Overview

Dataset statistics

Number of variables19
Number of observations181
Missing cells203
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.9 KiB
Average record size in memory157.7 B

Variable types

Categorical8
Text8
Numeric3

Alerts

엔진펌프대수(대) is highly overall correlated with 소재지우편번호 and 7 other fieldsHigh correlation
엔진펌프규모(kW(HP)) is highly overall correlated with 소재지우편번호 and 6 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 5 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 6 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
시군명 is highly overall correlated with 소재지우편번호 and 8 other fieldsHigh correlation
설치목적 is highly overall correlated with 소재지우편번호 and 8 other fieldsHigh correlation
모터펌프대수(대) is highly overall correlated with 엔진펌프대수(대)High correlation
한전전원공급방식(1회선 또는 2회선) is highly overall correlated with 시군명 and 4 other fieldsHigh correlation
비상발전기(kW(HP)) is highly overall correlated with 소재지우편번호 and 7 other fieldsHigh correlation
비상발전기대수(대) is highly overall correlated with WGS84위도 and 4 other fieldsHigh correlation
엔진펌프규모(kW(HP)) is highly imbalanced (64.1%)Imbalance
엔진펌프대수(대) is highly imbalanced (57.9%)Imbalance
비상발전기(kW(HP)) is highly imbalanced (59.4%)Imbalance
소재지우편번호 has 20 (11.0%) missing valuesMissing
소재지도로명주소 has 78 (43.1%) missing valuesMissing
WGS84위도 has 7 (3.9%) missing valuesMissing
WGS84경도 has 7 (3.9%) missing valuesMissing
사용전력량(kWH)/년 (최대/최소,고압/저압) has 46 (25.4%) missing valuesMissing
전기요금(원)/년 (최대/최소,고압/저압) has 43 (23.8%) missing valuesMissing

Reproduction

Analysis started2024-04-29 12:57:20.782943
Analysis finished2024-04-29 12:57:25.227480
Duration4.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
파주시
23 
광주시
15 
연천군
15 
동두천시
14 
김포시
11 
Other values (20)
103 

Length

Max length4
Median length3
Mean length3.160221
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row가평군
2nd row가평군
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
파주시 23
 
12.7%
광주시 15
 
8.3%
연천군 15
 
8.3%
동두천시 14
 
7.7%
김포시 11
 
6.1%
양평군 10
 
5.5%
남양주시 8
 
4.4%
고양시 8
 
4.4%
여주시 7
 
3.9%
광명시 7
 
3.9%
Other values (15) 63
34.8%

Length

2024-04-29T21:57:25.294708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
파주시 23
 
12.7%
연천군 15
 
8.3%
광주시 15
 
8.3%
동두천시 14
 
7.7%
김포시 11
 
6.1%
양평군 10
 
5.5%
남양주시 8
 
4.4%
고양시 8
 
4.4%
여주시 7
 
3.9%
광명시 7
 
3.9%
Other values (15) 63
34.8%

시설명
Text

Distinct174
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-29T21:57:25.467552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.2099448
Min length2

Characters and Unicode

Total characters1305
Distinct characters161
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)93.4%

Sample

1st row가평배수펌프장
2nd row청평배수펌프장
3rd row강매배수펌프장
4th row구산배수펌프장
5th row대화1.2배수펌프장
ValueCountFrequency (%)
배수펌프장 19
 
9.2%
초성배수펌프장 3
 
1.4%
두일배수펌프장 3
 
1.4%
간이펌프장 2
 
1.0%
통복 2
 
1.0%
신장 2
 
1.0%
군남배수펌프장 2
 
1.0%
덕천빗물배수펌프장 2
 
1.0%
연천배수펌프장 2
 
1.0%
가정배수펌프장 1
 
0.5%
Other values (169) 169
81.6%
2024-04-29T21:57:25.791912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 161
 
12.3%
펌 141
 
10.8%
프 141
 
10.8%
수 127
 
9.7%
배 120
 
9.2%
26
 
2.0%
물 22
 
1.7%
빗 22
 
1.7%
1 21
 
1.6%
2 21
 
1.6%
Other values (151) 503
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1195
91.6%
Decimal Number 65
 
5.0%
Space Separator 26
 
2.0%
Close Punctuation 5
 
0.4%
Open Punctuation 5
 
0.4%
Other Punctuation 5
 
0.4%
Uppercase Letter 3
 
0.2%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 161
 
13.5%
펌 141
 
11.8%
프 141
 
11.8%
수 127
 
10.6%
배 120
 
10.0%
물 22
 
1.8%
빗 22
 
1.8%
산 19
 
1.6%
동 17
 
1.4%
이 16
 
1.3%
Other values (137) 409
34.2%
Decimal Number
ValueCountFrequency (%)
1 21
32.3%
2 21
32.3%
3 15
23.1%
4 6
 
9.2%
5 2
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
G 1
33.3%
W 1
33.3%
P 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
. 2
40.0%
Space Separator
ValueCountFrequency (%)
26
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1194
91.5%
Common 107
 
8.2%
Latin 3
 
0.2%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 161
 
13.5%
펌 141
 
11.8%
프 141
 
11.8%
수 127
 
10.6%
배 120
 
10.1%
물 22
 
1.8%
빗 22
 
1.8%
산 19
 
1.6%
동 17
 
1.4%
이 16
 
1.3%
Other values (136) 408
34.2%
Common
ValueCountFrequency (%)
26
24.3%
1 21
19.6%
2 21
19.6%
3 15
14.0%
4 6
 
5.6%
) 5
 
4.7%
( 5
 
4.7%
, 3
 
2.8%
5 2
 
1.9%
. 2
 
1.9%
Latin
ValueCountFrequency (%)
G 1
33.3%
W 1
33.3%
P 1
33.3%
Han
ValueCountFrequency (%)
小 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1194
91.5%
ASCII 110
 
8.4%
CJK 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 161
 
13.5%
펌 141
 
11.8%
프 141
 
11.8%
수 127
 
10.6%
배 120
 
10.1%
물 22
 
1.8%
빗 22
 
1.8%
산 19
 
1.6%
동 17
 
1.4%
이 16
 
1.3%
Other values (136) 408
34.2%
ASCII
ValueCountFrequency (%)
26
23.6%
1 21
19.1%
2 21
19.1%
3 15
13.6%
4 6
 
5.5%
) 5
 
4.5%
( 5
 
4.5%
, 3
 
2.7%
5 2
 
1.8%
. 2
 
1.8%
Other values (4) 4
 
3.6%
CJK
ValueCountFrequency (%)
小 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct135
Distinct (%)83.9%
Missing20
Missing (%)11.0%
Infinite0
Infinite (%)0.0%
Mean12698.82
Minimum10012
Maximum18596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-29T21:57:25.947888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10012
5-th percentile10200
Q111017
median12211
Q313820
95-th percentile17878
Maximum18596
Range8584
Interquartile range (IQR)2803

