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

Description대전보훈병원에서 개방하는 진료정보 데이터로 대전보훈병원 다빈도 질환 환자 연령별 현황이 포함된 공공데이터입니다.
URLhttps://www.data.go.kr/data/15102127/fileData.do

Alerts

순위 is highly overall correlated with 연인원 and 1 other fieldsHigh correlation
실인원 is highly overall correlated with 연인원 and 7 other fieldsHigh correlation
연인원 is highly overall correlated with 순위 and 9 other fieldsHigh correlation
진료비(천원) is highly overall correlated with 순위 and 1 other fieldsHigh correlation
59이하 is highly overall correlated with 실인원 and 5 other fieldsHigh correlation
60-64 is highly overall correlated with 실인원 and 7 other fieldsHigh correlation
65-69 is highly overall correlated with 실인원 and 7 other fieldsHigh correlation
70-79 is highly overall correlated with 실인원 and 7 other fieldsHigh correlation
80-89 is highly overall correlated with 실인원 and 7 other fieldsHigh correlation
90이상 is highly overall correlated with 실인원 and 5 other fieldsHigh correlation
구분 is highly overall correlated with 실인원 and 5 other fieldsHigh correlation
59이하 has 6 (10.0%) zerosZeros
60-64 has 3 (5.0%) zerosZeros
65-69 has 1 (1.7%) zerosZeros
80-89 has 1 (1.7%) zerosZeros
90이상 has 9 (15.0%) zerosZeros

Reproduction

Analysis started2023-12-12 01:54:45.098278
Analysis finished2023-12-12 01:54:55.408790
Duration10.31 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

2023-12-12T10:54:55.514799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:54:55.654346image/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
2023-12-12T10:54:55.793421image/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
2023-12-12T10:54:55.952980image/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%

상병코드
Text

Distinct36
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T10:54:56.245188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9666667
Min length5

Characters and Unicode

Total characters418
Distinct characters26
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

Unique19 ?
Unique (%)31.7%

Sample

1st row M48
2nd row U07
3rd row K63
4th row H25
5th row E11
ValueCountFrequency (%)
m48 3
 
5.0%
m17 3
 
5.0%
u07 3
 
5.0%
m75 3
 
5.0%
n18 3
 
5.0%
k63 3
 
5.0%
e11 3
 
5.0%
c16 2
 
3.3%
k29 2
 
3.3%
i69 2
 
3.3%
Other values (26) 33
55.0%
2023-12-12T10:54:56.754490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
238
56.9%
1 19
 
4.5%
M 16
 
3.8%
6 14
 
3.3%
0 13
 
3.1%
2 13
 
3.1%
8 12
 
2.9%
3 11
 
2.6%
7 11
 
2.6%
4 10
 
2.4%
Other values (16) 61
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 238
56.9%
Decimal Number 120
28.7%
Uppercase Letter 60
 
14.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 16
26.7%
I 8
13.3%
K 7
11.7%
N 5
 
8.3%
C 4
 
6.7%
E 4
 
6.7%
S 4
 
6.7%
U 3
 
5.0%
R 2
 
3.3%
Z 2
 
3.3%
Other values (5) 5
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 19
15.8%
6 14
11.7%
0 13
10.8%
2 13
10.8%
8 12
10.0%
3 11
9.2%
7 11
9.2%
4 10
8.3%
5 9
7.5%
9 8
6.7%
Space Separator
ValueCountFrequency (%)
238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 358
85.6%
Latin 60
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 16
26.7%
I 8
13.3%
K 7
11.7%
N 5
 
8.3%
C 4
 
6.7%
E 4
 
6.7%
S 4
 
6.7%
U 3
 
5.0%
R 2
 
3.3%
Z 2
 
3.3%
Other values (5) 5
 
8.3%
Common
ValueCountFrequency (%)
238
66.5%
1 19
 
5.3%
6 14
 
3.9%
0 13
 
3.6%
2 13
 
3.6%
8 12
 
3.4%
3 11
 
3.1%
7 11
 
3.1%
4 10
 
2.8%
5 9
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
238
56.9%
1 19
 
4.5%
M 16
 
3.8%
6 14
 
3.3%
0 13
 
3.1%
2 13
 
3.1%
8 12
 
2.9%
3 11
 
2.6%
7 11
 
2.6%
4 10
 
2.4%
Other values (16) 61
 
14.6%

상병명칭
Text

Distinct49
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-12T10:54:57.068709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length13.666667
Min length7

