Overview

Dataset statistics

Number of variables13
Number of observations60
Missing cells20
Missing cells (%)2.6%
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

Description인천보훈병원에서 개방하는 진료정보 데이터로 인천보훈병원 다빈도 질환 환자 연령별 현황이 포함된 공공데이터 입니다.
URLhttps://www.data.go.kr/data/15102225/fileData.do

Alerts

순위 is highly overall correlated with 진료인원(실인원) and 2 other fieldsHigh correlation
진료인원(실인원) is highly overall correlated with 순위 and 9 other fieldsHigh correlation
진료인원(연인원) is highly overall correlated with 순위 and 3 other fieldsHigh correlation
진료비(천원) is highly overall correlated with 순위 and 7 other fieldsHigh correlation
연령별 59이하 is highly overall correlated with 진료인원(실인원) and 5 other fieldsHigh correlation
연령별(60-64) is highly overall correlated with 진료인원(실인원) and 4 other fieldsHigh correlation
연령별(65-69) is highly overall correlated with 진료인원(실인원) and 6 other fieldsHigh correlation
연령별(70-79) is highly overall correlated with 진료인원(실인원) and 7 other fieldsHigh correlation
연령별(80-89) is highly overall correlated with 진료인원(실인원) and 7 other fieldsHigh correlation
연령별(90이상) is highly overall correlated with 진료인원(실인원) and 3 other fieldsHigh correlation
구분 is highly overall correlated with 진료인원(실인원) and 2 other fieldsHigh correlation
진료인원(연인원) has 20 (33.3%) missing valuesMissing
연령별 59이하 has 24 (40.0%) zerosZeros
연령별(60-64) has 23 (38.3%) zerosZeros
연령별(65-69) has 19 (31.7%) zerosZeros
연령별(80-89) has 6 (10.0%) zerosZeros
연령별(90이상) has 20 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-12 17:05:05.279587
Analysis finished2023-12-12 17:05:16.397242
Duration11.12 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
입원(실인원)
20 
입원(연인원)
20 
외래
20 

Length

Max length7
Median length7
Mean length5.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row입원(실인원)
2nd row입원(실인원)
3rd row입원(실인원)
4th row입원(실인원)
5th row입원(실인원)

Common Values

ValueCountFrequency (%)
입원(실인원) 20
33.3%
입원(연인원) 20
33.3%
외래 20
33.3%

Length

2023-12-13T02:05:16.479668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:16.987455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
입원(실인원 20
33.3%
입원(연인원 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
2023-12-13T02:05:17.082755image/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
2023-12-13T02:05:17.196825image/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
2023-12-13T02:05:17.398041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters300
Distinct characters27
Distinct categories3 ?
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 row N40
2nd row U07
3rd row M48
4th row E11
5th row N31
ValueCountFrequency (%)
n40 3
 
5.0%
e11 3
 
5.0%
m54 3
 
5.0%
m19 3
 
5.0%
m17 3
 
5.0%
u07 3
 
5.0%
g81 3
 
5.0%
i63 3
 
5.0%
m48 3
 
5.0%
c61 2
 
3.3%
Other values (21) 31
51.7%
2023-12-13T02:05:17.707363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
40.0%
1 26
 
8.7%
4 20
 
6.7%
M 16
 
5.3%
0 13
 
4.3%
5 13
 
4.3%
7 11
 
3.7%
6 8
 
2.7%
9 8
 
2.7%
3 8
 
2.7%
Other values (17) 57
19.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator 120
40.0%
Decimal Number 120
40.0%
Uppercase Letter 60
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 16
26.7%
C 6
 
10.0%
I 5
 
8.3%
N 5
 
8.3%
G 5
 
8.3%
E 5
 
8.3%
K 4
 
6.7%
U 3
 
5.0%
H 3
 
5.0%
J 2
 
3.3%
Other values (6) 6
 
10.0%
Decimal Number
ValueCountFrequency (%)
1 26
21.7%
4 20
16.7%
0 13
10.8%
5 13
10.8%
7 11
9.2%
6 8
 
