Overview

Dataset statistics

Number of variables10
Number of observations51
Missing cells10
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory91.6 B

Variable types

Categorical2
Numeric8

Dataset

Description- 시도별로 평일 여가 시간이 자유로운 정도 통계를 제공합니다. - 항목(전혀 자유롭지 않음, 자유롭지 않음, 다소 자유롭지 않음, 보통, 다소 자유롱, 자유로움, 매우 자유로움)별 비율이 의미하는 것은 표본 수 중 해당 항목에 응답한 사람의 비율입니다. - 데이터 제공처: KOSIS 국가통계포털
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/1042

Alerts

표본 수(명) is highly overall correlated with 시도High correlation
전혀 자유롭지 않음(%) is highly overall correlated with 자유롭지 않음(%) and 1 other fieldsHigh correlation
자유롭지 않음(%) is highly overall correlated with 전혀 자유롭지 않음(%) and 1 other fieldsHigh correlation
다소 자유롭지 않음(%) is highly overall correlated with 자유로움(%)High correlation
보통(%) is highly overall correlated with 자유로움(%)High correlation
다소 자유로움(%) is highly overall correlated with 전혀 자유롭지 않음(%) and 2 other fieldsHigh correlation
자유로움(%) is highly overall correlated with 다소 자유롭지 않음(%) and 1 other fieldsHigh correlation
매우 자유로움(%) is highly overall correlated with 다소 자유로움(%)High correlation
시도 is highly overall correlated with 표본 수(명)High correlation
전혀 자유롭지 않음(%) has 8 (15.7%) missing valuesMissing
자유롭지 않음(%) has 1 (2.0%) missing valuesMissing
다소 자유롭지 않음(%) has 1 (2.0%) missing valuesMissing
전혀 자유롭지 않음(%) has 1 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-11 19:30:05.035581
Analysis finished2023-12-11 19:30:12.014058
Duration6.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준 연도
Categorical

Distinct3
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
2018
17 
2019
17 
2020
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 17
33.3%
2019 17
33.3%
2020 17
33.3%

Length

2023-12-12T04:30:12.117210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:30:12.245261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 17
33.3%
2019 17
33.3%
2020 17
33.3%

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size540.0 B
서울
 
3
부산
 
3
대구
 
3
인천
 
3
광주
 
3
Other values (12)
36 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주

Common Values

ValueCountFrequency (%)
서울 3
 
5.9%
부산 3
 
5.9%
대구 3
 
5.9%
인천 3
 
5.9%
광주 3
 
5.9%
대전 3
 
5.9%
울산 3
 
5.9%
세종 3
 
5.9%
경기 3
 
5.9%
강원 3
 
5.9%
Other values (7) 21
41.2%

Length

2023-12-12T04:30:12.372722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 3
 
5.9%
강원 3
 
5.9%
경남 3
 
5.9%
경북 3
 
5.9%
전남 3
 
5.9%
전북 3
 
5.9%
충남 3
 
5.9%
충북 3
 
5.9%
경기 3
 
5.9%
부산 3
 
5.9%
Other values (7) 21
41.2%

표본 수(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.90196
Minimum61
Maximum2408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:12.544791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile195
Q1390.5
median500
Q3652
95-th percentile1334.5
Maximum2408
Range2347
Interquartile range (IQR)261.5

Descriptive statistics

Standard deviation419.51545
Coefficient of variation (CV)0.69814292
Kurtosis7.6687193
Mean600.90196
Median Absolute Deviation (MAD)140
Skewness2.4869544
Sum30646
Variance175993.21
MonotonicityNot monotonic
2023-12-12T04:30:12.715043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
475 2
 
3.9%
463 2
 
3.9%
1220 1
 
2.0%
303 1
 
2.0%
500 1
 
2.0%
611 1
 
2.0%
682 1
 
2.0%
302 1
 
2.0%
1931 1
 
2.0%
681 1
 
2.0%
Other values (39) 39
76.5%
ValueCountFrequency (%)
61 1
2.0%
120 1
2.0%
189 1
2.0%
201 1
2.0%
221 1
2.0%
294 1
2.0%
302 1
2.0%
303 1
2.0%
308 1
2.0%
320 1
2.0%
ValueCountFrequency (%)
2408 1
2.0%
1931 1
2.0%
1344 1
2.0%
1325 1
2.0%
1220 1
2.0%
1191 1
2.0%
734 1
2.0%
713 1
2.0%
693 1
2.0%
682 1
2.0%

