Overview

Dataset statistics

Number of variables19
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory172.8 B

Variable types

Text1
Numeric18

Dataset

Description공무원 연령별(18세이상) 지역별(광역시, 도) 가입자 현황에 대한 데이터입니다. 18세 이상부터 시작되며 65세 이상까지의 데이터가 있습니다.
URLhttps://www.data.go.kr/data/15054015/fileData.do

Alerts

is highly overall correlated with 서울 and 16 other fieldsHigh correlation
서울 is highly overall correlated with and 16 other fieldsHigh correlation
부산 is highly overall correlated with and 16 other fieldsHigh correlation
대구 is highly overall correlated with and 16 other fieldsHigh correlation
인천 is highly overall correlated with and 16 other fieldsHigh correlation
광주 is highly overall correlated with and 16 other fieldsHigh correlation
대전 is highly overall correlated with and 16 other fieldsHigh correlation
세종 is highly overall correlated with and 16 other fieldsHigh correlation
울산 is highly overall correlated with and 16 other fieldsHigh correlation
경기 is highly overall correlated with and 16 other fieldsHigh correlation
강원 is highly overall correlated with and 16 other fieldsHigh correlation
충북 is highly overall correlated with and 16 other fieldsHigh correlation
충남 is highly overall correlated with and 16 other fieldsHigh correlation
경북 is highly overall correlated with and 16 other fieldsHigh correlation
경남 is highly overall correlated with and 16 other fieldsHigh correlation
전북 is highly overall correlated with and 16 other fieldsHigh correlation
전남 is highly overall correlated with and 16 other fieldsHigh correlation
제주 is highly overall correlated with and 16 other fieldsHigh correlation
구분 has unique valuesUnique
has unique valuesUnique
서울 has unique valuesUnique
부산 has unique valuesUnique
광주 has unique valuesUnique
충북 has unique valuesUnique
경북 has unique valuesUnique
전남 has unique valuesUnique
인천 has 1 (2.1%) zerosZeros
세종 has 1 (2.1%) zerosZeros
울산 has 1 (2.1%) zerosZeros
강원 has 1 (2.1%) zerosZeros
경북 has 1 (2.1%) zerosZeros
경남 has 1 (2.1%) zerosZeros
전북 has 1 (2.1%) zerosZeros
제주 has 1 (2.1%) zerosZeros

Reproduction

Analysis started2023-12-12 19:01:02.154138
Analysis finished2023-12-12 19:01:52.703619
Duration50.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-13T04:01:52.905149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0833333
Min length3

Characters and Unicode

Total characters148
Distinct characters13
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

Unique48 ?
Unique (%)100.0%

Sample

1st row18세이상
2nd row19세
3rd row20세
4th row21세
5th row22세
ValueCountFrequency (%)
18세이상 1
 
2.1%
19세 1
 
2.1%
53세 1
 
2.1%
44세 1
 
2.1%
45세 1
 
2.1%
46세 1
 
2.1%
47세 1
 
2.1%
48세 1
 
2.1%
49세 1
 
2.1%
50세 1
 
2.1%
Other values (38) 38
79.2%
2023-12-13T04:01:53.412790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
32.4%
2 15
 
10.1%
3 15
 
10.1%
4 15
 
10.1%
5 15
 
10.1%
6 10
 
6.8%
1 7
 
4.7%
8 5
 
3.4%
9 5
 
3.4%
0 5
 
3.4%
Other values (3) 8
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 96
64.9%
Other Letter 52
35.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Other Letter
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 96
64.9%
Hangul 52
35.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%
Hangul
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 96
64.9%
Hangul 52
35.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
48
92.3%
2
 
3.8%
2
 
3.8%
ASCII
ValueCountFrequency (%)
2 15
15.6%
3 15
15.6%
4 15
15.6%
5 15
15.6%
6 10
10.4%
1 7
7.3%
8 5
 
5.2%
9 5
 
5.2%
0 5
 
5.2%
7 4
 
4.2%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26687.375
Minimum15
Maximum44190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:53.634493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile738.45
Q111739.75
median34799.5
Q337867.5
95-th percentile43060.9
Maximum44190
Range44175
Interquartile range (IQR)26127.75

Descriptive statistics

Standard deviation15536.131
Coefficient of variation (CV)0.58215285
Kurtosis-1.0632425
Mean26687.375
Median Absolute Deviation (MAD)5758.5
Skewness-0.77326555
Sum1280994
Variance2.4137138 × 108
MonotonicityNot monotonic
2023-12-13T04:01:53.866204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15 1
 
