Overview

Dataset statistics

Number of variables9
Number of observations44
Missing cells21
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory78.0 B

Variable types

Categorical2
Text4
Numeric3

Dataset

Description김해시 민방위 급수시설 현황에 대한 데이터로 시설구분, 사업장명, 영업상태, 전화번호, 주소, 위도 및 경도, 소재지면적 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15033335/fileData.do

Alerts

영업상태명 has constant value ""Constant
경도 is highly overall correlated with 시설구분명High correlation
시설구분명 is highly overall correlated with 경도High correlation
시설구분명 is highly imbalanced (53.0%)Imbalance
전화번호 has 18 (40.9%) missing valuesMissing
도로명주소 has 3 (6.8%) missing valuesMissing
사업장명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:45:52.261971
Analysis finished2023-12-12 20:45:53.865182
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size484.0 B
민간시설
36 
공공시설
4 
정부지원시설
 
3
자치단체자체시설
 
1

Length

Max length8
Median length4
Mean length4.2272727
Min length4

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row공공시설
2nd row민간시설
3rd row민간시설
4th row민간시설
5th row민간시설

Common Values

ValueCountFrequency (%)
민간시설 36
81.8%
공공시설 4
 
9.1%
정부지원시설 3
 
6.8%
자치단체자체시설 1
 
2.3%

Length

2023-12-13T05:45:53.955520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:45:54.073674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간시설 36
81.8%
공공시설 4
 
9.1%
정부지원시설 3
 
6.8%
자치단체자체시설 1
 
2.3%

사업장명
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T05:45:54.292236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length5.8863636
Min length3

Characters and Unicode

Total characters259
Distinct characters116
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

Unique44 ?
Unique (%)100.0%

Sample

1st row부곡마을 식수대
2nd row대우유토피아아파트
3rd row송림탕
4th row홍익아파트
5th row한덕타워아파트
ValueCountFrequency (%)
부곡마을 1
 
2.1%
식수대 1
 
2.1%
장수사우나 1
 
2.1%
지내동 1
 
2.1%
동원1차아파트 1
 
2.1%
분성여자고등학교 1
 
2.1%
금호탕(2 1
 
2.1%
은하탕 1
 
2.1%
청해아파트 1
 
2.1%
청수탕 1
 
2.1%
Other values (38) 38
79.2%
2023-12-13T05:45:54.697690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
탕 17
 
6.6%
파 12
 
4.6%
아 12
 
4.6%
트 11
 
4.2%
동 10
 
3.9%
수 7
 
2.7%
) 6
 
2.3%
( 6
 
2.3%
한 5
 
1.9%
천 5
 
1.9%
Other values (106) 168
64.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 236
91.1%
Decimal Number 7
 
2.7%
Close Punctuation 6
 
2.3%
Open Punctuation 6
 
2.3%
Space Separator 4
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
탕 17
 
7.2%
파 12
 
5.1%
아 12
 
5.1%
트 11
 
4.7%
동 10
 
4.2%
수 7
 
3.0%
한 5
 
2.1%
천 5
 
2.1%
토 4
 
1.7%
원 4
 
1.7%
Other values (98) 149
63.1%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 1
 
14.3%
5 1
 
14.3%
3 1
 
14.3%
0 1
 
14.3%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 236
91.1%
Common 23
 
8.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
탕 17
 
7.2%
파 12
 
5.1%
아 12
 
5.1%
트 11
 
4.7%
동 10
 
4.2%
수 7
 
3.0%
한 5
 
2.1%
천 5
 
2.1%
토 4
 
1.7%
원 4
 
1.7%
Other values (98) 149
63.1%
Common
ValueCountFrequency (%)
) 6
26.1%
( 6
26.1%
4
17.4%
2 3
13.0%
1 1
 
