Overview

Dataset statistics

Number of variables11
Number of observations72
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory90.8 B

Variable types

Text4
Numeric1
Categorical6

Dataset

Description인천광역시 부평구 건강검진기관 데이터입니다.(연번,구분,검진기관명,일반검진,위암,유방암,대장암,간암,자궁경부암,폐암,구강검진,영유아검진,주소,전화번호)ex)1,보건소,부평구보건소,지정,지정,지정,인천광역시 부평구 부흥로 291 (부평동),032-509-8200
Author인천광역시 부평구
URLhttps://www.data.go.kr/data/15081741/fileData.do

Alerts

위암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
자궁경부암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
유방암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
대장암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
간암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
폐암 is highly overall correlated with 검진기관 코드 and 5 other fieldsHigh correlation
검진기관 코드 is highly overall correlated with 위암 and 5 other fieldsHigh correlation
폐암 is highly imbalanced (75.0%)Imbalance
팩스(FAX) has 1 (1.4%) missing valuesMissing
검진기관명 has unique valuesUnique
검진기관 코드 has unique valuesUnique
전화번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:42:20.609186
Analysis finished2023-12-12 04:42:21.479850
Duration0.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검진기관명
Text

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-12T13:42:21.626796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length16
Mean length8.0833333
Min length5

Characters and Unicode

Total characters582
Distinct characters151
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)100.0%

Sample

1st row가톨릭대학교인천성모병원
2nd row갈산중앙의원
3rd row굿모닝의원
4th row근로복지공단인천병원
5th row김명주산부인과의원
ValueCountFrequency (%)
가톨릭대학교인천성모병원 1
 
1.4%
성모내과의원 1
 
1.4%
이기섭내과의원 1
 
1.4%
의료법인나누리의료재단나누리병원 1
 
1.4%
의)상원의료재단부평힘찬병원 1
 
1.4%
우리들내과의원 1
 
1.4%
우리내과의원 1
 
1.4%
연세베스트항외과의원 1
 
1.4%
이수금내과의원 1
 
1.4%
연세미여성의원 1
 
1.4%
Other values (63) 63
86.3%
2023-12-12T13:42:21.992878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 74
 
12.7%
의 73
 
12.5%
과 53
 
9.1%
내 35
 
6.0%
부 21
 
3.6%
인 18
 
3.1%
평 13
 
2.2%
성 12
 
2.1%
산 11
 
1.9%
병 8
 
1.4%
Other values (141) 264
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 580
99.7%
Close Punctuation 1
 
0.2%
Space Separator 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 74
 
12.8%
의 73
 
12.6%
과 53
 
9.1%
내 35
 
6.0%
부 21
 
3.6%
인 18
 
3.1%
평 13
 
2.2%
성 12
 
2.1%
산 11
 
1.9%
병 8
 
1.4%
Other values (139) 262
45.2%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 580
99.7%
Common 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 74
 
12.8%
의 73
 
12.6%
과 53
 
9.1%
내 35
 
6.0%
부 21
 
3.6%
인 18
 
3.1%
평 13
 
2.2%
성 12
 
2.1%
산 11
 
1.9%
병 8
 
1.4%
Other values (139) 262
45.2%
Common
ValueCountFrequency (%)
) 1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 580
99.7%
ASCII 2
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 74
 
12.8%
의 73
 
12.6%
과 53
 
9.1%
내 35
 
6.0%
부 21
 
3.6%
인 18
 
3.1%
평 13
 
2.2%
성 12
 
2.1%
산 11
 
1.9%
병 8
 
1.4%
Other values (139) 262
45.2%
ASCII
ValueCountFrequency (%)
) 1
50.0%
1
50.0%

검진기관 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35646788
Minimum31100031
Maximum41385373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-12T13:42:22.128238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31100031
5-th percentile31204668
Q131343046
median31388030
Q341332390
95-th percentile41379952
Maximum41385373
Range10285342
Interquartile range (IQR)9989344

