Overview

Dataset statistics

Number of variables19
Number of observations82
Missing cells812
Missing cells (%)52.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory167.6 B

Variable types

Categorical2
Text3
Numeric14

Dataset

Description부산광역시 구군 보건소 검사정보 현황에 대한 데이터로 구,군별 보건소 검사 항목 및 비용 정보와 검사 여부 현황에 대한 정보를 제공합니다.
Author부산광역시
URLhttps://www.data.go.kr/data/15083359/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
중구 is highly overall correlated with 서구 and 12 other fieldsHigh correlation
서구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
동구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
영도구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
부산진구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
동래구 is highly overall correlated with 중구 and 13 other fieldsHigh correlation
남구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
북구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
사하구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
금정구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
연제구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
수영구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
사상구 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
기장군 is highly overall correlated with 중구 and 12 other fieldsHigh correlation
통합표준 검사분류 is highly overall correlated with 동래구High correlation
중구 has 52 (63.4%) missing valuesMissing
서구 has 53 (64.6%) missing valuesMissing
동구 has 56 (68.3%) missing valuesMissing
영도구 has 41 (50.0%) missing valuesMissing
부산진구 has 50 (61.0%) missing valuesMissing
동래구 has 45 (54.9%) missing valuesMissing
남구 has 50 (61.0%) missing valuesMissing
북구 has 40 (48.8%) missing valuesMissing
해운대구 has 54 (65.9%) missing valuesMissing
사하구 has 52 (63.4%) missing valuesMissing
금정구 has 53 (64.6%) missing valuesMissing
강서구 has 53 (64.6%) missing valuesMissing
연제구 has 56 (68.3%) missing valuesMissing
수영구 has 53 (64.6%) missing valuesMissing
사상구 has 59 (72.0%) missing valuesMissing
기장군 has 45 (54.9%) missing valuesMissing
중구 has 2 (2.4%) zerosZeros
서구 has 1 (1.2%) zerosZeros
동구 has 2 (2.4%) zerosZeros
영도구 has 1 (1.2%) zerosZeros
부산진구 has 2 (2.4%) zerosZeros
동래구 has 1 (1.2%) zerosZeros
남구 has 1 (1.2%) zerosZeros
북구 has 2 (2.4%) zerosZeros
사하구 has 1 (1.2%) zerosZeros
금정구 has 3 (3.7%) zerosZeros
연제구 has 1 (1.2%) zerosZeros
수영구 has 1 (1.2%) zerosZeros
사상구 has 1 (1.2%) zerosZeros
기장군 has 1 (1.2%) zerosZeros

Reproduction

Analysis started2024-04-06 08:47:19.885543
Analysis finished2024-04-06 08:48:19.405684
Duration59.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통합표준 검사분류
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size788.0 B
간기능검사
15 
성병검사
12 
소변검사
고지혈증검사
신장기능검사
Other values (18)
35 

Length

Max length12
Median length11
Mean length7.097561
Min length5

Unique

Unique8 ?
Unique (%)9.8%

Sample

1st row 신장기능검사
2nd row 신장기능검사
3rd row 암검사
4th row 암검사
5th row 암검사 (정밀)

Common Values

ValueCountFrequency (%)
간기능검사 15
18.3%
성병검사 12
14.6%
소변검사 8
9.8%
고지혈증검사 7
 
8.5%
신장기능검사 5
 
6.1%
간염검사 5
 
6.1%
기타검사 4
 
4.9%
산전검사 3
 
3.7%
전혈구검사(CBC) 3
 
3.7%
흉부(X-선)촬영 2
 
2.4%
Other values (13) 18
22.0%

Length

2024-04-06T17:48:19.633608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
간기능검사 15
17.9%
성병검사 12
14.3%
소변검사 8
9.5%
고지혈증검사 7
 
8.3%
신장기능검사 5
 
6.0%
간염검사 5
 
6.0%
기타검사 4
 
4.8%
산전검사 3
 
3.6%
전혈구검사(cbc 3
 
3.6%
암검사 3
 
3.6%
Other values (14) 19
22.6%
Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-04-06T17:48:20.226777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length118
Median length68
Mean length21.963415
Min length5

Characters and Unicode

Total characters1801
Distinct characters183
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

Unique80 ?
Unique (%)97.6%

Sample

1st row (3종) 요소질소(BUN), 크레아티닌(Creatinine), 요산(Uric Acid)
2nd row (7종) 요소질소(BUN), 크레아티닌(Creatinine), 요산(Uric Acid), 뇨스틱4종(뇨당,뇨단백,pH,잠혈)
3rd row 간암(AFT)
4th row 간암(AFP)
5th row 대장암(CEA)
ValueCountFrequency (%)
ast(sgot 9
 
4.8%
alt(sgpt 9
 
4.8%
r-gtp(지방간 6
 
3.2%
포함 5
 
2.6%
중성지방(tg 4
 
2.1%
4종 4
 
2.1%
알부민 4
 
2.1%
크레아티닌(creatinine 4
 
2.1%
요소질소(bun 4
 
2.1%
3종 3
 
1.6%
Other values (105) 137
72.5%
2024-04-06T17:48:21.233863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
 
14.4%
, 118
 
6.6%
( 112
 
6.2%
) 111
 
6.2%
T 54
 
3.0%
A 35
 
1.9%
G 32
 
1.8%
30
 
1.7%
29
 
1.6%
S 29
 
1.6%
Other values (173) 991
55.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 744
41.3%
Uppercase Letter 290
 
