Overview

Dataset statistics

Number of variables19
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory173.1 B

Variable types

Text1
Numeric18

Dataset

Description공무원 재직년수별(1년미만~40년이상), 지역별(서울,부산,대구, 인천 등)공무원연금 가입자 현황 데이터입니다. 1년 미만부터 구분됩니다.
URLhttps://www.data.go.kr/data/15053035/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
인천 has unique valuesUnique
광주 has unique valuesUnique
세종 has unique valuesUnique
경기 has unique valuesUnique
충북 has unique valuesUnique
경북 has unique valuesUnique
경남 has unique valuesUnique
전북 has unique valuesUnique
전남 has unique valuesUnique
제주 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:46:31.313396
Analysis finished2023-12-12 10:47:14.940733
Duration43.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T19:47:15.139762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters126
Distinct characters17
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

Unique42 ?
Unique (%)100.0%

Sample

1st row1년미만
2nd row1년이상
3rd row2년
4th row3년
5th row4년
ValueCountFrequency (%)
1년미만 1
 
2.4%
30년 1
 
2.4%
39년 1
 
2.4%
23년 1
 
2.4%
24년 1
 
2.4%
25년 1
 
2.4%
26년 1
 
2.4%
27년 1
 
2.4%
28년 1
 
2.4%
29년 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T19:47:15.659010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
33.3%
3 16
 
12.7%
1 15
 
11.9%
2 14
 
11.1%
4 5
 
4.0%
6 4
 
3.2%
0 4
 
3.2%
8 4
 
3.2%
7 4
 
3.2%
5 4
 
3.2%
Other values (7) 14
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
58.7%
Other Letter 52
41.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 16
21.6%
1 15
20.3%
2 14
18.9%
4 5
 
6.8%
6 4
 
5.4%
0 4
 
5.4%
8 4
 
5.4%
7 4
 
5.4%
5 4
 
5.4%
9 4
 
5.4%
Other Letter
ValueCountFrequency (%)
42
80.8%
3
 
5.8%
3
 
5.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 74
58.7%
Hangul 52
41.3%

Most frequent character per script

Common
ValueCountFrequency (%)
3 16
21.6%
1 15
20.3%
2 14
18.9%
4 5
 
6.8%
6 4
 
5.4%
0 4
 
5.4%
8 4
 
5.4%
7 4
 
5.4%
5 4
 
5.4%
9 4
 
5.4%
Hangul
ValueCountFrequency (%)
42
80.8%
3
 
5.8%
3
 
5.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
58.7%
Hangul 52
41.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
42
80.8%
3
 
5.8%
3
 
5.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
ASCII
ValueCountFrequency (%)
3 16
21.6%
1 15
20.3%
2 14
18.9%
4 5
 
6.8%
6 4
 
5.4%
0 4
 
5.4%
8 4
 
5.4%
7 4
 
5.4%
5 4
 
5.4%
9 4
 
5.4%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30499.857
Minimum1871
Maximum64070
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:15.929260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1871
5-th percentile3326.2
Q124209.5
median28052
Q337785.5
95-th percentile57381.55
Maximum64070
Range62199
Interquartile range (IQR)13576

Descriptive statistics

Standard deviation15242.84
Coefficient of variation (CV)0.4997676
Kurtosis0.007314927
Mean30499.857
Median Absolute Deviation (MAD)8546.5
Skewness0.16513203
Sum1280994
Variance2.3234418 × 108
MonotonicityNot monotonic
2023-12-12T19:47:16.118530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
50252 1
 
2.4%
26010 1
 
2.4%
17565 1
 
2.4%
26425 1
 
2.4%
24158 1
 
2.4%
27847 1
 
2.4%
26146 1
 
2.4%
24996 1
 
2.4%
31106 1
 
2.4%
27296 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
1871 1
2.4%
3108 1
2.4%
3194 1
2.4%
5838 1
2.4%
7187 1
2.4%
12151 1
2.4%
17565 1
2.4%
17934 1
2.4%
22488 1
2.4%
22584 1
2.4%
ValueCountFrequency (%)
64070 1
2.4%
61921 1
2.4%
57438 1
2.4%
56309 1
2.4%
50252 1
2.4%
46286 1
2.4%
45459 1
2.4%
44745 1
2.4%
42872 1
2.4%
38615 1
2.4%

서울
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5748.0952
Minimum297
Maximum13633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:16.298887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile630.35
Q14358.5
median5284
Q36775
95-th percentile11971.45
Maximum13633
Range13336
Interquartile range (IQR)2416.5

