Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory673.8 KiB
Average record size in memory69.0 B

Variable types

Categorical1
Text2
Numeric4

Dataset

Description병원급 이상 의료기관의 진료과목별 행위 통계 / 진료일자 기준(심사분은 각 진료년+4개월) (예) 진료년월: 2022.1월~12월, 심사년월: 2022.1월~2023.4월 / 보험자: 건강보험 / ※ 2019년 진료분부터 서면, DRG 청구건 제외
URLhttps://www.data.go.kr/data/15055566/fileData.do

Alerts

진료년도 has constant value ""Constant
환자수 is highly overall correlated with 명세서청구건수 and 2 other fieldsHigh correlation
명세서청구건수 is highly overall correlated with 환자수 and 2 other fieldsHigh correlation
총사용량 is highly overall correlated with 환자수 and 2 other fieldsHigh correlation
진료행위청구금액 is highly overall correlated with 환자수 and 2 other fieldsHigh correlation
환자수 is highly skewed (γ1 = 26.4470873)Skewed
명세서청구건수 is highly skewed (γ1 = 33.26762609)Skewed
총사용량 is highly skewed (γ1 = 28.42250584)Skewed
진료행위청구금액 is highly skewed (γ1 = 30.32912706)Skewed

Reproduction

Analysis started2023-12-12 15:12:11.102093
Analysis finished2023-12-12 15:12:14.229735
Duration3.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

진료년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 10000
100.0%

Length

2023-12-13T00:12:14.318542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:12:14.436030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 10000
100.0%

진료과목
Text

Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:12:14.645627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length4.5658
Min length2

Characters and Unicode

Total characters45658
Distinct characters79
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row신경외과
2nd row응급의학과
3rd row방사선종양학과
4th row정형외과
5th row외과
ValueCountFrequency (%)
내과 717
 
7.2%
외과 574
 
5.7%
소아청소년과 507
 
5.1%
신경외과 500
 
5.0%
정형외과 498
 
5.0%
재활의학과 477
 
4.8%
심장혈관흉부외과 474
 
4.7%
신경과 439
 
4.4%
가정의학과 434
 
4.3%
비뇨의학과 414
 
4.1%
Other values (41) 4966
49.7%
2023-12-13T00:12:15.062106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 10117
22.2%
학 3206
 
7.0%
의 3181
 
7.0%
외 2648
 
5.8%
정 1290
 
2.8%
신 1284
 
2.8%
소 1270
 
2.8%
부 1142
 
2.5%
경 1061
 
2.3%
형 865
 
1.9%
Other values (69) 19594
42.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 45595
99.9%
Other Punctuation 34
 
0.1%
Decimal Number 29
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 10117
22.2%
학 3206
 
7.0%
의 3181
 
7.0%
외 2648
 
5.8%
정 1290
 
2.8%
신 1284
 
2.8%
소 1270
 
2.8%
부 1142
 
2.5%
경 1061
 
2.3%
형 865
 
1.9%
Other values (66) 19531
42.8%
Decimal Number
ValueCountFrequency (%)
3 16
55.2%
2 13
44.8%
Other Punctuation
ValueCountFrequency (%)
· 34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 45595
99.9%
Common 63
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 10117
22.2%
학 3206
 
7.0%
의 3181
 
7.0%
외 2648
 
5.8%
정 1290
 
2.8%
신 1284
 
2.8%
소 1270
 
2.8%
부 1142
 
2.5%
경 1061
 
2.3%
형 865
 
1.9%
Other values (66) 19531
42.8%
Common
ValueCountFrequency (%)
· 34
54.0%
3 16
25.4%
2 13
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 45595
99.9%
None 34
 
0.1%
ASCII 29
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 10117
22.2%
학 3206
 
7.0%
의 3181
 
7.0%
외 2648
 
5.8%
정 1290
 
2.8%
신 1284
 
2.8%
소 1270
 
2.8%
부 1142
 
2.5%
경 1061
 
2.3%
형 865
 
1.9%
Other values (66) 19531
42.8%
None
ValueCountFrequency (%)
· 34
100.0%
ASCII
ValueCountFrequency (%)
3 16
55.2%
2 13
44.8%

행위코드
Text

Distinct4718
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T00:12:15.450665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9984
Min length1

Characters and Unicode

Total characters49984
Distinct characters37
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

Unique2072 ?
Unique (%)20.7%

Sample

1st rowAV561
2nd rowHJ605
3rd rowD6583
4th rowD5110
5th rowAU933
ValueCountFrequency (%)
d0113 11
 
0.1%
d6620 11
 
0.1%
g6404 10
 
0.1%
fy753 9
 
0.1%
j1130 9
 
0.1%
d1850 9
 
0.1%
f6001 8
 
0.1%
d4023 8
 
0.1%
d1830 8
 
0.1%
l1310 8
 
0.1%
Other values (4708) 9909
99.1%
2023-12-13T00:12:16.006505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7148
14.3%
1 6364
12.7%
2 4890
 
