Overview

Dataset statistics

Number of variables9
Number of observations26
Missing cells108
Missing cells (%)46.2%
Duplicate rows1
Duplicate rows (%)3.8%
Total size in memory2.0 KiB
Average record size in memory79.1 B

Variable types

Text3
Categorical4
Unsupported2

Dataset

Description2022년 질병관리청 및 협력병원과 연계하여 경상남도 인플루엔자 및 호흡기감염증의 원인병원체를 분석한 결과를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15069937

Alerts

Dataset has 1 (3.8%) duplicate rowsDuplicates
Unnamed: 1 is highly overall correlated with Unnamed: 2 and 2 other fieldsHigh correlation
Unnamed: 2 is highly overall correlated with Unnamed: 1 and 2 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 1 and 2 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 1 and 2 other fieldsHigh correlation
Unnamed: 0 has 14 (53.8%) missing valuesMissing
Unnamed: 5 has 18 (69.2%) missing valuesMissing
Unnamed: 6 has 26 (100.0%) missing valuesMissing
Unnamed: 7 has 26 (100.0%) missing valuesMissing
Unnamed: 8 has 24 (92.3%) missing valuesMissing
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 23:54:16.563456
Analysis finished2023-12-10 23:54:17.582161
Duration1.02 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing14
Missing (%)53.8%
Memory size340.0 B
2023-12-11T08:54:17.749600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length92
Median length84.5
Mean length30.666667
Min length2

Characters and Unicode

Total characters368
Distinct characters81
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)100.0%

Sample

1st row <최근 4주간 급성호흡기 감염증 중 인플루엔자 바이러스 검출 현황>
2nd row○ 2020-2021절기 45주까지 인플루엔자 바이러스 총 65건 검출 [A(H1N1)pdm09 50건, A(H3N2) 4건, B 11건]
3rd row○ 45주차에 9건의 호흡기 검체 중 양성 0건이고, 2020년 1주~45주까지 총 46건 검출[ [A(H1N1)pdm09 32건, A(H3N2) 4건, B 10건]
4th row< 2019 - 2020 절기 주별 인플루엔자 바이러스 검출 건수[검출률(%)]>
5th row구분
ValueCountFrequency (%)
인플루엔자 4
 
5.4%
바이러스 4
 
5.4%
검출 4
 
5.4%
20년 4
 
5.4%
a(h1n1)pdm09 3
 
4.1%
3
 
4.1%
b 3
 
4.1%
a(h3n2 3
 
4.1%
ifv 2
 
2.7%
중 2
 
2.7%
Other values (38) 42
56.8%
2023-12-11T08:54:18.103840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
22.8%
2 18
 
4.9%
0 18
 
4.9%
4 13
 
3.5%
1 12
 
3.3%
건 11
 
3.0%
주 11
 
3.0%
( 7
 
1.9%
, 7
 
1.9%
) 7
 
1.9%
Other values (71) 180
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
34.5%
Space Separator 84
22.8%
Decimal Number 79
21.5%
Uppercase Letter 27
 
7.3%
Other Punctuation 12
 
3.3%
Open Punctuation 11
 
3.0%
Close Punctuation 10
 
2.7%
Lowercase Letter 9
 
2.4%
Math Symbol 5
 
1.4%
Dash Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 11
 
8.7%
주 11
 
8.7%
이 6
 
4.7%
검 6
 
4.7%
출 5
 
3.9%
기 5
 
3.9%
년 5
 
3.9%
러 4
 
3.1%
스 4
 
3.1%
바 4
 
3.1%
Other values (38) 66
52.0%
Decimal Number
ValueCountFrequency (%)
2 18
22.8%
0 18
22.8%
4 13
16.5%
1 12
15.2%
5 6
 
7.6%
9 5
 
6.3%
3 5
 
6.3%
6 2
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
A 6
22.2%
H 6
22.2%
N 6
22.2%
B 3
11.1%
I 2
 
7.4%
V 2
 
7.4%
F 2
 
7.4%
Other Punctuation
ValueCountFrequency (%)
, 7
58.3%
: 2
 
16.7%
% 1
 
8.3%
/ 1
 
8.3%
* 1
 
8.3%
Lowercase Letter
ValueCountFrequency (%)
m 3
33.3%
d 3
33.3%
p 3
33.3%
Math Symbol
ValueCountFrequency (%)
< 2
40.0%
> 2
40.0%
~ 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 7
63.6%
[ 4
36.4%
Close Punctuation
ValueCountFrequency (%)
) 7
70.0%
] 3
30.0%
Space Separator
ValueCountFrequency (%)
84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Symbol
ValueCountFrequency (%)
○ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 205
55.7%
Hangul 127
34.5%
Latin 36
 
