Overview

Dataset statistics

Number of variables18
Number of observations24
Missing cells143
Missing cells (%)33.1%
Duplicate rows1
Duplicate rows (%)4.2%
Total size in memory3.8 KiB
Average record size in memory161.5 B

Variable types

Text3
Numeric8
Categorical6
DateTime1

Dataset

Description대구광역시 북구 관내 배수펌프장 현황 (펌프장명, 지번주소, 설치년도, 모터펌프규격, 사용전력량 등) 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15025686/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (4.2%) duplicate rowsDuplicates
한전전원공급방식 is highly overall correlated with 위도 and 12 other fieldsHigh correlation
모터펌프2 수(대) is highly overall correlated with 위도 and 10 other fieldsHigh correlation
한전계약전력 구분 is highly overall correlated with 비상발전기 용량(KW) and 4 other fieldsHigh correlation
모터펌프1 수(대) is highly overall correlated with 모터펌프2 용량(KW) and 3 other fieldsHigh correlation
비상발전기 수(대) is highly overall correlated with 위도 and 12 other fieldsHigh correlation
모터펌프2 용량(KW) is highly overall correlated with 위도 and 12 other fieldsHigh correlation
위도 is highly overall correlated with 최대 사용전력량(KWH) and 4 other fieldsHigh correlation
경도 is highly overall correlated with 모터펌프2 용량(KW) and 3 other fieldsHigh correlation
설치년도 is highly overall correlated with 모터펌프2 용량(KW) and 3 other fieldsHigh correlation
모터펌프1 용량(KW) is highly overall correlated with 모터펌프2 용량(KW) and 2 other fieldsHigh correlation
비상발전기 용량(KW) is highly overall correlated with 연평균 사용전력량(KWH) and 5 other fieldsHigh correlation
연평균 사용전력량(KWH) is highly overall correlated with 비상발전기 용량(KW) and 6 other fieldsHigh correlation
최대 사용전력량(KWH) is highly overall correlated with 위도 and 6 other fieldsHigh correlation
최소 사용전력량(KWH) is highly overall correlated with 연평균 사용전력량(KWH) and 6 other fieldsHigh correlation
모터펌프2 용량(KW) is highly imbalanced (62.9%)Imbalance
모터펌프2 수(대) is highly imbalanced (58.5%)Imbalance
펌프장명 has 12 (50.0%) missing valuesMissing
소재지지번주소 has 12 (50.0%) missing valuesMissing
위도 has 12 (50.0%) missing valuesMissing
경도 has 12 (50.0%) missing valuesMissing
설치년도 has 12 (50.0%) missing valuesMissing
모터펌프1 용량(KW) has 12 (50.0%) missing valuesMissing
비상발전기 용량(KW) has 12 (50.0%) missing valuesMissing
한전계약전력(KW) has 11 (45.8%) missing valuesMissing
연평균 사용전력량(KWH) has 12 (50.0%) missing valuesMissing
최대 사용전력량(KWH) has 12 (50.0%) missing valuesMissing
최소 사용전력량(KWH) has 12 (50.0%) missing valuesMissing
데이터기준일자 has 12 (50.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 13:18:15.567912
Analysis finished2023-12-12 13:18:23.895603
Duration8.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

펌프장명
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Memory size324.0 B
2023-12-12T22:18:23.982132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length13.333333
Min length13

Characters and Unicode

Total characters160
Distinct characters31
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

Unique12 ?
Unique (%)100.0%

Sample

1st row도청교 지하차도 배수펌프장
2nd row성북교 지하차도 배수펌프장
3rd row침산교 지하차도 배수펌프장
4th row대구역 지하차도 배수펌프장
5th row원대 지하차도 배수펌프장
ValueCountFrequency (%)
지하차도 12
33.3%
배수펌프장 12
33.3%
도청교 1
 
