Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory132.3 B

Variable types

Categorical5
Text2
Numeric8

Dataset

Description샘플 데이터
Author지디에스컨설팅그룹
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=153a4740-2e00-11ea-9713-eb3e5186fb38

Alerts

화력발전소ID has constant value ""Constant
화력발전소명 has constant value ""Constant
화력발전소주소 has constant value ""Constant
화력발전소위도 has constant value ""Constant
화력발전소경도 has constant value ""Constant
진료소인원합계 is highly overall correlated with 입원내원일수합계 and 4 other fieldsHigh correlation
입원내원일수합계 is highly overall correlated with 진료소인원합계 and 4 other fieldsHigh correlation
보험급여일수합계 is highly overall correlated with 진료소인원합계 and 4 other fieldsHigh correlation
진료비합계금액 is highly overall correlated with 진료소인원합계 and 4 other fieldsHigh correlation
보험급여합계금액 is highly overall correlated with 진료소인원합계 and 4 other fieldsHigh correlation
인당진료금액 is highly overall correlated with 인당본인부담금액High correlation
인당본인부담금액 is highly overall correlated with 인당진료금액High correlation
전국화력발전소진료비총액 is highly overall correlated with 진료소인원합계 and 4 other fieldsHigh correlation
질병코드 has unique valuesUnique
질병명 has unique valuesUnique
진료비합계금액 has unique valuesUnique
보험급여합계금액 has unique valuesUnique
인당진료금액 has unique valuesUnique
인당본인부담금액 has unique valuesUnique
전국화력발전소진료비총액 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:54:31.067383
Analysis finished2023-12-10 11:54:44.918186
Duration13.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

화력발전소ID
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T20:54:45.050516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:45.257934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

화력발전소명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광양복합화력발전소
100 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광양복합화력발전소 100
100.0%

Length

2023-12-10T20:54:45.422948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:45.593145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양복합화력발전소 100
100.0%

화력발전소주소
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전라남도 광양시 제철로 2148-567
100 

Length

Max length21
Median length21
Mean length21
Min length21

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 광양시 제철로 2148-567
2nd row전라남도 광양시 제철로 2148-567
3rd row전라남도 광양시 제철로 2148-567
4th row전라남도 광양시 제철로 2148-567
5th row전라남도 광양시 제철로 2148-567

Common Values

ValueCountFrequency (%)
전라남도 광양시 제철로 2148-567 100
100.0%

Length

2023-12-10T20:54:45.768955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:45.952297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 100
25.0%
광양시 100
25.0%
제철로 100
25.0%
2148-567 100
25.0%

화력발전소위도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
34.88715092
100 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row34.88715092
2nd row34.88715092
3rd row34.88715092
4th row34.88715092
5th row34.88715092

Common Values

ValueCountFrequency (%)
34.88715092 100
100.0%

Length

2023-12-10T20:54:46.138322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:46.291774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
34.88715092 100
100.0%

화력발전소경도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
127.7760381
100 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row127.7760381
2nd row127.7760381
3rd row127.7760381
4th row127.7760381
5th row127.7760381

Common Values

ValueCountFrequency (%)
127.7760381 100
100.0%

Length

2023-12-10T20:54:46.431817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:46.596313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
127.7760381 100
100.0%

질병코드
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:54:47.019804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowI00
2nd rowI01
3rd rowI05
4th rowI06
5th rowI07
ValueCountFrequency (%)
i00 1
 
1.0%
i82 1
 
1.0%
i99 1
 
1.0%
i98 1
 
1.0%
i97 1
 
1.0%
i95 1
 
1.0%
i89 1
 
1.0%
i88 1
 
1.0%
i87 1
 
1.0%
i86 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T20:54:47.715889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
I 74
24.7%
0 27
 
9.0%
1 27
 
9.0%
J 26
 
8.7%
3 25
 
8.3%
2 23
 
7.7%
6 19
 
6.3%
8 19
 
6.3%
4 18
 
6.0%
7 17
 
5.7%
Other values (2) 25
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
66.7%
Uppercase Letter 100
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27
13.5%
1 27
13.5%
3 25
12.5%
2 23
11.5%
6 19
9.5%
8 19
9.5%
4 18
9.0%
7 17
8.5%
5 14
7.0%
9 11
5.5%
Uppercase Letter
ValueCountFrequency (%)
I 74
74.0%
J 26
 