Descriptive statistics

Standard deviation3063.3667
Coefficient of variation (CV)0.53293596
Kurtosis0.54995685
Mean5748.0952
Median Absolute Deviation (MAD)1224
Skewness0.57230837
Sum241420
Variance9384215.4
MonotonicityNot monotonic
2023-12-12T19:47:16.491983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
10517 1
 
2.4%
4504 1
 
2.4%
3731 1
 
2.4%
5508 1
 
2.4%
4931 1
 
2.4%
5524 1
 
2.4%
5389 1
 
2.4%
4601 1
 
2.4%
5698 1
 
2.4%
4634 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
297 1
2.4%
565 1
2.4%
604 1
2.4%
1131 1
2.4%
1553 1
2.4%
2624 1
2.4%
3428 1
2.4%
3731 1
2.4%
3855 1
2.4%
4039 1
2.4%
ValueCountFrequency (%)
13633 1
2.4%
12431 1
2.4%
12048 1
2.4%
10517 1
2.4%
9835 1
2.4%
9019 1
2.4%
8766 1
2.4%
8719 1
2.4%
8199 1
2.4%
7444 1
2.4%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1865.5714
Minimum136
Maximum3410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:17.003253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum136
5-th percentile236.5
Q11560.75
median1831.5
Q32344.25
95-th percentile3150.55
Maximum3410
Range3274
Interquartile range (IQR)783.5

Descriptive statistics

Standard deviation828.78216
Coefficient of variation (CV)0.4442511
Kurtosis-0.045270904
Mean1865.5714
Median Absolute Deviation (MAD)410.5
Skewness-0.36263939
Sum78354
Variance686879.86
MonotonicityNot monotonic
2023-12-12T19:47:17.152695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2965 1
 
2.4%
1827 1
 
2.4%
1076 1
 
2.4%
1645 1
 
2.4%
1547 1
 
2.4%
2093 1
 
2.4%
1697 1
 
2.4%
1680 1
 
2.4%
2026 1
 
2.4%
1832 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
136 1
2.4%
148 1
2.4%
231 1
2.4%
341 1
2.4%
510 1
2.4%
855 1
2.4%
1076 1
2.4%
1268 1
2.4%
1466 1
2.4%
1495 1
2.4%
ValueCountFrequency (%)
3410 1
2.4%
3248 1
2.4%
3154 1
2.4%
3085 1
2.4%
2965 1
2.4%
2787 1
2.4%
2768 1
2.4%
2650 1
2.4%
2577 1
2.4%
2468 1
2.4%

대구
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1528.6905
Minimum123
Maximum2587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:17.325534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum123
5-th percentile161.25
Q11322.75
median1608.5
Q32039.25
95-th percentile2244.25
Maximum2587
Range2464
Interquartile range (IQR)716.5

Descriptive statistics

Standard deviation648.42855
Coefficient of variation (CV)0.42417256
Kurtosis0.10908522
Mean1528.6905
Median Absolute Deviation (MAD)360
Skewness-0.80982632
Sum64205
Variance420459.58
MonotonicityNot monotonic
2023-12-12T19:47:17.505616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1328 2
 
4.8%
1628 1
 
2.4%
1563 1
 
2.4%
1338 1
 
2.4%
948 1
 
2.4%
1559 1
 
2.4%
1321 1
 
2.4%
1625 1
 
2.4%
1368 1
 
2.4%
1646 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
123 1
2.4%
134 1
2.4%
157 1
2.4%
242 1
2.4%
274 1
2.4%
659 1
2.4%
948 1
2.4%
988 1
2.4%
1247 1
2.4%
1250 1
2.4%
ValueCountFrequency (%)
2587 1
2.4%
2503 1
2.4%
2247 1
2.4%
2192 1
2.4%
2184 1
2.4%
2165 1
2.4%
2151 1
2.4%
2142 1
2.4%
2101 1
2.4%
2094 1
2.4%

인천
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1533.4762
Minimum74
Maximum3286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:17.686983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile135.9
Q11088.75
median1502.5
Q32010
95-th percentile2964.8
Maximum3286
Range3212
Interquartile range (IQR)921.25

Descriptive statistics

Standard deviation785.70299
Coefficient of variation (CV)0.51236725
Kurtosis-0.11933007
Mean1533.4762
Median Absolute Deviation (MAD)466.5
Skewness0.10247398
Sum64406
Variance617329.18
MonotonicityNot monotonic
2023-12-12T19:47:17.911407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2341 1
 
