Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells2617
Missing cells (%)2.6%
Duplicate rows789
Duplicate rows (%)7.9%
Total size in memory878.9 KiB
Average record size in memory90.0 B

Variable types

Categorical7
Text1
Numeric1
Boolean1

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 789 (7.9%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서High correlation
출동소방서 is highly overall correlated with 시군명High correlation
외국인여부 is highly overall correlated with 국적High correlation
국적 is highly overall correlated with 외국인여부High correlation
외국인여부 is highly imbalanced (87.8%)Imbalance
국적 is highly imbalanced (96.7%)Imbalance
환자연령대 has 2617 (26.2%) missing valuesMissing
환자연령대 has 258 (2.6%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:05.179276
Analysis finished2024-03-12 23:27:06.388645
Duration1.21 second
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
2020
10000 

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 (%)
2020 10000
100.0%

Length

2024-03-13T08:27:06.433582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:06.499842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시
903 
성남시
757 
고양시
691 
용인시
639 
안산시
 
633
Other values (26)
6377 

Length

Max length4
Median length3
Mean length3.09
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남시
2nd row안양시
3rd row남양주시
4th row안성시
5th row양평군

Common Values

ValueCountFrequency (%)
수원시 903
 
9.0%
성남시 757
 
7.6%
고양시 691
 
6.9%
용인시 639
 
6.4%
안산시 633
 
6.3%
부천시 569
 
5.7%
평택시 532
 
5.3%
남양주시 446
 
4.5%
화성시 443
 
4.4%
안양시 396
 
4.0%
Other values (21) 3991
39.9%

Length

2024-03-13T08:27:06.577730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 903
 
9.0%
성남시 757
 
7.6%
고양시 691
 
6.9%
용인시 639
 
6.4%
안산시 633
 
6.3%
부천시 569
 
5.7%
평택시 532
 
5.3%
남양주시 446
 
4.5%
화성시 443
 
4.4%
안양시 396
 
4.0%
Other values (21) 3991
39.9%

출동소방서
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
용인소방서
 
639
안산소방서
 
633
부천소방서
 
569
수원남부소방서
 
510
남양주소방서
 
446
Other values (30)
7203 

Length

Max length7
Median length5
Mean length5.192
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남소방서
2nd row안양소방서
3rd row남양주소방서
4th row안성소방서
5th row양평소방서

Common Values

ValueCountFrequency (%)
용인소방서 639
 
6.4%
안산소방서 633
 
6.3%
부천소방서 569
 
5.7%
수원남부소방서 510
 
5.1%
남양주소방서 446
 
4.5%
화성소방서 443
 
4.4%
성남소방서 439
 
4.4%
안양소방서 396
 
4.0%
수원소방서 393
 
3.9%
일산소방서 372
 
3.7%
Other values (25) 5160
51.6%

Length

2024-03-13T08:27:06.701357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용인소방서 639
 
6.4%
안산소방서 633
 
6.3%
부천소방서 569
 
5.7%
수원남부소방서 510
 
5.1%
남양주소방서 446
 
4.5%
화성소방서 443
 
4.4%
성남소방서 439
 
4.4%
안양소방서 396
 
4.0%
수원소방서 393
 
3.9%
일산소방서 372
 
3.7%
Other values (25) 5160
51.6%

출동안전센터
Text

Distinct202
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:06.885427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length8.6258
Min length5

Characters and Unicode

Total characters86258
Distinct characters158
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 row수진119안전센터
2nd row석수119안전센터
3rd row와부119안전센터
4th row도기119안전센터
5th row강상119안전센터
ValueCountFrequency (%)
119구조대 610
 
6.1%
흥선119안전센터 160
 
1.6%
상록수출동대 160
 
1.6%
정자119안전센터 156
 
1.6%
매산119안전센터 148
 
1.5%
신장119안전센터 146
 
1.5%
남부119안전센터 136
 
1.4%
119구급대 128
 
1.3%
신흥119안전센터 127
 
1.3%
수진119안전센터 126
 
1.3%
Other values (192) 8103
81.0%
2024-03-13T08:27:07.171053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19152
22.2%
9 9576
11.1%
안 8990
10.4%
전 8853
10.3%
터 8639
10.0%
센 8639
10.0%
대 1641
 
1.9%
구 985
 
1.1%
조 707
 
0.8%
동 682
 
0.8%
Other values (148) 18394
21.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57530
66.7%
Decimal Number 28728
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 8990
15.6%
전 8853
15.4%
터 8639
15.0%
센 8639
15.0%
대 1641
 
2.9%
구 985
 
1.7%
조 707
 
1.2%
동 682
 
1.2%
수 615
 
1.1%
지 600
 
1.0%
Other values (146) 17179
29.9%
Decimal Number
ValueCountFrequency (%)
1 19152
66.7%
9 9576
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57530
66.7%
Common 28728
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 8990
15.6%
전 8853
15.4%
터 8639
15.0%
센 8639
15.0%
대 1641
 