Descriptive statistics

Standard deviation2264.8755
Coefficient of variation (CV)0.17835323
Kurtosis0.56502437
Mean12698.82
Median Absolute Deviation (MAD)1197
Skewness1.2176066
Sum2044510
Variance5129661.2
MonotonicityNot monotonic
2024-04-29T21:57:26.088609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11023 3
 
1.7%
12705 3
 
1.7%
11048 3
 
1.7%
10824 3
 
1.7%
11049 2
 
1.1%
11324 2
 
1.1%
13902 2
 
1.1%
10013 2
 
1.1%
10092 2
 
1.1%
11337 2
 
1.1%
Other values (125) 137
75.7%
(Missing) 20
 
11.0%
ValueCountFrequency (%)
10012 1
0.6%
10013 2
1.1%
10077 1
0.6%
10092 2
1.1%
10094 1
0.6%
10121 1
0.6%
10200 1
0.6%
10203 1
0.6%
10426 1
0.6%
10427 1
0.6%
ValueCountFrequency (%)
18596 1
0.6%
18554 1
0.6%
18347 1
0.6%
18260 1
0.6%
18116 1
0.6%
18115 1
0.6%
17923 1
0.6%
17892 1
0.6%
17878 1
0.6%
17548 1
0.6%

소재지도로명주소
Text

MISSING 

Distinct97
Distinct (%)94.2%
Missing78
Missing (%)43.1%
Memory size1.5 KiB
2024-04-29T21:57:26.369465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length25
Mean length19.708738
Min length13

Characters and Unicode

Total characters2030
Distinct characters153
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)90.3%

Sample

1st row경기도 가평군 가평읍 가평제방길 97
2nd row경기도 가평군 청평면 강변로 17
3rd row경기도 고양시 덕양구 강매로 103
4th row경기도 고양시 일산서구 이산포길 664
5th row경기도 고양시 일산서구 멱절길 72
ValueCountFrequency (%)
경기도 103
 
21.7%
연천군 13
 
2.7%
파주시 11
 
2.3%
고양시 8
 
1.7%
동두천시 8
 
1.7%
이천시 7
 
1.5%
김포시 7
 
1.5%
남양주시 6
 
1.3%
광명시 6
 
1.3%
광주시 5
 
1.1%
Other values (222) 301
63.4%
2024-04-29T21:57:26.824572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
372
 
18.3%
경 106
 
5.2%
도 104
 
5.1%
기 103
 
5.1%
시 91
 
4.5%
로 86
 
4.2%
1 86
 
4.2%
2 45
 
2.2%
천 43
 
2.1%
3 42
 
2.1%
Other values (143) 952
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1230
60.6%
Decimal Number 386
 
19.0%
Space Separator 372
 
18.3%
Dash Punctuation 29
 
1.4%
Close Punctuation 6
 
0.3%
Open Punctuation 6
 
0.3%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 106
 
8.6%
도 104
 
8.5%
기 103
 
8.4%
시 91
 
7.4%
로 86
 
7.0%
천 43
 
3.5%
길 38
 
3.1%
양 31
 
2.5%
주 28
 
2.3%
산 22
 
1.8%
Other values (128) 578
47.0%
Decimal Number
ValueCountFrequency (%)
1 86
22.3%
2 45
11.7%
3 42
10.9%
8 42
10.9%
7 34
 
8.8%
5 30
 
7.8%
0 30
 
7.8%
6 29
 
7.5%
4 26
 
6.7%
9 22
 
5.7%
Space Separator
ValueCountFrequency (%)
372
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1230
60.6%
Common 800
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 106
 
8.6%
도 104
 
8.5%
기 103
 
8.4%
시 91
 
7.4%
로 86
 
7.0%
천 43
 
3.5%
길 38
 
3.1%
양 31
 
2.5%
주 28
 
2.3%
산 22
 
1.8%
Other values (128) 578
47.0%
Common
ValueCountFrequency (%)
372
46.5%
1 86
 
10.8%
2 45
 
5.6%
3 42
 
5.2%
8 42
 
5.2%
7 34
 
4.2%
5 30
 
3.8%
0 30
 
3.8%
- 29
 
3.6%
6 29
 
3.6%
Other values (5) 61
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1230
60.6%
ASCII 800
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
372
46.5%
1 86
 
10.8%
2 45
 
5.6%
3 42
 
5.2%
8 42
 
5.2%
7 34
 
4.2%
5 30
 
3.8%
0 30
 
3.8%
- 29
 
3.6%
6 29
 
3.6%
Other values (5) 61
 
7.6%
Hangul
ValueCountFrequency (%)
경 106
 
8.6%
도 104
 
8.5%
기 103
 
8.4%
시 91
 
7.4%
로 86
 
7.0%
천 43
 
3.5%
길 38
 
3.1%
양 31
 
2.5%
주 28
 
2.3%
산 22
 
1.8%
Other values (128) 578
47.0%

소재지지번주소
Text

Distinct174
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-29T21:57:27.086131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length19.812155
Min length13

Characters and Unicode

Total characters3586
Distinct characters166
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique169 ?
Unique (%)93.4%

Sample

1st row경기도 가평군 가평읍 대곡리 11번지
2nd row경기도 가평군 청평면 청평리 619-39번지
3rd row경기도 고양시 덕양구 강매동 290-2번지
4th row경기도 고양시 일산서구 구산동 672-8번지
5th row경기도 고양시 일산서구 법곳동 740-11번지
ValueCountFrequency (%)
경기도 181
 
21.8%
파주시 23
 
2.8%
연천군 15
 
1.8%
광주시 15
 
1.8%
동두천시 14
 
1.7%
김포시 11
 
1.3%
양평군 10
 
1.2%
문산읍 9
 
1.1%
남양주시 8
 
1.0%
고양시 8
 
1.0%
Other values (369) 536
64.6%
2024-04-29T21:57:27.483383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
649
 
18.1%
도 184
 
5.1%
경 182
 
5.1%
기 181
 
5.0%
시 159
 
4.4%
- 140
 
3.9%
1 132
 
3.7%
동 115
 
3.2%
리 91
 
2.5%
2 87
 
2.4%
Other values (156) 1666
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2085
58.1%
Decimal Number 711
 
19.8%
Space Separator 649
 
18.1%
Dash Punctuation 140
 
3.9%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 184
 
8.8%
경 182
 
8.7%
기 181
 
8.7%
시 159
 
7.6%
동 115
 
5.5%
리 91
 
4.4%
양 59
 
2.8%
주 59
 
2.8%
천 54
 
2.6%
지 52
 
2.5%
Other values (143) 949
45.5%
Decimal Number
ValueCountFrequency (%)
1 132
18.6%
2 87
12.2%
4 83
11.7%
6 69
9.7%
3 66
9.3%
5 64
9.0%
7 55
7.7%
0 54
7.6%
8 52
 