2.4%
1039 1
 
2.4%
954 1
 
2.4%
1222 1
 
2.4%
1277 1
 
2.4%
1654 1
 
2.4%
1363 1
 
2.4%
1387 1
 
2.4%
1646 1
 
2.4%
1163 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
74 1
2.4%
114 1
2.4%
129 1
2.4%
267 1
2.4%
358 1
2.4%
536 1
2.4%
915 1
2.4%
944 1
2.4%
954 1
2.4%
1039 1
2.4%
ValueCountFrequency (%)
3286 1
2.4%
2995 1
2.4%
2966 1
2.4%
2942 1
2.4%
2341 1
2.4%
2264 1
2.4%
2132 1
2.4%
2102 1
2.4%
2096 1
2.4%
2079 1
2.4%

광주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean990.66667
Minimum63
Maximum1733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:18.129978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile102.5
Q1852.25
median1006.5
Q31297.75
95-th percentile1587.7
Maximum1733
Range1670
Interquartile range (IQR)445.5

Descriptive statistics

Standard deviation436.44527
Coefficient of variation (CV)0.44055713
Kurtosis-0.0074617788
Mean990.66667
Median Absolute Deviation (MAD)248
Skewness-0.65198913
Sum41608
Variance190484.47
MonotonicityNot monotonic
2023-12-12T19:47:18.343231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1122 1
 
2.4%
962 1
 
2.4%
668 1
 
2.4%
908 1
 
2.4%
758 1
 
2.4%
933 1
 
2.4%
898 1
 
2.4%
865 1
 
2.4%
1054 1
 
2.4%
1014 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
63 1
2.4%
75 1
2.4%
100 1
2.4%
150 1
2.4%
192 1
2.4%
364 1
2.4%
565 1
2.4%
668 1
2.4%
758 1
2.4%
759 1
2.4%
ValueCountFrequency (%)
1733 1
2.4%
1680 1
2.4%
1591 1
2.4%
1525 1
2.4%
1480 1
2.4%
1470 1
2.4%
1426 1
2.4%
1374 1
2.4%
1342 1
2.4%
1325 1
2.4%

대전
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1260.2619
Minimum78
Maximum2140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:18.553067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum78
5-th percentile147
Q11100
median1311.5
Q31651.25
95-th percentile2057.55
Maximum2140
Range2062
Interquartile range (IQR)551.25

Descriptive statistics

Standard deviation559.80441
Coefficient of variation (CV)0.44419688
Kurtosis-0.17528802
Mean1260.2619
Median Absolute Deviation (MAD)332
Skewness-0.72011821
Sum52931
Variance313380.98
MonotonicityNot monotonic
2023-12-12T19:47:18.722521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1659 2
 
4.8%
1561 2
 
4.8%
1799 1
 
2.4%
78 1
 
2.4%
1087 1
 
2.4%
1226 1
 
2.4%
1208 1
 
2.4%
1139 1
 
2.4%
1356 1
 
2.4%
1344 1
 
2.4%
Other values (30) 30
71.4%
ValueCountFrequency (%)
78 1
2.4%
122 1
2.4%
145 1
2.4%
185 1
2.4%
273 1
2.4%
361 1
2.4%
598 1
2.4%
691 1
2.4%
868 1
2.4%
921 1
2.4%
ValueCountFrequency (%)
2140 1
2.4%
2111 1
2.4%
2068 1
2.4%
1859 1
2.4%
1846 1
2.4%
1801 1
2.4%
1799 1
2.4%
1756 1
2.4%
1680 1
2.4%
1659 2
4.8%

세종
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean774.59524
Minimum30
Maximum1734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:18.911572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile46.5
Q1488.25
median794
Q31102.5
95-th percentile1436.9
Maximum1734
Range1704
Interquartile range (IQR)614.25

Descriptive statistics

Standard deviation446.16081
Coefficient of variation (CV)0.57599219
Kurtosis-0.73632159
Mean774.59524
Median Absolute Deviation (MAD)314
Skewness-0.084810709
Sum32533
Variance199059.47
MonotonicityNot monotonic
2023-12-12T19:47:19.129552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1106 1
 
