Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells2887
Missing cells (%)2.9%
Duplicate rows898
Duplicate rows (%)9.0%
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 898 (9.0%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서High correlation
출동소방서 is highly overall correlated with 시군명High correlation
외국인유무 is highly overall correlated with 국적High correlation
국적 is highly overall correlated with 외국인유무High correlation
외국인유무 is highly imbalanced (92.3%)Imbalance
국적 is highly imbalanced (97.6%)Imbalance
환자연령대 has 2887 (28.9%) missing valuesMissing
환자연령대 has 359 (3.6%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:14.313779
Analysis finished2024-03-12 23:27:15.300703
Duration0.99 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
2018
10000 

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 10000
100.0%

Length

2024-03-13T08:27:15.355955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:15.423856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시
944 
성남시
770 
고양시
715 
안산시
645 
용인시
644 
Other values (27)
6282 

Length

Max length4
Median length3
Mean length3.099
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수원시
2nd row고양시
3rd row고양시
4th row동두천시
5th row용인시

Common Values

ValueCountFrequency (%)
수원시 944
 
9.4%
성남시 770
 
7.7%
고양시 715
 
7.1%
안산시 645
 
6.5%
용인시 644
 
6.4%
부천시 609
 
6.1%
남양주시 485
 
4.9%
화성시 417
 
4.2%
의정부시 387
 
3.9%
파주시 371
 
3.7%
Other values (22) 4013
40.1%

Length

2024-03-13T08:27:15.499114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 944
 
9.4%
성남시 770
 
7.7%
고양시 715
 
7.1%
안산시 645
 
6.5%
용인시 644
 
6.4%
부천시 609
 
6.1%
남양주시 485
 
4.9%
화성시 417
 
4.2%
의정부시 387
 
3.9%
파주시 371
 
3.7%
Other values (22) 4013
40.1%

출동소방서
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원소방서
944 
용인소방서
 
629
부천소방서
 
609
안산소방서
 
479
남양주소방서
 
432
Other values (42)
6907 

Length

Max length9
Median length5
Mean length5.2469
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수원소방서
2nd row일산소방서
3rd row고양소방서
4th row불현119안전센터
5th row용인소방서

Common Values

ValueCountFrequency (%)
수원소방서 944
 
9.4%
용인소방서 629
 
6.3%
부천소방서 609
 
6.1%
안산소방서 479
 
4.8%
남양주소방서 432
 
4.3%
성남소방서 426
 
4.3%
일산소방서 424
 
4.2%
화성소방서 417
 
4.2%
의정부소방서 387
 
3.9%
파주소방서 371
 
3.7%
Other values (37) 4882
48.8%

Length

2024-03-13T08:27:15.597050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원소방서 944
 
9.4%
용인소방서 629
 
6.3%
부천소방서 609
 
6.1%
안산소방서 479
 
4.8%
남양주소방서 432
 
4.3%
성남소방서 426
 
4.3%
일산소방서 424
 
4.2%
화성소방서 417
 
4.2%
의정부소방서 387
 
3.9%
파주소방서 371
 
3.7%
Other values (37) 4882
48.8%

출동안전센터
Text

Distinct181
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:15.786573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.612
Min length5

Characters and Unicode

Total characters86120
Distinct characters149
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 row서둔119안전센터
2nd row119구조대
3rd row행신119안전센터
4th row광암지역대
5th row구갈119안전센터
ValueCountFrequency (%)
119구조대 834
 
8.3%
중앙119안전센터 336
 
3.4%
남부119안전센터 177
 
1.8%
매산119안전센터 169
 
1.7%
상록수출동대 166
 
1.7%
신장119안전센터 159
 
1.6%
정자119안전센터 149
 
1.5%
수지119안전센터 144
 
1.4%
수진119안전센터 126
 
1.3%
신흥119안전센터 120
 
1.2%
Other values (171) 7620
76.2%
2024-03-13T08:27:16.060235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19218
22.3%
9 9609
11.2%
안 9091
10.6%
전 8715
10.1%
센 8628
10.0%
터 8628
10.0%
대 1613
 
1.9%
구 1203
 
1.4%
조 958
 
1.1%
수 610
 
0.7%
Other values (139) 17847
20.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57293
66.5%
Decimal Number 28827
33.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 9091
15.9%
전 8715
15.2%
센 8628
15.1%
터 8628
15.1%
대 1613
 
2.8%
구 1203
 
2.1%
조 958
 
1.7%
수 610
 
1.1%
동 601
 
1.0%
중 551
 
1.0%
Other values (137) 16695
29.1%
Decimal Number
ValueCountFrequency (%)
1 19218
66.7%
9 9609
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57293
66.5%
Common 28827
33.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 9091
15.9%
전 8715
15.2%
센 8628
15.1%
터 8628
15.1%
대 1613
 
2.8%
구 1203
 
2.1%
조 958
 
1.7%
수 610
 
1.1%
동 601
 
1.0%
중 551
 
1.0%
Other values (137) 16695
29.1%
Common
ValueCountFrequency (%)
1 19218
66.7%
9 9609
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57293
66.5%
ASCII 28827
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19218
66.7%
9 9609
33.3%
Hangul
ValueCountFrequency (%)
안 9091
15.9%
전 8715
15.2%
센 8628
15.1%
터 8628
15.1%
대 1613
 