Characters and Unicode

Total characters820
Distinct characters130
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

Unique38 ?
Unique (%)63.3%

Sample

1st row 기타 척추병증
2nd row 병인이 불확실한 신종질환의 임시적 지정이나 응급사용
3rd row 장의 기타 질환
4th row 노년백내장
5th row 2형 당뇨병
ValueCountFrequency (%)
기타 11
 
7.4%
및 9
 
6.1%
신생물 4
 
2.7%
악성 4
 
2.7%
장의 3
 
2.0%
불확실한 3
 
2.0%
척추병증 3
 
2.0%
어깨병변 3
 
2.0%
응급사용 3
 
2.0%
지정이나 3
 
2.0%
Other values (64) 102
68.9%
2023-12-12T10:54:57.594838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
365
44.5%
증 22
 
2.7%
의 21
 
2.6%
병 17
 
2.1%
장 14
 
1.7%
기 12
 
1.5%
지 12
 
1.5%
신 11
 
1.3%
성 11
 
1.3%
타 11
 
1.3%
Other values (120) 324
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 450
54.9%
Space Separator 365
44.5%
Decimal Number 3
 
0.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
증 22
 
4.9%
의 21
 
4.7%
병 17
 
3.8%
장 14
 
3.1%
기 12
 
2.7%
지 12
 
2.7%
신 11
 
2.4%
성 11
 
2.4%
타 11
 
2.4%
질 10
 
2.2%
Other values (116) 309
68.7%
Space Separator
ValueCountFrequency (%)
365
100.0%
Decimal Number
ValueCountFrequency (%)
2 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 450
54.9%
Common 370
45.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
증 22
 
4.9%
의 21
 
4.7%
병 17
 
3.8%
장 14
 
3.1%
기 12
 
2.7%
지 12
 
2.7%
신 11
 
2.4%
성 11
 
2.4%
타 11
 
2.4%
질 10
 
2.2%
Other values (116) 309
68.7%
Common
ValueCountFrequency (%)
365
98.6%
2 3
 
0.8%
( 1
 
0.3%
) 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 450
54.9%
ASCII 370
45.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
365
98.6%
2 3
 
0.8%
( 1
 
0.3%
) 1
 
0.3%
Hangul
ValueCountFrequency (%)
증 22
 
4.9%
의 21
 
4.7%
병 17
 
3.8%
장 14
 
3.1%
기 12
 
2.7%
지 12
 
2.7%
신 11
 
2.4%
성 11
 
2.4%
타 11
 
2.4%
질 10
 
2.2%
Other values (116) 309
68.7%

실인원
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3503.5333
Minimum20
Maximum26265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:57.789553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile39.95
Q172.25
median186
Q35599.25
95-th percentile14610.45
Maximum26265
Range26245
Interquartile range (IQR)5527

Descriptive statistics

Standard deviation5717.6446
Coefficient of variation (CV)1.6319652
Kurtosis3.8781044
Mean3503.5333
Median Absolute Deviation (MAD)146
Skewness1.9344876
Sum210212
Variance32691460
MonotonicityNot monotonic
2023-12-12T10:54:57.956987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
674 2
 
3.3%
128 2
 
3.3%
446 2
 
3.3%
40 2
 
3.3%
56 2
 
3.3%
63 2
 
3.3%
78 2
 
3.3%
126 2
 
3.3%
70 2
 
3.3%
137 2
 
3.3%
Other values (36) 40
66.7%
ValueCountFrequency (%)
20 1
1.7%
36 1
1.7%
39 1
1.7%
40 2
3.3%
48 1
1.7%
53 1
1.7%
54 1
1.7%
56 2
3.3%
63 2
3.3%
64 1
1.7%
ValueCountFrequency (%)
26265 1
1.7%
19788 1
1.7%
14961 1
1.7%
14592 1
1.7%
13738 1
1.7%
11803 1
1.7%
10820 1
1.7%
9998 1
1.7%
9158 1
1.7%
8828 1
1.7%