2.4%
472 1
 
2.4%
519 1
 
2.4%
608 1
 
2.4%
625 1
 
2.4%
663 1
 
2.4%
642 1
 
2.4%
613 1
 
2.4%
648 1
 
2.4%
478 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
30 1
2.4%
41 1
2.4%
46 1
2.4%
56 1
2.4%
88 1
2.4%
136 1
2.4%
174 1
2.4%
238 1
2.4%
329 1
2.4%
472 1
2.4%
ValueCountFrequency (%)
1734 1
2.4%
1451 1
2.4%
1440 1
2.4%
1378 1
2.4%
1364 1
2.4%
1255 1
2.4%
1189 1
2.4%
1180 1
2.4%
1159 1
2.4%
1117 1
2.4%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean571.42857
Minimum27
Maximum1077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:19.340528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile63.35
Q1421.25
median560.5
Q3722.5
95-th percentile1046.75
Maximum1077
Range1050
Interquartile range (IQR)301.25

Descriptive statistics

Standard deviation275.53762
Coefficient of variation (CV)0.48219084
Kurtosis-0.31345062
Mean571.42857
Median Absolute Deviation (MAD)153
Skewness-0.18049993
Sum24000
Variance75920.983
MonotonicityNot monotonic
2023-12-12T19:47:19.549038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
691 2
 
4.8%
27 1
 
2.4%
413 1
 
2.4%
464 1
 
2.4%
582 1
 
2.4%
516 1
 
2.4%
420 1
 
2.4%
526 1
 
2.4%
502 1
 
2.4%
425 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
27 1
2.4%
54 1
2.4%
61 1
2.4%
108 1
2.4%
131 1
2.4%
171 1
2.4%
299 1
2.4%
385 1
2.4%
412 1
2.4%
413 1
2.4%
ValueCountFrequency (%)
1077 1
2.4%
1070 1
2.4%
1052 1
2.4%
947 1
2.4%
919 1
2.4%
917 1
2.4%
908 1
2.4%
824 1
2.4%
747 1
2.4%
724 1
2.4%

경기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5783.1667
Minimum221
Maximum12906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:19.749678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum221
5-th percentile561.3
Q13781.5
median5006.5
Q37842.25
95-th percentile11328.85
Maximum12906
Range12685
Interquartile range (IQR)4060.75

Descriptive statistics

Standard deviation3146.8861
Coefficient of variation (CV)0.54414584
Kurtosis-0.19288198
Mean5783.1667
Median Absolute Deviation (MAD)2003.5
Skewness0.2793227
Sum242893
Variance9902892
MonotonicityNot monotonic
2023-12-12T19:47:19.928522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
9371 1
 
2.4%
3739 1
 
2.4%
3425 1
 
2.4%
4756 1
 
2.4%
4671 1
 
2.4%
4853 1
 
2.4%
4475 1
 
2.4%
4729 1
 
2.4%
4823 1
 
2.4%
3909 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
221 1
2.4%
537 1
2.4%
541 1
2.4%
947 1
2.4%
1104 1
2.4%
2020 1
2.4%
3047 1
2.4%
3425 1
2.4%
3683 1
2.4%
3691 1
2.4%
ValueCountFrequency (%)
12906 1
2.4%
12378 1
2.4%
11350 1
2.4%
10927 1
2.4%
9398 1
2.4%
9371 1
2.4%
8700 1
2.4%
8348 1
2.4%
8317 1
2.4%
8100 1
2.4%

강원
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1317.9286
Minimum103
Maximum2801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:20.120568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile165.15
Q1982
median1289
Q31594.25
95-th percentile2560.4
Maximum2801
Range2698
Interquartile range (IQR)612.25

Descriptive statistics

Standard deviation683.07748
Coefficient of variation (CV)0.51829629
Kurtosis-0.19250368
Mean1317.9286
Median Absolute Deviation (MAD)330
Skewness0.30525461
Sum55353
Variance466594.85
MonotonicityNot monotonic
2023-12-12T19:47:20.317076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1327 2
 
4.8%
2267 1
 
2.4%
1310 1
 
2.4%
1115 1
 
2.4%
1048 1
 
2.4%
1100 1
 
2.4%
1268 1
 
2.4%
1158 1
 
2.4%
1432 1
 
2.4%
1409 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
103 1
2.4%
110 1
2.4%
157 1
2.4%
320 1
2.4%
344 1
2.4%
515 1
2.4%
727 1
2.4%
767 1
2.4%
783 1
2.4%
903 1
2.4%
ValueCountFrequency (%)
2801 1
2.4%
2629 1
2.4%
2564 1
2.4%
2492 1
2.4%
2267 1
2.4%
2210 1
2.4%
2204 1
2.4%
2033 1
2.4%
1960 1
2.4%
1765 1
2.4%

