Overview

Dataset statistics

Number of variables9
Number of observations51
Missing cells9
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory78.5 B

Variable types

Text3
Numeric4
Categorical2

Dataset

Description전북특별자치도 군산시 소재한 민방위 비상 급수시설현황(시설명,소재지주소,위도,경도,활용사항,일일생산량,관리기관 등)전쟁 및 상수도 체계의 파괴 등과 같은 민방위사태 발생으로 상수도 공급 중단시 최소의 음용 및 생활용수를 주민에게 공급하기 위함.
Author전북특별자치도 군산시
URLhttps://www.data.go.kr/data/15059770/fileData.do

Alerts

위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
심도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
활용사항 is highly overall correlated with 심도 and 1 other fieldsHigh correlation
관리기관명 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
소재지도로명주소 has 5 (9.8%) missing valuesMissing
심도 has 4 (7.8%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2024-04-13 11:24:24.795559
Analysis finished2024-04-13 11:24:32.629951
Duration7.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size536.0 B
2024-04-13T20:24:33.310668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.745098
Min length3

Characters and Unicode

Total characters344
Distinct characters117
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)100.0%

Sample

1st row늘푸른 도서관
2nd row은파 제2주차장
3rd row금강터널 앞
4th row시민문화회관
5th row군산시청
ValueCountFrequency (%)
군산대학교 4
 
6.1%
옆 2
 
3.0%
늘푸른 1
 
1.5%
파라다이스장 1
 
1.5%
스파월드사우나1 1
 
1.5%
천지목욕탕 1
 
1.5%
수정사우나 1
 
1.5%
현대아파트 1
 
1.5%
지곡초등학교 1
 
1.5%
군산중앙여자고등학교 1
 
1.5%
Other values (52) 52
78.8%
2024-04-13T20:24:34.573481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
교 26
 
7.6%
학 25
 
7.3%
15
 
4.4%
등 14
 
4.1%
산 13
 
3.8%
대 11
 
3.2%
초 11
 
3.2%
군 10
 
2.9%
나 6
 
1.7%
장 6
 
1.7%
Other values (107) 207
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 306
89.0%
Space Separator 15
 
4.4%
Decimal Number 15
 
4.4%
Open Punctuation 4
 
1.2%
Close Punctuation 4
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
교 26
 
8.5%
학 25
 
8.2%
등 14
 
4.6%
산 13
 
4.2%
대 11
 
3.6%
초 11
 
3.6%
군 10
 
3.3%
나 6
 
2.0%
장 6
 
2.0%
원 6
 
2.0%
Other values (98) 178
58.2%
Decimal Number
ValueCountFrequency (%)
2 5
33.3%
1 4
26.7%
3 2
 
13.3%
4 2
 
13.3%
6 1
 
6.7%
9 1
 
6.7%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 306
89.0%
Common 38
 
11.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
교 26
 
8.5%
학 25
 
8.2%
등 14
 
4.6%
산 13
 
4.2%
대 11
 
3.6%
초 11
 
3.6%
군 10
 
3.3%
나 6
 
2.0%
장 6
 
2.0%
원 6
 
2.0%
Other values (98) 178
58.2%
Common
ValueCountFrequency (%)
15
39.5%
2 5
 
13.2%
( 4
 
10.5%
) 4
 
10.5%
1 4
 
10.5%
3 2
 
5.3%
4 2
 
5.3%
6 1
 
2.6%
9 1
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 306
89.0%
ASCII 38
 
11.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
교 26
 
8.5%
학 25
 
8.2%
등 14
 
4.6%
산 13
 
4.2%
대 11
 
3.6%
초 11
 
3.6%
군 10
 
3.3%
나 6
 
2.0%
장 6
 
2.0%
원 6
 
2.0%
Other values (98) 178
58.2%
ASCII
ValueCountFrequency (%)
15
39.5%
2 5
 
13.2%
( 4
 
10.5%
) 4
 
10.5%
1 4
 
10.5%
3 2
 
5.3%
4 2
 
5.3%
6 1
 
2.6%
9 1
 
2.6%

소재지도로명주소
Text

MISSING 

Distinct38
Distinct (%)82.6%
Missing5
Missing (%)9.8%
Memory size536.0 B
2024-04-13T20:24:35.514494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length22.891304
Min length16

