Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells2578
Missing cells (%)2.6%
Duplicate rows863
Duplicate rows (%)8.6%
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 863 (8.6%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서명High correlation
출동소방서명 is highly overall correlated with 시군명High correlation
외국인여부 is highly overall correlated with 국적명High correlation
국적명 is highly overall correlated with 외국인여부High correlation
외국인여부 is highly imbalanced (94.0%)Imbalance
국적명 is highly imbalanced (98.4%)Imbalance
환자연령대 has 2578 (25.8%) missing valuesMissing
환자연령대 has 409 (4.1%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:30.322141
Analysis finished2024-03-12 23:27:31.487294
Duration1.17 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
2014
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2014
4th row2014
5th row2014

Common Values

ValueCountFrequency (%)
2014 10000
100.0%

Length

2024-03-13T08:27:31.547516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:31.615601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2014 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시
1004 
성남시
701 
고양시
686 
부천시
637 
용인시
 
633
Other values (27)
6339 

Length

Max length4
Median length3
Mean length3.099
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row성남시
2nd row성남시
3rd row광주시
4th row화성시
5th row부천시

Common Values

ValueCountFrequency (%)
수원시 1004
 
10.0%
성남시 701
 
7.0%
고양시 686
 
6.9%
부천시 637
 
6.4%
용인시 633
 
6.3%
안산시 600
 
6.0%
남양주시 466
 
4.7%
오산시 441
 
4.4%
안양시 419
 
4.2%
의정부시 397
 
4.0%
Other values (22) 4016
40.2%

Length

2024-03-13T08:27:31.688571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 1004
 
10.0%
성남시 701
 
7.0%
고양시 686
 
6.9%
부천시 637
 
6.4%
용인시 633
 
6.3%
안산시 600
 
6.0%
남양주시 466
 
4.7%
오산시 441
 
4.4%
안양시 419
 
4.2%
의정부시 397
 
4.0%
Other values (22) 4016
40.2%

출동소방서명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원소방서
1004 
부천소방서
 
637
용인소방서
 
633
안산소방서
 
600
남양주소방서
 
466
Other values (30)
6660 

Length

Max length8
Median length5
Mean length5.1002
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row분당소방서
2nd row분당소방서
3rd row광주소방서
4th row화성소방서
5th row부천소방서

Common Values

ValueCountFrequency (%)
수원소방서 1004
 
10.0%
부천소방서 637
 
6.4%
용인소방서 633
 
6.3%
안산소방서 600
 
6.0%
남양주소방서 466
 
4.7%
안양소방서 419
 
4.2%
의정부소방서 397
 
4.0%
일산소방서 393
 
3.9%
성남소방서 382
 
3.8%
파주소방서 366
 
3.7%
Other values (25) 4703
47.0%

Length

2024-03-13T08:27:31.812747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원소방서 1004
 
10.0%
부천소방서 637
 
6.4%
용인소방서 633
 
6.3%
안산소방서 600
 
6.0%
남양주소방서 466
 
4.7%
안양소방서 419
 
4.2%
의정부소방서 397
 
4.0%
일산소방서 393
 
3.9%
성남소방서 382
 
3.8%
파주소방서 366
 
3.7%
Other values (25) 4703
47.0%

출동안전센터명
Text

Distinct155
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:31.991449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5224
Min length5

Characters and Unicode

Total characters85224
Distinct characters141
Distinct categories4 ?
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 row판교119안전센터
2nd row수내119안전센터
3rd row경안119안전센터
4th row태안119안전센터
5th row범박119안전센터
ValueCountFrequency (%)
119구조대 1526
 
15.3%
중앙119안전센터 311
 
3.1%
사동119안전센터 222
 
2.2%
매산119안전센터 182
 
1.8%
정자119안전센터 164
 
1.6%
남부119안전센터 156
 
1.6%
신장119안전센터 151
 
1.5%
수지119안전센터 132
 
1.3%
안양119안전센터 125
 
1.2%
구갈119안전센터 119
 
1.2%
Other values (145) 6912
69.1%
2024-03-13T08:27:32.268233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19814
23.2%
9 9907
11.6%
안 8812
10.3%
전 8459
9.9%
센 8324
9.8%
터 8324
9.8%
구 1962
 
