Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells12789
Missing cells (%)12.8%
Duplicate rows841
Duplicate rows (%)8.4%
Total size in memory888.7 KiB
Average record size in memory91.0 B

Variable types

Categorical6
Text1
Numeric1
Boolean1
Unsupported1

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 841 (8.4%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서명High correlation
출동소방서명 is highly overall correlated with 시군명High correlation
외국인여부 is highly imbalanced (95.0%)Imbalance
환자연령대 has 2789 (27.9%) missing valuesMissing
국적명 has 10000 (100.0%) missing valuesMissing
국적명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
환자연령대 has 279 (2.8%) zerosZeros

Reproduction

Analysis started2024-03-12 23:27:26.458691
Analysis finished2024-03-12 23:27:27.441455
Duration0.98 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
2015
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 10000
100.0%

Length

2024-03-13T08:27:27.492234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:27.562552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원시
1034 
성남시
819 
고양시
677 
용인시
 
627
부천시
 
573
Other values (27)
6270 

Length

Max length4
Median length3
Mean length3.1038
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남양주시
2nd row오산시
3rd row의정부시
4th row남양주시
5th row양평군

Common Values

ValueCountFrequency (%)
수원시 1034
 
10.3%
성남시 819
 
8.2%
고양시 677
 
6.8%
용인시 627
 
6.3%
부천시 573
 
5.7%
안산시 570
 
5.7%
남양주시 514
 
5.1%
안양시 421
 
4.2%
의정부시 398
 
4.0%
화성시 396
 
4.0%
Other values (22) 3971
39.7%

Length

2024-03-13T08:27:27.630685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원시 1034
 
10.3%
성남시 819
 
8.2%
고양시 677
 
6.8%
용인시 627
 
6.3%
부천시 573
 
5.7%
안산시 570
 
5.7%
남양주시 514
 
5.1%
안양시 421
 
4.2%
의정부시 398
 
4.0%
화성시 396
 
4.0%
Other values (22) 3971
39.7%

출동소방서명
Categorical

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
수원소방서
1034 
용인소방서
 
627
부천소방서
 
573
안산소방서
 
570
남양주소방서
 
514
Other values (30)
6682 

Length

Max length8
Median length5
Mean length5.1056
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남양주소방서
2nd row송탄소방서
3rd row의정부소방서
4th row남양주소방서
5th row양평소방서

Common Values

ValueCountFrequency (%)
수원소방서 1034
 
10.3%
용인소방서 627
 
6.3%
부천소방서 573
 
5.7%
안산소방서 570
 
5.7%
남양주소방서 514
 
5.1%
성남소방서 461
 
4.6%
안양소방서 421
 
4.2%
의정부소방서 398
 
4.0%
화성소방서 396
 
4.0%
일산소방서 370
 
3.7%
Other values (25) 4636
46.4%

Length

2024-03-13T08:27:27.719896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수원소방서 1034
 
10.3%
용인소방서 627
 
6.3%
부천소방서 573
 
5.7%
안산소방서 570
 
5.7%
남양주소방서 514
 
5.1%
성남소방서 461
 
4.6%
안양소방서 421
 
4.2%
의정부소방서 398
 
4.0%
화성소방서 396
 
4.0%
일산소방서 370
 
3.7%
Other values (25) 4636
46.4%

출동안전센터명
Text

Distinct165
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T08:27:27.898456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.5996
Min length5

Characters and Unicode

Total characters85996
Distinct characters148
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 row평내119안전센터
2nd row119구조대
3rd row중앙119안전센터
4th row가운119안전센터
5th row공흥119안전센터
ValueCountFrequency (%)
119구조대 1084
 
10.8%
중앙119안전센터 381
 
3.8%
사동119안전센터 212
 
2.1%
매산119안전센터 185
 
1.8%
정자119안전센터 176
 
1.8%
남부119안전센터 162
 
1.6%
수지119안전센터 148
 
1.5%
수진119안전센터 143
 
1.4%
구갈119안전센터 125
 
1.2%
신장119안전센터 122
 
1.2%
Other values (155) 7262
72.6%
2024-03-13T08:27:28.188059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19556
22.7%
9 9778
11.4%
안 9065
10.5%
전 8714
10.1%
센 8609
10.0%
터 8609
10.0%
대 1622
 
1.9%
구 1456
 
1.7%
조 1164
 
1.4%
동 678
 
0.8%
Other values (138) 16745
19.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56640
65.9%
Decimal Number 29334
34.1%
Open Punctuation 11
 
< 0.1%
Close Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
안 9065
16.0%
전 8714
15.4%
센 8609
15.2%
터 8609
15.2%
대 1622
 
2.9%
구 1456
 
2.6%
조 1164
 
2.1%
동 678
 
1.2%
중 589
 
1.0%
수 481
 
0.8%
Other values (134) 15653
27.6%
Decimal Number
ValueCountFrequency (%)
1 19556
66.7%
9 9778
33.3%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 56640
65.9%
Common 29356
34.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
안 9065
16.0%
전 8714
15.4%
센 8609
15.2%
터 8609
15.2%
대 1622
 