16.1%
Space Separator 260
 
14.4%
Other Punctuation 121
 
6.7%
Open Punctuation 112
 
6.2%
Close Punctuation 111
 
6.2%
Lowercase Letter 108
 
6.0%
Decimal Number 34
 
1.9%
Dash Punctuation 21
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
30
 
4.0%
29
 
3.9%
24
 
3.2%
21
 
2.8%
21
 
2.8%
20
 
2.7%
19
 
2.6%
17
 
2.3%
17
 
2.3%
16
 
2.2%
Other values (120) 530
71.2%
Uppercase Letter
ValueCountFrequency (%)
T 54
18.6%
A 35
12.1%
G 32
11.0%
S 29
10.0%
P 28
9.7%
L 24
8.3%
H 14
 
4.8%
B 14
 
4.8%
C 11
 
3.8%
D 10
 
3.4%
Other values (10) 39
13.4%
Lowercase Letter
ValueCountFrequency (%)
i 20
18.5%
r 18
16.7%
e 13
12.0%
n 12
11.1%
c 7
 
6.5%
t 7
 
6.5%
a 6
 
5.6%
l 4
 
3.7%
o 3
 
2.8%
d 3
 
2.8%
Other values (7) 15
13.9%
Decimal Number
ValueCountFrequency (%)
4 7
20.6%
5 6
17.6%
2 5
14.7%
3 4
11.8%
1 4
11.8%
7 3
8.8%
6 2
 
5.9%
0 2
 
5.9%
8 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
, 118
97.5%
/ 2
 
1.7%
. 1
 
0.8%
Space Separator
ValueCountFrequency (%)
260
100.0%
Open Punctuation
ValueCountFrequency (%)
( 112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 744
41.3%
Common 659
36.6%
Latin 398
22.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
30
 
4.0%
29
 
3.9%
24
 
3.2%
21
 
2.8%
21
 
2.8%
20
 
2.7%
19
 
2.6%
17
 
2.3%
17
 
2.3%
16
 
2.2%
Other values (120) 530
71.2%
Latin
ValueCountFrequency (%)
T 54
13.6%
A 35
 
8.8%
G 32
 
8.0%
S 29
 
7.3%
P 28
 
7.0%
L 24
 
6.0%
i 20
 
5.0%
r 18
 
4.5%
H 14
 
3.5%
B 14
 
3.5%
Other values (27) 130
32.7%
Common
ValueCountFrequency (%)
260
39.5%
, 118
17.9%
( 112
17.0%
) 111
16.8%
- 21
 
3.2%
4 7
 
1.1%
5 6
 
0.9%
2 5
 
0.8%
3 4
 
0.6%
1 4
 
0.6%
Other values (6) 11
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1057
58.7%
Hangul 744
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
24.6%
, 118
11.2%
( 112
10.6%
) 111
10.5%
T 54
 
5.1%
A 35
 
3.3%
G 32
 
3.0%
S 29
 
2.7%
P 28
 
2.6%
L 24
 
2.3%
Other values (43) 254
24.0%
Hangul
ValueCountFrequency (%)
30
 
4.0%
29
 
3.9%
24
 
3.2%
21
 
2.8%
21
 
2.8%
20
 
2.7%
19
 
2.6%
17
 
2.3%
17
 
2.3%
16
 
2.2%
Other values (120) 530
71.2%

중구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)93.3%
Missing52
Missing (%)63.4%
Infinite0
Infinite (%)0.0%
Mean6846
Minimum0
Maximum40000
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:21.603575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile396
Q11792.5
median4310
Q38030
95-th percentile19464
Maximum40000
Range40000
Interquartile range (IQR)6237.5

Descriptive statistics

Standard deviation8361.5997
Coefficient of variation (CV)1.2213847
Kurtosis7.8619481
Mean6846
Median Absolute Deviation (MAD)3035
Skewness2.5202317
Sum205380
Variance69916349
MonotonicityNot monotonic
2024-04-06T17:48:21.983597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4310 2
 
2.4%
0 2
 
2.4%
20760 1
 
1.2%
1750 1
 
1.2%
1920 1
 
1.2%
880 1
 
1.2%
2180 1
 
1.2%
7760 1
 
1.2%
4480 1
 
1.2%
1790 1
 
1.2%
Other values (18) 18
 
22.0%
(Missing) 52
63.4%
ValueCountFrequency (%)
0 2
2.4%
880 1
1.2%
1170 1
1.2%
1190 1
1.2%
1550 1
1.2%
1750 1
1.2%
1790 1
1.2%
1800 1
1.2%
1820 1
1.2%
1920 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
17510 1
1.2%
14810 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%
7380 1
1.2%

서구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)93.1%
Missing53
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean4423.7931
Minimum0
Maximum18060
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:22.351566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile842
Q11490
median3200
Q36740
95-th percentile12658
Maximum18060
Range18060
Interquartile range (IQR)5250

Descriptive statistics

Standard deviation4291.5344
Coefficient of variation (CV)0.97010287
Kurtosis3.4365543
Mean4423.7931
Median Absolute Deviation (MAD)2190
Skewness1.7542391
Sum128290
Variance18417267
MonotonicityNot monotonic
2024-04-06T17:48:22.688324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7060 2
 
2.4%
3750 2
 
2.4%
6360 1
 
1.2%
3200 1
 
1.2%
1530 1
 
1.2%
1670 1
 
1.2%
770 1
 
1.2%
6740 1
 
1.2%
1560 1
 
1.2%
1040 1
 
1.2%
Other values (17) 17
 
20.7%
(Missing) 53
64.6%
ValueCountFrequency (%)
0 1
1.2%
770 1
1.2%
950 1
1.2%
1010 1
1.2%
1040 1
1.2%
1270 1
1.2%
1350 1
1.2%
1490 1
1.2%
1530 1
1.2%
1560 1
1.2%
ValueCountFrequency (%)
18060 1
1.2%
15550 1
1.2%
8320 1
1.2%
7270 1
1.2%
7190 1
1.2%
7060 2
2.4%
6740 1
1.2%
6380 1
1.2%
6360 1
1.2%
6210 1
1.2%