9.8%
3 3633
 
7.3%
5 3328
 
6.7%
4 3095
 
6.2%
6 2771
 
5.5%
A 2391
 
4.8%
7 2243
 
4.5%
D 1682
 
3.4%
Other values (27) 12439
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 36212
72.4%
Uppercase Letter 13768
 
27.5%
Currency Symbol 4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2391
17.4%
D 1682
12.2%
H 1243
 
9.0%
G 810
 
5.9%
U 728
 
5.3%
B 683
 
5.0%
E 682
 
5.0%
M 557
 
4.0%
N 476
 
3.5%
F 448
 
3.3%
Other values (16) 4068
29.5%
Decimal Number
ValueCountFrequency (%)
0 7148
19.7%
1 6364
17.6%
2 4890
13.5%
3 3633
10.0%
5 3328
9.2%
4 3095
8.5%
6 2771
 
7.7%
7 2243
 
6.2%
8 1521
 
4.2%
9 1219
 
3.4%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36216
72.5%
Latin 13768
 
27.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2391
17.4%
D 1682
12.2%
H 1243
 
9.0%
G 810
 
5.9%
U 728
 
5.3%
B 683
 
5.0%
E 682
 
5.0%
M 557
 
4.0%
N 476
 
3.5%
F 448
 
3.3%
Other values (16) 4068
29.5%
Common
ValueCountFrequency (%)
0 7148
19.7%
1 6364
17.6%
2 4890
13.5%
3 3633
10.0%
5 3328
9.2%
4 3095
8.5%
6 2771
 
7.7%
7 2243
 
6.2%
8 1521
 
4.2%
9 1219
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7148
14.3%
1 6364
12.7%
2 4890
 
9.8%
3 3633
 
7.3%
5 3328
 
6.7%
4 3095
 
6.2%
6 2771
 
5.5%
A 2391
 
4.8%
7 2243
 
4.5%
D 1682
 
3.4%
Other values (27) 12439
24.9%

환자수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2506
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13851.716
Minimum1
Maximum6109558
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:12:16.160987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median29
Q3411
95-th percentile21502.95
Maximum6109558
Range6109557
Interquartile range (IQR)408

Descriptive statistics

Standard deviation158920.25
Coefficient of variation (CV)11.472965
Kurtosis838.35132
Mean13851.716
Median Absolute Deviation (MAD)28
Skewness26.447087
Sum1.3851716 × 108
Variance2.5255645 × 1010
MonotonicityNot monotonic
2023-12-13T00:12:16.291296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1496
 
15.0%
2 685
 
6.9%
3 435
 
4.3%
4 308
 
3.1%
5 240
 
2.4%
6 208
 
2.1%
7 173
 
1.7%
9 137
 
1.4%
8 133
 
1.3%
10 116
 
1.2%
Other values (2496) 6069
60.7%
ValueCountFrequency (%)
1 1496
15.0%
2 685
6.9%
3 435
 
4.3%
4 308
 
3.1%
5 240
 
2.4%
6 208
 
2.1%
7 173
 
1.7%
8 133
 
1.3%
9 137
 
1.4%
10 116
 
1.2%
ValueCountFrequency (%)
6109558 1
< 0.1%
5738950 1
< 0.1%
5607782 1
< 0.1%
5165425 1
< 0.1%
4638607 1
< 0.1%
4563354 1
< 0.1%
3374633 1
< 0.1%
3098266 1
< 0.1%
2370338 1
< 0.1%
2368912 1
< 0.1%

명세서청구건수
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2649
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25322.258
Minimum1
Maximum18688053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:12:16.444794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median32
Q3477.75
95-th percentile30189.05
Maximum18688053
Range18688052
Interquartile range (IQR)474.75

Descriptive statistics

Standard deviation373975.23
Coefficient of variation (CV)14.768636
Kurtosis1286.895
Mean25322.258
Median Absolute Deviation (MAD)31
Skewness33.267626
Sum2.5322258 × 108
Variance1.3985747 × 1011
MonotonicityNot monotonic
2023-12-13T00:12:16.610340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1420
 
14.2%
2 672
 
6.7%
3 431
 
4.3%
4 322
 
3.2%
5 223
 
2.2%
6 207
 
2.1%
7 174
 
1.7%
9 140
 
1.4%
8 129
 
1.3%
10 122
 
1.2%
Other values (2639) 6160
61.6%
ValueCountFrequency (%)
1 1420
14.2%
2 672
6.7%
3 431
 
4.3%
4 322
 
3.2%
5 223
 
2.2%
6 207
 
2.1%
7 174
 
1.7%
8 129
 
1.3%
9 140
 
1.4%
10 122
 
1.2%
ValueCountFrequency (%)
18688053 1
< 0.1%
14232941 1
< 0.1%
13095953 1
< 0.1%
12645893 1
< 0.1%
11842758 1
< 0.1%
9213220 1
< 0.1%
8288646 1
< 0.1%
7026224 1
< 0.1%
6062609 1
< 0.1%
2974240 1
< 0.1%

총사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct2992
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49498.521
Minimum0
Maximum32479645
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:12:16.747946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median47
Q3829
95-th percentile57474.85
Maximum32479645
Range32479645
Interquartile range (IQR)824