26.0%

Most occurring scripts

ValueCountFrequency (%)
Common 200
66.7%
Latin 100
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27
13.5%
1 27
13.5%
3 25
12.5%
2 23
11.5%
6 19
9.5%
8 19
9.5%
4 18
9.0%
7 17
8.5%
5 14
7.0%
9 11
5.5%
Latin
ValueCountFrequency (%)
I 74
74.0%
J 26
 
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 74
24.7%
0 27
 
9.0%
1 27
 
9.0%
J 26
 
8.7%
3 25
 
8.3%
2 23
 
7.7%
6 19
 
6.3%
8 19
 
6.3%
4 18
 
6.0%
7 17
 
5.7%
Other values (2) 25
 
8.3%

질병명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:54:48.265719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length20
Mean length12.5
Min length2

Characters and Unicode

Total characters1250
Distinct characters163
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row심장 침습이 없는 류마티스열
2nd row심장 침습이 있는 류마티스 열
3rd row류마티스성 승모판 질환
4th row류마티스성 대동맥판 질환
5th row류마티스성 삼첨판 질환
ValueCountFrequency (%)
및 23
 
6.7%
기타 21
 
6.1%
질환 17
 
4.9%
장애 16
 
4.6%
급성 16
 
4.6%
심장 12
 
3.5%
달리 11
 
3.2%
않은 8
 
2.3%
질환에서의 7
 
2.0%
분류된 7
 
2.0%
Other values (144) 207
60.0%
2023-12-10T20:54:48.953513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
245
 
19.6%
성 50
 
4.0%
의 35
 
2.8%
기 32
 
2.6%
장 31
 
2.5%
환 29
 
2.3%
심 28
 
2.2%
염 28
 
2.2%
질 26
 
2.1%
류 25
 
2.0%
Other values (153) 721
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 991
79.3%
Space Separator 245
 
19.6%
Close Punctuation 6
 
0.5%
Open Punctuation 6
 
0.5%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 50
 
5.0%
의 35
 
3.5%
기 32
 
3.2%
장 31
 
3.1%
환 29
 
2.9%
심 28
 
2.8%
염 28
 
2.8%
질 26
 
2.6%
류 25
 
2.5%
혈 24
 
2.4%
Other values (147) 683
68.9%
Close Punctuation
ValueCountFrequency (%)
) 4
66.7%
] 2
33.3%
Open Punctuation
ValueCountFrequency (%)
( 4
66.7%
[ 2
33.3%
Space Separator
ValueCountFrequency (%)
245
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 991
79.3%
Common 259
 
20.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 50
 
5.0%
의 35
 
3.5%
기 32
 
3.2%
장 31
 
3.1%
환 29
 
2.9%
심 28
 
2.8%
염 28
 
2.8%
질 26
 
2.6%
류 25
 
2.5%
혈 24
 
2.4%
Other values (147) 683
68.9%
Common
ValueCountFrequency (%)
245
94.6%
) 4
 
1.5%
( 4
 
1.5%
[ 2
 
0.8%
] 2
 
0.8%
, 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 991
79.3%
ASCII 259
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
245
94.6%
) 4
 
1.5%
( 4
 
1.5%
[ 2
 
0.8%
] 2
 
0.8%
, 2
 
0.8%
Hangul
ValueCountFrequency (%)
성 50
 
5.0%
의 35
 
3.5%
기 32
 
3.2%
장 31
 
3.1%
환 29
 
2.9%
심 28
 
2.8%
염 28
 
2.8%
질 26
 
2.6%
류 25
 
2.5%
혈 24
 
2.4%
Other values (147) 683
68.9%

진료소인원합계
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2246.58
Minimum1
Maximum48802
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:49.529847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19.75
median48.5
Q3521.5
95-th percentile14421.2
Maximum48802
Range48801
Interquartile range (IQR)511.75

Descriptive statistics

Standard deviation6591.1729
Coefficient of variation (CV)2.9338697
Kurtosis26.895162
Mean2246.58
Median Absolute Deviation (MAD)46.5
Skewness4.6859819
Sum224658
Variance43443561
MonotonicityNot monotonic
2023-12-10T20:54:49.847865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 8
 