2.9%
구 985
 
1.7%
조 707
 
1.2%
동 682
 
1.2%
수 615
 
1.1%
지 600
 
1.0%
Other values (146) 17179
29.9%
Common
ValueCountFrequency (%)
1 19152
66.7%
9 9576
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57530
66.7%
ASCII 28728
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19152
66.7%
9 9576
33.3%
Hangul
ValueCountFrequency (%)
안 8990
15.6%
전 8853
15.4%
터 8639
15.0%
센 8639
15.0%
대 1641
 
2.9%
구 985
 
1.7%
조 707
 
1.2%
동 682
 
1.2%
수 615
 
1.1%
지 600
 
1.0%
Other values (146) 17179
29.9%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing2617
Missing (%)26.2%
Infinite0
Infinite (%)0.0%
Mean49.674929
Minimum0
Maximum100
Zeros258
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:07.268625image/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 deviation22.640691
Coefficient of variation (CV)0.45577702
Kurtosis-0.67766298
Mean49.674929
Median Absolute Deviation (MAD)20
Skewness-0.32251773
Sum366750
Variance512.6009
MonotonicityNot monotonic
2024-03-13T08:27:07.353275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1287
12.9%
60 1119
11.2%
70 1041
 
10.4%
40 914
 
9.1%
80 889
 
8.9%
30 682
 
6.8%
20 671
 
6.7%
10 328
 
3.3%
0 258
 
2.6%
90 185
 
1.8%
(Missing) 2617
26.2%
ValueCountFrequency (%)
0 258
 
2.6%
10 328
 
3.3%
20 671
6.7%
30 682
6.8%
40 914
9.1%
50 1287
12.9%
60 1119
11.2%
70 1041
10.4%
80 889
8.9%
90 185
 
1.8%
ValueCountFrequency (%)
100 9
 
0.1%
90 185
 
1.8%
80 889
8.9%
70 1041
10.4%
60 1119
11.2%
50 1287
12.9%
40 914
9.1%
30 682
6.8%
20 671
6.7%
10 328
 
3.3%

환자성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
4221 
여
3245 
<NA>
2530 
미상
 
4

Length

Max length4
Median length1
Mean length1.7594
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 4221
42.2%
여 3245
32.5%
<NA> 2530
25.3%
미상 4
 
< 0.1%

Length

2024-03-13T08:27:07.454629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:07.538501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 4221
42.2%
여 3245
32.5%
na 2530
25.3%
미상 4
 
< 0.1%

외국인여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9833 
True
 
167
ValueCountFrequency (%)
False 9833
98.3%
True 167
 
1.7%
2024-03-13T08:27:07.602900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

국적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9845 
중국
 
70
우즈베키스탄
 
11
베트남
 
8
러시아
 
8
Other values (29)
 
58

Length

Max length9
Median length4
Mean length3.9839
Min length2

Unique

Unique19 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9845
98.5%
중국 70
 
0.7%
우즈베키스탄 11
 
0.1%
베트남 8
 
0.1%
러시아 8
 
0.1%
몽골 8
 
0.1%
방글라데시 6
 
0.1%
미국 5
 
0.1%
필리핀 5
 
0.1%
태국 4
 
< 0.1%
Other values (24) 30
 
0.3%

Length

2024-03-13T08:27:07.688815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9845
98.4%
중국 70
 
0.7%
우즈베키스탄 11
 
0.1%
베트남 8
 
0.1%
러시아 8
 
0.1%
몽골 8
 
0.1%
방글라데시 6
 
0.1%
미국 5
 
< 0.1%
필리핀 5
 
< 0.1%
태국 4
 
< 0.1%
Other values (25) 31
 
0.3%

구급처종명
Categorical

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집
4807 
<NA>
1455 
도로
1165 
도로외교통지역
775 
상업시설
505 
Other values (9)
1293 

Length

Max length10
Median length9
Mean length2.8407
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
집 4807
48.1%
<NA> 1455
 
14.5%
도로 1165
 
11.7%
도로외교통지역 775
 
7.8%
상업시설 505
 
5.1%
기타 277
 
2.8%
집단거주시설 232
 
2.3%
공장/산업/건설시설 204
 
2.0%
의료관련시설 178
 
1.8%
바다/강/산/논밭 152
 
1.5%
Other values (4) 250
 
2.5%

Length

2024-03-13T08:27:07.802025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
집 4807
48.1%
na 1455
 
14.5%
도로 1165
 
11.7%
도로외교통지역 775
 
7.8%
상업시설 505
 
5.1%
기타 277
 
2.8%
집단거주시설 232
 
2.3%
공장/산업/건설시설 204
 
2.0%
의료관련시설 178
 
1.8%
바다/강/산/논밭 152
 
1.5%
Other values (4) 250
 
2.5%

환자증상1
Categorical

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3531 
기타통증
1446 
복통
688 
기타
643 
요통
438 
Other values (39)
3254 

Length

Max length6
Median length4
Mean length3.374
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3531
35.3%
기타통증 1446
14.5%
복통 688
 
6.9%
기타 643
 
6.4%
요통 438
 
4.4%
두통 364
 
3.6%
어지러움 339
 
3.4%
열상 280
 
2.8%
의식장애 271
 
2.7%
전신쇠약 271
 
2.7%
Other values (34) 1729
17.3%

Length

2024-03-13T08:27:07.910898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3531
34.9%
기타통증 1446
14.3%
복통 688
 
