Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells44
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical4
Text1
Numeric10

Dataset

Description노인장기요양보험 시군구별 등급판정 현황에 대한 데이터로 성별, 연령별, 자격별 장기요양 등급판정 현황을 제공합니다.(시도, 시군구, 자격별, 성별, 연령구분, 신청자, 1등급 , 2등급 , 3등급 , 4등급 , 5등급 , 인지지원등급 , 등급외 )발췌기준- 기준: 제도초기부터 현재까지 장기요양신청일자 기준, 인정자 수(누적)- 발췌구분: 실거주지 관할 시도-시군구별 등급판정결과- 등급판정자: 인정자(1~5등급, 인지지원등급), 등급외자(등급외 A~C)- 인정자: 사망자 제외. 중복건수 제외- 연령구분: 신청당시의 연령기준※ 기초수급, 의료급여자는 소속 시군구를 관할하는 운영센터에서 관리하고 있으니 참고하시기 바랍니다)※ 인정자가 감소한 지역은 인정자가 다른 지역으로 주소 변경하였거나 사망자가 발생하였기 때문입니다
Author국민건강보험공단
URLhttps://www.data.go.kr/data/3051421/fileData.do

Alerts

신청자 is highly overall correlated with 1등급 and 8 other fieldsHigh correlation
1등급 is highly overall correlated with 신청자 and 6 other fieldsHigh correlation
2등급 is highly overall correlated with 신청자 and 6 other fieldsHigh correlation
3등급 is highly overall correlated with 신청자 and 7 other fieldsHigh correlation
4등급 is highly overall correlated with 신청자 and 7 other fieldsHigh correlation
5등급 is highly overall correlated with 신청자 and 7 other fieldsHigh correlation
인지지원등급 is highly overall correlated with 신청자 and 7 other fieldsHigh correlation
등급외A is highly overall correlated with 신청자 and 8 other fieldsHigh correlation
등급외B is highly overall correlated with 신청자 and 6 other fieldsHigh correlation
등급외C is highly overall correlated with 신청자 and 2 other fieldsHigh correlation
신청자 has 386 (3.9%) zerosZeros
1등급 has 3687 (36.9%) zerosZeros
2등급 has 2800 (28.0%) zerosZeros
3등급 has 1545 (15.4%) zerosZeros
4등급 has 1114 (11.1%) zerosZeros
5등급 has 2596 (26.0%) zerosZeros
인지지원등급 has 4572 (45.7%) zerosZeros
등급외A has 3523 (35.2%) zerosZeros
등급외B has 4053 (40.5%) zerosZeros
등급외C has 5458 (54.6%) zerosZeros

Reproduction

Analysis started2024-04-06 08:08:23.773941
Analysis finished2024-04-06 08:08:54.112815
Duration30.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기
1377 
서울
1106 
전남
956 
경북
929 
경남
782 
Other values (12)
4850 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북
2nd row인천
3rd row경북
4th row전남
5th row인천

Common Values

ValueCountFrequency (%)
경기 1377
13.8%
서울 1106
11.1%
전남 956
9.6%
경북 929
9.3%
경남 782
7.8%
강원 765
7.6%
부산 709
7.1%
충남 645
 
6.5%
전북 607
 
6.1%
충북 499
 
5.0%
Other values (7) 1625
16.2%

Length

2024-04-06T17:08:54.242815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 1377
13.8%
서울 1106
11.1%
전남 956
9.6%
경북 929
9.3%
경남 782
7.8%
강원 765
7.6%
부산 709
7.1%
충남 645
 
6.5%
전북 607
 
6.1%
충북 499
 
5.0%
Other values (7) 1625
16.2%
Distinct207
Distinct (%)2.1%
Missing44
Missing (%)0.4%
Memory size156.2 KiB
2024-04-06T17:08:54.807462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9340096
Min length2

Characters and Unicode

Total characters29211
Distinct characters132
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

