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

Description광주보훈병원에서 개방하는 진료정보 데이터로 광주보훈병원 다빈도 질환 환자 연령별 현황이 포함된 공공데이터입니다.
URLhttps://www.data.go.kr/data/15102117/fileData.do

Alerts

순위 is highly overall correlated with 연인원 and 1 other fieldsHigh correlation
실인원 is highly overall correlated with 59세이하 and 5 other fieldsHigh correlation
연인원 is highly overall correlated with 순위 and 1 other fieldsHigh correlation
진료비(천원) is highly overall correlated with 순위 and 1 other fieldsHigh correlation
59세이하 is highly overall correlated with 실인원 and 4 other fieldsHigh correlation
60세-64세 is highly overall correlated with 실인원 and 5 other fieldsHigh correlation
65세-69세 is highly overall correlated with 실인원 and 5 other fieldsHigh correlation
70세-79세 is highly overall correlated with 실인원 and 6 other fieldsHigh correlation
80세-89세 is highly overall correlated with 59세이하 and 5 other fieldsHigh correlation
90세이상 is highly overall correlated with 실인원 and 4 other fieldsHigh correlation
구분 is highly overall correlated with 실인원 and 2 other fieldsHigh correlation
연인원 has 20 (33.3%) missing valuesMissing
59세이하 has 3 (5.0%) zerosZeros
60세-64세 has 5 (8.3%) zerosZeros
65세-69세 has 3 (5.0%) zerosZeros
70세-79세 has 1 (1.7%) zerosZeros
80세-89세 has 1 (1.7%) zerosZeros
90세이상 has 11 (18.3%) zerosZeros

Reproduction

Analysis started2023-12-12 22:39:22.944673
Analysis finished2023-12-12 22:39:33.102505
Duration10.16 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

2023-12-13T07:39:33.163732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:39:33.260489image/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
2023-12-13T07:39:33.357165image/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
2023-12-13T07:39:33.498715image/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

Distinct37
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T07:39:33.711027image/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

Unique20 ?
Unique (%)33.3%

Sample

1st row N40
2nd row M48
3rd row U07
4th row I20
5th row I63
ValueCountFrequency (%)
n40 3
 
5.0%
m17 3
 
5.0%
m48 3
 
5.0%
z49 3
 
5.0%
i63 3
 
5.0%
c61 3
 
5.0%
c16 2
 
3.3%
c22 2
 
3.3%
m51 2
 
3.3%
m75 2
 
3.3%
Other values (27) 34
56.7%
2023-12-13T07:39:34.028933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
118
39.6%
1 23
 
7.7%
4 16
 
5.4%
0 15
 
5.0%
2 14
 
4.7%
C 14
 
4.7%
6 12
 
4.0%
8 11
 
3.7%
M 10
 
3.4%
7 9
 
3.0%
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 (%)
C 14
23.3%
M 10
16.7%
I 6
10.0%
K 6
10.0%
N 4
 
6.7%
Z 4
 
6.7%
E 3
 
5.0%
R 3
 
5.0%
U 2
 
3.3%
G 2
 
3.3%
Other values (5) 6
10.0%
Decimal Number
ValueCountFrequency (%)
1 23
19.2%
4 16
13.3%
0 15
12.5%
2 14
11.7%
6 12
10.0%
8 11
9.2%
7 9
 
7.5%
5 8
 
6.7%
3 7
 
5.8%
9 5
 
4.2%
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 (%)
C 14
23.3%
M 10
16.7%
I 6
10.0%
K 6
10.0%
N 4
 
6.7%
Z 4
 
6.7%
E 3
 
5.0%
R 3
 
5.0%
U 2
 
3.3%
G 2
 
3.3%
Other values (5) 6
10.0%
Common
ValueCountFrequency (%)
118
49.6%
1 23
 
9.7%
4 16
 
6.7%
0 15
 
6.3%
2 14
 
5.9%
6 12
 
5.0%
8 11
 
4.6%
7 9
 
3.8%
5 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 23
 
7.7%
4 16
 
5.4%
0 15
 
5.0%
2 14
 
4.7%
C 14
 
4.7%
6 12
 
4.0%
8 11
 
3.7%
M 10
 
3.4%
7 9
 
3.0%
Other values (16) 56
18.8%

상병명칭
Text

Distinct37
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Memory size612.0 B
2023-12-13T07:39:34.278783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length20
Mean length12.433333
Min length5

