Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells153
Missing cells (%)50.0%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory2.8 KiB
Average record size in memory83.9 B

Variable types

Text1
Numeric8

Dataset

Description2021년 시도별 119생활안전 활동 현황에 대한 자료로 지역별 벌퇴치, 갇힘사고, 위해동물포획퇴치, 전기가스안전조치, 급배수지원 , 위험고드름 제거, 기타안전조치 항목등에 대한 자료를 제공함
Author소방청
URLhttps://www.data.go.kr/data/15062062/fileData.do

Alerts

Dataset has 1 (2.9%) duplicate rowsDuplicates
벌퇴치_벌집제거 is highly overall correlated with 갇힘사고 and 3 other fieldsHigh correlation
갇힘사고 is highly overall correlated with 벌퇴치_벌집제거 and 5 other fieldsHigh correlation
위해동물포획퇴치 is highly overall correlated with 벌퇴치_벌집제거 and 6 other fieldsHigh correlation
전기_가스안전조치 is highly overall correlated with 벌퇴치_벌집제거 and 5 other fieldsHigh correlation
급배수지원 is highly overall correlated with 갇힘사고 and 4 other fieldsHigh correlation
위험고드름제거 is highly overall correlated with 갇힘사고 and 1 other fieldsHigh correlation
기타안전조치 is highly overall correlated with 벌퇴치_벌집제거 and 5 other fieldsHigh correlation
비화재출동 is highly overall correlated with 위해동물포획퇴치 and 3 other fieldsHigh correlation
지역 has 17 (50.0%) missing valuesMissing
벌퇴치_벌집제거 has 17 (50.0%) missing valuesMissing
갇힘사고 has 17 (50.0%) missing valuesMissing
위해동물포획퇴치 has 17 (50.0%) missing valuesMissing
전기_가스안전조치 has 17 (50.0%) missing valuesMissing
급배수지원 has 17 (50.0%) missing valuesMissing
위험고드름제거 has 17 (50.0%) missing valuesMissing
기타안전조치 has 17 (50.0%) missing valuesMissing
비화재출동 has 17 (50.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:07:05.910327
Analysis finished2023-12-12 04:07:15.175904
Duration9.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Memory size404.0 B
2023-12-12T13:07:15.349931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Characters and Unicode

Total characters79
Distinct characters31
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

Unique17 ?
Unique (%)100.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시
ValueCountFrequency (%)
부산광역시 1
 
5.9%
충청북도 1
 
5.9%
제주특별자치도 1
 
5.9%
경상남도 1
 
5.9%
경상북도 1
 
5.9%
전라남도 1
 
5.9%
전라북도 1
 
5.9%
충청남도 1
 
5.9%
서울특별시 1
 
5.9%
대구광역시 1
 
5.9%
Other values (7) 7
41.2%
2023-12-12T13:07:15.880343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 9
 
11.4%
시 8
 
10.1%
광 7
 
8.9%
역 6
 
7.6%
별 3
 
3.8%
남 3
 
3.8%
북 3
 
3.8%
경 3
 
3.8%
전 3
 
3.8%
특 3
 
3.8%
Other values (21) 31
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 79
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 9
 
11.4%
시 8
 
10.1%
광 7
 
8.9%
역 6
 
7.6%
별 3
 
3.8%
남 3
 
3.8%
북 3
 
3.8%
경 3
 
3.8%
전 3
 
3.8%
특 3
 
3.8%
Other values (21) 31
39.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 9
 
11.4%
시 8
 
10.1%
광 7
 
8.9%
역 6
 
7.6%
별 3
 
3.8%
남 3
 
3.8%
북 3
 
3.8%
경 3
 
3.8%
전 3
 
3.8%
특 3
 
3.8%
Other values (21) 31
39.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 79
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 9
 
