Overview

Dataset statistics

Number of variables13
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 KiB
Average record size in memory116.2 B

Variable types

Categorical1
Numeric10
Text2

Dataset

Description대구보훈병원에서 개방하는 진료정보 데이터로 대구보훈병원 다빈도 질환 환자 연령별 현황인 포함된 공공데이터 입니다.
URLhttps://www.data.go.kr/data/15102220/fileData.do

Alerts

순위 is highly overall correlated with 연인원 and 3 other fieldsHigh correlation
실인원 is highly overall correlated with 연인원 and 7 other fieldsHigh correlation
연인원 is highly overall correlated with 순위 and 8 other fieldsHigh correlation
진료비(천원) is highly overall correlated with 순위 and 1 other fieldsHigh correlation
59세이하 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
60세-64세 is highly overall correlated with 순위 and 7 other fieldsHigh correlation
65세-69세 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
70세-79세 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
80세-89세 is highly overall correlated with 순위 and 7 other fieldsHigh correlation
90세이상 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
구분 is highly overall correlated with 실인원High correlation
70세-79세 has unique valuesUnique
59세이하 has 10 (16.7%) zerosZeros
60세-64세 has 5 (8.3%) zerosZeros
90세이상 has 6 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-12 22:52:04.455512
Analysis finished2023-12-12 22:52:14.633682
Duration10.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
입원실인원
20 
입원연인원
20 
외래
20 

Length

Max length5
Median length5
Mean length4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
입원실인원 20
33.3%
입원연인원 20
33.3%
외래 20
33.3%

Length

2023-12-13T07:52:14.690167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:52:14.795905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
입원실인원 20
33.3%
입원연인원 20
33.3%
외래 20
33.3%

순위
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.5
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:14.895197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q15.75
median10.5
Q315.25
95-th percentile19.05
Maximum20
Range19
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation5.8149428
Coefficient of variation (CV)0.55380407
Kurtosis-1.2058222
Mean10.5
Median Absolute Deviation (MAD)5
Skewness0
Sum630
Variance33.813559
MonotonicityNot monotonic
2023-12-13T07:52:15.010074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 3
 
5.0%
12 3
 
5.0%
20 3
 
5.0%
19 3
 
5.0%
18 3
 
5.0%
17 3
 
5.0%
16 3
 
5.0%
15 3
 
5.0%
14 3
 
5.0%
13 3
 
5.0%
Other values (10) 30
50.0%
ValueCountFrequency (%)
1 3
5.0%
2 3
5.0%
3 3
5.0%
4 3
5.0%
5 3
5.0%
6 3
5.0%
7 3
5.0%
8 3
5.0%
9 3
5.0%
10 3
5.0%
ValueCountFrequency (%)
20 3
5.0%
19 3
5.0%
18 3
5.0%
17 3
5.0%
16 3
5.0%
15 3
5.0%
14 3
5.0%
13 3
5.0%
12 3
5.0%
11 3
5.0%
Distinct31
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T07:52:15.178644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters180
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)18.3%

Sample

1st rowU07
2nd rowI20
3rd rowH25
4th rowM51
5th rowM48
ValueCountFrequency (%)
u07 3
 
5.0%
m48 3
 
5.0%
e11 3
 
5.0%
m17 3
 
5.0%
c61 3
 
5.0%
i20 3
 
5.0%
n18 3
 
5.0%
h25 3
 
5.0%
m51 3
 
5.0%
j18 2
 
3.3%
Other values (21) 31
51.7%
2023-12-13T07:52:15.470846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 26
14.4%
0 25
13.9%
5 13
 
7.2%
8 13
 
7.2%
2 11
 
6.1%
M 11
 
6.1%
7 10
 
5.6%
4 9
 
5.0%
Z 8
 
4.4%
K 8
 
4.4%
Other values (15) 46
25.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
66.7%
Uppercase Letter 60
33.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 11
18.3%
Z 8
13.3%
K 8
13.3%
I 7
11.7%
N 4
 
6.7%
U 3
 
5.0%
H 3
 
5.0%
C 3
 
5.0%
E 3
 
5.0%
R 2
 
3.3%
Other values (5) 8
13.3%
Decimal Number
ValueCountFrequency (%)
1 26
21.7%
0 25
20.8%
5 13
10.8%
8 13
10.8%
2 11
9.2%
7 10
 
8.3%
4 9
 
7.5%
6 6
 
5.0%
3 5
 
4.2%
9 2
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common 120
66.7%
Latin 60
33.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 11
18.3%
Z 8
13.3%
K 8
13.3%
I 7
11.7%
N 4
 
