Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory95.3 B

Variable types

Numeric6
Text2
Categorical3

Alerts

질병명 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
질병코드 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 진료실인원(명) and 7 other fieldsHigh correlation
진료실인원(명) is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
입내원일수(일) is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
급여일수(일) is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
진료비(원) is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
급여비(원) is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
시도 is highly overall correlated with 기본키High correlation
기본키 has unique valuesUnique
진료비(원) has unique valuesUnique
급여비(원) has unique valuesUnique
진료실인원(명) has 4 (4.0%) zerosZeros
입내원일수(일) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:19:49.272945
Analysis finished2023-12-10 12:19:54.235788
Duration4.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:54.325508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T21:19:54.780823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

지점
Text

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:19:55.032308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length14.1
Min length14

Characters and Unicode

Total characters1410
Distinct characters15
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

Unique38 ?
Unique (%)38.0%

Sample

1st rowA-1000-0123E-10
2nd rowA-0600-0002S-8
3rd rowA-0651-0026S-4
4th rowA-6000-0386E-4
5th rowA-0651-0108S-6
ValueCountFrequency (%)
a-1000-0123e-10 2
 
2.0%
a-0010-1305e-8 2
 
2.0%
a-1000-0745s-8 2
 
2.0%
a-1100-0228s-6 2
 
2.0%
a-0150-2835s-6 2
 
2.0%
a-0600-0035s-6 2
 
2.0%
a-0150-3309e-10 2
 
2.0%
a-0352-3252e-8 2
 
2.0%
a-0650-0294e-4 2
 
2.0%
a-0290-0315s-4 2
 
2.0%
Other values (59) 80
80.0%
2023-12-10T21:19:55.437846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 348
24.7%
- 300
21.3%
1 123
 
8.7%
A 100
 
7.1%
4 78
 
5.5%
5 73
 
5.2%
2 66
 
4.7%
6 66
 
4.7%
8 63
 
4.5%
3 54
 
3.8%
Other values (5) 139
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 910
64.5%
Dash Punctuation 300
 
21.3%
Uppercase Letter 200
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 348
38.2%
1 123
 
13.5%
4 78
 
8.6%
5 73
 
8.0%
2 66
 
7.3%
6 66
 
7.3%
8 63
 
6.9%
3 54
 
5.9%
9 21
 
2.3%
7 18
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
A 100
50.0%
E 54
27.0%
S 45
22.5%
C 1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1210
85.8%
Latin 200
 
14.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 348
28.8%
- 300
24.8%
1 123
 
10.2%
4 78
 
6.4%
5 73
 
6.0%
2 66
 
5.5%
6 66
 
5.5%
8 63
 
5.2%
3 54
 
4.5%
9 21
 
1.7%
Latin
ValueCountFrequency (%)
A 100
50.0%
E 54
27.0%
S 45
22.5%
C 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 348
24.7%
- 300
21.3%
1 123
 
8.7%
A 100
 
7.1%
4 78
 
5.5%
5 73
 
5.2%
2 66
 
4.7%
6 66
 
4.7%
8 63
 
4.5%
3 54
 
3.8%
Other values (5) 139
 
9.9%

시도
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
25 
강원도
11 
대구시
10 
인천시
8 
대전시
7 
Other values (10)
39 

Length

Max length4
Median length3
Mean length3.22
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울시
2nd row서울시
3rd row부산시
4th row부산시
5th row부산시

Common Values

ValueCountFrequency (%)
경기도 25
25.0%
강원도 11
11.0%
대구시 10
 
10.0%
인천시 8
 
8.0%
대전시 7
 
7.0%
부산시 6
 
6.0%
전라북도 5
 
5.0%
경상남도 5
 
5.0%
서울시 4
 
4.0%
광주시 4
 
4.0%
Other values (5) 15
15.0%

Length

2023-12-10T21:19:55.572783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 25
25.0%
강원도 11
11.0%
대구시 10
 