8.0%
1 6
 
6.0%
4 4
 
4.0%
13 3
 
3.0%
3 3
 
3.0%
126 2
 
2.0%
31 2
 
2.0%
82 2
 
2.0%
16 2
 
2.0%
28 2
 
2.0%
Other values (65) 66
66.0%
ValueCountFrequency (%)
1 6
6.0%
2 8
8.0%
3 3
 
3.0%
4 4
4.0%
5 2
 
2.0%
6 1
 
1.0%
9 1
 
1.0%
10 1
 
1.0%
11 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
48802 1
1.0%
26508 1
1.0%
19533 1
1.0%
18379 1
1.0%
14653 1
1.0%
14409 1
1.0%
13437 1
1.0%
10463 1
1.0%
9597 1
1.0%
8768 1
1.0%

입원내원일수합계
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7238.03
Minimum0
Maximum152653
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:50.256707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q147.75
median257
Q32561.5
95-th percentile34244.65
Maximum152653
Range152653
Interquartile range (IQR)2513.75

Descriptive statistics

Standard deviation21618.379
Coefficient of variation (CV)2.9867766
Kurtosis27.863526
Mean7238.03
Median Absolute Deviation (MAD)253
Skewness4.9687744
Sum723803
Variance4.6735429 × 108
MonotonicityNot monotonic
2023-12-10T20:54:50.565966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
5.0%
2 4
 
4.0%
4 3
 
3.0%
6 2
 
2.0%
122 1
 
1.0%
28040 1
 
1.0%
24535 1
 
1.0%
29195 1
 
1.0%
281 1
 
1.0%
341 1
 
1.0%
Other values (80) 80
80.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 1
 
1.0%
2 4
4.0%
4 3
3.0%
5 5
5.0%
6 2
 
2.0%
7 1
 
1.0%
8 1
 
1.0%
12 1
 
1.0%
13 1
 
1.0%
ValueCountFrequency (%)
152653 1
1.0%
120187 1
1.0%
71185 1
1.0%
41673 1
1.0%
35720 1
1.0%
34167 1
1.0%
29195 1
1.0%
28040 1
1.0%
24575 1
1.0%
24535 1
1.0%

보험급여일수합계
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71030.78
Minimum1
Maximum3628670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:50.859450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.9
Q1532.25
median3070
Q326595.25
95-th percentile166293
Maximum3628670
Range3628669
Interquartile range (IQR)26063

Descriptive statistics

Standard deviation370194.27
Coefficient of variation (CV)5.2117444
Kurtosis88.535585
Mean71030.78
Median Absolute Deviation (MAD)3056.5
Skewness9.1991485
Sum7103078
Variance1.370438 × 1011
MonotonicityNot monotonic
2023-12-10T20:54:51.140922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 2
 
2.0%
2 2
 
2.0%
182 2
 
2.0%
133149 1
 
1.0%
69 1
 
1.0%
2270 1
 
1.0%
3019 1
 
1.0%
1226 1
 
1.0%
4056 1
 
1.0%
103 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
1 1
1.0%
2 2
2.0%
5 2
2.0%
7 1
1.0%
8 1
1.0%
19 1
1.0%
33 1
1.0%
69 1
1.0%
74 1
1.0%
103 1
1.0%
ValueCountFrequency (%)
3628670 1
1.0%
681631 1
1.0%
383753 1
1.0%
274733 1
1.0%
243547 1
1.0%
162227 1
1.0%
144389 1
1.0%
140979 1
1.0%
133149 1
1.0%
119137 1
1.0%

진료비합계금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7620392 × 108
Minimum10600
Maximum6.9102895 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:51.427521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10600
5-th percentile218230
Q15580970
median55376010
Q32.9796094 × 108
95-th percentile1.7528709 × 109
Maximum6.9102895 × 109
Range6.9102789 × 109
Interquartile range (IQR)2.9237997 × 108

