Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells2761
Missing cells (%)3.1%
Duplicate rows817
Duplicate rows (%)8.2%
Total size in memory800.8 KiB
Average record size in memory82.0 B

Variable types

Categorical7
Text1
Numeric1

Dataset

Description경기도의 구급활동 현황입니다. 출동소방서명, 신고시각, 접수경로, 현장거리, 환자연령 등의 정보를 제공합니다. ※ Sheet탭에서는 최신 1개년 데이터를 확인하실 수 있으며, 전체 데이터는 File탭에서 내려받을 수 있는 파일의 형태로 제공됩니다.
Author경기도
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=SE00GA6F273B8PIJ9N8412495661&infSeq=1

Alerts

집계년도 has constant value ""Constant
Dataset has 817 (8.2%) duplicate rowsDuplicates
외국인유무 is highly imbalanced (90.6%)Imbalance
국적 is highly imbalanced (97.7%)Imbalance
환자연령대 has 2761 (27.6%) missing valuesMissing
환자연령대 has 358 (3.6%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:18.247273
Analysis finished2024-03-12 23:27:19.178698
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

집계년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2017
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 10000
100.0%

Length

2024-03-13T08:27:19.239465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:19.309944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 10000
100.0%

출동소방서
Categorical

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원소방서
862 
용인소방서
 
609
부천소방서
 
605
안산소방서
 
576
남양주소방서
 
455
Other values (42)
6893 

Length

Max length9
Median length5
Mean length5.1872
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row포천소방서
2nd row일산소방서
3rd row의정부소방서
4th row수원소방서
5th row포천소방서

Common Values

ValueCountFrequency (%)
수원소방서 862
 
8.6%
용인소방서 609
 
6.1%
부천소방서 605
 
6.0%
안산소방서 576
 
5.8%
남양주소방서 455
 
4.5%
일산소방서 439
 
4.4%
화성소방서 436
 
4.4%
성남소방서 430
 
4.3%
의정부소방서 397
 
4.0%
안양소방서 379
 
3.8%
Other values (37) 4812
48.1%

Length

2024-03-13T08:27:19.387913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원소방서 862
 
8.6%
용인소방서 609
 
6.1%
부천소방서 605
 
6.0%
안산소방서 576
 
5.8%
남양주소방서 455
 
4.5%
일산소방서 439
 
4.4%
화성소방서 436
 
4.4%
성남소방서 430
 
4.3%
의정부소방서 397
 
4.0%
안양소방서 379
 
3.8%
Other values (37) 4812
48.1%

출동안전센터
Text

Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:19.563917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5795
Min length5

Characters and Unicode

Total characters85795
Distinct characters150
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

Unique0 ?
Unique (%)0.0%

Sample

1st row내촌119안전센터
2nd row백석119안전센터
3rd row중앙119안전센터
4th row매산119안전센터
5th row군내119안전센터
ValueCountFrequency (%)
119구조대 892
 
8.9%
중앙119안전센터 363
 
3.6%
시흥119안전센터 153
 
1.5%
남부119안전센터 149
 
1.5%
매산119안전센터 139
 
1.4%
신장119안전센터 138
 
1.4%
정자119안전센터 136
 
1.4%
수진119안전센터 135
 
1.4%
수지119안전센터 129
 
1.3%
구갈119안전센터 126
 
1.3%
Other values (170) 7640
76.4%
2024-03-13T08:27:19.851592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19132
22.3%
9 9566
11.1%
안 8984
10.5%
전 8674
10.1%
센 8512
9.9%
터 8512
9.9%
대 1697
 
2.0%
구 1342
 
1.6%
조 1043
 
1.2%
동 681
 
0.8%
Other values (140) 17652
20.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57097
66.6%
Decimal Number 28698
33.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 8984
15.7%
전 8674
15.2%
센 8512
14.9%
터 8512
14.9%
대 1697
 
3.0%
구 1342
 
2.4%
조 1043
 
1.8%
동 681
 
1.2%
중 567
 
1.0%
수 566
 
1.0%
Other values (138) 16519
28.9%
Decimal Number
ValueCountFrequency (%)
1 19132
66.7%
9 9566
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57097
66.6%
Common 28698
33.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 8984
15.7%
전 8674
15.2%
센 8512
14.9%
터 8512
14.9%
대 1697
 
3.0%
구 1342
 
2.4%
조 1043
 
1.8%
동 681
 
1.2%
중 567
 
1.0%
수 566
 
1.0%
Other values (138) 16519
28.9%
Common
ValueCountFrequency (%)
1 19132
66.7%
9 9566
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57097
66.6%
ASCII 28698
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19132
66.7%
9 9566
33.3%
Hangul
ValueCountFrequency (%)
안 8984
15.7%
전 8674
15.2%
센 8512
14.9%
터 8512
14.9%
대 1697
 
