Overview

Dataset statistics

Number of variables19
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 KiB
Average record size in memory173.7 B

Variable types

Text1
Numeric17
Categorical1

Dataset

Description연도별 지역별(광역시, 도) 임대주택 현황(장부가, 세대수)에 대한 데이터 입니다. 1982년부터 시작되며 연 단위로 구분됩니다.
Author공무원연금공단
URLhttps://www.data.go.kr/data/15054036/fileData.do

Alerts

is highly overall correlated with 서울 and 9 other fieldsHigh correlation
서울 is highly overall correlated with and 7 other fieldsHigh correlation
부산 is highly overall correlated with and 13 other fieldsHigh correlation
인천 is highly overall correlated with and 5 other fieldsHigh correlation
대구 is highly overall correlated with 부산 and 10 other fieldsHigh correlation
광주 is highly overall correlated with and 14 other fieldsHigh correlation
대전 is highly overall correlated with 부산 and 11 other fieldsHigh correlation
울산 is highly overall correlated with 서울 and 10 other fieldsHigh correlation
경기 is highly overall correlated with and 3 other fieldsHigh correlation
강원 is highly overall correlated with 부산 and 11 other fieldsHigh correlation
충북 is highly overall correlated with 부산 and 10 other fieldsHigh correlation
충남 is highly overall correlated with 광주 and 5 other fieldsHigh correlation
전북 is highly overall correlated with 부산 and 11 other fieldsHigh correlation
전남 is highly overall correlated with and 12 other fieldsHigh correlation
경북 is highly overall correlated with and 12 other fieldsHigh correlation
경남 is highly overall correlated with and 11 other fieldsHigh correlation
제주 is highly overall correlated with and 4 other fieldsHigh correlation
세종 is highly overall correlated with and 16 other fieldsHigh correlation
세종 is highly imbalanced (68.8%)Imbalance
구분 has unique valuesUnique
서울 has 15 (41.7%) zerosZeros
부산 has 20 (55.6%) zerosZeros
인천 has 21 (58.3%) zerosZeros
대구 has 17 (47.2%) zerosZeros
광주 has 19 (52.8%) zerosZeros
대전 has 16 (44.4%) zerosZeros
울산 has 28 (77.8%) zerosZeros
경기 has 9 (25.0%) zerosZeros
강원 has 10 (27.8%) zerosZeros
충북 has 16 (44.4%) zerosZeros
충남 has 24 (66.7%) zerosZeros
전북 has 16 (44.4%) zerosZeros
전남 has 16 (44.4%) zerosZeros
경북 has 9 (25.0%) zerosZeros
경남 has 13 (36.1%) zerosZeros
제주 has 29 (80.6%) zerosZeros

Reproduction

Analysis started2023-12-12 16:21:43.895198
Analysis finished2023-12-12 16:22:11.081979
Duration27.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-13T01:22:11.211898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4
Min length3

Characters and Unicode

Total characters144
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)100.0%

Sample

1st row장부가
2nd row세 대
3rd row1982
4th row1983
5th row1984
ValueCountFrequency (%)
장부가 1
 
2.7%
2005 1
 
2.7%
2013 1
 
2.7%
2007 1
 
2.7%
2008 1
 
2.7%
2009 1
 
2.7%
2010 1
 
2.7%
2011 1
 
2.7%
2012 1
 
2.7%
2014 1
 
2.7%
Other values (27) 27
73.0%
2023-12-13T01:22:11.548721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36
25.0%
2 30
20.8%
1 24
16.7%
9 18
12.5%
8 11
 
7.6%
6 4
 
2.8%
7 4
 
2.8%
3
 
2.1%
3 3
 
2.1%
4 3
 
2.1%
Other values (6) 8
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 136
94.4%
Other Letter 5
 
3.5%
Space Separator 3
 
2.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36
26.5%
2 30
22.1%
1 24
17.6%
9 18
13.2%
8 11
 
8.1%
6 4
 
2.9%
7 4
 
2.9%
3 3
 
2.2%
4 3
 
2.2%
5 3
 
2.2%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 139
96.5%
Hangul 5
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36
25.9%
2 30
21.6%
1 24
17.3%
9 18
12.9%
8 11
 
7.9%
6 4
 
2.9%
7 4
 
2.9%
3
 
2.2%
3 3
 
2.2%
4 3
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139
96.5%
Hangul 5
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36
25.9%
2 30
21.6%
1 24
17.3%
9 18
12.9%
8 11
 
7.9%
6 4
 
2.9%
7 4
 
2.9%
3
 
2.2%
3 3
 
2.2%
4 3
 
2.2%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%


Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156871.39
Minimum-2403
Maximum5608438
Zeros0
Zeros (%)0.0%
Negative15
Negative (%)41.7%
Memory size456.0 B
2023-12-13T01:22:11.672261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2403
5-th percentile-645.25
Q1-142.5
median250
Q31106.75
95-th percentile8606.75
Maximum5608438
Range5610841
Interquartile range (IQR)1249.25

