Overview

Dataset statistics

Number of variables19
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory164.1 B

Variable types

Numeric7
Text10
Categorical2

Dataset

Description서울특별시 구로구 관내 위치한 공공태양광 발전시설의 2023년 월별 발전량 현황으로 연번, 설치년도, 개소명, 주소, 설치용량(KW), 월별 발전량(kWh), 데이터기준일의 자료를 제공합니다. 설비이상으로 인하여 발전량 정보가 누락된 경우 "불명"으로 표기하였습니다.
Author서울특별시 구로구
URLhttps://www.data.go.kr/data/15126217/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 설치년도 and 5 other fieldsHigh correlation
설치년도 is highly overall correlated with 연번 and 5 other fieldsHigh correlation
설치용량 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
4월 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
5월 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
6월 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
7월 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
용도 is highly overall correlated with 설치용량 and 4 other fieldsHigh correlation
용도 is highly imbalanced (59.8%)Imbalance
연번 has unique valuesUnique
개소명 has unique valuesUnique
1월 has unique valuesUnique
3월 has unique valuesUnique
4월 has unique valuesUnique
5월 has unique valuesUnique
6월 has unique valuesUnique
7월 has unique valuesUnique
8월 has unique valuesUnique

Reproduction

Analysis started2024-03-14 20:22:02.795117
Analysis finished2024-03-14 20:22:16.043214
Duration13.25 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:16.302321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-03-15T05:22:16.563714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

설치년도
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.16
Minimum2008
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:16.816037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2009.8
Q12015
median2018
Q32021
95-th percentile2021
Maximum2021
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8262253
Coefficient of variation (CV)0.0018968378
Kurtosis0.094000617
Mean2017.16
Median Absolute Deviation (MAD)3
Skewness-0.8657241
Sum50429
Variance14.64
MonotonicityIncreasing
2024-03-15T05:22:17.201163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2021 7
28.0%
2016 3
12.0%
2018 3
12.0%
2020 3
12.0%
2013 2
 
8.0%
2014 2
 
8.0%
2015 2
 
8.0%
2008 1
 
4.0%
2009 1
 
4.0%
2019 1
 
4.0%
ValueCountFrequency (%)
2008 1
 
4.0%
2009 1
 
4.0%
2013 2
 
8.0%
2014 2
 
8.0%
2015 2
 
8.0%
2016 3
12.0%
2018 3
12.0%
2019 1
 
4.0%
2020 3
12.0%
2021 7
28.0%
ValueCountFrequency (%)
2021 7
28.0%
2020 3
12.0%
2019 1
 
4.0%
2018 3
12.0%
2016 3
12.0%
2015 2
 
8.0%
2014 2
 
8.0%
2013 2
 
8.0%
2009 1
 
4.0%
2008 1
 
4.0%

개소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:17.951967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length18
Mean length11.44
Min length5

Characters and Unicode

Total characters286
Distinct characters95
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

Unique25 ?
Unique (%)100.0%

Sample

1st row고척근린공원
2nd row구로구청(1)
3rd row궁동종합사회복지관
4th row구로구민회관
5th row구로구민체육센터
ValueCountFrequency (%)
제로에너지건물 5
 
13.2%
고척근린공원 2
 
5.3%
리모델링 2
 
5.3%
구로희망 2
 
5.3%
궁동경로당 2
 
5.3%
공용주차장 1
 
2.6%
모아래 1
 
2.6%
중앙경로당 1
 
2.6%
화원경로당 1
 
2.6%
백곡경로당 1
 
2.6%
Other values (20) 20
52.6%
2024-03-15T05:22:19.144055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
7.7%
20
 
7.0%
16
 
5.6%
7
 
2.4%
7
 
2.4%
7
 
2.4%
6
 
2.1%
6
 
2.1%
6
 
2.1%
6
 
2.1%
Other values (85) 183
64.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 251
87.8%
Space Separator 20
 
7.0%
Close Punctuation 5
 
1.7%
Open Punctuation 5
 
1.7%
Decimal Number 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
8.8%
16
 
6.4%
7
 
2.8%
7
 
2.8%
7
 
2.8%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
Other values (79) 163
64.9%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
3 2
40.0%
1 1
20.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
87.8%
Common 35
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
8.8%
16
 
6.4%
7
 
2.8%
7
 
2.8%
7
 
2.8%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
Other values (79) 163
64.9%
Common
ValueCountFrequency (%)
20
57.1%
) 5
 
