Overview

Dataset statistics

Number of variables10
Number of observations85
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.5 KiB
Average record size in memory90.6 B

Variable types

Categorical2
Numeric8

Dataset

Description보건복지부에서 2018년~2022년까지 시도별/기관별 구급차 운용 현황을 제공하고 있습니다. (중앙응급의료센터에서 응급의료정보센터의 운영실적을 취합하여 작성)
Author보건복지부
URLhttps://www.data.go.kr/data/15065413/fileData.do

Alerts

국가기관_자치단체(119구급대) is highly overall correlated with 국가기관_자치단체(보건소등) and 7 other fieldsHigh correlation
국가기관_자치단체(보건소등) is highly overall correlated with 국가기관_자치단체(119구급대) and 7 other fieldsHigh correlation
의료기관(응급의료기관) is highly overall correlated with 국가기관_자치단체(119구급대) and 6 other fieldsHigh correlation
의료기관(기타의료기관) is highly overall correlated with 국가기관_자치단체(119구급대) and 4 other fieldsHigh correlation
민간 이송업체 is highly overall correlated with 국가기관_자치단체(119구급대) and 6 other fieldsHigh correlation
기타 is highly overall correlated with 국가기관_자치단체(119구급대) and 2 other fieldsHigh correlation
군 is highly overall correlated with 국가기관_자치단체(119구급대) and 4 other fieldsHigh correlation
경찰 is highly overall correlated with 국가기관_자치단체(119구급대) and 5 other fieldsHigh correlation
시도 is highly overall correlated with 국가기관_자치단체(119구급대) and 6 other fieldsHigh correlation
의료기관(응급의료기관) has 1 (1.2%) zerosZeros
민간 이송업체 has 2 (2.4%) zerosZeros
기타 has 13 (15.3%) zerosZeros
경찰 has 5 (5.9%) zerosZeros

Reproduction

Analysis started2024-05-04 08:12:29.609861
Analysis finished2024-05-04 08:12:49.643781
Duration20.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

Distinct5
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size812.0 B
2018
17 
2019
17 
2020
17 
2021
17 
2022
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 17
20.0%
2019 17
20.0%
2020 17
20.0%
2021 17
20.0%
2022 17
20.0%

Length

2024-05-04T08:12:49.824767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:12:50.165238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 17
20.0%
2019 17
20.0%
2020 17
20.0%
2021 17
20.0%
2022 17
20.0%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size812.0 B
서울 Seoul
 
5
부산 Busan
 
5
대구 Daegu
 
5
인천 Incheon
 
5
광주 Gwangju
 
5
Other values (12)
60 

Length

Max length12
Median length11
Mean length9.7058824
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울 Seoul
2nd row부산 Busan
3rd row대구 Daegu
4th row인천 Incheon
5th row광주 Gwangju

Common Values

ValueCountFrequency (%)
서울 Seoul 5
 
5.9%
부산 Busan 5
 
5.9%
대구 Daegu 5
 
5.9%
인천 Incheon 5
 
5.9%
광주 Gwangju 5
 
5.9%
대전 Daejeon 5
 
5.9%
울산 Ulsan 5
 
5.9%
세종 Sejong 5
 
5.9%
경기 Gyeonggi 5
 
5.9%
강원 Gangwon 5
 
5.9%
Other values (7) 35
41.2%

Length

2024-05-04T08:12:50.711823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 5
 
2.9%
jeonbuk 5
 
2.9%
gangwon 5
 
2.9%
충북 5
 
2.9%
chungbuk 5
 
2.9%
충남 5
 
2.9%
chungnam 5
 
2.9%
전북 5
 
2.9%
전남 5
 
2.9%
seoul 5
 
2.9%
Other values (24) 120
70.6%

국가기관_자치단체(119구급대)
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.070588
Minimum9
Maximum274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:51.207494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14.6
Q134
median71
Q3128
95-th percentile225
Maximum274
Range265
Interquartile range (IQR)94

Descriptive statistics

Standard deviation61.28559
Coefficient of variation (CV)0.68041734
Kurtosis1.1460878
Mean90.070588
Median Absolute Deviation (MAD)40
Skewness1.0686404
Sum7656
Variance3755.9235
MonotonicityNot monotonic
2024-05-04T08:12:51.702036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 5
 
5.9%
70 4
 
4.7%
30 3
 
3.5%
34 3
 
3.5%
67 3
 
3.5%
11 3
 
3.5%
69 3
 
3.5%
31 3
 
3.5%
78 2
 
2.4%
127 2
 
2.4%
Other values (48) 54
63.5%
ValueCountFrequency (%)
9 1
 
1.2%
11 3
3.5%
12 1
 
1.2%
25 2
 
2.4%
29 1
 
1.2%
30 3
3.5%
31 3
3.5%
32 5
5.9%
33 2
 
2.4%
34 3
3.5%
ValueCountFrequency (%)
274 1
1.2%
263 1
1.2%
261 1
1.2%
251 1
1.2%
237 1
1.2%
177 1
1.2%
167 1
1.2%
161 1
1.2%
151 1
1.2%
150 1
1.2%

