Overview

Dataset statistics

Number of variables8
Number of observations89
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.8 KiB
Average record size in memory66.5 B

Variable types

Numeric1
Categorical6
Text1

Dataset

Description인천광역시 남동구 보건소신고 감염병현황에 대한 데이터로 연번, 분류, 감염병명, 신고범위, 신고시기, 신고내용, 신고기관, 신고기관전화번호 항목을 개방합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15068002&srcSe=7661IVAWM27C61E190

Alerts

신고기관 has constant value ""Constant
신고기관전화번호 has constant value ""Constant
신고내용 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
신고시기 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
분류 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 분류 and 2 other fieldsHigh correlation
신고범위 is highly overall correlated with 신고내용High correlation
연번 has unique valuesUnique
감염병명 has unique valuesUnique

Reproduction

Analysis started2024-01-28 10:33:42.147712
Analysis finished2024-01-28 10:33:42.683257
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2024-01-28T19:33:42.748752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.4
Q123
median45
Q367
95-th percentile84.6
Maximum89
Range88
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.836021
Coefficient of variation (CV)0.57413381
Kurtosis-1.2
Mean45
Median Absolute Deviation (MAD)22
Skewness0
Sum4005
Variance667.5
MonotonicityStrictly increasing
2024-01-28T19:33:42.862227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
68 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
Other values (79) 79
88.8%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%

분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size844.0 B
제3급감염병
26 
제2급감염병
23 
제4급감염병
23 
제1급감염병
17 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제1급감염병
2nd row제1급감염병
3rd row제1급감염병
4th row제1급감염병
5th row제1급감염병

Common Values

ValueCountFrequency (%)
제3급감염병 26
29.2%
제2급감염병 23
25.8%
제4급감염병 23
25.8%
제1급감염병 17
19.1%

Length

2024-01-28T19:33:42.953798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:43.033132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제3급감염병 26
29.2%
제2급감염병 23
25.8%
제4급감염병 23
25.8%
제1급감염병 17
19.1%

감염병명
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2024-01-28T19:33:43.184011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length22
Mean length7.1460674
Min length2

Characters and Unicode

Total characters636
Distinct characters203
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)100.0%

Sample

1st row에볼라바이러스병
2nd row마버그열
3rd row라싸열
4th row크리미안콩고출혈열
5th row남아메리카출혈열
ValueCountFrequency (%)
감염증 9
 
8.8%
에볼라바이러스병 1
 
1.0%
변종크로이츠펠트-야콥병(vcjd 1
 
1.0%
치쿤구니야열 1
 
1.0%
유비저 1
 
1.0%
진드기매개뇌염 1
 
1.0%
라임병 1
 
1.0%
웨스트나일열 1
 
1.0%
큐열 1
 
1.0%
뎅기열 1
 
1.0%
Other values (84) 84
82.4%
2024-01-28T19:33:43.447312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
증 38
 
6.0%
염 26
 
4.1%
감 21
 
3.3%
성 16
 
2.5%
) 14
 
2.2%
열 14
 
2.2%
스 13
 
2.0%
13
 
2.0%
( 12
 
1.9%
이 11
 
1.7%
Other values (193) 458
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 540
84.9%
Uppercase Letter 49
 
7.7%
Close Punctuation 14
 
2.2%
Space Separator 13
 
2.0%
Open Punctuation 12
 
1.9%
Decimal Number 4
 
0.6%
Dash Punctuation 3
 
0.5%
Lowercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
증 38
 
7.0%
염 26
 
4.8%
감 21
 
3.9%
성 16
 
3.0%
열 14
 
2.6%
스 13
 
2.4%
이 11
 
2.0%
균 11
 
2.0%
라 10
 
1.9%
병 9
 
1.7%
Other values (171) 371
68.7%
Uppercase Letter
ValueCountFrequency (%)
R 8
16.3%
S 8
16.3%
A 7
14.3%
C 4
8.2%
M 4
8.2%
E 4
8.2%
D 3
 
6.1%
B 3
 
6.1%
V 2
 
4.1%
J 2
 
4.1%
Other values (4) 4
8.2%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
9 1
25.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
v 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 540
84.9%
Latin 50
 
7.9%
Common 46
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
증 38
 
7.0%
염 26
 
4.8%
감 21
 
3.9%
성 16
 
3.0%
열 14
 
2.6%
스 13
 
2.4%
이 11
 
2.0%
균 11
 
2.0%
라 10
 
1.9%
병 9
 
1.7%
Other values (171) 371
68.7%
Latin
ValueCountFrequency (%)
R 8
16.0%
S 8
16.0%
A 7
14.0%
C 4
8.0%
M 4
8.0%
E 4
8.0%
D 3
 
6.0%
B 3
 
6.0%
V 2
 
4.0%
J 2
 
4.0%
Other values (5) 5
10.0%
Common
ValueCountFrequency (%)
) 14
30.4%
13
28.3%
( 12
26.1%
- 3
 
6.5%
1 2
 
4.3%
2 1
 
2.2%
9 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 540
84.9%
ASCII 96
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
증 38
 
7.0%
염 26
 
4.8%
감 21
 
3.9%
성 16
 
3.0%
열 14
 
2.6%
스 13
 
2.4%
이 11
 
2.0%
균 11
 
2.0%
라 10
 
1.9%
병 9
 
1.7%
Other values (171) 371
68.7%
ASCII
ValueCountFrequency (%)
) 14
14.6%
13
13.5%
( 12
12.5%
R 8
8.3%
S 8
8.3%
A 7
 