Descriptive statistics

Standard deviation934560.5
Coefficient of variation (CV)5.9574949
Kurtosis35.998956
Mean156871.39
Median Absolute Deviation (MAD)546
Skewness5.9998733
Sum5647370
Variance8.7340333 × 1011
MonotonicityNot monotonic
2023-12-13T01:22:11.803920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
121 2
 
5.6%
-299 2
 
5.6%
799 1
 
2.8%
140 1
 
2.8%
-19 1
 
2.8%
-293 1
 
2.8%
-243 1
 
2.8%
383 1
 
2.8%
1211 1
 
2.8%
5608438 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
-2403 1
2.8%
-664 1
2.8%
-639 1
2.8%
-353 1
2.8%
-299 2
5.6%
-293 1
2.8%
-244 1
2.8%
-243 1
2.8%
-109 1
2.8%
-101 1
2.8%
ValueCountFrequency (%)
5608438 1
2.8%
19466 1
2.8%
4987 1
2.8%
2920 1
2.8%
2875 1
2.8%
2650 1
2.8%
2640 1
2.8%
1741 1
2.8%
1211 1
2.8%
1072 1
2.8%

서울
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61047
Minimum-1694
Maximum2188686
Zeros15
Zeros (%)41.7%
Negative13
Negative (%)36.1%
Memory size456.0 B
2023-12-13T01:22:11.931496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1694
5-th percentile-211.5
Q1-6.25
median0
Q30
95-th percentile2910.75
Maximum2188686
Range2190380
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation364739.48
Coefficient of variation (CV)5.9747322
Kurtosis35.999415
Mean61047
Median Absolute Deviation (MAD)5
Skewness5.9999289
Sum2197692
Variance1.3303489 × 1011
MonotonicityNot monotonic
2023-12-13T01:22:12.031396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 15
41.7%
-7 2
 
5.6%
-2 2
 
5.6%
2188686 1
 
2.8%
-6 1
 
2.8%
1703 1
 
2.8%
-690 1
 
2.8%
-4 1
 
2.8%
-1694 1
 
2.8%
-17 1
 
2.8%
Other values (10) 10
27.8%
ValueCountFrequency (%)
-1694 1
2.8%
-690 1
2.8%
-52 1
2.8%
-23 1
2.8%
-21 1
2.8%
-17 1
2.8%
-9 1
2.8%
-7 2
5.6%
-6 1
2.8%
-4 1
2.8%
ValueCountFrequency (%)
2188686 1
 
2.8%
4503 1
 
2.8%
2380 1
 
2.8%
2100 1
 
2.8%
1703 1
 
2.8%
690 1
 
2.8%
104 1
 
2.8%
60 1
 
2.8%
0 15
41.7%
-2 2
 
5.6%

부산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6431.9444
Minimum-566
Maximum229966
Zeros20
Zeros (%)55.6%
Negative8
Negative (%)22.2%
Memory size456.0 B
2023-12-13T01:22:12.129910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-566
5-th percentile-186.5
Q10
median0
Q30
95-th percentile685.5
Maximum229966
Range230532
Interquartile range (IQR)0

Descriptive statistics

Standard deviation38320.847
Coefficient of variation (CV)5.9578946
Kurtosis35.997041
Mean6431.9444
Median Absolute Deviation (MAD)0
Skewness5.9996405
Sum231550
Variance1.4684873 × 109
MonotonicityNot monotonic
2023-12-13T01:22:12.227922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 20
55.6%
229966 1
 
2.8%
-3 1
 
2.8%
20 1
 
2.8%
-14 1
 
2.8%
-173 1
 
2.8%
-566 1
 
2.8%
-227 1
 
2.8%
-37 1
 
2.8%
-107 1
 
2.8%
Other values (7) 7
 
19.4%
ValueCountFrequency (%)
-566 1
 
2.8%
-227 1
 
2.8%
-173 1
 
2.8%
-107 1
 
2.8%
-61 1
 
2.8%
-37 1
 
2.8%
-14 1
 
2.8%
-3 1
 
2.8%
0 20
55.6%
20 1
 
2.8%
ValueCountFrequency (%)
229966 1
 
2.8%
792 1
 
2.8%
650 1
 
2.8%
540 1
 
2.8%
440 1
 
2.8%
210 1
 
2.8%
120 1
 
2.8%
20 1
 
2.8%
0 20
55.6%
-3 1
 
2.8%

인천
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8461.8611
Minimum-185
Maximum301645
Zeros21
Zeros (%)58.3%
Negative8
Negative (%)22.2%
Memory size456.0 B
2023-12-13T01:22:12.322193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-185
5-th percentile-112.75
Q10
median0
Q30
95-th percentile905.25
Maximum301645
Range301830
Interquartile range (IQR)0