2.9%
구 1456
 
2.6%
조 1164
 
2.1%
동 678
 
1.2%
중 589
 
1.0%
수 481
 
0.8%
Other values (134) 15653
27.6%
Common
ValueCountFrequency (%)
1 19556
66.6%
9 9778
33.3%
( 11
 
< 0.1%
) 11
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 56640
65.9%
ASCII 29356
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19556
66.6%
9 9778
33.3%
( 11
 
< 0.1%
) 11
 
< 0.1%
Hangul
ValueCountFrequency (%)
안 9065
16.0%
전 8714
15.4%
센 8609
15.2%
터 8609
15.2%
대 1622
 
2.9%
구 1456
 
2.6%
조 1164
 
2.1%
동 678
 
1.2%
중 589
 
1.0%
수 481
 
0.8%
Other values (134) 15653
27.6%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing2789
Missing (%)27.9%
Infinite0
Infinite (%)0.0%
Mean49.439745
Minimum0
Maximum100
Zeros279
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:27:28.284983image/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 deviation23.00014
Coefficient of variation (CV)0.4652156
Kurtosis-0.54653386
Mean49.439745
Median Absolute Deviation (MAD)20
Skewness-0.19523147
Sum356510
Variance529.00646
MonotonicityNot monotonic
2024-03-13T08:27:28.367695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
50 1329
13.3%
40 1054
 
10.5%
70 1029
 
10.3%
60 882
 
8.8%
80 754
 
7.5%
30 738
 
7.4%
20 543
 
5.4%
10 320
 
3.2%
0 279
 
2.8%
90 187
 
1.9%
(Missing) 2789
27.9%
ValueCountFrequency (%)
0 279
 
2.8%
10 320
 
3.2%
20 543
5.4%
30 738
7.4%
40 1054
10.5%
50 1329
13.3%
60 882
8.8%
70 1029
10.3%
80 754
7.5%
90 187
 
1.9%
ValueCountFrequency (%)
100 96
 
1.0%
90 187
 
1.9%
80 754
7.5%
70 1029
10.3%
60 882
8.8%
50 1329
13.3%
40 1054
10.5%
30 738
7.4%
20 543
5.4%
10 320
 
3.2%

환자성별구분명
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
3918 
여
3356 
<NA>
2721 
미상
 
5

Length

Max length4
Median length1
Mean length1.8168
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남
2nd row여
3rd row남
4th row여
5th row남

Common Values

ValueCountFrequency (%)
남 3918
39.2%
여 3356
33.6%
<NA> 2721
27.2%
미상 5
 
0.1%

Length

2024-03-13T08:27:28.477913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:27:28.556068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 3918
39.2%
여 3356
33.6%
na 2721
27.2%
미상 5
 
< 0.1%

외국인여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9944 
True
 
56
ValueCountFrequency (%)
False 9944
99.4%
True 56
 
0.6%
2024-03-13T08:27:28.630714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

국적명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

구급발생장소유형
Categorical

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
가정
4529 
<NA>
2080 
기타
1001 
일반도로
934 
주택가
 
448
Other values (14)
1008 

Length

Max length4
Median length2
Mean length2.7781
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row산
2nd row가정
3rd row가정
4th row가정
5th row가정

Common Values

ValueCountFrequency (%)
가정 4529
45.3%
<NA> 2080
20.8%
기타 1001
 
10.0%
일반도로 934
 
9.3%
주택가 448
 
4.5%
공공장소 448
 
4.5%
공장 101
 
1.0%
고속도로 95
 
0.9%
병원 83
 
0.8%
사무실 72
 
0.7%
Other values (9) 209
 
2.1%

Length

2024-03-13T08:27:28.710075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정 4529
45.3%
na 2080
20.8%
기타 1001
 
10.0%
일반도로 934
 
9.3%
주택가 448
 
4.5%
공공장소 448
 
4.5%
공장 101
 
1.0%
고속도로 95
 
0.9%
병원 83
 
0.8%
숙박시설 72
 
0.7%
Other values (9) 209
 
2.1%

환자증상유형
Categorical

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
2880 
기타통증
2007 
기타
1025 
복통
748 
요통
492 
Other values (33)
2848 

Length

Max length6
Median length4
Mean length3.3822
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타통증
2nd row현훈
3rd row복통
4th row두통
5th row오심/구토

Common Values

ValueCountFrequency (%)
<NA> 2880
28.8%
기타통증 2007
20.1%
기타 1025
 
10.2%
복통 748
 
7.5%
요통 492
 
4.9%
오심/구토 428
 
4.3%
두통 281
 
2.8%
전신쇠약 270
 
2.7%
고열 229
 
2.3%
열상 225
 
2.2%
Other values (28) 1415
14.1%

Length

2024-03-13T08:27:28.810961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2880
28.2%
기타통증 2007
19.6%
기타 1025
 