전혀 자유롭지 않음(%)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct24
Distinct (%)55.8%
Missing8
Missing (%)15.7%
Infinite0
Infinite (%)0.0%
Mean1.4139535
Minimum0
Maximum5.4
Zeros1
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:12.911500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2
Q10.65
median1
Q31.8
95-th percentile3.8
Maximum5.4
Range5.4
Interquartile range (IQR)1.15

Descriptive statistics

Standard deviation1.1650836
Coefficient of variation (CV)0.82399002
Kurtosis3.1239827
Mean1.4139535
Median Absolute Deviation (MAD)0.5
Skewness1.6662392
Sum60.8
Variance1.3574197
MonotonicityNot monotonic
2023-12-12T04:30:13.037945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.9 5
 
9.8%
1.0 4
 
7.8%
1.8 4
 
7.8%
0.6 4
 
7.8%
0.2 3
 
5.9%
1.6 2
 
3.9%
1.3 2
 
3.9%
2.9 2
 
3.9%
0.8 2
 
3.9%
2.1 1
 
2.0%
Other values (14) 14
27.5%
(Missing) 8
15.7%
ValueCountFrequency (%)
0.0 1
 
2.0%
0.1 1
 
2.0%
0.2 3
5.9%
0.4 1
 
2.0%
0.5 1
 
2.0%
0.6 4
7.8%
0.7 1
 
2.0%
0.8 2
 
3.9%
0.9 5
9.8%
1.0 4
7.8%
ValueCountFrequency (%)
5.4 1
 
2.0%
4.6 1
 
2.0%
3.9 1
 
2.0%
2.9 2
3.9%
2.7 1
 
2.0%
2.5 1
 
2.0%
2.1 1
 
2.0%
2.0 1
 
2.0%
1.8 4
7.8%
1.6 2
3.9%

자유롭지 않음(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)70.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean4.026
Minimum0.4
Maximum12.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:13.204933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.845
Q11.95
median3.2
Q36
95-th percentile9.34
Maximum12.3
Range11.9
Interquartile range (IQR)4.05

Descriptive statistics

Standard deviation2.8001319
Coefficient of variation (CV)0.69551215
Kurtosis0.60348227
Mean4.026
Median Absolute Deviation (MAD)1.85
Skewness0.96257124
Sum201.3
Variance7.8407388
MonotonicityNot monotonic
2023-12-12T04:30:13.378429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2.3 4
 
7.8%
4.4 3
 
5.9%
5.3 2
 
3.9%
6.6 2
 
3.9%
0.9 2
 
3.9%
3.1 2
 
3.9%
2.1 2
 
3.9%
2.4 2
 
3.9%
1.5 2
 
3.9%
3.3 2
 
3.9%
Other values (25) 27
52.9%
ValueCountFrequency (%)
0.4 1
2.0%
0.6 1
2.0%
0.8 1
2.0%
0.9 2
3.9%
1.1 1
2.0%
1.2 1
2.0%
1.3 1
2.0%
1.4 2
3.9%
1.5 2
3.9%
1.9 1
2.0%
ValueCountFrequency (%)
12.3 1
2.0%
10.9 1
2.0%
9.7 1
2.0%
8.9 1
2.0%
7.6 1
2.0%
7.3 1
2.0%
6.6 2
3.9%
6.5 1
2.0%
6.4 1
2.0%
6.2 1
2.0%

다소 자유롭지 않음(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)88.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean10.234
Minimum2.2
Maximum21.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:13.552718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4.29
Q17.2
median9.6
Q313.9
95-th percentile17.285
Maximum21.3
Range19.1
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.441438
Coefficient of variation (CV)0.43398847
Kurtosis-0.58277897
Mean10.234
Median Absolute Deviation (MAD)3.25
Skewness0.36434891
Sum511.7
Variance19.726371
MonotonicityNot monotonic
2023-12-12T04:30:13.697013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
13.9 3
 