동구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)92.3%
Missing56
Missing (%)68.3%
Infinite0
Infinite (%)0.0%
Mean8084.2308
Minimum0
Maximum40000
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:23.060778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile272.5
Q12255
median6550
Q39635
95-th percentile20040
Maximum40000
Range40000
Interquartile range (IQR)7380

Descriptive statistics

Standard deviation8554.3333
Coefficient of variation (CV)1.0581506
Kurtosis7.0367645
Mean8084.2308
Median Absolute Deviation (MAD)4220
Skewness2.3170182
Sum210190
Variance73176617
MonotonicityNot monotonic
2024-04-06T17:48:23.582414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4310 2
 
2.4%
0 2
 
2.4%
7310 1
 
1.2%
2180 1
 
1.2%
7760 1
 
1.2%
1790 1
 
1.2%
1190 1
 
1.2%
1090 1
 
1.2%
20760 1
 
1.2%
13420 1
 
1.2%
Other values (14) 14
 
17.1%
(Missing) 56
68.3%
ValueCountFrequency (%)
0 2
2.4%
1090 1
1.2%
1190 1
1.2%
1790 1
1.2%
1800 1
1.2%
2180 1
1.2%
2480 1
1.2%
3670 1
1.2%
4310 2
2.4%
5030 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
16610 1
1.2%
13420 1
1.2%
11620 1
1.2%
10060 1
1.2%
8360 1
1.2%
8120 1
1.2%
7760 1
1.2%

영도구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)87.8%
Missing41
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean6393.1707
Minimum0
Maximum40000
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:23.967564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile880
Q11790
median4310
Q37760
95-th percentile17880
Maximum40000
Range40000
Interquartile range (IQR)5970

Descriptive statistics

Standard deviation7395.1523
Coefficient of variation (CV)1.1567269
Kurtosis10.042176
Mean6393.1707
Median Absolute Deviation (MAD)2850
Skewness2.7575558
Sum262120
Variance54688277
MonotonicityNot monotonic
2024-04-06T17:48:24.383245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
5950 2
 
2.4%
880 2
 
2.4%
1300 2
 
2.4%
4310 2
 
2.4%
1790 2
 
2.4%
1170 1
 
1.2%
7340 1
 
1.2%
11620 1
 
1.2%
13420 1
 
1.2%
20760 1
 
1.2%
Other values (26) 26
31.7%
(Missing) 41
50.0%
ValueCountFrequency (%)
0 1
1.2%
880 2
2.4%
1170 1
1.2%
1190 1
1.2%
1300 2
2.4%
1460 1
1.2%
1720 1
1.2%
1750 1
1.2%
1790 2
2.4%
1800 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
17510 1
1.2%
13420 1
1.2%
13260 1
1.2%
11620 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%

부산진구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)93.8%
Missing50
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean6062.1875
Minimum0
Maximum40000
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:24.777296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile484
Q11780
median4310
Q37317.5
95-th percentile19176
Maximum40000
Range40000
Interquartile range (IQR)5537.5

Descriptive statistics

Standard deviation7732.7443
Coefficient of variation (CV)1.27557
Kurtosis12.022254
Mean6062.1875
Median Absolute Deviation (MAD)2845
Skewness3.1568615
Sum193990
Variance59795334
MonotonicityNot monotonic
2024-04-06T17:48:25.155536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 2
 
2.4%
4310 2
 
2.4%
1800 1
 
1.2%
1750 1
 
1.2%
1920 1
 
1.2%
880 1
 
1.2%
7760 1
 
1.2%
4480 1
 
1.2%
1790 1
 
1.2%
1190 1
 
1.2%
Other values (20) 20
 
24.4%
(Missing) 50
61.0%
ValueCountFrequency (%)
0 2
2.4%
880 1
1.2%
1090 1
1.2%
1170 1
1.2%
1190 1
1.2%
1460 1
1.2%
1750 1
1.2%
1790 1
1.2%
1800 1
1.2%
1920 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%
7340 1
1.2%
7310 1
1.2%
7150 1
1.2%

동래구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)89.2%
Missing45
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean5392.4324
Minimum0
Maximum40000
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:25.598690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile880
Q11750
median2720
Q37310
95-th percentile14864
Maximum40000
Range40000
Interquartile range (IQR)5560

Descriptive statistics

Standard deviation7014.9588
Coefficient of variation (CV)1.3008895
Kurtosis16.650825
Mean5392.4324
Median Absolute Deviation (MAD)1590
Skewness3.6698307
Sum199520
Variance49209647
MonotonicityNot monotonic
2024-04-06T17:48:26.052603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4310 2
 
2.4%
880 2
 
2.4%
4480 2
 
2.4%
2180 2
 
2.4%
2720 1
 
1.2%
5030 1
 
1.2%
1800 1
 
1.2%
7310 1
 
1.2%
7340 1
 
1.2%
1170 1
 
1.2%
Other values (23) 23
28.0%
(Missing) 45
54.9%
ValueCountFrequency (%)
0 1
1.2%
880 2
2.4%
1170 1
1.2%
1190 1
1.2%
1380 1
1.2%
1460 1
1.2%
1550 1
1.2%
1720 1
1.2%
1750 1
1.2%
1790 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
17880 1
1.2%
14110 1
1.2%
9730 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%
7340 1
1.2%
7310 1
1.2%