14.3%
( 5
 
14.3%
2 2
 
5.7%
3 2
 
5.7%
1 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 251
87.8%
ASCII 35
 
12.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22
 
8.8%
16
 
6.4%
7
 
2.8%
7
 
2.8%
7
 
2.8%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
Other values (79) 163
64.9%
ASCII
ValueCountFrequency (%)
20
57.1%
) 5
 
14.3%
( 5
 
14.3%
2 2
 
5.7%
3 2
 
5.7%
1 1
 
2.9%

주소
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:20.010062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length19.36
Min length15

Characters and Unicode

Total characters484
Distinct characters49
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

Unique23 ?
Unique (%)92.0%

Sample

1st row서울특별시 구로구 고척동 산 9-14
2nd row서울특별시 구로구 가마산로 245
3rd row서울특별시 구로구 궁동 108-9
4th row서울특별시 구로구 가마산로25길 21
5th row서울특별시 구로구 고척로45길 39
ValueCountFrequency (%)
서울특별시 25
25.0%
구로구 25
25.0%
구로동 3
 
3.0%
부일로17길 2
 
2.0%
158-4 2
 
2.0%
고척동 2
 
2.0%
고척로45길 2
 
2.0%
14-41 1
 
1.0%
3 1
 
1.0%
산9-14 1
 
1.0%
Other values (36) 36
36.0%
2024-03-15T05:22:21.285651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
75
15.5%
54
 
11.2%
46
 
9.5%
1 28
 
5.8%
25
 
5.2%
25
 
5.2%
25
 
5.2%
25
 
5.2%
25
 
5.2%
15
 
3.1%
Other values (39) 141
29.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
61.4%
Decimal Number 102
 
21.1%
Space Separator 75
 
15.5%
Dash Punctuation 10
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
54
18.2%
46
15.5%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
15
 
5.1%
6
 
2.0%
6
 
2.0%
Other values (27) 45
15.2%
Decimal Number
ValueCountFrequency (%)
1 28
27.5%
5 14
13.7%
2 10
 
9.8%
9 10
 
9.8%
4 10
 
9.8%
8 10
 
9.8%
0 7
 
6.9%
3 6
 
5.9%
7 4
 
3.9%
6 3
 
2.9%
Space Separator
ValueCountFrequency (%)
75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 297
61.4%
Common 187
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
54
18.2%
46
15.5%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
15
 
5.1%
6
 
2.0%
6
 
2.0%
Other values (27) 45
15.2%
Common
ValueCountFrequency (%)
75
40.1%
1 28
 
15.0%
5 14
 
7.5%
- 10
 
5.3%
2 10
 
5.3%
9 10
 
5.3%
4 10
 
5.3%
8 10
 
5.3%
0 7
 
3.7%
3 6
 
3.2%
Other values (2) 7
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 297
61.4%
ASCII 187
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
75
40.1%
1 28
 
15.0%
5 14
 
7.5%
- 10
 
5.3%
2 10
 
5.3%
9 10
 
5.3%
4 10
 
5.3%
8 10
 
5.3%
0 7
 
3.7%
3 6
 
3.2%
Other values (2) 7
 
3.7%
Hangul
ValueCountFrequency (%)
54
18.2%
46
15.5%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
25
8.4%
15
 
5.1%
6
 
2.0%
6
 
2.0%
Other values (27) 45
15.2%

설치용량
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.8
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:21.614325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q18
median15
Q326
95-th percentile89.6
Maximum100
Range97
Interquartile range (IQR)18

Descriptive statistics

Standard deviation26.150207
Coefficient of variation (CV)1.0987482
Kurtosis4.069554
Mean23.8
Median Absolute Deviation (MAD)7
Skewness2.1126026
Sum595
Variance683.83333
MonotonicityNot monotonic
2024-03-15T05:22:22.001855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 3
12.0%
15 3
12.0%
20 2
 
8.0%
10 2
 
8.0%
8 2
 
8.0%
30 2
 
8.0%
21 2
 
8.0%
100 1
 
4.0%
13 1
 
4.0%
6 1
 
4.0%
Other values (6) 6
24.0%
ValueCountFrequency (%)
3 3
12.0%
5 1
 
4.0%
6 1
 
4.0%
8 2
8.0%
10 2
8.0%
11 1
 
4.0%
13 1
 
4.0%
15 3
12.0%
20 2
8.0%
21 2
8.0%
ValueCountFrequency (%)
100 1
 
4.0%
97 1
 
4.0%
60 1
 
4.0%
45 1
 
4.0%
30 2
8.0%
26 1
 
4.0%
21 2
8.0%
20 2
8.0%
15 3
12.0%
13 1
 
4.0%

용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
자가사용
23 
발전사업
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자가사용 23
92.0%
발전사업 2
 