Unique0 ?
Unique (%)0.0%

Sample

1st row순창군
2nd row미추홀구
3rd row영양군
4th row무안군
5th row연수구
ValueCountFrequency (%)
중구 261
 
2.6%
동구 260
 
2.6%
서구 226
 
2.3%
북구 181
 
1.8%
남구 180
 
1.8%
강서구 84
 
0.8%
고성군 80
 
0.8%
금정구 48
 
0.5%
노원구 48
 
0.5%
사천시 47
 
0.5%
Other values (197) 8541
85.8%
2024-04-06T17:08:55.767116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3624
 
12.4%
3463
 
11.9%
3271
 
11.2%
946
 
3.2%
892
 
3.1%
786
 
2.7%
781
 
2.7%
744
 
2.5%
707
 
2.4%
577
 
2.0%
Other values (122) 13420
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29211
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3624
 
12.4%
3463
 
11.9%
3271
 
11.2%
946
 
3.2%
892
 
3.1%
786
 
2.7%
781
 
2.7%
744
 
2.5%
707
 
2.4%
577
 
2.0%
Other values (122) 13420
45.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29211
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3624
 
12.4%
3463
 
11.9%
3271
 
11.2%
946
 
3.2%
892
 
3.1%
786
 
2.7%
781
 
2.7%
744
 
2.5%
707
 
2.4%
577
 
2.0%
Other values (122) 13420
45.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29211
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3624
 
12.4%
3463
 
11.9%
3271
 
11.2%
946
 
3.2%
892
 
3.1%
786
 
2.7%
781
 
2.7%
744
 
2.5%
707
 
2.4%
577
 
2.0%
Other values (122) 13420
45.9%

자격별
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
감경
2623 
일반
2616 
기초
2597 
의료
2164 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감경
2nd row일반
3rd row감경
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
감경 2623
26.2%
일반 2616
26.2%
기초 2597
26.0%
의료 2164
21.6%

Length

2024-04-06T17:08:56.021600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:56.233725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감경 2623
26.2%
일반 2616
26.2%
기초 2597
26.0%
의료 2164
21.6%

성별
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여자
5034 
남자
4966 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남자
2nd row여자
3rd row여자
4th row여자
5th row여자

Common Values

ValueCountFrequency (%)
여자 5034
50.3%
남자 4966
49.7%

Length

2024-04-06T17:08:56.585871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:56.922389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여자 5034
50.3%
남자 4966
49.7%

연령구분
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
85세이상
1721 
75-79세
1721 
80-84세
1693 
70-74세
1692 
65-69세
1649 

Length

Max length6
Median length6
Mean length5.6755
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80-84세
2nd row85세이상
3rd row75-79세
4th row65세미만
5th row85세이상

Common Values

ValueCountFrequency (%)
85세이상 1721
17.2%
75-79세 1721
17.2%
80-84세 1693
16.9%
70-74세 1692
16.9%
65-69세 1649
16.5%
65세미만 1524
15.2%

Length

2024-04-06T17:08:57.522907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:57.746789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
85세이상 1721
17.2%
75-79세 1721
17.2%
80-84세 1693
16.9%
70-74세 1692
16.9%
65-69세 1649
16.5%
65세미만 1524
15.2%

신청자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct917
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.8148
Minimum0
Maximum4506
Zeros386
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:08:58.035028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median50
Q3148
95-th percentile555
Maximum4506
Range4506
Interquartile range (IQR)137

Descriptive statistics

Standard deviation246.41559
Coefficient of variation (CV)1.8414674
Kurtosis43.616739
Mean133.8148
Median Absolute Deviation (MAD)46
Skewness5.082211
Sum1338148
Variance60720.641
MonotonicityNot monotonic
2024-04-06T17:08:58.319957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 491
 
4.9%
0 386
 
3.9%
2 330
 
3.3%
3 259
 
2.6%
4 189
 
1.9%
5 186
 
1.9%
6 150
 
1.5%
8 134
 
1.3%
7 129
 
1.3%
10 117
 
1.2%
Other values (907) 7629
76.3%
ValueCountFrequency (%)
0 386
3.9%
1 491
4.9%
2 330
3.3%
3 259
2.6%
4 189
 