Descriptive statistics

Standard deviation50260.867
Coefficient of variation (CV)5.9396941
Kurtosis35.997193
Mean8461.8611
Median Absolute Deviation (MAD)0
Skewness5.9996595
Sum304627
Variance2.5261547 × 109
MonotonicityNot monotonic
2023-12-13T01:22:12.437197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 21
58.3%
-58 2
 
5.6%
301645 1
 
2.8%
1491 1
 
2.8%
330 1
 
2.8%
260 1
 
2.8%
450 1
 
2.8%
480 1
 
2.8%
-94 1
 
2.8%
-185 1
 
2.8%
Other values (5) 5
 
13.9%
ValueCountFrequency (%)
-185 1
 
2.8%
-127 1
 
2.8%
-108 1
 
2.8%
-94 1
 
2.8%
-69 1
 
2.8%
-58 2
 
5.6%
-40 1
 
2.8%
0 21
58.3%
260 1
 
2.8%
330 1
 
2.8%
ValueCountFrequency (%)
301645 1
 
2.8%
1491 1
 
2.8%
710 1
 
2.8%
480 1
 
2.8%
450 1
 
2.8%
330 1
 
2.8%
260 1
 
2.8%
0 21
58.3%
-40 1
 
2.8%
-58 2
 
5.6%

대구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)52.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1695.0556
Minimum-163
Maximum60422
Zeros17
Zeros (%)47.2%
Negative13
Negative (%)36.1%
Memory size456.0 B
2023-12-13T01:22:12.577055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-163
5-th percentile-98.75
Q1-6.25
median0
Q30
95-th percentile300
Maximum60422
Range60585
Interquartile range (IQR)6.25

Descriptive statistics

Standard deviation10067.948
Coefficient of variation (CV)5.9395976
Kurtosis35.992658
Mean1695.0556
Median Absolute Deviation (MAD)2.5
Skewness5.9991087
Sum61022
Variance1.0136357 × 108
MonotonicityNot monotonic
2023-12-13T01:22:12.675738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 17
47.2%
300 2
 
5.6%
60422 1
 
2.8%
-6 1
 
2.8%
-3 1
 
2.8%
-2 1
 
2.8%
-7 1
 
2.8%
-70 1
 
2.8%
-35 1
 
2.8%
-20 1
 
2.8%
Other values (9) 9
25.0%
ValueCountFrequency (%)
-163 1
2.8%
-113 1
2.8%
-94 1
2.8%
-70 1
2.8%
-35 1
2.8%
-20 1
2.8%
-14 1
2.8%
-9 1
2.8%
-7 1
2.8%
-6 1
2.8%
ValueCountFrequency (%)
60422 1
 
2.8%
300 2
 
5.6%
240 1
 
2.8%
200 1
 
2.8%
100 1
 
2.8%
0 17
47.2%
-2 1
 
2.8%
-3 1
 
2.8%
-4 1
 
2.8%
-6 1
 
2.8%

광주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1389.5
Minimum-200
Maximum49196
Zeros19
Zeros (%)52.8%
Negative9
Negative (%)25.0%
Memory size456.0 B
2023-12-13T01:22:12.784168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-200
5-th percentile-88.5
Q1-3.25
median0
Q30
95-th percentile305.75
Maximum49196
Range49396
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation8196.133
Coefficient of variation (CV)5.8986204
Kurtosis35.985984
Mean1389.5
Median Absolute Deviation (MAD)0
Skewness5.9982995
Sum50022
Variance67176596
MonotonicityNot monotonic
2023-12-13T01:22:12.891834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 19
52.8%
49196 1
 
2.8%
-40 1
 
2.8%
35 1
 
2.8%
-200 1
 
2.8%
-44 1
 
2.8%
-150 1
 
2.8%
-68 1
 
2.8%
-13 1
 
2.8%
-27 1
 
2.8%
Other values (8) 8
22.2%
ValueCountFrequency (%)
-200 1
 
2.8%
-150 1
 
2.8%
-68 1
 
2.8%
-44 1
 
2.8%
-40 1
 
2.8%
-27 1
 
2.8%
-26 1
 
2.8%
-14 1
 
2.8%
-13 1
 
2.8%
0 19
52.8%
ValueCountFrequency (%)
49196 1
 
2.8%
413 1
 
2.8%
270 1
 
2.8%
200 1
 
2.8%
190 1
 
2.8%
180 1
 
2.8%
120 1
 
2.8%
35 1
 
2.8%
0 19
52.8%
-13 1
 
2.8%

대전
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7063.4444
Minimum-95
Maximum251432
Zeros16
Zeros (%)44.4%
Negative12
Negative (%)33.3%
Memory size456.0 B
2023-12-13T01:22:13.013963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-95
5-th percentile-79
Q1-15.25
median0
Q30
95-th percentile1050.25
Maximum251432
Range251527
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation41892.775
Coefficient of variation (CV)5.9309272
Kurtosis35.996174
Mean7063.4444
Median Absolute Deviation (MAD)5.5
Skewness5.999536
Sum254284
Variance1.7550046 × 109
MonotonicityNot monotonic
2023-12-13T01:22:13.135647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 16
44.4%
251432 1
 
