Overview

Dataset statistics

Number of variables6
Number of observations339
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.0 KiB
Average record size in memory51.4 B

Variable types

Categorical4
Numeric2

Dataset

Description한국지역난방공사의 각 지사별 PDCV 밸브 설치 현황 정보 입니다(주요 내용 : 지사명, 제작사명, 구분(공동주택, 건물), 중온수냉방 및 냉수냉방 구분 등)
URLhttps://www.data.go.kr/data/15119328/fileData.do

Alerts

중온수냉방용 has constant value ""Constant
난방 및 급탕용 is highly overall correlated with 냉수냉방용High correlation
냉수냉방용 is highly overall correlated with 난방 및 급탕용High correlation
난방 및 급탕용 has 35 (10.3%) zerosZeros
냉수냉방용 has 35 (10.3%) zerosZeros

Reproduction

Analysis started2023-12-12 06:13:04.657913
Analysis finished2023-12-12 06:13:05.492549
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지사
Categorical

Distinct19
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
고양사업소
27 
강남지사
24 
동탄지사
24 
수원사업소
24 
세종지사
24 
Other values (14)
216 

Length

Max length6
Median length4
Mean length4.380531
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양사업소 27
 
8.0%
강남지사 24
 
7.1%
동탄지사 24
 
7.1%
수원사업소 24
 
7.1%
세종지사 24
 
7.1%
분당사업소 21
 
6.2%
광주전남지사 21
 
6.2%
화성지사 21
 
6.2%
중앙지사 18
 
5.3%
양산지사 18
 
5.3%
Other values (9) 117
34.5%

Length

2023-12-12T15:13:05.573485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양사업소 27
 
8.0%
강남지사 24
 
7.1%
동탄지사 24
 
7.1%
수원사업소 24
 
7.1%
세종지사 24
 
7.1%
분당사업소 21
 
6.2%
광주전남지사 21
 
6.2%
화성지사 21
 
6.2%
삼송지사 18
 
5.3%
양산지사 18
 
5.3%
Other values (9) 117
34.5%

제작사명
Categorical

Distinct12
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
신우
57 
신한
57 
삼양
57 
기타1
36 
기타2
36 
Other values (7)
96 

Length

Max length11
Median length2
Mean length2.699115
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신우
2nd row신우
3rd row신우
4th row신한
5th row신한

Common Values

ValueCountFrequency (%)
신우 57
16.8%
신한 57
16.8%
삼양 57
16.8%
기타1 36
10.6%
기타2 36
10.6%
기타3 30
8.8%
기타4 18
 
5.3%
기타 18
 
5.3%
SAMSON 15
 
4.4%
CLORIUS(경영) 6
 
1.8%
Other values (2) 9
 
2.7%

Length

2023-12-12T15:13:05.727562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신우 57
16.8%
신한 57
16.8%
삼양 57
16.8%
기타1 36
10.6%
기타2 36
10.6%
기타3 30
8.8%
기타4 18
 
5.3%
기타 18
 
5.3%
samson 15
 
4.4%
clorius(경영 6
 
1.8%
Other values (2) 9
 
2.7%

구분
Categorical

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
공동주택
113 
건물
113 
소계
113 

Length

Max length4
Median length2
Mean length2.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공동주택
2nd row건물
3rd row소계
4th row공동주택
5th row건물

Common Values

ValueCountFrequency (%)
공동주택 113
33.3%
건물 113
33.3%
소계 113
33.3%

Length

2023-12-12T15:13:05.857811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:13:05.983492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 113
33.3%
건물 113
33.3%
소계 113
33.3%

난방 및 급탕용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct132
Distinct (%)38.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.412979
Minimum0
Maximum653
Zeros35
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T15:13:06.127280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q380
95-th percentile258.3
Maximum653
Range653
Interquartile range (IQR)78

Descriptive statistics

Standard deviation94.768453
Coefficient of variation (CV)1.6223869
Kurtosis8.3249401
Mean58.412979
Median Absolute Deviation (MAD)14
Skewness2.6002064
Sum19802
Variance8981.0597
MonotonicityNot monotonic
2023-12-12T15:13:06.266633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
10.3%
1 35
 