Characters and Unicode

Total characters1053
Distinct characters85
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

Unique36 ?
Unique (%)78.3%

Sample

1st row전라북도 군산시 하나운로 17, (나운동)
2nd row전라북도 군산시 대학로 308, (나운동)
3rd row전라북도 군산시 시청로 17, (조촌동)
4th row전라북도 군산시 서당길 56-6, (구암동)
5th row전라북도 군산시 중앙로 230, (금동)
ValueCountFrequency (%)
전라북도 46
19.7%
군산시 46
19.7%
미룡동 7
 
3.0%
나운동 7
 
3.0%
미제길 6
 
2.6%
8 6
 
2.6%
호원대학교 4
 
1.7%
대학로 4
 
1.7%
조촌동 4
 
1.7%
64 4
 
1.7%
Other values (79) 99
42.5%
2024-04-13T20:24:37.026052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
187
17.8%
북 47
 
4.5%
군 47
 
4.5%
산 47
 
4.5%
시 47
 
4.5%
전 46
 
4.4%
도 46
 
4.4%
라 46
 
4.4%
, 45
 
4.3%
동 43
 
4.1%
Other values (75) 452
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 629
59.7%
Space Separator 187
 
17.8%
Decimal Number 110
 
10.4%
Other Punctuation 45
 
4.3%
Open Punctuation 40
 
3.8%
Close Punctuation 40
 
3.8%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
북 47
 
7.5%
군 47
 
7.5%
산 47
 
7.5%
시 47
 
7.5%
전 46
 
7.3%
도 46
 
7.3%
라 46
 
7.3%
동 43
 
6.8%
길 29
 
4.6%
로 18
 
2.9%
Other values (60) 213
33.9%
Decimal Number
ValueCountFrequency (%)
1 23
20.9%
2 17
15.5%
6 13
11.8%
0 11
10.0%
3 11
10.0%
4 9
 
8.2%
8 9
 
8.2%
7 8
 
7.3%
9 6
 
5.5%
5 3
 
2.7%
Space Separator
ValueCountFrequency (%)
187
100.0%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 40
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 629
59.7%
Common 424
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
북 47
 
7.5%
군 47
 
7.5%
산 47
 
7.5%
시 47
 
7.5%
전 46
 
7.3%
도 46
 
7.3%
라 46
 
7.3%
동 43
 
6.8%
길 29
 
4.6%
로 18
 
2.9%
Other values (60) 213
33.9%
Common
ValueCountFrequency (%)
187
44.1%
, 45
 
10.6%
( 40
 
9.4%
) 40
 
9.4%
1 23
 
5.4%
2 17
 
4.0%
6 13
 
3.1%
0 11
 
2.6%
3 11
 
2.6%
4 9
 
2.1%
Other values (5) 28
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 629
59.7%
ASCII 424
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
187
44.1%
, 45
 
10.6%
( 40
 
9.4%
) 40
 
9.4%
1 23
 
5.4%
2 17
 
4.0%
6 13
 
3.1%
0 11
 
2.6%
3 11
 
2.6%
4 9
 
2.1%
Other values (5) 28
 
6.6%
Hangul
ValueCountFrequency (%)
북 47
 
7.5%
군 47
 
7.5%
산 47
 
7.5%
시 47
 
7.5%
전 46
 
7.3%
도 46
 
7.3%
라 46
 
7.3%
동 43
 
6.8%
길 29
 
4.6%
로 18
 
2.9%
Other values (60) 213
33.9%

소재지지번주소
Text

Distinct45
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size536.0 B
2024-04-13T20:24:37.906707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length20.470588
Min length17

Characters and Unicode

Total characters1044
Distinct characters69
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