2.8%
-76 1
 
2.8%
-1 1
 
2.8%
-47 1
 
2.8%
-40 1
 
2.8%
-95 1
 
2.8%
-34 1
 
2.8%
-36 1
 
2.8%
-2 1
 
2.8%
Other values (11) 11
30.6%
ValueCountFrequency (%)
-95 1
2.8%
-88 1
2.8%
-76 1
2.8%
-66 1
2.8%
-55 1
2.8%
-47 1
2.8%
-40 1
2.8%
-36 1
2.8%
-34 1
2.8%
-9 1
2.8%
ValueCountFrequency (%)
251432 1
 
2.8%
1426 1
 
2.8%
925 1
 
2.8%
400 1
 
2.8%
310 1
 
2.8%
200 1
 
2.8%
80 1
 
2.8%
60 1
 
2.8%
0 16
44.4%
-1 1
 
2.8%

울산
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1468.8889
Minimum-12
Maximum52486
Zeros28
Zeros (%)77.8%
Negative2
Negative (%)5.6%
Memory size456.0 B
2023-12-13T01:22:13.261969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-2
Q10
median0
Q30
95-th percentile109.25
Maximum52486
Range52498
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8745.8681
Coefficient of variation (CV)5.9540706
Kurtosis35.998608
Mean1468.8889
Median Absolute Deviation (MAD)0
Skewness5.9998311
Sum52880
Variance76490210
MonotonicityNot monotonic
2023-12-13T01:22:13.413864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 28
77.8%
52486 1
 
2.8%
197 1
 
2.8%
80 1
 
2.8%
60 1
 
2.8%
40 1
 
2.8%
-12 1
 
2.8%
-8 1
 
2.8%
37 1
 
2.8%
ValueCountFrequency (%)
-12 1
 
2.8%
-8 1
 
2.8%
0 28
77.8%
37 1
 
2.8%
40 1
 
2.8%
60 1
 
2.8%
80 1
 
2.8%
197 1
 
2.8%
52486 1
 
2.8%
ValueCountFrequency (%)
52486 1
 
2.8%
197 1
 
2.8%
80 1
 
2.8%
60 1
 
2.8%
40 1
 
2.8%
37 1
 
2.8%
0 28
77.8%
-8 1
 
2.8%
-12 1
 
2.8%

세종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Memory size420.0 B
0
32 
409581
 
1
1661
 
1
632
 
1
1029
 
1

Length

Max length6
Median length1
Mean length1.3611111
Min length1

Unique

Unique4 ?
Unique (%)11.1%

Sample

1st row409581
2nd row1661
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
88.9%
409581 1
 
2.8%
1661 1
 
2.8%
632 1
 
2.8%
1029 1
 
2.8%

Length

2023-12-13T01:22:13.541980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:22:13.664696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
88.9%
409581 1
 
2.8%
1661 1
 
2.8%
632 1
 
2.8%
1029 1
 
2.8%

경기
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44840.806
Minimum-132
Maximum1603865
Zeros9
Zeros (%)25.0%
Negative13
Negative (%)36.1%
Memory size456.0 B
2023-12-13T01:22:13.807497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-132
5-th percentile-100.75
Q1-20
median0
Q3345
95-th percentile2200.5
Maximum1603865
Range1603997
Interquartile range (IQR)365

Descriptive statistics

Standard deviation267262.78
Coefficient of variation (CV)5.9602583
Kurtosis35.999124
Mean44840.806
Median Absolute Deviation (MAD)67
Skewness5.9998937
Sum1614269
Variance7.1429396 × 1010
MonotonicityNot monotonic
2023-12-13T01:22:13.945334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 9
25.0%
120 2
 
5.6%
-20 2
 
5.6%
1603865 1
 
2.8%
-30 1
 
2.8%
-12 1
 
2.8%
-61 1
 
2.8%
-132 1
 
2.8%
580 1
 
2.8%
-14 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
-132 1
2.8%
-130 1
2.8%
-91 1
2.8%
-73 1
2.8%
-61 1
2.8%
-54 1
2.8%
-30 1
2.8%
-29 1
2.8%
-20 2
5.6%
-17 1
2.8%
ValueCountFrequency (%)
1603865 1
2.8%
5202 1
2.8%
1200 1
2.8%
734 1
2.8%
701 1
2.8%
630 1
2.8%
580 1
2.8%
502 1
2.8%
360 1
2.8%
340 1
2.8%

