Overview

Dataset statistics

Number of variables12
Number of observations60
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory107.2 B

Variable types

Categorical1
Numeric9
Text2

Dataset

Description중앙보훈병원에서 제공하는 진료 및 질환별 자료관련 사항 중 외래환자 및 입원환자분들의 진료시 관련된 질환에 대한 데이터중에 다빈도 질환별 연령별 분포에 대한 정보입니다.
URLhttps://www.data.go.kr/data/15102115/fileData.do

Alerts

실인원 is highly overall correlated with 연인원 and 7 other fieldsHigh correlation
연인원 is highly overall correlated with 실인원 and 7 other fieldsHigh correlation
59이하 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
60-64 is highly overall correlated with 실인원 and 6 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 6 other fieldsHigh correlation
구분 is highly overall correlated with 실인원 and 4 other fieldsHigh correlation
70-79 has unique valuesUnique
59이하 has 1 (1.7%) zerosZeros
60-64 has 3 (5.0%) zerosZeros
65-69 has 4 (6.7%) zerosZeros
90이상 has 2 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 20:37:06.636759
Analysis finished2023-12-12 20:37:15.455872
Duration8.82 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

2023-12-13T05:37:15.535834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:37:15.634291image/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)

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
2023-12-13T05:37:15.730829image/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
2023-12-13T05:37:15.847548image/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

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T05:37:16.088789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9666667
Min length3

Characters and Unicode

Total characters298
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

Unique26 ?
Unique (%)43.3%

Sample

1st row H25
2nd row U07
3rd row Z51
4th row M17
5th row I25
ValueCountFrequency (%)
i63 3
 
5.0%
c61 3
 
5.0%
n18 3
 
5.0%
m48 3
 
5.0%
z51 2
 
3.3%
g81 2
 
3.3%
j18 2
 
3.3%
h25 2
 
3.3%
m75 2
 
3.3%
u07 2
 
3.3%
Other values (31) 36
60.0%
2023-12-13T05:37:16.499216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
39.6%
1 24
 
8.1%
0 17
 
5.7%
5 16
 
5.4%
2 13
 
4.4%
8 13
 
4.4%
4 12
 
4.0%
I 11
 
3.7%
6 10
 
3.4%
7 8
 
2.7%
Other values (16) 56
18.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
40.3%
Space Separator 118
39.6%
Uppercase Letter 60
20.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 11
18.3%
M 8
13.3%
C 7
11.7%
K 6
10.0%
N 4
 
6.7%
Z 4
 
6.7%
H 4
 
6.7%
J 3
 
5.0%
E 2
 
3.3%
U 2
 
3.3%
Other values (6) 9
15.0%
Decimal Number
ValueCountFrequency (%)
1 24
20.0%
0 17
14.2%
5 16
13.3%
2 13
10.8%
8 13
10.8%
4 12
10.0%
6 10
8.3%
7 8
 
6.7%
3 7
 
5.8%
Space Separator
ValueCountFrequency (%)
118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238
79.9%
Latin 60
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 11
18.3%
M 8
13.3%
C 7
11.7%
K 6
10.0%
N 4
 
6.7%
Z 4
 
6.7%
H 4
 
6.7%
J 3
 
5.0%
E 2
 
3.3%
U 2
 
3.3%
Other values (6) 9
15.0%
Common
ValueCountFrequency (%)
118
49.6%
1 24
 
10.1%
0 17
 
7.1%
5 16
 
6.7%
2 13
 
5.5%
8 13
 
5.5%
4 12
 
5.0%
6 10
 
4.2%
7 8
 
3.4%
3 7
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
118
39.6%
1 24
 
8.1%
0 17
 
5.7%
5 16
 
5.4%
2 13
 
4.4%
8 13
 
4.4%
4 12
 
4.0%
I 11
 
3.7%
6 10
 
3.4%
7 8
 
2.7%
Other values (16) 56
18.8%

상병명
Text

Distinct41
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T05:37:16.861862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length44
Mean length33.65
Min length12

