Overview

Dataset statistics

Number of variables6
Number of observations49
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory52.7 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description인천광역시 중구 태양광 발전설비에 대한 정보입니다. 파일명 인천광역시 중구 태양광 발전설비 내용 설치장소명, 주소, 설비용량 등
URLhttps://www.data.go.kr/data/15067558/fileData.do

Alerts

기관명 has constant value ""Constant
데이터기준일자 has constant value ""Constant
설비용량(kWh) is highly overall correlated with 설치시기High correlation
설치시기 is highly overall correlated with 설비용량(kWh)High correlation
설치장소명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:16:23.001437
Analysis finished2023-12-12 04:16:23.972584
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
인천광역시 중구
49 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 중구
2nd row인천광역시 중구
3rd row인천광역시 중구
4th row인천광역시 중구
5th row인천광역시 중구

Common Values

ValueCountFrequency (%)
인천광역시 중구 49
100.0%

Length

2023-12-12T13:16:24.062963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:24.200480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 49
50.0%
중구 49
50.0%

설치장소명
Text

UNIQUE 

Distinct49
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T13:16:24.446560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length16
Mean length7.3265306
Min length3

Characters and Unicode

Total characters359
Distinct characters121
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)100.0%

Sample

1st row중앙동경로당
2nd row신포동경로당
3rd row연안동경로당
4th row신흥동경로당
5th row신선동경로당
ValueCountFrequency (%)
경로당 6
 
9.8%
중구청 2
 
3.3%
중앙동경로당 1
 
1.6%
새희망어린이집 1
 
1.6%
포내경로당(10통)(무의까치놀섬 1
 
1.6%
마을노인쉼터 1
 
1.6%
공항신도시 1
 
1.6%
신설마을 1
 
1.6%
바다어린이집 1
 
1.6%
하늘어린이집 1
 
1.6%
Other values (45) 45
73.8%
2023-12-12T13:16:24.944917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 37
 
10.3%
로 36
 
10.0%
당 36
 
10.0%
동 14
 
3.9%
13
 
3.6%
중 6
 
1.7%
통 6
 
1.7%
이 6
 
1.7%
1 5
 
1.4%
지 5
 
1.4%
Other values (111) 195
54.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
89.1%
Decimal Number 16
 
4.5%
Space Separator 13
 
3.6%
Close Punctuation 4
 
1.1%
Open Punctuation 4
 
1.1%
Dash Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 37
 
11.6%
로 36
 
11.2%
당 36
 
11.2%
동 14
 
4.4%
중 6
 
1.9%
통 6
 
1.9%
이 6
 
1.9%
지 5
 
1.6%
을 5
 
1.6%
신 5
 
1.6%
Other values (99) 164
51.2%
Decimal Number
ValueCountFrequency (%)
1 5
31.2%
2 4
25.0%
4 2
 
12.5%
8 1
 
6.2%
6 1
 
6.2%
9 1
 
6.2%
0 1
 
6.2%
5 1
 
6.2%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
89.1%
Common 39
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 37
 
11.6%
로 36
 
11.2%
당 36
 
11.2%
동 14
 
4.4%
중 6
 
1.9%
통 6
 
1.9%
이 6
 
1.9%
지 5
 
1.6%
을 5
 
1.6%
신 5
 
1.6%
Other values (99) 164
51.2%
Common
ValueCountFrequency (%)
13
33.3%
1 5
 
12.8%
) 4
 
10.3%
( 4
 
10.3%
2 4
 
10.3%
- 2
 
5.1%
4 2
 
5.1%
8 1
 
2.6%
6 1
 
2.6%
9 1
 
2.6%
Other values (2) 2
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
89.1%
ASCII 39
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 37
 
11.6%
로 36
 
11.2%
당 36
 
11.2%
동 14
 
4.4%
중 6
 
1.9%
통 6
 
1.9%
이 6
 
1.9%
지 5
 
1.6%
을 5
 
1.6%
신 5
 
1.6%
Other values (99) 164
51.2%
ASCII
ValueCountFrequency (%)
13
33.3%
1 5
 
12.8%
) 4
 
10.3%
( 4
 
10.3%
2 4
 
10.3%
- 2
 
5.1%
4 2
 
5.1%
8 1
 
2.6%
6 1
 
2.6%
9 1
 
2.6%
Other values (2) 2
 
5.1%

주소
Text

Distinct48
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-12-12T13:16:25.276046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length18.530612
Min length15

