Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells4590
Missing cells (%)4.6%
Duplicate rows1199
Duplicate rows (%)12.0%
Total size in memory878.9 KiB
Average record size in memory90.0 B

Variable types

Categorical7
Numeric1
Boolean1
Text1

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 1199 (12.0%) duplicate rowsDuplicates
시군명 is highly overall correlated with 출동소방서 and 1 other fieldsHigh correlation
출동안전센터 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
출동소방서 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
외국인여부 is highly overall correlated with 국적High correlation
국적 is highly overall correlated with 외국인여부High correlation
외국인여부 is highly imbalanced (91.4%)Imbalance
국적 is highly imbalanced (97.5%)Imbalance
환자연령대 has 2826 (28.3%) missing valuesMissing
구급처종명 has 1764 (17.6%) missing valuesMissing
환자연령대 has 429 (4.3%) zerosZeros

Reproduction

Analysis started2024-03-12 23:26:56.310088
Analysis finished2024-03-12 23:26:57.475319
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
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2024-03-13T08:26:57.521344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:26:57.587218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

시군명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광주시
2160 
광명시
1862 
김포시
1677 
구리시
1549 
군포시
1413 
Other values (3)
1339 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평군
2nd row광주시
3rd row광명시
4th row광주시
5th row김포시

Common Values

ValueCountFrequency (%)
광주시 2160
21.6%
광명시 1862
18.6%
김포시 1677
16.8%
구리시 1549
15.5%
군포시 1413
14.1%
가평군 789
 
7.9%
과천시 505
 
5.1%
고양시 45
 
0.4%

Length

2024-03-13T08:26:57.654930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:26:57.735215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주시 2160
21.6%
광명시 1862
18.6%
김포시 1677
16.8%
구리시 1549
15.5%
군포시 1413
14.1%
가평군 789
 
7.9%
과천시 505
 
5.1%
고양시 45
 
0.4%

출동소방서
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
광주소방서
2160 
광명소방서
1862 
김포소방서
1677 
구리소방서
1549 
군포소방서
1413 
Other values (4)
1339 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가평소방서
2nd row광주소방서
3rd row광명소방서
4th row광주소방서
5th row김포소방서

Common Values

ValueCountFrequency (%)
광주소방서 2160
21.6%
광명소방서 1862
18.6%
김포소방서 1677
16.8%
구리소방서 1549
15.5%
군포소방서 1413
14.1%
가평소방서 789
 
7.9%
과천소방서 505
 
5.1%
일산소방서 26
 
0.3%
고양소방서 19
 
0.2%

Length

2024-03-13T08:26:58.021568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:26:58.107511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주소방서 2160
21.6%
광명소방서 1862
18.6%
김포소방서 1677
16.8%
구리소방서 1549
15.5%
군포소방서 1413
14.1%
가평소방서 789
 
7.9%
과천소방서 505
 
5.1%
일산소방서 26
 
0.3%
고양소방서 19
 
0.2%

출동안전센터
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
송정119안전센터
974 
119구조대
966 
소하119안전센터
666 
교문119안전센터
 
541
오금119안전센터
 
481
Other values (41)
6372 

Length

Max length10
Median length9
Mean length8.5058
Min length5

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row조종119안전센터
2nd row곤지암119안전센터
3rd row소하119안전센터
4th row퇴촌지역대
5th row119구조대

Common Values

ValueCountFrequency (%)
송정119안전센터 974
 
9.7%
119구조대 966
 
9.7%
소하119안전센터 666
 
6.7%
교문119안전센터 541
 
5.4%
오금119안전센터 481
 
4.8%
하안119안전센터 418
 
4.2%
산본119안전센터 406
 
4.1%
광남119안전센터 396
 
4.0%
광명119안전센터 382
 
3.8%
인창119안전센터 373
 
3.7%
Other values (36) 4397
44.0%

Length

2024-03-13T08:26:58.208703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송정119안전센터 974
 