11.4%
시 8
 
10.1%
광 7
 
8.9%
역 6
 
7.6%
별 3
 
3.8%
남 3
 
3.8%
북 3
 
3.8%
경 3
 
3.8%
전 3
 
3.8%
특 3
 
3.8%
Other values (21) 31
39.2%

벌퇴치_벌집제거
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean11782.941
Minimum1656
Maximum53769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:16.039652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1656
5-th percentile1795.2
Q14168
median7702
Q314837
95-th percentile33225
Maximum53769
Range52113
Interquartile range (IQR)10669

Descriptive statistics

Standard deviation12869.002
Coefficient of variation (CV)1.0921723
Kurtosis7.0349624
Mean11782.941
Median Absolute Deviation (MAD)4263
Skewness2.4643906
Sum200310
Variance1.6561122 × 108
MonotonicityNot monotonic
2023-12-12T13:07:16.214886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
6051 1
 
2.9%
1830 1
 
2.9%
28089 1
 
2.9%
18247 1
 
2.9%
14837 1
 
2.9%
11290 1
 
2.9%
16016 1
 
2.9%
7702 1
 
2.9%
8913 1
 
2.9%
53769 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
1656 1
2.9%
1830 1
2.9%
3100 1
2.9%
3439 1
2.9%
4168 1
2.9%
4825 1
2.9%
5434 1
2.9%
6051 1
2.9%
7702 1
2.9%
8913 1
2.9%
ValueCountFrequency (%)
53769 1
2.9%
28089 1
2.9%
18247 1
2.9%
16016 1
2.9%
14837 1
2.9%
11290 1
2.9%
10944 1
2.9%
8913 1
2.9%
7702 1
2.9%
6051 1
2.9%

갇힘사고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean3607.4706
Minimum213
Maximum17108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:16.382425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum213
5-th percentile773
Q11458
median2459
Q33246
95-th percentile12176.8
Maximum17108
Range16895
Interquartile range (IQR)1788

Descriptive statistics

Standard deviation4217.9109
Coefficient of variation (CV)1.1692156
Kurtosis6.8225311
Mean3607.4706
Median Absolute Deviation (MAD)1001
Skewness2.5880428
Sum61327
Variance17790772
MonotonicityNot monotonic
2023-12-12T13:07:16.571911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2459 1
 
2.9%
913 1
 
2.9%
4268 1
 
2.9%
3246 1
 
2.9%
2210 1
 
2.9%
2201 1
 
2.9%
2779 1
 
2.9%
1619 1
 
2.9%
10944 1
 
2.9%
17108 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
213 1
2.9%
913 1
2.9%
1080 1
2.9%
1325 1
2.9%
1458 1
2.9%
1619 1
2.9%
2201 1
2.9%
2210 1
2.9%
2459 1
2.9%
2483 1
2.9%
ValueCountFrequency (%)
17108 1
2.9%
10944 1
2.9%
4465 1
2.9%
4268 1
2.9%
3246 1
2.9%
2779 1
2.9%
2556 1
2.9%
2483 1
2.9%
2459 1
2.9%
2210 1
2.9%

위해동물포획퇴치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean5599.5294
Minimum1001
Maximum21181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:16.757585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1008.2
Q12519
median4774
Q37108
95-th percentile12273
Maximum21181
Range20180
Interquartile range (IQR)4589

Descriptive statistics

Standard deviation4857.9977
Coefficient of variation (CV)0.86757249
Kurtosis6.3242451
Mean5599.5294
Median Absolute Deviation (MAD)2334
Skewness2.208068
Sum95192
Variance23600141
MonotonicityNot monotonic
2023-12-12T13:07:16.922014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3611 1
 