9.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
건 11
 
8.7%
주 11
 
8.7%
이 6
 
4.7%
검 6
 
4.7%
출 5
 
3.9%
기 5
 
3.9%
년 5
 
3.9%
러 4
 
3.1%
스 4
 
3.1%
바 4
 
3.1%
Other values (38) 66
52.0%
Common
ValueCountFrequency (%)
84
41.0%
2 18
 
8.8%
0 18
 
8.8%
4 13
 
6.3%
1 12
 
5.9%
( 7
 
3.4%
, 7
 
3.4%
) 7
 
3.4%
5 6
 
2.9%
9 5
 
2.4%
Other values (13) 28
 
13.7%
Latin
ValueCountFrequency (%)
A 6
16.7%
H 6
16.7%
N 6
16.7%
m 3
8.3%
d 3
8.3%
p 3
8.3%
B 3
8.3%
I 2
 
5.6%
V 2
 
5.6%
F 2
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239
64.9%
Hangul 127
34.5%
Geometric Shapes 2
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
35.1%
2 18
 
7.5%
0 18
 
7.5%
4 13
 
5.4%
1 12
 
5.0%
( 7
 
2.9%
, 7
 
2.9%
) 7
 
2.9%
A 6
 
2.5%
H 6
 
2.5%
Other values (22) 61
25.5%
Hangul
ValueCountFrequency (%)
건 11
 
8.7%
주 11
 
8.7%
이 6
 
4.7%
검 6
 
4.7%
출 5
 
3.9%
기 5
 
3.9%
년 5
 
3.9%
러 4
 
3.1%
스 4
 
3.1%
바 4
 
3.1%
Other values (38) 66
52.0%
Geometric Shapes
ValueCountFrequency (%)
○ 2
100.0%

Unnamed: 1
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
19 
0(0.0)
5 
A(H1N1)pdm09
 
1
50(76.9)
 
1

Length

Max length12
Median length4
Mean length4.8461538
Min length4

Unique

Unique2 ?
Unique (%)7.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
73.1%
0(0.0) 5
 
19.2%
A(H1N1)pdm09 1
 
3.8%
50(76.9) 1
 
3.8%

Length

2023-12-11T08:54:18.240515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:54:18.404552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
73.1%
0(0.0 5
 
19.2%
a(h1n1)pdm09 1
 
3.8%
50(76.9 1
 
3.8%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
19 
0(0.0)
5 
A(H3N2)
 
1
4(6.2)
 
1

Length

Max length7
Median length4
Mean length4.5769231
Min length4

Unique

Unique2 ?
Unique (%)7.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
73.1%
0(0.0) 5
 
19.2%
A(H3N2) 1
 
3.8%
4(6.2) 1
 
3.8%

Length

2023-12-11T08:54:18.522149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:54:18.624702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
73.1%
0(0.0 5
 
19.2%
a(h3n2 1
 
3.8%
4(6.2 1
 
3.8%

Unnamed: 3
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
19 
0(0.0)
6 
A(Not subtyped)
 
1

Length

Max length16
Median length4
Mean length4.9230769
Min length4

Unique

Unique1 ?
Unique (%)3.8%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
73.1%
0(0.0) 6
 
23.1%
A(Not subtyped) 1
 
3.8%

Length

2023-12-11T08:54:18.763774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:54:18.937008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
70.4%
0(0.0 6
 
22.2%
a(not 1
 
3.7%
subtyped 1
 
3.7%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
19 
0(0.0)
5 
B
 
1
11(16.9)
 
1

Length

Max length8
Median length4
Mean length4.4230769
Min length1

Unique

Unique2 ?
Unique (%)7.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 19
73.1%
0(0.0) 5
 
19.2%
B 1
 
3.8%
11(16.9) 1
 
3.8%

Length

2023-12-11T08:54:19.099619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:54:19.241879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
73.1%
0(0.0 5
 
19.2%
b 1
 
3.8%
11(16.9 1
 
3.8%

Unnamed: 5
Text

MISSING 

Distinct4
Distinct (%)50.0%
Missing18
Missing (%)69.2%
Memory size340.0 B
2023-12-11T08:54:19.365004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.875
Min length5

Characters and Unicode

Total characters55
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)37.5%

Sample

1st row양성건수[건수(%)]
2nd row총계(%)
3rd row0(0.0)
4th row0(0.0)
5th row0(0.0)
ValueCountFrequency (%)
0(0.0 5
62.5%
양성건수[건수 1
 