6.7%
9 8
 
6.7%
3 8
 
6.7%
2 7
 
5.8%
8 6
 
5.0%
Space Separator
ValueCountFrequency (%)
120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 240
80.0%
Latin 60
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 16
26.7%
C 6
 
10.0%
I 5
 
8.3%
N 5
 
8.3%
G 5
 
8.3%
E 5
 
8.3%
K 4
 
6.7%
U 3
 
5.0%
H 3
 
5.0%
J 2
 
3.3%
Other values (6) 6
 
10.0%
Common
ValueCountFrequency (%)
120
50.0%
1 26
 
10.8%
4 20
 
8.3%
0 13
 
5.4%
5 13
 
5.4%
7 11
 
4.6%
6 8
 
3.3%
9 8
 
3.3%
3 8
 
3.3%
2 7
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
40.0%
1 26
 
8.7%
4 20
 
6.7%
M 16
 
5.3%
0 13
 
4.3%
5 13
 
4.3%
7 11
 
3.7%
6 8
 
2.7%
9 8
 
2.7%
3 8
 
2.7%
Other values (17) 57
19.0%
Distinct31
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T02:05:17.991992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length17
Mean length11.366667
Min length4

Characters and Unicode

Total characters682
Distinct characters123
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)18.3%

Sample

1st row 전립선증식증
2nd row U07의 응급사용
3rd row 기타 척추병증
4th row 인슐린-비의존 당뇨병
5th row 달리 분류되지 않은 방광의 신경근육기능장애
ValueCountFrequency (%)
10
 
6.8%
기타 9
 
6.1%
신생물 7
 
4.8%
악성 6
 
4.1%
당뇨병 5
 
3.4%
방광의 4
 
2.7%
전립선증식증 3
 
2.0%
윤충증 3
 
2.0%
뇌경색증 3
 
2.0%
응급사용 3
 
2.0%
Other values (54) 94
63.9%
2023-12-13T02:05:18.366060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
207
30.4%
23
 
3.4%
21
 
3.1%
20
 
2.9%
16
 
2.3%
14
 
2.1%
12
 
1.8%
11
 
1.6%
11
 
1.6%
9
 
1.3%
Other values (113) 338
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 454
66.6%
Space Separator 207
30.4%
Decimal Number 6
 
0.9%
Close Punctuation 3
 
0.4%
Uppercase Letter 3
 
0.4%
Dash Punctuation 3
 
0.4%
Other Punctuation 3
 
0.4%
Open Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
 
5.1%
21
 
4.6%
20
 
4.4%
16
 
3.5%
14
 
3.1%
12
 
2.6%
11
 
2.4%
11
 
2.4%
9
 
2.0%
9
 
2.0%
Other values (105) 308
67.8%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
7 3
50.0%
Space Separator
ValueCountFrequency (%)
207
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 454
66.6%
Common 225
33.0%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
 
5.1%
21
 
4.6%
20
 
4.4%
16
 
3.5%
14
 
3.1%
12
 
2.6%
11
 
2.4%
11
 
2.4%
9
 
2.0%
9
 
2.0%
Other values (105) 308
67.8%
Common
ValueCountFrequency (%)
207
92.0%
) 3
 
1.3%
- 3
 
1.3%
, 3
 
1.3%
0 3
 
1.3%
7 3
 
1.3%
( 3
 
1.3%
Latin
ValueCountFrequency (%)
U 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 454
66.6%
ASCII 228
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
207
90.8%
) 3
 