Unique42 ?
Unique (%)82.4%

Sample

1st row전라북도 군산시 나운동 153-8번지
2nd row전라북도 군산시 나운동 1222-33번지
3rd row전라북도 군산시 사정동 552번지
4th row전라북도 군산시 나운동 790-3번지
5th row전라북도 군산시 조촌동 888번지
ValueCountFrequency (%)
전라북도 51
23.9%
군산시 51
23.9%
나운동 8
 
3.8%
월하리 4
 
1.9%
호원대학교 4
 
1.9%
미룡동 4
 
1.9%
임피면 4
 
1.9%
조촌동 4
 
1.9%
727 4
 
1.9%
장미동 3
 
1.4%
Other values (66) 76
35.7%
2024-04-13T20:24:39.172151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
19.2%
산 53
 
5.1%
북 52
 
5.0%
전 51
 
4.9%
도 51
 
4.9%
군 51
 
4.9%
시 51
 
4.9%
라 51
 
4.9%
동 46
 
4.4%
지 42
 
4.0%
Other values (59) 396
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 629
60.2%
Space Separator 200
 
19.2%
Decimal Number 183
 
17.5%
Dash Punctuation 32
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 53
 
8.4%
북 52
 
8.3%
전 51
 
8.1%
도 51
 
8.1%
군 51
 
8.1%
시 51
 
8.1%
라 51
 
8.1%
동 46
 
7.3%
지 42
 
6.7%
번 41
 
6.5%
Other values (47) 140
22.3%
Decimal Number
ValueCountFrequency (%)
1 30
16.4%
2 27
14.8%
5 22
12.0%
7 20
10.9%
3 20
10.9%
8 20
10.9%
9 14
7.7%
4 12
 
6.6%
0 11
 
6.0%
6 7
 
3.8%
Space Separator
ValueCountFrequency (%)
200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 629
60.2%
Common 415
39.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 53
 
8.4%
북 52
 
8.3%
전 51
 
8.1%
도 51
 
8.1%
군 51
 
8.1%
시 51
 
8.1%
라 51
 
8.1%
동 46
 
7.3%
지 42
 
6.7%
번 41
 
6.5%
Other values (47) 140
22.3%
Common
ValueCountFrequency (%)
200
48.2%
- 32
 
7.7%
1 30
 
7.2%
2 27
 
6.5%
5 22
 
5.3%
7 20
 
4.8%
3 20
 
4.8%
8 20
 
4.8%
9 14
 
3.4%
4 12
 
2.9%
Other values (2) 18
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 629
60.2%
ASCII 415
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
200
48.2%
- 32
 
7.7%
1 30
 
7.2%
2 27
 
6.5%
5 22
 
5.3%
7 20
 
4.8%
3 20
 
4.8%
8 20
 
4.8%
9 14
 
3.4%
4 12
 
2.9%
Other values (2) 18
 
4.3%
Hangul
ValueCountFrequency (%)
산 53
 
8.4%
북 52
 
8.3%
전 51
 
8.1%
도 51
 
8.1%
군 51
 
8.1%
시 51
 
8.1%
라 51
 
8.1%
동 46
 
7.3%
지 42
 
6.7%
번 41
 
6.5%
Other values (47) 140
22.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.893118
Minimum35
Maximum35.990665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-04-13T20:24:39.602958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile35
Q135.958087
median35.969099
Q335.977118
95-th percentile35.986645
Maximum35.990665
Range0.99066531
Interquartile range (IQR)0.0190315

Descriptive statistics

Standard deviation0.26339092
Coefficient of variation (CV)0.0073382013
Kurtosis8.7445944
Mean35.893118
Median Absolute Deviation (MAD)0.0091638
Skewness-3.2211113
Sum1830.549
Variance0.069374779
MonotonicityNot monotonic
2024-04-13T20:24:40.039121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
35.0 4
 
7.8%
35.94620182 2
 
3.9%
35.95808693 2
 
3.9%
35.96051837 1
 
2.0%
35.96419694 1
 
2.0%
35.96909938 1
 
2.0%
35.97489877 1
 
2.0%
35.96882618 1
 
2.0%
35.97941647 1
 
2.0%
35.95759463 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
35.0 4
7.8%
35.946036 1
 