연인원
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4724.6833
Minimum89
Maximum26265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:58.121925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile240.45
Q11375.5
median2592
Q35742.5
95-th percentile14610.45
Maximum26265
Range26176
Interquartile range (IQR)4367

Descriptive statistics

Standard deviation5161.494
Coefficient of variation (CV)1.0924529
Kurtosis4.9120668
Mean4724.6833
Median Absolute Deviation (MAD)1831
Skewness2.0230082
Sum283481
Variance26641020
MonotonicityNot monotonic
2023-12-12T10:54:58.283938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
5709 2
 
3.3%
1918 2
 
3.3%
3585 2
 
3.3%
1482 2
 
3.3%
1091 2
 
3.3%
1979 2
 
3.3%
2592 2
 
3.3%
1150 2
 
3.3%
1423 2
 
3.3%
3520 2
 
3.3%
Other values (38) 40
66.7%
ValueCountFrequency (%)
89 1
1.7%
117 1
1.7%
135 1
1.7%
246 1
1.7%
250 1
1.7%
694 1
1.7%
743 1
1.7%
827 1
1.7%
981 1
1.7%
1091 2
3.3%
ValueCountFrequency (%)
26265 1
1.7%
19788 1
1.7%
14961 1
1.7%
14592 1
1.7%
13738 1
1.7%
11803 1
1.7%
10820 1
1.7%
9998 1
1.7%
9158 1
1.7%
8828 1
1.7%

진료비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean947106.67
Minimum54664
Maximum4550024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:58.790259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54664
5-th percentile237094.8
Q1410333.75
median720677
Q31266638.5
95-th percentile2589662.7
Maximum4550024
Range4495360
Interquartile range (IQR)856304.75

Descriptive statistics

Standard deviation822596.26
Coefficient of variation (CV)0.86853603
Kurtosis5.7413149
Mean947106.67
Median Absolute Deviation (MAD)363393.5
Skewness2.1261091
Sum56826400
Variance6.7666461 × 1011
MonotonicityNot monotonic
2023-12-12T10:54:58.970782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2880149 2
 
3.3%
1033882 2
 
3.3%
1266568 2
 
3.3%
354120 2
 
3.3%
415246 2
 
3.3%
771613 2
 
3.3%
1289799 2
 
3.3%
632746 2
 
3.3%
777204 2
 
3.3%
1377971 2
 
3.3%
Other values (38) 40
66.7%
ValueCountFrequency (%)
54664 1
1.7%
131854 1
1.7%
155315 1
1.7%
241399 1
1.7%
259394 1
1.7%
272943 1
1.7%
302310 1
1.7%
304565 1
1.7%
308184 1
1.7%
330053 1
1.7%
ValueCountFrequency (%)
4550024 1
1.7%
2880149 2
3.3%
2574374 1
1.7%
2366164 2
3.3%
1930406 1
1.7%
1811203 1
1.7%
1491421 1
1.7%
1377971 2
3.3%
1312582 1
1.7%
1289799 2
3.3%

59이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.96667
Minimum0
Maximum5317
Zeros6
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:59.139082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median32
Q3320
95-th percentile1392
Maximum5317
Range5317
Interquartile range (IQR)316

Descriptive statistics

Standard deviation915.33147
Coefficient of variation (CV)2.3352278
Kurtosis18.771086
Mean391.96667
Median Absolute Deviation (MAD)32
Skewness4.1203494
Sum23518
Variance837831.69
MonotonicityNot monotonic
2023-12-12T10:54:59.353766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 6
 
10.0%
5 5
 
8.3%
2 5
 
8.3%
1 3
 
5.0%
7 2
 
3.3%
6 2
 
3.3%
4 2
 
3.3%
247 1
 
1.7%
1127 1
 
1.7%
960 1
 
1.7%
Other values (32) 32
53.3%
ValueCountFrequency (%)
0 6
10.0%
1 3
5.0%
2 5
8.3%
4 2
 
3.3%
5 5
8.3%
6 2
 
3.3%
7 2
 
3.3%
10 1
 
1.7%
13 1
 
1.7%
14 1
 
1.7%
ValueCountFrequency (%)
5317 1
1.7%
4273 1
1.7%
1677 1
1.7%
1377 1
1.7%
1127 1
1.7%
1030 1
1.7%
960 1
1.7%
948 1
1.7%
883 1
1.7%
794 1
1.7%