Characters and Unicode

Total characters908
Distinct characters65
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

Unique47 ?
Unique (%)95.9%

Sample

1st row인천광역시 중구 중앙동1가 3-2
2nd row인천광역시 중구 답동 63-1
3rd row인천광역시 중구 항동7가 27-107
4th row인천광역시 중구 신흥동1가 10-4
5th row인천광역시 중구 선화동 14-11
ValueCountFrequency (%)
인천광역시 49
25.0%
중구 49
25.0%
운서동 6
 
3.1%
을왕동 5
 
2.6%
운북동 5
 
2.6%
운남동 3
 
1.5%
444-3 2
 
1.0%
북성동1가 2
 
1.0%
덕교동 2
 
1.0%
중산동 2
 
1.0%
Other values (71) 71
36.2%
2023-12-12T13:16:26.075933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
148
16.3%
중 53
 
5.8%
인 49
 
5.4%
광 49
 
5.4%
역 49
 
5.4%
시 49
 
5.4%
구 49
 
5.4%
천 49
 
5.4%
1 47
 
5.2%
동 39
 
4.3%
Other values (55) 327
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 519
57.2%
Decimal Number 205
 
22.6%
Space Separator 148
 
16.3%
Dash Punctuation 36
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 53
10.2%
인 49
9.4%
광 49
9.4%
역 49
9.4%
시 49
9.4%
구 49
9.4%
천 49
9.4%
동 39
 
7.5%
운 16
 
3.1%
북 9
 
1.7%
Other values (43) 108
20.8%
Decimal Number
ValueCountFrequency (%)
1 47
22.9%
2 30
14.6%
7 26
12.7%
3 20
9.8%
4 18
 
8.8%
8 14
 
6.8%
9 14
 
6.8%
5 13
 
6.3%
6 12
 
5.9%
0 11
 
5.4%
Space Separator
ValueCountFrequency (%)
148
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 519
57.2%
Common 389
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 53
10.2%
인 49
9.4%
광 49
9.4%
역 49
9.4%
시 49
9.4%
구 49
9.4%
천 49
9.4%
동 39
 
7.5%
운 16
 
3.1%
북 9
 
1.7%
Other values (43) 108
20.8%
Common
ValueCountFrequency (%)
148
38.0%
1 47
 
12.1%
- 36
 
9.3%
2 30
 
7.7%
7 26
 
6.7%
3 20
 
5.1%
4 18
 
4.6%
8 14
 
3.6%
9 14
 
3.6%
5 13
 
3.3%
Other values (2) 23
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 519
57.2%
ASCII 389
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
148
38.0%
1 47
 
12.1%
- 36
 
9.3%
2 30
 
7.7%
7 26
 
6.7%
3 20
 
5.1%
4 18
 
4.6%
8 14
 
3.6%
9 14
 
3.6%
5 13
 
3.3%
Other values (2) 23
 
5.9%
Hangul
ValueCountFrequency (%)
중 53
10.2%
인 49
9.4%
광 49
9.4%
역 49
9.4%
시 49
9.4%
구 49
9.4%
천 49
9.4%
동 39
 
7.5%
운 16
 
3.1%
북 9
 
1.7%
Other values (43) 108
20.8%

설비용량(kWh)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9591837
Minimum3
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T13:16:26.232609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median6
Q36
95-th percentile41.2
Maximum86
Range83
Interquartile range (IQR)3

Descriptive statistics

Standard deviation15.514723
Coefficient of variation (CV)1.5578308
Kurtosis12.589471
Mean9.9591837
Median Absolute Deviation (MAD)3
Skewness3.3886659
Sum488
Variance240.70663
MonotonicityNot monotonic
2023-12-12T13:16:26.364852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 22
44.9%
6 14
28.6%
13 2
 