8.0%

Length

2024-03-15T05:22:22.412439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:22:22.824721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자가사용 23
92.0%
발전사업 2
 
8.0%

1월
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:23.848511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4
Min length2

Characters and Unicode

Total characters85
Distinct characters12
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

Unique25 ?
Unique (%)100.0%

Sample

1st row3196
2nd row2206
3rd row134
4th row967
5th row1735
ValueCountFrequency (%)
3196 1
 
4.0%
876 1
 
4.0%
101 1
 
4.0%
306 1
 
4.0%
440 1
 
4.0%
809 1
 
4.0%
1162 1
 
4.0%
235 1
 
4.0%
333 1
 
4.0%
524 1
 
4.0%
Other values (15) 15
60.0%
2024-03-15T05:22:25.037085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
16.5%
3 13
15.3%
6 10
11.8%
0 10
11.8%
5 10
11.8%
2 7
8.2%
4 7
8.2%
9 5
 
5.9%
7 5
 
5.9%
8 2
 
2.4%
Other values (2) 2
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 83
97.6%
Other Letter 2
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
16.9%
3 13
15.7%
6 10
12.0%
0 10
12.0%
5 10
12.0%
2 7
8.4%
4 7
8.4%
9 5
 
6.0%
7 5
 
6.0%
8 2
 
2.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83
97.6%
Hangul 2
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
16.9%
3 13
15.7%
6 10
12.0%
0 10
12.0%
5 10
12.0%
2 7
8.4%
4 7
8.4%
9 5
 
6.0%
7 5
 
6.0%
8 2
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83
97.6%
Hangul 2
 
2.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
16.9%
3 13
15.7%
6 10
12.0%
0 10
12.0%
5 10
12.0%
2 7
8.4%
4 7
8.4%
9 5
 
6.0%
7 5
 
6.0%
8 2
 
2.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

2월
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:25.637570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4
Min length2

Characters and Unicode

Total characters85
Distinct characters12
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

Unique23 ?
Unique (%)92.0%

Sample

1st row3903
2nd row2336
3rd row269
4th row1262
5th row1970
ValueCountFrequency (%)
불명 2
 
8.0%
3903 1
 
4.0%
908 1
 
4.0%
145 1
 
4.0%
341 1
 
4.0%
549 1
 
4.0%
946 1
 
4.0%
1308 1
 
4.0%
277 1
 
4.0%
865 1
 
4.0%
Other values (14) 14
56.0%
2024-03-15T05:22:26.840386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
12.9%
3 10
11.8%
9 9
10.6%
2 9
10.6%
4 9
10.6%
0 8
9.4%
6 8
9.4%
7 8
9.4%
5 5
5.9%
8 4
 
4.7%
Other values (2) 4
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
95.3%
Other Letter 4
 
4.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
13.6%
3 10
12.3%
9 9
11.1%
2 9
11.1%
4 9
11.1%
0 8
9.9%
6 8
9.9%
7 8
9.9%
5 5
6.2%
8 4
 
4.9%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 81
95.3%
Hangul 4
 
4.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
13.6%
3 10
12.3%
9 9
11.1%
2 9
11.1%
4 9
11.1%
0 8
9.9%
6 8
9.9%
7 8
9.9%
5 5
6.2%
8 4
 
4.9%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 81
95.3%
Hangul 4
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
13.6%
3 10
12.3%
9 9
11.1%
2 9
11.1%
4 9
11.1%
0 8
9.9%
6 8
9.9%
7 8
9.9%
5 5
6.2%
8 4
 
4.9%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

3월
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:27.698518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.72
Min length2

Characters and Unicode

Total characters93
Distinct characters12
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

Unique25 ?
Unique (%)100.0%

Sample

1st row4321
2nd row2871
3rd row383
4th row1629
5th row2181
ValueCountFrequency (%)
4321 1
 
4.0%
1364 1
 
4.0%
231 1
 
4.0%
425 1
 
4.0%
735 1
 
4.0%
1236 1
 
4.0%
1661 1
 
4.0%
362 1
 
4.0%
1805 1
 
4.0%
940 1
 
4.0%
Other values (15) 15
60.0%
2024-03-15T05:22:28.690969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
18.3%
2 14
15.1%
3 13
14.0%
6 13
14.0%
4 7
7.5%
8 7
7.5%
5 6
 