1.9%
5 186
 
1.9%
6 150
 
1.5%
7 129
 
1.3%
8 134
 
1.3%
9 105
 
1.1%
ValueCountFrequency (%)
4506 1
< 0.1%
4044 1
< 0.1%
3315 1
< 0.1%
3085 1
< 0.1%
3048 1
< 0.1%
3043 1
< 0.1%
2791 1
< 0.1%
2768 1
< 0.1%
2672 1
< 0.1%
2630 1
< 0.1%

1등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9378
Minimum0
Maximum290
Zeros3687
Zeros (%)36.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:08:58.614463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile21
Maximum290
Range290
Interquartile range (IQR)5

Descriptive statistics

Standard deviation11.766001
Coefficient of variation (CV)2.3828427
Kurtosis110.82639
Mean4.9378
Median Absolute Deviation (MAD)1
Skewness7.9774093
Sum49378
Variance138.43878
MonotonicityNot monotonic
2024-04-06T17:08:58.909722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3687
36.9%
1 1539
15.4%
2 921
 
9.2%
3 652
 
6.5%
4 490
 
4.9%
5 394
 
3.9%
6 332
 
3.3%
7 269
 
2.7%
8 212
 
2.1%
9 154
 
1.5%
Other values (95) 1350
 
13.5%
ValueCountFrequency (%)
0 3687
36.9%
1 1539
15.4%
2 921
 
9.2%
3 652
 
6.5%
4 490
 
4.9%
5 394
 
3.9%
6 332
 
3.3%
7 269
 
2.7%
8 212
 
2.1%
9 154
 
1.5%
ValueCountFrequency (%)
290 1
< 0.1%
271 1
< 0.1%
195 1
< 0.1%
191 1
< 0.1%
183 1
< 0.1%
169 1
< 0.1%
154 1
< 0.1%
148 2
< 0.1%
139 1
< 0.1%
135 1
< 0.1%

2등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct179
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1628
Minimum0
Maximum487
Zeros2800
Zeros (%)28.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:08:59.268955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q38
95-th percentile39
Maximum487
Range487
Interquartile range (IQR)8

Descriptive statistics

Standard deviation22.386179
Coefficient of variation (CV)2.4431592
Kurtosis80.859052
Mean9.1628
Median Absolute Deviation (MAD)3
Skewness7.194224
Sum91628
Variance501.14101
MonotonicityNot monotonic
2024-04-06T17:08:59.637159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2800
28.0%
1 1322
13.2%
2 866
 
8.7%
3 672
 
6.7%
4 512
 
5.1%
5 441
 
4.4%
6 348
 
3.5%
7 303
 
3.0%
8 268
 
2.7%
9 231
 
2.3%
Other values (169) 2237
22.4%
ValueCountFrequency (%)
0 2800
28.0%
1 1322
13.2%
2 866
 
8.7%
3 672
 
6.7%
4 512
 
5.1%
5 441
 
4.4%
6 348
 
3.5%
7 303
 
3.0%
8 268
 
2.7%
9 231
 
2.3%
ValueCountFrequency (%)
487 1
< 0.1%
397 1
< 0.1%
355 1
< 0.1%
338 1
< 0.1%
331 1
< 0.1%
300 1
< 0.1%
299 1
< 0.1%
290 1
< 0.1%
280 2
< 0.1%
277 1
< 0.1%

3등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct350
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.0992
Minimum0
Maximum1168
Zeros1545
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:08:59.949132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q327
95-th percentile120
Maximum1168
Range1168
Interquartile range (IQR)25

Descriptive statistics

Standard deviation61.095668
Coefficient of variation (CV)2.174285
Kurtosis63.385359
Mean28.0992
Median Absolute Deviation (MAD)9
Skewness6.2910168
Sum280992
Variance3732.6806
MonotonicityNot monotonic
2024-04-06T17:09:00.373658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1545
 