6.7%
U 3
 
5.0%
H 3
 
5.0%
C 3
 
5.0%
E 3
 
5.0%
R 2
 
3.3%
Other values (5) 8
13.3%
Common
ValueCountFrequency (%)
1 26
21.7%
0 25
20.8%
5 13
10.8%
8 13
10.8%
2 11
9.2%
7 10
 
8.3%
4 9
 
7.5%
6 6
 
5.0%
3 5
 
4.2%
9 2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 26
14.4%
0 25
13.9%
5 13
 
7.2%
8 13
 
7.2%
2 11
 
6.1%
M 11
 
6.1%
7 10
 
5.6%
4 9
 
5.0%
Z 8
 
4.4%
K 8
 
4.4%
Other values (15) 46
25.6%
Distinct31
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T07:52:15.699355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length27
Mean length13.15
Min length3

Characters and Unicode

Total characters789
Distinct characters153
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)18.3%

Sample

1st row바이러스가 확인된 코로나바이러스 질환 2019 [바이러스가 확인된 코로나-19]
2nd row협심증
3rd row노년백내장
4th row기타 추간판장애
5th row기타 척추병증
ValueCountFrequency (%)
18
 
8.9%
기타 13
 
6.4%
바이러스가 6
 
3.0%
확인된 6
 
3.0%
접하고 4
 
2.0%
위하여 4
 
2.0%
감염성 4
 
2.0%
있는 4
 
2.0%
사람 4
 
2.0%
의학적 4
 
2.0%
Other values (67) 135
66.8%
2023-12-13T07:52:16.059236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
142
 
18.0%
21
 
2.7%
20
 
2.5%
18
 
2.3%
17
 
2.2%
16
 
2.0%
16
 
2.0%
15
 
1.9%
13
 
1.6%
13
 
1.6%
Other values (143) 498
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 612
77.6%
Space Separator 142
 
18.0%
Decimal Number 21
 
2.7%
Dash Punctuation 4
 
0.5%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
21
 
3.4%
20
 
3.3%
18
 
2.9%
17
 
2.8%
16
 
2.6%
16
 
2.6%
15
 
2.5%
13
 
2.1%
13
 
2.1%
10
 
1.6%
Other values (132) 453
74.0%
Decimal Number
ValueCountFrequency (%)
9 6
28.6%
1 6
28.6%
2 6
28.6%
0 3
14.3%
Open Punctuation
ValueCountFrequency (%)
[ 3
75.0%
( 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
] 3
75.0%
) 1
 
25.0%
Space Separator
ValueCountFrequency (%)
142
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 612
77.6%
Common 177
 
22.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
21
 
3.4%
20
 
3.3%
18
 
2.9%
17
 
2.8%
16
 
2.6%
16
 
2.6%
15
 
2.5%
13
 
2.1%
13
 
2.1%
10
 
1.6%
Other values (132) 453
74.0%
Common
ValueCountFrequency (%)
142
80.2%
9 6
 
3.4%
1 6
 
3.4%
2 6
 
3.4%
- 4
 
2.3%
[ 3
 
1.7%
] 3
 
1.7%
0 3
 
1.7%
, 2
 
1.1%
( 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 612
77.6%
ASCII 177
 
22.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
142
80.2%
9 6
 
3.4%
1 6
 
3.4%
2 6
 
3.4%
- 4
 
2.3%
[ 3
 
1.7%
] 3
 
1.7%
0 3
 
1.7%
, 2
 
1.1%
( 1
 
0.6%
Hangul
ValueCountFrequency (%)
21
 
3.4%
20
 
3.3%
18
 
2.9%
17
 
2.8%
16
 
2.6%
16
 
2.6%
15
 
2.5%
13
 
2.1%
13
 
2.1%
10
 
1.6%
Other values (132) 453
74.0%

실인원
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3970.0333
Minimum54
Maximum29425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:16.184880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile58
Q194
median220.5
Q35404.5
95-th percentile19828.85
Maximum29425
Range29371
Interquartile range (IQR)5310.5

Descriptive statistics

Standard deviation6889.7874
Coefficient of variation (CV)1.7354483
Kurtosis4.1876714
Mean3970.0333
Median Absolute Deviation (MAD)160
Skewness2.1444468
Sum238202
Variance47469171
MonotonicityNot monotonic
2023-12-13T07:52:16.313642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
994 2
 
