Overview

Dataset statistics

Number of variables7
Number of observations3495
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory204.9 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Categorical2
Text1

Dataset

Description○ 내용: 질병에 따른 항생제 종류별 처방량○ 대상: 해당년도 국민 건강보험 가입자 또는 의료급여수급권자○ 산출식- 분자: 항생제 처방 건수- 분모: 전체 처방 건수
Author국민건강보험공단
URLhttps://www.data.go.kr/data/15042847/fileData.do

Alerts

지표명 has constant value ""Constant
지표연도 is highly overall correlated with 항생제 처방률High correlation
분모 is highly overall correlated with 분자High correlation
분자 is highly overall correlated with 분모 and 1 other fieldsHigh correlation
항생제 처방률 is highly overall correlated with 지표연도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 06:05:33.135312
Analysis finished2023-12-12 06:05:35.561571
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지표연도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.996
Minimum2009
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T15:05:35.624722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12012
median2015
Q32018
95-th percentile2021
Maximum2021
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7385178
Coefficient of variation (CV)0.0018553475
Kurtosis-1.2118935
Mean2014.996
Median Absolute Deviation (MAD)3
Skewness0.002185911
Sum7042411
Variance13.976515
MonotonicityIncreasing
2023-12-12T15:05:35.744101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2016 271
 
7.8%
2011 270
 
7.7%
2012 270
 
7.7%
2015 270
 
7.7%
2013 269
 
7.7%
2014 269
 
7.7%
2009 268
 
7.7%
2010 268
 
7.7%
2017 268
 
7.7%
2018 268
 
7.7%
Other values (3) 804
23.0%
ValueCountFrequency (%)
2009 268
7.7%
2010 268
7.7%
2011 270
7.7%
2012 270
7.7%
2013 269
7.7%
2014 269
7.7%
2015 270
7.7%
2016 271
7.8%
2017 268
7.7%
2018 268
7.7%
ValueCountFrequency (%)
2021 268
7.7%
2020 268
7.7%
2019 268
7.7%
2018 268
7.7%
2017 268
7.7%
2016 271
7.8%
2015 270
7.7%
2014 269
7.7%
2013 269
7.7%
2012 270
7.7%

시도
Categorical

Distinct18
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
경기도
579 
서울특별시
338 
경상북도
325 
전라남도
299 
경상남도
295 
Other values (13)
1659 

Length

Max length7
Median length5
Mean length4.1081545
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전국
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
경기도 579
16.6%
서울특별시 338
9.7%
경상북도 325
9.3%
전라남도 299
8.6%
경상남도 295
8.4%
강원도 247
7.1%
충청남도 228
 
6.5%
부산광역시 221
 
6.3%
전라북도 208
 
6.0%
충청북도 189
 
5.4%
Other values (8) 566
16.2%

Length

2023-12-12T15:05:35.895397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 579
16.6%
서울특별시 338
9.7%
경상북도 325
9.3%
전라남도 299
8.6%
경상남도 295
8.4%
강원도 247
7.1%
충청남도 228
 
6.5%
부산광역시 221
 
6.3%
전라북도 208
 
6.0%
충청북도 189
 
5.4%
Other values (8) 566
16.2%

시군구
Text

Distinct241
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2023-12-12T15:05:36.272697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.3633763
Min length2

Characters and Unicode

Total characters11755
Distinct characters145
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

Unique0 ?
Unique (%)0.0%

Sample

1st row전체
2nd row전체
3rd row종로구
4th row중구
5th row용산구
ValueCountFrequency (%)
전체 231
 
5.9%
동구 78
 
2.0%
중구 78
 
2.0%
남구 75
 
1.9%
서구 65
 
1.7%
북구 65
 
1.7%
창원시 57
 
1.5%
수원시 52
 
1.3%
용인시 42
 
1.1%
청주시 40
 
1.0%
Other values (237) 3130
80.0%
2023-12-12T15:05:36.840963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 1380
 
11.7%
시 1296
 
11.0%
군 1123
 
9.6%
418
 
3.6%
천 319
 
2.7%
주 300
 
2.6%
산 295
 
2.5%
양 286
 
2.4%
성 271
 
2.3%
동 260
 
2.2%
Other values (135) 5807
49.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11337
96.4%
Space Separator 418
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 1380
 
