Overview

Dataset statistics

Number of variables14
Number of observations122
Missing cells309
Missing cells (%)18.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.7 KiB
Average record size in memory123.1 B

Variable types

Numeric8
Text2
Categorical4

Dataset

Description에너지효율 조성사업으로 지원한 연간실적 데이터로 사업장 지원개소, 지원설비 품목, 지원대수, 설비용량, 정부보조금에 대한 데이터를 개방
Author한국에너지공단
URLhttps://www.data.go.kr/data/15086180/fileData.do

Alerts

3개체설비 has constant value ""Constant
총사업비(백만원) is highly overall correlated with 정부보조금(백만원) and 4 other fieldsHigh correlation
정부보조금(백만원) is highly overall correlated with 총사업비(백만원) and 3 other fieldsHigh correlation
설비합계 is highly overall correlated with 총사업비(백만원) and 6 other fieldsHigh correlation
1수량(개) is highly overall correlated with 총사업비(백만원) and 3 other fieldsHigh correlation
1총용량(kW) is highly overall correlated with 총사업비(백만원) and 3 other fieldsHigh correlation
2수량(개) is highly overall correlated with 설비합계 and 1 other fieldsHigh correlation
2총용량(kW) is highly overall correlated with 총사업비(백만원) and 2 other fieldsHigh correlation
1개체설비 is highly overall correlated with 설비합계 and 1 other fieldsHigh correlation
2개체설비 is highly overall correlated with 1개체설비High correlation
1개체설비 is highly imbalanced (67.9%)Imbalance
2개체설비 is highly imbalanced (50.4%)Imbalance
3수량(개) is highly imbalanced (93.1%)Imbalance
3총용량(kW) is highly imbalanced (93.1%)Imbalance
2수량(개) has 92 (75.4%) missing valuesMissing
2총용량(kW) has 95 (77.9%) missing valuesMissing
3개체설비 has 121 (99.2%) missing valuesMissing
관리번호 has unique valuesUnique
지원사업장 has unique valuesUnique

Reproduction

Analysis started2024-03-30 07:32:49.464876
Analysis finished2024-03-30 07:33:15.483354
Duration26.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.5
Minimum1
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:15.786361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.05
Q131.25
median61.5
Q391.75
95-th percentile115.95
Maximum122
Range121
Interquartile range (IQR)60.5

Descriptive statistics

Standard deviation35.362409
Coefficient of variation (CV)0.57499853
Kurtosis-1.2
Mean61.5
Median Absolute Deviation (MAD)30.5
Skewness0
Sum7503
Variance1250.5
MonotonicityStrictly increasing
2024-03-30T07:33:16.406133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
93 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
Other values (112) 112
91.8%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%

지원사업장
Text

UNIQUE 

Distinct122
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-03-30T07:33:17.190214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length7.0901639
Min length2

Characters and Unicode

Total characters865
Distinct characters173
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)100.0%

Sample

1st row(주)거양금속
2nd row(주)광일테크
3rd row(주)금영
4th row(주)금장
5th row(주)금화전선
ValueCountFrequency (%)
주식회사 9
 
6.7%
주)거양금속 1
 
0.7%
레이져전자 1
 
0.7%
세웅텍스타일 1
 
0.7%
성훈엔지니어링 1
 
0.7%
성화산업(주 1
 
0.7%
성창실업 1
 
0.7%
성진정밀(주 1
 
0.7%
선진파워테크(주 1
 
0.7%
새한비엔피(주 1
 
0.7%
Other values (116) 116
86.6%
2024-03-30T07:33:18.784511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 93
 
10.8%
( 80
 
9.2%
) 80
 
9.2%
이 24
 
2.8%
산 19
 
2.2%
스 19
 
2.2%
업 16
 
1.8%
성 15
 
1.7%
텍 15
 
1.7%
크 14
 
1.6%
Other values (163) 490
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 684
79.1%
Open Punctuation 80
 