7.3%
9 49
 
6.9%
Space Separator
ValueCountFrequency (%)
649
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 140
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2085
58.1%
Common 1500
41.8%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 184
 
8.8%
경 182
 
8.7%
기 181
 
8.7%
시 159
 
7.6%
동 115
 
5.5%
리 91
 
4.4%
양 59
 
2.8%
주 59
 
2.8%
천 54
 
2.6%
지 52
 
2.5%
Other values (143) 949
45.5%
Common
ValueCountFrequency (%)
649
43.3%
- 140
 
9.3%
1 132
 
8.8%
2 87
 
5.8%
4 83
 
5.5%
6 69
 
4.6%
3 66
 
4.4%
5 64
 
4.3%
7 55
 
3.7%
0 54
 
3.6%
Other values (2) 101
 
6.7%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2085
58.1%
ASCII 1501
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
649
43.2%
- 140
 
9.3%
1 132
 
8.8%
2 87
 
5.8%
4 83
 
5.5%
6 69
 
4.6%
3 66
 
4.4%
5 64
 
4.3%
7 55
 
3.7%
0 54
 
3.6%
Other values (3) 102
 
6.8%
Hangul
ValueCountFrequency (%)
도 184
 
8.8%
경 182
 
8.7%
기 181
 
8.7%
시 159
 
7.6%
동 115
 
5.5%
리 91
 
4.4%
양 59
 
2.8%
주 59
 
2.8%
천 54
 
2.6%
지 52
 
2.5%
Other values (143) 949
45.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct167
Distinct (%)96.0%
Missing7
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean37.581555
Minimum36.97694
Maximum38.089363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-29T21:57:27.623466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.97694
5-th percentile37.110554
Q137.406539
median37.59417
Q337.820995
95-th percentile38.004786
Maximum38.089363
Range1.1124232
Interquartile range (IQR)0.41445614

Descriptive statistics

Standard deviation0.27489024
Coefficient of variation (CV)0.0073144989
Kurtosis-0.6739611
Mean37.581555
Median Absolute Deviation (MAD)0.19849667
Skewness-0.16906612
Sum6539.1906
Variance0.075564646
MonotonicityNot monotonic
2024-04-29T21:57:27.771692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0142288322 3
 
1.7%
38.0047857444 3
 
1.7%
37.3888648328 2
 
1.1%
38.0617814484 2
 
1.1%
37.8565588989 2
 
1.1%
37.7486950508 1
 
0.6%
38.0024484459 1
 
0.6%
38.0018171802 1
 
0.6%
37.7279598885 1
 
0.6%
38.059951913 1
 
0.6%
Other values (157) 157
86.7%
(Missing) 7
 
3.9%
ValueCountFrequency (%)
36.9769397 1
0.6%
36.9851845 1
0.6%
36.9866865 1
0.6%
36.996553 1
0.6%
36.9991087 1
0.6%
37.0030529935 1
0.6%
37.0104876113 1
0.6%
37.0935943 1
0.6%
37.1012956722 1
0.6%
37.1155392262 1
0.6%
ValueCountFrequency (%)
38.0893628968 1
 
0.6%
38.0617814484 2
1.1%
38.059951913 1
 
0.6%
38.0237907214 1
 
0.6%
38.0142288322 3
1.7%
38.0047857444 3
1.7%
38.0024484459 1
 
0.6%
38.0018171802 1
 
0.6%
37.9873172447 1
 
0.6%
37.9724530782 1
 
0.6%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct167
Distinct (%)96.0%
Missing7
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean127.0501
Minimum126.52187
Maximum127.69274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-29T21:57:28.094662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.52187
5-th percentile126.70743
Q1126.83595
median127.05058
Q3127.19352
95-th percentile127.51475
Maximum127.69274
Range1.1708724
Interquartile range (IQR)0.35757671

Descriptive statistics

Standard deviation0.25576268
Coefficient of variation (CV)0.0020130853
Kurtosis-0.4636761
Mean127.0501
Median Absolute Deviation (MAD)0.1968121
Skewness0.53309594
Sum22106.717
Variance0.06541455
MonotonicityNot monotonic
2024-04-29T21:57:28.223623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9094942817 3
 
1.7%
127.0724496436 3
 
1.7%
126.9409561583 2
 
1.1%
127.0168362245 2
 
1.1%
126.7864274897 2
 
1.1%
127.0502423025 1
 
0.6%
126.9638523184 1
 
0.6%
126.9021550425 1
 
0.6%
127.0513663556 1
 
0.6%
127.0124824163 1
 
0.6%
Other values (157) 157
86.7%
(Missing) 7
 
3.9%
ValueCountFrequency (%)
126.52187097 1
0.6%
126.6568448158 1
0.6%
126.6587445045 1
0.6%
126.6693266954 1
0.6%
126.6765114043 1
0.6%
126.6829929542 1
0.6%
126.6842789485 1
0.6%
126.7011038952 1
0.6%
126.7046385241 1
0.6%
126.7089391663 1
0.6%
ValueCountFrequency (%)
127.6927434 1
0.6%
127.633822954 1
0.6%
127.6306729042 1
0.6%
127.6258319 1
0.6%
127.5563209685 1
0.6%
127.5477521 1
0.6%
127.5403942 1
0.6%
127.5360867 1
0.6%
127.5178309932 1
0.6%
127.5130930623 1
0.6%

설치일자
Text

Distinct91
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-29T21:57:28.464604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length6.8950276
Min length4

Characters and Unicode

Total characters1248
Distinct characters35
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)34.3%

Sample

1st row1995-10-20
2nd row1997-09-26
3rd row2000(‘12./’13수문펌프증설)
4th row1990
5th row1994 / 2015(증설)준공
ValueCountFrequency (%)
2001 12
 
6.1%
2011 11
 
5.6%
2007 9
 
4.6%
2003 7
 
3.6%
2012 5
 
2.6%
2012-05-01 5
 
2.6%
2014-05-01 5
 
2.6%
2013 5
 
2.6%
2009 4
 
2.0%
2000 4
 
2.0%
Other values (88) 129
65.8%
2024-04-29T21:57:28.823191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 363
29.1%
1 208
16.7%
2 206
16.5%
- 114
 
9.1%
9 78
 
6.2%
5 34
 
2.7%
3 30
 
2.4%
8 30
 
2.4%
7 27
 
2.2%
6 21
 
1.7%
Other values (25) 137
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1009
80.8%
Dash Punctuation 114
 
9.1%
Other Letter 58
 
4.6%
Other Punctuation 25
 
2.0%
Space Separator 16
 
1.3%
Close Punctuation 12
 
1.0%
Open Punctuation 12
 
1.0%
Initial Punctuation 1
 
0.1%
Final Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
년 14
24.1%
설 11
19.0%
증 8
13.8%
장 3
 
5.2%
확 3
 
5.2%
치 3
 
5.2%
공 2
 
3.4%
향 2
 
3.4%
준 2
 
3.4%
양 2
 
3.4%
Other values (6) 8
13.8%
Decimal Number
ValueCountFrequency (%)
0 363
36.0%
1 208
20.6%
2 206
20.4%
9 78
 