10.0%
인천시 8
 
8.0%
대전시 7
 
7.0%
부산시 6
 
6.0%
전라북도 5
 
5.0%
경상남도 5
 
5.0%
서울시 4
 
4.0%
광주시 4
 
4.0%
Other values (5) 15
15.0%

시군구
Text

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:19:55.877568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.93
Min length2

Characters and Unicode

Total characters293
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)37.0%

Sample

1st row송파구
2nd row강동구
3rd row해운대구
4th row금정구
5th row기장군
ValueCountFrequency (%)
서구 5
 
5.0%
동구 4
 
4.0%
북구 4
 
4.0%
기장군 2
 
2.0%
이천시 2
 
2.0%
김포시 2
 
2.0%
화성시 2
 
2.0%
포천시 2
 
2.0%
강동구 2
 
2.0%
충주시 2
 
2.0%
Other values (55) 73
73.0%
2023-12-10T21:19:56.411696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 48
 
16.4%
구 39
 
13.3%
군 19
 
6.5%
주 14
 
4.8%
천 10
 
3.4%
성 9
 
3.1%
동 9
 
3.1%
서 7
 
2.4%
광 6
 
2.0%
양 6
 
2.0%
Other values (64) 126
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 48
 
16.4%
구 39
 
13.3%
군 19
 
6.5%
주 14
 
4.8%
천 10
 
3.4%
성 9
 
3.1%
동 9
 
3.1%
서 7
 
2.4%
광 6
 
2.0%
양 6
 
2.0%
Other values (64) 126
43.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 48
 
16.4%
구 39
 
13.3%
군 19
 
6.5%
주 14
 
4.8%
천 10
 
3.4%
성 9
 
3.1%
동 9
 
3.1%
서 7
 
2.4%
광 6
 
2.0%
양 6
 
2.0%
Other values (64) 126
43.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 48
 
16.4%
구 39
 
13.3%
군 19
 
6.5%
주 14
 
4.8%
천 10
 
3.4%
성 9
 
3.1%
동 9
 
3.1%
서 7
 
2.4%
광 6
 
2.0%
양 6
 
2.0%
Other values (64) 126
43.0%

질병코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
O11
51 
I10
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO11
2nd rowO11
3rd rowO11
4th rowO11
5th rowO11

Common Values

ValueCountFrequency (%)
O11 51
51.0%
I10 49
49.0%

Length

2023-12-10T21:19:56.598098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:19:56.721687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o11 51
51.0%
i10 49
49.0%

질병명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
만성고혈압에겹친전자간
51 
본태성(원발성)고혈압
49 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
만성고혈압에겹친전자간 51
51.0%
본태성(원발성)고혈압 49
49.0%

Length

2023-12-10T21:19:56.847658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:19:56.982541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
만성고혈압에겹친전자간 51
51.0%
본태성(원발성)고혈압 49
49.0%

진료실인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14815.51
Minimum0
Maximum68534
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:57.123003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median7
Q326748.25
95-th percentile52282.8
Maximum68534
Range68534
Interquartile range (IQR)26746.25

Descriptive statistics

Standard deviation18316.937
Coefficient of variation (CV)1.2363352
Kurtosis-0.15992747
Mean14815.51
Median Absolute Deviation (MAD)7
Skewness0.95878823
Sum1481551
Variance3.3551017 × 108
MonotonicityNot monotonic
2023-12-10T21:19:57.295201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
18.0%
2 14
 
14.0%
3 10
 
10.0%
0 4
 
4.0%
7 2
 
2.0%
5 2
 
2.0%
15941 1
 
1.0%
37240 1
 
1.0%
21242 1
 
1.0%
24805 1
 
1.0%
Other values (46) 46
46.0%
ValueCountFrequency (%)
0 4
 
4.0%
1 18
18.0%
2 14
14.0%
3 10
10.0%
4 1
 
1.0%
5 2
 
2.0%
7 2
 
2.0%
5397 1
 
1.0%
7738 1
 
1.0%
7972 1
 
1.0%
ValueCountFrequency (%)
68534 1
1.0%
59433 1
1.0%
56763 1
1.0%
52803 1
1.0%
52526 1
1.0%
52270 1
1.0%
48728 1
1.0%
47108 1
1.0%
46290 1
1.0%
43898 1
1.0%