1.3%
U 3
 
1.3%
- 3
 
1.3%
, 3
 
1.3%
0 3
 
1.3%
7 3
 
1.3%
( 3
 
1.3%
Hangul
ValueCountFrequency (%)
23
 
5.1%
21
 
4.6%
20
 
4.4%
16
 
3.5%
14
 
3.1%
12
 
2.6%
11
 
2.4%
11
 
2.4%
9
 
2.0%
9
 
2.0%
Other values (105) 308
67.8%

진료인원(실인원)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1724.3333
Minimum5
Maximum18383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:18.493343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7.95
Q19
median43
Q32474.25
95-th percentile7676.95
Maximum18383
Range18378
Interquartile range (IQR)2465.25

Descriptive statistics

Standard deviation3349.4074
Coefficient of variation (CV)1.9424361
Kurtosis10.260194
Mean1724.3333
Median Absolute Deviation (MAD)35
Skewness2.8807284
Sum103460
Variance11218530
MonotonicityNot monotonic
2023-12-13T02:05:18.613255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
8 9
 
15.0%
10 8
 
13.3%
9 4
 
6.7%
71 2
 
3.3%
62 2
 
3.3%
56 2
 
3.3%
45 2
 
3.3%
41 2
 
3.3%
17 2
 
3.3%
11 2
 
3.3%
Other values (23) 25
41.7%
ValueCountFrequency (%)
5 2
 
3.3%
7 1
 
1.7%
8 9
15.0%
9 4
6.7%
10 8
13.3%
11 2
 
3.3%
17 2
 
3.3%
41 2
 
3.3%
45 2
 
3.3%
56 2
 
3.3%
ValueCountFrequency (%)
18383 1
1.7%
10702 1
1.7%
9310 1
1.7%
7591 1
1.7%
6625 1
1.7%
6359 1
1.7%
6025 1
1.7%
5575 1
1.7%
4225 1
1.7%
3737 1
1.7%

진료인원(연인원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)60.0%
Missing20
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean432.175
Minimum8
Maximum1584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:18.731427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile77.25
Q1185
median236
Q3557
95-th percentile1260.05
Maximum1584
Range1576
Interquartile range (IQR)372

Descriptive statistics

Standard deviation402.51045
Coefficient of variation (CV)0.93135986
Kurtosis2.0296147
Mean432.175
Median Absolute Deviation (MAD)59
Skewness1.6442337
Sum17287
Variance162014.66
MonotonicityNot monotonic
2023-12-13T02:05:18.870673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
237 2
 
3.3%
191 2
 
3.3%
197 2
 
3.3%
234 2
 
3.3%
466 2
 
3.3%
185 2
 
3.3%
179 2
 
3.3%
1584 2
 
3.3%
235 2
 
3.3%
267 2
 
3.3%
Other values (14) 20
33.3%
(Missing) 20
33.3%
ValueCountFrequency (%)
8 1
1.7%
63 1
1.7%
78 1
1.7%
143 1
1.7%
158 1
1.7%
175 1
1.7%
179 2
3.3%
184 1
1.7%
185 2
3.3%
191 2
3.3%
ValueCountFrequency (%)
1584 2
3.3%
1243 2
3.3%
864 2
3.3%
813 2
3.3%
626 2
3.3%
534 2
3.3%
466 2
3.3%
280 2
3.3%
267 2
3.3%
237 2
3.3%

진료비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200330.22
Minimum8859
Maximum1096520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:19.015582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8859
5-th percentile32371.4
Q152983.5
median123784.5
Q3267384
95-th percentile683489
Maximum1096520
Range1087661
Interquartile range (IQR)214400.5

Descriptive statistics

Standard deviation229355.85
Coefficient of variation (CV)1.144889
Kurtosis5.4742906
Mean200330.22
Median Absolute Deviation (MAD)77342.5
Skewness2.281896
Sum12019813
Variance5.2604108 × 1010
MonotonicityNot monotonic
2023-12-13T02:05:19.140560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
382206 2
 
