Overview

Dataset statistics

Number of variables11
Number of observations47
Missing cells21
Missing cells (%)4.1%
Duplicate rows1
Duplicate rows (%)2.1%
Total size in memory4.4 KiB
Average record size in memory94.8 B

Variable types

Categorical5
Text3
Numeric2
DateTime1

Dataset

Description경상남도 김해시 횡단보도 보행신호 음성안내 보조장치 설치 시설명, 위치도, 설치연도, 설치수량 , 관리기관 등의 데이터로 구성되어 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15106347/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 1 (2.1%) duplicate rowsDuplicates
관리기관명 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
설치연도 is highly overall correlated with 시설종류 and 2 other fieldsHigh correlation
시설종류 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
전화번호 is highly overall correlated with 위도 and 5 other fieldsHigh correlation
설치수량 is highly overall correlated with 시설종류 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 시설종류 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 시설종류 and 2 other fieldsHigh correlation
시설종류 is highly imbalanced (74.6%)Imbalance
관리기관명 is highly imbalanced (74.6%)Imbalance
전화번호 is highly imbalanced (74.6%)Imbalance
설치수량 is highly imbalanced (68.1%)Imbalance
대상시설명 has 2 (4.3%) missing valuesMissing
소재지도로명주소 has 11 (23.4%) missing valuesMissing
소재지지번주소 has 2 (4.3%) missing valuesMissing
위도 has 2 (4.3%) missing valuesMissing
경도 has 2 (4.3%) missing valuesMissing
데이터기준일자 has 2 (4.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 06:22:44.105659
Analysis finished2023-12-12 06:22:45.433305
Duration1.33 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
초등학교
45 
<NA>
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
초등학교 45
95.7%
<NA> 2
 
4.3%

Length

2023-12-12T15:22:45.515200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:45.673450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
초등학교 45
95.7%
na 2
 
4.3%

대상시설명
Text

MISSING 

Distinct45
Distinct (%)100.0%
Missing2
Missing (%)4.3%
Memory size508.0 B
2023-12-12T15:22:45.908064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length6.0888889
Min length6

Characters and Unicode

Total characters274
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row경운초등학교
2nd row삼계초등학교
3rd row봉명초등학교
4th row신어초등학교
5th row분성초등학교
ValueCountFrequency (%)
우암초등학교 1
 
2.2%
수남초등학교 1
 
2.2%
구봉초등학교 1
 
2.2%
구산초등학교 1
 
2.2%
구지초등학교 1
 
2.2%
내동초등학교 1
 
2.2%
임호초등학교 1
 
2.2%
대곡초등학교 1
 
2.2%
삼성초등학교 1
 
2.2%
신명초등학교 1
 
2.2%
Other values (35) 35
77.8%
2023-12-12T15:22:46.680339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
등 46
16.8%
초 45
16.4%
학 45
16.4%
교 45
16.4%
동 7
 
2.6%
봉 4
 
1.5%
산 4
 
1.5%
대 4
 
1.5%
구 3
 
1.1%
삼 3
 
1.1%
Other values (51) 68
24.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 274
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 46
16.8%
초 45
16.4%
학 45
16.4%
교 45
16.4%
동 7
 
2.6%
봉 4
 
1.5%
산 4
 
1.5%
대 4
 
1.5%
구 3
 
1.1%
삼 3
 
1.1%
Other values (51) 68
24.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 274
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 46
16.8%
초 45
16.4%
학 45
16.4%
교 45
16.4%
동 7
 
2.6%
봉 4
 
1.5%
산 4
 
1.5%
대 4
 
1.5%
구 3
 
1.1%
삼 3
 
1.1%
Other values (51) 68
24.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 274
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
등 46
16.8%
초 45
16.4%
학 45
16.4%
교 45
16.4%
동 7
 
2.6%
봉 4
 
1.5%
산 4
 
1.5%
대 4
 
1.5%
구 3
 
1.1%
삼 3
 
1.1%
Other values (51) 68
24.8%

소재지도로명주소
Text

MISSING 

Distinct36
Distinct (%)100.0%
Missing11
Missing (%)23.4%
Memory size508.0 B
2023-12-12T15:22:46.968562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length17.277778
Min length12

