Overview

Dataset statistics

Number of variables14
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory128.7 B

Variable types

Text1
Categorical1
Numeric12

Dataset

Description2022년 한국보훈복지의료공단 대구보훈병원에 내원한 응급환자를 진료과별로 분류한 인원통계를 월별로 제공합니다.
URLhttps://www.data.go.kr/data/15067503/fileData.do

Alerts

1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
1월 has 10 (35.7%) zerosZeros
2월 has 10 (35.7%) zerosZeros
3월 has 11 (39.3%) zerosZeros
4월 has 9 (32.1%) zerosZeros
5월 has 9 (32.1%) zerosZeros
6월 has 9 (32.1%) zerosZeros
7월 has 7 (25.0%) zerosZeros
8월 has 8 (28.6%) zerosZeros
9월 has 6 (21.4%) zerosZeros
10월 has 8 (28.6%) zerosZeros
11월 has 6 (21.4%) zerosZeros
12월 has 8 (28.6%) zerosZeros

Reproduction

Analysis started2023-12-12 04:20:28.419764
Analysis finished2023-12-12 04:20:46.117847
Duration17.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T13:20:46.230569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.0714286
Min length2

Characters and Unicode

Total characters114
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)7.1%

Sample

1st row가정의학과
2nd row가정의학과
3rd row내분비내과
4th row내분비내과
5th row비뇨의학과
ValueCountFrequency (%)
가정의학과 2
 
6.7%
내분비내과 2
 
6.7%
비뇨의학과 2
 
6.7%
소화기 2
 
6.7%
내과 2
 
6.7%
순환기내과 2
 
6.7%
신경과 2
 
6.7%
신경외과 2
 
6.7%
외과 2
 
6.7%
응급실 2
 
6.7%
Other values (6) 10
33.3%
2023-12-12T13:20:46.625420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 26
22.8%
내 8
 
7.0%
의 6
 
5.3%
학 6
 
5.3%
외 6
 
5.3%
비 6
 
5.3%
신 4
 
3.5%
경 4
 
3.5%
정 4
 
3.5%
기 4
 
3.5%
Other values (21) 40
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112
98.2%
Space Separator 2
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 26
23.2%
내 8
 
7.1%
의 6
 
5.4%
학 6
 
5.4%
외 6
 
5.4%
비 6
 
5.4%
신 4
 
3.6%
경 4
 
3.6%
정 4
 
3.6%
기 4
 
3.6%
Other values (20) 38
33.9%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112
98.2%
Common 2
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 26
23.2%
내 8
 
7.1%
의 6
 
5.4%
학 6
 
5.4%
외 6
 
5.4%
비 6
 
5.4%
신 4
 
3.6%
경 4
 
3.6%
정 4
 
3.6%
기 4
 
3.6%
Other values (20) 38
33.9%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112
98.2%
ASCII 2
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 26
23.2%
내 8
 
7.1%
의 6
 
5.4%
학 6
 
5.4%
외 6
 
5.4%
비 6
 
5.4%
신 4
 
3.6%
경 4
 
3.6%
정 4
 
3.6%
기 4
 
3.6%
Other values (20) 38
33.9%
ASCII
ValueCountFrequency (%)
2
100.0%

국사비
Categorical

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
국비
14 
사비
14 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국비
2nd row사비
3rd row국비
4th row사비
5th row국비

Common Values

ValueCountFrequency (%)
국비 14
50.0%
사비 14
50.0%

Length

2023-12-12T13:20:46.794614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:20:46.950670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국비 14
50.0%
사비 14
50.0%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.285714
Minimum0
Maximum295
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:47.075265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38
95-th percentile123.85
Maximum295
Range295
Interquartile range (IQR)8

Descriptive statistics

Standard deviation62.52517
Coefficient of variation (CV)2.9374241
Kurtosis15.079273
Mean21.285714
Median Absolute Deviation (MAD)2
Skewness3.8460532
Sum596
Variance3909.3968
MonotonicityNot monotonic
2023-12-12T13:20:47.247085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 10
35.7%
4 3
 