Characters and Unicode

Total characters746
Distinct characters164
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)33.3%

Sample

1st row 전립선증식증
2nd row 기타 척추병증
3rd row U07의 응급사용
4th row 협심증
5th row 뇌경색증
ValueCountFrequency (%)
악성 12
 
7.2%
신생물 12
 
7.2%
및 12
 
7.2%
기타 8
 
4.8%
전립선증식증 3
 
1.8%
보건서비스와 3
 
1.8%
전립선의 3
 
1.8%
뇌경색증 3
 
1.8%
사람 3
 
1.8%
접하고 3
 
1.8%
Other values (71) 104
62.7%
2023-12-13T07:39:34.951755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
224
30.0%
의 23
 
3.1%
성 22
 
2.9%
증 20
 
2.7%
신 15
 
2.0%
생 14
 
1.9%
기 14
 
1.9%
물 14
 
1.9%
악 14
 
1.9%
및 12
 
1.6%
Other values (154) 374
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 499
66.9%
Space Separator 224
30.0%
Decimal Number 6
 
0.8%
Dash Punctuation 4
 
0.5%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Uppercase Letter 3
 
0.4%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 23
 
4.6%
성 22
 
4.4%
증 20
 
4.0%
신 15
 
3.0%
생 14
 
2.8%
기 14
 
2.8%
물 14
 
2.8%
악 14
 
2.8%
및 12
 
2.4%
병 10
 
2.0%
Other values (143) 341
68.3%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
7 2
33.3%
3 1
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
U 2
66.7%
G 1
33.3%
Space Separator
ValueCountFrequency (%)
224
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 499
66.9%
Common 244
32.7%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 23
 
4.6%
성 22
 
4.4%
증 20
 
4.0%
신 15
 
3.0%
생 14
 
2.8%
기 14
 
2.8%
물 14
 
2.8%
악 14
 
2.8%
및 12
 
2.4%
병 10
 
2.0%
Other values (143) 341
68.3%
Common
ValueCountFrequency (%)
224
91.8%
- 4
 
1.6%
( 4
 
1.6%
) 4
 
1.6%
0 3
 
1.2%
7 2
 
0.8%
+ 1
 
0.4%
. 1
 
0.4%
3 1
 
0.4%
Latin
ValueCountFrequency (%)
U 2
66.7%
G 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 499
66.9%
ASCII 247
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
224
90.7%
- 4
 
1.6%
( 4
 
1.6%
) 4
 
1.6%
0 3
 
1.2%
U 2
 
0.8%
7 2
 
0.8%
G 1
 
0.4%
+ 1
 
0.4%
. 1
 
0.4%
Hangul
ValueCountFrequency (%)
의 23
 
4.6%
성 22
 
4.4%
증 20
 
4.0%
신 15
 
3.0%
생 14
 
2.8%
기 14
 
2.8%
물 14
 
2.8%
악 14
 
2.8%
및 12
 
2.4%
병 10
 
2.0%
Other values (143) 341
68.3%

실인원
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3511.4
Minimum14
Maximum24715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:35.102302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile36.95
Q169
median189
Q35367.5
95-th percentile17025.85
Maximum24715
Range24701
Interquartile range (IQR)5298.5

Descriptive statistics

Standard deviation5839.3602
Coefficient of variation (CV)1.6629721
Kurtosis2.8194774
Mean3511.4
Median Absolute Deviation (MAD)145.5
Skewness1.8495619
Sum210684
Variance34098127
MonotonicityNot monotonic
2023-12-13T07:39:35.307015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
57 3
 
5.0%
596 2
 
3.3%
92 2
 
3.3%
439 2
 
3.3%
61 2
 
3.3%
81 2
 
3.3%
88 2
 
3.3%
90 2
 
3.3%
69 2
 
3.3%
122 2
 
3.3%
Other values (36) 39
65.0%
ValueCountFrequency (%)
14 1
 
1.7%
32 1
 
1.7%
36 1
 
1.7%
37 1
 
1.7%
40 1
 
1.7%
41 1
 
1.7%
46 1
 
1.7%
57 3
5.0%
61 2
3.3%
63 1
 
1.7%
ValueCountFrequency (%)
24715 1
1.7%
17670 1
1.7%
17137 1
1.7%
17020 1
1.7%
16499 1
1.7%
13905 1
1.7%
13277 1
1.7%
11307 1
1.7%
9727 1
1.7%
7937 1
1.7%