4.3%
5 1
 
4.3%
3 1
 
4.3%
0 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 236
91.1%
ASCII 23
 
8.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
탕 17
 
7.2%
파 12
 
5.1%
아 12
 
5.1%
트 11
 
4.7%
동 10
 
4.2%
수 7
 
3.0%
한 5
 
2.1%
천 5
 
2.1%
토 4
 
1.7%
원 4
 
1.7%
Other values (98) 149
63.1%
ASCII
ValueCountFrequency (%)
) 6
26.1%
( 6
26.1%
4
17.4%
2 3
13.0%
1 1
 
4.3%
5 1
 
4.3%
3 1
 
4.3%
0 1
 
4.3%

영업상태명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size484.0 B
영업
44 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업 44
100.0%

Length

2023-12-13T05:45:54.854810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:45:54.960891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 44
100.0%

전화번호
Text

MISSING 

Distinct25
Distinct (%)96.2%
Missing18
Missing (%)40.9%
Memory size484.0 B
2023-12-13T05:45:55.122627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters312
Distinct characters11
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

Unique24 ?
Unique (%)92.3%

Sample

1st row055-332-2422
2nd row055-359-1601
3rd row055-333-7566
4th row055-321-1148
5th row055-336-3625
ValueCountFrequency (%)
055-321-7191 2
 
7.7%
055-337-2928 1
 
3.8%
055-332-2422 1
 
3.8%
055-334-1591 1
 
3.8%
055-330-7461 1
 
3.8%
055-332-0998 1
 
3.8%
055-327-6061 1
 
3.8%
055-336-2488 1
 
3.8%
055-340-8600 1
 
3.8%
055-312-4410 1
 
3.8%
Other values (15) 15
57.7%
2023-12-13T05:45:55.472817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 61
19.6%
3 53
17.0%
- 52
16.7%
0 42
13.5%
2 23
 
7.4%
1 23
 
7.4%
6 17
 
5.4%
9 12
 
3.8%
4 12
 
3.8%
7 9
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 260
83.3%
Dash Punctuation 52
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 61
23.5%
3 53
20.4%
0 42
16.2%
2 23
 
8.8%
1 23
 
8.8%
6 17
 
6.5%
9 12
 
4.6%
4 12
 
4.6%
7 9
 
3.5%
8 8
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 312
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 61
19.6%
3 53
17.0%
- 52
16.7%
0 42
13.5%
2 23
 
7.4%
1 23
 
7.4%
6 17
 
5.4%
9 12
 
3.8%
4 12
 
3.8%
7 9
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 61
19.6%
3 53
17.0%
- 52
16.7%
0 42
13.5%
2 23
 
7.4%
1 23
 
7.4%
6 17
 
5.4%
9 12
 
3.8%
4 12
 
3.8%
7 9
 
2.9%

지번주소
Text

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size484.0 B
2023-12-13T05:45:55.716233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length23
Mean length20.25
Min length17

Characters and Unicode

Total characters891
Distinct characters59
Distinct categories3 ?
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 (%)95.5%

Sample

1st row경상남도 김해시 부곡동 403번지 6호
2nd row경상남도 김해시 어방동 499번지 대우유토피아아파트
3rd row경상남도 김해시 내동 694번지
4th row경상남도 김해시 내동 121번지 1호
5th row경상남도 김해시 동상동 495번지 9호
ValueCountFrequency (%)
경상남도 44
21.3%
김해시 44
21.3%
삼계동 6
 
2.9%
2호 6
 
2.9%
1호 5
 
2.4%
삼방동 5
 
2.4%
어방동 4
 
1.9%
내동 4
 
1.9%
구산동 4
 
1.9%
4호 3
 
1.4%
Other values (70) 82
39.6%
2023-12-13T05:45:56.070317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
163
18.3%
상 49
 
5.5%
동 46
 
5.2%
지 46
 
5.2%
경 44
 
4.9%
남 44
 
4.9%
도 44
 
4.9%
김 44
 
4.9%
해 44
 
4.9%
시 44
 
4.9%
Other values (49) 323
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
63.2%
Decimal Number 165
 