10.7%
2 3
 
10.7%
1 2
 
7.1%
8 2
 
7.1%
3 1
 
3.6%
13 1
 
3.6%
40 1
 
3.6%
23 1
 
3.6%
6 1
 
3.6%
Other values (3) 3
 
10.7%
ValueCountFrequency (%)
0 10
35.7%
1 2
 
7.1%
2 3
 
10.7%
3 1
 
3.6%
4 3
 
10.7%
6 1
 
3.6%
8 2
 
7.1%
11 1
 
3.6%
13 1
 
3.6%
23 1
 
3.6%
ValueCountFrequency (%)
295 1
 
3.6%
169 1
 
3.6%
40 1
 
3.6%
23 1
 
3.6%
13 1
 
3.6%
11 1
 
3.6%
8 2
7.1%
6 1
 
3.6%
4 3
10.7%
3 1
 
3.6%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.142857
Minimum0
Maximum196
Zeros10
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:47.390884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile91.05
Maximum196
Range196
Interquartile range (IQR)5

Descriptive statistics

Standard deviation42.64558
Coefficient of variation (CV)2.8162176
Kurtosis13.651354
Mean15.142857
Median Absolute Deviation (MAD)2
Skewness3.6955556
Sum424
Variance1818.6455
MonotonicityNot monotonic
2023-12-12T13:20:47.552392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 10
35.7%
1 3
 
10.7%
5 3
 
10.7%
4 2
 
7.1%
2 2
 
7.1%
3 2
 
7.1%
17 1
 
3.6%
28 1
 
3.6%
15 1
 
3.6%
196 1
 
3.6%
Other values (2) 2
 
7.1%
ValueCountFrequency (%)
0 10
35.7%
1 3
 
10.7%
2 2
 
7.1%
3 2
 
7.1%
4 2
 
7.1%
5 3
 
10.7%
7 1
 
3.6%
15 1
 
3.6%
17 1
 
3.6%
28 1
 
3.6%
ValueCountFrequency (%)
196 1
 
3.6%
125 1
 
3.6%
28 1
 
3.6%
17 1
 
3.6%
15 1
 
3.6%
7 1
 
3.6%
5 3
10.7%
4 2
7.1%
3 2
7.1%
2 2
7.1%

3월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)46.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.571429
Minimum0
Maximum241
Zeros11
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:47.700969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37.75
95-th percentile136.25
Maximum241
Range241
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation55.953306
Coefficient of variation (CV)2.7199524
Kurtosis11.415891
Mean20.571429
Median Absolute Deviation (MAD)2
Skewness3.4563033
Sum576
Variance3130.7725
MonotonicityNot monotonic
2023-12-12T13:20:47.871069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11
39.3%
2 4
 
14.3%
3 2
 
7.1%
6 2
 
7.1%
24 1
 
3.6%
17 1
 
3.6%
42 1
 
3.6%
20 1
 
3.6%
1 1
 
3.6%
5 1
 
3.6%
Other values (3) 3
 
10.7%
ValueCountFrequency (%)
0 11
39.3%
1 1
 
3.6%
2 4
 
14.3%
3 2
 
7.1%
5 1
 
3.6%
6 2
 
7.1%
13 1
 
3.6%
17 1
 
3.6%
20 1
 
3.6%
24 1
 
3.6%
ValueCountFrequency (%)
241 1
3.6%
187 1
3.6%
42 1
3.6%
24 1
3.6%
20 1
3.6%
17 1
3.6%
13 1
3.6%
6 2
7.1%
5 1
3.6%
3 2
7.1%

4월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.178571
Minimum0
Maximum224
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:48.013349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile102.3
Maximum224
Range224
Interquartile range (IQR)10

Descriptive statistics

Standard deviation48.636257
Coefficient of variation (CV)2.6754719
Kurtosis13.625867
Mean18.178571
Median Absolute Deviation (MAD)2
Skewness3.6989256
Sum509
Variance2365.4854
MonotonicityNot monotonic
2023-12-12T13:20:48.158880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 9
32.1%
2 5
17.9%
7 2
 