2.8%
성북교 1
 
2.8%
침산교 1
 
2.8%
대구역 1
 
2.8%
원대 1
 
2.8%
칠성 1
 
2.8%
매천 1
 
2.8%
칠곡 1
 
2.8%
Other values (4) 4
 
11.1%
2023-12-12T22:18:24.232176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
15.0%
도 13
8.1%
배 12
7.5%
프 12
7.5%
펌 12
7.5%
수 12
7.5%
장 12
7.5%
차 12
7.5%
하 12
7.5%
지 12
7.5%
Other values (21) 27
16.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 136
85.0%
Space Separator 24
 
15.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 13
9.6%
배 12
8.8%
프 12
8.8%
펌 12
8.8%
수 12
8.8%
장 12
8.8%
차 12
8.8%
하 12
8.8%
지 12
8.8%
교 3
 
2.2%
Other values (20) 24
17.6%
Space Separator
ValueCountFrequency (%)
24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 136
85.0%
Common 24
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 13
9.6%
배 12
8.8%
프 12
8.8%
펌 12
8.8%
수 12
8.8%
장 12
8.8%
차 12
8.8%
하 12
8.8%
지 12
8.8%
교 3
 
2.2%
Other values (20) 24
17.6%
Common
ValueCountFrequency (%)
24
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 136
85.0%
ASCII 24
 
15.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
100.0%
Hangul
ValueCountFrequency (%)
도 13
9.6%
배 12
8.8%
프 12
8.8%
펌 12
8.8%
수 12
8.8%
장 12
8.8%
차 12
8.8%
하 12
8.8%
지 12
8.8%
교 3
 
2.2%
Other values (20) 24
17.6%

소재지지번주소
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Memory size324.0 B
2023-12-12T22:18:24.380940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length18
Min length15

Characters and Unicode

Total characters216
Distinct characters35
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

Unique12 ?
Unique (%)100.0%

Sample

1st row대구광역시 북구 침산동 22
2nd row대구광역시 북구 침산동 1662
3rd row대구광역시 북구 침산동 663-8
4th row대구광역시 북구 칠성동2가 302
5th row대구광역시 중구 태평로3가 251
ValueCountFrequency (%)
대구광역시 12
25.0%
북구 11
22.9%
침산동 3
 
6.2%
서변동 3
 
6.2%
매천동 1
 
2.1%
고성동1가 1
 
2.1%
1801 1
 
2.1%
1726 1
 
2.1%
1290-47 1
 
2.1%
991 1
 
2.1%
Other values (13) 13
27.1%
2023-12-12T22:18:24.683861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
16.7%
구 24
 
11.1%
대 12
 
5.6%
광 12
 
5.6%
역 12
 
5.6%
시 12
 
5.6%
동 11
 
5.1%
북 11
 
5.1%
1 11
 
5.1%
2 10
 
4.6%
Other values (25) 65
30.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
57.4%
Decimal Number 51
23.6%
Space Separator 36
 
16.7%
Dash Punctuation 5
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 24
19.4%
대 12
9.7%
광 12
9.7%
역 12
9.7%
시 12
9.7%
동 11
8.9%
북 11
8.9%
가 4
 
3.2%
서 3
 
2.4%
변 3
 
2.4%
Other values (13) 20
16.1%
Decimal Number
ValueCountFrequency (%)
1 11
21.6%
2 10
19.6%
3 6
11.8%
6 5
9.8%
9 4
 
7.8%
0 4
 
7.8%
5 3
 
5.9%
4 3
 
5.9%
8 3
 
5.9%
7 2
 
3.9%
Space Separator
ValueCountFrequency (%)
36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
57.4%
Common 92
42.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 24
19.4%
대 12
9.7%
광 12
9.7%
역 12
9.7%
시 12
9.7%
동 11
8.9%
북 11
8.9%
가 4
 
3.2%
서 3
 
2.4%
변 3
 
2.4%
Other values (13) 20
16.1%
Common
ValueCountFrequency (%)
36
39.1%
1 11
 
12.0%
2 10
 
10.9%
3 6
 
6.5%
6 5
 
5.4%
- 5
 
5.4%
9 4
 
4.3%
0 4
 
4.3%
5 3
 
3.3%
4 3
 
3.3%
Other values (2) 5
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
57.4%
ASCII 92
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
39.1%
1 11
 