18.5%
Space Separator 163
 
18.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 49
8.7%
동 46
8.2%
지 46
8.2%
경 44
 
7.8%
남 44
 
7.8%
도 44
 
7.8%
김 44
 
7.8%
해 44
 
7.8%
시 44
 
7.8%
번 44
 
7.8%
Other values (38) 114
20.2%
Decimal Number
ValueCountFrequency (%)
1 34
20.6%
2 22
13.3%
4 21
12.7%
9 17
10.3%
5 15
9.1%
7 15
9.1%
6 13
 
7.9%
3 12
 
7.3%
8 8
 
4.8%
0 8
 
4.8%
Space Separator
ValueCountFrequency (%)
163
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
63.2%
Common 328
36.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 49
8.7%
동 46
8.2%
지 46
8.2%
경 44
 
7.8%
남 44
 
7.8%
도 44
 
7.8%
김 44
 
7.8%
해 44
 
7.8%
시 44
 
7.8%
번 44
 
7.8%
Other values (38) 114
20.2%
Common
ValueCountFrequency (%)
163
49.7%
1 34
 
10.4%
2 22
 
6.7%
4 21
 
6.4%
9 17
 
5.2%
5 15
 
4.6%
7 15
 
4.6%
6 13
 
4.0%
3 12
 
3.7%
8 8
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
63.2%
ASCII 328
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163
49.7%
1 34
 
10.4%
2 22
 
6.7%
4 21
 
6.4%
9 17
 
5.2%
5 15
 
4.6%
7 15
 
4.6%
6 13
 
4.0%
3 12
 
3.7%
8 8
 
2.4%
Hangul
ValueCountFrequency (%)
상 49
8.7%
동 46
8.2%
지 46
8.2%
경 44
 
7.8%
남 44
 
7.8%
도 44
 
7.8%
김 44
 
7.8%
해 44
 
7.8%
시 44
 
7.8%
번 44
 
7.8%
Other values (38) 114
20.2%

도로명주소
Text

MISSING 

Distinct40
Distinct (%)97.6%
Missing3
Missing (%)6.8%
Memory size484.0 B
2023-12-13T05:45:56.320861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length27.804878
Min length20

Characters and Unicode

Total characters1140
Distinct characters99
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

Unique39 ?
Unique (%)95.1%

Sample

1st row경상남도 김해시 장유로116번길 44-24 (부곡동)
2nd row경상남도 김해시 인제로 167 (어방동, 대우유토피아아파트)
3rd row경상남도 김해시 금관대로1359번길 1 (내동)
4th row경상남도 김해시 평전로 211 (내동, 홍익아파트)
5th row경상남도 김해시 가야로515번길 7 (동상동, 한덕타워아파트)
ValueCountFrequency (%)
경상남도 41
 
18.6%
김해시 41
 
18.6%
삼계동 6
 
2.7%
삼방동 4
 
1.8%
내동 4
 
1.8%
어방동 4
 
1.8%
동상동 3
 
1.4%
7 3
 
1.4%
삼안로 3
 
1.4%
구산동 3
 
1.4%
Other values (98) 108
49.1%
2023-12-13T05:45:56.692489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
 
15.7%
동 48
 
4.2%
해 47
 
4.1%
상 46
 
4.0%
김 44
 
3.9%
로 42
 
3.7%
남 42
 
3.7%
1 42
 
3.7%
( 41
 
3.6%
) 41
 
3.6%
Other values (89) 568
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 668
58.6%
Decimal Number 181
 
15.9%
Space Separator 179
 
15.7%
Open Punctuation 41
 
3.6%
Close Punctuation 41
 
3.6%
Dash Punctuation 16
 
1.4%
Other Punctuation 14
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 48
 
