7%
8 22
 
6.1%
9 19
 
5.3%
1 17
 
4.7%
7 13
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 432
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 72
16.7%
3 62
14.4%
0 59
13.7%
2 58
13.4%
6 55
12.7%
5 31
7.2%
4 24
 
5.6%
8 22
 
5.1%
9 19
 
4.4%
1 17
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 72
16.7%
3 62
14.4%
0 59
13.7%
2 58
13.4%
6 55
12.7%
5 31
7.2%
4 24
 
5.6%
8 22
 
5.1%
9 19
 
4.4%
1 17
 
3.9%

팩스
Text

MISSING 

Distinct16
Distinct (%)72.7%
Missing15
Missing (%)40.5%
Memory size424.0 B
2024-03-15T10:04:19.890331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters264
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)45.5%

Sample

1st row063-228-4999
2nd row063-902-0220
3rd row063-221-0787
4th row063-901-3110
5th row063-228-3581
ValueCountFrequency (%)
063-901-3110 2
 
9.1%
063-228-3581 2
 
9.1%
063-901-5644 2
 
9.1%
063-285-6947 2
 
9.1%
063-225-0574 2
 
9.1%
063-221-0787 2
 
9.1%
063-225-1087 1
 
4.5%
063-904-6923 1
 
4.5%
063-902-0220 1
 
4.5%
063-902-0419 1
 
4.5%
Other values (6) 6
27.3%
2024-03-15T10:04:20.688005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 44
16.7%
0 41
15.5%
2 31
11.7%
3 28
10.6%
6 27
10.2%
9 21
8.0%
1 20
7.6%
4 18
6.8%
8 12
 
4.5%
5 12
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 220
83.3%
Dash Punctuation 44
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
18.6%
2 31
14.1%
3 28
12.7%
6 27
12.3%
9 21
9.5%
1 20
9.1%
4 18
8.2%
8 12
 
5.5%
5 12
 
5.5%
7 10
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 44
16.7%
0 41
15.5%
2 31
11.7%
3 28
10.6%
6 27
10.2%
9 21
8.0%
1 20
7.6%
4 18
6.8%
8 12
 
4.5%
5 12
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 44
16.7%
0 41
15.5%
2 31
11.7%
3 28
10.6%
6 27
10.2%
9 21
8.0%
1 20
7.6%
4 18
6.8%
8 12
 
4.5%
5 12
 
4.5%

도로명주소
Text

Distinct27
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size424.0 B
2024-03-15T10:04:21.505645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length23.297297
Min length21

Characters and Unicode

Total characters862
Distinct characters68
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

Unique19 ?
Unique (%)51.4%

Sample

1st row전북특별자치도 전주시 완산구 삼천천변3길 20
2nd row전북특별자치도 전주시 완산구 모악로 4651
3rd row전북특별자치도 전주시 완산구 유연로 217-6
4th row전북특별자치도 전주시 완산구 장승배기로 13
5th row전북특별자치도 전주시 완산구 새터로 100
ValueCountFrequency (%)
전북특별자치도 37
20.0%
전주시 37
20.0%
완산구 29
15.7%
덕진구 8
 
4.3%
호암로 4
 
2.2%
장승배기로 3
 
1.6%
13 3
 
1.6%
서신로 3
 
1.6%
102 3
 
1.6%
모악로 3
 
1.6%
Other values (44) 55
29.7%
2024-03-15T10:04:23.090132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
17.2%
전 74
 
8.6%
도 37
 
4.3%
북 37
 
4.3%
시 37
 
4.3%
주 37
 
4.3%
구 37
 
4.3%
치 37
 
4.3%
자 37
 
4.3%
별 37
 
4.3%
Other values (58) 344
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 609
70.6%
Space Separator 148
 
17.2%
Decimal Number 103
 
11.9%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 74
 
12.2%
도 37
 
6.1%
북 37
 
6.1%
시 37
 
6.1%
주 37
 
6.1%
구 37
 
6.1%
치 37
 
6.1%
자 37
 
6.1%
별 37
 
6.1%
특 37
 
6.1%
Other values (46) 202
33.2%
Decimal Number
ValueCountFrequency (%)
1 26
25.2%
2 19
18.4%
0 12
11.7%
5 10
 
