Overview

Dataset statistics

Number of variables18
Number of observations2206
Missing cells2724
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory314.7 KiB
Average record size in memory146.1 B

Variable types

Numeric2
Categorical9
DateTime4
Text3

Dataset

Description울산소방본부에서 제공하는 유해화학물질사고 현황 데이터임 긴급구조 분류명, 긴급구조 규모, 관할서 및 센터명, 접수일시와 상황종료 일시 등의 정보를 제공하는 데이터임
Author소방청
URLhttps://www.data.go.kr/data/15080940/fileData.do

Alerts

긴급구조 규모명 has constant value ""Constant
긴급구조 구군명 is highly overall correlated with 긴급구조 시도명 and 2 other fieldsHigh correlation
타시도신고여부 is highly overall correlated with 긴급구조 시도명High correlation
접수경로명 is highly overall correlated with 긴급구조 시도명High correlation
긴급구조 시도명 is highly overall correlated with 일련번호 and 8 other fieldsHigh correlation
관할서명 is highly overall correlated with 긴급구조 시도명 and 2 other fieldsHigh correlation
서센터명 is highly overall correlated with 긴급구조 시도명 and 2 other fieldsHigh correlation
긴급구조 분류명 is highly overall correlated with 긴급구조 시도명 and 1 other fieldsHigh correlation
긴급구조 종별명 is highly overall correlated with 긴급구조 시도명 and 1 other fieldsHigh correlation
일련번호 is highly overall correlated with 긴급구조 시도명High correlation
긴급구조 우편번호 is highly overall correlated with 긴급구조 시도명High correlation
접수경로명 is highly imbalanced (53.6%)Imbalance
긴급구조 시도명 is highly imbalanced (99.4%)Imbalance
긴급구조 분류명 is highly imbalanced (50.0%)Imbalance
긴급구조 리명 has 1681 (76.2%) missing valuesMissing
도로명 has 1014 (46.0%) missing valuesMissing
긴급구조 우편번호 is highly skewed (γ1 = -20.26449395)Skewed
일련번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:58:43.894395
Analysis finished2023-12-12 22:58:46.232116
Duration2.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2206
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1103.5
Minimum1
Maximum2206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2023-12-13T07:58:46.331503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile111.25
Q1552.25
median1103.5
Q31654.75
95-th percentile2095.75
Maximum2206
Range2205
Interquartile range (IQR)1102.5

Descriptive statistics

Standard deviation636.96167
Coefficient of variation (CV)0.57721945
Kurtosis-1.2
Mean1103.5
Median Absolute Deviation (MAD)551.5
Skewness0
Sum2434321
Variance405720.17
MonotonicityNot monotonic
2023-12-13T07:58:46.461547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1475 1
 
< 0.1%
1469 1
 
< 0.1%
1470 1
 
< 0.1%
1471 1
 
< 0.1%
1472 1
 
< 0.1%
1473 1
 
< 0.1%
1474 1
 
< 0.1%
1476 1
 
< 0.1%
1450 1
 
< 0.1%
Other values (2196) 2196
99.5%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2206 1
< 0.1%
2205 1
< 0.1%
2204 1
< 0.1%
2203 1
< 0.1%
2202 1
< 0.1%
2201 1
< 0.1%
2200 1
< 0.1%
2199 1
< 0.1%
2198 1
< 0.1%
2197 1
< 0.1%

접수경로명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
이동전화
1305 
일반전화
413 
기타
387 
사후각지
 
74
IP전화
 
18
Other values (6)
 
9

Length

Max length5
Median length4
Mean length3.6477788
Min length2

Unique

Unique4 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
이동전화 1305
59.2%
일반전화 413
 
18.7%
기타 387
 
17.5%
사후각지 74
 
3.4%
IP전화 18
 
0.8%
경찰 3
 
0.1%
공중전화 2
 
0.1%
MMS신고 1
 
< 0.1%
SMS신고 1
 
< 0.1%
WEB신고 1
 
< 0.1%

Length

2023-12-13T07:58:46.598920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이동전화 1305
59.2%
일반전화 413
 
18.7%
기타 387
 
17.5%
사후각지 74
 
3.4%
ip전화 18
 
0.8%
경찰 3
 
0.1%
공중전화 2
 
0.1%
mms신고 1
 
< 0.1%
sms신고 1
 
< 0.1%
web신고 1
 
< 0.1%

접수일시
Date

Distinct1678
Distinct (%)76.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2005-07-26 00:00:00
Maximum2020-12-30 00:00:00
2023-12-13T07:58:46.738040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:46.881349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