7.1%
8 2
 
7.1%
1 1
 
3.6%
14 1
 
3.6%
17 1
 
3.6%
23 1
 
3.6%
20 1
 
3.6%
13 1
 
3.6%
Other values (4) 4
14.3%
ValueCountFrequency (%)
0 9
32.1%
1 1
 
3.6%
2 5
17.9%
3 1
 
3.6%
7 2
 
7.1%
8 2
 
7.1%
9 1
 
3.6%
13 1
 
3.6%
14 1
 
3.6%
17 1
 
3.6%
ValueCountFrequency (%)
224 1
3.6%
145 1
3.6%
23 1
3.6%
20 1
3.6%
17 1
3.6%
14 1
3.6%
13 1
3.6%
9 1
3.6%
8 2
7.1%
7 2
7.1%

5월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.75
Minimum0
Maximum294
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:48.307820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q311.5
95-th percentile129.5
Maximum294
Range294
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation62.753855
Coefficient of variation (CV)2.7584112
Kurtosis14.402117
Mean22.75
Median Absolute Deviation (MAD)3
Skewness3.7618684
Sum637
Variance3938.0463
MonotonicityNot monotonic
2023-12-12T13:20:48.467748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 9
32.1%
5 3
 
10.7%
2 2
 
7.1%
3 2
 
7.1%
1 2
 
7.1%
17 1
 
3.6%
19 1
 
3.6%
45 1
 
3.6%
25 1
 
3.6%
4 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
0 9
32.1%
1 2
 
7.1%
2 2
 
7.1%
3 2
 
7.1%
4 1
 
3.6%
5 3
 
10.7%
7 1
 
3.6%
11 1
 
3.6%
13 1
 
3.6%
17 1
 
3.6%
ValueCountFrequency (%)
294 1
 
3.6%
175 1
 
3.6%
45 1
 
3.6%
25 1
 
3.6%
19 1
 
3.6%
17 1
 
3.6%
13 1
 
3.6%
11 1
 
3.6%
7 1
 
3.6%
5 3
10.7%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.642857
Minimum0
Maximum217
Zeros9
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:48.616381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q312
95-th percentile129.45
Maximum217
Range217
Interquartile range (IQR)12

Descriptive statistics

Standard deviation51.589702
Coefficient of variation (CV)2.4991551
Kurtosis10.899623
Mean20.642857
Median Absolute Deviation (MAD)4.5
Skewness3.4059868
Sum578
Variance2661.4974
MonotonicityNot monotonic
2023-12-12T13:20:48.757880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
32.1%
7 2
 
7.1%
4 2
 
7.1%
12 2
 
7.1%
5 2
 
7.1%
1 1
 
3.6%
9 1
 
3.6%
19 1
 
3.6%
183 1
 
3.6%
217 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
0 9
32.1%
1 1
 
3.6%
2 1
 
3.6%
3 1
 
3.6%
4 2
 
7.1%
5 2
 
7.1%
6 1
 
3.6%
7 2
 
7.1%
9 1
 
3.6%
12 2
 
7.1%
ValueCountFrequency (%)
217 1
3.6%
183 1
3.6%
30 1
3.6%
27 1
3.6%
25 1
3.6%
19 1
3.6%
12 2
7.1%
9 1
3.6%
7 2
7.1%
6 1
3.6%

7월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.357143
Minimum0
Maximum243
Zeros7
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:48.939543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median4
Q310.75
95-th percentile132.7
Maximum243
Range243
Interquartile range (IQR)10

Descriptive statistics

Standard deviation55.888258
Coefficient of variation (CV)2.6168415
Kurtosis11.750668
Mean21.357143
Median Absolute Deviation (MAD)4
Skewness3.5129413
Sum598
Variance3123.4974
MonotonicityNot monotonic
2023-12-12T13:20:49.096252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 7
25.0%
2 3
10.7%
9 3
10.7%
1 2
 