Descriptive statistics

Standard deviation4990099.6
Coefficient of variation (CV)0.13998736
Kurtosis-1.9726471
Mean35646788
Median Absolute Deviation (MAD)183533
Skewness0.28547967
Sum2.5665687 × 109
Variance2.4901094 × 1013
MonotonicityNot monotonic
2023-12-12T13:42:22.284851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31100031 1
 
1.4%
31389929 1
 
1.4%
31379362 1
 
1.4%
31328431 1
 
1.4%
31208665 1
 
1.4%
31206212 1
 
1.4%
41317955 1
 
1.4%
41321774 1
 
1.4%
31335748 1
 
1.4%
31353185 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
31100031 1
1.4%
31100171 1
1.4%
31101208 1
1.4%
31202781 1
1.4%
31206212 1
1.4%
31207651 1
1.4%
31208665 1
1.4%
31312195 1
1.4%
31320961 1
1.4%
31324126 1
1.4%
ValueCountFrequency (%)
41385373 1
1.4%
41382811 1
1.4%
41381131 1
1.4%
41380550 1
1.4%
41379462 1
1.4%
41374401 1
1.4%
41370384 1
1.4%
41366841 1
1.4%
41365755 1
1.4%
41363159 1
1.4%

전화번호
Text

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-12T13:42:22.554111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.027778
Min length12

Characters and Unicode

Total characters866
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)100.0%

Sample

1st row032-1544-9004
2nd row032-513-9303
3rd row032-521-5433
4th row032-500-0114
5th row032-522-3313
ValueCountFrequency (%)
032-1544-9004 1
 
1.4%
032-513-9303 1
 
1.4%
032-435-7070 1
 
1.4%
032-280-1109 1
 
1.4%
032-363-9121 1
 
1.4%
032-506-8808 1
 
1.4%
032-505-1608 1
 
1.4%
032-521-5211 1
 
1.4%
032-268-7575 1
 
1.4%
032-431-7582 1
 
1.4%
Other values (62) 62
86.1%
2023-12-12T13:42:23.066030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 144
16.6%
2 133
15.4%
0 128
14.8%
3 123
14.2%
5 101
11.7%
1 67
7.7%
7 53
 
6.1%
8 39
 
4.5%
4 29
 
3.3%
9 25
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 722
83.4%
Dash Punctuation 144
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 133
18.4%
0 128
17.7%
3 123
17.0%
5 101
14.0%
1 67
9.3%
7 53
 
7.3%
8 39
 
5.4%
4 29
 
4.0%
9 25
 
3.5%
6 24
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 144
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 866
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 144
16.6%
2 133
15.4%
0 128
14.8%
3 123
14.2%
5 101
11.7%
1 67
7.7%
7 53
 
6.1%
8 39
 
4.5%
4 29
 
3.3%
9 25
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 144
16.6%
2 133
15.4%
0 128
14.8%
3 123
14.2%
5 101
11.7%
1 67
7.7%
7 53
 
6.1%
8 39
 
4.5%
4 29
 
3.3%
9 25
 
2.9%

팩스(FAX)
Text

MISSING 

Distinct71
Distinct (%)100.0%
Missing1
Missing (%)1.4%
Memory size708.0 B
2023-12-12T13:42:23.370592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.014085
Min length12

Characters and Unicode

Total characters853
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)100.0%

Sample

1st row032-280-6052
2nd row032-521-5432
3rd row032-500-0884
4th row032-522-3391
5th row032-362-7303
ValueCountFrequency (%)
032-280-6052 1
 
1.4%
032-268-7573 1
 
1.4%
032-433-7071 1
 
1.4%
032-506-9522 1
 
1.4%
032-363-9507 1
 
1.4%
032-506-8838 1
 
1.4%
032-505-1609 1
 
1.4%
032-519-7323 1
 
1.4%
050-4023-9739 1
 
1.4%
032-428-3338 1
 
1.4%
Other values (61) 61
85.9%
2023-12-12T13:42:23.808859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 142
16.6%
2 130
15.2%
0 126
14.8%
3 122
14.3%
5 97
11.4%
7 56
 