2.9%
2519 1
 
2.9%
10046 1
 
2.9%
9469 1
 
2.9%
7304 1
 
2.9%
4355 1
 
2.9%
7108 1
 
2.9%
2859 1
 
2.9%
6133 1
 
2.9%
21181 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
1001 1
2.9%
1010 1
2.9%
1618 1
2.9%
2021 1
2.9%
2519 1
2.9%
2859 1
2.9%
3611 1
2.9%
4355 1
2.9%
4774 1
2.9%
4948 1
2.9%
ValueCountFrequency (%)
21181 1
2.9%
10046 1
2.9%
9469 1
2.9%
7304 1
2.9%
7108 1
2.9%
6133 1
2.9%
5235 1
2.9%
4948 1
2.9%
4774 1
2.9%
4355 1
2.9%

전기_가스안전조치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean366
Minimum33
Maximum2092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:17.090650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile42.6
Q196
median151
Q3277
95-th percentile1656.8
Maximum2092
Range2059
Interquartile range (IQR)181

Descriptive statistics

Standard deviation564.88428
Coefficient of variation (CV)1.5433997
Kurtosis6.0761383
Mean366
Median Absolute Deviation (MAD)104
Skewness2.5925598
Sum6222
Variance319094.25
MonotonicityNot monotonic
2023-12-12T13:07:17.236996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
92 1
 
2.9%
277 1
 
2.9%
266 1
 
2.9%
390 1
 
2.9%
136 1
 
2.9%
96 1
 
2.9%
343 1
 
2.9%
45 1
 
2.9%
2092 1
 
2.9%
1548 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
33 1
2.9%
45 1
2.9%
47 1
2.9%
92 1
2.9%
96 1
2.9%
136 1
2.9%
144 1
2.9%
147 1
2.9%
151 1
2.9%
207 1
2.9%
ValueCountFrequency (%)
2092 1
2.9%
1548 1
2.9%
390 1
2.9%
343 1
2.9%
277 1
2.9%
266 1
2.9%
208 1
2.9%
207 1
2.9%
151 1
2.9%
147 1
2.9%

급배수지원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)88.2%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean209.11765
Minimum2
Maximum1067
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:17.415205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.2
Q113
median38
Q3318
95-th percentile785.4
Maximum1067
Range1065
Interquartile range (IQR)305

Descriptive statistics

Standard deviation313.00058
Coefficient of variation (CV)1.4967679
Kurtosis2.4592867
Mean209.11765
Median Absolute Deviation (MAD)32
Skewness1.7545642
Sum3555
Variance97969.36
MonotonicityNot monotonic
2023-12-12T13:07:17.580867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
13 2
 
5.9%
6 2
 
5.9%
1067 1
 
2.9%
92 1
 
2.9%
38 1
 
2.9%
26 1
 
2.9%
32 1
 
2.9%
597 1
 
2.9%
411 1
 
2.9%
2 1
 
2.9%
Other values (5) 5
 
14.7%
(Missing) 17
50.0%
ValueCountFrequency (%)
2 1
2.9%
6 2
5.9%
13 2
5.9%
26 1
2.9%
32 1
2.9%
37 1
2.9%
38 1
2.9%
42 1
2.9%
92 1
2.9%
140 1
2.9%
ValueCountFrequency (%)
1067 1
2.9%
715 1
2.9%
597 1
2.9%
411 1
2.9%
318 1
2.9%
140 1
2.9%
92 1
2.9%
42 1
2.9%
38 1
2.9%
37 1
2.9%

위험고드름제거
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)88.2%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean163.05882
Minimum7
Maximum933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:17.756642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14.2
Q130
median69
Q3100
95-th percentile863.4
Maximum933
Range926
Interquartile range (IQR)70

Descriptive statistics

Standard deviation277.82109
Coefficient of variation (CV)1.703809
Kurtosis5.0828707
Mean163.05882
Median Absolute Deviation (MAD)39
Skewness2.4891935
Sum2772
Variance77184.559
MonotonicityNot monotonic
2023-12-12T13:07:17.894325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
69 2
 