Descriptive statistics

Standard deviation9.1921357 × 108
Coefficient of variation (CV)2.4433918
Kurtosis28.928282
Mean3.7620392 × 108
Median Absolute Deviation (MAD)54677985
Skewness4.8839123
Sum3.7620392 × 1010
Variance8.4495359 × 1017
MonotonicityNot monotonic
2023-12-10T20:54:51.725822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
497480 1
 
1.0%
717814440 1
 
1.0%
587771160 1
 
1.0%
559250 1
 
1.0%
158950 1
 
1.0%
221350 1
 
1.0%
23959180 1
 
1.0%
19013620 1
 
1.0%
38480980 1
 
1.0%
20239570 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10600 1
1.0%
39760 1
1.0%
76930 1
1.0%
127360 1
1.0%
158950 1
1.0%
221350 1
1.0%
228320 1
1.0%
429150 1
1.0%
497480 1
1.0%
559250 1
1.0%
ValueCountFrequency (%)
6910289520 1
1.0%
4313225920 1
1.0%
3046465630 1
1.0%
1858161490 1
1.0%
1814681340 1
1.0%
1749617770 1
1.0%
1383637170 1
1.0%
1366091730 1
1.0%
1169616100 1
1.0%
909777030 1
1.0%

보험급여합계금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8262586 × 108
Minimum6900
Maximum4.9581517 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:52.010122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6900
5-th percentile139689.35
Q13944568.5
median42699238
Q32.3537197 × 108
95-th percentile1.3214661 × 109
Maximum4.9581517 × 109
Range4.9581448 × 109
Interquartile range (IQR)2.314274 × 108

Descriptive statistics

Standard deviation6.7188705 × 108
Coefficient of variation (CV)2.3773021
Kurtosis26.797044
Mean2.8262586 × 108
Median Absolute Deviation (MAD)42253823
Skewness4.690009
Sum2.8262586 × 1010
Variance4.5143221 × 1017
MonotonicityNot monotonic
2023-12-10T20:54:52.253427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448450 1
 
1.0%
548944191 1
 
1.0%
424815012 1
 
1.0%
272950 1
 
1.0%
107947 1
 
1.0%
151341 1
 
1.0%
15341959 1
 
1.0%
13747510 1
 
1.0%
27688248 1
 
1.0%
14897926 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
6900 1
1.0%
27752 1
1.0%
41860 1
1.0%
58530 1
1.0%
107947 1
1.0%
141360 1
1.0%
151341 1
1.0%
231169 1
1.0%
272950 1
1.0%
379630 1
1.0%
ValueCountFrequency (%)
4958151695 1
1.0%
3164153424 1
1.0%
2247469855 1
1.0%
1474023899 1
1.0%
1382904041 1
1.0%
1318232474 1
1.0%
1050772860 1
1.0%
1026870173 1
1.0%
914899562 1
1.0%
669379813 1
1.0%

인당진료금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1027930.6
Minimum10600
Maximum5792577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:52.499191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10600
5-th percentile46460.7
Q1133857.25
median388592.5
Q31170403.5
95-th percentile4225189.7
Maximum5792577
Range5781977
Interquartile range (IQR)1036546.2

Descriptive statistics

Standard deviation1390521.6
Coefficient of variation (CV)1.3527388
Kurtosis3.2695499
Mean1027930.6
Median Absolute Deviation (MAD)322902.5
Skewness1.9618444
Sum1.0279306 × 108
Variance1.9335504 × 1012
MonotonicityNot monotonic
2023-12-10T20:54:52.745220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
248740 1
 
1.0%
339713 1
 
1.0%
40791 1
 
1.0%
139812 1
 
1.0%
79475 1
 
1.0%
44270 1
 
1.0%
156595 1
 
1.0%
190136 1
 
1.0%
296007 1
 
1.0%
134036 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10600 1
1.0%
19880 1
1.0%
38465 1
1.0%
40791 1
1.0%
44270 1
1.0%
46576 1
1.0%
49198 1
1.0%
52739 1
1.0%
58014 1
1.0%
63680 1
1.0%
ValueCountFrequency (%)
5792577 1
1.0%
5776913 1
1.0%
5505534 1
1.0%
4965422 1
1.0%
4761649 1
1.0%
4196955 1
1.0%
4127866 1
1.0%
3240376 1
1.0%
3240368 1
1.0%
3147175 1
1.0%