연인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)67.5%
Missing20
Missing (%)33.3%
Infinite0
Infinite (%)0.0%
Mean3114.525
Minimum523
Maximum9039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:35.466443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum523
5-th percentile965.45
Q11678
median2411
Q33059.25
95-th percentile8077.6
Maximum9039
Range8516
Interquartile range (IQR)1381.25

Descriptive statistics

Standard deviation2332.8485
Coefficient of variation (CV)0.74902225
Kurtosis1.6448889
Mean3114.525
Median Absolute Deviation (MAD)733
Skewness1.6794018
Sum124581
Variance5442182.3
MonotonicityNot monotonic
2023-12-13T07:39:35.588167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2389 2
 
3.3%
1569 2
 
3.3%
7906 2
 
3.3%
8027 2
 
3.3%
1678 2
 
3.3%
2433 2
 
3.3%
3011 2
 
3.3%
3156 2
 
3.3%
2800 2
 
3.3%
3027 2
 
3.3%
Other values (17) 20
33.3%
(Missing) 20
33.3%
ValueCountFrequency (%)
523 1
1.7%
784 1
1.7%
975 1
1.7%
1241 1
1.7%
1453 1
1.7%
1493 1
1.7%
1540 1
1.7%
1569 2
3.3%
1678 2
3.3%
1800 1
1.7%
ValueCountFrequency (%)
9039 2
3.3%
8027 2
3.3%
7906 2
3.3%
3412 2
3.3%
3156 2
3.3%
3027 2
3.3%
3011 2
3.3%
2800 2
3.3%
2601 1
1.7%
2575 1
1.7%

진료비(천원)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0925809 × 109
Minimum1.9257449 × 108
Maximum2.8935479 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:35.738931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.9257449 × 108
5-th percentile2.9572829 × 108
Q16.4630582 × 108
median9.1513425 × 108
Q31.4522949 × 109
95-th percentile2.3281302 × 109
Maximum2.8935479 × 109
Range2.7009734 × 109
Interquartile range (IQR)8.059891 × 108

Descriptive statistics

Standard deviation6.7594148 × 108
Coefficient of variation (CV)0.6186649
Kurtosis0.32223695
Mean1.0925809 × 109
Median Absolute Deviation (MAD)4.1519702 × 108
Skewness0.99036778
Sum6.5554857 × 1010
Variance4.5689688 × 1017
MonotonicityNot monotonic
2023-12-13T07:39:35.897620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1542267926 2
 
3.3%
790747830 2
 
3.3%
915134252 2
 
3.3%
707782784 2
 
3.3%
2893547863 2
 
3.3%
1815292395 2
 
3.3%
1141054590 2
 
3.3%
2249187216 2
 
3.3%
976881874 2
 
3.3%
1076301014 2
 
3.3%
Other values (37) 40
66.7%
ValueCountFrequency (%)
192574492 1
1.7%
219275080 1
1.7%
250859328 1
1.7%
298089818 1
1.7%
324467533 1
1.7%
344063663 1
1.7%
419552493 1
1.7%
426305686 1
1.7%
458750772 1
1.7%
463497893 1
1.7%
ValueCountFrequency (%)
2893547863 2
3.3%
2447505420 1
1.7%
2321847323 2
3.3%
2249187216 2
3.3%
1994781894 1
1.7%
1908427331 1
1.7%
1815292395 2
3.3%
1574621516 1
1.7%
1571187280 1
1.7%
1542267926 2
3.3%

59세이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean617.41667
Minimum0
Maximum16495
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:36.038145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q16.75
median56
Q3416.25
95-th percentile1384.05
Maximum16495
Range16495
Interquartile range (IQR)409.5

Descriptive statistics

Standard deviation2241.8915
Coefficient of variation (CV)3.6310835
Kurtosis44.640207
Mean617.41667
Median Absolute Deviation (MAD)55
Skewness6.4505966
Sum37045
Variance5026077.4
MonotonicityNot monotonic
2023-12-13T07:39:36.208910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 4
 
6.7%
1 3
 
5.0%
0 3
 
5.0%
17 2
 
3.3%
8 2
 
3.3%
4 2
 
3.3%
2 2
 
3.3%
211 2
 
3.3%
5921 1
 
1.7%
1537 1
 
1.7%
Other values (38) 38
63.3%
ValueCountFrequency (%)
0 3
5.0%
1 3
5.0%
2 2
3.3%
3 4
6.7%
4 2
3.3%
6 1
 