9.7%
7 10
 
9.7%
3 9
 
8.7%
6 5
 
4.9%
8 5
 
4.9%
4 5
 
4.9%
9 2
 
1.9%
Space Separator
ValueCountFrequency (%)
148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 609
70.6%
Common 253
29.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 74
 
12.2%
도 37
 
6.1%
북 37
 
6.1%
시 37
 
6.1%
주 37
 
6.1%
구 37
 
6.1%
치 37
 
6.1%
자 37
 
6.1%
별 37
 
6.1%
특 37
 
6.1%
Other values (46) 202
33.2%
Common
ValueCountFrequency (%)
148
58.5%
1 26
 
10.3%
2 19
 
7.5%
0 12
 
4.7%
5 10
 
4.0%
7 10
 
4.0%
3 9
 
3.6%
6 5
 
2.0%
8 5
 
2.0%
4 5
 
2.0%
Other values (2) 4
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 609
70.6%
ASCII 253
29.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
58.5%
1 26
 
10.3%
2 19
 
7.5%
0 12
 
4.7%
5 10
 
4.0%
7 10
 
4.0%
3 9
 
3.6%
6 5
 
2.0%
8 5
 
2.0%
4 5
 
2.0%
Other values (2) 4
 
1.6%
Hangul
ValueCountFrequency (%)
전 74
 
12.2%
도 37
 
6.1%
북 37
 
6.1%
시 37
 
6.1%
주 37
 
6.1%
구 37
 
6.1%
치 37
 
6.1%
자 37
 
6.1%
별 37
 
6.1%
특 37
 
6.1%
Other values (46) 202
33.2%

지번주소
Text

Distinct26
Distinct (%)70.3%
Missing0
Missing (%)0.0%
Memory size424.0 B
2024-03-15T10:04:23.880880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length27
Mean length26.432432
Min length24

Characters and Unicode

Total characters978
Distinct characters39
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

Unique17 ?
Unique (%)45.9%

Sample

1st row전북특별자치도 전주시 완산구 삼천동1가 546-29
2nd row전북특별자치도 전주시 완산구 평화동2가 457-4
3rd row전북특별자치도 전주시 완산구 효자동3가 1523-3
4th row전북특별자치도 전주시 완산구 삼천동1가 766-1
5th row전북특별자치도 전주시 완산구 서신동 965-3
ValueCountFrequency (%)
전북특별자치도 37
20.0%
전주시 37
20.0%
완산구 29
15.7%
덕진구 8
 
4.3%
서신동 7
 
3.8%
삼천동1가 5
 
2.7%
효자동2가 5
 
2.7%
평화동2가 5
 
2.7%
송천동1가 4
 
2.2%
766-1 3
 
1.6%
Other values (33) 45
24.3%
2024-03-15T10:04:25.108713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
 
15.1%
전 74
 
7.6%
1 46
 
4.7%
자 44
 
4.5%
주 37
 
3.8%
동 37
 
3.8%
구 37
 
3.8%
북 37
 
3.8%
시 37
 
3.8%
도 37
 
3.8%
Other values (29) 444
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 624
63.8%
Decimal Number 179
 
18.3%
Space Separator 148
 
15.1%
Dash Punctuation 27
 
2.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 74
 
11.9%
자 44
 
7.1%
주 37
 
5.9%
동 37
 
5.9%
구 37
 
5.9%
북 37
 
5.9%
시 37
 
5.9%
도 37
 
5.9%
치 37
 
5.9%
별 37
 
5.9%
Other values (17) 210
33.7%
Decimal Number
ValueCountFrequency (%)
1 46
25.7%
2 30
16.8%
6 23
12.8%
9 17
 
9.5%
3 15
 
8.4%
7 14
 
7.8%
5 13
 
7.3%
4 10
 
5.6%
0 7
 
3.9%
8 4
 
2.2%
Space Separator
ValueCountFrequency (%)
148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 624
63.8%
Common 354
36.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 74
 