충북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1089.7857
Minimum76
Maximum2352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:20.497302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile96
Q1765.75
median1031.5
Q31499.25
95-th percentile2247.9
Maximum2352
Range2276
Interquartile range (IQR)733.5

Descriptive statistics

Standard deviation594.45132
Coefficient of variation (CV)0.54547542
Kurtosis-0.22630348
Mean1089.7857
Median Absolute Deviation (MAD)386.5
Skewness0.32791354
Sum45771
Variance353372.37
MonotonicityNot monotonic
2023-12-12T19:47:20.680580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1774 1
 
2.4%
1011 1
 
2.4%
566 1
 
2.4%
830 1
 
2.4%
691 1
 
2.4%
928 1
 
2.4%
838 1
 
2.4%
837 1
 
2.4%
1103 1
 
2.4%
1101 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
76 1
2.4%
79 1
2.4%
91 1
2.4%
191 1
2.4%
306 1
2.4%
386 1
2.4%
525 1
2.4%
566 1
2.4%
597 1
2.4%
691 1
2.4%
ValueCountFrequency (%)
2352 1
2.4%
2313 1
2.4%
2254 1
2.4%
2132 1
2.4%
1774 1
2.4%
1773 1
2.4%
1701 1
2.4%
1620 1
2.4%
1614 1
2.4%
1559 1
2.4%

충남
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1349.881
Minimum114
Maximum3188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:20.851831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114
5-th percentile185.35
Q1918.5
median1252.5
Q31748.25
95-th percentile2990.75
Maximum3188
Range3074
Interquartile range (IQR)829.75

Descriptive statistics

Standard deviation788.32797
Coefficient of variation (CV)0.58399814
Kurtosis0.3811249
Mean1349.881
Median Absolute Deviation (MAD)409
Skewness0.76768423
Sum56695
Variance621460.99
MonotonicityNot monotonic
2023-12-12T19:47:21.045008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1236 2
 
4.8%
2739 1
 
2.4%
1279 1
 
2.4%
929 1
 
2.4%
933 1
 
2.4%
1097 1
 
2.4%
1070 1
 
2.4%
915 1
 
2.4%
1253 1
 
2.4%
1299 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
114 1
2.4%
131 1
2.4%
181 1
2.4%
268 1
2.4%
332 1
2.4%
584 1
2.4%
617 1
2.4%
694 1
2.4%
771 1
2.4%
820 1
2.4%
ValueCountFrequency (%)
3188 1
2.4%
3153 1
2.4%
2996 1
2.4%
2891 1
2.4%
2739 1
2.4%
2010 1
2.4%
1991 1
2.4%
1988 1
2.4%
1943 1
2.4%
1824 1
2.4%

경북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1567.119
Minimum143
Maximum3350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:21.239920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile278.35
Q11111.25
median1463
Q31908
95-th percentile3283
Maximum3350
Range3207
Interquartile range (IQR)796.75

Descriptive statistics

Standard deviation842.9092
Coefficient of variation (CV)0.53787183
Kurtosis-0.14114164
Mean1567.119
Median Absolute Deviation (MAD)424.5
Skewness0.51322323
Sum65819
Variance710495.91
MonotonicityNot monotonic
2023-12-12T19:47:21.432751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2862 1
 
2.4%
1638 1
 
2.4%
670 1
 
2.4%
1205 1
 
2.4%
1044 1
 
2.4%
1350 1
 
2.4%
1291 1
 
2.4%
1295 1
 
2.4%
1823 1
 
2.4%
1616 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
143 1
2.4%
258 1
2.4%
270 1
2.4%
437 1
2.4%
473 1
2.4%
645 1
2.4%
670 1
2.4%
704 1
2.4%
790 1
2.4%
1044 1
2.4%
ValueCountFrequency (%)
3350 1
2.4%
3302 1
2.4%
3289 1
2.4%
3169 1
2.4%
2862 1
2.4%
2511 1
2.4%
2491 1
2.4%
2479 1
2.4%
2223 1
2.4%
2060 1
2.4%

경남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1811.4524
Minimum129
Maximum3585
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:21.636149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile235.7
Q11351.25
median1695
Q32379.5
95-th percentile3299.7
Maximum3585
Range3456
Interquartile range (IQR)1028.25

Descriptive statistics

Standard deviation878.72641
Coefficient of variation (CV)0.48509496
Kurtosis-0.36484869
Mean1811.4524
Median Absolute Deviation (MAD)597
Skewness0.0070996053
Sum76081
Variance772160.11
MonotonicityNot monotonic
2023-12-12T19:47:21.797642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2815 1
 