2.1%
43063 1
 
2.1%
38190 1
 
2.1%
36196 1
 
2.1%
35247 1
 
2.1%
38289 1
 
2.1%
38458 1
 
2.1%
37481 1
 
2.1%
39132 1
 
2.1%
37760 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
15 1
2.1%
528 1
2.1%
652 1
2.1%
899 1
2.1%
1005 1
2.1%
1079 1
2.1%
1301 1
2.1%
1645 1
2.1%
2288 1
2.1%
4647 1
2.1%
ValueCountFrequency (%)
44190 1
2.1%
43953 1
2.1%
43063 1
2.1%
43057 1
2.1%
42465 1
2.1%
41606 1
2.1%
39132 1
2.1%
38458 1
2.1%
38368 1
2.1%
38359 1
2.1%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5029.5833
Minimum1
Maximum8179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:54.072305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile139.15
Q12096.25
median6611.5
Q37364.25
95-th percentile7972.85
Maximum8179
Range8178
Interquartile range (IQR)5268

Descriptive statistics

Standard deviation2959.8455
Coefficient of variation (CV)0.58848721
Kurtosis-1.1495118
Mean5029.5833
Median Absolute Deviation (MAD)1219
Skewness-0.73530878
Sum241420
Variance8760685.1
MonotonicityNot monotonic
2023-12-13T04:01:54.264585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
7829 1
 
2.1%
6801 1
 
2.1%
6661 1
 
2.1%
6630 1
 
2.1%
7400 1
 
2.1%
7720 1
 
2.1%
7607 1
 
2.1%
7832 1
 
2.1%
7555 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
93 1
2.1%
122 1
2.1%
171 1
2.1%
205 1
2.1%
229 1
2.1%
328 1
2.1%
466 1
2.1%
621 1
2.1%
676 1
2.1%
ValueCountFrequency (%)
8179 1
2.1%
8122 1
2.1%
7976 1
2.1%
7967 1
2.1%
7832 1
2.1%
7829 1
2.1%
7790 1
2.1%
7720 1
2.1%
7607 1
2.1%
7555 1
2.1%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1632.375
Minimum1
Maximum2815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:54.476180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30.2
Q1833.5
median2123
Q32308
95-th percentile2664.25
Maximum2815
Range2814
Interquartile range (IQR)1474.5

Descriptive statistics

Standard deviation946.90878
Coefficient of variation (CV)0.58008042
Kurtosis-1.0492136
Mean1632.375
Median Absolute Deviation (MAD)419.5
Skewness-0.73536285
Sum78354
Variance896636.24
MonotonicityNot monotonic
2023-12-13T04:01:54.688397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
2716 1
 
2.1%
2280 1
 
2.1%
2166 1
 
2.1%
2153 1
 
2.1%
2297 1
 
2.1%
2398 1
 
2.1%
2388 1
 
2.1%
2512 1
 
2.1%
2436 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
19 1
2.1%
26 1
2.1%
38 1
2.1%
67 1
2.1%
69 1
2.1%
101 1
2.1%
114 1
2.1%
181 1
2.1%
334 1
2.1%
ValueCountFrequency (%)
2815 1
2.1%
2716 1
2.1%
2666 1
2.1%
2661 1
2.1%
2610 1
2.1%
2512 1
2.1%
2461 1
2.1%
2455 1
2.1%
2436 1
2.1%
2398 1
2.1%

대구
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1337.6042
Minimum1
Maximum2498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:54.863214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21.25
Q1514.5
median1685.5
Q31987.5
95-th percentile2409.55
Maximum2498
Range2497
Interquartile range (IQR)1473

Descriptive statistics

Standard deviation835.87288
Coefficient of variation (CV)0.62490302
Kurtosis-1.2274253
Mean1337.6042
Median Absolute Deviation (MAD)399
Skewness-0.54429905
Sum64205
Variance698683.48
MonotonicityNot monotonic
2023-12-13T04:01:55.051124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
16 2
 
4.2%
1 1
 
2.1%
2082 1
 
2.1%
1946 1
 
2.1%
2004 1
 
2.1%
2150 1
 
2.1%
2087 1
 
2.1%
2020 1
 
2.1%
2024 1
 
2.1%
1982 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
16 2
4.2%
31 1
2.1%
46 1
2.1%
49 1
2.1%
62 1
2.1%
66 1
2.1%
104 1
2.1%
140 1
2.1%
229 1
2.1%
ValueCountFrequency (%)
2498 1
2.1%
2496 1
2.1%
2482 1
2.1%
2275 1
2.1%
2150 1
2.1%
2116 1
2.1%
2087 1
2.1%
2082 1
2.1%
2024 1
2.1%
2020 1
2.1%