2.3%
대 1881
 
2.2%
조 1661
 
1.9%
동 614
 
0.7%
Other values (131) 15466
18.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 55479
65.1%
Decimal Number 29721
34.9%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 8812
15.9%
전 8459
15.2%
센 8324
15.0%
터 8324
15.0%
구 1962
 
3.5%
대 1881
 
3.4%
조 1661
 
3.0%
동 614
 
1.1%
중 544
 
1.0%
산 464
 
0.8%
Other values (127) 14434
26.0%
Decimal Number
ValueCountFrequency (%)
1 19814
66.7%
9 9907
33.3%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 55479
65.1%
Common 29745
34.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 8812
15.9%
전 8459
15.2%
센 8324
15.0%
터 8324
15.0%
구 1962
 
3.5%
대 1881
 
3.4%
조 1661
 
3.0%
동 614
 
1.1%
중 544
 
1.0%
산 464
 
0.8%
Other values (127) 14434
26.0%
Common
ValueCountFrequency (%)
1 19814
66.6%
9 9907
33.3%
( 12
 
< 0.1%
) 12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 55479
65.1%
ASCII 29745
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19814
66.6%
9 9907
33.3%
( 12
 
< 0.1%
) 12
 
< 0.1%
Hangul
ValueCountFrequency (%)
안 8812
15.9%
전 8459
15.2%
센 8324
15.0%
터 8324
15.0%
구 1962
 
3.5%
대 1881
 
3.4%
조 1661
 
3.0%
동 614
 
1.1%
중 544
 
1.0%
산 464
 
0.8%
Other values (127) 14434
26.0%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.1%
Missing2578
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean47.954729
Minimum0
Maximum100
Zeros409
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:32.364601image/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 deviation23.008915
Coefficient of variation (CV)0.47980492
Kurtosis-0.63488943
Mean47.954729
Median Absolute Deviation (MAD)20
Skewness-0.32078981
Sum355920
Variance529.41016
MonotonicityNot monotonic
2024-03-13T08:27:32.446129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1356
13.6%
70 1114
11.1%
40 1087
10.9%
60 897
 
9.0%
80 769
 
7.7%
30 735
 
7.3%
20 553
 
5.5%
0 409
 
4.1%
10 338
 
3.4%
90 157
 
1.6%
(Missing) 2578
25.8%
ValueCountFrequency (%)
0 409
 
4.1%
10 338
 
3.4%
20 553
5.5%
30 735
7.3%
40 1087
10.9%
50 1356
13.6%
60 897
9.0%
70 1114
11.1%
80 769
7.7%
90 157
 
1.6%
ValueCountFrequency (%)
100 7
 
0.1%
90 157
 
1.6%
80 769
7.7%
70 1114
11.1%
60 897
9.0%
50 1356
13.6%
40 1087
10.9%
30 735
7.3%
20 553
5.5%
10 338
 
3.4%

환자성별구분명
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
4037 
여
3400 
<NA>
2555 
미상
 
8

Length

Max length4
Median length1
Mean length1.7673
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 4037
40.4%
여 3400
34.0%
<NA> 2555
25.6%
미상 8
 
0.1%

Length

2024-03-13T08:27:32.539410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:32.617956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 4037
40.4%
여 3400
34.0%
na 2555
25.6%
미상 8
 
0.1%

외국인여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9930 
True
 
70
ValueCountFrequency (%)
False 9930
99.3%
True 70
 
0.7%
2024-03-13T08:27:32.682004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

국적명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9942 
중국
 
30
몽골
 
4
태국
 
4
베트남
 
3
Other values (13)
 
17

Length

Max length6
Median length4
Mean length3.9915
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> 9942
99.4%
중국 30
 