2.0%
35.94620182 2
3.9%
35.946939 1
 
2.0%
35.948522 1
 
2.0%
35.95139332 1
 
2.0%
35.95283913 1
 
2.0%
35.95759463 1
 
2.0%
35.95808693 2
3.9%
35.95964442 1
 
2.0%
ValueCountFrequency (%)
35.99066531 1
2.0%
35.98872238 1
2.0%
35.98810265 1
2.0%
35.98518722 1
2.0%
35.98218993 1
2.0%
35.98215616 1
2.0%
35.98146846 1
2.0%
35.98107886 1
2.0%
35.97944457 1
2.0%
35.97941647 1
2.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.65317
Minimum126
Maximum126.75313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-04-13T20:24:40.441302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile126
Q1126.68968
median126.70623
Q3126.71519
95-th percentile126.74606
Maximum126.75313
Range0.7531344
Interquartile range (IQR)0.0255011

Descriptive statistics

Standard deviation0.19333427
Coefficient of variation (CV)0.0015264858
Kurtosis8.5760604
Mean126.65317
Median Absolute Deviation (MAD)0.0119903
Skewness-3.1786788
Sum6459.3116
Variance0.037378138
MonotonicityNot monotonic
2024-04-13T20:24:40.880129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
126.0 4
 
7.8%
126.6878441 2
 
3.9%
126.7151391 2
 
3.9%
126.6990479 1
 
2.0%
126.6941984 1
 
2.0%
126.7337308 1
 
2.0%
126.7435839 1
 
2.0%
126.7386023 1
 
2.0%
126.7515957 1
 
2.0%
126.7070549 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
126.0 4
7.8%
126.679086 1
 
2.0%
126.683076 1
 
2.0%
126.683554 1
 
2.0%
126.68405 1
 
2.0%
126.684343 1
 
2.0%
126.6853974 1
 
2.0%
126.6859232 1
 
2.0%
126.6878441 2
3.9%
126.6915254 1
 
2.0%
ValueCountFrequency (%)
126.7531344 1
2.0%
126.7515957 1
2.0%
126.7485428 1
2.0%
126.7435839 1
2.0%
126.7386023 1
2.0%
126.7381204 1
2.0%
126.7368362 1
2.0%
126.7337308 1
2.0%
126.7210539 1
2.0%
126.7163609 1
2.0%

일일생산량
Real number (ℝ)

Distinct13
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.60784
Minimum70
Maximum324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-04-13T20:24:41.249414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile70
Q190
median90
Q3150
95-th percentile244
Maximum324
Range254
Interquartile range (IQR)60

Descriptive statistics

Standard deviation60.637638
Coefficient of variation (CV)0.48662778
Kurtosis2.6569422
Mean124.60784
Median Absolute Deviation (MAD)10
Skewness1.7253011
Sum6355
Variance3676.9231
MonotonicityNot monotonic
2024-04-13T20:24:41.588877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
90 20
39.2%
150 6
 
11.8%
100 6
 
11.8%
200 5
 
9.8%
70 4
 
7.8%
80 3
 
5.9%
300 1
 
2.0%
288 1
 
2.0%
170 1
 
2.0%
324 1
 
2.0%
Other values (3) 3
 
5.9%
ValueCountFrequency (%)
70 4
 
7.8%
80 3
 
5.9%
90 20
39.2%
95 1
 
2.0%
100 6
 
11.8%
150 6
 
11.8%
170 1
 
2.0%
178 1
 
2.0%
180 1
 
2.0%
200 5
 
9.8%
ValueCountFrequency (%)
324 1
 
2.0%
300 1
 
2.0%
288 1
 
2.0%
200 5
9.8%
180 1
 
2.0%
178 1
 
2.0%
170 1
 
2.0%
150 6
11.8%
100 6
11.8%
95 1
 
2.0%

심도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)36.2%
Missing4
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean109.87234
Minimum40
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size587.0 B
2024-04-13T20:24:41.917388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile73.9
Q190
median100
Q3145
95-th percentile172.5
Maximum200
Range160
Interquartile range (IQR)55