3.3%
92746 2
 
3.3%
137462 2
 
3.3%
56551 2
 
3.3%
51146 2
 
3.3%
75110 2
 
3.3%
37132 2
 
3.3%
47763 2
 
3.3%
45630 2
 
3.3%
93727 2
 
3.3%
Other values (34) 40
66.7%
ValueCountFrequency (%)
8859 1
1.7%
15599 1
1.7%
22195 1
1.7%
32907 1
1.7%
37132 2
3.3%
37855 1
1.7%
38509 1
1.7%
42537 1
1.7%
45630 2
3.3%
47763 2
3.3%
ValueCountFrequency (%)
1096520 1
1.7%
984046 1
1.7%
805298 1
1.7%
677078 1
1.7%
646091 1
1.7%
482598 1
1.7%
409785 1
1.7%
382206 2
3.3%
292733 1
1.7%
290804 1
1.7%

연령별 59이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.73333
Minimum0
Maximum2185
Zeros24
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:19.258607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q3148
95-th percentile573.6
Maximum2185
Range2185
Interquartile range (IQR)148

Descriptive statistics

Standard deviation345.60264
Coefficient of variation (CV)2.404471
Kurtosis21.562928
Mean143.73333
Median Absolute Deviation (MAD)1.5
Skewness4.2437741
Sum8624
Variance119441.18
MonotonicityNot monotonic
2023-12-13T02:05:19.389593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 24
40.0%
1 6
 
10.0%
4 2
 
3.3%
163 2
 
3.3%
1187 1
 
1.7%
18 1
 
1.7%
147 1
 
1.7%
116 1
 
1.7%
190 1
 
1.7%
64 1
 
1.7%
Other values (20) 20
33.3%
ValueCountFrequency (%)
0 24
40.0%
1 6
 
10.0%
2 1
 
1.7%
4 2
 
3.3%
7 1
 
1.7%
11 1
 
1.7%
14 1
 
1.7%
18 1
 
1.7%
25 1
 
1.7%
32 1
 
1.7%
ValueCountFrequency (%)
2185 1
1.7%
1187 1
1.7%
775 1
1.7%
563 1
1.7%
517 1
1.7%
471 1
1.7%
434 1
1.7%
419 1
1.7%
265 1
1.7%
228 1
1.7%

연령별(60-64)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.28333
Minimum0
Maximum1393
Zeros23
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:19.504238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q390.25
95-th percentile565.25
Maximum1393
Range1393
Interquartile range (IQR)90.25

Descriptive statistics

Standard deviation229.81423
Coefficient of variation (CV)2.2037484
Kurtosis16.842854
Mean104.28333
Median Absolute Deviation (MAD)1.5
Skewness3.6875199
Sum6257
Variance52814.579
MonotonicityNot monotonic
2023-12-13T02:05:19.619076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 23
38.3%
1 7
 
11.7%
2 2
 
3.3%
71 2
 
3.3%
85 1
 
1.7%
182 1
 
1.7%
223 1
 
1.7%
236 1
 
1.7%
248 1
 
1.7%
116 1
 
1.7%
Other values (20) 20
33.3%
ValueCountFrequency (%)
0 23
38.3%
1 7
 
11.7%
2 2
 
3.3%
3 1
 
1.7%
5 1
 
1.7%
6 1
 
1.7%
12 1
 
1.7%
15 1
 
1.7%
25 1
 
1.7%
36 1
 
1.7%
ValueCountFrequency (%)
1393 1
1.7%
621 1
1.7%
608 1
1.7%
563 1
1.7%
504 1
1.7%
338 1
1.7%
300 1
1.7%
248 1
1.7%
236 1
1.7%
223 1
1.7%

연령별(65-69)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean144.06667
Minimum0
Maximum1109
Zeros19
Zeros (%)31.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:19.764048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.5
Q3166.25
95-th percentile794.75
Maximum1109
Range1109
Interquartile range (IQR)166.25