0.3%
몽골 4
 
< 0.1%
태국 4
 
< 0.1%
베트남 3
 
< 0.1%
우즈베키스탄 3
 
< 0.1%
필리핀 2
 
< 0.1%
미국 2
 
< 0.1%
이집트 1
 
< 0.1%
아일랜드 1
 
< 0.1%
Other values (8) 8
 
0.1%

Length

2024-03-13T08:27:32.769603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9942
99.4%
중국 30
 
0.3%
몽골 4
 
< 0.1%
태국 4
 
< 0.1%
베트남 3
 
< 0.1%
우즈베키스탄 3
 
< 0.1%
필리핀 2
 
< 0.1%
미국 2
 
< 0.1%
우즈벡 1
 
< 0.1%
영국 1
 
< 0.1%
Other values (8) 8
 
0.1%

구급발생장소유형
Categorical

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가정
4551 
<NA>
1902 
일반도로
991 
기타
983 
주택가
 
430
Other values (15)
1143 

Length

Max length4
Median length2
Mean length2.7578
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가정 4551
45.5%
<NA> 1902
19.0%
일반도로 991
 
9.9%
기타 983
 
9.8%
주택가 430
 
4.3%
공공장소 395
 
4.0%
공장 107
 
1.1%
고속도로 100
 
1.0%
사무실 87
 
0.9%
식당 78
 
0.8%
Other values (10) 376
 
3.8%

Length

2024-03-13T08:27:32.875676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정 4551
45.5%
na 1902
19.0%
일반도로 991
 
9.9%
기타 983
 
9.8%
주택가 430
 
4.3%
공공장소 395
 
4.0%
공장 107
 
1.1%
고속도로 100
 
1.0%
사무실 87
 
0.9%
식당 78
 
0.8%
Other values (10) 376
 
3.8%

환자증상유형
Categorical

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
2710 
기타통증
2251 
기타
1179 
복통
684 
요통
448 
Other values (28)
2728 

Length

Max length6
Median length4
Mean length3.4581
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 2710
27.1%
기타통증 2251
22.5%
기타 1179
11.8%
복통 684
 
6.8%
요통 448
 
4.5%
그 밖의출혈 376
 
3.8%
오심/구토 348
 
3.5%
전신쇠약 307
 
3.1%
고열 255
 
2.5%
의식장애 209
 
2.1%
Other values (23) 1233
12.3%

Length

2024-03-13T08:27:32.982992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2710
26.1%
기타통증 2251
21.7%
기타 1179
11.4%
복통 684
 
6.6%
요통 448
 
4.3%
그 376
 
3.6%
밖의출혈 376
 
3.6%
오심/구토 348
 
3.4%
전신쇠약 307
 
3.0%
고열 255
 
2.5%
Other values (24) 1442
13.9%

Interactions

2024-03-13T08:27:31.221204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:33.073104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서명환자연령대환자성별구분명외국인여부국적명구급발생장소유형환자증상유형
시군명1.0001.0000.1190.0410.1050.7250.3240.168
출동소방서명1.0001.0000.1250.0370.0970.7940.3320.148
환자연령대0.1190.1251.0000.1900.1050.7060.3020.425
환자성별구분명0.0410.0370.1901.0000.0260.0000.2250.200
외국인여부0.1050.0970.1050.0261.0001.0000.1120.000
국적명0.7250.7940.7060.0001.0001.0000.0000.633
구급발생장소유형0.3240.3320.3020.2250.1120.0001.0000.319
환자증상유형0.1680.1480.4250.2000.0000.6330.3191.000
2024-03-13T08:27:33.166316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인여부환자성별구분명국적명구급발생장소유형시군명환자증상유형출동소방서명
외국인여부1.0000.0430.8560.1000.0830.0000.082
환자성별구분명0.0431.0000.0000.1210.0200.1010.018
국적명0.8560.0001.0000.0000.2800.2550.328
구급발생장소유형0.1000.1210.0001.0000.0920.0900.093
시군명0.0830.0200.2800.0921.0000.0301.000
환자증상유형0.0000.1010.2550.0900.0301.0000.033
출동소방서명0.0820.0180.3280.0931.0000.0331.000
2024-03-13T08:27:33.251795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서명환자성별구분명외국인여부국적명구급발생장소유형환자증상유형
환자연령대1.0000.0410.0420.1150.0890.2520.1030.143
시군명0.0411.0001.0000.0200.0830.2800.0920.030
출동소방서명0.0421.0001.0000.0180.0820.3280.0930.033
환자성별구분명0.1150.0200.0181.0000.0430.0000.1210.101
외국인여부0.0890.0830.0820.0431.0000.8560.1000.000
국적명0.2520.2800.3280.0000.8561.0000.0000.255
구급발생장소유형0.1030.0920.0930.1210.1000.0001.0000.090
환자증상유형0.1430.0300.0330.1010.0000.2550.0901.000