인천
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341.7917
Minimum0
Maximum2439
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:55.541866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.4
Q1556.75
median1673
Q31972.25
95-th percentile2373.4
Maximum2439
Range2439
Interquartile range (IQR)1415.5

Descriptive statistics

Standard deviation823.15876
Coefficient of variation (CV)0.61347732
Kurtosis-1.1781076
Mean1341.7917
Median Absolute Deviation (MAD)441
Skewness-0.55780607
Sum64406
Variance677590.34
MonotonicityNot monotonic
2023-12-13T04:01:55.738540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1973 2
 
4.2%
0 1
 
2.1%
1438 1
 
2.1%
1947 1
 
2.1%
1882 1
 
2.1%
2020 1
 
2.1%
2000 1
 
2.1%
1850 1
 
2.1%
1972 1
 
2.1%
1867 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 1
2.1%
21 1
2.1%
23 1
2.1%
27 1
2.1%
29 1
2.1%
49 1
2.1%
52 1
2.1%
74 1
2.1%
82 1
2.1%
243 1
2.1%
ValueCountFrequency (%)
2439 1
2.1%
2412 1
2.1%
2407 1
2.1%
2311 1
2.1%
2291 1
2.1%
2209 1
2.1%
2135 1
2.1%
2035 1
2.1%
2020 1
2.1%
2000 1
2.1%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean866.83333
Minimum1
Maximum1584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:55.912826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.35
Q1316.25
median1056
Q31299
95-th percentile1508.9
Maximum1584
Range1583
Interquartile range (IQR)982.75

Descriptive statistics

Standard deviation533.15182
Coefficient of variation (CV)0.6150569
Kurtosis-1.2478071
Mean866.83333
Median Absolute Deviation (MAD)309.5
Skewness-0.54122219
Sum41608
Variance284250.87
MonotonicityNot monotonic
2023-12-13T04:01:56.109877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
1518 1
 
2.1%
1427 1
 
2.1%
1341 1
 
2.1%
1291 1
 
2.1%
1408 1
 
2.1%
1279 1
 
2.1%
1339 1
 
2.1%
1366 1
 
2.1%
1323 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
23 1
2.1%
34 1
2.1%
35 1
2.1%
38 1
2.1%
52 1
2.1%
66 1
2.1%
68 1
2.1%
92 1
2.1%
98 1
2.1%
ValueCountFrequency (%)
1584 1
2.1%
1547 1
2.1%
1518 1
2.1%
1492 1
2.1%
1427 1
2.1%
1408 1
2.1%
1403 1
2.1%
1366 1
2.1%
1341 1
2.1%
1339 1
2.1%

대전
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1102.7292
Minimum2
Maximum1964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:56.299443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile39.7
Q1399.5
median1352.5
Q31688
95-th percentile1878.65
Maximum1964
Range1962
Interquartile range (IQR)1288.5

Descriptive statistics

Standard deviation686.44817
Coefficient of variation (CV)0.62249934
Kurtosis-1.2933742
Mean1102.7292
Median Absolute Deviation (MAD)438.5
Skewness-0.53759419
Sum52931
Variance471211.1
MonotonicityNot monotonic
2023-12-13T04:01:56.479762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1687 2
 
4.2%
2 1
 
2.1%
39 1
 
2.1%
1691 1
 
2.1%
1801 1
 
2.1%
1709 1
 
2.1%
1865 1
 
2.1%
1797 1
 
2.1%
1665 1
 
2.1%
1888 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
2 1
2.1%
36 1
2.1%
39 1
2.1%
41 1
2.1%
44 1
2.1%
55 1
2.1%
60 1
2.1%
66 1
2.1%
85 1
2.1%
117 1
2.1%
ValueCountFrequency (%)
1964 1
2.1%
1888 1
2.1%
1886 1
2.1%
1865 1
2.1%
1853 1
2.1%
1823 1
2.1%
1801 1
2.1%
1797 1
2.1%
1785 1
2.1%
1747 1
2.1%

세종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677.77083
Minimum0
Maximum1434
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:56.679684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.05
Q1164.5
median888
Q31040.25
95-th percentile1341.8
Maximum1434
Range1434
Interquartile range (IQR)875.75

Descriptive statistics

Standard deviation470.01151
Coefficient of variation (CV)0.69346671
Kurtosis-1.4427393
Mean677.77083
Median Absolute Deviation (MAD)291
Skewness-0.27553696
Sum32533
Variance220910.82
MonotonicityNot monotonic
2023-12-13T04:01:56.867488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1089 2
 