7.3%
C 4
 
4.2%
M 4
 
4.2%
E 4
 
4.2%
D 3
 
3.1%
Other values (12) 19
19.8%

신고범위
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size844.0 B
환자+의사
47 
환자
17 
환자+병원체보유자
13 
환자+의사+병원체보유자
11 
병원체보유자
 
1

Length

Max length12
Median length5
Mean length5.8876404
Min length2

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row환자+의사
2nd row환자+의사
3rd row환자+의사
4th row환자+의사
5th row환자+의사

Common Values

ValueCountFrequency (%)
환자+의사 47
52.8%
환자 17
 
19.1%
환자+병원체보유자 13
 
14.6%
환자+의사+병원체보유자 11
 
12.4%
병원체보유자 1
 
1.1%

Length

2024-01-28T19:33:43.550533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:43.844229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환자+의사 47
52.8%
환자 17
 
19.1%
환자+병원체보유자 13
 
14.6%
환자+의사+병원체보유자 11
 
12.4%
병원체보유자 1
 
1.1%

신고시기
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size844.0 B
24시간 이내
49 
7일 이내
23 
즉시
17 

Length

Max length7
Median length7
Mean length5.5280899
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
24시간 이내 49
55.1%
7일 이내 23
25.8%
즉시 17
 
19.1%

Length

2024-01-28T19:33:43.935009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:44.012696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이내 72
44.7%
24시간 49
30.4%
7일 23
 
14.3%
즉시 17
 
10.6%

신고내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size844.0 B
발생+사망+병원체 검사결과
66 
발생+사망
23 

Length

Max length14
Median length14
Mean length11.674157
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발생+사망+병원체 검사결과
2nd row발생+사망+병원체 검사결과
3rd row발생+사망+병원체 검사결과
4th row발생+사망+병원체 검사결과
5th row발생+사망+병원체 검사결과

Common Values

ValueCountFrequency (%)
발생+사망+병원체 검사결과 66
74.2%
발생+사망 23
 
25.8%

Length

2024-01-28T19:33:44.095435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:44.170832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발생+사망+병원체 66
42.6%
검사결과 66
42.6%
발생+사망 23
 
14.8%

신고기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
남동구보건소
89 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구보건소 89
100.0%

Length

2024-01-28T19:33:44.245988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:44.317054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구보건소 89
100.0%

신고기관전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
032-453-8430
89 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row032-453-8430
2nd row032-453-8430
3rd row032-453-8430
4th row032-453-8430
5th row032-453-8430

Common Values

ValueCountFrequency (%)
032-453-8430 89
100.0%

Length

2024-01-28T19:33:44.389954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T19:33:44.464141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
032-453-8430 89
100.0%

Interactions

2024-01-28T19:33:42.440737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T19:33:44.511391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류감염병명신고범위신고시기신고내용
연번1.0000.9721.0000.7730.9480.993
분류0.9721.0001.0000.4341.0001.000
감염병명1.0001.0001.0001.0001.0001.000
신고범위0.7730.4341.0001.0000.5270.500
신고시기0.9481.0001.0000.5271.0001.000
신고내용0.9931.0001.0000.5001.0001.000
2024-01-28T19:33:44.593320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신고내용신고시기분류신고범위
신고내용1.0000.9940.9880.596
신고시기0.9941.0000.9940.459
분류0.9880.9941.0000.364
신고범위0.5960.4590.3641.000
2024-01-28T19:33:44.663774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류신고범위신고시기신고내용
연번1.0000.8790.4140.9000.881
분류0.8791.0000.3640.9940.988
신고범위0.4140.3641.0000.4590.596
신고시기0.9000.9940.4591.0000.994
신고내용0.8810.9880.5960.9941.000

Missing values

2024-01-28T19:33:42.521751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T19:33:42.632314image/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

연번분류감염병명신고범위신고시기신고내용신고기관신고기관전화번호
01제1급감염병에볼라바이러스병환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
12제1급감염병마버그열환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
23제1급감염병라싸열환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
34제1급감염병크리미안콩고출혈열환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
45제1급감염병남아메리카출혈열환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
56제1급감염병리프트밸리열환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
67제1급감염병두창환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
78제1급감염병페스트환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
89제1급감염병탄저환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
910제1급감염병보툴리눔독소증환자+의사즉시발생+사망+병원체 검사결과남동구보건소032-453-8430
연번분류감염병명신고범위신고시기신고내용신고기관신고기관전화번호
7980제4급감염병첨규콘딜롬환자+의사7일 이내발생+사망남동구보건소032-453-8430
8081제4급감염병반코마이신내성장알균(VRE) 감염증환자+병원체보유자7일 이내발생+사망남동구보건소032-453-8430
8182제4급감염병메티실린내성황색포도알균(MRSA) 감염증환자+병원체보유자7일 이내발생+사망남동구보건소032-453-8430
8283제4급감염병다제내성녹농균(MRPA) 감염증환자+병원체보유자7일 이내발생+사망남동구보건소032-453-8430
8384제4급감염병다제내성아시네토박터바우마니균(MRAB) 감염증환자+병원체보유자7일 이내발생+사망남동구보건소032-453-8430
8485제4급감염병장관감염증환자7일 이내발생+사망남동구보건소032-453-8430
8586제4급감염병급성호흡기감염증환자7일 이내발생+사망남동구보건소032-453-8430
8687제4급감염병해외유입기생충감염증환자7일 이내발생+사망남동구보건소032-453-8430
8788제4급감염병엔테로바이러스감염증환자7일 이내발생+사망남동구보건소032-453-8430
8889제4급감염병사람유두종바이러스 감염증병원체보유자7일 이내발생+사망남동구보건소032-453-8430