6.6%
1 52
 
6.1%
8 37
 
4.3%
6 36
 
4.2%
9 32
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 711
83.4%
Dash Punctuation 142
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 130
18.3%
0 126
17.7%
3 122
17.2%
5 97
13.6%
7 56
7.9%
1 52
 
7.3%
8 37
 
5.2%
6 36
 
5.1%
9 32
 
4.5%
4 23
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 853
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 142
16.6%
2 130
15.2%
0 126
14.8%
3 122
14.3%
5 97
11.4%
7 56
 
6.6%
1 52
 
6.1%
8 37
 
4.3%
6 36
 
4.2%
9 32
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 142
16.6%
2 130
15.2%
0 126
14.8%
3 122
14.3%
5 97
11.4%
7 56
 
6.6%
1 52
 
6.1%
8 37
 
4.3%
6 36
 
4.2%
9 32
 
3.8%

주소
Text

Distinct71
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-12T13:42:24.158584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length46
Mean length33.583333
Min length22

Characters and Unicode

Total characters2418
Distinct characters151
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)97.2%

Sample

1st row인천광역시 부평구 동수로 56 (부평동)
2nd row인천광역시 부평구 굴포로 42 (갈산동)
3rd row인천광역시 부평구 체육관로 38 (삼산동, 세원빌딩)
4th row인천광역시 부평구 무네미로 446 (구산동)
5th row인천광역시 부평구 부평대로 9 (부평동)
ValueCountFrequency (%)
인천광역시 72
 
14.8%
부평구 72
 
14.8%
부평동 33
 
6.8%
부평대로 14
 
2.9%
산곡동 9
 
1.8%
2층 9
 
1.8%
삼산동 8
 
1.6%
3층 7
 
1.4%
부개동 7
 
1.4%
십정동 6
 
1.2%
Other values (187) 251
51.4%
2023-12-12T13:42:24.723280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
421
 
17.4%
부 151
 
6.2%
평 129
 
5.3%
동 85
 
3.5%
천 79
 
3.3%
인 78
 
3.2%
) 77
 
3.2%
( 77
 
3.2%
광 74
 
3.1%
시 74
 
3.1%
Other values (141) 1173
48.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1367
56.5%
Space Separator 421
 
17.4%
Decimal Number 396
 
16.4%
Close Punctuation 77
 
3.2%
Open Punctuation 77
 
3.2%
Other Punctuation 66
 
2.7%
Uppercase Letter 7
 
0.3%
Math Symbol 5
 
0.2%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 151
 
11.0%
평 129
 
9.4%
동 85
 
6.2%
천 79
 
5.8%
인 78
 
5.7%
광 74
 
5.4%
시 74
 
5.4%
역 73
 
5.3%
구 73
 
5.3%
로 72
 
5.3%
Other values (119) 479
35.0%
Decimal Number
ValueCountFrequency (%)
2 66
16.7%
0 63
15.9%
1 62
15.7%
3 53
13.4%
4 45
11.4%
6 29
7.3%
5 27
6.8%
7 20
 
5.1%
8 18
 
4.5%
9 13
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
28.6%
I 1
14.3%
P 1
14.3%
R 1
14.3%
K 1
14.3%
F 1
14.3%
Space Separator
ValueCountFrequency (%)
421
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Other Punctuation
ValueCountFrequency (%)
, 66
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1367
56.5%
Common 1044
43.2%
Latin 7
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 151
 