10.3%
2 29
 
8.6%
4 22
 
6.5%
3 10
 
2.9%
7 9
 
2.7%
20 7
 
2.1%
6 7
 
2.1%
14 6
 
1.8%
19 5
 
1.5%
Other values (122) 174
51.3%
ValueCountFrequency (%)
0 35
10.3%
1 35
10.3%
2 29
8.6%
3 10
 
2.9%
4 22
6.5%
5 4
 
1.2%
6 7
 
2.1%
7 9
 
2.7%
8 4
 
1.2%
9 1
 
0.3%
ValueCountFrequency (%)
653 1
0.3%
514 1
0.3%
459 1
0.3%
413 1
0.3%
408 1
0.3%
383 1
0.3%
362 1
0.3%
359 1
0.3%
349 1
0.3%
340 1
0.3%

중온수냉방용
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
0
339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 339
100.0%

Length

2023-12-12T15:13:06.391842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:13:06.480499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 339
100.0%

냉수냉방용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.171091
Minimum0
Maximum652
Zeros35
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2023-12-12T15:13:06.585150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median14
Q379.5
95-th percentile256.5
Maximum652
Range652
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation94.456497
Coefficient of variation (CV)1.6237704
Kurtosis8.3885852
Mean58.171091
Median Absolute Deviation (MAD)14
Skewness2.6081039
Sum19720
Variance8922.0298
MonotonicityNot monotonic
2023-12-12T15:13:06.804168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 35
 
10.3%
0 35
 
10.3%
2 29
 
8.6%
4 22
 
6.5%
3 10
 
2.9%
7 9
 
2.7%
20 8
 
2.4%
6 7
 
2.1%
14 6
 
1.8%
105 5
 
1.5%
Other values (123) 173
51.0%
ValueCountFrequency (%)
0 35
10.3%
1 35
10.3%
2 29
8.6%
3 10
 
2.9%
4 22
6.5%
5 4
 
1.2%
6 7
 
2.1%
7 9
 
2.7%
8 4
 
1.2%
9 1
 
0.3%
ValueCountFrequency (%)
652 1
0.3%
514 1
0.3%
459 1
0.3%
413 1
0.3%
408 1
0.3%
372 1
0.3%
362 1
0.3%
359 1
0.3%
348 1
0.3%
338 1
0.3%

Interactions

2023-12-12T15:13:05.122737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:04.898159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:05.214010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:05.016075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:13:06.939433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사제작사명구분난방 및 급탕용냉수냉방용
지사1.0000.4240.0000.0000.000
제작사명0.4241.0000.0000.3780.379
구분0.0000.0001.0000.2730.276
난방 및 급탕용0.0000.3780.2731.0001.000
냉수냉방용0.0000.3790.2761.0001.000
2023-12-12T15:13:07.065289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지사제작사명구분
지사1.0000.1610.000
제작사명0.1611.0000.000
구분0.0000.0001.000
2023-12-12T15:13:07.167439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
난방 및 급탕용냉수냉방용지사제작사명구분
난방 및 급탕용1.0001.0000.0000.1690.123
냉수냉방용1.0001.0000.0000.1700.124
지사0.0000.0001.0000.1610.000
제작사명0.1690.1700.1611.0000.000
구분0.1230.1240.0000.0001.000

Missing values

2023-12-12T15:13:05.358218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:13:05.453061image/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

지사제작사명구분난방 및 급탕용중온수냉방용냉수냉방용
0강남지사신우공동주택1280128
1강남지사신우건물79079
2강남지사신우소계2070207
3강남지사신한공동주택1090105
4강남지사신한건물1190119
5강남지사신한소계2280224
6강남지사삼양공동주택2900290
7강남지사삼양건물1230123
8강남지사삼양소계4130413
9강남지사SAMSON공동주택50050
지사제작사명구분난방 및 급탕용중온수냉방용냉수냉방용
329화성지사기타1소계101
330화성지사기타2공동주택000
331화성지사기타2건물202
332화성지사기타2소계202
333화성지사기타3공동주택000
334화성지사기타3건물101
335화성지사기타3소계101
336화성지사기타4공동주택101
337화성지사기타4건물505
338화성지사기타4소계606