Overview

Dataset statistics

Number of variables10
Number of observations51
Missing cells16
Missing cells (%)3.1%
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 휴일 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/1043

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(%)High correlation
보통(%) is highly overall correlated with 자유로움(%)High correlation
다소 자유로움(%) is highly overall correlated with 전혀 자유롭지 않음(%) and 1 other fieldsHigh correlation
자유로움(%) is highly overall correlated with 보통(%)High 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 13 (25.5%) missing valuesMissing
자유롭지 않음(%) has 2 (3.9%) missing valuesMissing
다소 자유롭지 않음(%) has 1 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-11 20:04:53.855106
Analysis finished2023-12-11 20:05:02.667748
Duration8.81 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

2023-12-12T05:05:02.748317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:05:02.887330image/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

2023-12-12T05:05:03.045201image/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
2023-12-12T05:05:03.225381image/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
2023-12-12T05:05:03.436877image/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 

Distinct18
Distinct (%)47.4%
Missing13
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean1.1921053
Minimum0.2
Maximum5.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:03.590403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.2
Q10.7
median1.05
Q31.3
95-th percentile2.93
Maximum5.2
Range5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.97852111
Coefficient of variation (CV)0.82083448
Kurtosis6.996001
Mean1.1921053
Median Absolute Deviation (MAD)0.35
Skewness2.3125818
Sum45.3
Variance0.95750356
MonotonicityNot monotonic
2023-12-12T05:05:03.739111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1.1 5
 
9.8%
0.2 4
 
7.8%
0.7 4
 
7.8%
1.3 3
 
5.9%
1.4 3
 
5.9%
1.0 3
 
5.9%
2.9 2
 
3.9%
1.2 2
 
3.9%
0.8 2
 
3.9%
0.4 2
 
3.9%
Other values (8) 8
15.7%
(Missing) 13
25.5%
ValueCountFrequency (%)
0.2 4
7.8%
0.3 1
 
2.0%
0.4 2
 
3.9%
0.5 1
 
2.0%
0.6 1
 
2.0%
0.7 4
7.8%
0.8 2
 
3.9%
0.9 1
 
2.0%
1.0 3
5.9%
1.1 5
9.8%
ValueCountFrequency (%)
5.2 1
 
2.0%
3.1 1
 
2.0%
2.9 2
 
3.9%
2.3 1
 
2.0%
1.6 1
 
2.0%
1.4 3
5.9%
1.3 3
5.9%
1.2 2
 
3.9%
1.1 5
9.8%
1.0 3
5.9%

자유롭지 않음(%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)73.5%
Missing2
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean2.8857143
Minimum0.1
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:03.907873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.64
Q11.6
median2.5
Q33.7
95-th percentile6.72
Maximum8.8
Range8.7
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.9285141
Coefficient of variation (CV)0.66829697
Kurtosis1.6289657
Mean2.8857143
Median Absolute Deviation (MAD)1.1
Skewness1.2578107
Sum141.4
Variance3.7191667
MonotonicityNot monotonic
2023-12-12T05:05:04.057189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1.8 3
 
5.9%
2.1 3
 
5.9%
3.2 3
 
5.9%
0.8 2
 
3.9%
3.9 2
 
3.9%
3.7 2
 
3.9%
3.6 2
 
3.9%
2.5 2
 
3.9%
1.3 2
 
3.9%
2.7 2
 
3.9%
Other values (26) 26
51.0%
(Missing) 2
 
3.9%
ValueCountFrequency (%)
0.1 1
2.0%
0.4 1
2.0%
0.6 1
2.0%
0.7 1
2.0%
0.8 2
3.9%
1.1 1
2.0%
1.2 1
2.0%
1.3 2
3.9%
1.4 1
2.0%
1.5 1
2.0%
ValueCountFrequency (%)
8.8 1
2.0%
8.1 1
2.0%
6.8 1
2.0%
6.6 1
2.0%
6.5 1
2.0%
4.8 1
2.0%
4.6 1
2.0%
4.3 1
2.0%
4.0 1
2.0%
3.9 2
3.9%