7.7%
5 34
 
3.4%
3 30
 
3.0%
8 30
 
3.0%
7 27
 
2.7%
6 21
 
2.1%
4 12
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 17
68.0%
, 5
 
20.0%
/ 3
 
12.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1190
95.4%
Hangul 58
 
4.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 363
30.5%
1 208
17.5%
2 206
17.3%
- 114
 
9.6%
9 78
 
6.6%
5 34
 
2.9%
3 30
 
2.5%
8 30
 
2.5%
7 27
 
2.3%
6 21
 
1.8%
Other values (9) 79
 
6.6%
Hangul
ValueCountFrequency (%)
년 14
24.1%
설 11
19.0%
증 8
13.8%
장 3
 
5.2%
확 3
 
5.2%
치 3
 
5.2%
공 2
 
3.4%
향 2
 
3.4%
준 2
 
3.4%
양 2
 
3.4%
Other values (6) 8
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1188
95.2%
Hangul 58
 
4.6%
Punctuation 2
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 363
30.6%
1 208
17.5%
2 206
17.3%
- 114
 
9.6%
9 78
 
6.6%
5 34
 
2.9%
3 30
 
2.5%
8 30
 
2.5%
7 27
 
2.3%
6 21
 
1.8%
Other values (7) 77
 
6.5%
Hangul
ValueCountFrequency (%)
년 14
24.1%
설 11
19.0%
증 8
13.8%
장 3
 
5.2%
확 3
 
5.2%
치 3
 
5.2%
공 2
 
3.4%
향 2
 
3.4%
준 2
 
3.4%
양 2
 
3.4%
Other values (6) 8
13.8%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

설치목적
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
침수방지
23 
침수예방
20 
도시침수방지
18 
침수피해예방
15 
내수배제
15 
Other values (20)
90 

Length

Max length12
Median length9
Mean length5.3646409
Min length2

Unique

Unique4 ?
Unique (%)2.2%

Sample

1st row도시침수방지
2nd row도시침수방지
3rd row내수배제
4th row내수배제
5th row내수배제

Common Values

ValueCountFrequency (%)
침수방지 23
12.7%
침수예방 20
11.0%
도시침수방지 18
 
9.9%
침수피해예방 15
 
8.3%
내수배제 15
 
8.3%
침수피해 예방 11
 
6.1%
<NA> 10
 
5.5%
홍수피해 예방 8
 
4.4%
배수용 7
 
3.9%
재해예방 6
 
3.3%
Other values (15) 48
26.5%

Length

2024-04-29T21:57:28.952692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
예방 30
13.0%
침수방지 28
12.2%
침수예방 20
 
8.7%
도시침수방지 18
 
7.8%
침수피해예방 15
 
6.5%
내수배제 15
 
6.5%
침수 11
 
4.8%
침수피해 11
 
4.8%
na 10
 
4.3%
홍수피해 8
 
3.5%
Other values (19) 64
27.8%

모터펌프규모(kW(HP))
Text

Distinct140
Distinct (%)77.8%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-04-29T21:57:29.176202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length31
Mean length6.4
Min length1

Characters and Unicode

Total characters1152
Distinct characters38
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)66.7%

Sample

1st row300
2nd row150×2 100×2
3rd row750HP, 100HP, 375HP, 375HP
4th row500HP
5th row670HP, 1,430HP
ValueCountFrequency (%)
10
 
4.3%
150 7
 
3.0%
75 5
 
2.2%
기존 5
 
2.2%
260 4
 
1.7%
120 4
 
1.7%
200 3
 
1.3%
22 3
 
1.3%
130 3
 
1.3%
74.5 3
 
1.3%
Other values (164) 184
79.7%
2024-04-29T21:57:29.549080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 221
19.2%
1 104
 
9.0%
5 104
 
9.0%
2 94
 
8.2%
3 65
 
5.6%
P 56
 
4.9%
H 56
 
4.9%
7 52
 
4.5%
51
 
4.4%
, 43
 
3.7%
Other values (28) 306
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 744
64.6%
Uppercase Letter 116
 
10.1%
Other Punctuation 93
 
8.1%
Space Separator 51
 
4.4%
Other Letter 49
 
4.3%
Open Punctuation 29
 
2.5%
Close Punctuation 29
 
2.5%
Math Symbol 19
 
1.6%
Lowercase Letter 18
 
1.6%
Connector Punctuation 3
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221
29.7%
1 104
14.0%
5 104
14.0%
2 94
12.6%
3 65
 
8.7%
7 52
 
7.0%
4 40
 
5.4%
6 37
 
5.0%
8 17
 
2.3%
9 10
 
1.3%
Other Letter
ValueCountFrequency (%)
대 18
36.7%
증 5
 
10.2%
기 5
 
10.2%
존 5
 
10.2%
설 5
 
10.2%
양 3
 
6.1%
운 3
 
6.1%
산 2
 
4.1%
향 2
 
4.1%
상 1
 
2.0%
Other Punctuation
ValueCountFrequency (%)
, 43
46.2%
* 28
30.1%
. 11
 
11.8%
: 10
 
10.8%
/ 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
P 56
48.3%
H 56
48.3%
W 3
 
2.6%
K 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
× 13
68.4%
+ 6
31.6%
Lowercase Letter
ValueCountFrequency (%)
k 10
55.6%
w 8
44.4%
Space Separator
ValueCountFrequency (%)
51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 969
84.1%
Latin 134
 
11.6%
Hangul 49
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221
22.8%
1 104
10.7%
5 104
10.7%
2 94
9.7%
3 65
 
6.7%
7 52
 
5.4%
51
 
5.3%
, 43
 
4.4%
4 40
 
4.1%
6 37
 
3.8%
Other values (12) 158
16.3%
Hangul
ValueCountFrequency (%)
대 18
36.7%
증 5
 
10.2%
기 5
 
10.2%
존 5
 
10.2%
설 5
 
10.2%
양 3
 
6.1%
운 3
 
6.1%
산 2
 
4.1%
향 2
 
4.1%
상 1
 
2.0%
Latin
ValueCountFrequency (%)
P 56
41.8%
H 56
41.8%
k 10
 
7.5%
w 8
 
6.0%
W 3
 
2.2%
K 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1090
94.6%
Hangul 49
 
4.3%
None 13
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221
20.3%
1 104
9.5%
5 104
9.5%
2 94
 
8.6%
3 65
 
6.0%
P 56
 
5.1%
H 56
 
5.1%
7 52
 
4.8%
51
 
4.7%
, 43
 
3.9%
Other values (17) 244
22.4%
Hangul
ValueCountFrequency (%)
대 18
36.7%
증 5
 
10.2%
기 5
 
10.2%
존 5
 
10.2%
설 5
 
10.2%
양 3
 
6.1%
운 3
 
6.1%
산 2
 
4.1%
향 2
 
4.1%
상 1
 
2.0%
None
ValueCountFrequency (%)
× 13
100.0%

모터펌프대수(대)
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
3
44 
2
34 
4
29 
5
12 
6
10 
Other values (31)
52 