4.2%
0 1
 
2.1%
10 1
 
2.1%
1170 1
 
2.1%
1125 1
 
2.1%
1031 1
 
2.1%
1027 1
 
2.1%
996 1
 
2.1%
1093 1
 
2.1%
965 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 1
2.1%
5 1
2.1%
10 1
2.1%
13 1
2.1%
15 1
2.1%
21 1
2.1%
24 1
2.1%
28 1
2.1%
36 1
2.1%
39 1
2.1%
ValueCountFrequency (%)
1434 1
2.1%
1357 1
2.1%
1353 1
2.1%
1321 1
2.1%
1170 1
2.1%
1146 1
2.1%
1125 1
2.1%
1093 1
2.1%
1089 2
4.2%
1076 1
2.1%

울산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500
Minimum0
Maximum898
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:57.074527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.35
Q1227
median640.5
Q3735.5
95-th percentile844
Maximum898
Range898
Interquartile range (IQR)508.5

Descriptive statistics

Standard deviation300.67067
Coefficient of variation (CV)0.60134134
Kurtosis-1.1063113
Mean500
Median Absolute Deviation (MAD)163.5
Skewness-0.62962859
Sum24000
Variance90402.851
MonotonicityNot monotonic
2023-12-13T04:01:57.273178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
5 2
 
4.2%
659 2
 
4.2%
0 1
 
2.1%
531 1
 
2.1%
782 1
 
2.1%
729 1
 
2.1%
807 1
 
2.1%
831 1
 
2.1%
740 1
 
2.1%
753 1
 
2.1%
Other values (36) 36
75.0%
ValueCountFrequency (%)
0 1
2.1%
2 1
2.1%
4 1
2.1%
5 2
4.2%
11 1
2.1%
12 1
2.1%
21 1
2.1%
42 1
2.1%
103 1
2.1%
127 1
2.1%
ValueCountFrequency (%)
898 1
2.1%
867 1
2.1%
851 1
2.1%
831 1
2.1%
816 1
2.1%
807 1
2.1%
801 1
2.1%
782 1
2.1%
755 1
2.1%
753 1
2.1%

경기
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5060.2708
Minimum4
Maximum8841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:57.468817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile78.75
Q12037
median6449.5
Q37606.25
95-th percentile8590.85
Maximum8841
Range8837
Interquartile range (IQR)5569.25

Descriptive statistics

Standard deviation3074.4291
Coefficient of variation (CV)0.60756217
Kurtosis-1.1850254
Mean5060.2708
Median Absolute Deviation (MAD)1783
Skewness-0.61078695
Sum242893
Variance9452114.5
MonotonicityNot monotonic
2023-12-13T04:01:57.646524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
7154 2
 
4.2%
4 1
 
2.1%
7696 1
 
2.1%
7669 1
 
2.1%
7182 1
 
2.1%
7720 1
 
2.1%
7708 1
 
2.1%
7302 1
 
2.1%
6914 1
 
2.1%
6557 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
4 1
2.1%
55 1
2.1%
70 1
2.1%
95 1
2.1%
107 1
2.1%
138 1
2.1%
172 1
2.1%
289 1
2.1%
301 1
2.1%
1021 1
2.1%
ValueCountFrequency (%)
8841 1
2.1%
8817 1
2.1%
8734 1
2.1%
8325 1
2.1%
8287 1
2.1%
8178 1
2.1%
8145 1
2.1%
7720 1
2.1%
7708 1
2.1%
7696 1
2.1%

강원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1153.1875
Minimum0
Maximum1800
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:57.835948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.45
Q1497.5
median1466
Q31658
95-th percentile1759.3
Maximum1800
Range1800
Interquartile range (IQR)1160.5

Descriptive statistics

Standard deviation654.80781
Coefficient of variation (CV)0.56782423
Kurtosis-0.99738793
Mean1153.1875
Median Absolute Deviation (MAD)261.5
Skewness-0.84346848
Sum55353
Variance428773.26
MonotonicityNot monotonic
2023-12-13T04:01:58.016217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1534 2
 