9.2%
Close Punctuation 80
 
9.2%
Space Separator 12
 
1.4%
Uppercase Letter 7
 
0.8%
Decimal Number 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 93
 
13.6%
이 24
 
3.5%
산 19
 
2.8%
스 19
 
2.8%
업 16
 
2.3%
성 15
 
2.2%
텍 15
 
2.2%
크 14
 
2.0%
사 13
 
1.9%
동 13
 
1.9%
Other values (153) 443
64.8%
Uppercase Letter
ValueCountFrequency (%)
E 2
28.6%
S 2
28.6%
P 1
14.3%
G 1
14.3%
N 1
14.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 80
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 684
79.1%
Common 174
 
20.1%
Latin 7
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 93
 
13.6%
이 24
 
3.5%
산 19
 
2.8%
스 19
 
2.8%
업 16
 
2.3%
성 15
 
2.2%
텍 15
 
2.2%
크 14
 
2.0%
사 13
 
1.9%
동 13
 
1.9%
Other values (153) 443
64.8%
Common
ValueCountFrequency (%)
( 80
46.0%
) 80
46.0%
12
 
6.9%
1 1
 
0.6%
2 1
 
0.6%
Latin
ValueCountFrequency (%)
E 2
28.6%
S 2
28.6%
P 1
14.3%
G 1
14.3%
N 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 684
79.1%
ASCII 181
 
20.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 93
 
13.6%
이 24
 
3.5%
산 19
 
2.8%
스 19
 
2.8%
업 16
 
2.3%
성 15
 
2.2%
텍 15
 
2.2%
크 14
 
2.0%
사 13
 
1.9%
동 13
 
1.9%
Other values (153) 443
64.8%
ASCII
ValueCountFrequency (%)
( 80
44.2%
) 80
44.2%
12
 
6.6%
E 2
 
1.1%
S 2
 
1.1%
1 1
 
0.6%
P 1
 
0.6%
2 1
 
0.6%
G 1
 
0.6%
N 1
 
0.6%

총사업비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.40984
Minimum29
Maximum571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:19.559701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile42
Q1102.75
median161.5
Q3234.5
95-th percentile322.9
Maximum571
Range542
Interquartile range (IQR)131.75

Descriptive statistics

Standard deviation97.95038
Coefficient of variation (CV)0.55840871
Kurtosis2.4671817
Mean175.40984
Median Absolute Deviation (MAD)64.5
Skewness1.1261693
Sum21400
Variance9594.2769
MonotonicityNot monotonic
2024-03-30T07:33:20.269087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170 3
 
2.5%
76 3
 
2.5%
151 3
 
2.5%
237 3
 
2.5%
99 3
 
2.5%
153 2
 
1.6%
130 2
 
1.6%
171 2
 
1.6%
323 2
 
1.6%
37 2
 
1.6%
Other values (89) 97
79.5%
ValueCountFrequency (%)
29 1
0.8%
31 1
0.8%
37 2
1.6%
38 1
0.8%
41 1
0.8%
42 2
1.6%
43 1
0.8%
44 1
0.8%
51 1
0.8%
55 1
0.8%
ValueCountFrequency (%)
571 1
0.8%
528 1
0.8%
467 1
0.8%
373 1
0.8%
330 1
0.8%
323 2
1.6%
321 1
0.8%
307 1
0.8%
300 1
0.8%
298 1
0.8%

정부보조금(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)68.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.86885
Minimum20
Maximum285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:20.972004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile27
Q163.25
median98
Q3141.75
95-th percentile198.6
Maximum285
Range265
Interquartile range (IQR)78.5

Descriptive statistics

Standard deviation53.117203
Coefficient of variation (CV)0.51138721
Kurtosis0.17164969
Mean103.86885
Median Absolute Deviation (MAD)40
Skewness0.52724582
Sum12672
Variance2821.4372
MonotonicityNot monotonic
2024-03-30T07:33:21.625517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 5
 
4.1%
100 4
 
3.3%
165 3
 
2.5%
56 3
 
2.5%
90 3
 
2.5%
111 3
 
2.5%
199 2
 
1.6%
94 2
 
1.6%
34 2
 
1.6%
141 2
 
1.6%
Other values (74) 93
76.2%
ValueCountFrequency (%)
20 1
0.8%
21 1
0.8%
22 2
1.6%
23 1
0.8%
26 1
0.8%
27 2
1.6%
28 2
1.6%
30 1
0.8%
32 1
0.8%
33 1
0.8%
ValueCountFrequency (%)
285 1
0.8%
241 1
0.8%
209 2
1.6%
203 1
0.8%
199 2
1.6%
191 1
0.8%
180 1
0.8%
178 2
1.6%
177 1
0.8%
174 2
1.6%