Length

Max length23
Median length1
Mean length1.718232
Min length1

Unique

Unique23 ?
Unique (%)12.7%

Sample

1st row5
2nd row4
3rd row5, 2, 4, 2
4th row7
5th row7, 5

Common Values

ValueCountFrequency (%)
3 44
24.3%
2 34
18.8%
4 29
16.0%
5 12
 
6.6%
6 10
 
5.5%
1 7
 
3.9%
3대 5
 
2.8%
10 3
 
1.7%
2,1 3
 
1.7%
8 3
 
1.7%
Other values (26) 31
17.1%

Length

2024-04-29T21:57:29.685032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 44
22.3%
2 38
19.3%
4 32
16.2%
5 15
 
7.6%
6 10
 
5.1%
1 8
 
4.1%
3대 7
 
3.6%
2대 6
 
3.0%
7 5
 
2.5%
8 3
 
1.5%
Other values (22) 29
14.7%

엔진펌프규모(kW(HP))
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
139 
0
27 
해당없음
 
8
1,000HP
 
1
100
 
1
Other values (5)
 
5

Length

Max length9
Median length4
Mean length3.6629834
Min length1

Unique

Unique7 ?
Unique (%)3.9%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row0
5th row1,000HP

Common Values

ValueCountFrequency (%)
<NA> 139
76.8%
0 27
 
14.9%
해당없음 8
 
4.4%
1,000HP 1
 
0.6%
100 1
 
0.6%
650 1
 
0.6%
240m3/min 1
 
0.6%
60m3/min 1
 
0.6%
7.5m3/min 1
 
0.6%
325m3/min 1
 
0.6%

Length

2024-04-29T21:57:29.804636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T21:57:29.931401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 139
76.8%
0 27
 
14.9%
해당없음 8
 
4.4%
1,000hp 1
 
0.6%
100 1
 
0.6%
650 1
 
0.6%
240m3/min 1
 
0.6%
60m3/min 1
 
0.6%
7.5m3/min 1
 
0.6%
325m3/min 1
 
0.6%

엔진펌프대수(대)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
139 
0
35 
1
 
5
6
 
1
3
 
1

Length

Max length4
Median length4
Mean length3.3038674
Min length1

Unique

Unique2 ?
Unique (%)1.1%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row0
5th row6

Common Values

ValueCountFrequency (%)
<NA> 139
76.8%
0 35
 
19.3%
1 5
 
2.8%
6 1
 
0.6%
3 1
 
0.6%

Length

2024-04-29T21:57:30.072217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T21:57:30.175485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 139
76.8%
0 35
 
19.3%
1 5
 
2.8%
6 1
 
0.6%
3 1
 
0.6%

한전전원공급방식(1회선 또는 2회선)
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2
83 
<NA>
30 
한전2회선
14 
이중선로
11 
2회선
10 
Other values (11)
33 

Length

Max length16
Median length1
Mean length2.8895028
Min length1

Unique

Unique6 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
2 83
45.9%
<NA> 30
 
16.6%
한전2회선 14
 
7.7%
이중선로 11
 
6.1%
2회선 10
 
5.5%
22.9KV(2회선) 8
 
4.4%
1 8
 
4.4%
1회선 4
 
2.2%
지중 4
 
2.2%
380V 3
 
1.7%
Other values (6) 6
 
3.3%

Length

2024-04-29T21:57:30.297212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2 83
45.9%
na 30
 
16.6%
한전2회선 14
 
7.7%
이중선로 11
 
6.1%
2회선 10
 
5.5%
22.9kv(2회선 8
 
4.4%
1 8
 
4.4%
1회선 4
 
2.2%
지중 4
 
2.2%
380v 3
 
1.7%
Other values (6) 6
 
3.3%

비상발전기(kW(HP))
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
134 
없음
 
8
해당없음
 
8
0
 
8
750
 
3
Other values (17)
20 

Length

Max length11
Median length4
Mean length3.7734807
Min length1

Unique

Unique14 ?
Unique (%)7.7%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row0
5th row300kW

Common Values

ValueCountFrequency (%)
<NA> 134
74.0%
없음 8
 
4.4%
해당없음 8
 
4.4%
0 8
 
4.4%
750 3
 
1.7%
무(이중선로) 2
 
1.1%
60 2
 
1.1%
유 2
 
1.1%
300 1
 
0.6%
300kW 1
 
0.6%
Other values (12) 12
 
6.6%

Length

2024-04-29T21:57:30.415015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 134
73.6%
해당없음 8
 
4.4%
0 8
 
4.4%
없음 8
 
4.4%
750 3
 
1.6%
무(이중선로 2
 
1.1%
60 2
 
1.1%
유 2
 
1.1%
199 1
 
0.5%
105kw 1
 
0.5%
Other values (13) 13
 
7.1%

비상발전기대수(대)
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
<NA>
119 
1
31 
0
28 
5
 
1
2
 
1

Length

Max length4
Median length4
Mean length2.9723757
Min length1

Unique

Unique3 ?
Unique (%)1.7%

Sample

1st row<NA>
2nd row<NA>
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
<NA> 119
65.7%
1 31
 
17.1%
0 28
 
15.5%
5 1
 
0.6%
2 1
 
0.6%
3 1
 
0.6%

Length

2024-04-29T21:57:30.540985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-29T21:57:30.649688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 119
65.7%
1 31
 
17.1%
0 28
 
15.5%
5 1
 
0.6%
2 1
 
0.6%
3 1
 
0.6%

한전계약전력(kW, 고압/저압)
Text

Distinct143
Distinct (%)79.4%
Missing1
Missing (%)0.6%
Memory size1.5 KiB
2024-04-29T21:57:30.919057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length12
Mean length6.3555556
Min length1

Characters and Unicode

Total characters1144
Distinct characters33
Distinct categories8 ?
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 (%)67.8%

Sample

1st row1313
2nd row406
3rd row7250kW
4th row2281kW
5th row10000kW
ValueCountFrequency (%)
15
 
6.4%
고압 10
 
4.3%
농사용(갑 8
 
3.4%
저압 7
 
3.0%
고압a 4
 
1.7%
300 4
 
1.7%
90 4
 
1.7%
산업용(을)고압a 4
 
1.7%
600 3
 
1.3%
산업용(갑)2고압a 3
 
1.3%
Other values (145) 172
73.5%
2024-04-29T21:57:31.339295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 236
20.6%
5 69
 
6.0%
압 67
 
5.9%
W 57
 
5.0%
1 57
 
5.0%
54
 
4.7%
, 50
 
4.4%
2 48
 
4.2%
k 47
 
4.1%
고 47
 
4.1%
Other values (23) 412
36.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 579
50.6%
Other Letter 199
 