Characters and Unicode

Total characters622
Distinct characters62
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

Unique36 ?
Unique (%)100.0%

Sample

1st row경상남도 김해시 우암로73번길 8
2nd row경상남도 김해시 해반천로 211
3rd row경상남도 김해시 함박로 74
4th row경상남도 김해시 활천로 303
5th row경상남도 김해시 장등로 37
ValueCountFrequency (%)
경상남도 36
24.3%
김해시 34
23.0%
월산로 4
 
2.7%
활천로 3
 
2.0%
진영읍 3
 
2.0%
8 2
 
1.4%
장등로 2
 
1.4%
함박로 2
 
1.4%
율하2로 2
 
1.4%
42 1
 
0.7%
Other values (59) 59
39.9%
2023-12-12T15:22:47.472907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
114
18.3%
상 38
 
6.1%
경 36
 
5.8%
남 36
 
5.8%
도 36
 
5.8%
로 36
 
5.8%
해 35
 
5.6%
김 34
 
5.5%
시 34
 
5.5%
1 25
 
4.0%
Other values (52) 198
31.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 385
61.9%
Decimal Number 118
 
19.0%
Space Separator 114
 
18.3%
Dash Punctuation 5
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 38
9.9%
경 36
9.4%
남 36
9.4%
도 36
9.4%
로 36
9.4%
해 35
9.1%
김 34
8.8%
시 34
8.8%
번 9
 
2.3%
길 7
 
1.8%
Other values (40) 84
21.8%
Decimal Number
ValueCountFrequency (%)
1 25
21.2%
3 16
13.6%
5 15
12.7%
2 14
11.9%
4 12
10.2%
7 10
 
8.5%
0 9
 
7.6%
9 7
 
5.9%
8 6
 
5.1%
6 4
 
3.4%
Space Separator
ValueCountFrequency (%)
114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
61.9%
Common 237
38.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 38
9.9%
경 36
9.4%
남 36
9.4%
도 36
9.4%
로 36
9.4%
해 35
9.1%
김 34
8.8%
시 34
8.8%
번 9
 
2.3%
길 7
 
1.8%
Other values (40) 84
21.8%
Common
ValueCountFrequency (%)
114
48.1%
1 25
 
10.5%
3 16
 
6.8%
5 15
 
6.3%
2 14
 
5.9%
4 12
 
5.1%
7 10
 
4.2%
0 9
 
3.8%
9 7
 
3.0%
8 6
 
2.5%
Other values (2) 9
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
61.9%
ASCII 237
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114
48.1%
1 25
 
10.5%
3 16
 
6.8%
5 15
 
6.3%
2 14
 
5.9%
4 12
 
5.1%
7 10
 
4.2%
0 9
 
3.8%
9 7
 
3.0%
8 6
 
2.5%
Other values (2) 9
 
3.8%
Hangul
ValueCountFrequency (%)
상 38
9.9%
경 36
9.4%
남 36
9.4%
도 36
9.4%
로 36
9.4%
해 35
9.1%
김 34
8.8%
시 34
8.8%
번 9
 
2.3%
길 7
 
1.8%
Other values (40) 84
21.8%

소재지지번주소
Text

MISSING 

Distinct45
Distinct (%)100.0%
Missing2
Missing (%)4.3%
Memory size508.0 B
2023-12-12T15:22:47.779518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length21
Mean length17.333333
Min length7

Characters and Unicode

Total characters780
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row경상남도 내동 1098
2nd row경상남도 김해시 삼계동 1519-7
3rd row경상남도 김해시 외동 1449-2
4th row경상남도 김해시 삼방동 200
5th row경상남도 김해시 삼계동 1520-3
ValueCountFrequency (%)
경상남도 44
24.3%
김해시 41
22.7%
진영읍 5
 
2.8%
삼계동 4
 
2.2%
부곡동 4
 
2.2%
외동 4
 
2.2%
구산동 3
 
1.7%
진영리 3
 
1.7%
관동동 3
 
1.7%
내동 3
 
1.7%
Other values (61) 67
37.0%
2023-12-12T15:22:48.251161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
136
17.4%
상 46
 