국가기관_자치단체(보건소등)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.117647
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:52.265285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q110
median20
Q328
95-th percentile52
Maximum65
Range64
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.590299
Coefficient of variation (CV)0.70488054
Kurtosis0.44125928
Mean22.117647
Median Absolute Deviation (MAD)10
Skewness0.99264853
Sum1880
Variance243.05742
MonotonicityNot monotonic
2024-05-04T08:12:52.916601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20 6
 
7.1%
14 5
 
5.9%
16 5
 
5.9%
5 4
 
4.7%
28 4
 
4.7%
10 4
 
4.7%
7 3
 
3.5%
9 3
 
3.5%
27 3
 
3.5%
21 3
 
3.5%
Other values (30) 45
52.9%
ValueCountFrequency (%)
1 2
2.4%
2 3
3.5%
5 4
4.7%
6 2
2.4%
7 3
3.5%
8 2
2.4%
9 3
3.5%
10 4
4.7%
11 2
2.4%
12 2
2.4%
ValueCountFrequency (%)
65 1
1.2%
64 1
1.2%
63 1
1.2%
54 1
1.2%
52 2
2.4%
51 2
2.4%
45 1
1.2%
44 2
2.4%
43 1
1.2%
41 1
1.2%

의료기관(응급의료기관)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.070588
Minimum0
Maximum83
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:53.497809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.2
Q118
median26
Q340
95-th percentile71.6
Maximum83
Range83
Interquartile range (IQR)22

Descriptive statistics

Standard deviation18.626522
Coefficient of variation (CV)0.64073428
Kurtosis1.0362556
Mean29.070588
Median Absolute Deviation (MAD)11
Skewness1.0520378
Sum2471
Variance346.94734
MonotonicityNot monotonic
2024-05-04T08:12:53.995010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19 7
 
8.2%
26 5
 
5.9%
17 4
 
4.7%
12 4
 
4.7%
25 4
 
4.7%
44 4
 
4.7%
2 3
 
3.5%
27 3
 
3.5%
41 3
 
3.5%
18 3
 
3.5%
Other values (31) 45
52.9%
ValueCountFrequency (%)
0 1
 
1.2%
1 1
 
1.2%
2 3
3.5%
8 2
2.4%
9 1
 
1.2%
10 2
2.4%
11 3
3.5%
12 4
4.7%
17 4
4.7%
18 3
3.5%
ValueCountFrequency (%)
83 1
1.2%
81 1
1.2%
79 1
1.2%
74 1
1.2%
73 1
1.2%
66 1
1.2%
61 1
1.2%
58 1
1.2%
53 1
1.2%
50 1
1.2%

의료기관(기타의료기관)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.78824
Minimum4
Maximum468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:54.379983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.6
Q159
median112
Q3166
95-th percentile312.4
Maximum468
Range464
Interquartile range (IQR)107

Descriptive statistics

Standard deviation96.015511
Coefficient of variation (CV)0.74553014
Kurtosis2.4553298
Mean128.78824
Median Absolute Deviation (MAD)54
Skewness1.3648778
Sum10947
Variance9218.9784
MonotonicityNot monotonic
2024-05-04T08:12:54.795192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 5
 
5.9%
5 4
 
4.7%
111 3
 
3.5%
94 3
 
3.5%
142 2
 
2.4%
54 2
 
2.4%
99 2
 
2.4%
98 2
 
2.4%
230 2
 
2.4%
114 2
 
2.4%
Other values (56) 58
68.2%
ValueCountFrequency (%)
4 1
 
1.2%
5 4
4.7%
13 5
5.9%
23 1
 
1.2%
29 1
 
1.2%
38 1
 
1.2%
45 1
 
1.2%
48 1
 
1.2%
50 1
 
1.2%
52 1
 
1.2%
ValueCountFrequency (%)
468 1
1.2%
433 1
1.2%
414 1
1.2%
360 1
1.2%
322 1
1.2%
274 1
1.2%
256 1
1.2%
233 1
1.2%
230 2
2.4%
229 1
1.2%

민간 이송업체
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.470588
Minimum0
Maximum330
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:55.196180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q124
median42
Q355
95-th percentile244.8
Maximum330
Range330
Interquartile range (IQR)31

Descriptive statistics

Standard deviation70.760257
Coefficient of variation (CV)1.1701599
Kurtosis4.5637933
Mean60.470588
Median Absolute Deviation (MAD)18
Skewness2.2810837
Sum5140
Variance5007.014
MonotonicityNot monotonic
2024-05-04T08:12:55.624116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 5
 