7.1%
4 2
 
7.1%
5 2
 
7.1%
3 1
 
3.6%
6 1
 
3.6%
22 1
 
3.6%
17 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
0 7
25.0%
1 2
 
7.1%
2 3
10.7%
3 1
 
3.6%
4 2
 
7.1%
5 2
 
7.1%
6 1
 
3.6%
9 3
10.7%
16 1
 
3.6%
17 1
 
3.6%
ValueCountFrequency (%)
243 1
 
3.6%
188 1
 
3.6%
30 1
 
3.6%
22 1
 
3.6%
20 1
 
3.6%
17 1
 
3.6%
16 1
 
3.6%
9 3
10.7%
6 1
 
3.6%
5 2
7.1%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.857143
Minimum0
Maximum318
Zeros8
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:49.226340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q310.25
95-th percentile159.4
Maximum318
Range318
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation71.561274
Coefficient of variation (CV)2.8789018
Kurtosis12.678318
Mean24.857143
Median Absolute Deviation (MAD)3.5
Skewness3.6269552
Sum696
Variance5121.0159
MonotonicityNot monotonic
2023-12-12T13:20:49.371946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 8
28.6%
1 5
17.9%
5 3
 
10.7%
3 1
 
3.6%
32 1
 
3.6%
16 1
 
3.6%
22 1
 
3.6%
10 1
 
3.6%
11 1
 
3.6%
9 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
0 8
28.6%
1 5
17.9%
3 1
 
3.6%
4 1
 
3.6%
5 3
 
10.7%
8 1
 
3.6%
9 1
 
3.6%
10 1
 
3.6%
11 1
 
3.6%
15 1
 
3.6%
ValueCountFrequency (%)
318 1
3.6%
228 1
3.6%
32 1
3.6%
22 1
3.6%
16 1
3.6%
15 1
3.6%
11 1
3.6%
10 1
3.6%
9 1
3.6%
8 1
3.6%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.964286
Minimum0
Maximum272
Zeros6
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:49.539583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38.75
95-th percentile143.5
Maximum272
Range272
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation61.896259
Coefficient of variation (CV)2.695327
Kurtosis12.083913
Mean22.964286
Median Absolute Deviation (MAD)3.5
Skewness3.5486924
Sum643
Variance3831.1468
MonotonicityNot monotonic
2023-12-12T13:20:49.670837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 6
21.4%
1 4
14.3%
3 3
10.7%
4 3
10.7%
7 2
 
7.1%
33 1
 
3.6%
17 1
 
3.6%
27 1
 
3.6%
22 1
 
3.6%
6 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
0 6
21.4%
1 4
14.3%
3 3
10.7%
4 3
10.7%
5 1
 
3.6%
6 1
 
3.6%
7 2
 
7.1%
8 1
 
3.6%
11 1
 
3.6%
17 1
 
3.6%
ValueCountFrequency (%)
272 1
3.6%
203 1
3.6%
33 1
3.6%
27 1
3.6%
22 1
3.6%
17 1
3.6%
11 1
3.6%
8 1
3.6%
7 2
7.1%
6 1
3.6%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.178571
Minimum0
Maximum314
Zeros8
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:49.805869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q311
95-th percentile114.5
Maximum314
Range314
Interquartile range (IQR)11

Descriptive statistics

Standard deviation64.650659
Coefficient of variation (CV)2.9150055
Kurtosis16.807143
Mean22.178571
Median Absolute Deviation (MAD)3.5
Skewness4.0292588
Sum621
Variance4179.7077
MonotonicityNot monotonic
2023-12-12T13:20:50.277418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 8
28.6%
2 3
 