인당본인부담금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean823440.91
Minimum6900
Maximum4954694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:52.992249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6900
5-th percentile30228.7
Q195482.5
median283274
Q3868627.5
95-th percentile3729304.7
Maximum4954694
Range4947794
Interquartile range (IQR)773145

Descriptive statistics

Standard deviation1191735.9
Coefficient of variation (CV)1.4472634
Kurtosis3.7862434
Mean823440.91
Median Absolute Deviation (MAD)241051
Skewness2.0950135
Sum82344091
Variance1.4202343 × 1012
MonotonicityNot monotonic
2023-12-10T20:54:53.242024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
224225 1
 
1.0%
259793 1
 
1.0%
29482 1
 
1.0%
68237 1
 
1.0%
53973 1
 
1.0%
30268 1
 
1.0%
100274 1
 
1.0%
137475 1
 
1.0%
212986 1
 
1.0%
98661 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
6900 1
1.0%
13876 1
1.0%
20930 1
1.0%
29265 1
1.0%
29482 1
1.0%
30268 1
1.0%
32214 1
1.0%
35055 1
1.0%
38220 1
1.0%
42910 1
1.0%
ValueCountFrequency (%)
4954694 1
1.0%
4931853 1
1.0%
4648766 1
1.0%
4395831 1
1.0%
4236581 1
1.0%
3702606 1
1.0%
3631841 1
1.0%
2844580 1
1.0%
2635799 1
1.0%
2460826 1
1.0%

전국화력발전소진료비총액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2499077 × 108
Minimum127420
Maximum1.3301036 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:53.502326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127420
5-th percentile2749277.9
Q118692694
median82892182
Q35.1264738 × 108
95-th percentile2.2429669 × 109
Maximum1.3301036 × 1010
Range1.3300909 × 1010
Interquartile range (IQR)4.9395469 × 108

Descriptive statistics

Standard deviation1.6387864 × 109
Coefficient of variation (CV)2.622097
Kurtosis38.720386
Mean6.2499077 × 108
Median Absolute Deviation (MAD)79244122
Skewness5.67735
Sum6.2499077 × 1010
Variance2.685621 × 1018
MonotonicityNot monotonic
2023-12-10T20:54:53.780468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5308830 1
 
1.0%
1145125293 1
 
1.0%
1095585940 1
 
1.0%
6112204 1
 
1.0%
19455481 1
 
1.0%
2298482 1
 
1.0%
22977200 1
 
1.0%
21985814 1
 
1.0%
65953812 1
 
1.0%
33787479 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
127420 1
1.0%
201316 1
1.0%
283152 1
1.0%
1126160 1
1.0%
2298482 1
1.0%
2773004 1
1.0%
3540037 1
1.0%
3756084 1
1.0%
3913507 1
1.0%
4063330 1
1.0%
ValueCountFrequency (%)
13301036376 1
1.0%
7180681515 1
1.0%
4974559794 1
1.0%
2929218036 1
1.0%
2885045378 1
1.0%
2209173258 1
1.0%
2013268585 1
1.0%
1868808142 1
1.0%
1671938068 1
1.0%
1483711556 1
1.0%