17.4%
Uppercase Letter 89
 
7.8%
Other Punctuation 89
 
7.8%
Lowercase Letter 58
 
5.1%
Space Separator 54
 
4.7%
Close Punctuation 38
 
3.3%
Open Punctuation 38
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
압 67
33.7%
고 47
23.6%
저 21
 
10.6%
용 15
 
7.5%
갑 11
 
5.5%
농 8
 
4.0%
사 8
 
4.0%
업 7
 
3.5%
산 7
 
3.5%
을 4
 
2.0%
Other values (2) 4
 
2.0%
Decimal Number
ValueCountFrequency (%)
0 236
40.8%
5 69
 
11.9%
1 57
 
9.8%
2 48
 
8.3%
4 40
 
6.9%
3 32
 
5.5%
9 28
 
4.8%
6 26
 
4.5%
7 24
 
4.1%
8 19
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
W 57
64.0%
K 21
 
23.6%
A 11
 
12.4%
Other Punctuation
ValueCountFrequency (%)
, 50
56.2%
/ 37
41.6%
: 2
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
k 47
81.0%
w 11
 
19.0%
Space Separator
ValueCountFrequency (%)
54
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 798
69.8%
Hangul 199
 
17.4%
Latin 147
 
12.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236
29.6%
5 69
 
8.6%
1 57
 
7.1%
54
 
6.8%
, 50
 
6.3%
2 48
 
6.0%
4 40
 
5.0%
) 38
 
4.8%
( 38
 
4.8%
/ 37
 
4.6%
Other values (6) 131
16.4%
Hangul
ValueCountFrequency (%)
압 67
33.7%
고 47
23.6%
저 21
 
10.6%
용 15
 
7.5%
갑 11
 
5.5%
농 8
 
4.0%
사 8
 
4.0%
업 7
 
3.5%
산 7
 
3.5%
을 4
 
2.0%
Other values (2) 4
 
2.0%
Latin
ValueCountFrequency (%)
W 57
38.8%
k 47
32.0%
K 21
 
14.3%
A 11
 
7.5%
w 11
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 945
82.6%
Hangul 199
 
17.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236
25.0%
5 69
 
7.3%
W 57
 
6.0%
1 57
 
6.0%
54
 
5.7%
, 50
 
5.3%
2 48
 
5.1%
k 47
 
5.0%
4 40
 
4.2%
) 38
 
4.0%
Other values (11) 249
26.3%
Hangul
ValueCountFrequency (%)
압 67
33.7%
고 47
23.6%
저 21
 
10.6%
용 15
 
7.5%
갑 11
 
5.5%
농 8
 
4.0%
사 8
 
4.0%
업 7
 
3.5%
산 7
 
3.5%
을 4
 
2.0%
Other values (2) 4
 
2.0%

사용전력량(kWH)/년 (최대/최소,고압/저압)
Text

MISSING 

Distinct126
Distinct (%)93.3%
Missing46
Missing (%)25.4%
Memory size1.5 KiB
2024-04-29T21:57:31.537121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length43
Mean length10.333333
Min length1

Characters and Unicode

Total characters1395
Distinct characters29
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)89.6%

Sample

1st row133,860kWh
2nd row101,988kWh
3rd row165,168kWh
4th row24,876kWh
5th row127,224kWh
ValueCountFrequency (%)
35
 
16.2%
0kwh 9
 
4.2%
고압 8
 
3.7%
저압 7
 
3.2%
1500 5
 
2.3%
300 3
 
1.4%
0 2
 
0.9%
26659kw 2
 
0.9%
2500 2
 
0.9%
27000kwh 1
 
0.5%
Other values (142) 142
65.7%
2024-04-29T21:57:31.845824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130
 
9.3%
2 119
 
8.5%
1 115
 
8.2%
, 90
 
6.5%
89
 
6.4%
3 86
 
6.2%
5 76
 
5.4%
6 72
 
5.2%
8 64
 
4.6%
4 64
 
4.6%
Other values (19) 490
35.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 844
60.5%
Other Punctuation 156
 
11.2%
Lowercase Letter 104
 
7.5%
Space Separator 89
 
6.4%
Other Letter 87
 
6.2%
Uppercase Letter 47
 
3.4%
Open Punctuation 34
 
2.4%
Close Punctuation 34
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
15.4%
2 119
14.1%
1 115
13.6%
3 86
10.2%
5 76
9.0%
6 72
8.5%
8 64
7.6%
4 64
7.6%
9 62
7.3%
7 56
6.6%
Other Letter
ValueCountFrequency (%)
압 37
42.5%
고 21
24.1%
저 18
20.7%
최 4
 
4.6%
운 2
 
2.3%
양 2
 
2.3%
대 2
 
2.3%
년 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 90
57.7%
/ 50
32.1%
: 16
 
10.3%
Lowercase Letter
ValueCountFrequency (%)
k 52
50.0%
h 44
42.3%
w 8
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
W 44
93.6%
H 3
 
6.4%
Space Separator
ValueCountFrequency (%)
89
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1157
82.9%
Latin 151
 
10.8%
Hangul 87
 
6.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
11.2%
2 119
10.3%
1 115
9.9%
, 90
 
7.8%
89
 
7.7%
3 86
 
7.4%
5 76
 
6.6%
6 72
 
6.2%
8 64
 
5.5%
4 64
 
5.5%
Other values (6) 252
21.8%
Hangul
ValueCountFrequency (%)
압 37
42.5%
고 21
24.1%
저 18
20.7%
최 4
 
4.6%
운 2
 
2.3%
양 2
 
2.3%
대 2
 
2.3%
년 1
 
1.1%
Latin
ValueCountFrequency (%)
k 52
34.4%
W 44
29.1%
h 44
29.1%
w 8
 
5.3%
H 3
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1308
93.8%
Hangul 87
 
6.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
 
9.9%
2 119
 
9.1%
1 115
 
8.8%
, 90
 
6.9%
89
 
6.8%
3 86
 
6.6%
5 76
 
5.8%
6 72
 
5.5%
8 64
 
4.9%
4 64
 
4.9%
Other values (11) 403
30.8%
Hangul
ValueCountFrequency (%)
압 37
42.5%
고 21
24.1%
저 18
20.7%
최 4
 
4.6%
운 2
 
2.3%
양 2
 
2.3%
대 2
 
2.3%
년 1
 
1.1%

전기요금(원)/년 (최대/최소,고압/저압)
Text

MISSING 

Distinct121
Distinct (%)87.7%
Missing43
Missing (%)23.8%
Memory size1.5 KiB
2024-04-29T21:57:32.004821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length47
Mean length13.826087
Min length4

Characters and Unicode

Total characters1908
Distinct characters40
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique112 ?
Unique (%)81.2%

Sample

1st row산업용(을)
2nd row농사용(갑)
3rd row산업용(을)
4th row산업용(을)
5th row농사용(갑)
ValueCountFrequency (%)
35
 