15.4%
1 839
 
8.4%
2 572
 
5.7%
3 449
 
4.5%
4 394
 
3.9%
5 332
 
3.3%
6 298
 
3.0%
7 271
 
2.7%
8 251
 
2.5%
9 244
 
2.4%
Other values (340) 4805
48.0%
ValueCountFrequency (%)
0 1545
15.4%
1 839
8.4%
2 572
 
5.7%
3 449
 
4.5%
4 394
 
3.9%
5 332
 
3.3%
6 298
 
3.0%
7 271
 
2.7%
8 251
 
2.5%
9 244
 
2.4%
ValueCountFrequency (%)
1168 1
< 0.1%
1110 1
< 0.1%
970 1
< 0.1%
882 1
< 0.1%
864 1
< 0.1%
748 1
< 0.1%
739 2
< 0.1%
730 1
< 0.1%
727 1
< 0.1%
720 1
< 0.1%

4등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct464
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.5776
Minimum0
Maximum1606
Zeros1114
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:00.647176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q349
95-th percentile209
Maximum1606
Range1606
Interquartile range (IQR)46

Descriptive statistics

Standard deviation92.316578
Coefficient of variation (CV)1.940337
Kurtosis39.665294
Mean47.5776
Median Absolute Deviation (MAD)14
Skewness4.9576175
Sum475776
Variance8522.3506
MonotonicityNot monotonic
2024-04-06T17:09:00.938089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1114
 
11.1%
1 730
 
7.3%
2 483
 
4.8%
3 349
 
3.5%
4 286
 
2.9%
5 260
 
2.6%
6 240
 
2.4%
7 232
 
2.3%
8 214
 
2.1%
9 194
 
1.9%
Other values (454) 5898
59.0%
ValueCountFrequency (%)
0 1114
11.1%
1 730
7.3%
2 483
4.8%
3 349
 
3.5%
4 286
 
2.9%
5 260
 
2.6%
6 240
 
2.4%
7 232
 
2.3%
8 214
 
2.1%
9 194
 
1.9%
ValueCountFrequency (%)
1606 1
< 0.1%
1439 1
< 0.1%
1152 1
< 0.1%
1114 1
< 0.1%
1086 1
< 0.1%
1031 1
< 0.1%
1028 1
< 0.1%
1013 1
< 0.1%
1012 1
< 0.1%
1005 1
< 0.1%

5등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct176
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.8713
Minimum0
Maximum408
Zeros2596
Zeros (%)26.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:01.242021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q312
95-th percentile55
Maximum408
Range408
Interquartile range (IQR)12

Descriptive statistics

Standard deviation23.395257
Coefficient of variation (CV)1.970741
Kurtosis38.271863
Mean11.8713
Median Absolute Deviation (MAD)3
Skewness4.7896965
Sum118713
Variance547.33807
MonotonicityNot monotonic
2024-04-06T17:09:01.523459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2596
26.0%
1 1135
 
11.3%
2 781
 
7.8%
3 584
 
5.8%
4 468
 
4.7%
5 378
 
3.8%
7 329
 
3.3%
6 318
 
3.2%
9 231
 
2.3%
8 217
 
2.2%
Other values (166) 2963
29.6%
ValueCountFrequency (%)
0 2596
26.0%
1 1135
11.3%
2 781
 
7.8%
3 584
 
5.8%
4 468
 
4.7%
5 378
 
3.8%
6 318
 
3.2%
7 329
 
3.3%
8 217
 
2.2%
9 231
 
2.3%
ValueCountFrequency (%)
408 1
< 0.1%
400 1
< 0.1%
278 1
< 0.1%
275 1
< 0.1%
260 1
< 0.1%
259 1
< 0.1%
252 1
< 0.1%
250 1
< 0.1%
239 1
< 0.1%
233 1
< 0.1%