Descriptive statistics

Standard deviation35.351414
Coefficient of variation (CV)0.3217499
Kurtosis0.30525454
Mean109.87234
Median Absolute Deviation (MAD)18
Skewness0.84486828
Sum5164
Variance1249.7225
MonotonicityNot monotonic
2024-04-13T20:24:42.299973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
90 10
19.6%
150 8
15.7%
100 8
15.7%
80 4
 
7.8%
110 3
 
5.9%
120 2
 
3.9%
200 2
 
3.9%
155 1
 
2.0%
140 1
 
2.0%
85 1
 
2.0%
Other values (7) 7
13.7%
(Missing) 4
 
7.8%
ValueCountFrequency (%)
40 1
 
2.0%
60 1
 
2.0%
73 1
 
2.0%
76 1
 
2.0%
80 4
 
7.8%
82 1
 
2.0%
83 1
 
2.0%
85 1
 
2.0%
90 10
19.6%
100 8
15.7%
ValueCountFrequency (%)
200 2
 
3.9%
180 1
 
2.0%
155 1
 
2.0%
150 8
15.7%
140 1
 
2.0%
120 2
 
3.9%
110 3
 
5.9%
100 8
15.7%
90 10
19.6%
85 1
 
2.0%

활용사항
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size536.0 B
식수
25 
생활용수
25 
생활용수+음용수
 
1

Length

Max length8
Median length4
Mean length3.0980392
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

1st row식수
2nd row생활용수
3rd row식수
4th row생활용수
5th row생활용수

Common Values

ValueCountFrequency (%)
식수 25
49.0%
생활용수 25
49.0%
생활용수+음용수 1
 
2.0%

Length

2024-04-13T20:24:42.718029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:24:43.044064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
식수 25
49.0%
생활용수 25
49.0%
생활용수+음용수 1
 
2.0%

관리기관명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)31.4%
Missing0
Missing (%)0.0%
Memory size536.0 B
나운3동
10 
월명동
7 
조촌동
5 
신풍동
5 
흥남동
5 
Other values (11)
19 

Length

Max length4
Median length3
Mean length3.2941176
Min length3

Unique

Unique7 ?
Unique (%)13.7%

Sample

1st row나운3동
2nd row나운3동
3rd row개정동
4th row나운1동
5th row조촌동

Common Values

ValueCountFrequency (%)
나운3동 10
19.6%
월명동 7
13.7%
조촌동 5
9.8%
신풍동 5
9.8%
흥남동 5
9.8%
나운1동 4
 
7.8%
임피면 4
 
7.8%
구암동 2
 
3.9%
수송동 2
 
3.9%
개정동 1
 
2.0%
Other values (6) 6
11.8%

Length

2024-04-13T20:24:43.399208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
나운3동 10
19.6%
월명동 7
13.7%
조촌동 5
9.8%
신풍동 5
9.8%
흥남동 5
9.8%
나운1동 4
 
7.8%
임피면 4
 
7.8%
구암동 2
 
3.9%
수송동 2
 
3.9%
개정동 1
 
2.0%
Other values (6) 6
11.8%

Interactions

2024-04-13T20:24:30.529281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:27.541992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:28.542506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:29.558842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:30.774527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:27.789301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:28.790762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:29.797705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:31.037404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:28.045142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:29.051598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:30.048751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:31.280332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:28.283048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:29.294765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:24:30.276888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:24:43.642794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명소재지도로명주소소재지지번주소위도경도일일생산량심도활용사항관리기관명
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0000.8340.9790.7741.000
소재지지번주소1.0001.0001.0001.0001.0000.9361.0001.0001.000
위도1.0001.0001.0001.0000.9760.000NaN0.1371.000
경도1.0001.0001.0000.9761.0000.000NaN0.1371.000
일일생산량1.0000.8340.9360.0000.0001.0000.4290.0000.000
심도1.0000.9791.000NaNNaN0.4291.0000.9440.728
활용사항1.0000.7741.0000.1370.1370.0000.9441.0000.873
관리기관명1.0001.0001.0001.0001.0000.0000.7280.8731.000
2024-04-13T20:24:43.939403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명활용사항
관리기관명1.0000.635
활용사항0.6351.000
2024-04-13T20:24:44.180403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도일일생산량심도활용사항관리기관명
위도1.0000.580-0.1700.1500.2220.845
경도0.5801.0000.1000.5550.2220.845
일일생산량-0.1700.1001.0000.4220.0000.000
심도0.1500.5550.4221.0000.6690.360
활용사항0.2220.2220.0000.6691.0000.635
관리기관명0.8450.8450.0000.3600.6351.000