9.7%
119구조대 966
 
9.7%
소하119안전센터 666
 
6.7%
교문119안전센터 541
 
5.4%
오금119안전센터 481
 
4.8%
하안119안전센터 418
 
4.2%
산본119안전센터 406
 
4.1%
광남119안전센터 396
 
4.0%
광명119안전센터 382
 
3.8%
인창119안전센터 373
 
3.7%
Other values (36) 4397
44.0%

환자연령대
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)0.2%
Missing2826
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean49.85782
Minimum0
Maximum100
Zeros429
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T08:26:58.302299image/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 deviation24.140184
Coefficient of variation (CV)0.4841805
Kurtosis-0.70166957
Mean49.85782
Median Absolute Deviation (MAD)20
Skewness-0.42090968
Sum357680
Variance582.7485
MonotonicityNot monotonic
2024-03-13T08:26:58.401259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
60 1198
12.0%
50 1065
 
10.7%
80 986
 
9.9%
70 985
 
9.8%
40 766
 
7.7%
30 654
 
6.5%
20 548
 
5.5%
0 429
 
4.3%
10 318
 
3.2%
90 218
 
2.2%
(Missing) 2826
28.3%
ValueCountFrequency (%)
0 429
 
4.3%
10 318
 
3.2%
20 548
5.5%
30 654
6.5%
40 766
7.7%
50 1065
10.7%
60 1198
12.0%
70 985
9.8%
80 986
9.9%
90 218
 
2.2%
ValueCountFrequency (%)
100 7
 
0.1%
90 218
 
2.2%
80 986
9.9%
70 985
9.8%
60 1198
12.0%
50 1065
10.7%
40 766
7.7%
30 654
6.5%
20 548
5.5%
10 318
 
3.2%

환자성별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
남
3880 
여
3330 
<NA>
2788 
미상
 
2

Length

Max length4
Median length1
Mean length1.8366
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남 3880
38.8%
여 3330
33.3%
<NA> 2788
27.9%
미상 2
 
< 0.1%

Length

2024-03-13T08:26:58.516923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:26:58.604348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남 3880
38.8%
여 3330
33.3%
na 2788
27.9%
미상 2
 
< 0.1%

외국인여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size87.9 KiB
False
9892 
True
 
108
ValueCountFrequency (%)
False 9892
98.9%
True 108
 
1.1%
2024-03-13T08:26:58.669708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

국적
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9890 
중국
 
51
베트남
 
12
미국
 
6
우즈베키스탄
 
5
Other values (24)
 
36

Length

Max length7
Median length4
Mean length3.9875
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> 9890
98.9%
중국 51
 
0.5%
베트남 12
 
0.1%
미국 6
 
0.1%
우즈베키스탄 5
 
0.1%
태국 4
 
< 0.1%
몽골 4
 
< 0.1%
네팔 3
 
< 0.1%
러시아 3
 
< 0.1%
필리핀 3
 
< 0.1%
Other values (19) 19
 
0.2%

Length

2024-03-13T08:26:58.745098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9890
98.9%
중국 51
 
0.5%
베트남 12
 
0.1%
미국 6
 
0.1%
우즈베키스탄 5
 
< 0.1%
태국 4
 
< 0.1%
몽골 4
 
< 0.1%
러시아 3
 
< 0.1%
필리핀 3
 
< 0.1%
네팔 3
 
< 0.1%
Other values (19) 19
 
0.2%

구급처종명
Text

MISSING 

Distinct157
Distinct (%)1.9%
Missing1764
Missing (%)17.6%
Memory size156.2 KiB
2024-03-13T08:26:58.847559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length1
Mean length2.6692569
Min length1

Characters and Unicode

Total characters21984
Distinct characters208
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique128 ?
Unique (%)1.6%

Sample

1st row집
2nd row상업시설
3rd row집
4th row집
5th row집
ValueCountFrequency (%)
집 5083
61.3%
도로 863
 
10.4%
도로외교통지역 670
 
8.1%
상업시설 506
 
6.1%
의료관련시설 281
 
3.4%
공장/산업/건설시설 196
 
2.4%
오락/문화시설 129
 
1.6%
바다/강/산/논밭 109
 
1.3%
집단거주시설 97
 
1.2%
학교/교육시설 69
 
0.8%
Other values (173) 286
 
3.5%
2024-03-13T08:26:59.089160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
집 5188
23.6%
도 1553
 