4.1%
10 2
 
4.1%
40 2
 
4.1%
9 1
 
2.0%
42 1
 
2.0%
86 1
 
2.0%
8 1
 
2.0%
5 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
3 22
44.9%
5 1
 
2.0%
6 14
28.6%
8 1
 
2.0%
9 1
 
2.0%
10 2
 
4.1%
12 1
 
2.0%
13 2
 
4.1%
40 2
 
4.1%
42 1
 
2.0%
ValueCountFrequency (%)
86 1
 
2.0%
50 1
 
2.0%
42 1
 
2.0%
40 2
 
4.1%
13 2
 
4.1%
12 1
 
2.0%
10 2
 
4.1%
9 1
 
2.0%
8 1
 
2.0%
6 14
28.6%

설치시기
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.9592
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-12T13:16:26.494344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12010
median2010
Q32013
95-th percentile2018.6
Maximum2022
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.1620087
Coefficient of variation (CV)0.0015716068
Kurtosis1.7831032
Mean2011.9592
Median Absolute Deviation (MAD)0
Skewness1.6507442
Sum98586
Variance9.9982993
MonotonicityIncreasing
2023-12-12T13:16:26.641798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2010 28
57.1%
2011 6
 
12.2%
2015 3
 
6.1%
2012 2
 
4.1%
2014 2
 
4.1%
2018 2
 
4.1%
2013 1
 
2.0%
2016 1
 
2.0%
2017 1
 
2.0%
2019 1
 
2.0%
Other values (2) 2
 
4.1%
ValueCountFrequency (%)
2010 28
57.1%
2011 6
 
12.2%
2012 2
 
4.1%
2013 1
 
2.0%
2014 2
 
4.1%
2015 3
 
6.1%
2016 1
 
2.0%
2017 1
 
2.0%
2018 2
 
4.1%
2019 1
 
2.0%
ValueCountFrequency (%)
2022 1
 
2.0%
2020 1
 
2.0%
2019 1
 
2.0%
2018 2
4.1%
2017 1
 
2.0%
2016 1
 
2.0%
2015 3
6.1%
2014 2
4.1%
2013 1
 
2.0%
2012 2
4.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size524.0 B
2023-08-18
49 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-18
2nd row2023-08-18
3rd row2023-08-18
4th row2023-08-18
5th row2023-08-18

Common Values

ValueCountFrequency (%)
2023-08-18 49
100.0%

Length

2023-12-12T13:16:26.833643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:16:26.959172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-18 49
100.0%

Interactions

2023-12-12T13:16:23.477196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.273230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.596230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:16:23.367982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:16:27.040540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치장소명주소설비용량(kWh)설치시기
설치장소명1.0001.0001.0001.000
주소1.0001.0000.0001.000
설비용량(kWh)1.0000.0001.0000.918
설치시기1.0001.0000.9181.000
2023-12-12T13:16:27.166846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설비용량(kWh)설치시기
설비용량(kWh)1.0000.554
설치시기0.5541.000

Missing values

2023-12-12T13:16:23.757011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:16:23.914574image/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

기관명설치장소명주소설비용량(kWh)설치시기데이터기준일자
0인천광역시 중구중앙동경로당인천광역시 중구 중앙동1가 3-2620102023-08-18
1인천광역시 중구신포동경로당인천광역시 중구 답동 63-1620102023-08-18
2인천광역시 중구연안동경로당인천광역시 중구 항동7가 27-107620102023-08-18
3인천광역시 중구신흥동경로당인천광역시 중구 신흥동1가 10-4320102023-08-18
4인천광역시 중구신선동경로당인천광역시 중구 선화동 14-11620102023-08-18
5인천광역시 중구율목동 경로당인천광역시 중구 유동 35-1외 9필지920102023-08-18
6인천광역시 중구내경동경로당인천광역시 중구 경동 208-17620102023-08-18
7인천광역시 중구북성동경로당인천광역시 중구 북성동2가 9-33620102023-08-18
8인천광역시 중구월미경로당인천광역시 중구 북성동1가 98-497320102023-08-18
9인천광역시 중구송월동경로당인천광역시 중구 송월동1가 12-70320102023-08-18
기관명설치장소명주소설비용량(kWh)설치시기데이터기준일자
39인천광역시 중구도원경로당인천광역시 중구 도원서로 56620152023-08-18
40인천광역시 중구복사골경로당인천광역시 중구 도원로 21820152023-08-18
41인천광역시 중구큰무리경로당인천광역시 중구 무의동631320152023-08-18
42인천광역시 중구중구 무의지소인천광역시 중구 무의동164-8520162023-08-18
43인천광역시 중구중구청 서별관인천광역시 중구 신포27번길 805020172023-08-18
44인천광역시 중구동인천 행정복지센터인천광역시 중구 참외전로72번길 254020182023-08-18
45인천광역시 중구넙듸경로당인천광역시 중구 운서동 2891-12620182023-08-18
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