남구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)93.8%
Missing50
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean5852.5
Minimum0
Maximum40000
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:26.449380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1039.5
Q11700
median3425
Q37317.5
95-th percentile19176
Maximum40000
Range40000
Interquartile range (IQR)5617.5

Descriptive statistics

Standard deviation7767.219
Coefficient of variation (CV)1.3271626
Kurtosis12.135687
Mean5852.5
Median Absolute Deviation (MAD)2125
Skewness3.2025818
Sum187280
Variance60329690
MonotonicityNot monotonic
2024-04-06T17:48:27.465066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1300 2
 
2.4%
4310 2
 
2.4%
7150 1
 
1.2%
3670 1
 
1.2%
1750 1
 
1.2%
1920 1
 
1.2%
3180 1
 
1.2%
880 1
 
1.2%
1190 1
 
1.2%
1170 1
 
1.2%
Other values (20) 20
 
24.4%
(Missing) 50
61.0%
ValueCountFrequency (%)
0 1
1.2%
880 1
1.2%
1170 1
1.2%
1190 1
1.2%
1300 2
2.4%
1460 1
1.2%
1550 1
1.2%
1750 1
1.2%
1800 1
1.2%
1820 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
8570 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7340 1
1.2%
7310 1
1.2%
7150 1
1.2%

북구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)92.9%
Missing40
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean5960.4762
Minimum0
Maximum40000
Zeros2
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:27.836707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile890.5
Q11760
median4130
Q37332.5
95-th percentile17795.5
Maximum40000
Range40000
Interquartile range (IQR)5572.5

Descriptive statistics

Standard deviation7228.6479
Coefficient of variation (CV)1.2127635
Kurtosis11.602568
Mean5960.4762
Median Absolute Deviation (MAD)2625
Skewness3.0006745
Sum250340
Variance52253351
MonotonicityNot monotonic
2024-04-06T17:48:28.291850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4310 2
 
2.4%
4480 2
 
2.4%
0 2
 
2.4%
3670 1
 
1.2%
1750 1
 
1.2%
1800 1
 
1.2%
7310 1
 
1.2%
7340 1
 
1.2%
11620 1
 
1.2%
13420 1
 
1.2%
Other values (29) 29
35.4%
(Missing) 40
48.8%
ValueCountFrequency (%)
0 2
2.4%
880 1
1.2%
1090 1
1.2%
1170 1
1.2%
1190 1
1.2%
1380 1
1.2%
1460 1
1.2%
1550 1
1.2%
1720 1
1.2%
1750 1
1.2%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
16190 1
1.2%
13420 1
1.2%
11620 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%

해운대구
Text

MISSING 

Distinct26
Distinct (%)92.9%
Missing54
Missing (%)65.9%
Memory size788.0 B
2024-04-06T17:48:28.820507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.8571429
Min length1

Characters and Unicode

Total characters108
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)85.7%

Sample

1st row6150
2nd row5280
3rd row0
4th row5950
5th row2480
ValueCountFrequency (%)
1300 2
 
7.1%
880 2
 
7.1%
6150 1
 
3.6%
1
 
3.6%
1790 1
 
3.6%
1010 1
 
3.6%
1170 1
 
3.6%
20760 1
 
3.6%
7340 1
 
3.6%
7310 1
 
3.6%
Other values (16) 16
57.1%
2024-04-06T17:48:29.712692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
30.6%
1 17
15.7%
8 12
 
11.1%
3 10
 
9.3%
7 8
 
7.4%
5 7
 
6.5%
2 6
 
5.6%
6 5
 
4.6%
4 4
 
3.7%
9 3
 
2.8%
Other values (2) 3
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 105
97.2%
Space Separator 2
 
1.9%
Dash Punctuation 1
 
0.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
31.4%
1 17
16.2%
8 12
 
11.4%
3 10
 
9.5%
7 8
 
7.6%
5 7
 
6.7%
2 6
 
5.7%
6 5
 
4.8%
4 4
 
3.8%
9 3
 
2.9%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
30.6%
1 17
15.7%
8 12
 
11.1%
3 10
 
9.3%
7 8
 
7.4%
5 7
 
6.5%
2 6
 
5.6%
6 5
 
4.6%
4 4
 
3.7%
9 3
 
2.8%
Other values (2) 3
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
30.6%
1 17
15.7%
8 12
 
11.1%
3 10
 
9.3%
7 8
 
7.4%
5 7
 
6.5%
2 6
 
5.6%
6 5
 
4.6%
4 4
 
3.7%
9 3
 
2.8%
Other values (2) 3
 
2.8%

사하구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct27
Distinct (%)90.0%
Missing52
Missing (%)63.4%
Infinite0
Infinite (%)0.0%
Mean7689.6667
Minimum0
Maximum40000
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:30.070419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile880
Q12777.5
median6125
Q38322.5
95-th percentile19464
Maximum40000
Range40000
Interquartile range (IQR)5545

Descriptive statistics

Standard deviation7945.355
Coefficient of variation (CV)1.0332509
Kurtosis8.8969242
Mean7689.6667
Median Absolute Deviation (MAD)2940
Skewness2.5946022
Sum230690
Variance63128665
MonotonicityNot monotonic
2024-04-06T17:48:30.448224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
880 2
 