5.9%
76 2
 
5.9%
846 1
 
2.9%
205 1
 
2.9%
51 1
 
2.9%
64 1
 
2.9%
16 1
 
2.9%
29 1
 
2.9%
933 1
 
2.9%
100 1
 
2.9%
Other values (5) 5
 
14.7%
(Missing) 17
50.0%
ValueCountFrequency (%)
7 1
2.9%
16 1
2.9%
24 1
2.9%
29 1
2.9%
30 1
2.9%
51 1
2.9%
52 1
2.9%
64 1
2.9%
69 2
5.9%
76 2
5.9%
ValueCountFrequency (%)
933 1
2.9%
846 1
2.9%
205 1
2.9%
125 1
2.9%
100 1
2.9%
76 2
5.9%
69 2
5.9%
64 1
2.9%
52 1
2.9%
51 1
2.9%

기타안전조치
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean4495.7647
Minimum752
Maximum23661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:18.053244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum752
5-th percentile950.4
Q12209
median2398
Q33339
95-th percentile18895.4
Maximum23661
Range22909
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation6241.0901
Coefficient of variation (CV)1.3882155
Kurtosis6.3088312
Mean4495.7647
Median Absolute Deviation (MAD)432
Skewness2.6565817
Sum76428
Variance38951205
MonotonicityNot monotonic
2023-12-12T13:07:18.205120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2267 1
 
2.9%
2223 1
 
2.9%
4016 1
 
2.9%
3339 1
 
2.9%
2209 1
 
2.9%
2385 1
 
2.9%
2552 1
 
2.9%
1000 1
 
2.9%
23661 1
 
2.9%
17704 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
752 1
2.9%
1000 1
2.9%
1106 1
2.9%
1966 1
2.9%
2209 1
2.9%
2223 1
2.9%
2267 1
2.9%
2385 1
2.9%
2398 1
2.9%
2482 1
2.9%
ValueCountFrequency (%)
23661 1
2.9%
17704 1
2.9%
4016 1
2.9%
3550 1
2.9%
3339 1
2.9%
2818 1
2.9%
2552 1
2.9%
2482 1
2.9%
2398 1
2.9%
2385 1
2.9%

비화재출동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing17
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean5462.7059
Minimum884
Maximum33306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T13:07:18.382377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum884
5-th percentile916.8
Q11284
median2523
Q36295
95-th percentile15794.8
Maximum33306
Range32422
Interquartile range (IQR)5011

Descriptive statistics

Standard deviation7828.22
Coefficient of variation (CV)1.4330297
Kurtosis11.017251
Mean5462.7059
Median Absolute Deviation (MAD)1598
Skewness3.1248988
Sum92866
Variance61281028
MonotonicityNot monotonic
2023-12-12T13:07:18.548728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
884 1
 
2.9%
2523 1
 
2.9%
8326 1
 
2.9%
5560 1
 
2.9%
1483 1
 
2.9%
925 1
 
2.9%
6295 1
 
2.9%
1065 1
 
2.9%
33306 1
 
2.9%
11417 1
 
2.9%
Other values (7) 7
20.6%
(Missing) 17
50.0%
ValueCountFrequency (%)
884 1
2.9%
925 1
2.9%
1065 1
2.9%
1104 1
2.9%
1284 1
2.9%
1430 1
2.9%
1483 1
2.9%
1519 1
2.9%
2523 1
2.9%
3097 1
2.9%
ValueCountFrequency (%)
33306 1
2.9%
11417 1
2.9%
8326 1
2.9%
6609 1
2.9%
6295 1
2.9%
6039 1
2.9%
5560 1
2.9%
3097 1
2.9%
2523 1
2.9%
1519 1
2.9%