5.9%
경 44
 
5.6%
남 44
 
5.6%
도 44
 
5.6%
동 43
 
5.5%
김 41
 
5.3%
해 41
 
5.3%
시 41
 
5.3%
1 40
 
5.1%
Other values (42) 260
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 446
57.2%
Decimal Number 175
 
22.4%
Space Separator 136
 
17.4%
Dash Punctuation 23
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 46
10.3%
경 44
9.9%
남 44
9.9%
도 44
9.9%
동 43
9.6%
김 41
9.2%
해 41
9.2%
시 41
9.2%
삼 9
 
2.0%
영 9
 
2.0%
Other values (30) 84
18.8%
Decimal Number
ValueCountFrequency (%)
1 40
22.9%
3 25
14.3%
5 21
12.0%
6 17
9.7%
2 16
 
9.1%
0 16
 
9.1%
8 11
 
6.3%
4 11
 
6.3%
7 10
 
5.7%
9 8
 
4.6%
Space Separator
ValueCountFrequency (%)
136
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 446
57.2%
Common 334
42.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 46
10.3%
경 44
9.9%
남 44
9.9%
도 44
9.9%
동 43
9.6%
김 41
9.2%
해 41
9.2%
시 41
9.2%
삼 9
 
2.0%
영 9
 
2.0%
Other values (30) 84
18.8%
Common
ValueCountFrequency (%)
136
40.7%
1 40
 
12.0%
3 25
 
7.5%
- 23
 
6.9%
5 21
 
6.3%
6 17
 
5.1%
2 16
 
4.8%
0 16
 
4.8%
8 11
 
3.3%
4 11
 
3.3%
Other values (2) 18
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 445
57.1%
ASCII 334
42.8%
Compat Jamo 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
136
40.7%
1 40
 
12.0%
3 25
 
7.5%
- 23
 
6.9%
5 21
 
6.3%
6 17
 
5.1%
2 16
 
4.8%
0 16
 
4.8%
8 11
 
3.3%
4 11
 
3.3%
Other values (2) 18
 
5.4%
Hangul
ValueCountFrequency (%)
상 46
10.3%
경 44
9.9%
남 44
9.9%
도 44
9.9%
동 43
9.7%
김 41
9.2%
해 41
9.2%
시 41
9.2%
삼 9
 
2.0%
영 9
 
2.0%
Other values (29) 83
18.7%
Compat Jamo
ValueCountFrequency (%)
ㅏ 1
100.0%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)100.0%
Missing2
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean35.231913
Minimum35.163793
Maximum35.312681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:22:48.453540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.163793
5-th percentile35.170751
Q135.2005
median35.233567
Q335.252646
95-th percentile35.308692
Maximum35.312681
Range0.148888
Interquartile range (IQR)0.052146

Descriptive statistics

Standard deviation0.041603588
Coefficient of variation (CV)0.0011808495
Kurtosis-0.52114409
Mean35.231913
Median Absolute Deviation (MAD)0.031026
Skewness0.30780534
Sum1585.4361
Variance0.0017308585
MonotonicityNot monotonic
2023-12-12T15:22:48.645667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
35.202121 1
 
2.1%
35.244414 1
 
2.1%
35.252646 1
 
2.1%
35.244365 1
 
2.1%
35.242686 1
 
2.1%
35.234012 1
 
2.1%
35.242422 1
 
2.1%
35.232915 1
 
2.1%
35.269997 1
 
2.1%
35.253768 1
 
2.1%
Other values (35) 35
74.5%
(Missing) 2
 
4.3%
ValueCountFrequency (%)
35.163793 1
2.1%
35.167671 1
2.1%
35.170286 1
2.1%
35.172612 1
2.1%
35.177363 1
2.1%
35.177773 1
2.1%
35.178566 1
2.1%
35.185083 1
2.1%
35.189215 1
2.1%
35.198216 1
2.1%
ValueCountFrequency (%)
35.312681 1
2.1%
35.309707 1
2.1%
35.308973 1
2.1%
35.3075683 1
2.1%
35.303871 1
2.1%
35.302426 1
2.1%
35.269997 1
2.1%
35.266598 1
2.1%
35.264623 1
2.1%
35.263229 1
2.1%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)100.0%
Missing2
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean128.83673
Minimum128.72634
Maximum128.93045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T15:22:48.855460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.72634
5-th percentile128.73114
Q1128.80306
median128.84807
Q3128.8716
95-th percentile128.91169
Maximum128.93045
Range0.2041085
Interquartile range (IQR)0.068541