5.9%
7.2 2
 
3.9%
4.9 2
 
3.9%
11.8 2
 
3.9%
11.1 2
 
3.9%
6.3 1
 
2.0%
5.5 1
 
2.0%
9.9 1
 
2.0%
8.7 1
 
2.0%
10.9 1
 
2.0%
Other values (34) 34
66.7%
ValueCountFrequency (%)
2.2 1
2.0%
2.7 1
2.0%
4.2 1
2.0%
4.4 1
2.0%
4.9 2
3.9%
5.5 1
2.0%
5.8 1
2.0%
6.0 1
2.0%
6.1 1
2.0%
6.3 1
2.0%
ValueCountFrequency (%)
21.3 1
2.0%
17.8 1
2.0%
17.6 1
2.0%
16.9 1
2.0%
16.7 1
2.0%
16.3 1
2.0%
16.0 1
2.0%
15.6 1
2.0%
15.1 1
2.0%
14.7 1
2.0%

보통(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.286275
Minimum7.5
Maximum43.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:13.847400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.5
5-th percentile17.4
Q122.25
median25.6
Q330.95
95-th percentile35.8
Maximum43.5
Range36
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.7789681
Coefficient of variation (CV)0.25789003
Kurtosis0.44838805
Mean26.286275
Median Absolute Deviation (MAD)4.8
Skewness-0.095576217
Sum1340.6
Variance45.954408
MonotonicityNot monotonic
2023-12-12T04:30:14.006562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
25.6 2
 
3.9%
30.1 2
 
3.9%
22.9 2
 
3.9%
32.6 2
 
3.9%
25.0 2
 
3.9%
7.5 1
 
2.0%
19.6 1
 
2.0%
28.4 1
 
2.0%
22.2 1
 
2.0%
27.6 1
 
2.0%
Other values (36) 36
70.6%
ValueCountFrequency (%)
7.5 1
2.0%
13.2 1
2.0%
17.1 1
2.0%
17.7 1
2.0%
18.4 1
2.0%
18.6 1
2.0%
18.9 1
2.0%
19.0 1
2.0%
19.2 1
2.0%
19.6 1
2.0%
ValueCountFrequency (%)
43.5 1
2.0%
39.6 1
2.0%
36.1 1
2.0%
35.5 1
2.0%
34.2 1
2.0%
33.2 1
2.0%
32.9 1
2.0%
32.7 1
2.0%
32.6 2
3.9%
32.3 1
2.0%

다소 자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.656863
Minimum6.8
Maximum49.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:14.140180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile16.85
Q122.7
median30.2
Q334.2
95-th percentile39.45
Maximum49.7
Range42.9
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation8.1143393
Coefficient of variation (CV)0.28315519
Kurtosis0.20739948
Mean28.656863
Median Absolute Deviation (MAD)5.4
Skewness-0.19172944
Sum1461.5
Variance65.842502
MonotonicityNot monotonic
2023-12-12T04:30:14.273337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
24.5 2
 
3.9%
32.5 2
 
3.9%
34.1 2
 
3.9%
27.9 1
 
2.0%
35.4 1
 
2.0%
21.1 1
 
2.0%
31.9 1
 
2.0%
23.1 1
 
2.0%
49.7 1
 
2.0%
32.1 1
 
2.0%
Other values (38) 38
74.5%
ValueCountFrequency (%)
6.8 1
2.0%
14.7 1
2.0%
16.7 1
2.0%
17.0 1
2.0%
17.8 1
2.0%
18.7 1
2.0%
18.9 1
2.0%
19.2 1
2.0%
19.7 1
2.0%
20.6 1
2.0%
ValueCountFrequency (%)
49.7 1
2.0%
41.3 1
2.0%
39.5 1
2.0%
39.4 1
2.0%
38.9 1
2.0%
38.1 1
2.0%
36.0 1
2.0%
35.6 1
2.0%
35.5 1
2.0%
35.4 1
2.0%