7.2%
해 47
 
7.0%
상 46
 
6.9%
김 44
 
6.6%
로 42
 
6.3%
남 42
 
6.3%
경 41
 
6.1%
시 41
 
6.1%
도 41
 
6.1%
번 24
 
3.6%
Other values (74) 252
37.7%
Decimal Number
ValueCountFrequency (%)
1 42
23.2%
7 23
12.7%
2 21
11.6%
4 21
11.6%
3 20
11.0%
6 15
 
8.3%
5 13
 
7.2%
8 11
 
6.1%
9 9
 
5.0%
0 6
 
3.3%
Space Separator
ValueCountFrequency (%)
179
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 668
58.6%
Common 472
41.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 48
 
7.2%
해 47
 
7.0%
상 46
 
6.9%
김 44
 
6.6%
로 42
 
6.3%
남 42
 
6.3%
경 41
 
6.1%
시 41
 
6.1%
도 41
 
6.1%
번 24
 
3.6%
Other values (74) 252
37.7%
Common
ValueCountFrequency (%)
179
37.9%
1 42
 
8.9%
( 41
 
8.7%
) 41
 
8.7%
7 23
 
4.9%
2 21
 
4.4%
4 21
 
4.4%
3 20
 
4.2%
- 16
 
3.4%
6 15
 
3.2%
Other values (5) 53
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 668
58.6%
ASCII 472
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
37.9%
1 42
 
8.9%
( 41
 
8.7%
) 41
 
8.7%
7 23
 
4.9%
2 21
 
4.4%
4 21
 
4.4%
3 20
 
4.2%
- 16
 
3.4%
6 15
 
3.2%
Other values (5) 53
 
11.2%
Hangul
ValueCountFrequency (%)
동 48
 
7.2%
해 47
 
7.0%
상 46
 
6.9%
김 44
 
6.6%
로 42
 
6.3%
남 42
 
6.3%
경 41
 
6.1%
시 41
 
6.1%
도 41
 
6.1%
번 24
 
3.6%
Other values (74) 252
37.7%

위도
Real number (ℝ)

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.239872
Minimum35.186816
Maximum35.301505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:45:56.817418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.186816
5-th percentile35.199809
Q135.230425
median35.237713
Q335.249589
95-th percentile35.268852
Maximum35.301505
Range0.11468936
Interquartile range (IQR)0.019164313

Descriptive statistics

Standard deviation0.02062851
Coefficient of variation (CV)0.00058537415
Kurtosis1.9958508
Mean35.239872
Median Absolute Deviation (MAD)0.009497695
Skewness-0.071535745
Sum1550.5544
Variance0.00042553544
MonotonicityNot monotonic
2023-12-13T05:45:56.953703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
35.23771335 2
 
4.5%
35.21121301 1
 
2.3%
35.24391397 1
 
2.3%
35.22939183 1
 
2.3%
35.2662747 1
 
2.3%
35.22851086 1
 
2.3%
35.23382217 1
 
2.3%
35.22526599 1
 
2.3%
35.22191709 1
 
2.3%
35.23017438 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
35.18681553 1
2.3%
35.19135366 1
2.3%
35.19779599 1
2.3%
35.21121301 1
2.3%
35.22175482 1
2.3%
35.22191709 1
2.3%
35.22526599 1
2.3%
35.22851086 1
2.3%
35.22939183 1
2.3%
35.22986227 1
2.3%
ValueCountFrequency (%)
35.30150489 1
2.3%
35.27298685 1
2.3%
35.26930699 1
2.3%
35.2662747 1
2.3%
35.26309338 1
2.3%
35.26210891 1
2.3%
35.26174382 1
2.3%
35.25211284 1
2.3%
35.25202493 1
2.3%
35.25132068 1
2.3%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.87392
Minimum128.74051
Maximum128.92539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:45:57.075538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.74051
5-th percentile128.79367
Q1128.86486
median128.87582
Q3128.9018
95-th percentile128.91677
Maximum128.92539
Range0.1848777
Interquartile range (IQR)0.0369371