Descriptive statistics

Standard deviation262.76779
Coefficient of variation (CV)1.8239319
Kurtosis4.3129958
Mean144.06667
Median Absolute Deviation (MAD)6.5
Skewness2.2137399
Sum8644
Variance69046.911
MonotonicityNot monotonic
2023-12-13T02:05:19.927586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 19
31.7%
1 6
 
10.0%
2 3
 
5.0%
343 2
 
3.3%
13 2
 
3.3%
138 1
 
1.7%
89 1
 
1.7%
219 1
 
1.7%
161 1
 
1.7%
650 1
 
1.7%
Other values (23) 23
38.3%
ValueCountFrequency (%)
0 19
31.7%
1 6
 
10.0%
2 3
 
5.0%
4 1
 
1.7%
6 1
 
1.7%
7 1
 
1.7%
9 1
 
1.7%
13 2
 
3.3%
18 1
 
1.7%
31 1
 
1.7%
ValueCountFrequency (%)
1109 1
1.7%
953 1
1.7%
866 1
1.7%
791 1
1.7%
650 1
1.7%
618 1
1.7%
516 1
1.7%
364 1
1.7%
343 2
3.3%
327 1
1.7%

연령별(70-79)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1164.4667
Minimum1
Maximum13487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:20.188771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q120
median139
Q31659.5
95-th percentile5156.5
Maximum13487
Range13486
Interquartile range (IQR)1639.5

Descriptive statistics

Standard deviation2220.1696
Coefficient of variation (CV)1.9065978
Kurtosis16.0133
Mean1164.4667
Median Absolute Deviation (MAD)135.5
Skewness3.5261727
Sum69868
Variance4929153.2
MonotonicityNot monotonic
2023-12-13T02:05:20.380294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 4
 
6.7%
6 4
 
6.7%
5 2
 
3.3%
43 1
 
1.7%
3005 1
 
1.7%
24 1
 
1.7%
58 1
 
1.7%
13487 1
 
1.7%
6583 1
 
1.7%
5736 1
 
1.7%
Other values (43) 43
71.7%
ValueCountFrequency (%)
1 1
 
1.7%
2 1
 
1.7%
3 4
6.7%
4 1
 
1.7%
5 2
3.3%
6 4
6.7%
8 1
 
1.7%
14 1
 
1.7%
22 1
 
1.7%
23 1
 
1.7%
ValueCountFrequency (%)
13487 1
1.7%
6583 1
1.7%
5736 1
1.7%
5126 1
1.7%
4198 1
1.7%
3980 1
1.7%
3005 1
1.7%
2540 1
1.7%
2425 1
1.7%
2030 1
1.7%

연령별(80-89)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean229.13333
Minimum0
Maximum2374
Zeros6
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:20.554031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median63.5
Q3268.5
95-th percentile842.55
Maximum2374
Range2374
Interquartile range (IQR)263

Descriptive statistics

Standard deviation404.57948
Coefficient of variation (CV)1.7656946
Kurtosis14.110756
Mean229.13333
Median Absolute Deviation (MAD)63.5
Skewness3.3649226
Sum13748
Variance163684.56
MonotonicityNot monotonic
2023-12-13T02:05:20.733587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 6
 
10.0%
2 6
 
10.0%
11 3
 
5.0%
17 2
 
3.3%
7 2
 
3.3%
19 1
 
1.7%
216 1
 
1.7%
910 1
 
1.7%
839 1
 
1.7%
802 1
 
1.7%
Other values (36) 36
60.0%
ValueCountFrequency (%)
0 6
10.0%
1 1
 
1.7%
2 6
10.0%
3 1
 
1.7%
4 1
 
1.7%
6 1
 
1.7%
7 2
 
3.3%
8 1
 
1.7%
9 1
 
1.7%
11 3
5.0%
ValueCountFrequency (%)
2374 1
1.7%
1530 1
1.7%
910 1
1.7%
839 1
1.7%
802 1
1.7%
721 1
1.7%
609 1
1.7%
551 1
1.7%
501 1
1.7%
423 1
1.7%