강원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean698.22222
Minimum-147
Maximum24700
Zeros10
Zeros (%)27.8%
Negative16
Negative (%)44.4%
Memory size456.0 B
2023-12-13T01:22:14.075564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-147
5-th percentile-112.75
Q1-16.5
median0
Q314
95-th percentile233.5
Maximum24700
Range24847
Interquartile range (IQR)30.5

Descriptive statistics

Standard deviation4115.7111
Coefficient of variation (CV)5.8945576
Kurtosis35.957339
Mean698.22222
Median Absolute Deviation (MAD)17
Skewness5.9948266
Sum25136
Variance16939078
MonotonicityNot monotonic
2023-12-13T01:22:14.209536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 10
27.8%
-9 2
 
5.6%
24700 1
 
2.8%
-104 1
 
2.8%
57 1
 
2.8%
20 1
 
2.8%
12 1
 
2.8%
-15 1
 
2.8%
-118 1
 
2.8%
-72 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
-147 1
2.8%
-118 1
2.8%
-111 1
2.8%
-104 1
2.8%
-95 1
2.8%
-72 1
2.8%
-70 1
2.8%
-24 1
2.8%
-18 1
2.8%
-16 1
2.8%
ValueCountFrequency (%)
24700 1
2.8%
280 1
2.8%
218 1
2.8%
210 1
2.8%
190 1
2.8%
170 1
2.8%
100 1
2.8%
57 1
2.8%
20 1
2.8%
12 1
2.8%

충북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean859.11111
Minimum-80
Maximum30542
Zeros16
Zeros (%)44.4%
Negative15
Negative (%)41.7%
Memory size456.0 B
2023-12-13T01:22:14.323921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80
5-th percentile-42.5
Q1-19.5
median0
Q30
95-th percentile223.5
Maximum30542
Range30622
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation5088.9847
Coefficient of variation (CV)5.9235466
Kurtosis35.984925
Mean859.11111
Median Absolute Deviation (MAD)6.5
Skewness5.998174
Sum30928
Variance25897765
MonotonicityNot monotonic
2023-12-13T01:22:14.440637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 16
44.4%
30542 1
 
2.8%
-44 1
 
2.8%
-19 1
 
2.8%
-4 1
 
2.8%
-10 1
 
2.8%
-27 1
 
2.8%
-7 1
 
2.8%
-80 1
 
2.8%
-42 1
 
2.8%
Other values (11) 11
30.6%
ValueCountFrequency (%)
-80 1
2.8%
-44 1
2.8%
-42 1
2.8%
-35 1
2.8%
-29 1
2.8%
-27 1
2.8%
-25 1
2.8%
-22 1
2.8%
-21 1
2.8%
-19 1
2.8%
ValueCountFrequency (%)
30542 1
 
2.8%
270 1
 
2.8%
208 1
 
2.8%
193 1
 
2.8%
100 1
 
2.8%
0 16
44.4%
-4 1
 
2.8%
-6 1
 
2.8%
-7 1
 
2.8%
-10 1
 
2.8%

충남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1375.5556
Minimum-76
Maximum48526
Zeros24
Zeros (%)66.7%
Negative7
Negative (%)19.4%
Memory size456.0 B
2023-12-13T01:22:14.546607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-76
5-th percentile-16.25
Q10
median0
Q30
95-th percentile482
Maximum48526
Range48602
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8083.7548
Coefficient of variation (CV)5.8767199
Kurtosis35.984071
Mean1375.5556
Median Absolute Deviation (MAD)0
Skewness5.9980745
Sum49520
Variance65347091
MonotonicityNot monotonic
2023-12-13T01:22:14.650236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 24
66.7%
-1 2
 
5.6%
48526 1
 
2.8%
497 1
 
2.8%
100 1
 
2.8%
44 1
 
2.8%
-23 1
 
2.8%
-76 1
 
2.8%
-5 1
 
2.8%
477 1
 
2.8%
Other values (2) 2
 
5.6%
ValueCountFrequency (%)
-76 1
 
2.8%
-23 1
 
2.8%
-14 1
 
2.8%
-5 1
 
2.8%
-4 1
 
2.8%
-1 2
 
5.6%
0 24
66.7%
44 1
 
2.8%
100 1
 
2.8%
477 1
 
2.8%
ValueCountFrequency (%)
48526 1
 
2.8%
497 1
 
2.8%
477 1
 
2.8%
100 1
 
2.8%
44 1
 
2.8%
0 24
66.7%
-1 2
 
5.6%
-4 1
 
2.8%
-5 1
 
2.8%
-14 1
 
2.8%

전북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1362.7222
Minimum-83
Maximum48296
Zeros16
Zeros (%)44.4%
Negative13
Negative (%)36.1%
Memory size456.0 B
2023-12-13T01:22:14.779245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-83
5-th percentile-78.5
Q1-17.25
median0
Q30
95-th percentile320.25
Maximum48296
Range48379
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation8046.3478
Coefficient of variation (CV)5.9046133
Kurtosis35.987475
Mean1362.7222
Median Absolute Deviation (MAD)9.5
Skewness5.9984821
Sum49058
Variance64743713
MonotonicityNot monotonic
2023-12-13T01:22:14.901285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 16
44.4%
300 2
 