자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)94.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.480392
Minimum6.1
Maximum47.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:14.401702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.1
5-th percentile12.65
Q115.65
median18.4
Q324.75
95-th percentile31.3
Maximum47.6
Range41.5
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation7.1891869
Coefficient of variation (CV)0.35102779
Kurtosis2.7760045
Mean20.480392
Median Absolute Deviation (MAD)3
Skewness1.130918
Sum1044.5
Variance51.684408
MonotonicityNot monotonic
2023-12-12T04:30:14.527358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
15.4 2
 
3.9%
20.1 2
 
3.9%
16.8 2
 
3.9%
13.3 1
 
2.0%
15.6 1
 
2.0%
20.6 1
 
2.0%
14.4 1
 
2.0%
16.4 1
 
2.0%
26.8 1
 
2.0%
29.2 1
 
2.0%
Other values (38) 38
74.5%
ValueCountFrequency (%)
6.1 1
2.0%
8.1 1
2.0%
12.6 1
2.0%
12.7 1
2.0%
13.3 1
2.0%
14.3 1
2.0%
14.4 1
2.0%
14.7 1
2.0%
15.0 1
2.0%
15.4 2
3.9%
ValueCountFrequency (%)
47.6 1
2.0%
31.8 1
2.0%
31.6 1
2.0%
31.0 1
2.0%
30.3 1
2.0%
30.0 1
2.0%
29.2 1
2.0%
28.4 1
2.0%
27.9 1
2.0%
27.2 1
2.0%

매우 자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3921569
Minimum0.4
Maximum26.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T04:30:14.651267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile2.35
Q15.05
median7.2
Q312.45
95-th percentile21.3
Maximum26.3
Range25.9
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation6.0381733
Coefficient of variation (CV)0.64289528
Kurtosis0.34759746
Mean9.3921569
Median Absolute Deviation (MAD)2.6
Skewness0.98401102
Sum479
Variance36.459537
MonotonicityNot monotonic
2023-12-12T04:30:14.794799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.4 2
 
3.9%
6.3 2
 
3.9%
3.3 2
 
3.9%
4.9 2
 
3.9%
7.2 2
 
3.9%
5.0 2
 
3.9%
6.7 2
 
3.9%
9.6 2
 
3.9%
8.9 1
 
2.0%
9.8 1
 
2.0%
Other values (33) 33
64.7%
ValueCountFrequency (%)
0.4 1
2.0%
1.3 1
2.0%
2.1 1
2.0%
2.6 1
2.0%
2.8 1
2.0%
3.1 1
2.0%
3.3 2
3.9%
4.8 1
2.0%
4.9 2
3.9%
5.0 2
3.9%
ValueCountFrequency (%)
26.3 1
2.0%
22.4 1
2.0%
21.5 1
2.0%
21.1 1
2.0%
19.8 1
2.0%
19.0 1
2.0%
17.6 1
2.0%
16.9 1
2.0%
16.3 1
2.0%
16.0 1
2.0%