Characters and Unicode

Total characters2019
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)43.3%

Sample

1st row Senile incipient cataract,
2nd row Emergency use of U07
3rd row therapeutic radiology
4th row Osteoarthritis of patellofemoral joint, both
5th row Atherosclerotic cardiovascular disease
ValueCountFrequency (%)
of 17
 
7.3%
disease 6
 
2.6%
and 5
 
2.1%
to 5
 
2.1%
stenosis 4
 
1.7%
with 4
 
1.7%
prostate 4
 
1.7%
cerebral 4
 
1.7%
due 4
 
1.7%
spinal 3
 
1.3%
Other values (122) 177
76.0%
2023-12-13T05:37:17.404743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
292
14.5%
e 177
 
8.8%
i 166
 
8.2%
a 147
 
7.3%
r 132
 
6.5%
t 127
 
6.3%
o 127
 
6.3%
n 126
 
6.2%
s 107
 
5.3%
c 86
 
4.3%
Other values (44) 532
26.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1635
81.0%
Space Separator 292
 
14.5%
Uppercase Letter 57
 
2.8%
Decimal Number 18
 
0.9%
Other Punctuation 11
 
0.5%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 177
10.8%
i 166
10.2%
a 147
9.0%
r 132
 
8.1%
t 127
 
7.8%
o 127
 
7.8%
n 126
 
7.7%
s 107
 
6.5%
c 86
 
5.3%
l 75
 
4.6%
Other values (15) 365
22.3%
Uppercase Letter
ValueCountFrequency (%)
A 10
17.5%
C 7
12.3%
P 6
10.5%
O 5
8.8%
R 4
 
7.0%
I 4
 
7.0%
S 3
 
5.3%
E 3
 
5.3%
B 2
 
3.5%
D 2
 
3.5%
Other values (8) 11
19.3%
Decimal Number
ValueCountFrequency (%)
0 8
44.4%
1 5
27.8%
9 2
 
11.1%
7 2
 
11.1%
2 1
 
5.6%
Close Punctuation
ValueCountFrequency (%)
) 2
66.7%
] 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 2
66.7%
[ 1
33.3%
Space Separator
ValueCountFrequency (%)
292
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1692
83.8%
Common 327
 
16.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 177
10.5%
i 166
 
9.8%
a 147
 
8.7%
r 132
 
7.8%
t 127
 
7.5%
o 127
 
7.5%
n 126
 
7.4%
s 107
 
6.3%
c 86
 
5.1%
l 75
 
4.4%
Other values (33) 422
24.9%
Common
ValueCountFrequency (%)
292
89.3%
, 11
 
3.4%
0 8
 
2.4%
1 5
 
1.5%
) 2
 
0.6%
( 2
 
0.6%
9 2
 
0.6%
7 2
 
0.6%
2 1
 
0.3%
[ 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
292
14.5%
e 177
 
8.8%
i 166
 
8.2%
a 147
 
7.3%
r 132
 
6.5%
t 127
 
6.3%
o 127
 
6.3%
n 126
 
6.2%
s 107
 
5.3%
c 86
 
4.3%
Other values (44) 532
26.3%

실인원
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9140.15
Minimum73
Maximum57720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:17.578507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile82.75
Q1212.75
median378.5
Q316244.5
95-th percentile47639.25
Maximum57720
Range57647
Interquartile range (IQR)16031.75

Descriptive statistics

Standard deviation15070.12
Coefficient of variation (CV)1.6487826
Kurtosis2.8756194
Mean9140.15
Median Absolute Deviation (MAD)257.5
Skewness1.8590404
Sum548409
Variance2.2710852 × 108
MonotonicityNot monotonic
2023-12-13T05:37:17.716500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1182 2
 