3.3%
112 2
 
3.3%
670 2
 
3.3%
54 2
 
3.3%
58 2
 
3.3%
63 2
 
3.3%
70 2
 
3.3%
85 2
 
3.3%
88 2
 
3.3%
94 2
 
3.3%
Other values (30) 40
66.7%
ValueCountFrequency (%)
54 2
3.3%
58 2
3.3%
63 2
3.3%
70 2
3.3%
73 2
3.3%
85 2
3.3%
88 2
3.3%
94 2
3.3%
112 2
3.3%
116 2
3.3%
ValueCountFrequency (%)
29425 1
1.7%
24269 1
1.7%
23721 1
1.7%
19624 1
1.7%
19323 1
1.7%
13609 1
1.7%
12019 1
1.7%
10461 1
1.7%
9759 1
1.7%
8389 1
1.7%

연인원
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6369.5333
Minimum155
Maximum29425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:16.441569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile182
Q12233
median5098
Q38432
95-th percentile19828.85
Maximum29425
Range29270
Interquartile range (IQR)6199

Descriptive statistics

Standard deviation6261.4766
Coefficient of variation (CV)0.98303537
Kurtosis3.6680703
Mean6369.5333
Median Absolute Deviation (MAD)3172
Skewness1.8032711
Sum382172
Variance39206089
MonotonicityNot monotonic
2023-12-13T07:52:16.600568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
10726 2
 
3.3%
155 2
 
3.3%
5480 2
 
3.3%
612 2
 
3.3%
184 2
 
3.3%
487 2
 
3.3%
182 2
 
3.3%
5824 2
 
3.3%
3131 2
 
3.3%
2343 2
 
3.3%
Other values (30) 40
66.7%
ValueCountFrequency (%)
155 2
3.3%
182 2
3.3%
184 2
3.3%
487 2
3.3%
612 2
3.3%
624 2
3.3%
640 2
3.3%
2233 2
3.3%
2343 2
3.3%
3131 2
3.3%
ValueCountFrequency (%)
29425 1
1.7%
24269 1
1.7%
23721 1
1.7%
19624 1
1.7%
19323 1
1.7%
13609 1
1.7%
12019 1
1.7%
10726 2
3.3%
10461 1
1.7%
9759 1
1.7%

진료비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1310742.2
Minimum80273.243
Maximum4432312.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:16.780889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80273.243
5-th percentile82840.102
Q1379776.16
median1112285.7
Q32080648.6
95-th percentile2526784.5
Maximum4432312.6
Range4352039.4
Interquartile range (IQR)1700872.4

Descriptive statistics

Standard deviation1003310.2
Coefficient of variation (CV)0.76545202
Kurtosis1.1643096
Mean1310742.2
Median Absolute Deviation (MAD)770013.86
Skewness0.94250073
Sum78644530
Variance1.0066314 × 1012
MonotonicityNot monotonic
2023-12-13T07:52:16.951137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2468469.692 2
 
3.3%
80273.243 2
 
3.3%
4432312.625 2
 
3.3%
176521.173 2
 
3.3%
115625.804 2
 
3.3%
342271.875 2
 
3.3%
82840.102 2
 
3.3%
1592207.672 2
 
3.3%
778177.202 2
 
3.3%
832322.192 2
 
3.3%
Other values (30) 40
66.7%
ValueCountFrequency (%)
80273.243 2
3.3%
82840.102 2
3.3%
115625.804 2
3.3%
176521.173 2
3.3%
189250.77 1
1.7%
301315.946 1
1.7%
342271.875 2
3.3%
353555.929 2
3.3%
364410.856 1
1.7%
384897.93 1
1.7%
ValueCountFrequency (%)
4432312.625 2
3.3%
2526784.514 2
3.3%
2502313.719 2
3.3%
2468469.692 2
3.3%
2319941.563 1
1.7%
2300262.157 2
3.3%
2180723.96 2
3.3%
2113938.282 1
1.7%
2098006.204 1
1.7%
2074862.667 2
3.3%

59세이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453.68333
Minimum0
Maximum8028
Zeros10
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:17.115916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median21.5
Q3360
95-th percentile1862.7
Maximum8028
Range8028
Interquartile range (IQR)356.25

Descriptive statistics

Standard deviation1191.3861
Coefficient of variation (CV)2.6260302
Kurtosis28.682056
Mean453.68333
Median Absolute Deviation (MAD)21.5
Skewness4.9527993
Sum27221
Variance1419400.9
MonotonicityNot monotonic
2023-12-13T07:52:17.299941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 10
 