5.9%
5 5
 
5.9%
55 4
 
4.7%
53 4
 
4.7%
44 3
 
3.5%
36 3
 
3.5%
6 3
 
3.5%
77 2
 
2.4%
54 2
 
2.4%
27 2
 
2.4%
Other values (41) 52
61.2%
ValueCountFrequency (%)
0 2
 
2.4%
5 5
5.9%
6 3
3.5%
7 5
5.9%
17 1
 
1.2%
20 2
 
2.4%
21 1
 
1.2%
22 1
 
1.2%
23 1
 
1.2%
24 2
 
2.4%
ValueCountFrequency (%)
330 1
1.2%
283 1
1.2%
270 1
1.2%
267 1
1.2%
249 1
1.2%
228 1
1.2%
207 2
2.4%
192 1
1.2%
166 1
1.2%
78 1
1.2%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6941176
Minimum0
Maximum26
Zeros13
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:55.999079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q310
95-th percentile20.6
Maximum26
Range26
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.2068003
Coefficient of variation (CV)0.92720215
Kurtosis1.3060133
Mean6.6941176
Median Absolute Deviation (MAD)4
Skewness1.1753481
Sum569
Variance38.52437
MonotonicityNot monotonic
2024-05-04T08:12:56.369492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 13
15.3%
1 9
10.6%
5 8
9.4%
2 8
9.4%
8 6
 
7.1%
9 5
 
5.9%
7 5
 
5.9%
11 4
 
4.7%
4 4
 
4.7%
10 4
 
4.7%
Other values (10) 19
22.4%
ValueCountFrequency (%)
0 13
15.3%
1 9
10.6%
2 8
9.4%
3 1
 
1.2%
4 4
 
4.7%
5 8
9.4%
6 3
 
3.5%
7 5
 
5.9%
8 6
7.1%
9 5
 
5.9%
ValueCountFrequency (%)
26 1
 
1.2%
24 2
2.4%
23 1
 
1.2%
22 1
 
1.2%
15 3
3.5%
14 3
3.5%
13 1
 
1.2%
12 3
3.5%
11 4
4.7%
10 4
4.7%

군
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)65.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.16471
Minimum4
Maximum919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:56.743614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.2
Q122
median36
Q363
95-th percentile669.2
Maximum919
Range915
Interquartile range (IQR)41

Descriptive statistics

Standard deviation212.86866
Coefficient of variation (CV)1.9148943
Kurtosis5.2407971
Mean111.16471
Median Absolute Deviation (MAD)19
Skewness2.5666086
Sum9449
Variance45313.068
MonotonicityNot monotonic
2024-05-04T08:12:57.189819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 5
 
5.9%
5 4
 
4.7%
63 3
 
3.5%
17 3
 
3.5%
11 3
 
3.5%
30 3
 
3.5%
32 2
 
2.4%
50 2
 
2.4%
22 2
 
2.4%
9 2
 
2.4%
Other values (46) 56
65.9%
ValueCountFrequency (%)
4 1
 
1.2%
5 4
4.7%
6 1
 
1.2%
8 2
2.4%
9 2
2.4%
11 3
3.5%
14 1
 
1.2%
16 1
 
1.2%
17 3
3.5%
18 1
 
1.2%
ValueCountFrequency (%)
919 1
1.2%
807 1
1.2%
797 1
1.2%
684 1
1.2%
683 1
1.2%
614 1
1.2%
611 1
1.2%
599 1
1.2%
539 1
1.2%
508 1
1.2%

경찰
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2588235
Minimum0
Maximum9
Zeros5
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size897.0 B
2024-05-04T08:12:57.772171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q11
median2
Q35
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8290708
Coefficient of variation (CV)0.86812641
Kurtosis-0.47385383
Mean3.2588235
Median Absolute Deviation (MAD)1
Skewness0.93175522
Sum277
Variance8.0036415
MonotonicityNot monotonic
2024-05-04T08:12:58.148791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 28
32.9%
2 16
18.8%
9 8
 