3.3%
234 2
 
3.3%
897 2
 
3.3%
187 2
 
3.3%
209 2
 
3.3%
214 2
 
3.3%
233 2
 
3.3%
204 2
 
3.3%
296 2
 
3.3%
321 2
 
3.3%
Other values (36) 40
66.7%
ValueCountFrequency (%)
73 1
1.7%
76 1
1.7%
78 1
1.7%
83 1
1.7%
85 1
1.7%
123 1
1.7%
174 1
1.7%
184 1
1.7%
187 2
3.3%
200 1
1.7%
ValueCountFrequency (%)
57720 1
1.7%
55804 1
1.7%
49278 1
1.7%
47553 1
1.7%
40172 1
1.7%
27516 1
1.7%
26133 1
1.7%
24547 1
1.7%
22082 1
1.7%
20376 1
1.7%

연인원
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11883.467
Minimum669
Maximum57720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:17.876032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum669
5-th percentile1750.8
Q13228.75
median5945
Q316244.5
95-th percentile47639.25
Maximum57720
Range57051
Interquartile range (IQR)13015.75

Descriptive statistics

Standard deviation13606.715
Coefficient of variation (CV)1.1450122
Kurtosis3.7847145
Mean11883.467
Median Absolute Deviation (MAD)2935
Skewness2.0351536
Sum713008
Variance1.8514269 × 108
MonotonicityNot monotonic
2023-12-13T05:37:18.014464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
3987 2
 
3.3%
3985 2
 
3.3%
8163 2
 
3.3%
5945 2
 
3.3%
5094 2
 
3.3%
3036 2
 
3.3%
3012 2
 
3.3%
3010 2
 
3.3%
4981 2
 
3.3%
6350 2
 
3.3%
Other values (37) 40
66.7%
ValueCountFrequency (%)
669 1
1.7%
873 1
1.7%
1120 1
1.7%
1784 1
1.7%
1815 1
1.7%
2573 1
1.7%
2688 1
1.7%
2798 1
1.7%
2969 1
1.7%
3010 2
3.3%
ValueCountFrequency (%)
57720 1
1.7%
55804 1
1.7%
49278 1
1.7%
47553 1
1.7%
40172 1
1.7%
27516 1
1.7%
26133 1
1.7%
24547 1
1.7%
22082 1
1.7%
20376 1
1.7%

59이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean737.85
Minimum0
Maximum6680
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:18.168592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q112
median116
Q3893.25
95-th percentile3141.1
Maximum6680
Range6680
Interquartile range (IQR)881.25

Descriptive statistics

Standard deviation1285.7365
Coefficient of variation (CV)1.7425446
Kurtosis7.6356489
Mean737.85
Median Absolute Deviation (MAD)113.5
Skewness2.5538687
Sum44271
Variance1653118.4
MonotonicityNot monotonic
2023-12-13T05:37:18.321294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 3
 
5.0%
12 2
 
3.3%
8 2
 
3.3%
2 2
 
3.3%
7 2
 
3.3%
14 1
 
1.7%
3960 1
 
1.7%
352 1
 
1.7%
1422 1
 
1.7%
1930 1
 
1.7%
Other values (44) 44
73.3%
ValueCountFrequency (%)
0 1
 
1.7%
1 1
 
1.7%
2 2
3.3%
3 3
5.0%
4 1
 
1.7%
7 2
3.3%
8 2
3.3%
10 1
 
1.7%
11 1
 
1.7%
12 2
3.3%
ValueCountFrequency (%)
6680 1
1.7%
4132 1
1.7%
3960 1
1.7%
3098 1
1.7%
2800 1
1.7%
2668 1
1.7%
2324 1
1.7%
1930 1
1.7%
1881 1
1.7%
1871 1
1.7%

60-64
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean355.33333
Minimum0
Maximum2783
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:18.478317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q19
median74
Q3515
95-th percentile1509.7
Maximum2783
Range2783
Interquartile range (IQR)506