16.1%
고압 7
 
3.2%
저압 7
 
3.2%
0원 7
 
3.2%
산업용(을 6
 
2.8%
월별상이 4
 
1.8%
3459190 3
 
1.4%
2321010 3
 
1.4%
농사용(갑 2
 
0.9%
943380 2
 
0.9%
Other values (138) 141
65.0%
2024-04-29T21:57:32.307069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 333
17.5%
, 196
 
10.3%
1 152
 
8.0%
2 122
 
6.4%
8 107
 
5.6%
9 107
 
5.6%
5 101
 
5.3%
4 99
 
5.2%
7 91
 
4.8%
3 91
 
4.8%
Other values (30) 509
26.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1275
66.8%
Other Punctuation 260
 
13.6%
Other Letter 188
 
9.9%
Space Separator 86
 
4.5%
Open Punctuation 45
 
2.4%
Close Punctuation 45
 
2.4%
Math Symbol 5
 
0.3%
Connector Punctuation 2
 
0.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
압 40
21.3%
원 39
20.7%
고 24
12.8%
저 18
9.6%
용 10
 
5.3%
산 8
 
4.3%
을 8
 
4.3%
업 8
 
4.3%
이 4
 
2.1%
상 4
 
2.1%
Other values (10) 25
13.3%
Decimal Number
ValueCountFrequency (%)
0 333
26.1%
1 152
11.9%
2 122
 
9.6%
8 107
 
8.4%
9 107
 
8.4%
5 101
 
7.9%
4 99
 
7.8%
7 91
 
7.1%
3 91
 
7.1%
6 72
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 196
75.4%
/ 42
 
16.2%
: 16
 
6.2%
. 6
 
2.3%
Space Separator
ValueCountFrequency (%)
86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1718
90.0%
Hangul 188
 
9.9%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
압 40
21.3%
원 39
20.7%
고 24
12.8%
저 18
9.6%
용 10
 
5.3%
산 8
 
4.3%
을 8
 
4.3%
업 8
 
4.3%
이 4
 
2.1%
상 4
 
2.1%
Other values (10) 25
13.3%
Common
ValueCountFrequency (%)
0 333
19.4%
, 196
11.4%
1 152
8.8%
2 122
 
7.1%
8 107
 
6.2%
9 107
 
6.2%
5 101
 
5.9%
4 99
 
5.8%
7 91
 
5.3%
3 91
 
5.3%
Other values (9) 319
18.6%
Latin
ValueCountFrequency (%)
A 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1720
90.1%
Hangul 188
 
9.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 333
19.4%
, 196
11.4%
1 152
8.8%
2 122
 
7.1%
8 107
 
6.2%
9 107
 
6.2%
5 101
 
5.9%
4 99
 
5.8%
7 91
 
5.3%
3 91
 
5.3%
Other values (10) 321
18.7%
Hangul
ValueCountFrequency (%)
압 40
21.3%
원 39
20.7%
고 24
12.8%
저 18
9.6%
용 10
 
5.3%
산 8
 
4.3%
을 8
 
4.3%
업 8
 
4.3%
이 4
 
2.1%
상 4
 
2.1%
Other values (10) 25
13.3%

Interactions

2024-04-29T21:57:24.245698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:23.729863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:24.012246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:24.332734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:23.864815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:24.083713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:24.410413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:23.941723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-29T21:57:24.161963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-29T21:57:32.403108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호소재지도로명주소WGS84위도WGS84경도설치일자설치목적모터펌프대수(대)엔진펌프규모(kW(HP))엔진펌프대수(대)한전전원공급방식(1회선 또는 2회선)비상발전기(kW(HP))비상발전기대수(대)
시군명1.0000.9941.0000.9750.9380.9580.9980.7040.9040.8700.9440.9700.795
소재지우편번호0.9941.0001.0000.8130.9010.8900.9780.4430.8760.9360.7560.9180.350
소재지도로명주소1.0001.0001.0001.0001.0000.9991.0000.9991.0001.0000.9041.0001.000
WGS84위도0.9750.8131.0001.0000.8300.9370.9560.5860.8530.9840.7810.9510.922
WGS84경도0.9380.9011.0000.8301.0000.8360.9100.0000.8860.4410.6170.8470.364
설치일자0.9580.8900.9990.9370.8361.0000.9150.9750.9561.0000.8480.9680.000
설치목적0.9980.9781.0000.9560.9100.9151.0000.6830.8850.9780.9090.9660.772
모터펌프대수(대)0.7040.4430.9990.5860.0000.9750.6831.0000.7830.8750.5470.0000.000
엔진펌프규모(kW(HP))0.9040.8761.0000.8530.8860.9560.8850.7831.0001.0000.7550.9110.669
엔진펌프대수(대)0.8700.9361.0000.9840.4411.0000.9780.8751.0001.0000.7891.0000.000
한전전원공급방식(1회선 또는 2회선)0.9440.7560.9040.7810.6170.8480.9090.5470.7550.7891.0000.9370.950
비상발전기(kW(HP))0.9700.9181.0000.9510.8470.9680.9660.0000.9111.0000.9371.0000.912
비상발전기대수(대)0.7950.3501.0000.9220.3640.0000.7720.0000.6690.0000.9500.9121.000
2024-04-29T21:57:32.545704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
모터펌프대수(대)엔진펌프대수(대)설치목적비상발전기대수(대)엔진펌프규모(kW(HP))비상발전기(kW(HP))한전전원공급방식(1회선 또는 2회선)시군명
모터펌프대수(대)1.0000.6370.2130.0000.4490.0000.1680.220
엔진펌프대수(대)0.6371.0000.7560.0000.9320.8530.5560.773
설치목적0.2130.7561.0000.5170.6850.6840.5610.872
비상발전기대수(대)0.0000.0000.5171.0000.4640.5070.8200.522
엔진펌프규모(kW(HP))0.4490.9320.6850.4641.0000.7010.4590.755
비상발전기(kW(HP))0.0000.8530.6840.5070.7011.0000.6550.708
한전전원공급방식(1회선 또는 2회선)0.1680.5560.5610.8200.4590.6551.0000.651
시군명0.2200.7730.8720.5220.7550.7080.6511.000
2024-04-29T21:57:32.671415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도시군명설치목적모터펌프대수(대)엔진펌프규모(kW(HP))엔진펌프대수(대)한전전원공급방식(1회선 또는 2회선)비상발전기(kW(HP))비상발전기대수(대)
소재지우편번호1.000-0.8070.4900.9130.8240.1540.7070.6640.4180.5890.211
WGS84위도-0.8071.000-0.3930.7890.7400.2270.6270.7860.4190.6340.600
WGS84경도0.490-0.3931.0000.6580.6110.0000.6510.2860.2760.4570.252
시군명0.9130.7890.6581.0000.8720.2200.7550.7730.6510.7080.522
설치목적0.8240.7400.6110.8721.0000.2130.6850.7560.5610.6840.517
모터펌프대수(대)0.1540.2270.0000.2200.2131.0000.4490.6370.1680.0000.000
엔진펌프규모(kW(HP))0.7070.6270.6510.7550.6850.4491.0000.9320.4590.7010.464
엔진펌프대수(대)0.6640.7860.2860.7730.7560.6370.9321.0000.5560.8530.000
한전전원공급방식(1회선 또는 2회선)0.4180.4190.2760.6510.5610.1680.4590.5561.0000.6550.820
비상발전기(kW(HP))0.5890.6340.4570.7080.6840.0000.7010.8530.6551.0000.507
비상발전기대수(대)0.2110.6000.2520.5220.5170.0000.4640.0000.8200.5071.000