12.0%
2 10
 
10.9%
3 6
 
6.5%
6 5
 
5.4%
- 5
 
5.4%
9 4
 
4.3%
0 4
 
4.3%
5 3
 
3.3%
4 3
 
3.3%
Other values (2) 5
 
5.4%
Hangul
ValueCountFrequency (%)
구 24
19.4%
대 12
9.7%
광 12
9.7%
역 12
9.7%
시 12
9.7%
동 11
8.9%
북 11
8.9%
가 4
 
3.2%
서 3
 
2.4%
변 3
 
2.4%
Other values (13) 20
16.1%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean35.89985
Minimum35.876036
Maximum35.944232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:24.883238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.876036
5-th percentile35.876816
Q135.877937
median35.898528
Q335.914969
95-th percentile35.931744
Maximum35.944232
Range0.068196
Interquartile range (IQR)0.03703175

Descriptive statistics

Standard deviation0.021912389
Coefficient of variation (CV)0.0006103755
Kurtosis-0.33296112
Mean35.89985
Median Absolute Deviation (MAD)0.020712
Skewness0.64401785
Sum430.7982
Variance0.00048015279
MonotonicityNot monotonic
2023-12-12T22:18:24.986454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
35.887858 1
 
4.2%
35.897832 1
 
4.2%
35.899224 1
 
4.2%
35.877574 1
 
4.2%
35.878058 1
 
4.2%
35.876036 1
 
4.2%
35.904121 1
 
4.2%
35.944232 1
 
4.2%
35.912796 1
 
4.2%
35.921527 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
35.876036 1
4.2%
35.877455 1
4.2%
35.877574 1
4.2%
35.878058 1
4.2%
35.887858 1
4.2%
35.897832 1
4.2%
35.899224 1
4.2%
35.904121 1
4.2%
35.912796 1
4.2%
35.921487 1
4.2%
ValueCountFrequency (%)
35.944232 1
4.2%
35.921527 1
4.2%
35.921487 1
4.2%
35.912796 1
4.2%
35.904121 1
4.2%
35.899224 1
4.2%
35.897832 1
4.2%
35.887858 1
4.2%
35.878058 1
4.2%
35.877574 1
4.2%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean128.58621
Minimum128.54145
Maximum128.60495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:25.101483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.54145
5-th percentile128.54463
Q1128.58548
median128.59353
Q3128.59933
95-th percentile128.60203
Maximum128.60495
Range0.0635
Interquartile range (IQR)0.013843

Descriptive statistics

Standard deviation0.020512458
Coefficient of variation (CV)0.000159523
Kurtosis1.8035709
Mean128.58621
Median Absolute Deviation (MAD)0.0060435
Skewness-1.6857535
Sum1543.0345
Variance0.00042076094
MonotonicityNot monotonic
2023-12-12T22:18:25.256396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
128.59927 1
 
4.2%
128.585819 1
 
4.2%
128.592746 1
 
4.2%
128.594307 1
 
4.2%
128.584478 1
 
4.2%
128.604948 1
 
4.2%
128.541448 1
 
4.2%
128.547225 1
 
4.2%
128.599643 1
 
4.2%
128.596693 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
128.541448 1
4.2%
128.547225 1
4.2%
128.584478 1
4.2%
128.585819 1
4.2%
128.588426 1
4.2%
128.592746 1
4.2%
128.594307 1
4.2%
128.596693 1
4.2%
128.59927 1
4.2%
128.599497 1
4.2%
ValueCountFrequency (%)
128.604948 1
4.2%
128.599643 1
4.2%
128.599497 1
4.2%
128.59927 1
4.2%
128.596693 1
4.2%
128.594307 1
4.2%
128.592746 1
4.2%
128.588426 1
4.2%
128.585819 1
4.2%
128.584478 1
4.2%