Descriptive statistics

Standard deviation569.22953
Coefficient of variation (CV)1.6019593
Kurtosis6.5328138
Mean355.33333
Median Absolute Deviation (MAD)72.5
Skewness2.4300999
Sum21320
Variance324022.26
MonotonicityNot monotonic
2023-12-13T05:37:18.624598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
9 5
 
8.3%
2 4
 
6.7%
0 3
 
5.0%
74 2
 
3.3%
4 2
 
3.3%
1 2
 
3.3%
515 2
 
3.3%
557 2
 
3.3%
33 1
 
1.7%
537 1
 
1.7%
Other values (36) 36
60.0%
ValueCountFrequency (%)
0 3
5.0%
1 2
 
3.3%
2 4
6.7%
4 2
 
3.3%
6 1
 
1.7%
8 1
 
1.7%
9 5
8.3%
11 1
 
1.7%
13 1
 
1.7%
15 1
 
1.7%
ValueCountFrequency (%)
2783 1
1.7%
2221 1
1.7%
1770 1
1.7%
1496 1
1.7%
1209 1
1.7%
1134 1
1.7%
1055 1
1.7%
882 1
1.7%
708 1
1.7%
668 1
1.7%

65-69
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean642.56667
Minimum0
Maximum4380
Zeros4
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:18.776145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.25
median157.5
Q3978.75
95-th percentile2932.5
Maximum4380
Range4380
Interquartile range (IQR)961.5

Descriptive statistics

Standard deviation994.58115
Coefficient of variation (CV)1.5478256
Kurtosis4.981037
Mean642.56667
Median Absolute Deviation (MAD)152
Skewness2.2265438
Sum38554
Variance989191.67
MonotonicityNot monotonic
2023-12-13T05:37:19.286655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
6.7%
7 3
 
5.0%
82 2
 
3.3%
26 2
 
3.3%
51 1
 
1.7%
962 1
 
1.7%
71 1
 
1.7%
50 1
 
1.7%
2303 1
 
1.7%
3919 1
 
1.7%
Other values (43) 43
71.7%
ValueCountFrequency (%)
0 4
6.7%
3 1
 
1.7%
5 1
 
1.7%
6 1
 
1.7%
7 3
5.0%
9 1
 
1.7%
11 1
 
1.7%
13 1
 
1.7%
14 1
 
1.7%
15 1
 
1.7%
ValueCountFrequency (%)
4380 1
1.7%
3919 1
1.7%
3550 1
1.7%
2900 1
1.7%
2303 1
1.7%
1996 1
1.7%
1489 1
1.7%
1455 1
1.7%
1137 1
1.7%
1122 1
1.7%

70-79
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct60
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6927.7167
Minimum76
Maximum41658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:19.456536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile127.95
Q1259
median2692.5
Q310046.75
95-th percentile30351
Maximum41658
Range41582
Interquartile range (IQR)9787.75

Descriptive statistics

Standard deviation9820.7894
Coefficient of variation (CV)1.4176084
Kurtosis3.603924
Mean6927.7167
Median Absolute Deviation (MAD)2526
Skewness1.9853651
Sum415663
Variance96447904
MonotonicityNot monotonic
2023-12-13T05:37:19.610461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
877 1
 
1.7%
3482 1
 
1.7%
1119 1
 
1.7%
2144 1
 
1.7%
2210 1
 
1.7%
1678 1
 
1.7%
1491 1
 
1.7%
2320 1
 
1.7%
1674 1
 
1.7%
41658 1
 
1.7%
Other values (50) 50
83.3%
ValueCountFrequency (%)
76 1
1.7%
107 1
1.7%
127 1
1.7%
128 1
1.7%
129 1
1.7%
130 1
1.7%
137 1
1.7%
147 1
1.7%
163 1
1.7%
170 1
1.7%
ValueCountFrequency (%)
41658 1
1.7%
37582 1
1.7%
32118 1
1.7%
30258 1
1.7%
28028 1
1.7%
20276 1
1.7%
18880 1
1.7%
16480 1
1.7%
14515 1
1.7%
13045 1
1.7%