7.1%
로 1541
 
7.0%
설 1537
 
7.0%
시 1345
 
6.1%
/ 937
 
4.3%
교 818
 
3.7%
업 716
 
3.3%
지 704
 
3.2%
역 677
 
3.1%
Other values (198) 6968
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20627
93.8%
Other Punctuation 937
 
4.3%
Open Punctuation 174
 
0.8%
Close Punctuation 174
 
0.8%
Space Separator 53
 
0.2%
Decimal Number 19
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
집 5188
25.2%
도 1553
 
7.5%
로 1541
 
7.5%
설 1537
 
7.5%
시 1345
 
6.5%
교 818
 
4.0%
업 716
 
3.5%
지 704
 
3.4%
역 677
 
3.3%
통 673
 
3.3%
Other values (187) 5875
28.5%
Decimal Number
ValueCountFrequency (%)
1 9
47.4%
9 3
 
15.8%
4 2
 
10.5%
0 2
 
10.5%
2 1
 
5.3%
3 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
/ 937
100.0%
Open Punctuation
ValueCountFrequency (%)
( 174
100.0%
Close Punctuation
ValueCountFrequency (%)
) 174
100.0%
Space Separator
ValueCountFrequency (%)
53
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20627
93.8%
Common 1357
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
집 5188
25.2%
도 1553
 
7.5%
로 1541
 
7.5%
설 1537
 
7.5%
시 1345
 
6.5%
교 818
 
4.0%
업 716
 
3.5%
지 704
 
3.4%
역 677
 
3.3%
통 673
 
3.3%
Other values (187) 5875
28.5%
Common
ValueCountFrequency (%)
/ 937
69.0%
( 174
 
12.8%
) 174
 
12.8%
53
 
3.9%
1 9
 
0.7%
9 3
 
0.2%
4 2
 
0.1%
0 2
 
0.1%
2 1
 
0.1%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20627
93.8%
ASCII 1357
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
집 5188
25.2%
도 1553
 
7.5%
로 1541
 
7.5%
설 1537
 
7.5%
시 1345
 
6.5%
교 818
 
4.0%
업 716
 
3.5%
지 704
 
3.4%
역 677
 
3.3%
통 673
 
3.3%
Other values (187) 5875
28.5%
ASCII
ValueCountFrequency (%)
/ 937
69.0%
( 174
 
12.8%
) 174
 
12.8%
53
 
3.9%
1 9
 
0.7%
9 3
 
0.2%
4 2
 
0.1%
0 2
 
0.1%
2 1
 
0.1%
3 1
 
0.1%

환자증상1
Categorical

Distinct44
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
3822 
기타통증
1190 
복통
709 
기타
676 
요통
 
367
Other values (39)
3236 

Length

Max length6
Median length4
Mean length3.3556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3822
38.2%
기타통증 1190
 
11.9%
복통 709
 
7.1%
기타 676
 
6.8%
요통 367
 
3.7%
두통 356
 
3.6%
고열 354
 
3.5%
어지러움 349
 
3.5%
호흡곤란 304
 
3.0%
열상 252
 
2.5%
Other values (34) 1621
16.2%

Length

2024-03-13T08:26:59.198320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3822
37.8%
기타통증 1190
 