2.8%
구 1203
 
2.1%
조 958
 
1.7%
수 610
 
1.1%
동 601
 
1.0%
중 551
 
1.0%
Other values (137) 16695
29.1%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing2887
Missing (%)28.9%
Infinite0
Infinite (%)0.0%
Mean49.891748
Minimum0
Maximum100
Zeros359
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:16.166571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median50
Q370
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)40

Descriptive statistics

Standard deviation23.143123
Coefficient of variation (CV)0.46386675
Kurtosis-0.60353692
Mean49.891748
Median Absolute Deviation (MAD)20
Skewness-0.38843747
Sum354880
Variance535.60414
MonotonicityNot monotonic
2024-03-13T08:27:16.247655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1228
12.3%
70 1064
 
10.6%
60 1006
 
10.1%
40 914
 
9.1%
80 885
 
8.8%
30 641
 
6.4%
20 558
 
5.6%
0 359
 
3.6%
10 255
 
2.5%
90 196
 
2.0%
(Missing) 2887
28.9%
ValueCountFrequency (%)
0 359
 
3.6%
10 255
 
2.5%
20 558
5.6%
30 641
6.4%
40 914
9.1%
50 1228
12.3%
60 1006
10.1%
70 1064
10.6%
80 885
8.8%
90 196
 
2.0%
ValueCountFrequency (%)
100 7
 
0.1%
90 196
 
2.0%
80 885
8.8%
70 1064
10.6%
60 1006
10.1%
50 1228
12.3%
40 914
9.1%
30 641
6.4%
20 558
5.6%
10 255
 
2.5%

환자성별
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
3810 
여
3435 
<NA>
2755 

Length

Max length4
Median length1
Mean length1.8265
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 3810
38.1%
여 3435
34.4%
<NA> 2755
27.6%

Length

2024-03-13T08:27:16.343739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:16.431821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 3810
38.1%
여 3435
34.4%
na 2755
27.6%

외국인유무
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9905 
True
 
95
ValueCountFrequency (%)
False 9905
99.1%
True 95
 
0.9%
2024-03-13T08:27:16.508784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

국적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9905 
중국
 
47
베트남
 
8
러시아
 
8
몽골
 
4
Other values (17)
 
28

Length

Max length7
Median length4
Mean length3.987
Min length2

Unique

Unique10 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9905
99.1%
중국 47
 
0.5%
베트남 8
 
0.1%
러시아 8
 
0.1%
몽골 4
 
< 0.1%
네팔 3
 
< 0.1%
일본 3
 
< 0.1%
우즈벡 3
 
< 0.1%
우즈베키스탄 3
 
< 0.1%
태국 2
 
< 0.1%
Other values (12) 14
 
0.1%

Length

2024-03-13T08:27:16.594929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9905
99.1%
중국 47
 
0.5%
베트남 8
 
0.1%
러시아 8
 
0.1%
몽골 4
 
< 0.1%
우즈베키스탄 4
 
< 0.1%
네팔 3
 
< 0.1%
일본 3
 
< 0.1%
우즈벡 3
 
< 0.1%
필리핀 2
 
< 0.1%
Other values (11) 13
 
0.1%

구급처종명
Categorical

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
집
4707 
<NA>
1646 
도로
1315 
기타
635 
상업시설
487 
Other values (11)
1210 

Length

Max length10
Median length9
Mean length2.5546
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
집 4707
47.1%
<NA> 1646
 
16.5%
도로 1315
 
13.2%
기타 635
 
6.3%
상업시설 487
 
4.9%
도로외교통지역 359
 
3.6%
의료관련시설 242
 
2.4%
집단거주시설 213
 
2.1%
공장/산업/건설시설 149
 
1.5%
오락/문화시설 104
 
1.0%
Other values (6) 143
 
1.4%

Length

2024-03-13T08:27:16.686098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
집 4707
47.1%
na 1646
 
16.5%
도로 1315
 
13.2%
기타 635
 
6.3%
상업시설 487
 
4.9%
도로외교통지역 359
 
3.6%
의료관련시설 242
 
2.4%
집단거주시설 213
 
2.1%
공장/산업/건설시설 149
 
1.5%
오락/문화시설 104
 
1.0%
Other values (6) 143
 
1.4%

환자증상1
Categorical

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3289 
기타통증
1601 
기타
1035 
복통
642 
요통
417 
Other values (38)
3016 

Length

Max length6
Median length4
Mean length3.3358
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row비출혈
3rd row요통
4th row복통
5th row의식장애

Common Values

ValueCountFrequency (%)
<NA> 3289
32.9%
기타통증 1601
16.0%
기타 1035
 
10.3%
복통 642
 
6.4%
요통 417
 
4.2%
오심/구토 394
 
3.9%
두통 355
 
3.5%
고열 316
 
3.2%
전신쇠약 310
 
3.1%
열상 295
 
2.9%
Other values (33) 1346
13.5%

Length

2024-03-13T08:27:16.797557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3289
32.5%
기타통증 1601
15.8%
기타 1035
 