설치년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)58.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean1998.9167
Minimum1971
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:25.396256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1971
5-th percentile1982
Q11991
median1999.5
Q32005.5
95-th percentile2017.35
Maximum2019
Range48
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation13.581125
Coefficient of variation (CV)0.006794243
Kurtosis0.3955643
Mean1998.9167
Median Absolute Deviation (MAD)8.5
Skewness-0.24552072
Sum23987
Variance184.44697
MonotonicityNot monotonic
2023-12-12T22:18:25.524226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1991 4
 
16.7%
2016 2
 
8.3%
2000 2
 
8.3%
1971 1
 
4.2%
2002 1
 
4.2%
1999 1
 
4.2%
2019 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
1971 1
 
4.2%
1991 4
16.7%
1999 1
 
4.2%
2000 2
8.3%
2002 1
 
4.2%
2016 2
8.3%
2019 1
 
4.2%
ValueCountFrequency (%)
2019 1
 
4.2%
2016 2
8.3%
2002 1
 
4.2%
2000 2
8.3%
1999 1
 
4.2%
1991 4
16.7%
1971 1
 
4.2%

모터펌프1 용량(KW)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)58.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean49.583333
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:25.653772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q130
median50
Q360
95-th percentile86.25
Maximum100
Range80
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.592982
Coefficient of variation (CV)0.47582484
Kurtosis0.41573549
Mean49.583333
Median Absolute Deviation (MAD)15
Skewness0.66687556
Sum595
Variance556.62879
MonotonicityNot monotonic
2023-12-12T22:18:25.784204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
60 3
 
12.5%
20 2
 
8.3%
50 2
 
8.3%
30 2
 
8.3%
40 1
 
4.2%
75 1
 
4.2%
100 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
20 2
8.3%
30 2
8.3%
40 1
 
4.2%
50 2
8.3%
60 3
12.5%
75 1
 
4.2%
100 1
 
4.2%
ValueCountFrequency (%)
100 1
 
4.2%
75 1
 
4.2%
60 3
12.5%
50 2
8.3%
40 1
 
4.2%
30 2
8.3%
20 2
8.3%

모터펌프1 수(대)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
12 
3
6 
1
3 
2
2 
6
 
1

Length

Max length4
Median length2.5
Mean length2.5
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row1
2nd row1
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
<NA> 12
50.0%
3 6
25.0%
1 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%

Length

2023-12-12T22:18:25.948279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:26.084602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
50.0%
3 6
25.0%
1 3
 
12.5%
2 2
 
8.3%
6 1
 
4.2%

모터펌프2 용량(KW)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
21 
100
 
1
75
 
1
25
 
1

Length

Max length4
Median length4
Mean length3.7916667
Min length2

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row100
2nd row75
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
87.5%
100 1
 
4.2%
75 1
 
4.2%
25 1
 
4.2%

Length

2023-12-12T22:18:26.245326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:26.401425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
87.5%
100 1
 
4.2%
75 1
 
4.2%
25 1
 
4.2%

모터펌프2 수(대)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
21 
3
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.625
Min length1

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row3
2nd row3
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
87.5%
3 2
 
8.3%
4 1
 
4.2%

Length

2023-12-12T22:18:26.863813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:26.993240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
87.5%
3 2
 
8.3%
4 1
 
4.2%

한전전원공급방식
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
1회선
12 
<NA>
12 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1회선
2nd row1회선
3rd row1회선
4th row1회선
5th row1회선

Common Values

ValueCountFrequency (%)
1회선 12
50.0%
<NA> 12
50.0%

Length

2023-12-12T22:18:27.129413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:27.231368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1회선 12
50.0%
na 12
50.0%

비상발전기 용량(KW)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)58.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean181.91667
Minimum105
Maximum273
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:27.336049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum105
5-th percentile112.15
Q1148.75
median171
Q3227
95-th percentile273
Maximum273
Range168
Interquartile range (IQR)78.25