10.7%
1 2
 
7.1%
4 2
 
7.1%
5 2
 
7.1%
11 2
 
7.1%
3 1
 
3.6%
28 1
 
3.6%
13 1
 
3.6%
30 1
 
3.6%
Other values (5) 5
17.9%
ValueCountFrequency (%)
0 8
28.6%
1 2
 
7.1%
2 3
 
10.7%
3 1
 
3.6%
4 2
 
7.1%
5 2
 
7.1%
6 1
 
3.6%
7 1
 
3.6%
11 2
 
7.1%
12 1
 
3.6%
ValueCountFrequency (%)
314 1
3.6%
160 1
3.6%
30 1
3.6%
28 1
3.6%
13 1
3.6%
12 1
3.6%
11 2
7.1%
7 1
3.6%
6 1
3.6%
5 2
7.1%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.857143
Minimum0
Maximum260
Zeros6
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:50.433486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.5
Q312
95-th percentile106.55
Maximum260
Range260
Interquartile range (IQR)11

Descriptive statistics

Standard deviation54.197659
Coefficient of variation (CV)2.5985179
Kurtosis15.419734
Mean20.857143
Median Absolute Deviation (MAD)4.5
Skewness3.8643561
Sum584
Variance2937.3862
MonotonicityNot monotonic
2023-12-12T13:20:50.597274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 6
21.4%
1 3
10.7%
3 3
10.7%
10 2
 
7.1%
12 2
 
7.1%
5 2
 
7.1%
2 1
 
3.6%
144 1
 
3.6%
260 1
 
3.6%
4 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
0 6
21.4%
1 3
10.7%
2 1
 
3.6%
3 3
10.7%
4 1
 
3.6%
5 2
 
7.1%
6 1
 
3.6%
8 1
 
3.6%
10 2
 
7.1%
12 2
 
7.1%
ValueCountFrequency (%)
260 1
3.6%
144 1
3.6%
37 1
3.6%
21 1
3.6%
20 1
3.6%
16 1
3.6%
12 2
7.1%
10 2
7.1%
8 1
3.6%
6 1
3.6%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.464286
Minimum0
Maximum253
Zeros8
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T13:20:50.721551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q311
95-th percentile151.6
Maximum253
Range253
Interquartile range (IQR)11

Descriptive statistics

Standard deviation60.67917
Coefficient of variation (CV)2.7011395
Kurtosis11.075975
Mean22.464286
Median Absolute Deviation (MAD)3
Skewness3.4502468
Sum629
Variance3681.9616
MonotonicityNot monotonic
2023-12-12T13:20:50.864190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 8
28.6%
3 3
 
10.7%
2 2
 
7.1%
1 2
 
7.1%
11 2
 
7.1%
14 1
 
3.6%
30 1
 
3.6%
32 1
 
3.6%
17 1
 
3.6%
4 1
 
3.6%
Other values (6) 6
21.4%
ValueCountFrequency (%)
0 8
28.6%
1 2
 
7.1%
2 2
 
7.1%
3 3
 
10.7%
4 1
 
3.6%
5 1
 
3.6%
6 1
 
3.6%
7 1
 
3.6%
8 1
 
3.6%
11 2
 
7.1%
ValueCountFrequency (%)
253 1
3.6%
216 1
3.6%
32 1
3.6%
30 1
3.6%
17 1
3.6%
14 1
3.6%
11 2
7.1%
8 1
3.6%
7 1
3.6%
6 1
3.6%