Interactions

2023-12-10T20:54:42.731930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.646497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.733910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.151727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:36.657640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:38.505896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.821194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:41.256667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:42.996250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.788872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.888940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.341644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:36.824780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:38.649975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.989427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:41.419158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:43.311330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:32.017751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.093967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.578886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:37.388660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:38.831004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:40.195833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:41.598021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:43.497288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:32.527966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.284615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.762111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:37.565177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.020204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:40.386394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:41.817556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:43.655888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:32.953891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.477925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.945381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:37.747952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.185697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:40.587577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:42.004432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:43.818824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.189643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.656907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:36.103407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:37.922120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.332176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:40.755397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:42.203591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:43.975701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.390531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.832934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:36.273151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:38.106590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.489680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:40.927121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:42.367996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:44.118152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.553649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.992749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:36.458620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:38.314368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:39.642055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:41.089025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:42.522173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:54:53.964228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
질병코드질병명진료소인원합계입원내원일수합계보험급여일수합계진료비합계금액보험급여합계금액인당진료금액인당본인부담금액전국화력발전소진료비총액
질병코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
질병명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
진료소인원합계1.0001.0001.0000.9160.8690.7850.7900.0000.0000.766
입원내원일수합계1.0001.0000.9161.0001.0000.9720.9670.0000.0000.962
보험급여일수합계1.0001.0000.8691.0001.0000.9930.9910.0000.0000.991
진료비합계금액1.0001.0000.7850.9720.9931.0001.0000.0000.0000.994
보험급여합계금액1.0001.0000.7900.9670.9911.0001.0000.0000.0000.993
인당진료금액1.0001.0000.0000.0000.0000.0000.0001.0000.9950.000
인당본인부담금액1.0001.0000.0000.0000.0000.0000.0000.9951.0000.000
전국화력발전소진료비총액1.0001.0000.7660.9620.9910.9940.9930.0000.0001.000
2023-12-10T20:54:54.195280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진료소인원합계입원내원일수합계보험급여일수합계진료비합계금액보험급여합계금액인당진료금액인당본인부담금액전국화력발전소진료비총액
진료소인원합계1.0000.9700.9170.9100.902-0.195-0.1670.875
입원내원일수합계0.9701.0000.9500.9670.962-0.0220.0050.912
보험급여일수합계0.9170.9501.0000.9400.9320.0290.0470.924
진료비합계금액0.9100.9670.9401.0000.9990.1650.1900.939
보험급여합계금액0.9020.9620.9320.9991.0000.1820.2080.935
인당진료금액-0.195-0.0220.0290.1650.1821.0000.9970.101
인당본인부담금액-0.1670.0050.0470.1900.2080.9971.0000.119
전국화력발전소진료비총액0.8750.9120.9240.9390.9350.1010.1191.000

Missing values

2023-12-10T20:54:44.388765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:54:44.758497image/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

화력발전소ID화력발전소명화력발전소주소화력발전소위도화력발전소경도질병코드질병명진료소인원합계입원내원일수합계보험급여일수합계진료비합계금액보험급여합계금액인당진료금액인당본인부담금액전국화력발전소진료비총액
01광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I00심장 침습이 없는 류마티스열2554974804484502487402242255308830
11광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I01심장 침습이 있는 류마티스 열16421801610544910801610544910283152
21광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I05류마티스성 승모판 질환351778498172944601125246249412732149882704388
31광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I06류마티스성 대동맥판 질환96116045959860416977966220646330814438474
41광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I07류마티스성 삼첨판 질환12538754444300326893737035827241124888469
51광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I08다발성 판막 질환13104191064550490571458104965422439583130874421
61광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I09기타 류마티스성 심장 질환265467106804423803553402211905973399
71광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I10본태성(원발성) 고혈압1465312018736286706910289520495815169547159533837113301036376
81광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I11고혈압성 심장 질환469203862087180424650128074147384700273079726123800
91광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038I12고혈압성 신장 질환16702275142265901014638088916163414872106700
화력발전소ID화력발전소명화력발전소주소화력발전소위도화력발전소경도질병코드질병명진료소인원합계입원내원일수합계보험급여일수합계진료비합계금액보험급여합계금액인당진료금액인당본인부담금액전국화력발전소진료비총액
901광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J20급성 기관지염488021526536816314313225920316415342488382648367180681515
911광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J21급성 세기관지염959735720140979116961610091489956212187395331787304641
921광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J22상세불명의 급성 하기도 감염595110557489512927787302086142384919835055229010373
931광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J30혈관운동성 및 알레르기성 비염26508711853837531814681340131823247468457497292209173258
941광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J31만성 비염,비인두염 및 인두염7851412813353145210360450296770045917317158249
951광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J32만성 부비동염45171173254345483462990342734145107031758761181221254
961광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J33비용종912151903173257901165497019039312807647444932
971광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J34코 및 비동의 기타 장애27995473230562343626501669586758373059649482614565
981광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J35편도 및 아데노이드의 만성 질환1270273110356183767180137675891144698108406331869470
991광양복합화력발전소전라남도 광양시 제철로 2148-56734.887151127.776038J36편도주위 농양49518874374212611140501892277995273938220296829543