Missing values

2024-03-13T08:27:31.311623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:31.425291image/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

집계년도시군명출동소방서명출동안전센터명환자연령대환자성별구분명외국인여부국적명구급발생장소유형환자증상유형
257682014성남시분당소방서판교119안전센터<NA><NA>N<NA>가정<NA>
177912014성남시분당소방서수내119안전센터40여N<NA>가정기타통증
744222014광주시광주소방서경안119안전센터<NA><NA>N<NA><NA><NA>
159882014화성시화성소방서태안119안전센터0남N<NA>가정고열
701022014부천시부천소방서범박119안전센터70남N<NA>가정기타통증
760282014평택시평택소방서포승119안전센터<NA><NA>N<NA><NA><NA>
911992014용인시용인소방서이동119안전센터50남N<NA>기타기타통증
415522014성남시성남소방서상대원119안전센터<NA><NA>N<NA>공공장소<NA>
618492014김포시김포소방서통진119안전센터40여N<NA>가정요통
974202014과천시과천소방서119구조대<NA><NA>N<NA><NA><NA>
집계년도시군명출동소방서명출동안전센터명환자연령대환자성별구분명외국인여부국적명구급발생장소유형환자증상유형
911692014광주시광주소방서경안119안전센터<NA><NA>N<NA><NA><NA>
562014연천군연천소방서전곡119안전센터70여N<NA>가정복통
651802014용인시용인소방서백암119안전센터70여N<NA>가정기타통증
988322014화성시화성소방서남양119안전센터20남Y스리랑카공장두통
368782014고양시고양소방서119구조대70남N<NA>일반도로기타통증
282552014오산시송탄소방서고덕119안전센터40여N<NA>가정복통
525502014고양시일산소방서중산119안전센터30여N<NA>가정분만진통
71482014안산시안산소방서선부119안전센터20여N<NA>가정그 밖의출혈
295742014평택시평택소방서안중119안전센터60여N<NA>가정기타
210502014군포시군포소방서산본119안전센터<NA><NA>N<NA>가정<NA>

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서명출동안전센터명환자연령대환자성별구분명외국인여부국적명구급발생장소유형환자증상유형# duplicates
3572014수원시수원소방서매산119안전센터<NA><NA>N<NA><NA><NA>80
4132014수원시수원소방서지만119안전센터<NA><NA>N<NA><NA><NA>45
3482014수원시수원소방서남부119안전센터<NA><NA>N<NA><NA><NA>40
4932014안산시안산소방서사동119안전센터<NA><NA>N<NA><NA><NA>40
4582014안산시안산소방서119구조대<NA><NA>N<NA><NA><NA>28
6732014용인시용인소방서수지119안전센터<NA><NA>N<NA><NA><NA>28
7472014이천시이천소방서관고119안전센터<NA><NA>N<NA><NA><NA>26
5672014안양시안양소방서안양119안전센터<NA><NA>N<NA><NA><NA>25
6822014용인시용인소방서역북119안전센터<NA><NA>N<NA><NA><NA>25
6122014오산시송탄소방서119구조대<NA><NA>N<NA><NA><NA>24