12.2%
시 1296
 
11.4%
군 1123
 
9.9%
천 319
 
2.8%
주 300
 
2.6%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5550
49.0%
Space Separator
ValueCountFrequency (%)
418
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11337
96.4%
Common 418
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 1380
 
12.2%
시 1296
 
11.4%
군 1123
 
9.9%
천 319
 
2.8%
주 300
 
2.6%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5550
49.0%
Common
ValueCountFrequency (%)
418
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11337
96.4%
ASCII 418
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 1380
 
12.2%
시 1296
 
11.4%
군 1123
 
9.9%
천 319
 
2.8%
주 300
 
2.6%
산 295
 
2.6%
양 286
 
2.5%
성 271
 
2.4%
동 260
 
2.3%
전 257
 
2.3%
Other values (134) 5550
49.0%
ASCII
ValueCountFrequency (%)
418
100.0%

지표명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
항생제처방률
3495 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
항생제처방률 3495
100.0%

Length

2023-12-12T15:05:37.003290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:05:37.093106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
항생제처방률 3495
100.0%

분모
Real number (ℝ)

HIGH CORRELATION 

Distinct3484
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8126251.5
Minimum13351
Maximum8.0259524 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T15:05:37.216312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13351
5-th percentile299012
Q1944943
median2768338
Q34950360
95-th percentile21703053
Maximum8.0259524 × 108
Range8.0258189 × 108
Interquartile range (IQR)4005417

Descriptive statistics

Standard deviation46451939
Coefficient of variation (CV)5.7162812
Kurtosis218.95783
Mean8126251.5
Median Absolute Deviation (MAD)1927213
Skewness14.29993
Sum2.8401249 × 1010
Variance2.1577826 × 1015
MonotonicityNot monotonic
2023-12-12T15:05:37.395387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5929833 2
 
0.1%
4076945 2
 
0.1%
1515936 2
 
0.1%
3562743 2
 
0.1%
39269 2
 
0.1%
2089883 2
 
0.1%
2953713 2
 
0.1%
1665491 2
 
0.1%
2652940 2
 
0.1%
3590971 2
 
0.1%
Other values (3474) 3475
99.4%
ValueCountFrequency (%)
13351 1
< 0.1%
15344 1
< 0.1%
17703 1
< 0.1%
33021 1
< 0.1%
33329 1
< 0.1%
33610 1
< 0.1%
39269 2
0.1%
44246 1
< 0.1%
46613 1
< 0.1%
56157 1
< 0.1%
ValueCountFrequency (%)
802595242 1
< 0.1%
790703281 1
< 0.1%
773027097 1
< 0.1%
772157528 1
< 0.1%
745655310 1
< 0.1%
740342977 1
< 0.1%
732562496 1
< 0.1%
731887756 1
< 0.1%
719750209 1
< 0.1%
689980003 1
< 0.1%

분자
Real number (ℝ)

HIGH CORRELATION 

Distinct3483
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1663830.7
Minimum620
Maximum1.6799403 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T15:05:37.566797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum620
5-th percentile37956.4
Q1144139.5
median538258
Q31023921
95-th percentile4209374.4
Maximum1.6799403 × 108
Range1.6799341 × 108
Interquartile range (IQR)879781.5

Descriptive statistics

Standard deviation9637183.7
Coefficient of variation (CV)5.7921659
Kurtosis229.29341
Mean1663830.7
Median Absolute Deviation (MAD)414661
Skewness14.569894
Sum5.8150884 × 109
Variance9.2875309 × 1013
MonotonicityNot monotonic
2023-12-12T15:05:37.761826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
941723 2
 
0.1%
536288 2
 
0.1%
86324 2
 
0.1%
299669 2
 
0.1%
656834 2
 
0.1%
11016 2
 
0.1%
756408 2
 
0.1%
681691 2
 
0.1%
351623 2
 
0.1%
698873 2
 
0.1%
Other values (3473) 3475
99.4%
ValueCountFrequency (%)
620 1
< 0.1%
804 1
< 0.1%
1189 1
< 0.1%
2339 1
< 0.1%
2376 1
< 0.1%
3478 1
< 0.1%
3817 1
< 0.1%
3923 1
< 0.1%
4400 1
< 0.1%
4421 1
< 0.1%
ValueCountFrequency (%)
167994027 1
< 0.1%
165392977 1
< 0.1%
164516142 1
< 0.1%
162852894 1
< 0.1%
160584849 1
< 0.1%
160419878 1
< 0.1%
157514861 1
< 0.1%
157247382 1
< 0.1%
152746908 1
< 0.1%
151461368 1
< 0.1%