60-64
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.66667
Minimum0
Maximum1103
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:59.541933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q13
median15.5
Q3248.25
95-th percentile731.75
Maximum1103
Range1103
Interquartile range (IQR)245.25

Descriptive statistics

Standard deviation278.51767
Coefficient of variation (CV)1.5765151
Kurtosis2.2837107
Mean176.66667
Median Absolute Deviation (MAD)14.5
Skewness1.735988
Sum10600
Variance77572.09
MonotonicityNot monotonic
2023-12-12T10:54:59.739560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
3 5
 
8.3%
2 5
 
8.3%
13 4
 
6.7%
11 3
 
5.0%
4 3
 
5.0%
1 3
 
5.0%
0 3
 
5.0%
9 1
 
1.7%
803 1
 
1.7%
1103 1
 
1.7%
Other values (31) 31
51.7%
ValueCountFrequency (%)
0 3
5.0%
1 3
5.0%
2 5
8.3%
3 5
8.3%
4 3
5.0%
5 1
 
1.7%
7 1
 
1.7%
9 1
 
1.7%
11 3
5.0%
13 4
6.7%
ValueCountFrequency (%)
1103 1
1.7%
1028 1
1.7%
803 1
1.7%
728 1
1.7%
716 1
1.7%
600 1
1.7%
581 1
1.7%
576 1
1.7%
558 1
1.7%
454 1
1.7%

65-69
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean256.83333
Minimum0
Maximum1492
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:54:59.891000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.95
Q17
median39.5
Q3366
95-th percentile1143.35
Maximum1492
Range1492
Interquartile range (IQR)359

Descriptive statistics

Standard deviation383.27364
Coefficient of variation (CV)1.4923049
Kurtosis1.9772811
Mean256.83333
Median Absolute Deviation (MAD)39
Skewness1.693409
Sum15410
Variance146898.68
MonotonicityNot monotonic
2023-12-12T10:55:00.067427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2 4
 
6.7%
7 3
 
5.0%
3 3
 
5.0%
1 2
 
3.3%
6 2
 
3.3%
157 2
 
3.3%
19 2
 
3.3%
16 2
 
3.3%
1226 1
 
1.7%
779 1
 
1.7%
Other values (38) 38
63.3%
ValueCountFrequency (%)
0 1
 
1.7%
1 2
3.3%
2 4
6.7%
3 3
5.0%
4 1
 
1.7%
5 1
 
1.7%
6 2
3.3%
7 3
5.0%
10 1
 
1.7%
11 1
 
1.7%
ValueCountFrequency (%)
1492 1
1.7%
1310 1
1.7%
1226 1
1.7%
1139 1
1.7%
984 1
1.7%
863 1
1.7%
830 1
1.7%
805 1
1.7%
779 1
1.7%
657 1
1.7%

70-79
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2689.6833
Minimum36
Maximum19647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:55:00.255622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile41.75
Q1132
median1181.5
Q33707.25
95-th percentile9367.45
Maximum19647
Range19611
Interquartile range (IQR)3575.25

Descriptive statistics

Standard deviation3796.5111
Coefficient of variation (CV)1.4115086
Kurtosis6.820701
Mean2689.6833
Median Absolute Deviation (MAD)1111.5
Skewness2.3811927
Sum161381
Variance14413496
MonotonicityNot monotonic
2023-12-12T10:55:00.492093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1183 2
 
3.3%
36 2
 
3.3%
543 1
 
1.7%
1180 1
 
1.7%
1023 1
 
1.7%
534 1
 
1.7%
407 1
 
1.7%
618 1
 
1.7%
843 1
 
1.7%
19647 1
 
1.7%
Other values (48) 48
80.0%
ValueCountFrequency (%)
36 2
3.3%
37 1
1.7%
42 1
1.7%
44 1
1.7%
48 1
1.7%
50 1
1.7%
62 1
1.7%
78 1
1.7%
79 1
1.7%
82 1
1.7%
ValueCountFrequency (%)
19647 1
1.7%
13961 1
1.7%
11162 1
1.7%
9273 1
1.7%
9272 1
1.7%
8181 1
1.7%
7234 1
1.7%
6145 1
1.7%
5933 1
1.7%
5659 1
1.7%