1.7%
7 1
 
1.7%
8 2
3.3%
9 1
 
1.7%
11 1
 
1.7%
ValueCountFrequency (%)
16495 1
1.7%
5921 1
1.7%
1537 1
1.7%
1376 1
1.7%
1177 1
1.7%
1062 1
1.7%
1039 1
1.7%
990 1
1.7%
907 1
1.7%
662 1
1.7%

60세-64세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202.16667
Minimum0
Maximum1045
Zeros5
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:36.379340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median46.5
Q3290
95-th percentile890.95
Maximum1045
Range1045
Interquartile range (IQR)286

Descriptive statistics

Standard deviation293.98542
Coefficient of variation (CV)1.4541736
Kurtosis1.592425
Mean202.16667
Median Absolute Deviation (MAD)46
Skewness1.6176595
Sum12130
Variance86427.429
MonotonicityNot monotonic
2023-12-13T07:39:36.549000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 5
 
8.3%
3 4
 
6.7%
2 3
 
5.0%
4 3
 
5.0%
33 2
 
3.3%
9 2
 
3.3%
5 2
 
3.3%
1 2
 
3.3%
675 2
 
3.3%
149 1
 
1.7%
Other values (34) 34
56.7%
ValueCountFrequency (%)
0 5
8.3%
1 2
 
3.3%
2 3
5.0%
3 4
6.7%
4 3
5.0%
5 2
 
3.3%
8 1
 
1.7%
9 2
 
3.3%
11 1
 
1.7%
20 1
 
1.7%
ValueCountFrequency (%)
1045 1
1.7%
1016 1
1.7%
1004 1
1.7%
885 1
1.7%
703 1
1.7%
700 1
1.7%
675 2
3.3%
641 1
1.7%
486 1
1.7%
408 1
1.7%

65세-69세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.26667
Minimum0
Maximum1830
Zeros3
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:36.740581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.95
Q17
median53.5
Q3346.75
95-th percentile1297.1
Maximum1830
Range1830
Interquartile range (IQR)339.75

Descriptive statistics

Standard deviation427.65006
Coefficient of variation (CV)1.5764932
Kurtosis3.5521744
Mean271.26667
Median Absolute Deviation (MAD)53.5
Skewness2.0236318
Sum16276
Variance182884.57
MonotonicityNot monotonic
2023-12-13T07:39:36.906345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
7 5
 
8.3%
3 4
 
6.7%
0 3
 
5.0%
119 2
 
3.3%
4 2
 
3.3%
2 2
 
3.3%
35 1
 
1.7%
1394 1
 
1.7%
1830 1
 
1.7%
1463 1
 
1.7%
Other values (38) 38
63.3%
ValueCountFrequency (%)
0 3
5.0%
1 1
 
1.7%
2 2
 
3.3%
3 4
6.7%
4 2
 
3.3%
7 5
8.3%
9 1
 
1.7%
11 1
 
1.7%
13 1
 
1.7%
16 1
 
1.7%
ValueCountFrequency (%)
1830 1
1.7%
1463 1
1.7%
1394 1
1.7%
1292 1
1.7%
1212 1
1.7%
918 1
1.7%
835 1
1.7%
813 1
1.7%
786 1
1.7%
516 1
1.7%

70세-79세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2766.5
Minimum0
Maximum18209
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:37.044117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37.9
Q187.25
median1405
Q33851.25
95-th percentile10662.75
Maximum18209
Range18209
Interquartile range (IQR)3764

Descriptive statistics

Standard deviation3707.2112
Coefficient of variation (CV)1.3400366
Kurtosis4.6660779
Mean2766.5
Median Absolute Deviation (MAD)1355.5
Skewness2.0362918
Sum165990
Variance13743415
MonotonicityNot monotonic
2023-12-13T07:39:37.190794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 2
 
3.3%
476 1
 
1.7%
9134 1
 
1.7%
828 1
 
1.7%
1136 1
 
1.7%
604 1
 
1.7%
745 1
 
1.7%
1052 1
 
1.7%
660 1
 
1.7%
18209 1
 
1.7%
Other values (49) 49
81.7%
ValueCountFrequency (%)
0 1
1.7%
29 1
1.7%
36 1
1.7%
38 1
1.7%
39 1
1.7%
42 1
1.7%
44 1
1.7%
47 2
3.3%
49 1
1.7%
50 1
1.7%
ValueCountFrequency (%)
18209 1
1.7%
11473 1
1.7%
11380 1
1.7%
10625 1
1.7%
10066 1
1.7%
9134 1
1.7%
7934 1
1.7%
6563 1
1.7%
6024 1
1.7%
5077 1
1.7%