2.4%
1874 1
 
2.4%
956 1
 
2.4%
1543 1
 
2.4%
1253 1
 
2.4%
1352 1
 
2.4%
1351 1
 
2.4%
1442 1
 
2.4%
2083 1
 
2.4%
2015 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
129 1
2.4%
214 1
2.4%
225 1
2.4%
439 1
2.4%
540 1
2.4%
691 1
2.4%
956 1
2.4%
1112 1
2.4%
1156 1
2.4%
1253 1
2.4%
ValueCountFrequency (%)
3585 1
2.4%
3535 1
2.4%
3305 1
2.4%
3199 1
2.4%
2815 1
2.4%
2729 1
2.4%
2659 1
2.4%
2516 1
2.4%
2506 1
2.4%
2494 1
2.4%

전북
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1346.0714
Minimum127
Maximum3012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:21.974600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127
5-th percentile152.6
Q11139.75
median1286.5
Q31571.75
95-th percentile2566.9
Maximum3012
Range2885
Interquartile range (IQR)432

Descriptive statistics

Standard deviation662.78983
Coefficient of variation (CV)0.49238831
Kurtosis0.54917791
Mean1346.0714
Median Absolute Deviation (MAD)248.5
Skewness0.30982141
Sum56535
Variance439290.36
MonotonicityNot monotonic
2023-12-12T19:47:22.227738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
2217 1
 
2.4%
1320 1
 
2.4%
728 1
 
2.4%
1222 1
 
2.4%
1137 1
 
2.4%
1202 1
 
2.4%
1187 1
 
2.4%
1205 1
 
2.4%
1704 1
 
2.4%
1396 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
127 1
2.4%
134 1
2.4%
146 1
2.4%
278 1
2.4%
350 1
2.4%
607 1
2.4%
728 1
2.4%
813 1
2.4%
871 1
2.4%
1091 1
2.4%
ValueCountFrequency (%)
3012 1
2.4%
2817 1
2.4%
2583 1
2.4%
2261 1
2.4%
2217 1
2.4%
2081 1
2.4%
1934 1
2.4%
1874 1
2.4%
1715 1
2.4%
1704 1
2.4%

전남
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1478.9762
Minimum106
Maximum3776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:22.427575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile210.3
Q11043
median1343.5
Q31776.75
95-th percentile3044.5
Maximum3776
Range3670
Interquartile range (IQR)733.75

Descriptive statistics

Standard deviation852.25876
Coefficient of variation (CV)0.57624914
Kurtosis0.65057581
Mean1478.9762
Median Absolute Deviation (MAD)366.5
Skewness0.80799592
Sum62117
Variance726345
MonotonicityNot monotonic
2023-12-12T19:47:22.636166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3047 1
 
2.4%
1465 1
 
2.4%
673 1
 
2.4%
1093 1
 
2.4%
1040 1
 
2.4%
1267 1
 
2.4%
1216 1
 
2.4%
976 1
 
2.4%
1764 1
 
2.4%
1540 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
106 1
2.4%
174 1
2.4%
210 1
2.4%
216 1
2.4%
410 1
2.4%
673 1
2.4%
741 1
2.4%
757 1
2.4%
976 1
2.4%
978 1
2.4%
ValueCountFrequency (%)
3776 1
2.4%
3368 1
2.4%
3047 1
2.4%
2997 1
2.4%
2966 1
2.4%
2390 1
2.4%
2282 1
2.4%
2208 1
2.4%
2111 1
2.4%
1885 1
2.4%

제주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.69048
Minimum17
Maximum1117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T19:47:23.229689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile68.5
Q1337.75
median433
Q3650.25
95-th percentile955.9
Maximum1117
Range1100
Interquartile range (IQR)312.5

Descriptive statistics

Standard deviation265.67737
Coefficient of variation (CV)0.55040938
Kurtosis0.016790766
Mean482.69048
Median Absolute Deviation (MAD)120.5
Skewness0.45222928
Sum20273
Variance70584.463
MonotonicityNot monotonic
2023-12-12T19:47:23.434997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
763 1
 
2.4%
387 1
 
2.4%
236 1
 
2.4%
380 1
 
2.4%
331 1
 
2.4%
398 1
 
2.4%
369 1
 
2.4%
406 1
 
2.4%
521 1
 
2.4%
481 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
17 1
2.4%
52 1
2.4%
67 1
2.4%
97 1
2.4%
120 1
2.4%
148 1
2.4%
236 1
2.4%
260 1
2.4%
317 1
2.4%
327 1
2.4%
ValueCountFrequency (%)
1117 1
2.4%
1076 1
2.4%
960 1
2.4%
878 1
2.4%
817 1
2.4%
787 1
2.4%
763 1
2.4%
751 1
2.4%
729 1
2.4%
680 1
2.4%