Interactions

2023-12-12T04:30:10.904985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.354381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.231129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.975269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.824772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.666504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.374118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.070940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.979042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.489632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.314904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.066201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.936675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.765586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.455744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.139273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.059412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.616035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.404552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.167579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.037980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.852189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.555933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.209363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.146891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.731258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.492213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.273741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.143720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.976374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.655238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.540792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.237736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.855678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.602464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.401953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.264206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.068718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.754721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.615169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.338559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:05.974787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.698558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.500390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.392673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.146385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.833326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.692184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.449699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.068074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.792982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.604248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.495957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.218201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.905115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.771477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:11.526641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.146708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:06.887103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:07.707825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:08.575871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.293571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:09.985267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:30:10.838748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:30:14.893775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도시도표본 수(명)전혀 자유롭지 않음(%)자유롭지 않음(%)다소 자유롭지 않음(%)보통(%)다소 자유로움(%)자유로움(%)매우 자유로움(%)
기준 연도1.0000.0000.0000.0000.0000.4870.3400.3290.4120.000
시도0.0001.0000.8410.0000.6290.6230.4870.5380.0000.397
표본 수(명)0.0000.8411.0000.0000.0000.0000.0000.0000.0000.000
전혀 자유롭지 않음(%)0.0000.0000.0001.0000.7380.7430.7540.4070.5640.715
자유롭지 않음(%)0.0000.6290.0000.7381.0000.0000.7650.6910.6330.626
다소 자유롭지 않음(%)0.4870.6230.0000.7430.0001.0000.0000.3710.4500.000
보통(%)0.3400.4870.0000.7540.7650.0001.0000.8290.7740.585
다소 자유로움(%)0.3290.5380.0000.4070.6910.3710.8291.0000.5440.477
자유로움(%)0.4120.0000.0000.5640.6330.4500.7740.5441.0000.000
매우 자유로움(%)0.0000.3970.0000.7150.6260.0000.5850.4770.0001.000
2023-12-12T04:30:15.021119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도시도
기준 연도1.0000.000
시도0.0001.000
2023-12-12T04:30:15.378898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표본 수(명)전혀 자유롭지 않음(%)자유롭지 않음(%)다소 자유롭지 않음(%)보통(%)다소 자유로움(%)자유로움(%)매우 자유로움(%)기준 연도시도
표본 수(명)1.000-0.009-0.0830.1390.0400.0650.064-0.1870.0000.511
전혀 자유롭지 않음(%)-0.0091.0000.7090.4240.033-0.626-0.3910.2140.0000.000
자유롭지 않음(%)-0.0830.7091.0000.2450.107-0.597-0.3630.1720.0000.262
다소 자유롭지 않음(%)0.1390.4240.2451.0000.248-0.349-0.511-0.2420.3040.258
보통(%)0.0400.0330.1070.2481.000-0.202-0.680-0.3530.0980.166
다소 자유로움(%)0.065-0.626-0.597-0.349-0.2021.0000.216-0.5020.1830.204
자유로움(%)0.064-0.391-0.363-0.511-0.6800.2161.0000.0700.2390.000
매우 자유로움(%)-0.1870.2140.172-0.242-0.353-0.5020.0701.0000.0000.127
기준 연도0.0000.0000.0000.3040.0980.1830.2390.0001.0000.000
시도0.5110.0000.2620.2580.1660.2040.0000.1270.0001.000

Missing values

2023-12-12T04:30:11.649469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:30:11.836266image/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.
2023-12-12T04:30:11.951466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준 연도시도표본 수(명)전혀 자유롭지 않음(%)자유롭지 않음(%)다소 자유롭지 않음(%)보통(%)다소 자유로움(%)자유로움(%)매우 자유로움(%)
02018서울12202.92.713.925.627.918.28.9
12018부산734<NA>1.115.123.533.218.88.3
22018대구638<NA>1.37.625.639.419.76.4
32018인천6372.55.417.830.120.717.26.3
42018광주475<NA>0.914.734.229.114.76.5
52018대전4930.83.17.432.935.617.03.3
62018울산425<NA>1.921.319.024.515.717.6
72018세종1891.42.416.926.332.515.54.9
82018경기13251.84.416.324.930.916.15.6
92018강원4785.47.312.843.517.86.17.2
기준 연도시도표본 수(명)전혀 자유롭지 않음(%)자유롭지 않음(%)다소 자유롭지 않음(%)보통(%)다소 자유로움(%)자유로움(%)매우 자유로움(%)
412020세종612.99.714.57.56.847.610.9
422020경기24081.36.18.223.829.024.67.2
432020강원3201.04.49.331.819.218.316.0
442020충북3210.96.42.218.433.131.08.0
452020충남4300.22.34.935.534.317.75.1
462020전북3751.01.54.223.335.327.96.7
472020전남3604.67.66.313.217.024.926.3
482020경북5570.96.67.318.926.127.212.9
492020경남6602.76.55.532.716.714.321.5
502020제주1201.112.316.736.122.38.13.3