12.5%
총계 1
 
12.5%
65(100.0 1
 
12.5%
2023-12-11T08:54:19.684651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18
32.7%
( 8
14.5%
) 8
14.5%
. 6
 
10.9%
건 2
 
3.6%
수 2
 
3.6%
% 2
 
3.6%
양 1
 
1.8%
성 1
 
1.8%
[ 1
 
1.8%
Other values (6) 6
 
10.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21
38.2%
Open Punctuation 9
16.4%
Close Punctuation 9
16.4%
Other Punctuation 8
 
14.5%
Other Letter 8
 
14.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
건 2
25.0%
수 2
25.0%
양 1
12.5%
성 1
12.5%
총 1
12.5%
계 1
12.5%
Decimal Number
ValueCountFrequency (%)
0 18
85.7%
6 1
 
4.8%
5 1
 
4.8%
1 1
 
4.8%
Open Punctuation
ValueCountFrequency (%)
( 8
88.9%
[ 1
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 8
88.9%
] 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 6
75.0%
% 2
 
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47
85.5%
Hangul 8
 
14.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18
38.3%
( 8
17.0%
) 8
17.0%
. 6
 
12.8%
% 2
 
4.3%
[ 1
 
2.1%
] 1
 
2.1%
6 1
 
2.1%
5 1
 
2.1%
1 1
 
2.1%
Hangul
ValueCountFrequency (%)
건 2
25.0%
수 2
25.0%
양 1
12.5%
성 1
12.5%
총 1
12.5%
계 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47
85.5%
Hangul 8
 
14.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18
38.3%
( 8
17.0%
) 8
17.0%
. 6
 
12.8%
% 2
 
4.3%
[ 1
 
2.1%
] 1
 
2.1%
6 1
 
2.1%
5 1
 
2.1%
1 1
 
2.1%
Hangul
ValueCountFrequency (%)
건 2
25.0%
수 2
25.0%
양 1
12.5%
성 1
12.5%
총 1
12.5%
계 1
12.5%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing26
Missing (%)100.0%
Memory size366.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing26
Missing (%)100.0%
Memory size366.0 B

Unnamed: 8
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing24
Missing (%)92.3%
Memory size340.0 B
2023-12-11T08:54:19.852510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length37
Mean length37
Min length34

Characters and Unicode

Total characters74
Distinct characters31
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

Unique2 ?
Unique (%)100.0%

Sample

1st row< 2020-2021 절기 경남 IFV 최근 4주 검출현황 >
2nd row< 2020-2021 절기 IFV 검출현황 - 질병관리본부(44주차) >
ValueCountFrequency (%)
5
29.4%
2020-2021 2
 
11.8%
절기 2
 
11.8%
ifv 2
 
11.8%
검출현황 2
 
11.8%
경남 1
 
5.9%
최근 1
 
5.9%
4주 1
 
5.9%
질병관리본부(44주차 1
 
5.9%
2023-12-11T08:54:20.179105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
20.3%
2 8
 
10.8%
0 6
 
8.1%
4 3
 
4.1%
- 3
 
4.1%
V 2
 
2.7%
> 2
 
2.7%
황 2
 
2.7%
현 2
 
2.7%
출 2
 
2.7%
Other values (21) 29
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25
33.8%
Decimal Number 19
25.7%
Space Separator 15
20.3%
Uppercase Letter 6
 
8.1%
Math Symbol 4
 
5.4%
Dash Punctuation 3
 
4.1%
Open Punctuation 1
 
1.4%
Close Punctuation 1
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
황 2
 
8.0%
현 2
 
8.0%
출 2
 
8.0%
검 2
 
8.0%
주 2
 
8.0%
기 2
 
8.0%
절 2
 
8.0%
최 1
 
4.0%
리 1
 
4.0%
차 1
 
4.0%
Other values (8) 8
32.0%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
0 6
31.6%
4 3
 
15.8%
1 2
 
10.5%
Uppercase Letter
ValueCountFrequency (%)
V 2
33.3%
F 2
33.3%
I 2
33.3%
Math Symbol
ValueCountFrequency (%)
> 2
50.0%
< 2
50.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43
58.1%
Hangul 25
33.8%
Latin 6
 
8.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
황 2
 
8.0%
현 2
 
8.0%
출 2
 
8.0%
검 2
 
8.0%
주 2
 
8.0%
기 2
 
8.0%
절 2
 
8.0%
최 1
 
4.0%
리 1
 
4.0%
차 1
 
4.0%
Other values (8) 8
32.0%
Common
ValueCountFrequency (%)
15
34.9%
2 8
18.6%
0 6
 