3.0%
구 1342
 
2.4%
조 1043
 
1.8%
동 681
 
1.2%
중 567
 
1.0%
수 566
 
1.0%
Other values (138) 16519
28.9%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing2761
Missing (%)27.6%
Infinite0
Infinite (%)0.0%
Mean48.48736
Minimum0
Maximum100
Zeros358
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:19.947067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q130
median50
Q370
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.180803
Coefficient of variation (CV)0.4780793
Kurtosis-0.70469133
Mean48.48736
Median Absolute Deviation (MAD)20
Skewness-0.30932552
Sum351000
Variance537.34963
MonotonicityNot monotonic
2024-03-13T08:27:20.027665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1199
12.0%
70 1070
 
10.7%
40 1028
 
10.3%
60 947
 
9.5%
80 817
 
8.2%
30 698
 
7.0%
20 610
 
6.1%
0 358
 
3.6%
10 330
 
3.3%
90 179
 
1.8%
(Missing) 2761
27.6%
ValueCountFrequency (%)
0 358
 
3.6%
10 330
 
3.3%
20 610
6.1%
30 698
7.0%
40 1028
10.3%
50 1199
12.0%
60 947
9.5%
70 1070
10.7%
80 817
8.2%
90 179
 
1.8%
ValueCountFrequency (%)
100 3
 
< 0.1%
90 179
 
1.8%
80 817
8.2%
70 1070
10.7%
60 947
9.5%
50 1199
12.0%
40 1028
10.3%
30 698
7.0%
20 610
6.1%
10 330
 
3.3%

환자성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
3968 
여
3391 
<NA>
2637 
미상
 
4

Length

Max length4
Median length1
Mean length1.7915
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row남
3rd row여
4th row<NA>
5th row여

Common Values

ValueCountFrequency (%)
남 3968
39.7%
여 3391
33.9%
<NA> 2637
26.4%
미상 4
 
< 0.1%

Length

2024-03-13T08:27:20.120526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:20.200454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 3968
39.7%
여 3391
33.9%
na 2637
26.4%
미상 4
 
< 0.1%

외국인유무
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
N
9791 
Y
 
205
D
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
N 9791
97.9%
Y 205
 
2.1%
D 4
 
< 0.1%

Length

2024-03-13T08:27:20.281705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:20.350343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 9791
97.9%
y 205
 
2.1%
d 4
 
< 0.1%

국적
Categorical

IMBALANCE 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9905 
중국
 
31
미국
 
7
러시아
 
7
우즈베키스탄
 
7
Other values (27)
 
43

Length

Max length8
Median length4
Mean length3.9928
Min length2

Unique

Unique18 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9905
99.1%
중국 31
 
0.3%
미국 7
 
0.1%
러시아 7
 
0.1%
우즈베키스탄 7
 
0.1%
베트남 5
 
0.1%
1377107 4
 
< 0.1%
몽골 3
 
< 0.1%
590017 3
 
< 0.1%
배트남 2
 
< 0.1%
Other values (22) 26
 
0.3%

Length

2024-03-13T08:27:20.430189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9905
99.1%
중국 31
 
0.3%
미국 7
 
0.1%
러시아 7
 
0.1%
우즈베키스탄 7
 
0.1%
베트남 5
 
< 0.1%
1377107 4
 
< 0.1%
몽골 3
 
< 0.1%
590017 3
 
< 0.1%
태국 2
 
< 0.1%
Other values (22) 26
 
0.3%

구급처종명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집
4539 
<NA>
1931 
도로
1152 
기타
895 
상업시설
 
317
Other values (9)
1166 

Length

Max length10
Median length9
Mean length2.58
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도로
2nd row집
3rd row집단거주시설
4th row<NA>
5th row학교/교육시설

Common Values

ValueCountFrequency (%)
집 4539
45.4%
<NA> 1931
19.3%
도로 1152
 
11.5%
기타 895
 
8.9%
상업시설 317
 
3.2%
도로외교통지역 267
 
2.7%
집단거주시설 227
 
2.3%
의료관련시설 179
 
1.8%
공장/산업/건설시설 155
 
1.6%
오락/문화시설 147
 
1.5%
Other values (4) 191
 
1.9%

Length

2024-03-13T08:27:20.527704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
집 4539
45.4%
na 1931
19.3%
도로 1152
 
11.5%
기타 895
 
8.9%
상업시설 317
 
3.2%
도로외교통지역 267
 
2.7%
집단거주시설 227
 
2.3%
의료관련시설 179
 
1.8%
공장/산업/건설시설 155
 
1.6%
오락/문화시설 147
 
1.5%
Other values (4) 191
 
1.9%

환자증상1
Categorical

Distinct42
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3109 
기타통증
1822 
기타
1241 
복통
741 
요통
427 
Other values (37)
2660 

Length

Max length6
Median length4
Mean length3.3448
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row기타통증
2nd row복통
3rd row기타통증
4th row<NA>
5th row오심/구토