6.5%
7 5
 
5.4%
0 5
 
5.4%
9 4
 
4.3%
Other values (2) 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 91
97.8%
Other Letter 2
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
18.7%
2 14
15.4%
3 13
14.3%
6 13
14.3%
4 7
7.7%
8 7
7.7%
5 6
 
6.6%
7 5
 
5.5%
0 5
 
5.5%
9 4
 
4.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 91
97.8%
Hangul 2
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
18.7%
2 14
15.4%
3 13
14.3%
6 13
14.3%
4 7
7.7%
8 7
7.7%
5 6
 
6.6%
7 5
 
5.5%
0 5
 
5.5%
9 4
 
4.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91
97.8%
Hangul 2
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
18.7%
2 14
15.4%
3 13
14.3%
6 13
14.3%
4 7
7.7%
8 7
7.7%
5 6
 
6.6%
7 5
 
5.5%
0 5
 
5.5%
9 4
 
4.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

4월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2124.08
Minimum248
Maximum10962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:28.910821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum248
5-th percentile353.6
Q1976
median1426
Q32218
95-th percentile5201.4
Maximum10962
Range10714
Interquartile range (IQR)1242

Descriptive statistics

Standard deviation2281.5209
Coefficient of variation (CV)1.0741219
Kurtosis9.077189
Mean2124.08
Median Absolute Deviation (MAD)685
Skewness2.7281722
Sum53102
Variance5205337.5
MonotonicityNot monotonic
2024-03-15T05:22:29.135067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4182 1
 
4.0%
2437 1
 
4.0%
1025 1
 
4.0%
248 1
 
4.0%
372 1
 
4.0%
664 1
 
4.0%
1174 1
 
4.0%
1319 1
 
4.0%
349 1
 
4.0%
2026 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
248 1
4.0%
349 1
4.0%
372 1
4.0%
413 1
4.0%
664 1
4.0%
757 1
4.0%
976 1
4.0%
1025 1
4.0%
1102 1
4.0%
1174 1
4.0%
ValueCountFrequency (%)
10962 1
4.0%
5347 1
4.0%
4619 1
4.0%
4182 1
4.0%
3472 1
4.0%
2437 1
4.0%
2218 1
4.0%
2111 1
4.0%
2026 1
4.0%
1657 1
4.0%

5월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2541.72
Minimum312
Maximum12624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:29.362969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum312
5-th percentile421
Q11304
median1892
Q32659
95-th percentile6185.8
Maximum12624
Range12312
Interquartile range (IQR)1355

Descriptive statistics

Standard deviation2585.5184
Coefficient of variation (CV)1.0172318
Kurtosis9.4422388
Mean2541.72
Median Absolute Deviation (MAD)767
Skewness2.7638121
Sum63543
Variance6684905.4
MonotonicityNot monotonic
2024-03-15T05:22:29.689500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4321 1
 
4.0%
2828 1
 
4.0%
1304 1
 
4.0%
312 1
 
4.0%
419 1
 
4.0%
818 1
 
4.0%
1452 1
 
4.0%
1892 1
 
4.0%
429 1
 
4.0%
2550 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
312 1
4.0%
419 1
4.0%
429 1
4.0%
534 1
4.0%
818 1
4.0%
874 1
4.0%
1304 1
4.0%
1330 1
4.0%
1452 1
4.0%
1517 1
4.0%
ValueCountFrequency (%)
12624 1
4.0%
6502 1
4.0%
4921 1
4.0%
4321 1
4.0%
4182 1
4.0%
2828 1
4.0%
2659 1
4.0%
2550 1
4.0%
2453 1
4.0%
2181 1
4.0%

6월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2339.16
Minimum294
Maximum11137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:30.062166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum294
5-th percentile363
Q11128
median1828
Q32358
95-th percentile5536
Maximum11137
Range10843
Interquartile range (IQR)1230

Descriptive statistics

Standard deviation2306.4661
Coefficient of variation (CV)0.98602325
Kurtosis8.4072834
Mean2339.16
Median Absolute Deviation (MAD)700
Skewness2.5762254
Sum58479
Variance5319786.1
MonotonicityNot monotonic
2024-03-15T05:22:30.701773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4182 1
 