4.2%
0 1
 
2.1%
1424 1
 
2.1%
1443 1
 
2.1%
1436 1
 
2.1%
1636 1
 
2.1%
1682 1
 
2.1%
1702 1
 
2.1%
1760 1
 
2.1%
1762 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 1
2.1%
22 1
2.1%
25 1
2.1%
32 1
2.1%
50 1
2.1%
65 1
2.1%
75 1
2.1%
96 1
2.1%
108 1
2.1%
233 1
2.1%
ValueCountFrequency (%)
1800 1
2.1%
1762 1
2.1%
1760 1
2.1%
1758 1
2.1%
1754 1
2.1%
1753 1
2.1%
1733 1
2.1%
1722 1
2.1%
1712 1
2.1%
1702 1
2.1%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean953.5625
Minimum3
Maximum1628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:58.177411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile31.35
Q1428.75
median1177
Q31361.25
95-th percentile1513.25
Maximum1628
Range1625
Interquartile range (IQR)932.5

Descriptive statistics

Standard deviation548.7788
Coefficient of variation (CV)0.57550375
Kurtosis-1.003193
Mean953.5625
Median Absolute Deviation (MAD)254
Skewness-0.78573738
Sum45771
Variance301158.17
MonotonicityNot monotonic
2023-12-13T04:01:58.368110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
3 1
 
2.1%
1442 1
 
2.1%
1238 1
 
2.1%
1098 1
 
2.1%
1166 1
 
2.1%
1253 1
 
2.1%
1165 1
 
2.1%
1123 1
 
2.1%
1250 1
 
2.1%
1209 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
3 1
2.1%
22 1
2.1%
31 1
2.1%
32 1
2.1%
39 1
2.1%
51 1
2.1%
54 1
2.1%
62 1
2.1%
94 1
2.1%
159 1
2.1%
ValueCountFrequency (%)
1628 1
2.1%
1553 1
2.1%
1522 1
2.1%
1497 1
2.1%
1496 1
2.1%
1493 1
2.1%
1462 1
2.1%
1442 1
2.1%
1420 1
2.1%
1387 1
2.1%

충남
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1181.1458
Minimum1
Maximum2166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:58.589472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28.45
Q1604
median1385.5
Q31644.75
95-th percentile1952.35
Maximum2166
Range2165
Interquartile range (IQR)1040.75

Descriptive statistics

Standard deviation679.31023
Coefficient of variation (CV)0.57512816
Kurtosis-0.90729484
Mean1181.1458
Median Absolute Deviation (MAD)408
Skewness-0.73249946
Sum56695
Variance461462.38
MonotonicityNot monotonic
2023-12-13T04:01:58.748432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1577 2
 
4.2%
1 1
 
2.1%
1306 1
 
2.1%
1404 1
 
2.1%
1284 1
 
2.1%
1301 1
 
2.1%
1361 1
 
2.1%
1331 1
 
2.1%
1599 1
 
2.1%
1504 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
1 1
2.1%
20 1
2.1%
26 1
2.1%
33 1
2.1%
34 1
2.1%
44 1
2.1%
52 1
2.1%
92 1
2.1%
106 1
2.1%
243 1
2.1%
ValueCountFrequency (%)
2166 1
2.1%
1995 1
2.1%
1994 1
2.1%
1875 1
2.1%
1868 1
2.1%
1839 1
2.1%
1836 1
2.1%
1829 1
2.1%
1817 1
2.1%
1778 1
2.1%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1371.2292
Minimum0
Maximum2273
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:58.904323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.4
Q1782.25
median1679.5
Q31881.75
95-th percentile2160.15
Maximum2273
Range2273
Interquartile range (IQR)1099.5

Descriptive statistics

Standard deviation757.8654
Coefficient of variation (CV)0.55269055
Kurtosis-0.76344604
Mean1371.2292
Median Absolute Deviation (MAD)307.5
Skewness-0.90261762
Sum65819
Variance574359.97
MonotonicityNot monotonic
2023-12-13T04:01:59.068010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 1
 
2.1%
1965 1
 
2.1%
1576 1
 
2.1%
1549 1
 
2.1%
1442 1
 
2.1%
1604 1
 
2.1%
1620 1
 
2.1%
1687 1
 
2.1%
1874 1
 
2.1%
1796 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
0 1
2.1%
24 1
2.1%
31 1
2.1%
35 1
2.1%
37 1
2.1%
53 1
2.1%
55 1
2.1%
118 1
2.1%
133 1
2.1%
355 1
2.1%
ValueCountFrequency (%)
2273 1
2.1%
2209 1
2.1%
2199 1
2.1%
2088 1
2.1%
2069 1
2.1%
2055 1
2.1%
2047 1
2.1%
2037 1
2.1%
2009 1
2.1%
1965 1
2.1%

경남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1585.0208
Minimum0
Maximum2740
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:59.268665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24.45
Q1790.5
median2007
Q32198.5
95-th percentile2653.5
Maximum2740
Range2740
Interquartile range (IQR)1408