접수일시(상세)
Date

Distinct2174
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2023-12-13 00:00:08
Maximum2023-12-13 23:59:21
2023-12-13T07:58:46.998381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:47.109205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

타시도신고여부
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
아니오
1420 
<NA>
779 
예
 
7

Length

Max length4
Median length3
Mean length3.3467815
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
아니오 1420
64.4%
<NA> 779
35.3%
예 7
 
0.3%

Length

2023-12-13T07:58:47.299872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:47.435810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아니오 1420
64.4%
na 779
35.3%
예 7
 
0.3%

긴급구조 우편번호
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct156
Distinct (%)7.1%
Missing14
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean34742.274
Minimum-1
Maximum35227
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)0.2%
Memory size19.5 KiB
2023-12-13T07:58:47.600749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile34467
Q134560
median34809
Q335038
95-th percentile35195
Maximum35227
Range35228
Interquartile range (IQR)478

Descriptive statistics

Standard deviation1678.2071
Coefficient of variation (CV)0.048304468
Kurtosis417.35328
Mean34742.274
Median Absolute Deviation (MAD)235
Skewness-20.264494
Sum76155065
Variance2816379
MonotonicityNot monotonic
2023-12-13T07:58:47.756796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34560 173
 
7.8%
34467 128
 
5.8%
34523 103
 
4.7%
34730 101
 
4.6%
34787 76
 
3.4%
34645 73
 
3.3%
34491 69
 
3.1%
35030 60
 
2.7%
34687 54
 
2.4%
35207 49
 
2.2%
Other values (146) 1306
59.2%
ValueCountFrequency (%)
-1 5
 
0.2%
34463 1
 
< 0.1%
34465 1
 
< 0.1%
34466 4
 
0.2%
34467 128
5.8%
34489 8
 
0.4%
34490 22
 
1.0%
34491 69
3.1%
34522 22
 
1.0%
34523 103
4.7%
ValueCountFrequency (%)
35227 2
 
0.1%
35218 24
1.1%
35217 12
 
0.5%
35207 49
2.2%
35206 2
 
0.1%
35204 6
 
0.3%
35195 27
1.2%
35194 6
 
0.3%
35191 12
 
0.5%
35184 28
1.3%

긴급구조 시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
울산
2205 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0009066
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
울산 2205
> 99.9%
<NA> 1
 
< 0.1%

Length

2023-12-13T07:58:47.900354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:48.024028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
울산 2205
> 99.9%
na 1
 
< 0.1%

긴급구조 구군명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
남구
781 
울주군
525 
중구
341 
동구
291 
북구
254 

Length

Max length4
Median length2
Mean length2.25068
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남구
2nd row남구
3rd row울주군
4th row북구
5th row중구

Common Values

ValueCountFrequency (%)
남구 781
35.4%
울주군 525
23.8%
중구 341
15.5%
동구 291
 
13.2%
북구 254
 
11.5%
<NA> 14
 
0.6%

Length

2023-12-13T07:58:48.160339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:48.279414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남구 781
35.4%
울주군 525
23.8%
중구 341
15.5%
동구 291
 
13.2%
북구 254
 
11.5%
na 14
 
0.6%

긴급구조 동명
Text

Distinct81
Distinct (%)3.7%
Missing15
Missing (%)0.7%
Memory size17.4 KiB
2023-12-13T07:58:48.521622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9169329
Min length2

Characters and Unicode

Total characters6391
Distinct characters84
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

Unique4 ?
Unique (%)0.2%

Sample

1st row달동
2nd row신정동
3rd row언양읍
4th row중산동
5th row성안동
ValueCountFrequency (%)
신정동 173
 
7.9%
온산읍 157
 
7.2%
달동 128
 
5.8%
삼산동 103
 
4.7%
방어동 101
 
4.6%
화정동 76
 
3.5%
야음동 73
 
3.3%
무거동 69
 
3.1%
범서읍 67
 
3.1%
청량읍 57
 
2.6%
Other values (71) 1187
54.2%
2023-12-13T07:58:48.889786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1706
26.7%
읍 388
 