80-89
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean524.68333
Minimum0
Maximum3208
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:55:00.710280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.9
Q121.25
median330
Q3718
95-th percentile1703.15
Maximum3208
Range3208
Interquartile range (IQR)696.75

Descriptive statistics

Standard deviation631.24519
Coefficient of variation (CV)1.2030975
Kurtosis4.4232036
Mean524.68333
Median Absolute Deviation (MAD)313.5
Skewness1.8350086
Sum31481
Variance398470.49
MonotonicityNot monotonic
2023-12-12T10:55:00.897637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3
 
5.0%
19 2
 
3.3%
13 2
 
3.3%
16 2
 
3.3%
75 1
 
1.7%
1744 1
 
1.7%
614 1
 
1.7%
1365 1
 
1.7%
1189 1
 
1.7%
1701 1
 
1.7%
Other values (45) 45
75.0%
ValueCountFrequency (%)
0 1
 
1.7%
3 1
 
1.7%
4 1
 
1.7%
6 1
 
1.7%
11 3
5.0%
12 1
 
1.7%
13 2
3.3%
16 2
3.3%
17 1
 
1.7%
19 2
3.3%
ValueCountFrequency (%)
3208 1
1.7%
1812 1
1.7%
1744 1
1.7%
1701 1
1.7%
1624 1
1.7%
1375 1
1.7%
1365 1
1.7%
1354 1
1.7%
1351 1
1.7%
1262 1
1.7%

90이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.45
Minimum0
Maximum1390
Zeros9
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-12T10:55:01.083186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median57.5
Q3232.25
95-th percentile591.15
Maximum1390
Range1390
Interquartile range (IQR)228.5

Descriptive statistics

Standard deviation252.67561
Coefficient of variation (CV)1.4824031
Kurtosis8.3556166
Mean170.45
Median Absolute Deviation (MAD)57.5
Skewness2.4832479
Sum10227
Variance63844.964
MonotonicityNot monotonic
2023-12-12T10:55:01.240133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 9
 
15.0%
2 3
 
5.0%
1 2
 
3.3%
4 2
 
3.3%
15 2
 
3.3%
31 2
 
3.3%
17 1
 
1.7%
371 1
 
1.7%
709 1
 
1.7%
196 1
 
1.7%
Other values (36) 36
60.0%
ValueCountFrequency (%)
0 9
15.0%
1 2
 
3.3%
2 3
 
5.0%
3 1
 
1.7%
4 2
 
3.3%
6 1
 
1.7%
7 1
 
1.7%
8 1
 
1.7%
13 1
 
1.7%
15 2
 
3.3%
ValueCountFrequency (%)
1390 1
1.7%
709 1
1.7%
708 1
1.7%
585 1
1.7%
575 1
1.7%
542 1
1.7%
465 1
1.7%
447 1
1.7%
430 1
1.7%
409 1
1.7%