Interactions

2023-12-12T19:47:12.535577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:32.120240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:34.457571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:37.281160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.543253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.788918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:44.251425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:47.058771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:49.220830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:51.563286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:54.052560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:56.085273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:58.223024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.135710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:03.035546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:05.069828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:07.534041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:10.357130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:12.640056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:32.239952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:34.612889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:37.400713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.676310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.904774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:44.401269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:47.185503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T19:46:48.465537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:50.756339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:52.992164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.306572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:57.558014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.429670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.234335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.347121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:06.642449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:09.199334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:11.803517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:13.899830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:33.791914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:36.249668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:38.854226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.143474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:43.467785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:46.378107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:48.595556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:50.895269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:53.111558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.418909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:57.673096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.583205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.357981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.449760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:06.804706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:09.719198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:11.908080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:14.026231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:33.916960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:36.391704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.000928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.279693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:43.606990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:46.519541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:48.730838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:51.032543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:53.210036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.537819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:57.814892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.688121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.493374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.598894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:06.959325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:09.854441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:12.029075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:14.133579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:34.051070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:36.873908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.130047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.399868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:43.742407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:46.676165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:48.852801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:51.175890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:53.663960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.676285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:57.930873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.780565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.632972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.725746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:07.111124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:09.998882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:12.173893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:14.235047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:34.189765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:37.008845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.262182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.527533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:43.915106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:46.809126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:48.969275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:51.309743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:53.791296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.849334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:58.026798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:59.884626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.772931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.832377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:07.254755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:10.110166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:12.305351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:14.356462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:34.319470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:37.156658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:39.407322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:41.660108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:44.074477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:46.933551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:49.098614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:51.445401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:53.943543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:55.970445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:58.121942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:00.003884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:02.916643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:04.953739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:07.399341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:10.233091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:47:12.433877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:47:23.598628image/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
1.0001.0000.9740.9600.9320.9750.9310.9760.8410.9430.9860.9590.8170.8590.9230.9580.9790.9560.955
서울1.0000.9741.0000.9080.8680.9690.8900.9420.8660.9160.9700.9710.7580.8270.9290.9380.9930.9370.933
부산1.0000.9600.9081.0000.8550.9400.8300.9300.7270.9390.9490.9350.8820.8660.9550.9240.9210.9360.934
대구1.0000.9320.8680.8551.0000.9120.9770.9130.6840.8490.8940.8420.8700.9100.6660.8250.8860.7450.844
인천1.0000.9750.9690.9400.9121.0000.8810.9620.8930.9480.9690.9530.8100.8860.9310.9280.9680.9150.904
광주1.0000.9310.8900.8300.9770.8811.0000.9160.7540.8890.8960.8680.8600.8830.7090.8350.8860.7570.819
대전1.0000.9760.9420.9300.9130.9620.9161.0000.8650.9300.9580.9530.7680.8050.8590.9360.9480.8290.931
세종1.0000.8410.8660.7270.6840.8930.7540.8651.0000.8600.9290.8100.6390.7430.8000.8050.8450.8680.815
울산1.0000.9430.9160.9390.8490.9480.8890.9300.8601.0000.9380.9190.7780.8000.8240.8530.9010.8900.865
경기1.0000.9860.9700.9490.8940.9690.8960.9580.9290.9381.0000.9390.7970.8370.8870.9240.9670.9330.921
강원1.0000.9590.9710.9350.8420.9530.8680.9530.8100.9190.9391.0000.8540.8760.9610.9570.9820.9570.940
충북1.0000.8170.7580.8820.8700.8100.8600.7680.6390.7780.7970.8541.0000.9510.9170.8280.8170.8210.810
충남1.0000.8590.8270.8660.9100.8860.8830.8050.7430.8000.8370.8760.9511.0000.9240.8320.8750.9310.828
경북1.0000.9230.9290.9550.6660.9310.7090.8590.8000.8240.8870.9610.9170.9241.0000.9070.9580.9660.916
경남1.0000.9580.9380.9240.8250.9280.8350.9360.8050.8530.9240.9570.8280.8320.9071.0000.9740.8970.934
전북1.0000.9790.9930.9210.8860.9680.8860.9480.8450.9010.9670.9820.8170.8750.9580.9741.0000.9600.946
전남1.0000.9560.9370.9360.7450.9150.7570.8290.8680.8900.9330.9570.8210.9310.9660.8970.9601.0000.958
제주1.0000.9550.9330.9340.8440.9040.8190.9310.8150.8650.9210.9400.8100.8280.9160.9340.9460.9581.000
2023-12-12T19:47:23.848221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
1.0000.9520.9650.9310.9400.9160.9610.9210.9300.9670.9370.9760.9250.9270.9550.9350.9060.962
서울0.9521.0000.8890.8460.9170.8280.8920.8800.8710.9170.8850.9200.8740.8640.8750.8820.8560.898
부산0.9650.8891.0000.9180.9130.8780.9190.8540.9020.9290.9160.9470.9010.9290.9250.9410.9100.924
대구0.9310.8460.9181.0000.9090.9660.9400.8990.9160.9280.8680.8950.8430.8400.9060.8760.8180.899
인천0.9400.9170.9130.9091.0000.8990.9550.9110.9180.9610.8640.8870.8280.8210.8790.8610.7980.903
광주0.9160.8280.8780.9660.8991.0000.9370.8830.9210.9140.8480.8860.8290.8120.8980.8410.7930.894
대전0.9610.8920.9190.9400.9550.9371.0000.9290.9320.9670.9070.9240.8690.8570.9410.8930.8480.942
세종0.9210.8800.8540.8990.9110.8830.9291.0000.8740.9440.8350.8850.8340.8080.8750.8180.7720.895
울산0.9300.8710.9020.9160.9180.9210.9320.8741.0000.9490.8500.8750.7980.7860.8770.8330.7690.886
경기0.9670.9170.9290.9280.9610.9140.9670.9440.9491.0000.8840.9190.8520.8440.9160.8770.8230.927
강원0.9370.8850.9160.8680.8640.8480.9070.8350.8500.8841.0000.9570.9350.9420.9530.9560.9520.977
충북0.9760.9200.9470.8950.8870.8860.9240.8850.8750.9190.9571.0000.9650.9600.9690.9520.9410.970
충남0.9250.8740.9010.8430.8280.8290.8690.8340.7980.8520.9350.9651.0000.9720.9530.9480.9620.943
경북0.9270.8640.9290.8400.8210.8120.8570.8080.7860.8440.9420.9600.9721.0000.9470.9710.9730.939
경남0.9550.8750.9250.9060.8790.8980.9410.8750.8770.9160.9530.9690.9530.9471.0000.9520.9350.973
전북0.9350.8820.9410.8760.8610.8410.8930.8180.8330.8770.9560.9520.9480.9710.9521.0000.9610.945
전남0.9060.8560.9100.8180.7980.7930.8480.7720.7690.8230.9520.9410.9620.9730.9350.9611.0000.924
제주0.9620.8980.9240.8990.9030.8940.9420.8950.8860.9270.9770.9700.9430.9390.9730.9450.9241.000