80-89
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1549.2333
Minimum11
Maximum8712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:19.763353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20.9
Q159
median667
Q31942.5
95-th percentile5298.8
Maximum8712
Range8701
Interquartile range (IQR)1883.5

Descriptive statistics

Standard deviation2088.8682
Coefficient of variation (CV)1.3483238
Kurtosis3.0976651
Mean1549.2333
Median Absolute Deviation (MAD)629.5
Skewness1.841735
Sum92954
Variance4363370.6
MonotonicityNot monotonic
2023-12-13T05:37:19.921484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 2
 
3.3%
59 2
 
3.3%
49 2
 
3.3%
34 2
 
3.3%
5170 1
 
1.7%
4227 1
 
1.7%
3172 1
 
1.7%
5049 1
 
1.7%
7837 1
 
1.7%
178 1
 
1.7%
Other values (46) 46
76.7%
ValueCountFrequency (%)
11 1
1.7%
13 1
1.7%
19 1
1.7%
21 1
1.7%
28 1
1.7%
32 2
3.3%
34 2
3.3%
41 1
1.7%
43 1
1.7%
47 1
1.7%
ValueCountFrequency (%)
8712 1
1.7%
7837 1
1.7%
7746 1
1.7%
5170 1
1.7%
5049 1
1.7%
4291 1
1.7%
4265 1
1.7%
4227 1
1.7%
4059 1
1.7%
3429 1
1.7%

90이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean455.33333
Minimum0
Maximum3387
Zeros2
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T05:37:20.091432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q124
median192
Q3640.25
95-th percentile1544.6
Maximum3387
Range3387
Interquartile range (IQR)616.25

Descriptive statistics

Standard deviation637.26249
Coefficient of variation (CV)1.3995516
Kurtosis7.2087509
Mean455.33333
Median Absolute Deviation (MAD)186.5
Skewness2.3438244
Sum27320
Variance406103.48
MonotonicityNot monotonic
2023-12-13T05:37:20.301434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 2
 
3.3%
1 2
 
3.3%
30 2
 
3.3%
0 2
 
3.3%
104 2
 
3.3%
1708 1
 
1.7%
46 1
 
1.7%
564 1
 
1.7%
3387 1
 
1.7%
1536 1
 
1.7%
Other values (45) 45
75.0%
ValueCountFrequency (%)
0 2
3.3%
1 2
3.3%
2 1
1.7%
5 1
1.7%
6 1
1.7%
8 2
3.3%
9 1
1.7%
10 1
1.7%
11 1
1.7%
14 1
1.7%
ValueCountFrequency (%)
3387 1
1.7%
2257 1
1.7%
1708 1
1.7%
1536 1
1.7%
1427 1
1.7%
1192 1
1.7%
1147 1
1.7%
1056 1
1.7%
1041 1
1.7%
1036 1
1.7%