Common Values

ValueCountFrequency (%)
<NA> 3109
31.1%
기타통증 1822
18.2%
기타 1241
 
12.4%
복통 741
 
7.4%
요통 427
 
4.3%
오심/구토 414
 
4.1%
두통 293
 
2.9%
열상 268
 
2.7%
전신쇠약 252
 
2.5%
심정지 230
 
2.3%
Other values (32) 1203
 
12.0%

Length

2024-03-13T08:27:20.633320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3109
30.6%
기타통증 1822
17.9%
기타 1241
 
12.2%
복통 741
 
7.3%
요통 427
 
4.2%
오심/구토 414
 
4.1%
두통 293
 
2.9%
열상 268
 
2.6%
전신쇠약 252
 
2.5%
심정지 230
 
2.3%
Other values (33) 1360
13.4%

Interactions

2024-03-13T08:27:18.891284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:20.699932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출동소방서환자연령대환자성별외국인유무국적구급처종명환자증상1
출동소방서1.0000.1390.0000.1270.8610.2520.131
환자연령대0.1391.0000.1650.0800.0000.2690.404
환자성별0.0000.1651.0000.0280.0000.2080.237
외국인유무0.1270.0800.0281.0000.6950.0740.019
국적0.8610.0000.0000.6951.0000.8320.000
구급처종명0.2520.2690.2080.0740.8321.0000.369
환자증상10.1310.4040.2370.0190.0000.3691.000
2024-03-13T08:27:20.791309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인유무환자증상1환자성별국적출동소방서구급처종명
외국인유무1.0000.0090.0080.4990.0620.041
환자증상10.0091.0000.1190.0000.0250.121
환자성별0.0080.1191.0000.0000.0000.119
국적0.4990.0000.0001.0000.3540.401
출동소방서0.0620.0250.0000.3541.0000.079
구급처종명0.0410.1210.1190.4010.0791.000
2024-03-13T08:27:21.093868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대출동소방서환자성별외국인유무국적구급처종명환자증상1
환자연령대1.0000.0490.1030.0410.0000.1070.131
출동소방서0.0491.0000.0000.0620.3540.0790.025
환자성별0.1030.0001.0000.0080.0000.1190.119
외국인유무0.0410.0620.0081.0000.4990.0410.009
국적0.0000.3540.0000.4991.0000.4010.000
구급처종명0.1070.0790.1190.0410.4011.0000.121
환자증상10.1310.0250.1190.0090.0000.1211.000

Missing values

2024-03-13T08:27:18.983084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:19.109059image/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

집계년도출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
636112017포천소방서내촌119안전센터60남N<NA>도로기타통증
846702017일산소방서백석119안전센터60남N<NA>집복통
516322017의정부소방서중앙119안전센터50여N<NA>집단거주시설기타통증
474522017수원소방서매산119안전센터<NA><NA>N<NA><NA><NA>
634732017포천소방서군내119안전센터40여N<NA>학교/교육시설오심/구토
959812017의정부소방서호원119안전센터10남N<NA>도로외교통지역기타통증
271282017성남소방서단대119안전센터10남N<NA>도로기타통증
30292017남양주소방서평내119안전센터<NA><NA>N<NA><NA><NA>
814812017파주소방서광탄119안전센터80여N<NA>기타호흡곤란
110652017군포소방서산본119안전센터<NA><NA>N<NA>기타기타통증
집계년도출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
695502017안산소방서월피119안전센터30남N<NA>도로기타
279152017고양소방서행신119안전센터30남N<NA>기타요통
955562017용인소방서모현119안전센터90남N<NA>집기타
703962017용인소방서백암119안전센터<NA><NA>N<NA><NA><NA>
755502017일산소방서고봉119안전센터50남N<NA>기타의식장애
145302017일산소방서119구조대10여N<NA>집단거주시설기타
586402017고양소방서능곡119안전센터50남N<NA>집고열
49662017수원소방서원천119안전센터80남N<NA>집어지러움
74862017부천소방서괴안119안전센터70여N<NA>집기타통증
830122017광주소방서초월119안전센터40남N<NA>공장/산업/건설시설기타통증

Duplicate rows

Most frequently occurring

집계년도출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1# duplicates
4072017시흥소방서시흥119안전센터<NA><NA>N<NA><NA><NA>34
8012017화성소방서동탄119안전센터<NA><NA>N<NA><NA><NA>34
4302017안산소방서119구조대<NA><NA>N<NA><NA><NA>29
5772017용인소방서구갈119안전센터<NA><NA>N<NA><NA><NA>29
4462017안산소방서상록수출동대<NA><NA>N<NA><NA><NA>28
7932017화성소방서119구조대<NA><NA>N<NA><NA><NA>27
7222017파주소방서금촌119안전센터<NA><NA>N<NA><NA><NA>24
1642017남양주소방서평내119안전센터<NA><NA>N<NA><NA><NA>23
2382017부천소방서중앙119안전센터<NA><NA>N<NA><NA><NA>23
3342017수원소방서남부119안전센터<NA><NA>N<NA><NA><NA>23