10.0%
복통 748
 
7.3%
요통 492
 
4.8%
오심/구토 428
 
4.2%
두통 281
 
2.8%
전신쇠약 270
 
2.6%
고열 229
 
2.2%
열상 225
 
2.2%
Other values (29) 1633
16.0%

Interactions

2024-03-13T08:27:27.144101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:27:28.879247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서명환자연령대환자성별구분명외국인여부구급발생장소유형환자증상유형
시군명1.0001.0000.1310.1140.0690.3380.143
출동소방서명1.0001.0000.1390.1140.0630.3510.156
환자연령대0.1310.1391.0000.1920.0880.2670.393
환자성별구분명0.1140.1140.1921.0000.0160.2440.220
외국인여부0.0690.0630.0880.0161.0000.1750.004
구급발생장소유형0.3380.3510.2670.2440.1751.0000.381
환자증상유형0.1430.1560.3930.2200.0040.3811.000
2024-03-13T08:27:28.970719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인여부환자성별구분명구급발생장소유형시군명환자증상유형출동소방서명
외국인여부1.0000.0260.1380.0550.0030.053
환자성별구분명0.0261.0000.1140.0560.1110.056
구급발생장소유형0.1380.1141.0000.0980.1090.100
시군명0.0550.0560.0981.0000.0311.000
환자증상유형0.0030.1110.1090.0311.0000.033
출동소방서명0.0530.0560.1001.0000.0331.000
2024-03-13T08:27:29.057964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서명환자성별구분명외국인여부구급발생장소유형환자증상유형
환자연령대1.0000.0480.0490.1170.0740.0950.135
시군명0.0481.0001.0000.0560.0550.0980.031
출동소방서명0.0491.0001.0000.0560.0530.1000.033
환자성별구분명0.1170.0560.0561.0000.0260.1140.111
외국인여부0.0740.0550.0530.0261.0000.1380.003
구급발생장소유형0.0950.0980.1000.1140.1381.0000.109
환자증상유형0.1350.0310.0330.1110.0030.1091.000

Missing values

2024-03-13T08:27:27.236377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:27:27.375297image/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
148942015남양주시남양주소방서평내119안전센터40남N<NA>산기타통증
10262015오산시송탄소방서119구조대40여N<NA>가정현훈
704232015의정부시의정부소방서중앙119안전센터70남N<NA>가정복통
608842015남양주시남양주소방서가운119안전센터50여N<NA>가정두통
969312015양평군양평소방서공흥119안전센터50남N<NA>가정오심/구토
205912015하남시하남소방서119구조대0여N<NA>가정고열
884112015시흥시시흥소방서시흥119안전센터10남N<NA>가정복통
290402015화성시화성소방서봉담119안전센터40남N<NA>기타열상
457112015화성시화성소방서동탄119안전센터0여N<NA>가정기타통증
248942015용인시용인소방서구갈119안전센터70남N<NA>가정의식장애
집계년도시군명출동소방서명출동안전센터명환자연령대환자성별구분명외국인여부국적명구급발생장소유형환자증상유형
833042015고양시고양소방서신도119안전센터20남N<NA>주택가복통
862582015화성시화성소방서119구조대40여N<NA>가정기타통증
38742015고양시일산소방서주엽119안전센터60여N<NA>가정기타
444392015양평군양평소방서공흥119안전센터50여N<NA>일반도로기타통증
262542015성남시성남소방서신흥119안전센터20여N<NA>가정고열
429972015양평군양평소방서용문119안전센터60여N<NA>지하철기타통증
906782015양평군양평소방서공흥119안전센터70여N<NA>기타기타통증
841122015양주시양주소방서백석119안전센터<NA><NA>N<NA>기타<NA>
181032015하남시하남소방서덕풍119안전센터10남N<NA>가정요통
517042015화성시화성소방서장안119안전센터60남N<NA>가정복통

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서명출동안전센터명환자연령대환자성별구분명외국인여부구급발생장소유형환자증상유형# duplicates
3712015수원시수원소방서매산119안전센터<NA><NA>N<NA><NA>76
4962015안산시안산소방서사동119안전센터<NA><NA>N<NA><NA>52
7172015의정부시의정부소방서중앙119안전센터<NA><NA>N<NA><NA>43
6702015용인시용인소방서수지119안전센터<NA><NA>N<NA><NA>41
3572015수원시수원소방서남부119안전센터<NA><NA>N<NA><NA>38
4152015수원시수원소방서정자119안전센터<NA><NA>N<NA><NA>31
6302015오산시오산소방서청학119안전센터<NA><NA>N<NA><NA>28
2652015부천시부천소방서중앙119안전센터<NA><NA>N<NA><NA>24
3872015수원시수원소방서원천119안전센터<NA><NA>N<NA><NA>24
6422015용인시용인소방서구갈119안전센터<NA><NA>N<NA><NA>24