인지지원등급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4531
Minimum0
Maximum123
Zeros4572
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:01.825180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum123
Range123
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.0965673
Coefficient of variation (CV)2.0776027
Kurtosis78.736365
Mean2.4531
Median Absolute Deviation (MAD)1
Skewness6.3129877
Sum24531
Variance25.974998
MonotonicityNot monotonic
2024-04-06T17:09:02.286428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4572
45.7%
1 1771
 
17.7%
2 979
 
9.8%
3 640
 
6.4%
4 444
 
4.4%
5 313
 
3.1%
6 225
 
2.2%
7 188
 
1.9%
8 147
 
1.5%
9 105
 
1.1%
Other values (45) 616
 
6.2%
ValueCountFrequency (%)
0 4572
45.7%
1 1771
 
17.7%
2 979
 
9.8%
3 640
 
6.4%
4 444
 
4.4%
5 313
 
3.1%
6 225
 
2.2%
7 188
 
1.9%
8 147
 
1.5%
9 105
 
1.1%
ValueCountFrequency (%)
123 1
< 0.1%
104 1
< 0.1%
85 1
< 0.1%
72 1
< 0.1%
66 2
< 0.1%
61 1
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
49 1
< 0.1%
48 2
< 0.1%

등급외A
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9369
Minimum0
Maximum199
Zeros3523
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:02.675421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile32
Maximum199
Range199
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.88144
Coefficient of variation (CV)2.0011014
Kurtosis30.218188
Mean6.9369
Median Absolute Deviation (MAD)2
Skewness4.4027712
Sum69369
Variance192.69439
MonotonicityNot monotonic
2024-04-06T17:09:02.976601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3523
35.2%
1 1473
14.7%
2 791
 
7.9%
3 542
 
5.4%
4 398
 
4.0%
5 340
 
3.4%
6 272
 
2.7%
7 194
 
1.9%
8 178
 
1.8%
9 171
 
1.7%
Other values (107) 2118
21.2%
ValueCountFrequency (%)
0 3523
35.2%
1 1473
14.7%
2 791
 
7.9%
3 542
 
5.4%
4 398
 
4.0%
5 340
 
3.4%
6 272
 
2.7%
7 194
 
1.9%
8 178
 
1.8%
9 171
 
1.7%
ValueCountFrequency (%)
199 1
< 0.1%
191 1
< 0.1%
188 1
< 0.1%
168 1
< 0.1%
165 1
< 0.1%
162 1
< 0.1%
158 1
< 0.1%
147 1
< 0.1%
143 1
< 0.1%
141 1
< 0.1%

등급외B
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2858
Minimum0
Maximum106
Zeros4053
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:03.305985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile19
Maximum106
Range106
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.9485116
Coefficient of variation (CV)1.8546156
Kurtosis21.145837
Mean4.2858
Median Absolute Deviation (MAD)1
Skewness3.7846795
Sum42858
Variance63.178836
MonotonicityNot monotonic
2024-04-06T17:09:03.599422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4053
40.5%
1 1464
 
14.6%
2 809
 
8.1%
3 554
 
5.5%
4 417
 
4.2%
5 345
 
3.5%
6 297
 
3.0%
7 250
 
2.5%
8 197
 
2.0%
9 190
 
1.9%
Other values (61) 1424
 
14.2%
ValueCountFrequency (%)
0 4053
40.5%
1 1464
 
14.6%
2 809
 
8.1%
3 554
 
5.5%
4 417
 
4.2%
5 345
 
3.5%
6 297
 
3.0%
7 250
 
2.5%
8 197
 
2.0%
9 190
 
1.9%
ValueCountFrequency (%)
106 1
< 0.1%
87 2
< 0.1%
78 2
< 0.1%
76 2
< 0.1%
75 2
< 0.1%
74 1
< 0.1%
71 1
< 0.1%
70 1
< 0.1%
68 2
< 0.1%
65 1
< 0.1%