4.0%
3358 1
 
4.0%
1180 1
 
4.0%
294 1
 
4.0%
358 1
 
4.0%
732 1
 
4.0%
1342 1
 
4.0%
1739 1
 
4.0%
383 1
 
4.0%
2325 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
294 1
4.0%
358 1
4.0%
383 1
4.0%
467 1
4.0%
732 1
4.0%
802 1
4.0%
1128 1
4.0%
1180 1
4.0%
1341 1
4.0%
1342 1
4.0%
ValueCountFrequency (%)
11137 1
4.0%
5779 1
4.0%
4564 1
4.0%
4182 1
4.0%
3730 1
4.0%
3358 1
4.0%
2358 1
4.0%
2325 1
4.0%
2163 1
4.0%
2111 1
4.0%

7월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2021.24
Minimum259
Maximum9531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size353.0 B
2024-03-15T05:22:31.072400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum259
5-th percentile314.6
Q1776
median1274
Q32181
95-th percentile4791.4
Maximum9531
Range9272
Interquartile range (IQR)1405

Descriptive statistics

Standard deviation2029.4137
Coefficient of variation (CV)1.0040439
Kurtosis7.1556921
Mean2021.24
Median Absolute Deviation (MAD)716
Skewness2.3887421
Sum50531
Variance4118520
MonotonicityNot monotonic
2024-03-15T05:22:31.453466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4321 1
 
4.0%
2860 1
 
4.0%
1031 1
 
4.0%
259 1
 
4.0%
317 1
 
4.0%
613 1
 
4.0%
1128 1
 
4.0%
1255 1
 
4.0%
314 1
 
4.0%
1990 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
259 1
4.0%
314 1
4.0%
317 1
4.0%
400 1
4.0%
613 1
4.0%
681 1
4.0%
776 1
4.0%
957 1
4.0%
1031 1
4.0%
1128 1
4.0%
ValueCountFrequency (%)
9531 1
4.0%
4909 1
4.0%
4321 1
4.0%
4196 1
4.0%
3174 1
4.0%
2860 1
4.0%
2181 1
4.0%
2047 1
4.0%
1990 1
4.0%
1919 1
4.0%

8월
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:32.156587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6
Min length2

Characters and Unicode

Total characters90
Distinct characters12
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

Unique25 ?
Unique (%)100.0%

Sample

1st row3530
2nd row2946
3rd row382
4th row1690
5th row1869
ValueCountFrequency (%)
3530 1
 
4.0%
1161 1
 
4.0%
235 1
 
4.0%
320 1
 
4.0%
572 1
 
4.0%
1114 1
 
4.0%
1404 1
 
4.0%
290 1
 
4.0%
1988 1
 
4.0%
680 1
 
4.0%
Other values (15) 15
60.0%
2024-03-15T05:22:33.527624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 14
15.6%
1 14
15.6%
0 10
11.1%
9 10
11.1%
3 9
10.0%
2 8
8.9%
4 7
7.8%
8 6
6.7%
5 5
 
5.6%
7 5
 
5.6%
Other values (2) 2
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
97.8%
Other Letter 2
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 14
15.9%
1 14
15.9%
0 10
11.4%
9 10
11.4%
3 9
10.2%
2 8
9.1%
4 7
8.0%
8 6
6.8%
5 5
 
5.7%
7 5
 
5.7%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
97.8%
Hangul 2
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
6 14
15.9%
1 14
15.9%
0 10
11.4%
9 10
11.4%
3 9
10.2%
2 8
9.1%
4 7
8.0%
8 6
6.8%
5 5
 
5.7%
7 5
 
5.7%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
97.8%
Hangul 2
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 14
15.9%
1 14
15.9%
0 10
11.4%
9 10
11.4%
3 9
10.2%
2 8
9.1%
4 7
8.0%
8 6
6.8%
5 5
 
5.7%
7 5
 
5.7%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

9월
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:34.353980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.56
Min length2

Characters and Unicode

Total characters89
Distinct characters12
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

Unique23 ?
Unique (%)92.0%

Sample

1st row4619
2nd row3009
3rd row337
4th row1682
5th row1894
ValueCountFrequency (%)
불명 2
 
8.0%
4619 1
 
4.0%
1091 1
 
4.0%
182 1
 
4.0%
328 1
 
4.0%
542 1
 
4.0%
1030 1
 
4.0%
1290 1
 
4.0%
1683 1
 
4.0%
647 1
 
4.0%
Other values (14) 14
56.0%
2024-03-15T05:22:35.655609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
19.1%
9 13
14.6%
3 11
12.4%
2 11
12.4%
4 7
7.9%
8 7
7.9%
6 6
 