11.0%
평 129
 
9.4%
동 85
 
6.2%
천 79
 
5.8%
인 78
 
5.7%
광 74
 
5.4%
시 74
 
5.4%
역 73
 
5.3%
구 73
 
5.3%
로 72
 
5.3%
Other values (119) 479
35.0%
Common
ValueCountFrequency (%)
421
40.3%
) 77
 
7.4%
( 77
 
7.4%
2 66
 
6.3%
, 66
 
6.3%
0 63
 
6.0%
1 62
 
5.9%
3 53
 
5.1%
4 45
 
4.3%
6 29
 
2.8%
Other values (6) 85
 
8.1%
Latin
ValueCountFrequency (%)
A 2
28.6%
I 1
14.3%
P 1
14.3%
R 1
14.3%
K 1
14.3%
F 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1367
56.5%
ASCII 1051
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
421
40.1%
) 77
 
7.3%
( 77
 
7.3%
2 66
 
6.3%
, 66
 
6.3%
0 63
 
6.0%
1 62
 
5.9%
3 53
 
5.0%
4 45
 
4.3%
6 29
 
2.8%
Other values (12) 92
 
8.8%
Hangul
ValueCountFrequency (%)
부 151
 
11.0%
평 129
 
9.4%
동 85
 
6.2%
천 79
 
5.8%
인 78
 
5.7%
광 74
 
5.4%
시 74
 
5.4%
역 73
 
5.3%
구 73
 
5.3%
로 72
 
5.3%
Other values (119) 479
35.0%

위암
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
O
55 
<NA>
17 

Length

Max length4
Median length1
Mean length1.7083333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 55
76.4%
<NA> 17
 
23.6%

Length

2023-12-12T13:42:24.905404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:25.044216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 55
76.4%
na 17
 
23.6%

간암
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
O
52 
<NA>
20 

Length

Max length4
Median length1
Mean length1.8333333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 52
72.2%
<NA> 20
 
27.8%

Length

2023-12-12T13:42:25.175890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:25.394506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 52
72.2%
na 20
 
27.8%

대장암
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
O
43 
<NA>
29 

Length

Max length4
Median length1
Mean length2.2083333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 43
59.7%
<NA> 29
40.3%

Length

2023-12-12T13:42:25.521618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:25.634682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 43
59.7%
na 29
40.3%

유방암
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
<NA>
38 
O
34 

Length

Max length4
Median length4
Mean length2.5833333
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 38
52.8%
O 34
47.2%

Length

2023-12-12T13:42:25.800287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:25.915235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 38
52.8%
o 34
47.2%

자궁경부암
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
O
44 
<NA>
28 

Length

Max length4
Median length1
Mean length2.1666667
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
O 44
61.1%
<NA> 28
38.9%

Length

2023-12-12T13:42:26.024005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:26.131495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 44
61.1%
na 28
38.9%

폐암
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
<NA>
69 
O
 
3

Length

Max length4
Median length4
Mean length3.875
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 69
95.8%
O 3
 
4.2%

Length

2023-12-12T13:42:26.261791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:42:26.631315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 69
95.8%
o 3
 
4.2%

Interactions

2023-12-12T13:42:21.135439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:42:26.701386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검진기관명검진기관 코드전화번호팩스(FAX)주소
검진기관명1.0001.0001.0001.0001.000
검진기관 코드1.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.000
팩스(FAX)1.0001.0001.0001.0001.000
주소1.0001.0001.0001.0001.000
2023-12-12T13:42:26.827959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위암자궁경부암유방암대장암간암폐암
위암1.0001.0001.0001.0001.0001.000
자궁경부암1.0001.0001.0001.0001.0001.000
유방암1.0001.0001.0001.0001.0001.000
대장암1.0001.0001.0001.0001.0001.000
간암1.0001.0001.0001.0001.0001.000
폐암1.0001.0001.0001.0001.0001.000
2023-12-12T13:42:26.973099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검진기관 코드위암간암대장암유방암자궁경부암폐암
검진기관 코드1.0001.0001.0001.0001.0001.0001.000
위암1.0001.0001.0001.0001.0001.0001.000
간암1.0001.0001.0001.0001.0001.0001.000
대장암1.0001.0001.0001.0001.0001.0001.000
유방암1.0001.0001.0001.0001.0001.0001.000
자궁경부암1.0001.0001.0001.0001.0001.0001.000
폐암1.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T13:42:21.250559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:42:21.416275image/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