등급외C
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6465
Minimum0
Maximum48
Zeros5458
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-06T17:09:03.857579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7
Maximum48
Range48
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0440211
Coefficient of variation (CV)1.8487829
Kurtosis20.830096
Mean1.6465
Median Absolute Deviation (MAD)0
Skewness3.546459
Sum16465
Variance9.2660644
MonotonicityNot monotonic
2024-04-06T17:09:04.150486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 5458
54.6%
1 1547
 
15.5%
2 805
 
8.1%
3 571
 
5.7%
4 410
 
4.1%
5 323
 
3.2%
6 198
 
2.0%
7 189
 
1.9%
8 106
 
1.1%
9 99
 
1.0%
Other values (24) 294
 
2.9%
ValueCountFrequency (%)
0 5458
54.6%
1 1547
 
15.5%
2 805
 
8.1%
3 571
 
5.7%
4 410
 
4.1%
5 323
 
3.2%
6 198
 
2.0%
7 189
 
1.9%
8 106
 
1.1%
9 99
 
1.0%
ValueCountFrequency (%)
48 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
31 1
 
< 0.1%
30 1
 
< 0.1%
29 2
< 0.1%
27 2
< 0.1%
26 2
< 0.1%
25 1
 
< 0.1%
24 3
< 0.1%

Interactions

2024-04-06T17:08:51.513000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:31.459431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.993831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:36.247148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:38.320581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:40.474040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:42.741258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:45.374284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:47.312373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:49.497585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:51.689372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:31.979681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:34.248179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:36.420700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:38.502230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:40.734113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:42.974817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:45.578805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:47.489292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:49.676052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:51.897586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:32.170583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:34.538673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:36.612227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:38.673530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:40.948248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:43.180542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:45.763915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:47.697815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:49.913264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:52.076547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:32.342180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:34.857614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:36.801570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:38.852195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:41.126009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:43.368655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:45.939202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:47.901263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:50.124538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:52.263921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:32.519700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:35.034248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:37.077132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:39.163749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:41.358835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:43.594196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:46.133785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:48.123182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:50.317303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:52.512623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:32.762594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:35.228249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:37.280613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:39.444377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:41.563319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:43.805422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:46.350595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:48.357321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:50.519177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:52.702982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.055927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:35.425562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:37.463985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:39.640680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:41.795491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:44.406491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:46.548701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:48.582951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:50.737213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:52.861591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.270060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:35.608060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:37.628964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:39.816801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:42.065907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:44.592947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:46.731607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:48.787322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:50.911340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:53.052688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.540496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:35.846244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:37.910793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:40.006475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:42.286181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:44.854413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:46.943384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:49.044161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:51.123674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:53.242868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.787384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:36.059716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:38.083512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:40.215353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:42.488111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:45.140336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:47.119086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:49.222487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:51.287063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:09:04.360513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도자격별성별연령구분신청자1등급2등급3등급4등급5등급인지지원등급등급외A등급외B등급외C
시도1.0000.0000.0000.0000.0970.1150.0860.1020.0760.1190.1010.1020.1360.145
자격별0.0001.0000.0000.0620.2400.1990.1570.1910.2260.3190.1870.4580.4360.372
성별0.0000.0001.0000.0000.2670.2070.2300.2470.2800.2700.1340.2120.2010.050
연령구분0.0000.0620.0001.0000.2460.1850.2420.2580.2680.2430.1750.1270.1490.115
신청자0.0970.2400.2670.2461.0000.8260.9530.9470.9750.8230.8380.8120.8400.714
1등급0.1150.1990.2070.1850.8261.0000.8600.8200.7960.7510.6120.4670.3970.383
2등급0.0860.1570.2300.2420.9530.8601.0000.9650.9530.6900.7060.6080.5150.355
3등급0.1020.1910.2470.2580.9470.8200.9651.0000.9460.6820.6800.6100.5560.407
4등급0.0760.2260.2800.2680.9750.7960.9530.9461.0000.7840.7770.7230.6280.450
5등급0.1190.3190.2700.2430.8230.7510.6900.6820.7841.0000.7920.5940.6250.585
인지지원등급0.1010.1870.1340.1750.8380.6120.7060.6800.7770.7921.0000.7140.7330.892
등급외A0.1020.4580.2120.1270.8120.4670.6080.6100.7230.5940.7141.0000.8890.654
등급외B0.1360.4360.2010.1490.8400.3970.5150.5560.6280.6250.7330.8891.0000.752
등급외C0.1450.3720.0500.1150.7140.3830.3550.4070.4500.5850.8920.6540.7521.000
2024-04-06T17:09:04.609132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자격별시도연령구분성별
자격별1.0000.0000.0400.000
시도0.0001.0000.0000.000
연령구분0.0400.0001.0000.000
성별0.0000.0000.0001.000
2024-04-06T17:09:05.178845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
신청자1등급2등급3등급4등급5등급인지지원등급등급외A등급외B등급외C시도자격별성별연령구분
신청자1.0000.8260.9020.9580.9740.9160.7920.7040.6620.5510.0380.1460.2050.132
1등급0.8261.0000.8170.8340.7890.7210.6470.5250.4590.4020.0480.0890.1510.102
2등급0.9020.8171.0000.9020.8860.8200.7070.5400.4880.4040.0340.0940.1760.130
3등급0.9580.8340.9021.0000.9430.8750.7480.5840.5310.4410.0400.1150.1890.139
4등급0.9740.7890.8860.9431.0000.9160.7750.6070.5750.4560.0290.1370.2150.144
5등급0.9160.7210.8200.8750.9161.0000.7860.5720.5540.4380.0500.1470.2030.137
인지지원등급0.7920.6470.7070.7480.7750.7861.0000.5210.5120.4330.0400.1200.1340.088
등급외A0.7040.5250.5400.5840.6070.5720.5211.0000.8210.7330.0400.2900.1620.067
등급외B0.6620.4590.4880.5310.5750.5540.5120.8211.0000.7380.0530.2750.1540.079
등급외C0.5510.4020.4040.4410.4560.4380.4330.7330.7381.0000.0580.2460.0500.057
시도0.0380.0480.0340.0400.0290.0500.0400.0400.0530.0581.0000.0000.0000.000
자격별0.1460.0890.0940.1150.1370.1470.1200.2900.2750.2460.0001.0000.0000.040
성별0.2050.1510.1760.1890.2150.2030.1340.1620.1540.0500.0000.0001.0000.000
연령구분0.1320.1020.1300.1390.1440.1370.0880.0670.0790.0570.0000.0400.0001.000