연령별(90이상)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.5
Minimum0
Maximum962
Zeros20
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T02:05:20.907598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.5
Q382.75
95-th percentile250.4
Maximum962
Range962
Interquartile range (IQR)82.75

Descriptive statistics

Standard deviation155.65558
Coefficient of variation (CV)1.9828737
Kurtosis18.860763
Mean78.5
Median Absolute Deviation (MAD)11.5
Skewness3.932008
Sum4710
Variance24228.661
MonotonicityNot monotonic
2023-12-13T02:05:21.093601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 20
33.3%
2 3
 
5.0%
7 2
 
3.3%
6 2
 
3.3%
24 1
 
1.7%
60 1
 
1.7%
81 1
 
1.7%
22 1
 
1.7%
64 1
 
1.7%
65 1
 
1.7%
Other values (27) 27
45.0%
ValueCountFrequency (%)
0 20
33.3%
1 1
 
1.7%
2 3
 
5.0%
4 1
 
1.7%
6 2
 
3.3%
7 2
 
3.3%
11 1
 
1.7%
12 1
 
1.7%
20 1
 
1.7%
22 1
 
1.7%
ValueCountFrequency (%)
962 1
1.7%
598 1
1.7%
315 1
1.7%
247 1
1.7%
237 1
1.7%
235 1
1.7%
198 1
1.7%
187 1
1.7%
166 1
1.7%
153 1
1.7%

Interactions

2023-12-13T02:05:15.052281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:05.660970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.411980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.148399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.958251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.906064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.212789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.582306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.620725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.095249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.153652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:05.747267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.488935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.223930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.069809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.015882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.383651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.716249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.732390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.195262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.239797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:05.828653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.558012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.295410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.184191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.126697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.511239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.848045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.995288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.295052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.351591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:05.912963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.631532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.376691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.292662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.571521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.653311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.003601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.272119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.397971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.460891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.001731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.701332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.461249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.388647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.658889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.791318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.102863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.409286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.495841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.554122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.071856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.767256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.545018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.475869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.745512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.929923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.186611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.539464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.598294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.664952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.143467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.852681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.622389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.560979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.835238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.046264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.273775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.667811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.710790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.770491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.207741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.934227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.702069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.641170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:09.926342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.163972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.369268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.799986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.802083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.873257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.282040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.017102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.785485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.736873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.025871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.328177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.468408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:13.911164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.905247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:15.977850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:06.344431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.082657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:07.872698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:08.818736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:10.114486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:11.437357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:12.541580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.007323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:14.974591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:05:21.216127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위상병명 코드상병명칭진료인원(실인원)진료인원(연인원)진료비(천원)연령별 59이하연령별(60-64)연령별(65-69)연령별(70-79)연령별(80-89)연령별(90이상)
구분1.0000.0000.0000.0000.7160.0000.1520.6700.7480.8870.8950.6960.528
순위0.0001.0000.8740.8740.4220.8300.5370.1500.0000.4760.0000.0430.126
상병명 코드0.0000.8741.0001.0000.6831.0000.7830.5890.0000.4330.0000.0000.000
상병명칭0.0000.8741.0001.0000.6831.0000.7830.5890.0000.4330.0000.0000.000
진료인원(실인원)0.7160.4220.6830.6831.000NaN0.8700.8020.8830.9370.9270.8270.821
진료인원(연인원)0.0000.8301.0001.000NaN1.0000.937NaNNaNNaNNaN0.6540.282
진료비(천원)0.1520.5370.7830.7830.8700.9371.0000.7210.8840.8800.9030.8940.874
연령별 59이하0.6700.1500.5890.5890.802NaN0.7211.0000.9760.9800.9100.8880.908
연령별(60-64)0.7480.0000.0000.0000.883NaN0.8840.9761.0000.9630.9810.9530.932
연령별(65-69)0.8870.4760.4330.4330.937NaN0.8800.9800.9631.0000.9340.8600.881
연령별(70-79)0.8950.0000.0000.0000.927NaN0.9030.9100.9810.9341.0000.9530.956
연령별(80-89)0.6960.0430.0000.0000.8270.6540.8940.8880.9530.8600.9531.0000.967
연령별(90이상)0.5280.1260.0000.0000.8210.2820.8740.9080.9320.8810.9560.9671.000
2023-12-13T02:05:21.392996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위진료인원(실인원)진료인원(연인원)진료비(천원)연령별 59이하연령별(60-64)연령별(65-69)연령별(70-79)연령별(80-89)연령별(90이상)구분
순위1.000-0.511-0.949-0.726-0.149-0.183-0.233-0.284-0.445-0.2400.000
진료인원(실인원)-0.5111.0000.8480.8350.7500.8070.7600.8050.8320.5660.612
진료인원(연인원)-0.9490.8481.0000.9670.0720.0480.3110.4580.6080.3150.000
진료비(천원)-0.7260.8350.9671.0000.5010.4970.6250.6900.7490.5660.097
연령별 59이하-0.1490.7500.0720.5011.0000.7860.8190.7250.6930.4710.345
연령별(60-64)-0.1830.8070.0480.4970.7861.0000.7050.7290.7080.4340.414
연령별(65-69)-0.2330.7600.3110.6250.8190.7051.0000.8100.6960.4780.583
연령별(70-79)-0.2840.8050.4580.6900.7250.7290.8101.0000.8780.7060.597
연령별(80-89)-0.4450.8320.6080.7490.6930.7080.6960.8781.0000.6760.367
연령별(90이상)-0.2400.5660.3150.5660.4710.4340.4780.7060.6761.0000.246
구분0.0000.6120.0000.0970.3450.4140.5830.5970.3670.2461.000