11.9%
자 44
 
7.1%
주 37
 
5.9%
동 37
 
5.9%
구 37
 
5.9%
북 37
 
5.9%
시 37
 
5.9%
도 37
 
5.9%
치 37
 
5.9%
별 37
 
5.9%
Other values (17) 210
33.7%
Common
ValueCountFrequency (%)
148
41.8%
1 46
 
13.0%
2 30
 
8.5%
- 27
 
7.6%
6 23
 
6.5%
9 17
 
4.8%
3 15
 
4.2%
7 14
 
4.0%
5 13
 
3.7%
4 10
 
2.8%
Other values (2) 11
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 624
63.8%
ASCII 354
36.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
41.8%
1 46
 
13.0%
2 30
 
8.5%
- 27
 
7.6%
6 23
 
6.5%
9 17
 
4.8%
3 15
 
4.2%
7 14
 
4.0%
5 13
 
3.7%
4 10
 
2.8%
Other values (2) 11
 
3.1%
Hangul
ValueCountFrequency (%)
전 74
 
11.9%
자 44
 
7.1%
주 37
 
5.9%
동 37
 
5.9%
구 37
 
5.9%
북 37
 
5.9%
시 37
 
5.9%
도 37
 
5.9%
치 37
 
5.9%
별 37
 
5.9%
Other values (17) 210
33.7%

위도
Real number (ℝ)

Distinct27
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.818183
Minimum35.78616
Maximum35.877992
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size461.0 B
2024-03-15T10:04:25.503621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.78616
5-th percentile35.790337
Q135.795616
median35.810022
Q335.835279
95-th percentile35.860885
Maximum35.877992
Range0.09183193
Interquartile range (IQR)0.03966235

Descriptive statistics

Standard deviation0.025130463
Coefficient of variation (CV)0.00070161189
Kurtosis-0.67270947
Mean35.818183
Median Absolute Deviation (MAD)0.01890163
Skewness0.59846192
Sum1325.2728
Variance0.00063154019
MonotonicityNot monotonic
2024-03-15T10:04:25.892694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
35.79316275 3
 
8.1%
35.83527881 3
 
8.1%
35.7967368 2
 
5.4%
35.83426465 2
 
5.4%
35.80748439 2
 
5.4%
35.80923055 2
 
5.4%
35.79561646 2
 
5.4%
35.79112053 2
 
5.4%
35.79049694 1
 
2.7%
35.8433447 1
 
2.7%
Other values (17) 17
45.9%
ValueCountFrequency (%)
35.78615975 1
 
2.7%
35.78969789 1
 
2.7%
35.79049694 1
 
2.7%
35.79112053 2
5.4%
35.79316275 3
8.1%
35.79329229 1
 
2.7%
35.79561646 2
5.4%
35.7967368 2
5.4%
35.80425299 1
 
2.7%
35.80748439 2
5.4%
ValueCountFrequency (%)
35.87799168 1
 
2.7%
35.86260798 1
 
2.7%
35.86045431 1
 
2.7%
35.85720716 1
 
2.7%
35.85683613 1
 
2.7%
35.8433447 1
 
2.7%
35.83783767 1
 
2.7%
35.8374086 1
 
2.7%
35.83527881 3
8.1%
35.83426465 2
5.4%

경도
Real number (ℝ)

Distinct27
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12218
Minimum127.09614
Maximum127.16755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size461.0 B
2024-03-15T10:04:26.185066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.09614
5-th percentile127.10208
Q1127.1121
median127.11805
Q3127.13106
95-th percentile127.14424
Maximum127.16755
Range0.071413
Interquartile range (IQR)0.0189633

Descriptive statistics

Standard deviation0.015034909
Coefficient of variation (CV)0.00011827133
Kurtosis1.2309105
Mean127.12218
Median Absolute Deviation (MAD)0.0092438
Skewness0.79244606
Sum4703.5205
Variance0.00022604848
MonotonicityNot monotonic
2024-03-15T10:04:26.422191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
127.1144878 3
 
8.1%
127.1169762 3
 
8.1%
127.112096 2
 
5.4%
127.1180498 2
 
5.4%
127.0961354 2
 
5.4%
127.1035639 2
 
5.4%
127.1418577 2
 
5.4%
127.1328385 2
 
5.4%
127.1302943 1
 
2.7%
127.11207 1
 
2.7%
Other values (17) 17
45.9%
ValueCountFrequency (%)
127.0961354 2
5.4%
127.1035639 2
5.4%
127.1073517 1
 