6.7%
0 6
 
6.7%
7 5
 
5.6%
2
 
2.2%
Other values (2) 4
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 85
95.5%
Other Letter 4
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 17
20.0%
9 13
15.3%
3 11
12.9%
2 11
12.9%
4 7
8.2%
8 7
8.2%
6 6
 
7.1%
0 6
 
7.1%
7 5
 
5.9%
5 2
 
2.4%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 85
95.5%
Hangul 4
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 17
20.0%
9 13
15.3%
3 11
12.9%
2 11
12.9%
4 7
8.2%
8 7
8.2%
6 6
 
7.1%
0 6
 
7.1%
7 5
 
5.9%
5 2
 
2.4%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
95.5%
Hangul 4
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
20.0%
9 13
15.3%
3 11
12.9%
2 11
12.9%
4 7
8.2%
8 7
8.2%
6 6
 
7.1%
0 6
 
7.1%
7 5
 
5.9%
5 2
 
2.4%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

10월
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:36.263093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.64
Min length2

Characters and Unicode

Total characters91
Distinct characters12
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

Unique23 ?
Unique (%)92.0%

Sample

1st row4221
2nd row3509
3rd row315
4th row1859
5th row1676
ValueCountFrequency (%)
불명 2
 
8.0%
4221 1
 
4.0%
1165 1
 
4.0%
136 1
 
4.0%
374 1
 
4.0%
531 1
 
4.0%
1052 1
 
4.0%
1325 1
 
4.0%
1231 1
 
4.0%
633 1
 
4.0%
Other values (14) 14
56.0%
2024-03-15T05:22:37.456355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
24.2%
3 12
13.2%
5 12
13.2%
2 10
11.0%
4 6
 
6.6%
0 6
 
6.6%
8 5
 
5.5%
6 5
 
5.5%
7 5
 
5.5%
9 4
 
4.4%
Other values (2) 4
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87
95.6%
Other Letter 4
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
25.3%
3 12
13.8%
5 12
13.8%
2 10
11.5%
4 6
 
6.9%
0 6
 
6.9%
8 5
 
5.7%
6 5
 
5.7%
7 5
 
5.7%
9 4
 
4.6%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 87
95.6%
Hangul 4
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
25.3%
3 12
13.8%
5 12
13.8%
2 10
11.5%
4 6
 
6.9%
0 6
 
6.9%
8 5
 
5.7%
6 5
 
5.7%
7 5
 
5.7%
9 4
 
4.6%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
95.6%
Hangul 4
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
25.3%
3 12
13.8%
5 12
13.8%
2 10
11.5%
4 6
 
6.9%
0 6
 
6.9%
8 5
 
5.7%
6 5
 
5.7%
7 5
 
5.7%
9 4
 
4.6%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

11월
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:38.171786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.32
Min length2

Characters and Unicode

Total characters83
Distinct characters12
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

Unique23 ?
Unique (%)92.0%

Sample

1st row2365
2nd row2835
3rd row200
4th row1277
5th row1542
ValueCountFrequency (%)
불명 2
 
8.0%
2365 1
 
4.0%
842 1
 
4.0%
91 1
 
4.0%
286 1
 
4.0%
405 1
 
4.0%
728 1
 
4.0%
1066 1
 
4.0%
541 1
 
4.0%
474 1
 
4.0%
Other values (14) 14
56.0%
2024-03-15T05:22:39.065756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 12
14.5%
1 10
12.0%
3 8
9.6%
6 8
9.6%
7 8
9.6%
4 8
9.6%
8 8
9.6%
5 7
8.4%
0 6
7.2%
9 4
 
4.8%
Other values (2) 4
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79
95.2%
Other Letter 4
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 12
15.2%
1 10
12.7%
3 8
10.1%
6 8
10.1%
7 8
10.1%
4 8
10.1%
8 8
10.1%
5 7
8.9%
0 6
7.6%
9 4
 
5.1%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79
95.2%
Hangul 4
 
4.8%

Most frequent character per script

Common
ValueCountFrequency (%)
2 12
15.2%
1 10
12.7%
3 8
10.1%
6 8
10.1%
7 8
10.1%
4 8
10.1%
8 8
10.1%
5 7
8.9%
0 6
7.6%
9 4
 
5.1%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79
95.2%
Hangul 4
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12
15.2%
1 10
12.7%
3 8
10.1%
6 8
10.1%
7 8
10.1%
4 8
10.1%
8 8
10.1%
5 7
8.9%
0 6
7.6%
9 4
 
5.1%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

12월
Text

Distinct24
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-03-15T05:22:39.708667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.16
Min length2