Missing values

2024-04-06T17:08:53.486136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:08:53.898410image/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

시도시군구자격별성별연령구분신청자1등급2등급3등급4등급5등급인지지원등급등급외A등급외B등급외C
7544전북순창군감경남자80-84세450452771000
2435인천미추홀구일반여자85세이상121765131341415631048232
9255경북영양군감경여자75-79세290261610110
8346전남무안군일반여자65세미만12110401010
2483인천연수구일반여자85세이상875399126233133334102
5867충북충주시의료남자85세이상6010401000
7620전북부안군기초여자80-84세70001125174631
6944충남태안군의료여자85세이상11005310100
647서울양천구일반남자65-69세16310113533701465
7227전북남원시의료여자75-79세5011100200
시도시군구자격별성별연령구분신청자1등급2등급3등급4등급5등급인지지원등급등급외A등급외B등급외C
4777경기포천시기초남자65세미만98431919851059
7927전남곡성군일반남자65-69세18004201501
2616인천계양구일반남자65-69세119352124701693
2892광주남구일반여자65세미만45615720932
9600경북울진군감경남자70-74세300091540010
7704전남여수시기초남자70-74세9803153993797
3883경기동두천시일반남자65세미만26213300820
7102전북익산시일반남자75-79세2835832764092693
8005전남구례군의료여자85세이상11011441000
7899전남담양군기초여자65-69세15012420130