11.8%
복통 709
 
7.0%
기타 676
 
6.7%
요통 367
 
3.6%
두통 356
 
3.5%
고열 354
 
3.5%
어지러움 349
 
3.4%
호흡곤란 304
 
3.0%
열상 252
 
2.5%
Other values (35) 1738
17.2%

Interactions

2024-03-13T08:26:57.094497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:26:59.267277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적환자증상1
시군명1.0001.0000.9900.0480.0000.0630.0000.108
출동소방서1.0001.0000.9920.0440.0000.0460.0000.088
출동안전센터0.9900.9921.0000.1010.0740.1050.7100.000
환자연령대0.0480.0440.1011.0000.1630.1290.4830.442
환자성별0.0000.0000.0740.1631.0000.0000.2410.223
외국인여부0.0630.0460.1050.1290.0001.0000.9480.049
국적0.0000.0000.7100.4830.2410.9481.0000.000
환자증상10.1080.0880.0000.4420.2230.0490.0001.000
2024-03-13T08:26:59.353920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
외국인여부환자증상1시군명출동안전센터환자성별국적출동소방서
외국인여부1.0000.0410.0470.0840.0000.7270.046
환자증상10.0411.0000.0410.0000.1110.0000.032
시군명0.0470.0411.0000.9210.0000.0001.000
출동안전센터0.0840.0000.9211.0000.0360.2350.925
환자성별0.0000.1110.0000.0361.0000.1750.000
국적0.7270.0000.0000.2350.1751.0000.000
출동소방서0.0460.0321.0000.9250.0000.0001.000
2024-03-13T08:26:59.445200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자연령대시군명출동소방서출동안전센터환자성별외국인여부국적환자증상1
환자연령대1.0000.0240.0210.0340.1290.1100.2090.150
시군명0.0241.0001.0000.9210.0000.0470.0000.041
출동소방서0.0211.0001.0000.9250.0000.0460.0000.032
출동안전센터0.0340.9210.9251.0000.0360.0840.2350.000
환자성별0.1290.0000.0000.0361.0000.0000.1750.111
외국인여부0.1100.0470.0460.0840.0001.0000.7270.041
국적0.2090.0000.0000.2350.1750.7271.0000.000
환자증상10.1500.0410.0320.0000.1110.0410.0001.000

Missing values

2024-03-13T08:26:57.196792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:26:57.320402image/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.
2024-03-13T08:26:57.417351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1
33892022가평군가평소방서조종119안전센터50여N<NA>집<NA>
519222022광주시광주소방서곤지암119안전센터<NA><NA>N<NA><NA><NA>
267362022광명시광명소방서소하119안전센터50남N<NA>상업시설요통
349392022광주시광주소방서퇴촌지역대80남N<NA>집흉통
996812022김포시김포소방서119구조대<NA><NA>N<NA>집<NA>
585642022구리시구리소방서인창119안전센터<NA><NA>N<NA><NA><NA>
979322022김포시김포소방서양촌119안전센터60여Y중국집복통
432782022광주시광주소방서오포119안전센터90여N<NA>집고열
376872022광주시광주소방서곤지암119안전센터50남N<NA>공장/산업/건설시설경련/발작
712482022군포시군포소방서오금119안전센터40남N<NA>도로기타통증
집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1
531712022구리시구리소방서교문119안전센터<NA><NA>N<NA><NA><NA>
295262022광명시광명소방서광명119안전센터<NA><NA>N<NA>공장/산업/건설시설<NA>
160832022광명시광명소방서소하119안전센터<NA><NA>N<NA><NA><NA>
255052022광명시광명소방서광명119안전센터0여N<NA>집기타통증
343202022광주시광주소방서송정119안전센터<NA><NA>N<NA><NA><NA>
989492022김포시김포소방서대곶119안전센터60여N<NA>집흉통
742772022군포시군포소방서송정119안전센터20여N<NA>도로외교통지역어지러움
697232022군포시군포소방서금정출동대<NA><NA>N<NA><NA><NA>
500582022광주시광주소방서송정119안전센터<NA><NA>N<NA><NA><NA>
237302022광명시광명소방서광명119안전센터<NA><NA>N<NA><NA><NA>

Duplicate rows

Most frequently occurring

집계년도시군명출동소방서출동안전센터환자연령대환자성별외국인여부국적구급처종명환자증상1# duplicates
3162022광명시광명소방서소하119안전센터<NA><NA>N<NA><NA><NA>134
5592022광주시광주소방서송정119안전센터<NA><NA>N<NA><NA><NA>121
2262022광명시광명소방서광명119안전센터<NA><NA>N<NA><NA><NA>90
3702022광명시광명소방서하안119안전센터<NA><NA>N<NA><NA><NA>89
8152022구리시구리소방서교문119안전센터<NA><NA>N<NA><NA><NA>86
10322022군포시군포소방서오금119안전센터<NA><NA>N<NA><NA><NA>82
1792022광명시광명소방서광남119안전센터<NA><NA>N<NA><NA><NA>71
9462022군포시군포소방서산본119안전센터<NA><NA>N<NA><NA><NA>62
8672022구리시구리소방서인창119안전센터<NA><NA>N<NA><NA><NA>59
6972022구리시구리소방서119구조대<NA><NA>N<NA><NA><NA>57