Descriptive statistics

Standard deviation0.038221991
Coefficient of variation (CV)0.00029658438
Kurtosis3.1428827
Mean128.87392
Median Absolute Deviation (MAD)0.01392375
Skewness-1.6233009
Sum5670.4523
Variance0.0014609206
MonotonicityNot monotonic
2023-12-13T05:45:57.291072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
128.9028253 2
 
4.5%
128.7955909 1
 
2.3%
128.901774 1
 
2.3%
128.9245035 1
 
2.3%
128.8742113 1
 
2.3%
128.8803154 1
 
2.3%
128.8927378 1
 
2.3%
128.8740811 1
 
2.3%
128.8591561 1
 
2.3%
128.8990557 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
128.74051 1
2.3%
128.7907707 1
2.3%
128.7933362 1
2.3%
128.7955909 1
2.3%
128.7970316 1
2.3%
128.8591561 1
2.3%
128.8596415 1
2.3%
128.8615376 1
2.3%
128.8643641 1
2.3%
128.8645055 1
2.3%
ValueCountFrequency (%)
128.9253877 1
2.3%
128.9245035 1
2.3%
128.9177331 1
2.3%
128.9112881 1
2.3%
128.9110119 1
2.3%
128.910715 1
2.3%
128.9068351 1
2.3%
128.9065417 1
2.3%
128.9028253 2
4.5%
128.9018769 1
2.3%

소재지면적
Real number (ℝ)

Distinct19
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean204.59091
Minimum40
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:45:57.490179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile73
Q1100
median120
Q3202.5
95-th percentile600
Maximum1000
Range960
Interquartile range (IQR)102.5

Descriptive statistics

Standard deviation193.71056
Coefficient of variation (CV)0.94681902
Kurtosis6.629799
Mean204.59091
Median Absolute Deviation (MAD)30
Skewness2.4791045
Sum9002
Variance37523.782
MonotonicityNot monotonic
2023-12-13T05:45:57.660576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100 16
36.4%
200 6
 
13.6%
120 3
 
6.8%
90 2
 
4.5%
600 2
 
4.5%
150 2
 
4.5%
210 1
 
2.3%
450 1
 
2.3%
70 1
 
2.3%
40 1
 
2.3%
Other values (9) 9
20.5%
ValueCountFrequency (%)
40 1
 
2.3%
60 1
 
2.3%
70 1
 
2.3%
90 2
 
4.5%
100 16
36.4%
120 3
 
6.8%
139 1
 
2.3%
150 2
 
4.5%
200 6
 
13.6%
210 1
 
2.3%
ValueCountFrequency (%)
1000 1
2.3%
700 1
2.3%
600 2
4.5%
450 1
2.3%
400 1
2.3%
323 1
2.3%
300 1
2.3%
240 1
2.3%
230 1
2.3%
210 1
2.3%

Interactions

2023-12-13T05:45:53.251487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:52.706846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:52.968529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:53.339024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:52.794246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:53.057130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:53.430882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:52.883772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:45:53.152596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:45:57.757247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설구분명사업장명전화번호지번주소도로명주소위도경도소재지면적
시설구분명1.0001.0001.0001.0001.0000.6160.7320.000
사업장명1.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0000.983
지번주소1.0001.0001.0001.0001.0001.0001.0000.984
도로명주소1.0001.0001.0001.0001.0001.0001.0000.977
위도0.6161.0001.0001.0001.0001.0000.6200.000
경도0.7321.0001.0001.0001.0000.6201.0000.000
소재지면적0.0001.0000.9830.9840.9770.0000.0001.000
2023-12-13T05:45:57.881226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도소재지면적시설구분명
위도1.0000.0250.1530.291
경도0.0251.000-0.2630.530
소재지면적0.153-0.2631.0000.000
시설구분명0.2910.5300.0001.000