2.7%
127.109427 1
 
2.7%
127.1096328 1
 
2.7%
127.11207 1
 
2.7%
127.112096 2
5.4%
127.1144878 3
8.1%
127.1154577 1
 
2.7%
127.1169762 3
8.1%
ValueCountFrequency (%)
127.1675484 1
2.7%
127.1537743 1
2.7%
127.1418577 2
5.4%
127.1396979 1
2.7%
127.1331825 1
2.7%
127.1328385 2
5.4%
127.1317947 1
2.7%
127.1310593 1
2.7%
127.1308078 1
2.7%
127.1302943 1
2.7%

세대
Real number (ℝ)

Distinct25
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean652.75676
Minimum110
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size461.0 B
2024-03-15T10:04:26.660759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile176.8
Q1390
median702
Q3826
95-th percentile1318.8
Maximum1650
Range1540
Interquartile range (IQR)436

Descriptive statistics

Standard deviation391.84779
Coefficient of variation (CV)0.60029679
Kurtosis0.49819599
Mean652.75676
Median Absolute Deviation (MAD)312
Skewness0.8645366
Sum24152
Variance153544.69
MonotonicityNot monotonic
2024-03-15T10:04:26.879529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
390 5
 
13.5%
804 3
 
8.1%
709 2
 
5.4%
949 2
 
5.4%
856 2
 
5.4%
205 2
 
5.4%
1650 2
 
5.4%
1223 2
 
5.4%
110 1
 
2.7%
367 1
 
2.7%
Other values (15) 15
40.5%
ValueCountFrequency (%)
110 1
 
2.7%
164 1
 
2.7%
180 1
 
2.7%
205 2
 
5.4%
208 1
 
2.7%
270 1
 
2.7%
367 1
 
2.7%
369 1
 
2.7%
390 5
13.5%
419 1
 
2.7%
ValueCountFrequency (%)
1650 2
5.4%
1236 1
 
2.7%
1223 2
5.4%
949 2
5.4%
856 2
5.4%
826 1
 
2.7%
804 3
8.1%
800 1
 
2.7%
753 1
 
2.7%
720 1
 
2.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size424.0 B
Minimum2024-01-17 00:00:00
Maximum2024-01-17 00:00:00
2024-03-15T10:04:27.132790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:27.353324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T10:04:11.716280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:09.002010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.036551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.691904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:11.898871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:09.286339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.243905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.940592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:12.149337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:09.526076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.379291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:11.190597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:12.416441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:09.782821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:10.535416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:11.457731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:04:27.495715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정연도단지명사무실 전화번호팩스도로명주소지번주소위도경도세대
지정연도1.0000.9460.5180.3800.4050.4050.6510.4990.653
단지명0.9461.0000.9981.0000.9961.0001.0001.0001.000
사무실 전화번호0.5180.9981.0000.9971.0001.0001.0001.0001.000
팩스0.3801.0000.9971.0000.9981.0001.0001.0001.000
도로명주소0.4050.9961.0000.9981.0001.0001.0001.0001.000
지번주소0.4051.0001.0001.0001.0001.0001.0001.0001.000
위도0.6511.0001.0001.0001.0001.0001.0000.7150.643
경도0.4991.0001.0001.0001.0001.0000.7151.0000.788
세대0.6531.0001.0001.0001.0001.0000.6430.7881.000
2024-03-15T10:04:27.729740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정연도위도경도세대
지정연도1.000-0.1740.0190.296
위도-0.1741.000-0.028-0.441
경도0.019-0.0281.0000.021
세대0.296-0.4410.0211.000

Missing values

2024-03-15T10:04:12.739552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:04:13.032912image/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.
2024-03-15T10:04:13.358930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