Interactions

2023-12-12T13:07:13.335361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:06.306362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.598440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.666373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.735654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.727139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.718349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.543459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.425248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:06.418857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.716769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.765826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.844702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.852030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.827950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.642075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.518822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:06.545979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.854876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.906346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.973289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.973706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.931874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.728482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.657811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:06.685949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.968859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.081917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.131213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.119107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.058010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.827901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.775384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.122173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.088163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.244474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.234292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.237290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.166009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.916663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.861744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.234134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.236011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.376297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.360711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.360900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.275129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.044450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.980817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.366480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.420459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.493835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.466962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.471540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.370495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.151588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:14.096582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:07.480297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:08.518958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:09.613426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:10.588629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:11.591818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:12.448942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:07:13.233772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:07:18.679044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역벌퇴치_벌집제거갇힘사고위해동물포획퇴치전기_가스안전조치급배수지원위험고드름제거기타안전조치비화재출동
지역1.0001.0001.0001.0001.0001.0001.0001.0001.000
벌퇴치_벌집제거1.0001.0000.6550.9490.7670.8510.0000.7910.773
갇힘사고1.0000.6551.0000.8140.7990.8100.6840.8040.960
위해동물포획퇴치1.0000.9490.8141.0000.8510.7140.5090.8760.722
전기_가스안전조치1.0000.7670.7990.8511.0000.9330.8200.9820.858
급배수지원1.0000.8510.8100.7140.9331.0000.6170.9200.894
위험고드름제거1.0000.0000.6840.5090.8200.6171.0000.8150.453
기타안전조치1.0000.7910.8040.8760.9820.9200.8151.0000.847
비화재출동1.0000.7730.9600.7220.8580.8940.4530.8471.000
2023-12-12T13:07:18.844620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
벌퇴치_벌집제거갇힘사고위해동물포획퇴치전기_가스안전조치급배수지원위험고드름제거기타안전조치비화재출동
벌퇴치_벌집제거1.0000.7480.9000.5200.4160.4280.5590.377
갇힘사고0.7481.0000.8600.6370.5550.7850.7770.480
위해동물포획퇴치0.9000.8601.0000.6620.5990.5260.6960.547
전기_가스안전조치0.5200.6370.6621.0000.7670.3750.7550.824
급배수지원0.4160.5550.5990.7671.0000.3180.6450.533
위험고드름제거0.4280.7850.5260.3750.3181.0000.4900.286
기타안전조치0.5590.7770.6960.7550.6450.4901.0000.669
비화재출동0.3770.4800.5470.8240.5330.2860.6691.000

Missing values

2023-12-12T13:07:14.249882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:07:14.429543image/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.
2023-12-12T13:07:15.003523image/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

지역벌퇴치_벌집제거갇힘사고위해동물포획퇴치전기_가스안전조치급배수지원위험고드름제거기타안전조치비화재출동
0서울특별시8913109446133209210678462366133306
1부산광역시6051245936119292692267884
2대구광역시543424835235151387623986609
3인천광역시4825446549481472620528181284
4광주광역시343913252021144325135503097
5대전광역시3100145810104713647521104
6울산광역시41681080161820761619666039
7세종특별자치시165621310013362911061430
8경기도53769171082118115485979331770411417
9강원도109442556477420841110024821519
지역벌퇴치_벌집제거갇힘사고위해동물포획퇴치전기_가스안전조치급배수지원위험고드름제거기타안전조치비화재출동
24<NA><NA><NA><NA><NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA><NA><NA><NA><NA>
26<NA><NA><NA><NA><NA><NA><NA><NA><NA>
27<NA><NA><NA><NA><NA><NA><NA><NA><NA>
28<NA><NA><NA><NA><NA><NA><NA><NA><NA>
29<NA><NA><NA><NA><NA><NA><NA><NA><NA>
30<NA><NA><NA><NA><NA><NA><NA><NA><NA>
31<NA><NA><NA><NA><NA><NA><NA><NA><NA>
32<NA><NA><NA><NA><NA><NA><NA><NA><NA>
33<NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

지역벌퇴치_벌집제거갇힘사고위해동물포획퇴치전기_가스안전조치급배수지원위험고드름제거기타안전조치비화재출동# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>17