검진기관명검진기관 코드전화번호팩스(FAX)주소위암간암대장암유방암자궁경부암폐암
0가톨릭대학교인천성모병원31100031032-1544-9004032-280-6052인천광역시 부평구 동수로 56 (부평동)OOOOOO
1갈산중앙의원31328326032-513-9303<NA>인천광역시 부평구 굴포로 42 (갈산동)O<NA><NA><NA><NA><NA>
2굿모닝의원31367747032-521-5433032-521-5432인천광역시 부평구 체육관로 38 (삼산동, 세원빌딩)OOOOO<NA>
3근로복지공단인천병원31101208032-500-0114032-500-0884인천광역시 부평구 무네미로 446 (구산동)OOOOOO
4김명주산부인과의원31396186032-522-3313032-522-3391인천광역시 부평구 부평대로 9 (부평동)<NA><NA><NA><NA>O<NA>
5김방수내과의원31370594032-528-7303032-362-7303인천광역시 부평구 부영로 165 109호, 213호 (산곡동, 우성아파트)OO<NA><NA><NA><NA>
6나은내과의원41348346032-529-7588032-502-7588인천광역시 부평구 길주남로10번길 21 202, 203, 204(일부), 205, 206호 (부평동, 래미안부평)OOO<NA><NA><NA>
7노내과의원31312195032-523-5244032-523-5248인천광역시 부평구 부평문화로 100-1 (부평동)OOO<NA><NA><NA>
8다나은한방병원31921868032-523-1075032-507-8883인천광역시 부평구 경인로999번길 1 (부평동, 지하1층, 지상1층일부, 지상2~5층)<NA><NA>O<NA><NA><NA>
9다인이비인후과병원31207651032-515-2325032-515-2321인천광역시 부평구 경원대로 1242 지하1(일부)층, 1(일부)층, 3층, 4(일부)층, 5층, 6층, 7(일부)층 우암빌딩 (산곡동)OOO<NA><NA><NA>
검진기관명검진기관 코드전화번호팩스(FAX)주소위암간암대장암유방암자궁경부암폐암
62정속편안내과의원41338031032-519-7887032-519-7886인천광역시 부평구 마장로 310 롯데프라자 2층 201호 (산곡동)OOOOO<NA>
63제일외과의원31393101032-519-2314032-515-7093인천광역시 부평구 부평대로 11 3층 일부호 (부평동)O<NA>O<NA><NA><NA>
64중앙성모의원31378013032-511-4792032-518-9192인천광역시 부평구 후정동로 12 (삼산동, 인천삼산벽산블루밍 상가동)OOO<NA>O<NA>
65참조은내과의원41325141032-525-7582032-525-7583인천광역시 부평구 경인로 883 2층 (부평동, 재현빌딩)OO<NA><NA><NA><NA>
66탁월한내과의원41365755032-710-8575032-710-8578인천광역시 부평구 체육관로 40 신영프라자 701호, 702호, 703호 (삼산동)OOOOO<NA>
67파티마산부인과의원31332960032-511-7600032-511-0056인천광역시 부평구 평천로 337 (갈산동)<NA><NA><NA><NA>O<NA>
68필산부인과의원31324126032-528-0878032-525-9294인천광역시 부평구 부평대로 24 (부평동)<NA><NA><NA><NA>O<NA>
69하이큐영상의원41318307032-363-3292032-363-3295인천광역시 부평구 부평대로 301 (청천동)OOOOO<NA>
70행복플러스의원41328825032-511-5475032-511-5470인천광역시 부평구 주부토로 261 201,202호 (갈산동, 신협빌딩)OO<NA><NA><NA><NA>
71호내과의원41304209032-526-1728032-525-1728인천광역시 부평구 경원대로 1270 2층 201호 (부평동)OOOOO<NA>