입내원일수(일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115205.88
Minimum0
Maximum507987
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:57.472231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median52.5
Q3217268
95-th percentile378344.75
Maximum507987
Range507987
Interquartile range (IQR)217260

Descriptive statistics

Standard deviation140556.49
Coefficient of variation (CV)1.2200462
Kurtosis-0.4172247
Mean115205.88
Median Absolute Deviation (MAD)52.5
Skewness0.8797802
Sum11520588
Variance1.9756127 × 1010
MonotonicityNot monotonic
2023-12-10T21:19:57.683858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
5.0%
13 4
 
4.0%
5 4
 
4.0%
0 4
 
4.0%
3 4
 
4.0%
10 3
 
3.0%
8 3
 
3.0%
17 2
 
2.0%
2 2
 
2.0%
7 2
 
2.0%
Other values (65) 67
67.0%
ValueCountFrequency (%)
0 4
4.0%
1 5
5.0%
2 2
 
2.0%
3 4
4.0%
4 1
 
1.0%
5 4
4.0%
6 1
 
1.0%
7 2
 
2.0%
8 3
3.0%
9 2
 
2.0%
ValueCountFrequency (%)
507987 1
1.0%
439402 1
1.0%
429374 1
1.0%
412573 1
1.0%
392039 1
1.0%
377624 1
1.0%
373824 1
1.0%
368176 1
1.0%
345362 1
1.0%
340339 1
1.0%

급여일수(일)
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3815997.1
Minimum1
Maximum17487768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:57.865414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.95
Q125
median327
Q36880572.5
95-th percentile13300489
Maximum17487768
Range17487767
Interquartile range (IQR)6880547.5

Descriptive statistics

Standard deviation4728554.4
Coefficient of variation (CV)1.2391399
Kurtosis-0.16970913
Mean3815997.1
Median Absolute Deviation (MAD)326
Skewness0.96282171
Sum3.8159971 × 108
Variance2.2359227 × 1013
MonotonicityNot monotonic
2023-12-10T21:19:58.069201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 5
 
5.0%
3 4
 
4.0%
38 3
 
3.0%
23 2
 
2.0%
27 2
 
2.0%
6 2
 
2.0%
12 2
 
2.0%
10 2
 
2.0%
25 2
 
2.0%
7373353 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
1 5
5.0%
2 1
 
1.0%
3 4
4.0%
6 2
 
2.0%
10 2
 
2.0%
12 2
 
2.0%
13 1
 
1.0%
14 1
 
1.0%
16 1
 
1.0%
19 1
 
1.0%
ValueCountFrequency (%)
17487768 1
1.0%
15157555 1
1.0%
14725598 1
1.0%
14143164 1
1.0%
13728947 1
1.0%
13277939 1
1.0%
12350245 1
1.0%
12200621 1
1.0%
11804251 1
1.0%
11606473 1
1.0%

진료비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2373348 × 109
Minimum8130
Maximum3.3901963 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:58.251204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8130
5-th percentile14928.5
Q11326050
median8597785
Q31.3340903 × 1010
95-th percentile2.4107015 × 1010
Maximum3.3901963 × 1010
Range3.3901955 × 1010
Interquartile range (IQR)1.3339577 × 1010

Descriptive statistics

Standard deviation8.900925 × 109
Coefficient of variation (CV)1.2298623
Kurtosis-0.23710062
Mean7.2373348 × 109
Median Absolute Deviation (MAD)8588955
Skewness0.92828564
Sum7.2373348 × 1011
Variance7.9226466 × 1019
MonotonicityNot monotonic
2023-12-10T21:19:58.429185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1987360 1
 