Descriptive statistics

Standard deviation922.36972
Coefficient of variation (CV)0.58192908
Kurtosis-0.98955084
Mean1585.0208
Median Absolute Deviation (MAD)330
Skewness-0.7577966
Sum76081
Variance850765.89
MonotonicityNot monotonic
2023-12-13T04:01:59.444981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
78 2
 
4.2%
0 1
 
2.1%
2292 1
 
2.1%
2198 1
 
2.1%
1994 1
 
2.1%
1947 1
 
2.1%
2008 1
 
2.1%
2006 1
 
2.1%
1974 1
 
2.1%
2134 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 1
2.1%
21 1
2.1%
22 1
2.1%
29 1
2.1%
33 1
2.1%
78 2
4.2%
79 1
2.1%
163 1
2.1%
245 1
2.1%
411 1
2.1%
ValueCountFrequency (%)
2740 1
2.1%
2739 1
2.1%
2685 1
2.1%
2595 1
2.1%
2574 1
2.1%
2421 1
2.1%
2356 1
2.1%
2318 1
2.1%
2292 1
2.1%
2280 1
2.1%

전북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1177.8125
Minimum0
Maximum1944
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:01:59.642526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.35
Q1578.25
median1453.5
Q31658.75
95-th percentile1841.25
Maximum1944
Range1944
Interquartile range (IQR)1080.5

Descriptive statistics

Standard deviation660.96318
Coefficient of variation (CV)0.56117861
Kurtosis-0.91724094
Mean1177.8125
Median Absolute Deviation (MAD)255.5
Skewness-0.86813425
Sum56535
Variance436872.33
MonotonicityNot monotonic
2023-12-13T04:01:59.834952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1764 2
 
4.2%
1656 1
 
2.1%
1451 1
 
2.1%
1538 1
 
2.1%
1514 1
 
2.1%
1381 1
 
2.1%
1614 1
 
2.1%
1587 1
 
2.1%
1595 1
 
2.1%
1763 1
 
2.1%
Other values (37) 37
77.1%
ValueCountFrequency (%)
0 1
2.1%
34 1
2.1%
40 1
2.1%
41 1
2.1%
48 1
2.1%
76 1
2.1%
81 1
2.1%
86 1
2.1%
146 1
2.1%
181 1
2.1%
ValueCountFrequency (%)
1944 1
2.1%
1873 1
2.1%
1871 1
2.1%
1786 1
2.1%
1764 2
4.2%
1763 1
2.1%
1724 1
2.1%
1706 1
2.1%
1695 1
2.1%
1687 1
2.1%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct48
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1294.1042
Minimum1
Maximum2263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:02:00.034797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.75
Q1714.5
median1630.5
Q31759.25
95-th percentile2070
Maximum2263
Range2262
Interquartile range (IQR)1044.75

Descriptive statistics

Standard deviation714.9303
Coefficient of variation (CV)0.55245189
Kurtosis-0.76025652
Mean1294.1042
Median Absolute Deviation (MAD)204.5
Skewness-0.88543559
Sum62117
Variance511125.33
MonotonicityNot monotonic
2023-12-13T04:02:00.246577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1 1
 
2.1%
1719 1
 
2.1%
1539 1
 
2.1%
1459 1
 
2.1%
1427 1
 
2.1%
1572 1
 
2.1%
1571 1
 
2.1%
1615 1
 
2.1%
1810 1
 
2.1%
1769 1
 
2.1%
Other values (38) 38
79.2%
ValueCountFrequency (%)
1 1
2.1%
29 1
2.1%
30 1
2.1%
35 1
2.1%
48 1
2.1%
49 1
2.1%
70 1
2.1%
99 1
2.1%
148 1
2.1%
318 1
2.1%
ValueCountFrequency (%)
2263 1
2.1%
2159 1
2.1%
2119 1
2.1%
1979 1
2.1%
1931 1
2.1%
1917 1
2.1%
1847 1
2.1%
1836 1
2.1%
1826 1
2.1%
1810 1
2.1%

제주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422.35417
Minimum0
Maximum741
Zeros1
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-13T04:02:00.424735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.85
Q1223.75
median521.5
Q3588.25
95-th percentile676.05
Maximum741
Range741
Interquartile range (IQR)364.5

Descriptive statistics

Standard deviation240.66031
Coefficient of variation (CV)0.56980687
Kurtosis-0.93115496
Mean422.35417
Median Absolute Deviation (MAD)93
Skewness-0.79341099
Sum20273
Variance57917.383
MonotonicityNot monotonic
2023-12-13T04:02:00.978165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
589 2
 