16.7%
2 4
 
6.7%
5 3
 
5.0%
18 2
 
3.3%
4 2
 
3.3%
977 1
 
1.7%
430 1
 
1.7%
1358 1
 
1.7%
8028 1
 
1.7%
1061 1
 
1.7%
Other values (34) 34
56.7%
ValueCountFrequency (%)
0 10
16.7%
2 4
 
6.7%
3 1
 
1.7%
4 2
 
3.3%
5 3
 
5.0%
7 1
 
1.7%
9 1
 
1.7%
10 1
 
1.7%
14 1
 
1.7%
16 1
 
1.7%
ValueCountFrequency (%)
8028 1
1.7%
3557 1
1.7%
2769 1
1.7%
1815 1
1.7%
1358 1
1.7%
1061 1
1.7%
1049 1
1.7%
1012 1
1.7%
977 1
1.7%
596 1
1.7%

60세-64세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.2
Minimum0
Maximum3741
Zeros5
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:17.457785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median23.5
Q3285.75
95-th percentile1103.75
Maximum3741
Range3741
Interquartile range (IQR)281.75

Descriptive statistics

Standard deviation544.06318
Coefficient of variation (CV)2.2009028
Kurtosis29.24474
Mean247.2
Median Absolute Deviation (MAD)23.5
Skewness4.8704076
Sum14832
Variance296004.74
MonotonicityNot monotonic
2023-12-13T07:52:17.613794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 5
 
8.3%
2 4
 
6.7%
7 3
 
5.0%
3 3
 
5.0%
1 2
 
3.3%
4 2
 
3.3%
20 2
 
3.3%
6 2
 
3.3%
270 1
 
1.7%
389 1
 
1.7%
Other values (35) 35
58.3%
ValueCountFrequency (%)
0 5
8.3%
1 2
 
3.3%
2 4
6.7%
3 3
5.0%
4 2
 
3.3%
6 2
 
3.3%
7 3
5.0%
9 1
 
1.7%
10 1
 
1.7%
11 1
 
1.7%
ValueCountFrequency (%)
3741 1
1.7%
1233 1
1.7%
1118 1
1.7%
1103 1
1.7%
794 1
1.7%
652 1
1.7%
598 1
1.7%
505 1
1.7%
453 1
1.7%
425 1
1.7%

65세-69세
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean344.43333
Minimum1
Maximum3588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:17.789293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.95
Q17
median75
Q3482.5
95-th percentile1349.4
Maximum3588
Range3587
Interquartile range (IQR)475.5

Descriptive statistics

Standard deviation614.73184
Coefficient of variation (CV)1.7847629
Kurtosis13.870969
Mean344.43333
Median Absolute Deviation (MAD)72
Skewness3.3591746
Sum20666
Variance377895.23
MonotonicityNot monotonic
2023-12-13T07:52:17.941827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 4
 
6.7%
6 4
 
6.7%
2 2
 
3.3%
14 2
 
3.3%
5 2
 
3.3%
66 2
 
3.3%
7 2
 
3.3%
27 2
 
3.3%
2171 1
 
1.7%
3588 1
 
1.7%
Other values (38) 38
63.3%
ValueCountFrequency (%)
1 1
 
1.7%
2 2
3.3%
3 4
6.7%
4 1
 
1.7%
5 2
3.3%
6 4
6.7%
7 2
3.3%
9 1
 
1.7%
12 1
 
1.7%
14 2
3.3%
ValueCountFrequency (%)
3588 1
1.7%
2171 1
1.7%
1927 1
1.7%
1319 1
1.7%
860 1
1.7%
811 1
1.7%
776 1
1.7%
775 1
1.7%
753 1
1.7%
686 1
1.7%

70세-79세
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3192.25
Minimum26
Maximum22169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:18.420427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile44.95
Q1141.5
median1654
Q34589.75
95-th percentile12090.55
Maximum22169
Range22143
Interquartile range (IQR)4448.25

Descriptive statistics

Standard deviation4432.2857
Coefficient of variation (CV)1.3884519
Kurtosis6.1167847
Mean3192.25
Median Absolute Deviation (MAD)1593
Skewness2.2807295
Sum191535
Variance19645156
MonotonicityNot monotonic
2023-12-13T07:52:18.576643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221 1
 