6.1%
산 353
 
5.5%
정 303
 
4.7%
온 210
 
3.3%
신 190
 
3.0%
삼 152
 
2.4%
화 145
 
2.3%
면 137
 
2.1%
달 132
 
2.1%
Other values (74) 2675
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6391
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1706
26.7%
읍 388
 
6.1%
산 353
 
5.5%
정 303
 
4.7%
온 210
 
3.3%
신 190
 
3.0%
삼 152
 
2.4%
화 145
 
2.3%
면 137
 
2.1%
달 132
 
2.1%
Other values (74) 2675
41.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6391
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1706
26.7%
읍 388
 
6.1%
산 353
 
5.5%
정 303
 
4.7%
온 210
 
3.3%
신 190
 
3.0%
삼 152
 
2.4%
화 145
 
2.3%
면 137
 
2.1%
달 132
 
2.1%
Other values (74) 2675
41.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6391
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1706
26.7%
읍 388
 
6.1%
산 353
 
5.5%
정 303
 
4.7%
온 210
 
3.3%
신 190
 
3.0%
삼 152
 
2.4%
화 145
 
2.3%
면 137
 
2.1%
달 132
 
2.1%
Other values (74) 2675
41.9%

긴급구조 리명
Text

MISSING 

Distinct84
Distinct (%)16.0%
Missing1681
Missing (%)76.2%
Memory size17.4 KiB
2023-12-13T07:58:49.131618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9695238
Min length2

Characters and Unicode

Total characters1559
Distinct characters93
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

Unique18 ?
Unique (%)3.4%

Sample

1st row동부리
2nd row작동리
3rd row화산리
4th row학남리
5th row구영리
ValueCountFrequency (%)
덕신리 60
 
11.4%
구영리 27
 
5.1%
교동리 23
 
4.4%
화산리 22
 
4.2%
천상리 22
 
4.2%
상남리 19
 
3.6%
대안리 19
 
3.6%
원산리 17
 
3.2%
학남리 16
 
3.0%
동부리 14
 
2.7%
Other values (74) 286
54.5%
2023-12-13T07:58:49.506813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 525
33.7%
덕 72
 
4.6%
신 69
 
4.4%
산 58
 
3.7%
천 58
 
3.7%
동 48
 
3.1%
화 48
 
3.1%
상 47
 
3.0%
남 45
 
2.9%
대 42
 
2.7%
Other values (83) 547
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1559
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 525
33.7%
덕 72
 
4.6%
신 69
 
4.4%
산 58
 
3.7%
천 58
 
3.7%
동 48
 
3.1%
화 48
 
3.1%
상 47
 
3.0%
남 45
 
2.9%
대 42
 
2.7%
Other values (83) 547
35.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1559
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 525
33.7%
덕 72
 
4.6%
신 69
 
4.4%
산 58
 
3.7%
천 58
 
3.7%
동 48
 
3.1%
화 48
 
3.1%
상 47
 
3.0%
남 45
 
2.9%
대 42
 
2.7%
Other values (83) 547
35.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1559
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 525
33.7%
덕 72
 
4.6%
신 69
 
4.4%
산 58
 
3.7%
천 58
 
3.7%
동 48
 
3.1%
화 48
 
3.1%
상 47
 
3.0%
남 45
 
2.9%
대 42
 
2.7%
Other values (83) 547
35.1%

긴급구조 종별명
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
구조
1531 
구급
410 
기타
265 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구급
2nd row구조
3rd row구조
4th row구조
5th row구조

Common Values

ValueCountFrequency (%)
구조 1531
69.4%
구급 410
 
18.6%
기타 265
 
12.0%

Length

2023-12-13T07:58:49.638721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:49.731788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구조 1531
69.4%
구급 410
 
18.6%
기타 265
 
12.0%

긴급구조 분류명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
기타안전사고
1342 
기타출동
149 
교통사고
 
118
질병
 
98
구급기타
 
81
Other values (28)
418 

Length

Max length9
Median length6
Mean length5.1722575
Min length2

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st row구급기타
2nd row기타안전사고
3rd row기타안전사고
4th row기타안전사고
5th row교통사고