Descriptive statistics

Standard deviation57.425776
Coefficient of variation (CV)0.31567078
Kurtosis-0.91689041
Mean181.91667
Median Absolute Deviation (MAD)53
Skewness0.39325203
Sum2183
Variance3297.7197
MonotonicityNot monotonic
2023-12-12T22:18:27.482296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
227 2
 
8.3%
182 2
 
8.3%
273 2
 
8.3%
118 2
 
8.3%
159 2
 
8.3%
160 1
 
4.2%
105 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
105 1
4.2%
118 2
8.3%
159 2
8.3%
160 1
4.2%
182 2
8.3%
227 2
8.3%
273 2
8.3%
ValueCountFrequency (%)
273 2
8.3%
227 2
8.3%
182 2
8.3%
160 1
4.2%
159 2
8.3%
118 2
8.3%
105 1
4.2%

비상발전기 수(대)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
1
12 
<NA>
12 

Length

Max length4
Median length2.5
Mean length2.5
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 12
50.0%
<NA> 12
50.0%

Length

2023-12-12T22:18:27.649898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:27.774080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 12
50.0%
na 12
50.0%

한전계약전력(KW)
Text

MISSING 

Distinct12
Distinct (%)92.3%
Missing11
Missing (%)45.8%
Memory size324.0 B
2023-12-12T22:18:27.922868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.6153846
Min length1

Characters and Unicode

Total characters34
Distinct characters10
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

Unique11 ?
Unique (%)84.6%

Sample

1st row300
2nd row250
3rd row200
4th row200
5th row140
ValueCountFrequency (%)
200 2
16.7%
300 1
8.3%
250 1
8.3%
140 1
8.3%
110 1
8.3%
83 1
8.3%
130 1
8.3%
150 1
8.3%
45 1
8.3%
600 1
8.3%
2023-12-12T22:18:28.207010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
41.2%
1 5
 
14.7%
2 3
 
8.8%
3 3
 
8.8%
5 3
 
8.8%
4 2
 
5.9%
8 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
1
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33
97.1%
Space Separator 1
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
42.4%
1 5
 
15.2%
2 3
 
9.1%
3 3
 
9.1%
5 3
 
9.1%
4 2
 
6.1%
8 1
 
3.0%
6 1
 
3.0%
9 1
 
3.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
41.2%
1 5
 
14.7%
2 3
 
8.8%
3 3
 
8.8%
5 3
 
8.8%
4 2
 
5.9%
8 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
41.2%
1 5
 
14.7%
2 3
 
8.8%
3 3
 
8.8%
5 3
 
8.8%
4 2
 
5.9%
8 1
 
2.9%
6 1
 
2.9%
9 1
 
2.9%
1
 
2.9%

한전계약전력 구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
12 
고압
6 
저압
6 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고압
2nd row고압
3rd row고압
4th row고압
5th row저압

Common Values

ValueCountFrequency (%)
<NA> 12
50.0%
고압 6
25.0%
저압 6
25.0%

Length

2023-12-12T22:18:28.347277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:18:28.462085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 12
50.0%
고압 6
25.0%
저압 6
25.0%

연평균 사용전력량(KWH)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean13207.25
Minimum1592
Maximum28860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:28.557422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1592
5-th percentile3532.95
Q17921.25
median13803.5
Q317467.25
95-th percentile23746.1
Maximum28860
Range27268
Interquartile range (IQR)9546

Descriptive statistics

Standard deviation7551.5234
Coefficient of variation (CV)0.57177107
Kurtosis0.24404059
Mean13207.25
Median Absolute Deviation (MAD)4735.5
Skewness0.43116683
Sum158487
Variance57025506
MonotonicityNot monotonic
2023-12-12T22:18:28.672817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
15620 1
 