항생제 처방률
Real number (ℝ)

HIGH CORRELATION 

Distinct1759
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.129368
Minimum3.5
Maximum45.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T15:05:37.922778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile10.037
Q114.315
median18.93
Q323.485
95-th percentile28.972
Maximum45.69
Range42.19
Interquartile range (IQR)9.17

Descriptive statistics

Standard deviation6.0072941
Coefficient of variation (CV)0.31403516
Kurtosis-0.313361
Mean19.129368
Median Absolute Deviation (MAD)4.59
Skewness0.26734224
Sum66857.14
Variance36.087583
MonotonicityNot monotonic
2023-12-12T15:05:38.052401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.15 8
 
0.2%
17.79 7
 
0.2%
12.14 7
 
0.2%
27.2 6
 
0.2%
21.09 6
 
0.2%
18.93 6
 
0.2%
15.27 6
 
0.2%
11.4 6
 
0.2%
13.29 6
 
0.2%
16.49 6
 
0.2%
Other values (1749) 3431
98.2%
ValueCountFrequency (%)
3.5 1
< 0.1%
5.1 1
< 0.1%
5.24 1
< 0.1%
5.29 1
< 0.1%
5.78 1
< 0.1%
5.79 1
< 0.1%
6.19 1
< 0.1%
6.3 1
< 0.1%
6.4 1
< 0.1%
6.45 2
0.1%
ValueCountFrequency (%)
45.69 1
< 0.1%
45.28 1
< 0.1%
43.81 1
< 0.1%
41.04 1
< 0.1%
40.58 1
< 0.1%
40.4 1
< 0.1%
38.76 1
< 0.1%
38.37 1
< 0.1%
37.04 1
< 0.1%
36.61 1
< 0.1%

Interactions

2023-12-12T15:05:34.933010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:33.593411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.018950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.478383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:35.022158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:33.697350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.116155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.596264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:35.143764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:33.801974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.250683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.718008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:35.275167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:33.922748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.376145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:05:34.826818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:05:38.438819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도시도분모분자항생제 처방률
지표연도1.0000.0000.0000.0000.559
시도0.0001.0000.7920.7950.421
분모0.0000.7921.0000.9570.000
분자0.0000.7950.9571.0000.000
항생제 처방률0.5590.4210.0000.0001.000
2023-12-12T15:05:38.543430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지표연도분모분자항생제 처방률시도
지표연도1.0000.029-0.114-0.5010.000
분모0.0291.0000.9660.3950.448
분자-0.1140.9661.0000.5930.451
항생제 처방률-0.5010.3950.5931.0000.175
시도0.0000.4480.4510.1751.000

Missing values

2023-12-12T15:05:35.388403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:05:35.503728image/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

지표연도시도시군구지표명분모분자항생제 처방률
02009전국전체항생제처방률63792293416451614225.79
12009서울특별시전체항생제처방률1308888083321555925.38
22009서울특별시종로구항생제처방률477208977168116.17
32009서울특별시중구항생제처방률361532588502824.48
42009서울특별시용산구항생제처방률250222264952125.96
52009서울특별시성동구항생제처방률357285898510927.57
62009서울특별시광진구항생제처방률4534525120308826.53
72009서울특별시동대문구항생제처방률5485119119157121.72
82009서울특별시중랑구항생제처방률4430945121967527.53
92009서울특별시성북구항생제처방률5289543138812326.24
지표연도시도시군구지표명분모분자항생제 처방률
34852021경상남도고성군항생제처방률6095716507310.68
34862021경상남도남해군항생제처방률618545418436.76
34872021경상남도하동군항생제처방률575011425117.39
34882021경상남도산청군항생제처방률318907184455.78
34892021경상남도함양군항생제처방률562754500998.9
34902021경상남도거창군항생제처방률8996069043910.05
34912021경상남도합천군항생제처방률519097513899.9
34922021제주특별자치도전체항생제처방률9864002133193213.5
34932021제주특별자치도제주시항생제처방률7396573103035913.93
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