Missing values

2024-04-29T21:57:24.546298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-29T21:57:24.787108image/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.
2024-04-29T21:57:25.057160image/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

시군명시설명소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도설치일자설치목적모터펌프규모(kW(HP))모터펌프대수(대)엔진펌프규모(kW(HP))엔진펌프대수(대)한전전원공급방식(1회선 또는 2회선)비상발전기(kW(HP))비상발전기대수(대)한전계약전력(kW, 고압/저압)사용전력량(kWH)/년 (최대/최소,고압/저압)전기요금(원)/년 (최대/최소,고압/저압)
0가평군가평배수펌프장12419경기도 가평군 가평읍 가평제방길 97경기도 가평군 가평읍 대곡리 11번지37.8239127.5178311995-10-20도시침수방지3005<NA><NA>이중선로<NA><NA>1313<NA><NA>
1가평군청평배수펌프장12453경기도 가평군 청평면 강변로 17경기도 가평군 청평면 청평리 619-39번지37.73469127.4144221997-09-26도시침수방지150×2 100×24<NA><NA>이중선로<NA><NA>406<NA><NA>
2고양시강매배수펌프장10441경기도 고양시 덕양구 강매로 103경기도 고양시 덕양구 강매동 290-2번지37.598164126.8428452000(‘12./’13수문펌프증설)내수배제750HP, 100HP, 375HP, 375HP5, 2, 4, 200이중선로007250kW133,860kWh산업용(을)
3고양시구산배수펌프장10200경기도 고양시 일산서구 이산포길 664경기도 고양시 일산서구 구산동 672-8번지37.695125126.6829931990내수배제500HP700이중선로002281kW101,988kWh농사용(갑)
4고양시대화1.2배수펌프장10426경기도 고양시 일산서구 멱절길 72경기도 고양시 일산서구 법곳동 740-11번지37.654439126.7301281994 / 2015(증설)준공내수배제670HP, 1,430HP7, 51,000HP6이중선로300kW110000kW165,168kWh산업용(을)
5고양시도내배수펌프장10536경기도 고양시 덕양구 중앙로 299-36경기도 고양시 덕양구 도내동 916번지37.60967126.8572041998내수배제660HP300이중선로002000kW24,876kWh산업용(을)
6고양시송포배수펌프장10203경기도 고양시 일산서구 이산포길 132경기도 고양시 일산서구 법곳동 1552번지37.662322126.7147422001내수배제760HP, 290HP7, 100이중선로003912kW127,224kWh농사용(갑)
7고양시신평1.2배수펌프장10427경기도 고양시 덕양구 신평길 107경기도 고양시 덕양구 신평동 25-2번지37.621774126.7943251995 / 2019(증설)준공내수배제870HP, 1750HP17, 500이중선로0022000kW317,808kWh산업용(을)
8고양시행신배수펌프장10486경기도 고양시 덕양구 서정마을로 12경기도 고양시 덕양구 행신동 1072번지37.617947126.8477862007내수배제200HP400단일선로800kW1900kW37,621kWh산업용(을)
9고양시현천배수펌프장10542경기도 고양시 덕양구 해포길 170경기도 고양시 덕양구 현천동 534-1번지37.598334126.8492881998내수배제800HP600이중선로004000kW58,788kWh산업용(을)
시군명시설명소재지우편번호소재지도로명주소소재지지번주소WGS84위도WGS84경도설치일자설치목적모터펌프규모(kW(HP))모터펌프대수(대)엔진펌프규모(kW(HP))엔진펌프대수(대)한전전원공급방식(1회선 또는 2회선)비상발전기(kW(HP))비상발전기대수(대)한전계약전력(kW, 고압/저압)사용전력량(kWH)/년 (최대/최소,고압/저압)전기요금(원)/년 (최대/최소,고압/저압)
171평택시유천 배수펌프장17878경기도 평택시 신평로 139 (합정동)경기도 평택시 합정동 859-5336.97694127.0991761998-05농경지침수방지350,1254,2<NA><NA>2<NA><NA>1,226/10192/0 , 2,742/707190,720/64,950 , 308,960/110,370
172평택시통복 1수문 펌프장<NA><NA>경기도 평택시 신대동 22-236.996553127.0784182007-06도심침수방지1202<NA><NA>1<NA><NA>200/31,136/0 , 60/44569,460/70,030 , 9,310/7,170
173평택시통복 배수펌프장17892경기도 평택시 통복시장로55번길 36 (통복동)경기도 평택시 통복동 19-936.999109127.0834761997-08도심침수방지120,602,4<NA><NA>2<NA><NA>331/1022/0 , 529/457892,600/142,810 , 110,400/58,670
174평택시포승 배수펌프장<NA><NA>경기도 평택시 포승읍 도곡리 115836.985185126.8541911997-08농경지침수방지55026503125011,084/95499/0 , 1,518/861202,830/57,710 , 74,850/58,890
175하남시덕풍12965<NA>경기도 하남시 덕풍동 511-5237.534042127.2033422013재난 대비1504<NA><NA>2<NA><NA>4294002,974,190
176하남시신장<NA><NA>경기도 하남시 신장동 492-937.536628127.2135321993재난 대비54<NA><NA>162162112395,200
177화성시남양배수펌프장18260<NA>경기도 화성시 남양읍 남양리 513-137.20644126.8112222001년저지대 침수 예방175HP2대240m3/min1지중무(이중선로)0310kw3650kwh월별상이
178화성시발안배수펌프장18596경기도 화성시 향남읍 3.1만세로 1043경기도 화성시 향남읍 발안리 304-1번지37.128449126.9040072005년저지대 침수 예방60HP3대60m3/min1지중유1119kw1428kwh월별상이
179화성시서신배수펌프장18554<NA>경기도 화성시 서신면 전곡리 557-314번지37.189467126.6765112015년저지대 침수 예방2HP3대7.5m3/min1지중유174kw1007kwh월별상이
180화성시황계배수펌프장18347<NA>경기도 화성시 황계동 134번지37.218019127.0197262001년저지대 침수 예방200HP3대325m3/min1지중무(이중선로)0610kw7320kwh월별상이