5.6%
48296 1
 
2.8%
-18 1
 
2.8%
40 1
 
2.8%
-4 1
 
2.8%
-33 1
 
2.8%
-31 1
 
2.8%
-83 1
 
2.8%
-28 1
 
2.8%
Other values (10) 10
27.8%
ValueCountFrequency (%)
-83 1
2.8%
-80 1
2.8%
-78 1
2.8%
-51 1
2.8%
-37 1
2.8%
-33 1
2.8%
-31 1
2.8%
-28 1
2.8%
-18 1
2.8%
-17 1
2.8%
ValueCountFrequency (%)
48296 1
 
2.8%
381 1
 
2.8%
300 2
 
5.6%
130 1
 
2.8%
90 1
 
2.8%
40 1
 
2.8%
0 16
44.4%
-4 1
 
2.8%
-8 1
 
2.8%
-11 1
 
2.8%

전남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean985.72222
Minimum-72
Maximum34636
Zeros16
Zeros (%)44.4%
Negative12
Negative (%)33.3%
Memory size456.0 B
2023-12-13T01:22:15.021826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-72
5-th percentile-71
Q1-12.25
median0
Q30
95-th percentile355.25
Maximum34636
Range34708
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation5769.4544
Coefficient of variation (CV)5.8530226
Kurtosis35.977306
Mean985.72222
Median Absolute Deviation (MAD)10.5
Skewness5.9972554
Sum35486
Variance33286604
MonotonicityNot monotonic
2023-12-13T01:22:15.156516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 16
44.4%
-71 2
 
5.6%
34636 1
 
2.8%
-9 1
 
2.8%
-13 1
 
2.8%
-72 1
 
2.8%
-39 1
 
2.8%
332 1
 
2.8%
-36 1
 
2.8%
-16 1
 
2.8%
Other values (10) 10
27.8%
ValueCountFrequency (%)
-72 1
2.8%
-71 2
5.6%
-39 1
2.8%
-36 1
2.8%
-19 1
2.8%
-16 1
2.8%
-15 1
2.8%
-13 1
2.8%
-12 1
2.8%
-9 1
2.8%
ValueCountFrequency (%)
34636 1
 
2.8%
425 1
 
2.8%
332 1
 
2.8%
122 1
 
2.8%
120 1
 
2.8%
100 1
 
2.8%
70 1
 
2.8%
60 1
 
2.8%
0 16
44.4%
-6 1
 
2.8%

경북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1172.4167
Minimum-62
Maximum40995
Zeros9
Zeros (%)25.0%
Negative18
Negative (%)50.0%
Memory size456.0 B
2023-12-13T01:22:15.283251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-62
5-th percentile-52.5
Q1-24.25
median-0.5
Q310
95-th percentile474
Maximum40995
Range41057
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation6828.0318
Coefficient of variation (CV)5.8238952
Kurtosis35.970084
Mean1172.4167
Median Absolute Deviation (MAD)24
Skewness5.996387
Sum42207
Variance46622018
MonotonicityNot monotonic
2023-12-13T01:22:15.407232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 9
25.0%
-17 2
 
5.6%
-33 2
 
5.6%
-1 1
 
2.8%
-6 1
 
2.8%
-46 1
 
2.8%
430 1
 
2.8%
-12 1
 
2.8%
-25 1
 
2.8%
-60 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
-62 1
2.8%
-60 1
2.8%
-50 1
2.8%
-46 1
2.8%
-34 1
2.8%
-33 2
5.6%
-30 1
2.8%
-25 1
2.8%
-24 1
2.8%
-18 1
2.8%
ValueCountFrequency (%)
40995 1
 
2.8%
606 1
 
2.8%
430 1
 
2.8%
203 1
 
2.8%
170 1
 
2.8%
110 1
 
2.8%
80 1
 
2.8%
60 1
 
2.8%
40 1
 
2.8%
0 9
25.0%

경남
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4819.6944
Minimum-90
Maximum171955
Zeros13
Zeros (%)36.1%
Negative14
Negative (%)38.9%
Memory size456.0 B
2023-12-13T01:22:15.517629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-90
5-th percentile-56.25
Q1-14.25
median0
Q32.5
95-th percentile614.25
Maximum171955
Range172045
Interquartile range (IQR)16.75

Descriptive statistics

Standard deviation28652.248
Coefficient of variation (CV)5.9448268
Kurtosis35.997364
Mean4819.6944
Median Absolute Deviation (MAD)12
Skewness5.9996802
Sum173509
Variance8.2095134 × 108
MonotonicityNot monotonic
2023-12-13T01:22:15.669954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 13
36.1%
-47 2
 