다소 자유롭지 않음(%)
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)80.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean7.488
Minimum1
Maximum15.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:04.180353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.79
Q15.025
median7.4
Q39.2
95-th percentile13.07
Maximum15.4
Range14.4
Interquartile range (IQR)4.175

Descriptive statistics

Standard deviation3.2721234
Coefficient of variation (CV)0.4369823
Kurtosis0.028684827
Mean7.488
Median Absolute Deviation (MAD)2.25
Skewness0.35273666
Sum374.4
Variance10.706792
MonotonicityNot monotonic
2023-12-12T05:05:04.321201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7.3 3
 
5.9%
9.2 3
 
5.9%
7.4 3
 
5.9%
8.8 2
 
3.9%
4.6 2
 
3.9%
5.1 2
 
3.9%
11.0 2
 
3.9%
4.9 1
 
2.0%
3.1 1
 
2.0%
5.2 1
 
2.0%
Other values (30) 30
58.8%
ValueCountFrequency (%)
1.0 1
2.0%
1.4 1
2.0%
2.7 1
2.0%
2.9 1
2.0%
3.1 1
2.0%
4.0 1
2.0%
4.2 1
2.0%
4.3 1
2.0%
4.6 2
3.9%
4.7 1
2.0%
ValueCountFrequency (%)
15.4 1
2.0%
15.3 1
2.0%
13.7 1
2.0%
12.3 1
2.0%
11.7 1
2.0%
11.5 1
2.0%
11.0 2
3.9%
10.6 1
2.0%
10.2 1
2.0%
9.5 1
2.0%

보통(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.382353
Minimum5.1
Maximum44.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:04.492015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.1
5-th percentile13.3
Q118.9
median22.8
Q327.3
95-th percentile36.3
Maximum44.3
Range39.2
Interquartile range (IQR)8.4

Descriptive statistics

Standard deviation7.4264313
Coefficient of variation (CV)0.31760838
Kurtosis1.3153333
Mean23.382353
Median Absolute Deviation (MAD)4.3
Skewness0.53684109
Sum1192.5
Variance55.151882
MonotonicityNot monotonic
2023-12-12T05:05:04.920853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
27.3 3
 
5.9%
13.4 2
 
3.9%
22.8 2
 
3.9%
24.1 2
 
3.9%
13.2 2
 
3.9%
18.9 2
 
3.9%
28.2 1
 
2.0%
20.0 1
 
2.0%
21.0 1
 
2.0%
23.5 1
 
2.0%
Other values (34) 34
66.7%
ValueCountFrequency (%)
5.1 1
2.0%
13.2 2
3.9%
13.4 2
3.9%
14.0 1
2.0%
14.3 1
2.0%
15.7 1
2.0%
16.4 1
2.0%
17.0 1
2.0%
17.3 1
2.0%
18.7 1
2.0%
ValueCountFrequency (%)
44.3 1
2.0%
43.4 1
2.0%
36.9 1
2.0%
35.7 1
2.0%
34.6 1
2.0%
28.9 1
2.0%
28.7 1
2.0%
28.5 1
2.0%
28.3 1
2.0%
28.2 1
2.0%

다소 자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.315686
Minimum15.4
Maximum47.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:05.101843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.4
5-th percentile18.5
Q124.45
median30
Q335.3
95-th percentile43.5
Maximum47.4
Range32
Interquartile range (IQR)10.85

Descriptive statistics

Standard deviation7.8266052
Coefficient of variation (CV)0.25817015
Kurtosis-0.59388969
Mean30.315686
Median Absolute Deviation (MAD)5.3
Skewness0.21957349
Sum1546.1
Variance61.255749
MonotonicityNot monotonic
2023-12-12T05:05:05.255477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
34.8 2
 