Missing values

2023-12-13T02:05:16.133991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:05:16.326622image/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

구분순위상병명 코드상병명칭진료인원(실인원)진료인원(연인원)진료비(천원)연령별 59이하연령별(60-64)연령별(65-69)연령별(70-79)연령별(80-89)연령별(90이상)
0입원(실인원)1N40전립선증식증71158438220601243196
1입원(실인원)2U07U07의 응급사용67466137462256131472
2입원(실인원)3M48기타 척추병증6281326738401048112
3입원(실인원)4E11인슐린-비의존 당뇨병5686420112741436110
4입원(실인원)5N31달리 분류되지 않은 방광의 신경근육기능장애45124329021610122174
5입원(실인원)6C67방광의 악성 신생물4162618527510023116
6입원(실인원)7C61전립선의 악성 신생물17534143682000287
7입원(실인원)8M54배통1126757679020360
8입원(실인원)9M19연기, 불 및 화염에 노출1023593727002800
9입원(실인원)10M17윤충증1023792746002620
구분순위상병명 코드상병명칭진료인원(실인원)진료인원(연인원)진료비(천원)연령별 59이하연령별(60-64)연령별(65-69)연령별(70-79)연령별(80-89)연령별(90이상)
50외래11M51기타 추간판장애3326<NA>110107419248364203021649
51외래12I63뇌경색증2719<NA>290804100851981670551115
52외래13H04눈물기관의 장애2706<NA>1731301561451821769318136
53외래14K04치수 및 치근단주위조직의 질환2605<NA>48259816371132198320155
54외래15M19연기, 불 및 화염에 노출2526<NA>29273364116205184023665
55외래16B35피부사상균증2457<NA>80236163138161175018164
56외래17G81편마비2194<NA>1432911902219165610522
57외래18L24자극성 접촉피부염2119<NA>535961168989143331181
58외래19K29위염 및 십이지장염1916<NA>16906014794138128519260
59외래20H25노인성 백내장1415<NA>961761844114106614924