2.4%
4310 2
 
2.4%
4480 2
 
2.4%
13420 1
 
1.2%
3670 1
 
1.2%
9550 1
 
1.2%
7760 1
 
1.2%
0 1
 
1.2%
1790 1
 
1.2%
1170 1
 
1.2%
Other values (17) 17
 
20.7%
(Missing) 52
63.4%
ValueCountFrequency (%)
0 1
1.2%
880 2
2.4%
1090 1
1.2%
1170 1
1.2%
1790 1
1.2%
1800 1
1.2%
2480 1
1.2%
3670 1
1.2%
4310 2
2.4%
4480 2
2.4%
ValueCountFrequency (%)
40000 1
1.2%
20760 1
1.2%
17880 1
1.2%
14960 1
1.2%
13420 1
1.2%
11620 1
1.2%
9550 1
1.2%
8360 1
1.2%
8210 1
1.2%
7760 1
1.2%

금정구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)89.7%
Missing53
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean6376.8966
Minimum0
Maximum29180
Zeros3
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:30.841657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11300
median4480
Q38120
95-th percentile19608
Maximum29180
Range29180
Interquartile range (IQR)6820

Descriptive statistics

Standard deviation6782.5154
Coefficient of variation (CV)1.0636076
Kurtosis3.879863
Mean6376.8966
Median Absolute Deviation (MAD)3280
Skewness1.8687063
Sum184930
Variance46002515
MonotonicityNot monotonic
2024-04-06T17:48:31.168936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 3
 
3.7%
1300 2
 
2.4%
7310 1
 
1.2%
3180 1
 
1.2%
880 1
 
1.2%
7760 1
 
1.2%
4480 1
 
1.2%
1790 1
 
1.2%
1190 1
 
1.2%
1170 1
 
1.2%
Other values (16) 16
 
19.5%
(Missing) 53
64.6%
ValueCountFrequency (%)
0 3
3.7%
880 1
 
1.2%
1170 1
 
1.2%
1190 1
 
1.2%
1300 2
2.4%
1790 1
 
1.2%
1800 1
 
1.2%
2720 1
 
1.2%
3180 1
 
1.2%
4130 1
 
1.2%
ValueCountFrequency (%)
29180 1
1.2%
20760 1
1.2%
17880 1
1.2%
14810 1
1.2%
8570 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%
7340 1
1.2%

강서구
Text

MISSING 

Distinct29
Distinct (%)100.0%
Missing53
Missing (%)64.6%
Memory size788.0 B
2024-04-06T17:48:31.672801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length4
Mean length4.6206897
Min length1

Characters and Unicode

Total characters134
Distinct characters20
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

Unique29 ?
Unique (%)100.0%

Sample

1st row6150
2nd row0
3rd row5950
4th row2480
5th row40000
ValueCountFrequency (%)
6150 1
 
3.4%
1800 1
 
3.4%
1750 1
 
3.4%
1920 1
 
3.4%
1300 1
 
3.4%
880 1
 
3.4%
1790 1
 
3.4%
1170 1
 
3.4%
1090 1
 
3.4%
10540(강서구민)/21070(타지역민 1
 
3.4%
Other values (19) 19
65.5%
2024-04-06T17:48:32.530174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40
29.9%
1 18
13.4%
8 11
 
8.2%
7 10
 
7.5%
3 9
 
6.7%
5 8
 
6.0%
2 8
 
6.0%
6 6
 
4.5%
4 6
 
4.5%
9 5
 
3.7%
Other values (10) 13
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
90.3%
Other Letter 8
 
6.0%
Close Punctuation 2
 
1.5%
Open Punctuation 2
 
1.5%
Other Punctuation 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
33.1%
1 18
14.9%
8 11
 
9.1%
7 10
 
8.3%
3 9
 
7.4%
5 8
 
6.6%
2 8
 
6.6%
6 6
 
5.0%
4 6
 
5.0%
9 5
 
4.1%
Other Letter
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 126
94.0%
Hangul 8
 
6.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40
31.7%
1 18
14.3%
8 11
 
8.7%
7 10
 
7.9%
3 9
 
7.1%
5 8
 
6.3%
2 8
 
6.3%
6 6
 
4.8%
4 6
 
4.8%
9 5
 
4.0%
Other values (3) 5
 
4.0%
Hangul
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126
94.0%
Hangul 8
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40
31.7%
1 18
14.3%
8 11
 
8.7%
7 10
 
7.9%
3 9
 
7.1%
5 8
 
6.3%
2 8
 
6.3%
6 6
 
4.8%
4 6
 
4.8%
9 5
 
4.0%
Other values (3) 5
 
4.0%
Hangul
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

연제구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct25
Distinct (%)96.2%
Missing56
Missing (%)68.3%
Infinite0
Infinite (%)0.0%
Mean5677.6923
Minimum0
Maximum19970
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:32.904598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile912.5
Q11625
median4395
Q37655
95-th percentile17112.5
Maximum19970
Range19970
Interquartile range (IQR)6030

Descriptive statistics

Standard deviation5153.6847
Coefficient of variation (CV)0.90770765
Kurtosis2.003349
Mean5677.6923
Median Absolute Deviation (MAD)3020
Skewness1.4947423
Sum147620
Variance26560466
MonotonicityNot monotonic
2024-04-06T17:48:33.316791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1300 2
 
2.4%
0 1
 
1.2%
3670 1
 
1.2%
880 1
 
1.2%
1790 1
 
1.2%
7760 1
 
1.2%
4480 1
 
1.2%
1190 1
 
1.2%
1010 1
 
1.2%
19970 1
 
1.2%
Other values (15) 15
 
18.3%
(Missing) 56
68.3%
ValueCountFrequency (%)
0 1
1.2%
880 1
1.2%
1010 1
1.2%
1190 1
1.2%
1300 2
2.4%
1570 1
1.2%
1790 1
1.2%
2480 1
1.2%
3340 1
1.2%
3670 1
1.2%
ValueCountFrequency (%)
19970 1
1.2%
17880 1
1.2%
14810 1
1.2%
8360 1
1.2%
8230 1
1.2%
8120 1
1.2%
7760 1
1.2%
7340 1
1.2%
7310 1
1.2%
7150 1
1.2%