9.4%
4 8
 
9.4%
0 5
 
5.9%
8 5
 
5.9%
3 5
 
5.9%
6 4
 
4.7%
5 4
 
4.7%
7 2
 
2.4%
ValueCountFrequency (%)
0 5
 
5.9%
1 28
32.9%
2 16
18.8%
3 5
 
5.9%
4 8
 
9.4%
5 4
 
4.7%
6 4
 
4.7%
7 2
 
2.4%
8 5
 
5.9%
9 8
 
9.4%
ValueCountFrequency (%)
9 8
 
9.4%
8 5
 
5.9%
7 2
 
2.4%
6 4
 
4.7%
5 4
 
4.7%
4 8
 
9.4%
3 5
 
5.9%
2 16
18.8%
1 28
32.9%
0 5
 
5.9%

Interactions

2024-05-04T08:12:46.390478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:30.479249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:32.890054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:35.544346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:37.778050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:40.092424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:42.594984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:44.304923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:46.668116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:30.739768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:33.257084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:35.870957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:38.082251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:40.445681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:42.833419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:44.689135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:47.022613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:30.999148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:33.537715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:36.151030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:38.330557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:40.788673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:43.033744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:44.923999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:47.266790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:31.295583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:33.796529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:36.385609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:38.589874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:41.139134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:43.185982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:45.149591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:47.483459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:31.554878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:34.098063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:36.668573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:38.903537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:41.407825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:43.372413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:45.374900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:47.790143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:31.827310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:34.533924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:37.002124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:39.346913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:41.767637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:43.612378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:45.664280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:48.058686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:32.348226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:34.898614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:37.310853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:39.591108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:42.064634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:43.846910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:45.935067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:48.273868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:32.600641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:35.249709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:37.538870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:39.824271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:42.360902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:44.074747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:12:46.157660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T08:12:58.398640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도국가기관_자치단체(119구급대)국가기관_자치단체(보건소등)의료기관(응급의료기관)의료기관(기타의료기관)민간 이송업체기타군경찰
연도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
시도0.0001.0000.9420.8930.9170.8930.8850.9100.7790.936
국가기관_자치단체(119구급대)0.0000.9421.0000.8840.8050.8110.8510.7260.8520.733
국가기관_자치단체(보건소등)0.0000.8930.8841.0000.9210.8950.8490.7770.6980.862
의료기관(응급의료기관)0.0000.9170.8050.9211.0000.9250.8540.7150.8070.840
의료기관(기타의료기관)0.0000.8930.8110.8950.9251.0000.8610.8210.8460.813
민간 이송업체0.0000.8850.8510.8490.8540.8611.0000.8670.7330.717
기타0.0000.9100.7260.7770.7150.8210.8671.0000.7260.653
군0.0000.7790.8520.6980.8070.8460.7330.7261.0000.653
경찰0.0000.9360.7330.8620.8400.8130.7170.6530.6531.000
2024-05-04T08:12:58.761044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도
연도1.0000.000
시도0.0001.000
2024-05-04T08:12:59.318032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국가기관_자치단체(119구급대)국가기관_자치단체(보건소등)의료기관(응급의료기관)의료기관(기타의료기관)민간 이송업체기타군경찰연도시도
국가기관_자치단체(119구급대)1.0000.9340.7980.5870.9010.5430.8110.7840.0000.720
국가기관_자치단체(보건소등)0.9341.0000.7650.5330.8970.5610.7400.7520.0000.597
의료기관(응급의료기관)0.7980.7651.0000.6510.7600.3920.6630.5450.0000.652
의료기관(기타의료기관)0.5870.5330.6511.0000.6370.0960.3570.4120.0000.596
민간 이송업체0.9010.8970.7600.6371.0000.4890.7170.7560.0000.596
기타0.5430.5610.3920.0960.4891.0000.4670.4830.0000.634
군0.8110.7400.6630.3570.7170.4671.0000.7660.0000.472
경찰0.7840.7520.5450.4120.7560.4830.7661.0000.0000.703
연도0.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
시도0.7200.5970.6520.5960.5960.6340.4720.7030.0001.000

Missing values

2024-05-04T08:12:48.701299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T08:12:49.236011image/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.

Sample

연도시도국가기관_자치단체(119구급대)국가기관_자치단체(보건소등)의료기관(응급의료기관)의료기관(기타의료기관)민간 이송업체기타군경찰
02018서울 Seoul15045662742497381
12018부산 Busan671627223534242
22018대구 Daegu50911166390252
32018인천 Incheon671439113177731
42018광주 Gwangju30629141200141
52018대전 Daejeon3251910670241
62018울산 Ulsan2551048241261
72018세종 Sejong910501170
82018경기 Gyeonggi2375183468166249198
92018강원 Gangwon12027196353115393
연도시도국가기관_자치단체(119구급대)국가기관_자치단체(보건소등)의료기관(응급의료기관)의료기관(기타의료기관)민간 이송업체기타군경찰
752022세종 Sejong1222461160
762022경기 Gyeonggi2746581322228227979
772022강원 Gangwon12930235455125085
782022충북 Chungbuk70232254553644
792022충남 Chungnam1202017111468509
802022전북 Jeonbuk1071926110342342
812022전남 Jeonnam1273837945415506
822022경북 Gyeongbuk148444498729439
832022경남 Gyeongnam14131402147111633
842022제주 Jeju321211136541