14.0%
4 3
 
7.0%
- 3
 
7.0%
> 2
 
4.7%
< 2
 
4.7%
1 2
 
4.7%
( 1
 
2.3%
) 1
 
2.3%
Latin
ValueCountFrequency (%)
V 2
33.3%
F 2
33.3%
I 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49
66.2%
Hangul 25
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
30.6%
2 8
16.3%
0 6
 
12.2%
4 3
 
6.1%
- 3
 
6.1%
V 2
 
4.1%
> 2
 
4.1%
< 2
 
4.1%
F 2
 
4.1%
I 2
 
4.1%
Other values (3) 4
 
8.2%
Hangul
ValueCountFrequency (%)
황 2
 
8.0%
현 2
 
8.0%
출 2
 
8.0%
검 2
 
8.0%
주 2
 
8.0%
기 2
 
8.0%
절 2
 
8.0%
최 1
 
4.0%
리 1
 
4.0%
차 1
 
4.0%
Other values (8) 8
32.0%

Correlations

2023-12-11T08:54:20.299673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 8
Unnamed: 01.0001.0001.0001.0001.0001.000NaN
Unnamed: 11.0001.0001.0001.0001.0001.000NaN
Unnamed: 21.0001.0001.0001.0001.0001.000NaN
Unnamed: 31.0001.0001.0001.0001.0001.000NaN
Unnamed: 41.0001.0001.0001.0001.0001.000NaN
Unnamed: 51.0001.0001.0001.0001.0001.000NaN
Unnamed: 8NaNNaNNaNNaNNaNNaN1.000
2023-12-11T08:54:20.451036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 4Unnamed: 3
Unnamed: 11.0001.0001.0000.894
Unnamed: 21.0001.0001.0000.894
Unnamed: 41.0001.0001.0000.894
Unnamed: 30.8940.8940.8941.000
2023-12-11T08:54:20.569678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4
Unnamed: 11.0001.0000.8941.000
Unnamed: 21.0001.0000.8941.000
Unnamed: 30.8940.8941.0000.894
Unnamed: 41.0001.0000.8941.000

Missing values

2023-12-11T08:54:17.227219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:54:17.373763image/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.
2023-12-11T08:54:17.495293image/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

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA><NA><NA><NA><NA><NA><NA><NA>
1<NA><NA><NA><NA><NA><NA><NA><NA><NA>
2<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3<최근 4주간 급성호흡기 감염증 중 인플루엔자 바이러스 검출 현황><NA><NA><NA><NA><NA><NA><NA><NA>
4<NA><NA><NA><NA><NA><NA><NA><NA><NA>
5○ 2020-2021절기 45주까지 인플루엔자 바이러스 총 65건 검출 [A(H1N1)pdm09 50건, A(H3N2) 4건, B 11건]<NA><NA><NA><NA><NA><NA><NA><NA>
6<NA><NA><NA><NA><NA><NA><NA><NA><NA>
7○ 45주차에 9건의 호흡기 검체 중 양성 0건이고, 2020년 1주~45주까지 총 46건 검출[ [A(H1N1)pdm09 32건, A(H3N2) 4건, B 10건]<NA><NA><NA><NA><NA><NA><NA><NA>
8<NA><NA><NA><NA><NA><NA><NA><NA><NA>
9<NA><NA><NA><NA><NA><NA><NA><NA><NA>
Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
1620년 45주0(0.0)0(0.0)0(0.0)0(0.0)0(0.0)<NA><NA><NA>
17최근4주 누계0(0.0)0(0.0)0(0.0)0(0.0)0(0.0)<NA><NA><NA>
18이번절기 누계50(76.9)4(6.2)0(0.0)11(16.9)65(100.0)<NA><NA><NA>
19<NA><NA><NA><NA><NA><NA><NA><NA><NA>
20*IFV: 인플루엔자 바이러스 / IFV 아형: A(H1N1)pdm09, A(H3N2), B<NA><NA><NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA><NA><NA><NA>
24<NA><NA><NA><NA><NA><NA><NA><NA><NA>
25<NA><NA><NA><NA><NA><NA><NA><NA>< 2020-2021 절기 IFV 검출현황 - 질병관리본부(44주차) >

Duplicate rows

Most frequently occurring

Unnamed: 0Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 8# duplicates
0<NA><NA><NA><NA><NA><NA><NA>12