Descriptive statistics

Standard deviation640284.02
Coefficient of variation (CV)12.935417
Kurtosis1032.4753
Mean49498.521
Median Absolute Deviation (MAD)46
Skewness28.422506
Sum4.9498521 × 108
Variance4.0996362 × 1011
MonotonicityNot monotonic
2023-12-13T00:12:16.930374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1181
 
11.8%
2 607
 
6.1%
3 391
 
3.9%
4 307
 
3.1%
5 218
 
2.2%
7 195
 
1.9%
6 188
 
1.9%
8 131
 
1.3%
10 129
 
1.3%
9 123
 
1.2%
Other values (2982) 6530
65.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1181
11.8%
2 607
6.1%
3 391
 
3.9%
4 307
 
3.1%
5 218
 
2.2%
6 188
 
1.9%
7 195
 
1.9%
8 131
 
1.3%
9 123
 
1.2%
ValueCountFrequency (%)
32479645 1
< 0.1%
19060988 1
< 0.1%
18738102 1
< 0.1%
17750230 1
< 0.1%
17386348 1
< 0.1%
16470093 1
< 0.1%
13494679 1
< 0.1%
11350751 1
< 0.1%
11256474 1
< 0.1%
10748316 1
< 0.1%

진료행위청구금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9759
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8739549 × 108
Minimum0
Maximum2.50522 × 1011
Zeros42
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T00:12:17.070058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14508.45
Q1219916.5
median2045922.5
Q328323483
95-th percentile1.3394317 × 109
Maximum2.50522 × 1011
Range2.50522 × 1011
Interquartile range (IQR)28103567

Descriptive statistics

Standard deviation4.3855437 × 109
Coefficient of variation (CV)8.9979161
Kurtosis1381.3453
Mean4.8739549 × 108
Median Absolute Deviation (MAD)2021106
Skewness30.329127
Sum4.8739549 × 1012
Variance1.9232993 × 1019
MonotonicityNot monotonic
2023-12-13T00:12:17.213857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
0.4%
144183 5
 
0.1%
432549 4
 
< 0.1%
237341 3
 
< 0.1%
47220 3
 
< 0.1%
25530 3
 
< 0.1%
502650 3
 
< 0.1%
167925 3
 
< 0.1%
369120 3
 
< 0.1%
23740 3
 
< 0.1%
Other values (9749) 9928
99.3%
ValueCountFrequency (%)
0 42
0.4%
25 1
 
< 0.1%
40 2
 
< 0.1%
100 1
 
< 0.1%
110 1
 
< 0.1%
210 2
 
< 0.1%
275 1
 
< 0.1%
300 1
 
< 0.1%
330 1
 
< 0.1%
372 1
 
< 0.1%
ValueCountFrequency (%)
250522000000 1
< 0.1%
175825000000 1
< 0.1%
85910472624 1
< 0.1%
80391304521 1
< 0.1%
65446411410 1
< 0.1%
64824100775 1
< 0.1%
64278400025 1
< 0.1%
63406968541 1
< 0.1%
59638666630 1
< 0.1%
57640181768 1
< 0.1%

Interactions

2023-12-13T00:12:13.521181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:11.808445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.515809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.065042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.637958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.192617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.642590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.171793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.755475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.298890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.784070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.291981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.850868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.402670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:12.946214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:12:13.398599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:12:17.320785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진료과목환자수명세서청구건수총사용량진료행위청구금액
진료과목1.0000.0000.0000.0000.000
환자수0.0001.0000.9170.7690.661
명세서청구건수0.0000.9171.0000.9170.726
총사용량0.0000.7690.9171.0000.498
진료행위청구금액0.0000.6610.7260.4981.000
2023-12-13T00:12:17.418816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
환자수명세서청구건수총사용량진료행위청구금액
환자수1.0000.9970.9700.814
명세서청구건수0.9971.0000.9790.818
총사용량0.9700.9791.0000.829
진료행위청구금액0.8140.8180.8291.000

Missing values

2023-12-13T00:12:14.005505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:12:14.154362image/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

진료년도진료과목행위코드환자수명세서청구건수총사용량진료행위청구금액
254042022신경외과AV5614504761965255721270
769932022응급의학과HJ605222288366
624032022방사선종양학과D658359968573844729802
212392022정형외과D51105511958611138830777785062
153312022외과AU93318263296000
189772022외과Q72855595764428
824922022구강악안면외과UA1476672292080
98962022신경과HF105534539524164374814
170022022외과G810228263077339427285045
782392022응급의학과V23007412979220840163071449180
진료년도진료과목행위코드환자수명세서청구건수총사용량진료행위청구금액
361392022성형외과N09662116217126131576076852
588712022비뇨의학과KK083111146263
733042022가정의학과IA8215557572484060
739452022가정의학과Q2722111544513
913312022한방재활의학과153054324815526247080268
79252022신경과AO100183618867552438418690
911912022침구과G25011115412
311942022심장혈관흉부외과G180511111726
905402022한방소아과407024765142740
344412022성형외과D584240531111271394