4.2%
557 2
 
4.2%
0 1
 
2.1%
608 1
 
2.1%
507 1
 
2.1%
581 1
 
2.1%
505 1
 
2.1%
545 1
 
2.1%
547 1
 
2.1%
588 1
 
2.1%
Other values (36) 36
75.0%
ValueCountFrequency (%)
0 1
2.1%
1 1
2.1%
4 1
2.1%
15 1
2.1%
20 1
2.1%
21 1
2.1%
26 1
2.1%
33 1
2.1%
51 1
2.1%
87 1
2.1%
ValueCountFrequency (%)
741 1
2.1%
735 1
2.1%
682 1
2.1%
665 1
2.1%
664 1
2.1%
629 1
2.1%
623 1
2.1%
621 1
2.1%
608 1
2.1%
605 1
2.1%

Interactions

2023-12-13T04:01:50.551267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:03.077169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.970983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:09.137487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.958881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:17.448641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:21.302770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:25.534577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.812558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:30.211117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:32.423279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:35.371061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.852110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:40.286927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:42.214474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:44.182424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:46.006878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:48.112245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.665428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:03.217172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:06.129961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:09.318927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:14.116961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:17.628129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:21.515581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:25.676040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.918300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T04:01:34.707222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:36.951060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:39.616237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:41.344416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.466522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.446512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.355900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:49.910459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:51.843625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.135240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:08.234505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.118880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:16.502661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:20.336470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:24.049898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.238196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:29.499675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:31.843544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:34.803571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.103339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:39.742806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:41.424793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.575966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.539874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.486437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.007352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:51.930280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.273961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:08.384907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.248204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:16.662272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:20.512056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:24.217491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.346809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:29.616183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:31.938550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:34.909979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.229495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:39.837823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:41.798826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.679793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.637414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.606342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.094059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:52.017512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.423795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:08.545540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.406516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:16.831800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:20.682241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:24.418483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.472701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:29.747099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:32.058299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:35.029211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.394390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:39.942628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:41.885105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.788255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.709256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.728698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.188360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:52.119853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.601483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:08.737102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.607621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:17.052524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:20.899904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:24.653496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.598947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:29.893335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:32.209717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:35.146464