Descriptive statistics

Standard deviation0.053413868
Coefficient of variation (CV)0.00041458571
Kurtosis-0.35116725
Mean128.83673
Median Absolute Deviation (MAD)0.039109
Skewness-0.45289463
Sum5797.6529
Variance0.0028530413
MonotonicityNot monotonic
2023-12-12T15:22:49.031716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
128.803061 1
 
2.1%
128.872168 1
 
2.1%
128.871786 1
 
2.1%
128.87221 1
 
2.1%
128.860619 1
 
2.1%
128.870536 1
 
2.1%
128.90048 1
 
2.1%
128.900168 1
 
2.1%
128.864942 1
 
2.1%
128.907569 1
 
2.1%
Other values (35) 35
74.5%
(Missing) 2
 
4.3%
ValueCountFrequency (%)
128.726341 1
2.1%
128.727273 1
2.1%
128.730095 1
2.1%
128.73533 1
2.1%
128.738666 1
2.1%
128.790852 1
2.1%
128.794066 1
2.1%
128.794299 1
2.1%
128.799573 1
2.1%
128.80027 1
2.1%
ValueCountFrequency (%)
128.9304495 1
2.1%
128.9214314 1
2.1%
128.912717 1
2.1%
128.907569 1
2.1%
128.90048 1
2.1%
128.900168 1
2.1%
128.899238 1
2.1%
128.887184 1
2.1%
128.87221 1
2.1%
128.872168 1
2.1%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
경상남도 김해시청
45 
<NA>
 
2

Length

Max length9
Median length9
Mean length8.787234
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경상남도 김해시청
2nd row경상남도 김해시청
3rd row경상남도 김해시청
4th row경상남도 김해시청
5th row경상남도 김해시청

Common Values

ValueCountFrequency (%)
경상남도 김해시청 45
95.7%
<NA> 2
 
4.3%

Length

2023-12-12T15:22:49.235427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:49.370486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 45
48.9%
김해시청 45
48.9%
na 2
 
2.2%

전화번호
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size508.0 B
055-330-4668
45 
<NA>
 
2

Length

Max length12
Median length12
Mean length11.659574
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row055-330-4668
2nd row055-330-4668
3rd row055-330-4668
4th row055-330-4668
5th row055-330-4668

Common Values

ValueCountFrequency (%)
055-330-4668 45
95.7%
<NA> 2
 
4.3%

Length

2023-12-12T15:22:49.494552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:49.622688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
055-330-4668 45
95.7%
na 2
 
4.3%

설치연도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2022
20 
2021
19 
2020
6 
<NA>
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2022 20
42.6%
2021 19
40.4%
2020 6
 
12.8%
<NA> 2
 
4.3%

Length

2023-12-12T15:22:49.738039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:49.869369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 20
42.6%
2021 19
40.4%
2020 6
 
12.8%
na 2
 
4.3%

설치수량
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size508.0 B
2
43 
4
 
2
<NA>
 
2

Length

Max length4
Median length1
Mean length1.1276596
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 43
91.5%
4 2
 
4.3%
<NA> 2
 
4.3%

Length

2023-12-12T15:22:49.998425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:22:50.124072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 43
91.5%
4 2
 
4.3%
na 2
 
4.3%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)2.2%
Missing2
Missing (%)4.3%
Memory size508.0 B
Minimum2022-08-31 00:00:00
Maximum2022-08-31 00:00:00
2023-12-12T15:22:50.225632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:22:50.352040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T15:22:44.733213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:22:44.543941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:22:44.832438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:22:44.640802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:22:50.450032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대상시설명소재지도로명주소소재지지번주소위도경도설치연도설치수량
대상시설명1.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0001.0000.6890.0000.302
경도1.0001.0001.0000.6891.0000.2900.546
설치연도1.0001.0001.0000.0000.2901.0000.086
설치수량1.0001.0001.0000.3020.5460.0861.000
2023-12-12T15:22:50.562384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명설치연도시설종류전화번호설치수량
관리기관명1.0001.0001.0001.0001.000
설치연도1.0001.0001.0001.0000.136
시설종류1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000
설치수량1.0000.1361.0001.0001.000
2023-12-12T15:22:50.668984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도시설종류관리기관명전화번호설치연도설치수량
위도1.0000.2351.0001.0001.0000.0000.269
경도0.2351.0001.0001.0001.0000.1530.383
시설종류1.0001.0001.0001.0001.0001.0001.000
관리기관명1.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.000
설치연도0.0000.1531.0001.0001.0001.0000.136
설치수량0.2690.3831.0001.0001.0000.1361.000