설비합계
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1229508
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:22.006004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2620764
Coefficient of variation (CV)0.72433942
Kurtosis12.60252
Mean3.1229508
Median Absolute Deviation (MAD)1
Skewness2.7978715
Sum381
Variance5.1169896
MonotonicityNot monotonic
2024-03-30T07:33:22.380821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 33
27.0%
2 29
23.8%
1 25
20.5%
4 14
11.5%
5 9
 
7.4%
6 6
 
4.9%
8 3
 
2.5%
17 1
 
0.8%
9 1
 
0.8%
12 1
 
0.8%
ValueCountFrequency (%)
1 25
20.5%
2 29
23.8%
3 33
27.0%
4 14
11.5%
5 9
 
7.4%
6 6
 
4.9%
8 3
 
2.5%
9 1
 
0.8%
12 1
 
0.8%
17 1
 
0.8%
ValueCountFrequency (%)
17 1
 
0.8%
12 1
 
0.8%
9 1
 
0.8%
8 3
 
2.5%
6 6
 
4.9%
5 9
 
7.4%
4 14
11.5%
3 33
27.0%
2 29
23.8%
1 25
20.5%

1개체설비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
공기압축기
103 
덮개제어형 전기용해로
 
8
삼상유도전동기
 
6
공기압축기 통합제어시스템
 
1
멀티전기히트펌프시스템
 
1
Other values (3)
 
3

Length

Max length13
Median length5
Mean length5.6639344
Min length5

Unique

Unique5 ?
Unique (%)4.1%

Sample

1st row공기압축기
2nd row덮개제어형 전기용해로
3rd row공기압축기
4th row공기압축기
5th row공기압축기

Common Values

ValueCountFrequency (%)
공기압축기 103
84.4%
덮개제어형 전기용해로 8
 
6.6%
삼상유도전동기 6
 
4.9%
공기압축기 통합제어시스템 1
 
0.8%
멀티전기히트펌프시스템 1
 
0.8%
항온항습기 1
 
0.8%
센츄리에어(주) 1
 
0.8%
인버터 스크롤칠러 1
 
0.8%

Length

2024-03-30T07:33:23.085682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:33:23.614649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공기압축기 104
78.8%
덮개제어형 8
 
6.1%
전기용해로 8
 
6.1%
삼상유도전동기 6
 
4.5%
통합제어시스템 1
 
0.8%
멀티전기히트펌프시스템 1
 
0.8%
항온항습기 1
 
0.8%
센츄리에어(주 1
 
0.8%
인버터 1
 
0.8%
스크롤칠러 1
 
0.8%

1수량(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4508197
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:24.151649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4.95
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5593093
Coefficient of variation (CV)0.63623993
Kurtosis11.697588
Mean2.4508197
Median Absolute Deviation (MAD)1
Skewness2.5395184
Sum299
Variance2.4314456
MonotonicityNot monotonic
2024-03-30T07:33:24.507484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 36
29.5%
2 34
27.9%
3 33
27.0%
4 12
 
9.8%
6 3
 
2.5%
5 2
 
1.6%
8 1
 
0.8%
12 1
 
0.8%
ValueCountFrequency (%)
1 36
29.5%
2 34
27.9%
3 33
27.0%
4 12
 
9.8%
5 2
 
1.6%
6 3
 
2.5%
8 1
 
0.8%
12 1
 
0.8%
ValueCountFrequency (%)
12 1
 
0.8%
8 1
 
0.8%
6 3
 
2.5%
5 2
 
1.6%
4 12
 
9.8%
3 33
27.0%
2 34
27.9%
1 36
29.5%

1총용량(kW)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)33.9%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean193.47934
Minimum22
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:25.159153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile37
Q175
median150
Q3225
95-th percentile540
Maximum600
Range578
Interquartile range (IQR)150

Descriptive statistics

Standard deviation139.42418
Coefficient of variation (CV)0.72061534
Kurtosis1.5344958
Mean193.47934
Median Absolute Deviation (MAD)75
Skewness1.2934897
Sum23411
Variance19439.102
MonotonicityNot monotonic
2024-03-30T07:33:25.636765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
225 15
 