4.2%
8534 1
 
4.2%
9602 1
 
4.2%
17305 1
 
4.2%
28860 1
 
4.2%
17954 1
 
4.2%
5121 1
 
4.2%
19562 1
 
4.2%
11987 1
 
4.2%
1592 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
1592 1
4.2%
5121 1
4.2%
6083 1
4.2%
8534 1
4.2%
9602 1
4.2%
11987 1
4.2%
15620 1
4.2%
16267 1
4.2%
17305 1
4.2%
17954 1
4.2%
ValueCountFrequency (%)
28860 1
4.2%
19562 1
4.2%
17954 1
4.2%
17305 1
4.2%
16267 1
4.2%
15620 1
4.2%
11987 1
4.2%
9602 1
4.2%
8534 1
4.2%
6083 1
4.2%

최대 사용전력량(KWH)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean1570.5
Minimum160
Maximum3014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:28.774051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160
5-th percentile463.05
Q1915.25
median1427.5
Q32187.25
95-th percentile2941.4
Maximum3014
Range2854
Interquartile range (IQR)1272

Descriptive statistics

Standard deviation907.99544
Coefficient of variation (CV)0.57815692
Kurtosis-1.0103716
Mean1570.5
Median Absolute Deviation (MAD)705
Skewness0.22931056
Sum18846
Variance824455.73
MonotonicityNot monotonic
2023-12-12T22:18:28.872702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1420 1
 
4.2%
1018 1
 
4.2%
979 1
 
4.2%
2347 1
 
4.2%
3014 1
 
4.2%
2882 1
 
4.2%
724 1
 
4.2%
2134 1
 
4.2%
1435 1
 
4.2%
160 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
160 1
4.2%
711 1
4.2%
724 1
4.2%
979 1
4.2%
1018 1
4.2%
1420 1
4.2%
1435 1
4.2%
2022 1
4.2%
2134 1
4.2%
2347 1
4.2%
ValueCountFrequency (%)
3014 1
4.2%
2882 1
4.2%
2347 1
4.2%
2134 1
4.2%
2022 1
4.2%
1435 1
4.2%
1420 1
4.2%
1018 1
4.2%
979 1
4.2%
724 1
4.2%

최소 사용전력량(KWH)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean767.08333
Minimum111
Maximum1478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T22:18:28.978983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile112.65
Q1570.5
median767
Q31036.75
95-th percentile1342.7
Maximum1478
Range1367
Interquartile range (IQR)466.25

Descriptive statistics

Standard deviation424.90073
Coefficient of variation (CV)0.5539173
Kurtosis-0.55291223
Mean767.08333
Median Absolute Deviation (MAD)279.5
Skewness-0.081086621
Sum9205
Variance180540.63
MonotonicityNot monotonic
2023-12-12T22:18:29.136274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
984 1
 