Missing values

2023-12-12T15:22:44.973821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:22:45.117855image/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.
2023-12-12T15:22:45.292453image/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

시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명전화번호설치연도설치수량데이터기준일자
0초등학교경운초등학교경상남도 김해시 우암로73번길 8경상남도 내동 109835.237316128.861245경상남도 김해시청055-330-4668202022022-08-31
1초등학교삼계초등학교경상남도 김해시 해반천로 211경상남도 김해시 삼계동 1519-735.266598128.865272경상남도 김해시청055-330-4668202022022-08-31
2초등학교봉명초등학교경상남도 김해시 함박로 74경상남도 김해시 외동 1449-235.232648128.86219경상남도 김해시청055-330-4668202022022-08-31
3초등학교신어초등학교경상남도 김해시 활천로 303경상남도 김해시 삼방동 20035.243776128.912717경상남도 김해시청055-330-4668202022022-08-31
4초등학교분성초등학교<NA>경상남도 김해시 삼계동 1520-335.264623128.871602경상남도 김해시청055-330-4668202022022-08-31
5초등학교금병초등학교경상남도 김해시 장등로 37경상남도 김해시 진영읍 진영리ㅏ 1611-535.309707128.73533경상남도 김해시청055-330-4668202022022-08-31
6초등학교가야초등학교<NA>경상남도 김해시 외동 76035.233567128.848075경상남도 김해시청055-330-4668202122022-08-31
7초등학교어방초등학교경상남도 김해시 활천로 159경상남도 김해시 어방동 767-335.239578128.899238경상남도 김해시청055-330-4668202122022-08-31
8초등학교외동초등학교경상남도 김해시 금관대로1190번길 55-7경상남도 김해시 외동 121835.233074128.858936경상남도 김해시청055-330-4668202122022-08-31
9초등학교화정초등학교<NA>경상남도 김해시 삼계동 1520-235.263229128.867152경상남도 김해시청055-330-4668202122022-08-31
시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명전화번호설치연도설치수량데이터기준일자
37초등학교율산초등학교경상남도 김해시 율하5로 55경상남도 김해시 장유동 58435.163793128.828564경상남도 김해시청055-330-4668202222022-08-31
38초등학교율하초등학교경상남도 율하2로 50경상남도 김해시 율하동 1299-135.172612128.808622경상남도 김해시청055-330-4668202222022-08-31
39초등학교진영중앙초등학교경상남도 김해시 구지로 204경상남도 김해시 진영읍 진영리 15735.308973128.727273경상남도 김해시청055-330-4668202222022-08-31
40초등학교모산초등학교경상남도 김해시 율하로 483경상남도 김해시 장유동 84835.167671128.82695경상남도 김해시청055-330-4668202222022-08-31
41초등학교활천초등학교경상남도 김해시 분성로 727경상남도 김해시 지내동 312-735.231546128.921431경상남도 김해시청055-330-4668202222022-08-31
42초등학교금동초등학교경상남도 김해시 상동면 상동로 554경상남도 김해시 상동면 대감리 623-335.307568128.93045경상남도 김해시청055-330-4668202222022-08-31
43초등학교석봉초등학교경상남도 김해시 월산로 27경상남도 김해시 부곡동 1165-235.2005128.809261경상남도 김해시청055-330-4668202222022-08-31
44초등학교주촌초등학교경상남도 김해시 주촌면 천곡로 39경상남도 김해시 천곡리 1535-1335.232349128.837176경상남도 김해시청055-330-4668202222022-08-31
45<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
46<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시설종류대상시설명소재지도로명주소소재지지번주소위도경도관리기관명전화번호설치연도설치수량데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2