수영구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct26
Distinct (%)89.7%
Missing53
Missing (%)64.6%
Infinite0
Infinite (%)0.0%
Mean5223.4483
Minimum0
Maximum20760
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:33.773457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile880
Q11460
median4310
Q37340
95-th percentile14072
Maximum20760
Range20760
Interquartile range (IQR)5880

Descriptive statistics

Standard deviation4804.6758
Coefficient of variation (CV)0.91982834
Kurtosis3.9144562
Mean5223.4483
Median Absolute Deviation (MAD)3010
Skewness1.7944763
Sum151480
Variance23084909
MonotonicityNot monotonic
2024-04-06T17:48:34.167294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8360 2
 
2.4%
880 2
 
2.4%
4310 2
 
2.4%
7340 1
 
1.2%
3670 1
 
1.2%
2770 1
 
1.2%
1300 1
 
1.2%
7760 1
 
1.2%
1790 1
 
1.2%
1190 1
 
1.2%
Other values (16) 16
 
19.5%
(Missing) 53
64.6%
ValueCountFrequency (%)
0 1
1.2%
880 2
2.4%
1090 1
1.2%
1170 1
1.2%
1190 1
1.2%
1300 1
1.2%
1460 1
1.2%
1790 1
1.2%
1800 1
1.2%
2480 1
1.2%
ValueCountFrequency (%)
20760 1
1.2%
17880 1
1.2%
8360 2
2.4%
8260 1
1.2%
8120 1
1.2%
7760 1
1.2%
7340 1
1.2%
7310 1
1.2%
7150 1
1.2%
6150 1
1.2%

사상구
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)95.7%
Missing59
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean9656.9565
Minimum0
Maximum41120
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:34.468151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile909
Q12140
median5950
Q38310
95-th percentile38918
Maximum41120
Range41120
Interquartile range (IQR)6170

Descriptive statistics

Standard deviation11728.781
Coefficient of variation (CV)1.2145422
Kurtosis2.9106236
Mean9656.9565
Median Absolute Deviation (MAD)3470
Skewness1.9241352
Sum222110
Variance1.3756431 × 108
MonotonicityNot monotonic
2024-04-06T17:48:34.869585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4310 2
 
2.4%
5030 1
 
1.2%
3670 1
 
1.2%
880 1
 
1.2%
41120 1
 
1.2%
1790 1
 
1.2%
1190 1
 
1.2%
1170 1
 
1.2%
7340 1
 
1.2%
7310 1
 
1.2%
Other values (12) 12
 
14.6%
(Missing) 59
72.0%
ValueCountFrequency (%)
0 1
1.2%
880 1
1.2%
1170 1
1.2%
1190 1
1.2%
1790 1
1.2%
1800 1
1.2%
2480 1
1.2%
3670 1
1.2%
4310 2
2.4%
5030 1
1.2%
ValueCountFrequency (%)
41120 1
1.2%
40000 1
1.2%
29180 1
1.2%
17880 1
1.2%
14810 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 1
1.2%
7340 1
1.2%
7310 1
1.2%

기장군
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)89.2%
Missing45
Missing (%)54.9%
Infinite0
Infinite (%)0.0%
Mean5858.3784
Minimum0
Maximum26340
Zeros1
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size870.0 B
2024-04-06T17:48:35.208273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1154
Q11920
median4310
Q37760
95-th percentile18456
Maximum26340
Range26340
Interquartile range (IQR)5840

Descriptive statistics

Standard deviation5655.226
Coefficient of variation (CV)0.96532276
Kurtosis4.8498824
Mean5858.3784
Median Absolute Deviation (MAD)2840
Skewness2.0681942
Sum216760
Variance31981581
MonotonicityNot monotonic
2024-04-06T17:48:35.624607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4310 2
 
2.4%
5950 2
 
2.4%
8120 2
 
2.4%
3180 2
 
2.4%
1090 1
 
1.2%
1170 1
 
1.2%
1190 1
 
1.2%
1790 1
 
1.2%
2250 1
 
1.2%
7760 1
 
1.2%
Other values (23) 23
28.0%
(Missing) 45
54.9%
ValueCountFrequency (%)
0 1
1.2%
1090 1
1.2%
1170 1
1.2%
1190 1
1.2%
1300 1
1.2%
1460 1
1.2%
1750 1
1.2%
1790 1
1.2%
1800 1
1.2%
1920 1
1.2%
ValueCountFrequency (%)
26340 1
1.2%
20760 1
1.2%
17880 1
1.2%
11620 1
1.2%
9550 1
1.2%
8360 1
1.2%
8260 1
1.2%
8120 2
2.4%
7760 1
1.2%
7340 1
1.2%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
2024-03-01
82 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-01
2nd row2024-03-01
3rd row2024-03-01
4th row2024-03-01
5th row2024-03-01

Common Values

ValueCountFrequency (%)
2024-03-01 82
100.0%

Length

2024-04-06T17:48:36.046446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:48:36.355692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-01 82
100.0%