4.2%
619 1
 
4.2%
732 1
 
4.2%
1195 1
 
4.2%
1478 1
 
4.2%
802 1
 
4.2%
111 1
 
4.2%
1232 1
 
4.2%
623 1
 
4.2%
114 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
111 1
4.2%
114 1
4.2%
425 1
4.2%
619 1
4.2%
623 1
4.2%
732 1
4.2%
802 1
4.2%
890 1
4.2%
984 1
4.2%
1195 1
4.2%
ValueCountFrequency (%)
1478 1
4.2%
1232 1
4.2%
1195 1
4.2%
984 1
4.2%
890 1
4.2%
802 1
4.2%
732 1
4.2%
623 1
4.2%
619 1
4.2%
425 1
4.2%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)8.3%
Missing12
Missing (%)50.0%
Memory size324.0 B
Minimum2023-05-23 00:00:00
Maximum2023-05-23 00:00:00
2023-12-12T22:18:29.258254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:29.345097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T22:18:22.528107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.444915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.305991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.150248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.921278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.755497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.863094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.753463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.624079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.540728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.404400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.237201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.018476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.870347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.964039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.842678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.714539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.630916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.514596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.325222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.125378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.974964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.094462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.937816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.797704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.727060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.627774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.417276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.217388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.066179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.210817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.037949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.900086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.856973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.728411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.517911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.332843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.169456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.334070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.143870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.989496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:16.966711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.830652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.609973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.432491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.555249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.461747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.239069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:23.077904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.085555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.931392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.692884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.534283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.659524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.552668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.347742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:23.174282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:17.191428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.042048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:18.813393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:19.640178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:20.764953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:21.635842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:18:22.436070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:18:29.429707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
펌프장명소재지지번주소위도경도설치년도모터펌프1 용량(KW)모터펌프1 수(대)모터펌프2 용량(KW)모터펌프2 수(대)비상발전기 용량(KW)한전계약전력(KW)한전계약전력 구분연평균 사용전력량(KWH)최대 사용전력량(KWH)최소 사용전력량(KWH)
펌프장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.0001.0000.0000.0001.0001.0000.2230.9220.3810.7730.5000.728
경도1.0001.0000.0001.0000.0000.6210.0001.0001.0000.2891.0000.0000.0000.5290.462
설치년도1.0001.0001.0000.0001.0000.4460.628NaNNaN0.4931.0000.7420.9000.7800.928
모터펌프1 용량(KW)1.0001.0000.0000.6210.4461.0000.4241.0000.0000.5470.7510.4400.5250.6700.689
모터펌프1 수(대)1.0001.0000.0000.0000.6280.4241.000NaNNaN0.0001.0000.2890.0000.0000.430
모터펌프2 용량(KW)1.0001.0001.0001.000NaN1.000NaN1.0001.0001.0001.0001.0001.0001.0001.000
모터펌프2 수(대)1.0001.0001.0001.000NaN0.000NaN1.0001.0001.0001.0000.0001.0001.0001.000
비상발전기 용량(KW)1.0001.0000.2230.2890.4930.5470.0001.0001.0001.0000.8060.6840.0000.6720.790
한전계약전력(KW)1.0001.0000.9221.0001.0000.7511.0001.0001.0000.8061.0001.0000.8890.5110.778
한전계약전력 구분1.0001.0000.3810.0000.7420.4400.2891.0000.0000.6841.0001.0000.6370.7141.000
연평균 사용전력량(KWH)1.0001.0000.7730.0000.9000.5250.0001.0001.0000.0000.8890.6371.0000.6300.638
최대 사용전력량(KWH)1.0001.0000.5000.5290.7800.6700.0001.0001.0000.6720.5110.7140.6301.0000.716
최소 사용전력량(KWH)1.0001.0000.7280.4620.9280.6890.4301.0001.0000.7900.7781.0000.6380.7161.000
2023-12-12T22:18:29.625885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
한전전원공급방식모터펌프2 수(대)한전계약전력 구분모터펌프1 수(대)비상발전기 수(대)모터펌프2 용량(KW)
한전전원공급방식1.0001.0001.0001.0001.0001.000
모터펌프2 수(대)1.0001.0000.0001.0001.0001.000
한전계약전력 구분1.0000.0001.0000.0751.0001.000
모터펌프1 수(대)1.0001.0000.0751.0001.0001.000
비상발전기 수(대)1.0001.0001.0001.0001.0001.000
모터펌프2 용량(KW)1.0001.0001.0001.0001.0001.000
2023-12-12T22:18:29.800507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치년도모터펌프1 용량(KW)비상발전기 용량(KW)연평균 사용전력량(KWH)최대 사용전력량(KWH)최소 사용전력량(KWH)모터펌프1 수(대)모터펌프2 용량(KW)모터펌프2 수(대)한전전원공급방식비상발전기 수(대)한전계약전력 구분
위도1.000-0.154-0.2790.021-0.473-0.483-0.615-0.4060.0001.0001.0001.0001.0000.204
경도-0.1541.0000.0070.4250.325-0.077-0.035-0.1400.0001.0001.0001.0001.0000.000
설치년도-0.2790.0071.0000.392-0.0250.0540.114-0.1430.0001.0001.0001.0001.0000.425
모터펌프1 용량(KW)0.0210.4250.3921.0000.3110.2940.3220.1170.0341.0000.0001.0001.0000.269
비상발전기 용량(KW)-0.4730.325-0.0250.3111.0000.5010.4970.4440.0001.0001.0001.0001.0000.675
연평균 사용전력량(KWH)-0.483-0.0770.0540.2940.5011.0000.9580.9370.0001.0001.0001.0001.0000.269
최대 사용전력량(KWH)-0.615-0.0350.1140.3220.4970.9581.0000.8460.0001.0001.0001.0001.0000.465
최소 사용전력량(KWH)-0.406-0.140-0.1430.1170.4440.9370.8461.0000.0001.0001.0001.0001.0000.548
모터펌프1 수(대)0.0000.0000.0000.0340.0000.0000.0000.0001.0001.0001.0001.0001.0000.075
모터펌프2 용량(KW)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
모터펌프2 수(대)1.0001.0001.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
한전전원공급방식1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
비상발전기 수(대)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
한전계약전력 구분0.2040.0000.4250.2690.6750.2690.4650.5480.0751.0000.0001.0001.0001.000