12.3%
150 15
 
12.3%
75 11
 
9.0%
37 7
 
5.7%
110 6
 
4.9%
300 5
 
4.1%
220 5
 
4.1%
22 5
 
4.1%
55 5
 
4.1%
600 4
 
3.3%
Other values (31) 43
35.2%
ValueCountFrequency (%)
22 5
4.1%
37 7
5.7%
44 1
 
0.8%
55 5
4.1%
59 1
 
0.8%
74 2
 
1.6%
75 11
9.0%
88 1
 
0.8%
96 1
 
0.8%
110 6
4.9%
ValueCountFrequency (%)
600 4
3.3%
555 1
 
0.8%
550 1
 
0.8%
540 1
 
0.8%
480 3
2.5%
440 1
 
0.8%
375 1
 
0.8%
365 1
 
0.8%
330 1
 
0.8%
320 1
 
0.8%

2개체설비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
91 
인버터
 
8
공기압축기 통합제어시스템
 
6
삼상유도전동기
 
5
전기냉난방기기
 
4
Other values (3)
 
8

Length

Max length13
Median length4
Mean length5.0081967
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row삼상유도전동기
4th row전기냉난방기기
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 91
74.6%
인버터 8
 
6.6%
공기압축기 통합제어시스템 6
 
4.9%
삼상유도전동기 5
 
4.1%
전기냉난방기기 4
 
3.3%
멀티전기히트펌프시스템 4
 
3.3%
덮개제어형 전기용해로 3
 
2.5%
공기압축기 1
 
0.8%

Length

2024-03-30T07:33:26.110010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:33:26.576207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 91
69.5%
인버터 8
 
6.1%
공기압축기 7
 
5.3%
통합제어시스템 6
 
4.6%
삼상유도전동기 5
 
3.8%
전기냉난방기기 4
 
3.1%
멀티전기히트펌프시스템 4
 
3.1%
덮개제어형 3
 
2.3%
전기용해로 3
 
2.3%

2수량(개)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)23.3%
Missing92
Missing (%)75.4%
Infinite0
Infinite (%)0.0%
Mean2.6666667
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:27.040075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33.75
95-th percentile5.55
Maximum16
Range15
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation2.9046138
Coefficient of variation (CV)1.0892302
Kurtosis15.663294
Mean2.6666667
Median Absolute Deviation (MAD)1
Skewness3.5667606
Sum80
Variance8.4367816
MonotonicityNot monotonic
2024-03-30T07:33:27.530798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 14
 
11.5%
4 5
 
4.1%
2 5
 
4.1%
3 3
 
2.5%
6 1
 
0.8%
16 1
 
0.8%
5 1
 
0.8%
(Missing) 92
75.4%
ValueCountFrequency (%)
1 14
11.5%
2 5
 
4.1%
3 3
 
2.5%
4 5
 
4.1%
5 1
 
0.8%
6 1
 
0.8%
16 1
 
0.8%
ValueCountFrequency (%)
16 1
 
0.8%
6 1
 
0.8%
5 1
 
0.8%
4 5
 
4.1%
3 3
 
2.5%
2 5
 
4.1%
1 14
11.5%

2총용량(kW)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)100.0%
Missing95
Missing (%)77.9%
Infinite0
Infinite (%)0.0%
Mean213.82593
Minimum13
Maximum660
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-03-30T07:33:28.041683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile20.81
Q163.5
median144
Q3330
95-th percentile610.3
Maximum660
Range647
Interquartile range (IQR)266.5

Descriptive statistics

Standard deviation201.01742
Coefficient of variation (CV)0.94009844
Kurtosis-0.11475324
Mean213.82593
Median Absolute Deviation (MAD)108
Skewness1.0558612
Sum5773.3
Variance40408.003
MonotonicityNot monotonic
2024-03-30T07:33:28.551819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
144.0 1
 