지정연도단지명사무실 전화번호팩스도로명주소지번주소위도경도세대데이터기준일자
02013삼천호반리젠시빌063-903-3337063-228-4999전북특별자치도 전주시 완산구 삼천천변3길 20전북특별자치도 전주시 완산구 삼천동1가 546-2935.796737127.1120967092024-01-17
12013평화동 동도미소드림063-236-0017063-902-0220전북특별자치도 전주시 완산구 모악로 4651전북특별자치도 전주시 완산구 평화동2가 457-435.78616127.1272945412024-01-17
22013우미린063-228-4664<NA>전북특별자치도 전주시 완산구 유연로 217-6전북특별자치도 전주시 완산구 효자동3가 1523-335.825789127.1094273692024-01-17
32013삼천 주공 4,5단지063-228-3582<NA>전북특별자치도 전주시 완산구 장승배기로 13전북특별자치도 전주시 완산구 삼천동1가 766-135.793163127.1144888042024-01-17
42013서신대우대창063-252-6923<NA>전북특별자치도 전주시 완산구 새터로 100전북특별자치도 전주시 완산구 서신동 965-335.834265127.118053902024-01-17
52013광진선수촌063-253-5604<NA>전북특별자치도 전주시 완산구 서신로 102전북특별자치도 전주시 완산구 서신동 965-235.835279127.1169763902024-01-17
62013인후한신휴플러스063-247-5230<NA>전북특별자치도 전주시 덕진구 안덕원로 251전북특별자치도 전주시 덕진구 인후동1가 102935.837838127.15377412362024-01-17
72013중화산동 현대2차063-282-8015<NA>전북특별자치도 전주시 완산구 안행로 178전북특별자치도 전주시 완산구 중화산동1가 33135.811713127.1317951642024-01-17
82013서신대우063-255-4566<NA>전북특별자치도 전주시 완산구 여울로 79-1전북특별자치도 전주시 완산구 서신동 960-235.837409127.1154582082024-01-17
92013금호타운063-286-5950<NA>전북특별자치도 전주시 완산구 안행5길 35전북특별자치도 전주시 완산구 효자동1가 9835.804253127.1310597202024-01-17
지정연도단지명사무실 전화번호팩스도로명주소지번주소위도경도세대데이터기준일자
272015평화주공1단지063-285-6946063-285-6947전북특별자치도 전주시 완산구 덕적골2길 25전북특별자치도 전주시 완산구 평화동1가 445-635.795616127.14185816502024-01-17
282018효자휴먼시아 1단지063-229-9490063-229-9491전북특별자치도 전주시 완산구 호암로 2전북특별자치도 전주시 완산구 효자동2가 1191-135.807484127.0961359492024-01-17
292018평화주공4단지063-224-7840063-225-0574전북특별자치도 전주시 완산구 모악로 4726전북특별자치도 전주시 완산구 평화동2가 230-2735.791121127.13283912232024-01-17
302018평화주공063-285-6946063-285-6947전북특별자치도 전주시 완산구 덕적골2길 25전북특별자치도 전주시 완산구 평화동1가 445-635.795616127.14185816502024-01-17
312019아중마을 부영6차063-246-0374<NA>전북특별자치도 전주시 덕진구 무삼지로 10전북특별자치도 전주시 덕진구 인후동1가 918-135.827337127.1675488002024-01-17
322019평화현대엠코타운063-226-7251<NA>전북특별자치도 전주시 완산구 양지3길 15전북특별자치도 전주시 완산구 평화동2가 95335.789698127.1184315102024-01-17
332020삼천호반리젠시빌063-228-0999<NA>전북특별자치도 전주시 완산구 삼천천변3길 20전북특별자치도 전주시 완산구 삼천동1가 546-2935.796737127.1120967092024-01-17
342020에코시티더샵2차063-251-9331<NA>전북특별자치도 전주시 덕진구 세병로 175전북특별자치도 전주시 덕진구 송천동2가 130635.877992127.1331837022024-01-17
352021효자휴먼시아4-1단지 아파트063-229-9490063-224-9491전북특별자치도 전주시 완산구 호암로 2전북특별자치도 전주시 완산구 효자동2가 1191-135.807484127.0961359492024-01-17
362022덕진휴먼빌2차아파트063-251-0465<NA>전북특별자치도 전주시 덕진구 하가로 10전북특별자치도 전주시 덕진구 덕진동2가 72735.843345127.112073672024-01-17