Interactions

2023-12-12T13:20:44.471792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:28.835795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.139126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.499272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.637104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.016850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.477296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.071737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.604074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.106697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.448557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.116140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.577868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:28.932174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.261752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.619864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.744631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.110150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.604997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.201847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.726810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.243076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.554555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.218757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.704558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.029873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.400223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.729905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.850142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.212843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.743458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.320523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.866315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.358828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.677068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.347432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.817009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.129722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.499855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.814678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.943773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.373065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.860492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.438258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.976627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.453304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.096036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.468942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.924168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.215966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.601868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.901803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.027367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.475161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.999991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.554815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.094402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.554165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.179639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.570525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.030152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.306244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.712551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.989401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.127209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.592055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.135608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.695204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.208413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.664115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.296574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.694966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.158361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.446425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.822214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.071227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.194950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.726393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.275089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.815994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.318697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.761135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.420986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.815836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.259638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.566257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.916631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.162853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.265884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.830650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.418352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:37.915767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.445847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:40.888831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.556687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.936739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.356631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.698035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.022070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.246628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.344207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:34.989347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.549020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.044904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.585155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.008263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.663701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.061258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.455465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.790797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.149424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.333664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.430537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.144537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.665039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.171784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.705531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.150815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.767105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.167902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.541166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:29.899856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.271305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.416567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.820727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.247659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.805012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.329135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.839884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.252799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:42.893867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.268141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:45.641505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:30.019659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:31.379886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:32.503519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:33.933622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:35.344825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:36.948103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:38.469116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:39.975792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:41.345218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:43.008450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:20:44.362367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:20:51.011076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분국사비1월2월3월4월5월6월7월8월9월10월11월12월
구분1.0000.0000.0000.0000.0000.0000.0000.6750.0000.0000.0000.0000.0000.782
국사비0.0001.0000.0600.0600.0600.0600.0600.0000.0600.0600.0600.0000.0600.000
1월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
2월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
3월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
4월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
5월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
6월0.6750.0000.9900.9900.9900.9900.9901.0000.9900.9900.9901.0000.9900.997
7월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
8월0.0000.0600.9770.9770.9770.9770.9770.9900.9771.0001.0001.0000.9770.977
9월0.0000.0600.9770.9770.9770.9770.9770.9900.9771.0001.0001.0000.9770.977
10월0.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
11월0.0000.0601.0001.0001.0001.0001.0000.9901.0000.9770.9771.0001.0000.994
12월0.7820.0000.9940.9940.9940.9940.9940.9970.9940.9770.9771.0000.9941.000
2023-12-12T13:20:51.240288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월국사비
1월1.0000.9200.9570.9350.9500.9390.9130.8920.8880.9500.9440.9720.000
2월0.9201.0000.9210.8760.8810.8950.8810.8220.8560.9210.8780.9110.000
3월0.9570.9211.0000.9350.9690.9470.9160.9110.8770.9610.9070.9500.000
4월0.9350.8760.9351.0000.9220.9320.9280.9190.9430.9500.9350.9270.000
5월0.9500.8810.9690.9221.0000.9500.9410.9020.8510.9500.8990.9300.000
6월0.9390.8950.9470.9320.9501.0000.9380.8860.9340.9570.9160.9180.000
7월0.9130.8810.9160.9280.9410.9381.0000.9000.8750.9590.9210.8830.000
8월0.8920.8220.9110.9190.9020.8860.9001.0000.8620.9070.8280.8990.000
9월0.8880.8560.8770.9430.8510.9340.8750.8621.0000.8960.9030.8780.000
10월0.9500.9210.9610.9500.9500.9570.9590.9070.8961.0000.9350.9200.000
11월0.9440.8780.9070.9350.8990.9160.9210.8280.9030.9351.0000.9040.000
12월0.9720.9110.9500.9270.9300.9180.8830.8990.8780.9200.9041.0000.000
국사비0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T13:20:45.845853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:20:46.048157image/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

구분국사비1월2월3월4월5월6월7월8월9월10월11월12월
0가정의학과국비000101113110
1가정의학과사비110200213152
2내분비내과국비412757457288
3내분비내과사비3422273074103
4비뇨의학과국비123233433363
5비뇨의학과사비000214614230
6소화기 내과국비131724141727223233282017
7소화기 내과사비8317171912171617131614
8순환기내과국비402842234525302227303730
9순환기내과사비231520202530201022122132
구분국사비1월2월3월4월5월6월7월8월9월10월11월12월
18응급실사비169125187145175183188228203160144216
19이비인후과국비000000011000
20이비인후과사비000010000000
21재활의학과국비000000000010
22재활의학과사비000000001000
23정형외과국비876811121615116107
24정형외과사비1141391319988111211
25치과사비000000000001
26피부과국비000000000010
27피부과사비000000100000