Missing values

2023-12-12T19:47:14.537606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:47:14.839457image/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
01년미만502521051729651628234111221799110691993712267177427392862281522173047763
11년이상64070136333410258729951680214010521077129062801231328913302353530123776960
22년57438120483248196229661426175610921070109272492235231533289319925832997878
33년61921124313085250332861733211111809471237826292254318833503585281733681076
44년56309983531542184294214802068173410521135025642132299631693305226129661117
55년46286901927682053226411341801144066993982033162020102479250620812282729
66년45459876625772151213212001859137865187002204170119912491272919342208787
77년44745871927871998195412051659136472483172210161419432511265918742390817
88년42872819926502165197113251626145174781001960177319882223219717152111671
99년38615687122432094196713041561100972371961765151118242060235614951885751
구분서울부산대구인천광주대전세종울산경기강원충북충남경북경남전북전남제주
3232년260104504182715921039962116647242537391310101112791638187413201465387
3333년187129713612374637830272211107611414312912710617
3433년초과243644039169612509158489213294123683119699013791662160815061515415
3534년이상225843855178413281064759868238385369196079511941300150111911354317
3635년17934342812689889445655981742993047783525950108111128131099260
3736년1215126248556595363643611361712020515386617704691607757148
3837년7187155351027435819227388108110432030633247343935041097
3938년5838113134124226715018556131947344191268437540278210120
4039년31086041481341297514541615371039118125821414617467
4140년이상319456523115711410012246545411577913127022513421652