1.7%
1662 1
 
1.7%
536 1
 
1.7%
507 1
 
1.7%
321 1
 
1.7%
177 1
 
1.7%
144 1
 
1.7%
143 1
 
1.7%
137 1
 
1.7%
22169 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
26 1
1.7%
37 1
1.7%
44 1
1.7%
45 1
1.7%
47 1
1.7%
51 1
1.7%
53 1
1.7%
57 1
1.7%
65 1
1.7%
88 1
1.7%
ValueCountFrequency (%)
22169 1
1.7%
16074 1
1.7%
15350 1
1.7%
11919 1
1.7%
10089 1
1.7%
8365 1
1.7%
6889 1
1.7%
6248 1
1.7%
6199 1
1.7%
6195 1
1.7%

80세-89세
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean663.98333
Minimum1
Maximum3381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:18.735715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q124.75
median312.5
Q3985.25
95-th percentile2373.8
Maximum3381
Range3380
Interquartile range (IQR)960.5

Descriptive statistics

Standard deviation834.02877
Coefficient of variation (CV)1.2560989
Kurtosis1.3040762
Mean663.98333
Median Absolute Deviation (MAD)303.5
Skewness1.3906405
Sum39839
Variance695603.98
MonotonicityNot monotonic
2023-12-13T07:52:18.894287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 3
 
5.0%
8 2
 
3.3%
565 2
 
3.3%
1 2
 
3.3%
20 2
 
3.3%
1058 1
 
1.7%
44 1
 
1.7%
16 1
 
1.7%
3381 1
 
1.7%
2736 1
 
1.7%
Other values (44) 44
73.3%
ValueCountFrequency (%)
1 2
3.3%
7 3
5.0%
8 2
3.3%
10 1
 
1.7%
13 1
 
1.7%
14 1
 
1.7%
15 1
 
1.7%
16 1
 
1.7%
20 2
3.3%
21 1
 
1.7%
ValueCountFrequency (%)
3381 1
1.7%
2736 1
1.7%
2617 1
1.7%
2361 1
1.7%
2160 1
1.7%
1997 1
1.7%
1893 1
1.7%
1810 1
1.7%
1685 1
1.7%
1502 1
1.7%

90세이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.23333
Minimum0
Maximum1591
Zeros6
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:52:19.077212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.75
median59
Q3355
95-th percentile1106.15
Maximum1591
Range1591
Interquartile range (IQR)348.25

Descriptive statistics

Standard deviation404.89586
Coefficient of variation (CV)1.5094912
Kurtosis2.4064702
Mean268.23333
Median Absolute Deviation (MAD)59
Skewness1.7737627
Sum16094
Variance163940.66
MonotonicityNot monotonic
2023-12-13T07:52:19.235644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 6
 
10.0%
6 3
 
5.0%
4 2
 
3.3%
355 2
 
3.3%
1 2
 
3.3%
7 2
 
3.3%
55 1
 
1.7%
206 1
 
1.7%
19 1
 
1.7%
1474 1
 
1.7%
Other values (39) 39
65.0%
ValueCountFrequency (%)
0 6
10.0%
1 2
 
3.3%
2 1
 
1.7%
3 1
 
1.7%
4 2
 
3.3%
6 3
5.0%
7 2
 
3.3%
9 1
 
1.7%
10 1
 
1.7%
11 1
 
1.7%
ValueCountFrequency (%)
1591 1
1.7%
1474 1
1.7%
1356 1
1.7%
1093 1
1.7%
957 1
1.7%
937 1
1.7%
900 1
1.7%
812 1
1.7%
722 1
1.7%
697 1
1.7%