Characters and Unicode

Total characters79
Distinct characters12
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

Unique23 ?
Unique (%)92.0%

Sample

1st row1579
2nd row2064
3rd row117
4th row853
5th row1264
ValueCountFrequency (%)
불명 2
 
8.0%
1579 1
 
4.0%
556 1
 
4.0%
74 1
 
4.0%
223 1
 
4.0%
276 1
 
4.0%
424 1
 
4.0%
663 1
 
4.0%
226 1
 
4.0%
368 1
 
4.0%
Other values (14) 14
56.0%
2024-03-15T05:22:40.967461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
16.5%
6 11
13.9%
7 10
12.7%
3 8
10.1%
4 8
10.1%
5 7
8.9%
1 6
7.6%
9 4
 
5.1%
8 4
 
5.1%
0 4
 
5.1%
Other values (2) 4
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
94.9%
Other Letter 4
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
17.3%
6 11
14.7%
7 10
13.3%
3 8
10.7%
4 8
10.7%
5 7
9.3%
1 6
8.0%
9 4
 
5.3%
8 4
 
5.3%
0 4
 
5.3%
Other Letter
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75
94.9%
Hangul 4
 
5.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13
17.3%
6 11
14.7%
7 10
13.3%
3 8
10.7%
4 8
10.7%
5 7
9.3%
1 6
8.0%
9 4
 
5.3%
8 4
 
5.3%
0 4
 
5.3%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75
94.9%
Hangul 4
 
5.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13
17.3%
6 11
14.7%
7 10
13.3%
3 8
10.7%
4 8
10.7%
5 7
9.3%
1 6
8.0%
9 4
 
5.3%
8 4
 
5.3%
0 4
 
5.3%
Hangul
ValueCountFrequency (%)
2
50.0%
2
50.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size328.0 B
2024-01-03
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-03
2nd row2024-01-03
3rd row2024-01-03
4th row2024-01-03
5th row2024-01-03

Common Values

ValueCountFrequency (%)
2024-01-03 25
100.0%

Length

2024-03-15T05:22:41.383517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:22:41.729291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-03 25
100.0%

Interactions

2024-03-15T05:22:13.850036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:04.087115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:05.754823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:07.130121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:08.927294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:10.912011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:12.728800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.992020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:04.322665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.013026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:07.342241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:09.198511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:11.179530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:12.868496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:14.135601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:04.567405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.331460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:07.591057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:09.507669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:11.430128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.016495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:14.278942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:04.806766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.523786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:07.862845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:09.866789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:11.790462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.171970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:14.488266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:05.045758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.670076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:08.141231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:10.108718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:12.002944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.417969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:14.634135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:05.289534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.821273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:08.398532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:10.383755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:12.271459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.561410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:14.782609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:05.523602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:06.975578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:08.686904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:10.632159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:12.509665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:22:13.713912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:22:41.975054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치년도개소명주소설치용량용도1월2월3월4월5월6월7월8월9월10월11월12월
연번1.0000.7871.0001.0000.6010.1261.0001.0001.0000.7170.4070.4070.3411.0000.9140.9140.9140.914
설치년도0.7871.0001.0001.0000.5240.4241.0000.7091.0000.7340.6800.6800.4471.0000.9550.9550.9550.955
개소명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
주소1.0001.0001.0001.0000.9881.0001.0000.9871.0001.0000.9660.9660.9701.0000.9940.9940.9940.994
설치용량0.6010.5241.0000.9881.0000.9561.0000.0001.0000.9590.9760.9760.8801.0000.9880.9880.9880.988
용도0.1260.4241.0001.0000.9561.0001.0000.0001.0000.9540.9110.9110.7371.0001.0001.0001.0001.000
1월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2월1.0000.7091.0000.9870.0000.0001.0001.0001.0000.0000.0000.0000.0001.0000.9870.9870.9870.987
3월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
4월0.7170.7341.0001.0000.9590.9541.0000.0001.0001.0000.9920.9920.8771.0000.9640.9640.9640.964
5월0.4070.6801.0000.9660.9760.9111.0000.0001.0000.9921.0001.0000.9471.0000.9660.9660.9660.966
6월0.4070.6801.0000.9660.9760.9111.0000.0001.0000.9921.0001.0000.9471.0000.9660.9660.9660.966
7월0.3410.4471.0000.9700.8800.7371.0000.0001.0000.8770.9470.9471.0001.0001.0001.0001.0001.000
8월1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
9월0.9140.9551.0000.9940.9881.0001.0000.9871.0000.9640.9660.9661.0001.0001.0001.0001.0001.000
10월0.9140.9551.0000.9940.9881.0001.0000.9871.0000.9640.9660.9661.0001.0001.0001.0001.0001.000
11월0.9140.9551.0000.9940.9881.0001.0000.9871.0000.9640.9660.9661.0001.0001.0001.0001.0001.000
12월0.9140.9551.0000.9940.9881.0001.0000.9871.0000.9640.9660.9661.0001.0001.0001.0001.0001.000
2024-03-15T05:22:42.417620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치년도설치용량4월5월6월7월용도
연번1.0000.986-0.610-0.508-0.547-0.555-0.5630.000
설치년도0.9861.000-0.610-0.502-0.541-0.553-0.5700.278
설치용량-0.610-0.6101.0000.9210.9840.9870.9670.737
4월-0.508-0.5020.9211.0000.9520.9470.9330.737
5월-0.547-0.5410.9840.9521.0000.9940.9670.669
6월-0.555-0.5530.9870.9470.9941.0000.9760.669
7월-0.563-0.5700.9670.9330.9670.9761.0000.706
용도0.0000.2780.7370.7370.6690.6690.7061.000