5.6%
171955 1
 
2.8%
10 1
 
2.8%
-2 1
 
2.8%
-15 1
 
2.8%
-7 1
 
2.8%
-20 1
 
2.8%
-45 1
 
2.8%
-19 1
 
2.8%
Other values (13) 13
36.1%
ValueCountFrequency (%)
-90 1
2.8%
-84 1
2.8%
-47 2
5.6%
-45 1
2.8%
-34 1
2.8%
-20 1
2.8%
-19 1
2.8%
-15 1
2.8%
-14 1
2.8%
-9 1
2.8%
ValueCountFrequency (%)
171955 1
 
2.8%
777 1
 
2.8%
560 1
 
2.8%
200 1
 
2.8%
150 1
 
2.8%
128 1
 
2.8%
100 1
 
2.8%
70 1
 
2.8%
10 1
 
2.8%
0 13
36.1%

제주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1729.9167
Minimum-53
Maximum61509
Zeros29
Zeros (%)80.6%
Negative3
Negative (%)8.3%
Memory size456.0 B
2023-12-13T01:22:15.812604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-53
5-th percentile-22.25
Q10
median0
Q30
95-th percentile352.5
Maximum61509
Range61562
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10248.232
Coefficient of variation (CV)5.9241187
Kurtosis35.994053
Mean1729.9167
Median Absolute Deviation (MAD)0
Skewness5.9992792
Sum62277
Variance1.0502625 × 108
MonotonicityNot monotonic
2023-12-13T01:22:15.928072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 29
80.6%
-53 2
 