0.8%
660.0 1
 
0.8%
150.0 1
 
0.8%
37.0 1
 
0.8%
112.0 1
 
0.8%
252.0 1
 
0.8%
110.0 1
 
0.8%
96.0 1
 
0.8%
364.0 1
 
0.8%
300.0 1
 
0.8%
Other values (17) 17
 
13.9%
(Missing) 95
77.9%
ValueCountFrequency (%)
13.0 1
0.8%
17.3 1
0.8%
29.0 1
0.8%
30.0 1
0.8%
32.0 1
0.8%
37.0 1
0.8%
52.0 1
0.8%
75.0 1
0.8%
92.0 1
0.8%
96.0 1
0.8%
ValueCountFrequency (%)
660.0 1
0.8%
634.0 1
0.8%
555.0 1
0.8%
528.0 1
0.8%
486.0 1
0.8%
364.0 1
0.8%
360.0 1
0.8%
300.0 1
0.8%
252.0 1
0.8%
200.0 1
0.8%

3개체설비
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing121
Missing (%)99.2%
Memory size1.1 KiB
2024-03-30T07:33:28.985947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters4
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

Unique1 ?
Unique (%)100.0%

Sample

1st row항온항습기
ValueCountFrequency (%)
항온항습기 1
100.0%
2024-03-30T07:33:30.331185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 2
40.0%
온 1
20.0%
습 1
20.0%
기 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 2
40.0%
온 1
20.0%
습 1
20.0%
기 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 2
40.0%
온 1
20.0%
습 1
20.0%
기 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 2
40.0%
온 1
20.0%
습 1
20.0%
기 1
20.0%

3수량(개)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
121 
2
 
1

Length

Max length4
Median length4
Mean length3.9754098
Min length1

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 121
99.2%
2 1
 
0.8%

Length

2024-03-30T07:33:31.032723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:33:31.676890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 121
99.2%
2 1
 
0.8%

3총용량(kW)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
<NA>
121 
56
 
1

Length

Max length4
Median length4
Mean length3.9836066
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 121
99.2%
56 1
 
0.8%

Length

2024-03-30T07:33:32.459208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T07:33:33.047481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 121
99.2%
56 1
 
0.8%

Interactions

2024-03-30T07:33:10.884679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:51.209219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:53.821608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:56.687603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:59.878659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:02.603265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:05.252545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:08.163569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:11.251012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:51.462998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:54.195102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:57.015727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:00.217347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:02.951803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:05.539396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:08.499507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:11.636212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:51.831935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:54.412180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:57.438264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:00.518528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:03.296690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:05.832392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:08.823861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:11.928151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:52.148410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:54.735927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:57.746500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:00.857845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:03.566471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:06.470120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:09.163948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:12.264949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:52.487618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:55.091775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:58.253510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:01.215981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:03.843214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:06.821375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:09.483899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:12.594960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:52.876557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:55.520098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:58.675282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:01.662081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:04.205367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:07.133594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:09.875936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:12.917947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:53.227650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:55.862314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:59.141690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:01.984465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:04.560376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:07.467944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:10.184923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:13.237537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:53.532713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:56.233132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:32:59.510652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:02.335863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:04.816396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:07.824074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:33:10.585619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T07:33:33.332227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호총사업비(백만원)정부보조금(백만원)설비합계1개체설비1수량(개)1총용량(kW)2개체설비2수량(개)2총용량(kW)
관리번호1.0000.0000.2010.0000.0000.0000.0000.0000.0000.747
총사업비(백만원)0.0001.0000.9060.7120.4280.8080.6820.0000.4670.363
정부보조금(백만원)0.2010.9061.0000.6920.2900.8470.8250.5010.5910.538
설비합계0.0000.7120.6921.0000.8600.8430.7270.0000.8140.339
1개체설비0.0000.4280.2900.8601.0000.6960.6070.8280.0000.426
1수량(개)0.0000.8080.8470.8430.6961.0000.7730.0900.2860.713
1총용량(kW)0.0000.6820.8250.7270.6070.7731.0000.5010.0000.768
2개체설비0.0000.0000.5010.0000.8280.0900.5011.0000.3750.000
2수량(개)0.0000.4670.5910.8140.0000.2860.0000.3751.0000.679
2총용량(kW)0.7470.3630.5380.3390.4260.7130.7680.0000.6791.000
2024-03-30T07:33:33.842531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
3총용량(kW)2개체설비1개체설비3수량(개)
3총용량(kW)1.000NaNNaNNaN
2개체설비NaN1.0000.694NaN
1개체설비NaN0.6941.000NaN
3수량(개)NaNNaNNaN1.000
2024-03-30T07:33:34.255791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호총사업비(백만원)정부보조금(백만원)설비합계1수량(개)1총용량(kW)2수량(개)2총용량(kW)1개체설비2개체설비3수량(개)3총용량(kW)
관리번호1.0000.1410.1120.0600.0390.116-0.2420.1060.0000.000NaNNaN
총사업비(백만원)0.1411.0000.9760.6910.7000.7930.3890.5040.2240.000NaNNaN
정부보조금(백만원)0.1120.9761.0000.6770.7010.7770.3100.4020.1390.173NaNNaN
설비합계0.0600.6910.6771.0000.7190.5800.7870.5920.5010.100NaNNaN
1수량(개)0.0390.7000.7010.7191.0000.843-0.0750.1870.4650.000NaNNaN
1총용량(kW)0.1160.7930.7770.5800.8431.000-0.0170.3420.3580.262NaNNaN
2수량(개)-0.2420.3890.3100.787-0.075-0.0171.0000.6620.0000.228NaNNaN
2총용량(kW)0.1060.5040.4020.5920.1870.3420.6621.0000.2280.000NaNNaN
1개체설비0.0000.2240.1390.5010.4650.3580.0000.2281.0000.694NaNNaN
2개체설비0.0000.0000.1730.1000.0000.2620.2280.0000.6941.000NaNNaN
3수량(개)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaN
3총용량(kW)NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000