Interactions

2023-12-13T05:37:14.275467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.102593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.028522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.906487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.826627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.718949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.621665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.724144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.487738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.385793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.207950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.144752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.012976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.944268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.821461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.727815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.817420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.596737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.489142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.315501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.239987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.121211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.045686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.931603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.826911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.903863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.682178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.586803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.427235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.349297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.216731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.148888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.043259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.924368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.989147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.766750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.703970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.526206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.448544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.317408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.254838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.156377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.303964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.085243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.855971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.795458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.625203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.538330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.413856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.353229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.241138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.396995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.169518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.935075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.878287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.719901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.641896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.514257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.436321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.322936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.480190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.245147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.011575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.968068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.821040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.738260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.607098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.523065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.411968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.556268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.327601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.094965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:15.055123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:07.921091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:08.813048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:09.705014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:10.617802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:11.528656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:12.632497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:13.398890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:37:14.174308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:37:20.454879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위상병코드상병명실인원연인원59이하60-6465-6970-7980-8990이상
구분1.0000.0000.0000.0000.7390.7550.4530.7850.8300.7500.7280.598
순위0.0001.0000.4940.4940.5620.6560.2880.0000.0000.3660.2230.165
상병코드0.0000.4941.0001.0000.9230.9540.9320.9490.9360.9120.9240.696
상병명0.0000.4941.0001.0000.9230.9540.9320.9490.9360.9120.9240.696
실인원0.7390.5620.9230.9231.0000.9840.6130.8300.8660.9580.8450.776
연인원0.7550.6560.9540.9540.9841.0000.7950.8280.8680.9900.9350.897
59이하0.4530.2880.9320.9320.6130.7951.0000.8940.8390.7990.7210.394
60-640.7850.0000.9490.9490.8300.8280.8941.0000.9790.8490.8210.784
65-690.8300.0000.9360.9360.8660.8680.8390.9791.0000.8710.8920.760
70-790.7500.3660.9120.9120.9580.9900.7990.8490.8711.0000.9330.879
80-890.7280.2230.9240.9240.8450.9350.7210.8210.8920.9331.0000.933
90이상0.5980.1650.6960.6960.7760.8970.3940.7840.7600.8790.9331.000
2023-12-13T05:37:20.635401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위실인원연인원59이하60-6465-6970-7980-8990이상구분
순위1.000-0.406-0.434-0.233-0.302-0.275-0.272-0.226-0.1370.000
실인원-0.4061.0000.8320.5470.7030.7780.7450.6900.5810.642
연인원-0.4340.8321.0000.7240.7970.7980.8310.8340.6980.630
59이하-0.2330.5470.7241.0000.8820.8760.7500.7730.6070.306
60-64-0.3020.7030.7970.8821.0000.8930.8190.7980.6200.462
65-69-0.2750.7780.7980.8760.8931.0000.8510.8110.6410.510
70-79-0.2720.7450.8310.7500.8190.8511.0000.9310.8390.622
80-89-0.2260.6900.8340.7730.7980.8110.9311.0000.9280.595
90이상-0.1370.5810.6980.6070.6200.6410.8390.9281.0000.444
구분0.0000.6420.6300.3060.4620.5100.6220.5950.4441.000

Missing values

2023-12-13T05:37:15.206260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:37:15.386699image/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

구분순위상병코드상병명실인원연인원59이하60-6465-6970-7980-8990이상
0입원실인원1H25Senile incipient cataract,1182398714335187717829
1입원실인원2U07Emergency use of U07897816341874822108726
2입원실인원3Z51therapeutic radiology636821447512338110034
3입원실인원4M17Osteoarthritis of patellofemoral joint, both4268724121848284595
4입원실인원5I25Atherosclerotic cardiovascular disease421408989213184916
5입원실인원6C34lung cancer of hilus336635022142534718
6입원실인원7M48spinal stenosis3214981109182324111
7입원실인원8C61Acinar Prostate Adenocarcinoma29639853211261136
8입원실인원9I65Occlusion and stenosis of vertebral artery2891784207246322
9입원실인원10Z04Examination and observation following work accident280873000251281
구분순위상병코드상병명실인원연인원59이하60-6465-6970-7980-8990이상
50외래11M51Lumbar and other intervertebral disc disorders with myelopathy19706197061452403996130453429381
51외래12H35radiation retinopathy17644176441922696591240530781041
52외래13Z11Special screening examination for Coronavirus disease 2019[COVID019]17337173376680149619966115925125
53외래14R42psychophysiologic dizziness16810168106575151122104483136932
54외래15G81flaccid hemiplegia173131731323246681455109941558314
55외래16R52acute pain160231602326681055105891631480599
56외래17C61Acinar Prostate Adenocarcinoma16056160561971277121882815686
57외래18H42Glaucoma in endocrine, nutritional and metabolic diseases144621446236637276798092523625
58외래19K02Caries limited to enamel14323143231871515110989161662250
59외래20K04suppurative pulpitis1409414094108455791199131431198