Common Values

ValueCountFrequency (%)
기타안전사고 1342
60.8%
기타출동 149
 
6.8%
교통사고 118
 
5.3%
질병 98
 
4.4%
구급기타 81
 
3.7%
사고부상 72
 
3.3%
질병외 41
 
1.9%
시건개방 40
 
1.8%
예방경계 37
 
1.7%
지원출동(환경) 34
 
1.5%
Other values (23) 194
 
8.8%

Length

2023-12-13T07:58:50.125629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타안전사고 1342
60.8%
기타출동 149
 
6.8%
교통사고 118
 
5.3%
질병 98
 
4.4%
구급기타 81
 
3.7%
사고부상 72
 
3.3%
질병외 41
 
1.9%
시건개방 40
 
1.8%
예방경계 37
 
1.7%
지원출동(환경 34
 
1.5%
Other values (23) 194
 
8.8%

긴급구조 규모명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
1차출동
2206 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1차출동
2nd row1차출동
3rd row1차출동
4th row1차출동
5th row1차출동

Common Values

ValueCountFrequency (%)
1차출동 2206
100.0%

Length

2023-12-13T07:58:50.237357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:50.322665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1차출동 2206
100.0%

상황종료일시
Date

Distinct1682
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2005-07-26 00:00:00
Maximum2020-12-30 00:00:00
2023-12-13T07:58:50.427255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:50.581015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상황종료일시(상세)
Date

Distinct2172
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2023-12-13 00:00:10
Maximum2023-12-13 23:59:53
2023-12-13T07:58:50.716309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:50.850029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관할서명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
남부소방서
779 
중부소방서
655 
동부소방서
380 
온산소방서
322 
북부소방서
 
55

Length

Max length5
Median length5
Mean length4.9932004
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남부소방서
2nd row남부소방서
3rd row중부소방서
4th row중부소방서
5th row중부소방서

Common Values

ValueCountFrequency (%)
남부소방서 779
35.3%
중부소방서 655
29.7%
동부소방서 380
17.2%
온산소방서 322
14.6%
북부소방서 55
 
2.5%
<NA> 15
 
0.7%

Length

2023-12-13T07:58:50.988102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:58:51.122605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남부소방서 779
35.3%
중부소방서 655
29.7%
동부소방서 380
17.2%
온산소방서 322
14.6%
북부소방서 55
 
2.5%
na 15
 
0.7%

서센터명
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
신정119안전센터
204 
삼산119안전센터
152 
병영119안전센터
152 
화암119안전센터
146 
온산119안전센터
143 
Other values (23)
1409 

Length

Max length10
Median length9
Mean length8.9170444
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row삼산119안전센터
2nd row신정119안전센터
3rd row언양119안전센터
4th row농소119안전센터
5th row성남119안전센터

Common Values

ValueCountFrequency (%)
신정119안전센터 204
 
9.2%
삼산119안전센터 152
 
6.9%
병영119안전센터 152
 
6.9%
화암119안전센터 146
 
6.6%
온산119안전센터 143
 
6.5%
여천119안전센터 141
 
6.4%
언양119안전센터 123
 
5.6%
성남119안전센터 110
 
5.0%
염포119안전센터 92
 
4.2%
공단119안전센터 85
 
3.9%
Other values (18) 858
38.9%

Length

2023-12-13T07:58:51.273229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신정119안전센터 204
 
9.2%
삼산119안전센터 152
 
6.9%
병영119안전센터 152
 
6.9%
화암119안전센터 146
 
6.6%
온산119안전센터 143
 
6.5%
여천119안전센터 141
 
6.4%
언양119안전센터 123
 
5.6%
성남119안전센터 110
 
5.0%
염포119안전센터 92
 
4.2%
공단119안전센터 85
 
3.9%
Other values (18) 858
38.9%

도로명
Text

MISSING 

Distinct679
Distinct (%)57.0%
Missing1014
Missing (%)46.0%
Memory size17.4 KiB
2023-12-13T07:58:51.544052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.5889262
Min length3

Characters and Unicode

Total characters5470
Distinct characters209
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique464 ?
Unique (%)38.9%

Sample

1st row번영로
2nd row중앙로
3rd row약수9길
4th row함월5길
5th row은현작동로
ValueCountFrequency (%)
삼산로 24
 
2.0%
방어진순환도로 22
 
1.8%
번영로 16
 
1.3%
돋질로 14
 
1.2%
대암로 10
 
0.8%
화합로 10
 
0.8%
중앙로 9
 
0.8%
월평로 9
 
0.8%
봉수로 8
 
0.7%
산업로 8
 
0.7%
Other values (669) 1062
89.1%
2023-12-13T07:58:51.970176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 731
 