Interactions

2024-04-06T17:48:13.649076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:21.550899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:25.951601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:29.726849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:33.609942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:37.461076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:41.626420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:45.510738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:49.307271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:52.857902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:57.574222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:00.921190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:04.697346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:08.668079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:13.848908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:21.845215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:26.218197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:30.074094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:33.833023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:37.764327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:41.862777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:45.806231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:49.550601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:53.168971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:57.819014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:01.140743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:04.909039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:09.018216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:14.163550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:22.091725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:26.474654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:30.311166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:34.065401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:38.032182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:42.138207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:46.143512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:49.782979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:53.409441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:58.069979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:01.383216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:05.172966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:09.384376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:14.365142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:22.395950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:26.711818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:30.573246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:34.318675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:38.295064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:42.419665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:46.446999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:50.015208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:53.715445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:58.281019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:01.610853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:05.385816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:09.768928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:14.582603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:22.649328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:26.946925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:30.799818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:34.640957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:38.495022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:42.709308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:46.694276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:50.251113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:54.003632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:58.472951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:01.865983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:05.664004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:10.058795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:14.826135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:22.923659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:27.165466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:31.084301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:34.866264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:38.756080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:42.954055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:46.987766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:50.600943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:54.299298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:58.683947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:02.141209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:06.013881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:10.332254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:15.092198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:23.222949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:27.519826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:31.378994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:35.154832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:39.102615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:43.244476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:47.247270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:50.878906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:54.616761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:58.913084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:02.426215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:06.303969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:10.615274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:15.312994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:23.479948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:27.789569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:31.692291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:35.436265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:39.371005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:43.588271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:47.504076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:51.138028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:54.890222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:59.092116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:02.777034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:06.585357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:10.905275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:15.519479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:24.283584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:28.062515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:32.026797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:35.707174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:39.665210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:43.893559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:47.732794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:51.428804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:55.906357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:59.272356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:03.079160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:06.830913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:11.193641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:15.809237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:24.554646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:28.400388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:32.405701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:36.069213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:39.897977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:44.192902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:48.000892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:51.695978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:56.178463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:59.552377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:03.346371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:07.161583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:11.621595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:16.046166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:24.781047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:28.686086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:32.610611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:36.310383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:40.647069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:44.447863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:48.242701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:51.900465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:56.434911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:59.928714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:03.595816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:07.482491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:12.581286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:16.400819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:25.052715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:28.983563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:32.875646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:36.601959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:40.883645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:44.717481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:48.547465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:52.163326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:56.720198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:00.220982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:03.846164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:07.758269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:12.864546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:16.692189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:25.352707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:29.215390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:33.073568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:36.873985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:41.095863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:44.983635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:48.735166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:52.386176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:56.997292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:00.478174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:04.109723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:08.045600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:13.142142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:16.941054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:25.682895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:29.478704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:33.350948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:37.235961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:41.351490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:45.280029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:49.081730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:52.642209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:47:57.294912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:00.732158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:04.398529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:08.424679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:48:13.337105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:48:36.629653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통합표준 검사분류검사명중구서구동구영도구부산진구동래구남구북구해운대구사하구금정구강서구연제구수영구사상구기장군
통합표준 검사분류1.0001.0000.5330.0000.0000.8050.5280.9010.7510.2820.9790.0610.0001.0000.0000.0000.0000.157
검사명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
중구0.5331.0001.0000.8990.9931.0000.9930.9960.9970.9931.0000.9850.9841.0000.9580.8710.9960.942
서구0.0001.0000.8991.0000.9240.9020.8770.9490.9240.9051.0000.9220.9901.0000.9890.9990.7930.900
동구0.0001.0000.9930.9241.0000.9910.9941.0000.9990.9981.0000.9990.9831.0000.9330.8641.0000.931
영도구0.8051.0001.0000.9020.9911.0000.9970.9940.9960.9981.0000.9860.9711.0000.9240.8900.9740.903
부산진구0.5281.0000.9930.8770.9940.9971.0001.0001.0001.0001.0000.9930.9821.0000.8770.8820.9980.946
동래구0.9011.0000.9960.9491.0000.9941.0001.0001.0000.9991.0000.9960.9811.0000.9390.9150.9980.946
남구0.7511.0000.9970.9240.9990.9961.0001.0001.0001.0001.0000.9970.9851.0000.9640.9020.9980.957
북구0.2821.0000.9930.9050.9980.9981.0000.9991.0001.0001.0000.9970.9831.0000.9060.8870.9980.944
해운대구0.9791.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사하구0.0611.0000.9850.9220.9990.9860.9930.9960.9970.9971.0001.0000.9871.0000.9130.9300.9800.932
금정구0.0001.0000.9840.9900.9830.9710.9820.9810.9850.9831.0000.9871.0001.0001.0000.8960.9931.000
강서구1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
연제구0.0001.0000.9580.9890.9330.9240.8770.9390.9640.9061.0000.9131.0001.0001.0000.9820.9580.950
수영구0.0001.0000.8710.9990.8640.8900.8820.9150.9020.8871.0000.9300.8961.0000.9821.0000.7670.867
사상구0.0001.0000.9960.7931.0000.9740.9980.9980.9980.9981.0000.9800.9931.0000.9580.7671.0000.870
기장군0.1571.0000.9420.9000.9310.9030.9460.9460.9570.9441.0000.9321.0001.0000.9500.8670.8701.000
2024-04-06T17:48:37.288995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
중구서구동구영도구부산진구동래구남구북구사하구금정구연제구수영구사상구기장군통합표준 검사분류
중구1.0000.8660.9600.9940.8340.9840.9810.8530.9240.9450.9560.7850.9560.9490.175
서구0.8661.0000.9560.9230.8370.9381.0000.8510.9910.8930.9931.0000.9250.9380.000
동구0.9600.9561.0000.8520.8140.9750.9820.8780.9940.9580.9160.8360.9990.9800.000
영도구0.9940.9230.8521.0000.9750.9820.9850.9780.8350.9570.9810.8370.9510.8830.378
부산진구0.8340.8370.8140.9751.0000.9661.0001.0000.8140.8280.8100.7311.0000.8550.187
동래구0.9840.9380.9750.9820.9661.0000.9990.9860.9810.9990.9560.8331.0000.9950.561
남구0.9811.0000.9820.9851.0000.9991.0001.0001.0001.0000.9980.8541.0001.0000.412
북구0.8530.8510.8780.9781.0000.9861.0001.0000.8630.8160.8040.7171.0000.8770.055
사하구0.9240.9910.9940.8350.8140.9811.0000.8631.0000.9880.8380.9950.6840.9950.000
금정구0.9450.8930.9580.9570.8280.9991.0000.8160.9881.0000.9980.7440.9990.9780.000
연제구0.9560.9930.9160.9810.8100.9560.9980.8040.8380.9981.0000.8500.9990.8670.000
수영구0.7851.0000.8360.8370.7310.8330.8540.7170.9950.7440.8501.0000.7650.8000.000
사상구0.9560.9250.9990.9511.0001.0001.0001.0000.6840.9990.9990.7651.0001.0000.000
기장군0.9490.9380.9800.8830.8550.9951.0000.8770.9950.9780.8670.8001.0001.0000.000
통합표준 검사분류0.1750.0000.0000.3780.1870.5610.4120.0550.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-06T17:48:17.362927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:48:18.072408image/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.
2024-04-06T17:48:18.807280image/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