10.2%
복통 642
 
6.4%
요통 417
 
4.1%
오심/구토 394
 
3.9%
두통 355
 
3.5%
고열 316
 
3.1%
전신쇠약 310
 
3.1%
열상 295
 
2.9%
Other values (34) 1456
14.4%

Interactions

2024-03-13T08:27:15.040003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:16.872152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서환자연령대환자성별외국인유무국적구급처종명환자증상1
시군명1.0001.0000.1040.0490.0990.8910.2430.155
출동소방서1.0001.0000.1160.0470.0890.8220.3600.173
환자연령대0.1040.1161.0000.2070.1300.0000.3380.438
환자성별0.0490.0470.2071.0000.0200.0000.1860.190
외국인유무0.0990.0890.1300.0201.0000.6780.0540.185
국적0.8910.8220.0000.0000.6781.0000.3450.420
구급처종명0.2430.3600.3380.1860.0540.3451.0000.399
환자증상10.1550.1730.4380.1900.1850.4200.3991.000
2024-03-13T08:27:16.963097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인유무환자증상1시군명국적환자성별출동소방서구급처종명
외국인유무1.0000.1470.0780.5390.0130.0740.049
환자증상10.1471.0000.0330.1290.1510.0330.124
시군명0.0780.0331.0000.3790.0390.9990.074
국적0.5390.1290.3791.0000.0000.3590.097
환자성별0.0130.1510.0390.0001.0000.0390.169
출동소방서0.0740.0330.9990.3590.0391.0000.109
구급처종명0.0490.1240.0740.0970.1690.1091.000
2024-03-13T08:27:17.048495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서환자성별외국인유무국적구급처종명환자증상1
환자연령대1.0000.0370.0380.1600.1130.0000.1260.143
시군명0.0371.0000.9990.0390.0780.3790.0740.033
출동소방서0.0380.9991.0000.0390.0740.3590.1090.033
환자성별0.1600.0390.0391.0000.0130.0000.1690.151
외국인유무0.1130.0780.0740.0131.0000.5390.0490.147
국적0.0000.3790.3590.0000.5391.0000.0970.129
구급처종명0.1260.0740.1090.1690.0490.0971.0000.124
환자증상10.1430.0330.0330.1510.1470.1290.1241.000

Missing values

2024-03-13T08:27:15.132571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:15.240781image/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

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
288492018수원시수원소방서서둔119안전센터<NA><NA>N<NA><NA><NA>
676912018고양시일산소방서119구조대50여N<NA>집비출혈
89602018고양시고양소방서행신119안전센터10여N<NA>집요통
805092018동두천시불현119안전센터광암지역대80여N<NA>집복통
34112018용인시용인소방서구갈119안전센터40여N<NA>집의식장애
600642018부천시부천소방서서부119안전센터50남N<NA>공장/산업/건설시설열상
218482018의정부시의정부소방서금오119안전센터<NA><NA>N<NA>상업시설<NA>
311742018의왕시의왕소방서고천119안전센터<NA><NA>N<NA>상업시설<NA>
152232018포천시포천소방서군내119안전센터70남N<NA>집기타
889812018안산시사동119안전센터상록수출동대50여N<NA>도로외교통지역기타통증
집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1
359632018포천시포천소방서소흘119안전센터60여N<NA>집호흡곤란
475692018시흥시시흥소방서시흥119안전센터<NA><NA>N<NA>상업시설<NA>
238692018안산시안산소방서119구조대30여N<NA>집기타통증
856822018수원시수원소방서지만119안전센터80여N<NA>집기타
137392018양평군양평소방서공흥119안전센터0여N<NA>집고열
944072018평택시평택소방서119구조대<NA><NA>N<NA><NA><NA>
902018화성시화성소방서장안119안전센터<NA><NA>N<NA>집<NA>
68422018파주시파주소방서월롱119안전센터60여N<NA>집두통
71172018용인시용인소방서구갈119안전센터30여N<NA>오락/문화시설두통
69102018의왕시의왕소방서백운119안전센터<NA><NA>N<NA>집<NA>

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인유무국적구급처종명환자증상1# duplicates
6912018용인시용인소방서수지119안전센터<NA><NA>N<NA><NA><NA>36
2762018부천시부천소방서중앙119안전센터<NA><NA>N<NA><NA><NA>31
6542018용인시용인소방서구갈119안전센터<NA><NA>N<NA><NA><NA>30
4362018시흥시시흥소방서시흥119안전센터<NA><NA>N<NA><NA><NA>29
4782018안산시안산소방서119구조대<NA><NA>N<NA><NA><NA>28
3582018수원시수원소방서남부119안전센터<NA><NA>N<NA><NA><NA>26
2062018남양주시남양주소방서평내119안전센터<NA><NA>N<NA><NA><NA>25
2892018성남시분당소방서서현119안전센터<NA><NA>N<NA><NA><NA>25
8062018평택시평택소방서119구조대<NA><NA>N<NA><NA><NA>23
3762018수원시수원소방서매산119안전센터<NA><NA>N<NA><NA><NA>22