1.0%
15469570990 1
 
1.0%
10827535470 1
 
1.0%
10788704930 1
 
1.0%
11597431910 1
 
1.0%
11635539130 1
 
1.0%
11711666110 1
 
1.0%
21936608350 1
 
1.0%
14409719340 1
 
1.0%
15147737600 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8130 1
1.0%
9530 1
1.0%
10050 1
1.0%
10600 1
1.0%
12240 1
1.0%
15070 1
1.0%
17530 1
1.0%
30400 1
1.0%
41790 1
1.0%
43710 1
1.0%
ValueCountFrequency (%)
33901962800 1
1.0%
27556368950 1
1.0%
26744545250 1
1.0%
26505727810 1
1.0%
25858200640 1
1.0%
24014847190 1
1.0%
22431859600 1
1.0%
21936608350 1
1.0%
21852222800 1
1.0%
21066661810 1
1.0%

급여비(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1337752 × 109
Minimum1200
Maximum2.4035081 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:19:58.609826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile7034
Q11172335
median6753288.5
Q39.5114425 × 109
95-th percentile1.7037955 × 1010
Maximum2.4035081 × 1010
Range2.403508 × 1010
Interquartile range (IQR)9.5102702 × 109

Descriptive statistics

Standard deviation6.3088102 × 109
Coefficient of variation (CV)1.2288832
Kurtosis-0.24988088
Mean5.1337752 × 109
Median Absolute Deviation (MAD)6750623.5
Skewness0.92422132
Sum5.1337752 × 1011
Variance3.9801086 × 1019
MonotonicityNot monotonic
2023-12-10T21:19:58.749499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1558260 1
 
1.0%
10934387061 1
 
1.0%
7708985474 1
 
1.0%
7592906630 1
 
1.0%
8238410683 1
 
1.0%
8329533211 1
 
1.0%
8290069626 1
 
1.0%
15617077057 1
 
1.0%
10307504254 1
 
1.0%
10731876488 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1200 1
1.0%
4130 1
1.0%
6140 1
1.0%
6400 1
1.0%
6730 1
1.0%
7050 1
1.0%
9070 1
1.0%
9810 1
1.0%
12330 1
1.0%
20990 1
1.0%
ValueCountFrequency (%)
24035081267 1
1.0%
19503553374 1
1.0%
19006274158 1
1.0%
18489284468 1
1.0%
18381433030 1
1.0%
16967245338 1
1.0%
15808784769 1
1.0%
15617077057 1
1.0%
15491640981 1
1.0%
14803869598 1
1.0%