Interactions

2023-12-12T10:54:53.757231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:45.606062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.904736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.733397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.622440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.424445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.186643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.018070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.038680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.906336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.876127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:45.710488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.993406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.813881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.712851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.504720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.291065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.093752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.117106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.993606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.975555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.103027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.073418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.885544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.792570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.580999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.372627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.164801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.203481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.069047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.073978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.212290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.159070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.957061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.873967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.652527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.456388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.233540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.289206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.146453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.177033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.306135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.237947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.024351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.943053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.727412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.539751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.550222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.364970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.229442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.299147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.409755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.308163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.097786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.017531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.799035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.617986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.623962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.442447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.301213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.406715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.515552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.380491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.181665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.089859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.875348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.700206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.698459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.522408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.376072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.570176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.613941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.483337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.274986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.172270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.950429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.782435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.784150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.614761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.457389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.736772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.719696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.575856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.388671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.258845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.030427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.872023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.873853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.703513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.542739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:54.873805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:46.808449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:47.657186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:48.502019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:49.346845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.101785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:50.947526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:51.958383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:52.795009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:54:53.639980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:55:01.357563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위상병코드상병명칭실인원연인원진료비(천원)59이하60-6465-6970-7980-8990이상
구분1.0000.0000.0000.0000.7540.6570.0000.5730.8280.7340.6750.6860.458
순위0.0001.0000.2400.6360.4750.4790.4270.1010.0000.0000.1770.0000.000
상병코드0.0000.2401.0001.0000.0000.0000.8590.8640.0000.0000.0000.3460.554
상병명칭0.0000.6361.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
실인원0.7540.4750.0000.0001.0000.9960.8650.6420.8460.9250.9730.7990.753
연인원0.6570.4790.0000.0000.9961.0000.9140.5940.8360.9100.9720.8000.741
진료비(천원)0.0000.4270.8590.0000.8650.9141.0000.4660.5710.7450.9000.7310.630
59이하0.5730.1010.8640.0000.6420.5940.4661.0000.8230.8360.4140.3650.158
60-640.8280.0000.0000.0000.8460.8360.5710.8231.0000.8840.8140.6920.622
65-690.7340.0000.0000.0000.9250.9100.7450.8360.8841.0000.8990.8060.770
70-790.6750.1770.0000.0000.9730.9720.9000.4140.8140.8991.0000.8350.781
80-890.6860.0000.3460.0000.7990.8000.7310.3650.6920.8060.8351.0000.924
90이상0.4580.0000.5540.0000.7530.7410.6300.1580.6220.7700.7810.9241.000
2023-12-12T10:55:01.525974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위실인원연인원진료비(천원)59이하60-6465-6970-7980-8990이상구분
순위1.000-0.402-0.501-0.710-0.251-0.246-0.253-0.342-0.311-0.1790.000
실인원-0.4021.0000.8270.4870.6290.7830.7590.7250.6570.5030.628
연인원-0.5010.8271.0000.5990.7230.7930.7960.8530.7930.6530.509
진료비(천원)-0.7100.4870.5991.0000.3190.3640.3800.4410.4240.2980.000
59이하-0.2510.6290.7230.3191.0000.7420.7290.7260.5940.4040.275
60-64-0.2460.7830.7930.3640.7421.0000.8660.8430.7950.6780.507
65-69-0.2530.7590.7960.3800.7290.8661.0000.8660.8650.6580.560
70-79-0.3420.7250.8530.4410.7260.8430.8661.0000.8900.7160.529
80-89-0.3110.6570.7930.4240.5940.7950.8650.8901.0000.8830.574
90이상-0.1790.5030.6530.2980.4040.6780.6580.7160.8831.0000.332
구분0.0000.6280.5090.0000.2750.5070.5600.5290.5740.3321.000

Missing values

2023-12-12T10:54:55.068954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:54:55.303582image/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)59이하60-6465-6970-7980-8990이상
0입원실인원1M48기타 척추병증6745709288014979235437517
1입원실인원2U07병인이 불확실한 신종질환의 임시적 지정이나 응급사용4463585126656814326271209832
2입원실인원3K63장의 기타 질환355144474063371316276403
3입원실인원4H25노년백내장2502503081841517200252
4입원실인원5E112형 당뇨병190352013779715310152191
5입원실인원6M17무릎관절증186526023661646117136224
6입원실인원7M51기타 추간판장애14714237772041447109130
7입원실인원8M96달리 분류되지 않은 처치후 근골격장애13719181033882213110192
8입원실인원9M75어깨병변128259212897994211100110
9입원실인원10N18만성 신장병1261979771613231822315
구분순위상병코드상병명칭실인원연인원진료비(천원)59이하60-6465-6970-7980-8990이상
50외래11M17무릎관절증87728772126685039535977956591354226
51외래12I69뇌혈관질환의 후유증8529852966033524716743659331375371
52외래13K29위염 및 십이지장염8335833576779013778038634403730159
53외래14N18만성 신장병80908090122945879460026045101351575
54외래15F03상세불명의 치매5843584355139714616433001624708
55외래16M75어깨병변551855188267085052615713685396100
56외래17R42어지럼증 및 어지럼496549655620312041563783230715282
57외래18I20협심증4786478649665292811883774520131
58외래19L29가려움469846981553151961191853487576135
59외래20Z26기타 단일 감염성 질환에 대한 예방접종의 필요440544052729431677558451151018227