Missing values

2024-04-13T20:24:31.639034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:24:32.085488image/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.
2024-04-13T20:24:32.437798image/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

시설명소재지도로명주소소재지지번주소위도경도일일생산량심도활용사항관리기관명
0늘푸른 도서관전라북도 군산시 하나운로 17, (나운동)전라북도 군산시 나운동 153-8번지35.960518126.699048300150식수나운3동
1은파 제2주차장<NA>전라북도 군산시 나운동 1222-33번지35.952839126.691525288150생활용수나운3동
2금강터널 앞<NA>전라북도 군산시 사정동 552번지35.969101126.748543170150식수개정동
3시민문화회관전라북도 군산시 대학로 308, (나운동)전라북도 군산시 나운동 790-3번지35.96691126.69424332485생활용수나운1동
4군산시청전라북도 군산시 시청로 17, (조촌동)전라북도 군산시 조촌동 888번지35.967636126.736836178155생활용수조촌동
5감리교회 옆전라북도 군산시 서당길 56-6, (구암동)전라북도 군산시 구암동 59-18번지35.976053126.75313470150생활용수구암동
6명산시장 옆<NA>전라북도 군산시 송창동 3-92번지35.982156126.7106368080생활용수월명동
7둔율동169<NA>전라북도 군산시 둔율동 169-3번지35.981079126.715233150120생활용수월명동
8서초등학교전라북도 군산시 중앙로 230, (금동)전라북도 군산시 금동 20-3번지35.990665126.7065719090식수해신동
9군산여자고등학교전라북도 군산시 월명1길 16, (월명동)전라북도 군산시 월명동 37-1번지35.985187126.70336790100생활용수월명동
시설명소재지도로명주소소재지지번주소위도경도일일생산량심도활용사항관리기관명
41군산대학교 1(두드림센터)전라북도 군산시 미제길 8, (미룡동)전라북도 군산시 신관동 290-235.946939126.6840510076식수나운3동
42군산대학교 2(군산대학교후문)전라북도 군산시 미제길 8, (미룡동)전라북도 군산시 미룡동 458-435.948522126.68434315080식수나운3동
43군산대학교 3(황룡도서관)전라북도 군산시 미제길 8, (미룡동)전라북도 군산시 신관동 290-235.946036126.68355415083식수나운3동
44군산대학교 4(공대2호관)전라북도 군산시 미제길 8, (미룡동)전라북도 군산시 신관동 290-235.97378126.67908610073식수나운3동
45은적사 위 체육공원<NA>전라북도 군산시 소룡동 산127-1번지35.974543126.6859237060생활용수+음용수소룡동
46산북중학교전라북도 군산시 동아로 109, (산북동)전라북도 군산시 산북동 3581번지35.965049126.6830769090식수미성동
47호원대학교1전라북도 군산시 임피면 호원대3길 64, 호원대학교전라북도 군산시 임피면 월하리 727 호원대학교35.0126.090<NA>식수임피면
48호원대학교2전라북도 군산시 임피면 호원대3길 64, 호원대학교전라북도 군산시 임피면 월하리 727 호원대학교35.0126.090<NA>식수임피면
49호원대학교3전라북도 군산시 임피면 호원대3길 64, 호원대학교전라북도 군산시 임피면 월하리 727 호원대학교35.0126.090<NA>식수임피면
50호원대학교4전라북도 군산시 임피면 호원대3길 64, 호원대학교전라북도 군산시 임피면 월하리 727 호원대학교35.0126.090<NA>식수임피면