Interactions

2023-12-10T21:19:53.206034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:49.834612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.422630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.188002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.897314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.530380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.324106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:49.940786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.530729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.288497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.999557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.643664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.408775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.048740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.655618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.435384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.108546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.753293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.499229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.159398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.785573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.544713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.210181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.883147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.628695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.253080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.932856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.663044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.316663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.001214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.777770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:50.344675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.062518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:51.786974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:52.418462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:19:53.109869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:19:58.890257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점시도시군구질병코드질병명진료실인원(명)입내원일수(일)급여일수(일)진료비(원)급여비(원)
기본키1.0000.0000.9110.0001.0001.0000.7860.8220.7930.8040.804
지점0.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.000
시도0.9111.0001.0000.9930.4150.4150.0000.0000.0000.0000.000
시군구0.0001.0000.9931.0000.0000.0000.0000.0000.0000.0000.000
질병코드1.0000.0000.4150.0001.0000.9990.9990.9990.9990.9990.999
질병명1.0000.0000.4150.0000.9991.0000.9990.9990.9990.9990.999
진료실인원(명)0.7860.0000.0000.0000.9990.9991.0000.9870.9950.9910.991
입내원일수(일)0.8220.0000.0000.0000.9990.9990.9871.0000.9890.9890.989
급여일수(일)0.7930.0000.0000.0000.9990.9990.9950.9891.0000.9930.993
진료비(원)0.8040.0000.0000.0000.9990.9990.9910.9890.9931.0001.000
급여비(원)0.8040.0000.0000.0000.9990.9990.9910.9890.9931.0001.000
2023-12-10T21:19:59.066999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
질병명질병코드시도
질병명1.0000.9800.351
질병코드0.9801.0000.351
시도0.3510.3511.000
2023-12-10T21:19:59.173041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키진료실인원(명)입내원일수(일)급여일수(일)진료비(원)급여비(원)시도질병코드질병명
기본키1.0000.6690.6730.6560.6720.6760.6200.9390.939
진료실인원(명)0.6691.0000.9640.9310.9620.9590.0000.9370.937
입내원일수(일)0.6730.9641.0000.9450.9910.9880.0000.9370.937
급여일수(일)0.6560.9310.9451.0000.9450.9450.0000.9370.937
진료비(원)0.6720.9620.9910.9451.0000.9990.0000.9370.937
급여비(원)0.6760.9590.9880.9450.9991.0000.0000.9370.937
시도0.6200.0000.0000.0000.0000.0001.0000.3510.351
질병코드0.9390.9370.9370.9370.9370.9370.3511.0000.980
질병명0.9390.9370.9370.9370.9370.9370.3510.9801.000

Missing values

2023-12-10T21:19:53.941400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:19:54.157735image/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

기본키지점시도시군구질병코드질병명진료실인원(명)입내원일수(일)급여일수(일)진료비(원)급여비(원)
01A-1000-0123E-10서울시송파구O11만성고혈압에겹친전자간292419873601558260
12A-0600-0002S-8서울시강동구O11만성고혈압에겹친전자간122304001200
23A-0651-0026S-4부산시해운대구O11만성고혈압에겹친전자간2134828493202175940
34A-6000-0386E-4부산시금정구O11만성고혈압에겹친전자간111106006400
45A-0651-0108S-6부산시기장군O11만성고혈압에겹친전자간3324463244704968210
56A-0010-1185E-8대구시동구O11만성고혈압에겹친전자간282522709101779860
67A-4510-0285E-6대구시서구O11만성고혈압에겹친전자간255425626086460
78A-0010-1305E-8대구시북구O11만성고혈압에겹친전자간2538271240106540
89A-4510-0204S-8대구시달서구O11만성고혈압에겹친전자간7296469801105412380
910A-0450-0480S-4대구시달성군O11만성고혈압에겹친전자간4318357812604741410
기본키지점시도시군구질병코드질병명진료실인원(명)입내원일수(일)급여일수(일)진료비(원)급여비(원)
9091A-0550-3717E-4강원도춘천시I10본태성(원발성)고혈압3124124828582240541583702031011320041111
9192A-0500-1206S-6강원도원주시I10본태성(원발성)고혈압3693427261994814881821690974012921737787
9293A-0650-0294E-4강원도강릉시I10본태성(원발성)고혈압3103423105782863591545902013011073844346
9394A-0650-0140E-4강원도동해시I10본태성(원발성)고혈압14579129727398517786890966206181748848
9495A-0650-1189E-4강원도속초시I10본태성(원발성)고혈압1162594742312948259654184804250973659
9596A-0600-0850E-4강원도홍천군I10본태성(원발성)고혈압1061188846288321951622144803675898341
9697A-0500-1780S-4강원도횡성군I10본태성(원발성)고혈압797263877199556539923917002855902527
9798A-0500-2020S-4강원도평창군I10본태성(원발성)고혈압773852922206909435418565002503652657
9899A-0600-1417S-4강원도양양군I10본태성(원발성)고혈압539741901126624123307916101675842211
99100A-0400-0834S-4충청북도충주시I10본태성(원발성)고혈압282642210187044586139024813109911978487