80세-89세
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean565.98333
Minimum0
Maximum2647
Zeros1
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:37.324769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.85
Q121.25
median431
Q3777.25
95-th percentile1862.65
Maximum2647
Range2647
Interquartile range (IQR)756

Descriptive statistics

Standard deviation641.71641
Coefficient of variation (CV)1.133808
Kurtosis1.6556034
Mean565.98333
Median Absolute Deviation (MAD)406
Skewness1.4162401
Sum33959
Variance411799.95
MonotonicityNot monotonic
2023-12-13T07:39:37.472171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 2
 
3.3%
18 2
 
3.3%
13 2
 
3.3%
3 2
 
3.3%
14 2
 
3.3%
28 1
 
1.7%
1857 1
 
1.7%
1369 1
 
1.7%
1347 1
 
1.7%
0 1
 
1.7%
Other values (45) 45
75.0%
ValueCountFrequency (%)
0 1
1.7%
3 2
3.3%
6 1
1.7%
7 1
1.7%
11 1
1.7%
12 1
1.7%
13 2
3.3%
14 2
3.3%
16 1
1.7%
18 2
3.3%
ValueCountFrequency (%)
2647 1
1.7%
2388 1
1.7%
1970 1
1.7%
1857 1
1.7%
1788 1
1.7%
1615 1
1.7%
1397 1
1.7%
1369 1
1.7%
1347 1
1.7%
1269 1
1.7%

90세이상
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.15
Minimum0
Maximum1226
Zeros11
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size672.0 B
2023-12-13T07:39:37.604256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median80.5
Q3185.25
95-th percentile428.85
Maximum1226
Range1226
Interquartile range (IQR)182.25

Descriptive statistics

Standard deviation232.19847
Coefficient of variation (CV)1.5997139
Kurtosis10.530016
Mean145.15
Median Absolute Deviation (MAD)80
Skewness2.9768842
Sum8709
Variance53916.13
MonotonicityNot monotonic
2023-12-13T07:39:37.733275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 11
 
18.3%
6 5
 
8.3%
8 2
 
3.3%
1 2
 
3.3%
3 2
 
3.3%
7 1
 
1.7%
137 1
 
1.7%
299 1
 
1.7%
371 1
 
1.7%
271 1
 
1.7%
Other values (33) 33
55.0%
ValueCountFrequency (%)
0 11
18.3%
1 2
 
3.3%
2 1
 
1.7%
3 2
 
3.3%
5 1
 
1.7%
6 5
8.3%
7 1
 
1.7%
8 2
 
3.3%
9 1
 
1.7%
12 1
 
1.7%
ValueCountFrequency (%)
1226 1
1.7%
1040 1
1.7%
692 1
1.7%
415 1
1.7%
371 1
1.7%
367 1
1.7%
351 1
1.7%
299 1
1.7%
280 1
1.7%
272 1
1.7%