Missing values

2023-12-12T22:18:23.296863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:18:23.491209image/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.
2023-12-12T22:18:23.692580image/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

펌프장명소재지지번주소위도경도설치년도모터펌프1 용량(KW)모터펌프1 수(대)모터펌프2 용량(KW)모터펌프2 수(대)한전전원공급방식비상발전기 용량(KW)비상발전기 수(대)한전계약전력(KW)한전계약전력 구분연평균 사용전력량(KWH)최대 사용전력량(KWH)최소 사용전력량(KWH)데이터기준일자
0도청교 지하차도 배수펌프장대구광역시 북구 침산동 2235.887858128.59927199120110031회선2271300고압1562014209842023-05-23
1성북교 지하차도 배수펌프장대구광역시 북구 침산동 166235.897832128.58581919912017531회선1821250고압853410186192023-05-23
2침산교 지하차도 배수펌프장대구광역시 북구 침산동 663-835.899224128.5927461991403<NA><NA>1회선1601200고압96029797322023-05-23
3대구역 지하차도 배수펌프장대구광역시 북구 칠성동2가 30235.877574128.5943071971503<NA><NA>1회선1821200고압17305234711952023-05-23
4원대 지하차도 배수펌프장대구광역시 중구 태평로3가 25135.878058128.5844782016753<NA><NA>1회선2731140저압28860301414782023-05-23
5칠성 지하차도 배수펌프장대구광역시 북구 칠성동1가 302-24135.876036128.60494820161002<NA><NA>1회선2731110저압1795428828022023-05-23
6매천 지하차도 배수펌프장대구광역시 북구 매천동 543-935.904121128.5414482002303<NA><NA>1회선118183저압51217241112023-05-23
7칠곡 지하차도 배수펌프장대구광역시 북구 관음동 99135.944232128.54722519915012541회선1591130저압19562213412322023-05-23
8무태 지하차도 배수펌프장대구광역시 북구 서변동 1290-4735.912796128.5996431999603<NA><NA>1회선1591150고압1198714356232023-05-23
9고촌 지하차도 배수펌프장대구광역시 북구 서변동 172635.921527128.5966932000602<NA><NA>1회선105145저압15921601142023-05-23
펌프장명소재지지번주소위도경도설치년도모터펌프1 용량(KW)모터펌프1 수(대)모터펌프2 용량(KW)모터펌프2 수(대)한전전원공급방식비상발전기 용량(KW)비상발전기 수(대)한전계약전력(KW)한전계약전력 구분연평균 사용전력량(KWH)최대 사용전력량(KWH)최소 사용전력량(KWH)데이터기준일자
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Duplicate rows

Most frequently occurring

펌프장명소재지지번주소위도경도설치년도모터펌프1 용량(KW)모터펌프1 수(대)모터펌프2 용량(KW)모터펌프2 수(대)한전전원공급방식비상발전기 용량(KW)비상발전기 수(대)한전계약전력(KW)한전계약전력 구분연평균 사용전력량(KWH)최대 사용전력량(KWH)최소 사용전력량(KWH)데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11