통합표준 검사분류검사명중구서구동구영도구부산진구동래구남구북구해운대구사하구금정구강서구연제구수영구사상구기장군데이터기준일자
0신장기능검사(3종) 요소질소(BUN), 크레아티닌(Creatinine), 요산(Uric Acid)6150<NA>367061506150<NA>615061506150<NA>61506150<NA>6150<NA>61502024-03-01
1신장기능검사(7종) 요소질소(BUN), 크레아티닌(Creatinine), 요산(Uric Acid), 뇨스틱4종(뇨당,뇨단백,pH,잠혈)<NA><NA><NA>70307030<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01
2암검사간암(AFT)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01
3암검사간암(AFP)<NA><NA><NA><NA><NA>9730<NA>52805280<NA><NA><NA>4590<NA><NA><NA>2024-03-01
4암검사 (정밀)대장암(CEA)<NA><NA><NA><NA><NA>14110<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01
5에이즈(AIDS)에이즈(AIDS)000000000<NA>0000002024-03-01
6전혈구검사(CBC)(5종) 혈색소, 헤마토크리트, 적혈구수, 백혈구수,혈소판수<NA><NA>59505950<NA>5950<NA>595059505950<NA>5950<NA>5950595059502024-03-01
7전혈구검사(CBC)(6종) 혈색소,헤마토크리트,적혈구수,백혈구수,혈소판수,백혈구백분율<NA><NA><NA><NA><NA><NA>8570<NA><NA><NA>8570<NA><NA><NA><NA><NA>2024-03-01
8전혈구검사(CBC)(7종) 혈색소,헤마토크리트,적혈구수,백혈구수,혈소판수,MCV,MCHC<NA>8320<NA><NA>5950<NA><NA><NA><NA><NA><NA><NA>8230<NA><NA><NA>2024-03-01
9통풍(요산)검사요산(Uric Acid)2480216024802480248024802480248024802480<NA>248024802480248024802024-03-01
통합표준 검사분류검사명중구서구동구영도구부산진구동래구남구북구해운대구사하구금정구강서구연제구수영구사상구기장군데이터기준일자
72소변검사(10종) Ph, 백혈구(뇨), 비중(뇨), 빌리루빈(뇨),아질산염(뇨), 요단백, 요당, 유로빌리노젠(뇨), 적혈구(뇨), 케톤체(뇨)<NA><NA><NA><NA><NA><NA><NA><NA>1300<NA><NA><NA><NA><NA><NA><NA>2024-03-01
73소변검사뇨Gram stain<NA><NA><NA><NA><NA><NA>3180<NA><NA><NA>3180<NA><NA>2770<NA>31802024-03-01
74신장기능검사크레아티닌(Creatinine)19201670<NA>19201920192019201920<NA><NA><NA>1920<NA><NA><NA>19202024-03-01
75신장기능검사요소질소(BUN)17501530<NA>17501750175017501750<NA><NA><NA>1750<NA><NA><NA>17502024-03-01
76신장기능검사(2종) 요소질소(BUN), 크레아티닌(Creatinine)<NA>3200<NA>3670<NA><NA>3670367036703670<NA>367036703670367036702024-03-01
77산전검사전혈구(5종),소변,B형간염, 신장((2종),간기능(4종),통풍<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01
78한방난임검사AST(SGOT), ALT(SGPT) ,(4종) pH, 요단백, 요당, 잠혈 ,총콜레스테롤정량,트리글리세라이드(중성지방),크레아티닌,혈색소(HB)빈혈검사,당검사(반정량)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01
79산전검사전혈구(5종),소변,B형간염,매독,에이즈,풍진(항원,항체)0<NA><NA><NA><NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>2024-03-01
80산전검사풍진검사, C형간염, 빈혈검사, B형간염검사, 에이즈, 매독검사, 요당요단백 검사<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>0<NA><NA><NA><NA><NA>2024-03-01
81신혼부부 예비부모검사전혈구(5종),소변,B형간염, 신장(2종),간기능(3종)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2024-03-01