Interactions

2023-12-13T07:52:13.560823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:04.988046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.880311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.667135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.794456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.702697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.854611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.959441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.916869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.632498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.633315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.095165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.962709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.747679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.871907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.806785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.974021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.084932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.009528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.704575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.706407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.194627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.046276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.826185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.953465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.906734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.082782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.180990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.080194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.768517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.799388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.281542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.121317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.904199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.027651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.028668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.189175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.286189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.146696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.074597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.874078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.370494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.186716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.984358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.131715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.134952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.290101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.378206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.211187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.137432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.970423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.468579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.266658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.067481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.243948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.279474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.392050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.512556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.287099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.211450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:14.045405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.552188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.346619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.454092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.335342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.398568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.498614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.593318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.372796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.291635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:14.125638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.645831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.429961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.534693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.443181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.507529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.621304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.674256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.441366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.360821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:14.194233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.720008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.498848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.621994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.524012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.608045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.733061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.749274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.497911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.420664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:14.295971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:05.796204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:06.585161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:07.707152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:08.607299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:09.731529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:10.850060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:11.830234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:12.564943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:52:13.489189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:52:19.358904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위상병코드상병명칭실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상
구분1.0000.0000.0000.0000.7540.4350.0000.4480.4060.5640.7690.6540.563
순위0.0001.0000.4850.4850.2430.2800.5480.1380.0000.2130.2710.5130.305
상병코드0.0000.4851.0001.0000.8430.8420.9110.7880.0000.8050.7570.6970.000
상병명칭0.0000.4851.0001.0000.8430.8420.9110.7880.0000.8050.7570.6970.000
실인원0.7540.2430.8430.8431.0000.9880.0000.6510.8400.8860.8870.8350.678
연인원0.4350.2800.8420.8420.9881.0000.4910.6080.8350.8600.8810.8370.626
진료비(천원)0.0000.5480.9110.9110.0000.4911.0000.0000.2980.0000.1170.2720.000
59세이하0.4480.1380.7880.7880.6510.6080.0001.0000.7830.7380.4270.7370.407
60세-64세0.4060.0000.0000.0000.8400.8350.2980.7831.0000.9110.8490.9400.659
65세-69세0.5640.2130.8050.8050.8860.8600.0000.7380.9111.0000.8990.9000.686
70세-79세0.7690.2710.7570.7570.8870.8810.1170.4270.8490.8991.0000.8820.839
80세-89세0.6540.5130.6970.6970.8350.8370.2720.7370.9400.9000.8821.0000.794
90세이상0.5630.3050.0000.0000.6780.6260.0000.4070.6590.6860.8390.7941.000
2023-12-13T07:52:19.561255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상구분
순위1.000-0.477-0.704-0.782-0.444-0.519-0.471-0.453-0.544-0.4230.000
실인원-0.4771.0000.8040.3990.6680.7310.7660.8070.7060.5790.628
연인원-0.7040.8041.0000.6640.7210.7320.7370.7400.7630.6100.306
진료비(천원)-0.7820.3990.6641.0000.3940.3810.3430.3570.4610.3690.000
59세이하-0.4440.6680.7210.3941.0000.8390.8410.7530.7010.5160.198
60세-64세-0.5190.7310.7320.3810.8391.0000.9060.8260.7790.5920.329
65세-69세-0.4710.7660.7370.3430.8410.9061.0000.8560.8370.7130.435
70세-79세-0.4530.8070.7400.3570.7530.8260.8561.0000.8900.7310.446
80세-89세-0.5440.7060.7630.4610.7010.7790.8370.8901.0000.8860.469
90세이상-0.4230.5790.6100.3690.5160.5920.7130.7310.8861.0000.278
구분0.0000.6280.3060.0000.1980.3290.4350.4460.4690.2781.000

Missing values

2023-12-13T07:52:14.416711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:52:14.577664image/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

구분순위상병코드상병명칭실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상
0입원실인원1U07바이러스가 확인된 코로나바이러스 질환 2019 [바이러스가 확인된 코로나-19]994107262468469.692387346622123155
1입원실인원2I20협심증67054804432312.6251920394948216
2입원실인원3H25노년백내장481640840790.769012273973411
3입원실인원4M51기타 추간판장애25292362502313.7191697183316
4입원실인원5M48기타 척추병증23278152074862.6673471593920
5입원실인원6Z51기타 의학적 관리를 위하여 보건서비스와 접하고 있는 사람20951772526784.514171325884224
6입원실인원7E112형 당뇨병19885612300262.1571066151214
7입원실인원8R07목구멍 및 가슴의 통증136624353555.92997599133
8입원실인원9M17무릎관절증13446031721315.1542212102151
9입원실인원10I48심방세동 및 조동13322332180723.96523100149
구분순위상병코드상병명칭실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상
50외래11Z26기타 단일 감염성 질환에 대한 예방접종의 필요81518151364410.856276911038112962363143
51외래12H25노년백내장75367536665015.035371833915908803214
52외래13K05치은염 및 치주질환662266221279451.852555453567455042671
53외래14M17무릎관절증60426042562060.2812382316864087669131
54외래15K21위-식도역류병56975697476163.9843852115293929536107
55외래16F00알츠하이머병에서의 치매53075307384897.93525115207219971093
56외래17Z11감염성 및 기생충성 질환에 대한 특수선별검사50195019189250.771815357471203228163
57외래18C61전립선의 악성 신생물49664966940050.01820933610960265
58외래19K04치수 및 근단주위조직의 질환487548751089046.15341175343344449973
59외래20G62기타 다발신경병증48844884301315.946240547091397