3.9%
22.6 2
 
3.9%
35.3 2
 
3.9%
18.5 2
 
3.9%
25.6 1
 
2.0%
27.3 1
 
2.0%
19.0 1
 
2.0%
24.0 1
 
2.0%
31.7 1
 
2.0%
23.5 1
 
2.0%
Other values (37) 37
72.5%
ValueCountFrequency (%)
15.4 1
2.0%
17.8 1
2.0%
18.5 2
3.9%
19.0 1
2.0%
20.1 1
2.0%
20.9 1
2.0%
22.6 2
3.9%
22.9 1
2.0%
23.5 1
2.0%
23.8 1
2.0%
ValueCountFrequency (%)
47.4 1
2.0%
45.9 1
2.0%
44.1 1
2.0%
42.9 1
2.0%
41.7 1
2.0%
41.1 1
2.0%
39.8 1
2.0%
39.3 1
2.0%
38.6 1
2.0%
38.3 1
2.0%

자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.121569
Minimum7.4
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:05.449647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.4
5-th percentile12.95
Q118.55
median22.1
Q328.9
95-th percentile38.8
Maximum59.8
Range52.4
Interquartile range (IQR)10.35

Descriptive statistics

Standard deviation9.1491708
Coefficient of variation (CV)0.37929419
Kurtosis3.4197145
Mean24.121569
Median Absolute Deviation (MAD)4.5
Skewness1.3297396
Sum1230.2
Variance83.707325
MonotonicityNot monotonic
2023-12-12T05:05:05.649546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
24.3 2
 
3.9%
26.6 2
 
3.9%
19.9 1
 
2.0%
34.0 1
 
2.0%
41.0 1
 
2.0%
15.8 1
 
2.0%
22.3 1
 
2.0%
24.9 1
 
2.0%
17.8 1
 
2.0%
40.0 1
 
2.0%
Other values (39) 39
76.5%
ValueCountFrequency (%)
7.4 1
2.0%
11.8 1
2.0%
12.2 1
2.0%
13.7 1
2.0%
13.8 1
2.0%
14.1 1
2.0%
15.6 1
2.0%
15.8 1
2.0%
16.9 1
2.0%
17.3 1
2.0%
ValueCountFrequency (%)
59.8 1
2.0%
41.0 1
2.0%
40.0 1
2.0%
37.6 1
2.0%
37.3 1
2.0%
36.6 1
2.0%
34.0 1
2.0%
32.2 1
2.0%
31.3 1
2.0%
30.9 1
2.0%

매우 자유로움(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)86.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.194118
Minimum1
Maximum29.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2023-12-12T05:05:05.835901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q16.2
median8.4
Q316.45
95-th percentile24.5
Maximum29.8
Range28.8
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation7.016193
Coefficient of variation (CV)0.626775
Kurtosis-0.062202938
Mean11.194118
Median Absolute Deviation (MAD)3.5
Skewness0.85556781
Sum570.9
Variance49.226965
MonotonicityNot monotonic
2023-12-12T05:05:06.026236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
9.7 2
 
3.9%
7.4 2
 
3.9%
6.2 2
 
3.9%
2.6 2
 
3.9%
8.3 2
 
3.9%
24.5 2
 
3.9%
2.8 2
 
3.9%
6.0 1
 
2.0%
6.6 1
 
2.0%
17.7 1
 
2.0%
Other values (34) 34
66.7%
ValueCountFrequency (%)
1.0 1
2.0%
2.6 2
3.9%
2.8 2
3.9%
3.4 1
2.0%
4.7 1
2.0%
5.5 1
2.0%
5.6 1
2.0%
5.9 1
2.0%
6.0 1
2.0%
6.1 1
2.0%
ValueCountFrequency (%)
29.8 1
2.0%
27.3 1
2.0%
24.5 2
3.9%
21.7 1
2.0%
20.5 1
2.0%
20.4 1
2.0%
18.8 1
2.0%
18.5 1
2.0%
18.0 1
2.0%
17.7 1
2.0%