13.4%
로 692
 
12.7%
번 279
 
5.1%
1 246
 
4.5%
2 169
 
3.1%
산 145
 
2.7%
3 118
 
2.2%
4 109
 
2.0%
7 85
 
1.6%
대 80
 
1.5%
Other values (199) 2816
51.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4427
80.9%
Decimal Number 1043
 
19.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 731
 
16.5%
로 692
 
15.6%
번 279
 
6.3%
산 145
 
3.3%
대 80
 
1.8%
삼 78
 
1.8%
진 73
 
1.6%
남 72
 
1.6%
신 70
 
1.6%
화 67
 
1.5%
Other values (189) 2140
48.3%
Decimal Number
ValueCountFrequency (%)
1 246
23.6%
2 169
16.2%
3 118
11.3%
4 109
10.5%
7 85
 
8.1%
6 77
 
7.4%
5 77
 
7.4%
8 64
 
6.1%
0 55
 
5.3%
9 43
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4427
80.9%
Common 1043
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 731
 
16.5%
로 692
 
15.6%
번 279
 
6.3%
산 145
 
3.3%
대 80
 
1.8%
삼 78
 
1.8%
진 73
 
1.6%
남 72
 
1.6%
신 70
 
1.6%
화 67
 
1.5%
Other values (189) 2140
48.3%
Common
ValueCountFrequency (%)
1 246
23.6%
2 169
16.2%
3 118
11.3%
4 109
10.5%
7 85
 
8.1%
6 77
 
7.4%
5 77
 
7.4%
8 64
 
6.1%
0 55
 
5.3%
9 43
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4427
80.9%
ASCII 1043
 
19.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 731
 
16.5%
로 692
 
15.6%
번 279
 
6.3%
산 145
 
3.3%
대 80
 
1.8%
삼 78
 
1.8%
진 73
 
1.6%
남 72
 
1.6%
신 70
 
1.6%
화 67
 
1.5%
Other values (189) 2140
48.3%
ASCII
ValueCountFrequency (%)
1 246
23.6%
2 169
16.2%
3 118
11.3%
4 109
10.5%
7 85
 
8.1%
6 77
 
7.4%
5 77
 
7.4%
8 64
 
6.1%
0 55
 
5.3%
9 43
 
4.1%

Interactions

2023-12-13T07:58:45.504462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:45.337510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:45.606033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:58:45.413210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:58:52.070293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호접수경로명타시도신고여부긴급구조 우편번호긴급구조 구군명긴급구조 동명긴급구조 리명긴급구조 종별명긴급구조 분류명관할서명서센터명
일련번호1.0000.7790.1850.0780.0600.2680.3690.2580.3970.3240.370
접수경로명0.7791.0000.1540.0420.0000.0000.0000.1810.3870.0390.151
타시도신고여부0.1850.1541.0000.0000.0290.2490.8640.0230.0000.0000.000
긴급구조 우편번호0.0780.0420.0001.0000.0190.0000.4550.0050.0000.0440.105
긴급구조 구군명0.0600.0000.0290.0191.0001.000NaN0.0000.1760.9750.992
긴급구조 동명0.2680.0000.2490.0001.0001.0001.0000.2340.2970.9860.995
긴급구조 리명0.3690.0000.8640.455NaN1.0001.0000.4650.7301.0000.984
긴급구조 종별명0.2580.1810.0230.0050.0000.2340.4651.0001.0000.0000.200
긴급구조 분류명0.3970.3870.0000.0000.1760.2970.7301.0001.0000.1770.321
관할서명0.3240.0390.0000.0440.9750.9861.0000.0000.1771.0000.983
서센터명0.3700.1510.0000.1050.9920.9950.9840.2000.3210.9831.000
2023-12-13T07:58:52.223474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
긴급구조 구군명타시도신고여부접수경로명긴급구조 시도명관할서명서센터명긴급구조 분류명긴급구조 종별명
긴급구조 구군명1.0000.0360.0001.0000.7720.9640.0830.000
타시도신고여부0.0361.0000.1531.0000.0000.0000.0000.038
접수경로명0.0000.1531.0001.0000.0210.0520.1270.106
긴급구조 시도명1.0001.0001.0001.0001.0001.0001.0001.000
관할서명0.7720.0000.0211.0001.0000.9270.0840.000
서센터명0.9640.0000.0521.0000.9271.0000.0790.092
긴급구조 분류명0.0830.0000.1271.0000.0840.0791.0000.969
긴급구조 종별명0.0000.0380.1061.0000.0000.0920.9691.000
2023-12-13T07:58:52.391583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련번호긴급구조 우편번호접수경로명타시도신고여부긴급구조 시도명긴급구조 구군명긴급구조 종별명긴급구조 분류명관할서명서센터명
일련번호1.0000.0340.4760.1851.0000.0250.1590.1500.1400.142
긴급구조 우편번호0.0341.0000.0400.0001.0000.0240.0090.0000.0540.089
접수경로명0.4760.0401.0000.1531.0000.0000.1060.1270.0210.052
타시도신고여부0.1850.0000.1531.0001.0000.0360.0380.0000.0000.000
긴급구조 시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
긴급구조 구군명0.0250.0240.0000.0361.0001.0000.0000.0830.7720.964
긴급구조 종별명0.1590.0090.1060.0381.0000.0001.0000.9690.0000.092
긴급구조 분류명0.1500.0000.1270.0001.0000.0830.9691.0000.0840.079
관할서명0.1400.0540.0210.0001.0000.7720.0000.0841.0000.927
서센터명0.1420.0890.0520.0001.0000.9640.0920.0790.9271.000