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.556226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:40.067188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:41.986838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.906942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.797201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.843134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.319186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:52.203195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:05.764683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:08.924501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:13.777795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:17.231538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:21.067744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:25.393135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:27.691944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:30.047997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:32.311608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:35.234943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:37.702318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:40.187174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:42.075110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:43.999109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:45.886940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:47.982274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:01:50.431650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:02:01.179315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.9800.9540.9550.9840.9430.9470.9130.9790.9830.9670.9000.9000.9220.9390.9700.9740.976
서울1.0000.9801.0000.9560.9600.9790.9520.9480.9160.9600.9360.9060.8730.7970.8300.9080.9870.9610.970
부산1.0000.9540.9561.0000.9820.9170.9750.9640.8950.9460.9430.9120.8230.8300.8580.9040.9670.9540.974
대구1.0000.9550.9600.9821.0000.9460.9930.9820.9370.9400.9260.8490.8110.7450.7820.9050.9610.9360.972
인천1.0000.9840.9790.9170.9461.0000.9310.9410.9180.9420.9730.9140.9110.8550.8680.9220.9600.9450.945
광주1.0000.9430.9520.9750.9930.9311.0000.9810.9180.9350.9280.8170.7880.7260.7930.8970.9550.9320.965
대전1.0000.9470.9480.9640.9820.9410.9811.0000.8960.9370.9320.8510.8420.7640.7680.9030.9440.9360.949
세종1.0000.9130.9160.8950.9370.9180.9180.8961.0000.9480.9160.7310.7350.6090.6640.8270.8550.8340.881
울산1.0000.9790.9600.9460.9400.9420.9350.9370.9481.0000.9690.9070.8720.8750.8890.8540.9400.9560.955
경기1.0000.9830.9360.9430.9260.9730.9280.9320.9160.9691.0000.9340.9280.9000.9300.8780.9280.9530.947
강원1.0000.9670.9060.9120.8490.9140.8170.8510.7310.9070.9341.0000.9020.9100.9800.8810.9100.8970.906
충북1.0000.9000.8730.8230.8110.9110.7880.8420.7350.8720.9280.9021.0000.9710.9250.9520.8880.8820.843
충남1.0000.9000.7970.8300.7450.8550.7260.7640.6090.8750.9000.9100.9711.0000.9370.9000.8370.9470.880
경북1.0000.9220.8300.8580.7820.8680.7930.7680.6640.8890.9300.9800.9250.9371.0000.8200.8810.9420.902
경남1.0000.9390.9080.9040.9050.9220.8970.9030.8270.8540.8780.8810.9520.9000.8201.0000.9070.8730.901
전북1.0000.9700.9870.9670.9610.9600.9550.9440.8550.9400.9280.9100.8880.8370.8810.9071.0000.9800.983
전남1.0000.9740.9610.9540.9360.9450.9320.9360.8340.9560.9530.8970.8820.9470.9420.8730.9801.0000.978
제주1.0000.9760.9700.9740.9720.9450.9650.9490.8810.9550.9470.9060.8430.8800.9020.9010.9830.9781.000
2023-12-13T04:02:01.423997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1.0000.9830.9240.9190.9690.8970.9270.9500.9410.9490.9420.8930.8170.8430.9220.8780.7870.942
서울0.9831.0000.9340.9210.9320.9010.9250.9270.9370.9140.9590.8840.8040.8270.8950.8720.7830.928
부산0.9240.9341.0000.9630.8770.9290.9540.8780.9060.8500.9220.8100.7120.7850.8640.8940.7330.861
대구0.9190.9210.9631.0000.9010.9780.9830.9140.9460.8840.8780.7580.6430.7060.8370.8320.6530.834
인천0.9690.9320.8770.9011.0000.8930.9180.9830.9520.9880.8630.8580.7810.7940.9110.8150.7230.899
광주0.8970.9010.9290.9780.8931.0000.9760.9180.9570.8800.8410.7470.6360.6630.8360.7860.6180.812
대전0.9270.9250.9540.9830.9180.9761.0000.9330.9590.8940.8820.7650.6520.6940.8370.8140.6430.832
세종0.9500.9270.8780.9140.9830.9180.9331.0000.9720.9770.8440.8210.7400.7420.8810.7660.6660.852
울산0.9410.9370.9060.9460.9520.9570.9590.9721.0000.9500.8550.7910.6980.6980.8470.7680.6380.836
경기0.9490.9140.8500.8840.9880.8800.8940.9770.9501.0000.8360.8470.7740.7730.8830.7760.6910.869
강원0.9420.9590.9220.8780.8630.8410.8820.8440.8550.8361.0000.8830.8270.8680.8760.9080.8460.928
충북0.8930.8840.8100.7580.8580.7470.7650.8210.7910.8470.8831.0000.9410.9320.9370.8230.8970.932
충남0.8170.8040.7120.6430.7810.6360.6520.7400.6980.7740.8270.9411.0000.9440.8860.8000.9380.875
경북0.8430.8270.7850.7060.7940.6630.6940.7420.6980.7730.8680.9320.9441.0000.9050.8960.9600.913
경남0.9220.8950.8640.8370.9110.8360.8370.8810.8470.8830.8760.9370.8860.9051.0000.8680.8470.944
전북0.8780.8720.8940.8320.8150.7860.8140.7660.7680.7760.9080.8230.8000.8960.8681.0000.8830.903
전남0.7870.7830.7330.6530.7230.6180.6430.6660.6380.6910.8460.8970.9380.9600.8470.8831.0000.884
제주0.9420.9280.8610.8340.8990.8120.8320.8520.8360.8690.9280.9320.8750.9130.9440.9030.8841.000

Missing values

2023-12-13T04:01:52.352221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:01:52.602050image/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

구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
018세이상1511101200403100010
119세52893191629233910495222220312134491
220세6521223816273441215107253134552240304
321세899171263152384428111385039525333487015
422세164532869497452553621289965410613378869920
523세46476763341402439811773103102223315924337524518131887
624세985514736163514512332781742302022481416532741552465677163
725세167692684991616773412523294397329781767994812109898241048267
826세2320738551224800108956672849847045231127101314261553140610941478357
927세29626506215491074138271099060851759361335120817781873187714001836491
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
3856세2948154582081168312961053118451449045641389109912321778181016581665527
3957세2860153321930151112531005117638944945621263110713581717182415331684508
4058세2544150091645131511328219832814144143115290413221507147913951486453
4159세2146743241434105896274777428529933778917279621313137911951329411
4260세1236823049065695923444401362182042503433628796870616727244
4361세57931055419229261139185391271021299187288355411307369102
4462세22884661811048292851542301108949211816314614851
4563세107922911466216660135707562333778814821
4664세10052051016223686652556551443579763533
4765세이상1301621674649353624121723232262429412926