Interactions

2023-12-13T07:39:31.795192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.406844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.219822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.977663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.670589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.434637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.447314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.895612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.912474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.891669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.882515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.497041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.311917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.048424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.744467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.514957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.545501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.988923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.030471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.996238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.968232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.569798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.404322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.114701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.814618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.591066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.641739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.077678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.126518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.091707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.092794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.639282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.475386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.176919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.897666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.671418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.144532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.149260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.209676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.176693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.200990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.715675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.544736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.247365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.975832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.800982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.250453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.268222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.299430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.258842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.285788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.791351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.610750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.325851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.056739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.929654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.357535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.381304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.385229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.348097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.374400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.882405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.681286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.402738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.127524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.037987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.445310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.470526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.479511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.424958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.470258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:23.967937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.756101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.465280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.220604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.142974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.569953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.572833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.586116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.523627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.582658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.053362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.828738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.532567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.293839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.246787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.690699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.689696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.678134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.613694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:32.661369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.143609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:24.898990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:25.600028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:26.359760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:27.356622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:28.789887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:29.795440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:30.783861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:39:31.702798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:39:37.823683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위상병코드상병명칭실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상
구분1.0000.0000.0000.0000.9080.0000.0000.1150.5990.5600.6910.8320.512
순위0.0001.0000.8110.8110.4870.5310.6280.0880.1750.0000.0450.0000.234
상병코드0.0000.8111.0001.0000.8391.0000.9191.0000.2260.4130.0000.0000.538
상병명칭0.0000.8111.0001.0000.8391.0000.9191.0000.2260.4130.0000.0000.538
실인원0.9080.4870.8390.8391.000NaN0.7520.8470.8010.8490.9140.9010.842
연인원0.0000.5311.0001.000NaN1.0000.889NaN0.6640.5740.5620.4250.109
진료비(천원)0.0000.6280.9190.9190.7520.8891.0000.5490.3170.6050.7360.5140.376
59세이하0.1150.0881.0001.0000.847NaN0.5491.0000.0000.3460.0000.0000.000
60세-64세0.5990.1750.2260.2260.8010.6640.3170.0001.0000.9380.9220.7270.693
65세-69세0.5600.0000.4130.4130.8490.5740.6050.3460.9381.0000.9150.7420.782
70세-79세0.6910.0450.0000.0000.9140.5620.7360.0000.9220.9151.0000.7880.771
80세-89세0.8320.0000.0000.0000.9010.4250.5140.0000.7270.7420.7881.0000.809
90세이상0.5120.2340.5380.5380.8420.1090.3760.0000.6930.7820.7710.8091.000
2023-12-13T07:39:37.959209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상구분
순위1.000-0.449-0.700-0.720-0.285-0.345-0.317-0.303-0.178-0.2320.000
실인원-0.4491.0000.4890.3170.6600.6520.6740.6050.3590.5290.614
연인원-0.7000.4891.0000.8560.3150.4260.4680.4740.3400.2910.019
진료비(천원)-0.7200.3170.8561.0000.2530.2600.2250.2280.1310.1180.000
59세이하-0.2850.6600.3150.2531.0000.6570.7080.6580.5120.4000.025
60세-64세-0.3450.6520.4260.2600.6571.0000.9160.7870.6180.5160.444
65세-69세-0.3170.6740.4680.2250.7080.9161.0000.8830.7360.5900.405
70세-79세-0.3030.6050.4740.2280.6580.7870.8831.0000.8250.6990.548
80세-89세-0.1780.3590.3400.1310.5120.6180.7360.8251.0000.6650.513
90세이상-0.2320.5290.2910.1180.4000.5160.5900.6990.6651.0000.383
구분0.0000.6140.0190.0000.0250.4440.4050.5480.5130.3831.000

Missing values

2023-12-13T07:39:32.813573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:39:33.035077image/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
0입원실인원1N40전립선증식증59631561542267926173335476287
1입원실인원2M48기타 척추병증43980272321847323243050273566
2입원실인원3U07U07의 응급사용3062800915134252168202164258
3입원실인원4I20협심증2511493129478891381111203180
4입원실인원5I63뇌경색증22990392893547863119161443712
5입원실인원6C61전립선의 악성 신생물18930271815292395139143258
6입원실인원7M17윤충증1223412114105459019791131
7입원실인원8C67방광의 악성 신생물101145366567685532368196
8입원실인원9C18결장의 악성 신생물92238997688187410176113
9입원실인원10C16위의 악성 신생물903011107630101421344976
구분순위상병코드상병명칭실인원연인원진료비(천원)59세이하60세-64세65세-69세70세-79세80세-89세90세이상
50외래11K21위-식도역류병7260<NA>8050745964794085075024705137
51외래12H25노인성 백내장6131<NA>324467533421492925077445126
52외래13F00알츠하이머병에서의 치매(G30.-+)6065<NA>2980898181744119219826471040
53외래14M51기타 추간판장애5816<NA>419552493990276346393619276
54외래15M75어깨병변5780<NA>426305686641366491382342831
55외래16Z49투석을 포함한 치료를 위하여 보건서비스와 접하고 있는 사람5230<NA>1422303917117740434927805200
56외래17C61전립선의 악성 신생물5118<NA>95795836120871183951737205
57외래18R41인지기능 및 자각에 관한 기타 증상 및 징후5054<NA>468229553641063963576755157
58외래19J44기타 만성 폐쇄성 폐질환4676<NA>4634978931111091423453593268
59외래20R63음식 및 수액섭취에 관계된 증상 및 징후4573<NA>192574492155852133314627179