Missing values

2023-12-13T05:45:53.558301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:45:53.719014image/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.
2023-12-13T05:45:53.813966image/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

시설구분명사업장명영업상태명전화번호지번주소도로명주소위도경도소재지면적
0공공시설부곡마을 식수대영업<NA>경상남도 김해시 부곡동 403번지 6호경상남도 김해시 장유로116번길 44-24 (부곡동)35.211213128.79559190
1민간시설대우유토피아아파트영업<NA>경상남도 김해시 어방동 499번지 대우유토피아아파트경상남도 김해시 인제로 167 (어방동, 대우유토피아아파트)35.243914128.901774100
2민간시설송림탕영업055-332-2422경상남도 김해시 내동 694번지경상남도 김해시 금관대로1359번길 1 (내동)35.24403128.865895200
3민간시설홍익아파트영업<NA>경상남도 김해시 내동 121번지 1호경상남도 김해시 평전로 211 (내동, 홍익아파트)35.247506128.864364700
4민간시설한덕타워아파트영업<NA>경상남도 김해시 동상동 495번지 9호경상남도 김해시 가야로515번길 7 (동상동, 한덕타워아파트)35.237146128.889385600
5민간시설광남아파트영업<NA>경상남도 김해시 동상동 835번지경상남도 김해시 분성로335번길 38 (동상동, 광남아파트)35.237222128.882242400
6민간시설장복2차아파트(203동)영업<NA>경상남도 김해시 진영읍 344번지경상남도 김해시 진영읍 진영로 262-24 (장복2차아파트)35.301505128.74051150
7정부지원시설내외동행정복지센터영업055-359-1601경상남도 김해시 내동 1116번지 4호경상남도 김해시 내외로 67 (내동, 내외동행정복지센터)35.235181128.864622200
8민간시설가야탕영업055-333-7566경상남도 김해시 삼방동 690번지 1호경상남도 김해시 삼안로 232 (삼방동, 동원아파트)35.248701128.911012300
9민간시설장안탕영업055-321-1148경상남도 김해시 서상동 25번지 1호경상남도 김해시 가락로 100-7 (서상동)35.235655128.8816760
시설구분명사업장명영업상태명전화번호지번주소도로명주소위도경도소재지면적
34민간시설금호탕영업055-321-7191경상남도 김해시 어방동 1129번지 11호경상남도 김해시 분성로501번길 34 (어방동, 금호빌딩)35.237713128.902825200
35민간시설녹수탕영업055-334-1591경상남도 김해시 삼방동 578번지 12호경상남도 김해시 삼안로297번길 15 (삼방동)35.250632128.906542139
36민간시설화인아파트영업<NA>경상남도 김해시 삼방동 692번지경상남도 김해시 삼안로 264 (삼방동)35.251321128.91128840
37민간시설한일아파트영업<NA>경상남도 김해시 삼방동 691번지 2호경상남도 김해시 삼안로 244 (삼방동, 한일아파트)35.249241128.91071570
38민간시설제5주공아파트영업<NA>경상남도 김해시 구산동 410번지경상남도 김해시 가락로294번길 17-1 (구산동)35.252025128.873994120
39민간시설천호탕영업<NA>경상남도 김해시 구산동 530번지 4호경상남도 김해시 해반천로 34-17 (구산동)35.252113128.871109100
40민간시설용수탕영업055-336-6812경상남도 김해시 내동 639번지경상남도 김해시 금관대로1347번길 7 (내동)35.243766128.864943600
41정부지원시설북부동새동네공원영업<NA>경상남도 김해시 구산동 281번지<NA>35.248195128.870624200
42정부지원시설삼안동주민센터(맞은편)영업<NA>경상남도 김해시 삼방동 246번지 녹지공간 내<NA>35.244416128.906835100
43민간시설무계리 간이상수도(한국토지공사)영업<NA>경상남도 김해시 삼문동 63번지 7호<NA>35.197796128.793336450