Missing values

2024-03-30T07:33:13.744593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T07:33:14.740628image/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.
2024-03-30T07:33:15.195274image/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

관리번호지원사업장총사업비(백만원)정부보조금(백만원)설비합계1개체설비1수량(개)1총용량(kW)2개체설비2수량(개)2총용량(kW)3개체설비3수량(개)3총용량(kW)
01(주)거양금속2641453공기압축기3260<NA><NA><NA><NA><NA><NA>
12(주)광일테크123792덮개제어형 전기용해로2260<NA><NA><NA><NA><NA><NA>
23(주)금영97633공기압축기274삼상유도전동기1200.0<NA><NA><NA>
34(주)금장105706공기압축기175전기냉난방기기6144.0<NA><NA><NA>
45(주)금화전선2041323공기압축기3225<NA><NA><NA><NA><NA><NA>
56(주)기명물산76431공기압축기175<NA><NA><NA><NA><NA><NA>
67(주)대경벤드158862공기압축기2220<NA><NA><NA><NA><NA><NA>
78(주)대동씨엠144933공기압축기3225<NA><NA><NA><NA><NA><NA>
89(주)대불중공업2371523공기압축기3295<NA><NA><NA><NA><NA><NA>
910(주)대진종합기계58402공기압축기244<NA><NA><NA><NA><NA><NA>
관리번호지원사업장총사업비(백만원)정부보조금(백만원)설비합계1개체설비1수량(개)1총용량(kW)2개체설비2수량(개)2총용량(kW)3개체설비3수량(개)3총용량(kW)
112113토와한국 주식회사155885공기압축기155멀티전기히트펌프시스템4252.0<NA><NA><NA>
113114티에스오토모티브135942공기압축기2150<NA><NA><NA><NA><NA><NA>
114115티엠씨2921745공기압축기3225전기냉난방기기2112.0<NA><NA><NA>
115116한국알박(주)57114112인버터 스크롤칠러12480<NA><NA><NA><NA><NA><NA>
116117한두철강(주)진주공장97632공기압축기2150<NA><NA><NA><NA><NA><NA>
117118한올텍스1701002공기압축기2185<NA><NA><NA><NA><NA><NA>
118119한일캐스팅151983덮개제어형 전기용해로2200공기압축기137.0<NA><NA><NA>
119120현대노즐 1공장1721003공기압축기2150공기압축기 통합제어시스템1150.0<NA><NA><NA>
120121호남수산2591426삼상유도전동기3555인버터3660.0<NA><NA><NA>
121122화성교역1701002공기압축기2185<NA><NA><NA><NA><NA><NA>