5.6%
61509 1
 
2.8%
384 1
 
2.8%
160 1
 
2.8%
342 1
 
2.8%
-12 1
 
2.8%
ValueCountFrequency (%)
-53 2
 
5.6%
-12 1
 
2.8%
0 29
80.6%
160 1
 
2.8%
342 1
 
2.8%
384 1
 
2.8%
61509 1
 
2.8%
ValueCountFrequency (%)
61509 1
 
2.8%
384 1
 
2.8%
342 1
 
2.8%
160 1
 
2.8%
0 29
80.6%
-12 1
 
2.8%
-53 2
 
5.6%

Interactions

2023-12-13T01:22:09.063616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:44.487131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:45.690066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:46.819806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:48.616039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:50.263495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:51.767871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:53.666623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:55.014059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.442025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:58.156557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:00.132060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:01.429478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:02.775621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:04.348273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:06.143561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:07.443941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:09.155915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:44.561909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:45.752359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:46.892241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:48.729072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:50.344049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:51.870061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:53.733982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:55.104557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.546695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:58.252479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:00.222354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T01:21:54.681021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.135725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:57.697047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:59.683522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:01.137671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:02.415481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:03.974311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:05.819760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:07.090295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:08.672977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:10.255823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:45.495385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:46.609393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:48.314945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:50.010648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:51.512092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:53.089665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:54.775036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.214977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:57.842821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:59.797557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:01.223745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:02.491030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:04.070319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:05.901895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:07.192636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:08.781035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:10.323972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:45.556388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:46.680569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:48.399178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:50.087683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:51.590851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:53.475243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:54.858965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.280467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:57.928071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:59.902961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:01.288970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:02.605057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:04.146610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:05.978623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:07.272774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:08.860861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:10.403074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:45.622980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:46.751317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:48.527460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:50.175861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:51.681941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:53.572160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:54.941854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:56.360711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:21:58.048207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:00.026927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:01.359549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:02.689660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:04.257102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:06.060382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:07.355985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:22:08.965888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:22:16.032401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분서울부산인천대구광주대전울산세종경기강원충북충남전북전남경북경남제주
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1.0001.0000.6630.6620.6620.6620.6630.6630.6631.0000.6620.6620.6630.6630.6630.6630.6630.6630.663
서울1.0000.6631.0000.6620.6620.6620.6630.6630.6631.0000.6620.6620.6630.6630.6630.6630.6630.6630.663
부산1.0000.6620.6621.0000.6620.6620.6620.6630.6631.0000.6630.6620.6630.6630.6630.6630.6630.6630.663
인천1.0000.6620.6620.6621.0000.6620.6620.6630.6631.0000.6620.6620.6630.6630.6630.6630.6630.6630.663
대구1.0000.6620.6620.6620.6621.0000.6620.6630.6631.0000.6620.6630.6630.6630.6630.6630.6630.6630.663
광주1.0000.6630.6630.6620.6620.6621.0000.6630.6631.0000.6620.6620.6630.6630.6630.6630.6630.6630.663
대전1.0000.6630.6630.6630.6630.6630.6631.0000.6641.0000.6630.6630.6640.6640.6640.6640.6640.6640.664
울산1.0000.6630.6630.6630.6630.6630.6630.6641.0001.0000.6630.6630.6640.6640.6640.6640.6640.6640.664
세종1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
경기1.0000.6620.6620.6630.6620.6620.6620.6630.6631.0001.0000.6620.6630.6630.6630.6630.6630.6630.663
강원1.0000.6620.6620.6620.6620.6630.6620.6630.6631.0000.6621.0000.6630.6630.6630.6630.6630.6630.663
충북1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6631.0000.6640.6640.6640.6640.6640.664
충남1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6641.0000.6640.6640.6640.6640.664
전북1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6640.6641.0000.6640.6640.6640.664
전남1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6640.6640.6641.0000.6640.6640.664
경북1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6640.6640.6640.6641.0000.6640.664
경남1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6640.6640.6640.6640.6641.0000.664
제주1.0000.6630.6630.6630.6630.6630.6630.6640.6641.0000.6630.6630.6640.6640.6640.6640.6640.6641.000
2023-12-13T01:22:16.211053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
서울부산인천대구광주대전울산경기강원충북충남전북전남경북경남제주세종
1.0000.7400.6400.5980.4310.5490.4890.4880.7010.4600.3520.4230.4840.5490.6380.5430.5640.955
서울0.7401.0000.6250.5950.3800.5710.3570.5390.5560.4020.2960.4100.3820.6020.4120.4190.4430.955
부산0.6400.6251.0000.4010.7540.7670.6230.7490.3510.7070.6700.3920.7740.5640.5660.7950.5060.955
인천0.5980.5950.4011.0000.4210.5110.4350.4360.3470.5540.4000.4070.4890.4890.5150.3900.3320.955
대구0.4310.3800.7540.4211.0000.7490.8090.6740.0770.7690.7120.4600.8180.5270.5570.7400.4140.955
광주0.5490.5710.7670.5110.7491.0000.7400.7210.2900.7530.7880.6200.8870.7700.6290.7580.4250.955
대전0.4890.3570.6230.4350.8090.7401.0000.5840.0930.7160.6410.5550.7380.5520.5010.6740.3450.955
울산0.4880.5390.7490.4360.6740.7210.5841.0000.3920.6450.5690.3000.6150.6380.4290.6400.4440.955
경기0.7010.5560.3510.3470.0770.2900.0930.3921.0000.0870.1720.1190.1360.3150.3240.2220.5640.955
강원0.4600.4020.7070.5540.7690.7530.7160.6450.0871.0000.6880.4380.8220.5960.6630.7570.2000.955
충북0.3520.2960.6700.4000.7120.7880.6410.5690.1720.6881.0000.4030.7670.5940.6050.6610.4060.955
충남0.4230.4100.3920.4070.4600.6200.5550.3000.1190.4380.4031.0000.5970.6150.4110.5070.2580.955
전북0.4840.3820.7740.4890.8180.8870.7380.6150.1360.8220.7670.5971.0000.6570.6430.8160.3890.955
전남0.5490.6020.5640.4890.5270.7700.5520.6380.3150.5960.5940.6150.6571.0000.6310.4950.4740.955
경북0.6380.4120.5660.5150.5570.6290.5010.4290.3240.6630.6050.4110.6430.6311.0000.6540.5800.955
경남0.5430.4190.7950.3900.7400.7580.6740.6400.2220.7570.6610.5070.8160.4950.6541.0000.3720.955
제주0.5640.4430.5060.3320.4140.4250.3450.4440.5640.2000.4060.2580.3890.4740.5800.3721.0000.955
세종0.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9550.9551.000

Missing values

2023-12-13T01:22:10.804583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:22:11.010705image/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

구분서울부산인천대구광주대전울산세종경기강원충북충남전북전남경북경남제주
0장부가56084382188686229966301645604224919625143252486409581160386524700305424852648296346364099517195561509
1세 대1946645037921491300413142619716615202218193497381425606777384
2198229200440330300120808003602802700300100602000
31983287569002600200200002951900100901201705600
419844987238065045002700000210208443001222031500
5198526506021048020019031060034017010001306080100160
6198610041041200100180604001201000007040700
71987360000240000012000000000
8198826402100540000000000000000
91996-1700000000-1700000000
구분서울부산인천대구광주대전울산세종경기강원충북충남전북전남경북경남제주
2620131211000-140-360632701-16-420000-140
272014643-2-370-20-68-3401029-14-72-800-280-12-190
282015515-21-2270-35-150-95-120580-118-7-5-83-39430-45342
292016-664-17-5660-70-44-40-80-132-15-27477-31-72-46-20-53
302017-2403-1694-1730-7-200-4700-610-10-14-33-71-33-7-53
312018-101-4-140-20000-120-4-4-4-13-17-15-12
322019-34-200-30000-2000-100-6-20
332020-639-690000000001200400-100
3420211210200035-137002000000100
352022174117030000000057-19000000