Missing values

2024-03-15T05:22:15.071622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:22:15.771956image/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

연번설치년도개소명주소설치용량용도1월2월3월4월5월6월7월8월9월10월11월12월데이터기준일
012008고척근린공원서울특별시 구로구 고척동 산 9-14100자가사용3196390343214182432141824321353046194221236515792024-01-03
122009구로구청(1)서울특별시 구로구 가마산로 24530자가사용2206233628712437282833582860294630093509283520642024-01-03
232013궁동종합사회복지관서울특별시 구로구 궁동 108-95자가사용1342693834135344674003823373152001172024-01-03
342013구로구민회관서울특별시 구로구 가마산로25길 2120자가사용96712621629142617011872168316901682185912778532024-01-03
452014구로구민체육센터서울특별시 구로구 고척로45길 3920자가사용1735197021812111218121112181186918941676154212642024-01-03
562014구로3동자치회관서울특별시 구로구 디지털로31길 10926자가사용불명불명불명9762453216319191863161415157135012024-01-03
672015구로보건소서울특별시 구로구 구로중앙로28길 6610자가사용713989124811021330112895797591510847023282024-01-03
782015구로희망 햇빛발전소(고척도서관)서울특별시 구로구 고척로45길 3197발전사업5029불명1262410962126241113795319665979211179869964762024-01-03
892016신도림빗물펌프장서울특별시 구로구 신도림로19길 10845자가사용3405421457235347650257794909493747434851342322522024-01-03
9102016구로희망 햇빛발전소2호기(신구로펌프장)서울특별시 구로구 구로동 689-760발전사업4551502153604619492145644196420639614728383729732024-01-03
연번설치년도개소명주소설치용량용도1월2월3월4월5월6월7월8월9월10월11월12월데이터기준일
15162020고척근린공원 공용주차장서울특별시 구로구 고척동 산9-1430자가사용2055270736973472418237303174310629833048198513572024-01-03
16172020구립온새미어린이집서울특별시 구로구 새말로18길 1308자가사용5246439407578748026816806476334743682024-01-03
17182020천왕동청소년문화의집서울특별시 구로구 오리로 111521자가사용333865180520262550232519901988168312315412262024-01-03
18192021궁동경로당 제로에너지건물 리모델링(경로당)서울특별시 구로구 부일로17길 158-43자가사용235277362349429383314290불명불명불명불명2024-01-03
19202021궁동경로당 제로에너지건물 리모델링(궁동데이케어센터)서울특별시 구로구 부일로17길 158-415자가사용116213081661131918921739125514041290132510666632024-01-03
20212021나래어린이집 제로에너지건물서울특별시 구로구 고척로1길 14-4110자가사용809946123611741452134211281114103010527284242024-01-03
21222021백곡경로당 제로에너지건물 리모델링서울특별시 구로구 고척로21길 856자가사용4405497356648187326135725425314052762024-01-03
22232021화원경로당 제로에너지건물 리모델링서울특별시 구로구 벚꽃로 4903자가사용3063414253724193583173203283742862232024-01-03
23242021중앙경로당서울특별시 구로구 개봉로15길 58-23자가사용10114523124831229425923518213691742024-01-03
24252021모아래 마을활력소서울특별시 구로구 남부순환로105길 1988자가사용604736103210251304118010319979129326234002024-01-03