6.8%
기타 643
 
6.4%
요통 438
 
4.3%
두통 364
 
3.6%
어지러움 339
 
3.3%
열상 280
 
2.8%
의식장애 271
 
2.7%
전신쇠약 271
 
2.7%
Other values (35) 1852
18.3%

Interactions

2024-03-13T08:27:05.941847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:07.983140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서환자연령대환자성별외국인여부국적구급처종명환자증상1
시군명1.0001.0000.1400.0800.1010.8040.1870.117
출동소방서1.0001.0000.1440.0720.1040.8120.2000.132
환자연령대0.1400.1441.0000.1770.1540.1900.3300.369
환자성별0.0800.0720.1771.0000.0060.1970.2620.244
외국인여부0.1010.1040.1540.0061.0001.0000.1020.111
국적0.8040.8120.1900.1971.0001.0000.6360.000
구급처종명0.1870.2000.3300.2620.1020.6361.0000.428
환자증상10.1170.1320.3690.2440.1110.0000.4281.000
2024-03-13T08:27:08.069820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인여부환자증상1시군명국적환자성별출동소방서구급처종명
외국인여부1.0000.0930.0860.8930.0100.0880.095
환자증상10.0931.0000.0250.0000.1230.0270.143
시군명0.0860.0251.0000.3150.0391.0000.060
국적0.8930.0000.3151.0000.1440.3090.246
환자성별0.0100.1230.0390.1441.0000.0350.152
출동소방서0.0880.0271.0000.3090.0351.0000.063
구급처종명0.0950.1430.0600.2460.1520.0631.000
2024-03-13T08:27:08.153243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서환자성별외국인여부국적구급처종명환자증상1
환자연령대1.0000.0420.0430.1060.1300.0000.1320.125
시군명0.0421.0001.0000.0390.0860.3150.0600.025
출동소방서0.0431.0001.0000.0350.0880.3090.0630.027
환자성별0.1060.0390.0351.0000.0100.1440.1520.123
외국인여부0.1300.0860.0880.0101.0000.8930.0950.093
국적0.0000.3150.3090.1440.8931.0000.2460.000
구급처종명0.1320.0600.0630.1520.0950.2461.0000.143
환자증상10.1250.0250.0270.1230.0930.0000.1431.000

Missing values

2024-03-13T08:27:06.222292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:06.331611image/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

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1
998292020성남시성남소방서수진119안전센터70여N<NA>집가슴불편감
624672020안양시안양소방서석수119안전센터70여N<NA>집골절
834752020남양주시남양주소방서와부119안전센터<NA><NA>N<NA>도로외교통지역<NA>
20762020안성시안성소방서도기119안전센터50여N<NA>집기타
795192020양평군양평소방서강상119안전센터50남N<NA><NA><NA>
168282020수원시수원소방서정자119안전센터80남N<NA>집어지러움
1862020안산시안산소방서신길119안전센터50여N<NA>집복통
853582020의정부시의정부소방서흥선119안전센터<NA><NA>N<NA><NA><NA>
333442020부천시부천소방서범박119안전센터40남N<NA>집정신장애
630692020수원시수원남부소방서지만119안전센터40여N<NA>집복통
집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1
638822020연천군연천소방서전곡119안전센터50남N<NA>기타열상
638172020성남시분당소방서야탑119안전센터<NA><NA>N<NA><NA><NA>
655692020수원시수원소방서원천119안전센터70남N<NA>집호흡곤란
712412020용인시용인소방서수지119안전센터80여N<NA>상업시설의식장애
872312020안성시안성소방서119구조대60여N<NA>집기타통증
479822020수원시수원남부소방서서둔119안전센터60여N<NA>도로두통
725362020포천시포천소방서소흘119안전센터70여N<NA>집두통
431022020광주시광주소방서초월119안전센터70여N<NA>집전신쇠약
188802020안양시안양소방서박달119안전센터50여N<NA>도로전신쇠약
129872020화성시화성소방서목동119안전센터30남N<NA>집단거주시설열상

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1# duplicates
3942020시흥시시흥소방서은행119안전센터<NA><NA>N<NA><NA><NA>28
3332020수원시수원남부소방서매산119안전센터<NA><NA>N<NA><NA><NA>27
3252020수원시수원남부소방서남부119안전센터<NA><NA>N<NA><NA><NA>23
7602020화성시화성소방서119구조대<NA><NA>N<NA><NA><NA>22
3872020시흥시시흥소방서시흥119안전센터<NA><NA>N<NA><NA><NA>19
7732020화성시화성소방서반송119안전센터<NA><NA>N<NA><NA><NA>19
4362020안산시안산소방서상록수출동대<NA><NA>N<NA><NA><NA>18
4082020안산시안산소방서119구조대<NA><NA>N<NA><NA><NA>17
5142020안양시안양소방서안양119안전센터<NA><NA>N<NA><NA><NA>16
6372020의정부시의정부소방서흥선119안전센터<NA><NA>N<NA><NA><NA>16