Missing values

2023-12-13T07:58:45.755635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:58:45.965611image/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.
2023-12-13T07:58:46.121409image/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

일련번호접수경로명접수일시접수일시(상세)타시도신고여부긴급구조 우편번호긴급구조 시도명긴급구조 구군명긴급구조 동명긴급구조 리명긴급구조 종별명긴급구조 분류명긴급구조 규모명상황종료일시상황종료일시(상세)관할서명서센터명도로명
01IP전화2014-09-2210:13:18아니오34467울산남구달동<NA>구급구급기타1차출동2014-09-2210:43:14남부소방서삼산119안전센터번영로
12IP전화2014-11-2009:46:06아니오34560울산남구신정동<NA>구조기타안전사고1차출동2014-11-2010:15:58남부소방서신정119안전센터중앙로
23IP전화2014-12-0212:05:33아니오35012울산울주군언양읍동부리구조기타안전사고1차출동2014-12-0212:40:41중부소방서언양119안전센터<NA>
34IP전화2014-12-1817:16:10아니오34860울산북구중산동<NA>구조기타안전사고1차출동2014-12-1817:32:36중부소방서농소119안전센터약수9길
45IP전화2015-02-1319:26:37아니오35184울산중구성안동<NA>구조교통사고1차출동2015-02-1320:28:09중부소방서성남119안전센터함월5길
56IP전화2015-02-1612:35:58아니오34965울산울주군삼동면작동리구조기타안전사고1차출동2015-02-1613:50:50온산소방서웅촌119안전센터은현작동로
67IP전화2015-04-1809:13:24아니오34716울산남구황성동<NA>구급구급기타1차출동2015-04-1810:18:20남부소방서공단119안전센터<NA>
78IP전화2015-08-0114:04:47아니오34645울산남구야음동<NA>구조기타안전사고1차출동2015-08-0114:23:56남부소방서여천119안전센터꽃대나리로46번길
89IP전화2016-01-2117:13:49아니오34490울산남구매암동<NA>구급구급기타1차출동2016-01-2117:25:09남부소방서남부화학구조대장생포고래로
910IP전화2016-08-0110:40:11아니오34730울산동구방어동<NA>구조기타안전사고1차출동2016-08-0111:29:29동부소방서화암119안전센터<NA>
일련번호접수경로명접수일시접수일시(상세)타시도신고여부긴급구조 우편번호긴급구조 시도명긴급구조 구군명긴급구조 동명긴급구조 리명긴급구조 종별명긴급구조 분류명긴급구조 규모명상황종료일시상황종료일시(상세)관할서명서센터명도로명
21962197일반전화2020-03-1002:28:35아니오34730울산동구방어동<NA>구조자살1차출동2020-03-1003:02:15동부소방서화암119안전센터월봉2길
21972198일반전화2020-03-1312:25:05아니오34730울산동구방어동<NA>구조기타안전사고1차출동2020-03-1312:50:59동부소방서화암119안전센터북진3길
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