Interactions

2023-12-12T05:05:01.268068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:54.954100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.753555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.511119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.430959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.302265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.153635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.035937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.370986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.034993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.836074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.582118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.550467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.415439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.250947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.144832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.460578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.126901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.919706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.657973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.676124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.506465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.366196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.257600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.554057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.225104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.039116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.731448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.771746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.614476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.465024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.374239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.692301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.323808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.149840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.977726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.878926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.722086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.585868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.529792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.815853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.410149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.255304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.104533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.991529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.833436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.708711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.681959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.948516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.521901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.330424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.237821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.103515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.948614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.821843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:00.801373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:02.062606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:55.649078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:56.427052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:57.325320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:58.202612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.047841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:04:59.923861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:05:01.146871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:05:06.209751image/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 연도1.0000.0000.0000.0000.2610.3930.3250.0000.1520.317
시도0.0001.0000.8410.4590.6220.1880.0000.3700.0000.381
표본 수(명)0.0000.8411.0000.1990.1600.0000.0000.4120.0000.000
전혀 자유롭지 않음(%)0.0000.4590.1991.0000.6220.4800.7370.5680.4600.000
자유롭지 않음(%)0.2610.6220.1600.6221.0000.0000.6190.2200.6680.327
다소 자유롭지 않음(%)0.3930.1880.0000.4800.0001.0000.0000.0000.5850.000
보통(%)0.3250.0000.0000.7370.6190.0001.0000.3790.7740.474
다소 자유로움(%)0.0000.3700.4120.5680.2200.0000.3791.0000.4060.256
자유로움(%)0.1520.0000.0000.4600.6680.5850.7740.4061.0000.000
매우 자유로움(%)0.3170.3810.0000.0000.3270.0000.4740.2560.0001.000
2023-12-12T05:05:06.399106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준 연도시도
기준 연도1.0000.000
시도0.0001.000
2023-12-12T05:05:06.502827image/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)1.000-0.1690.0400.106-0.0640.1920.191-0.1140.0000.511
전혀 자유롭지 않음(%)-0.1691.0000.6410.1200.197-0.723-0.2330.2130.1860.147
자유롭지 않음(%)0.0400.6411.0000.0890.201-0.309-0.306-0.0180.1520.268
다소 자유롭지 않음(%)0.1060.1200.0891.0000.281-0.139-0.403-0.1600.2300.000
보통(%)-0.0640.1970.2010.2811.000-0.116-0.741-0.1910.1300.000
다소 자유로움(%)0.192-0.723-0.309-0.139-0.1161.0000.022-0.5330.0000.035
자유로움(%)0.191-0.233-0.306-0.403-0.7410.0221.000-0.0430.0750.000
매우 자유로움(%)-0.1140.213-0.018-0.160-0.191-0.533-0.0431.0000.1750.116
기준 연도0.0000.1860.1520.2300.1300.0000.0750.1751.0000.000
시도0.5110.1470.2680.0000.0000.0350.0000.1160.0001.000

Missing values

2023-12-12T05:05:02.226583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:05:02.440271image/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.
2023-12-12T05:05:02.596973image/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(%)
02018서울12201.21.88.626.332.019.910.2
12018부산734<NA>1.19.221.128.629.910.0
22018대구6380.21.56.722.442.919.66.8
32018인천6371.32.615.327.326.319.47.8
42018광주475<NA>2.213.724.630.017.312.3
52018대전493<NA>1.84.627.338.325.32.8
62018울산425<NA>0.110.624.025.720.818.8
72018세종1891.42.715.426.823.824.15.9
82018경기13250.73.212.318.735.323.26.5
92018강원4785.26.68.144.320.97.47.5
기준 연도시도표본 수(명)전혀 자유롭지 않음(%)자유롭지 않음(%)다소 자유롭지 않음(%)보통(%)다소 자유로움(%)자유로움(%)매우 자유로움(%)
412020세종612.93.95.15.118.559.84.7
422020경기24081.13.95.020.930.130.88.3
432020강원3201.12.59.228.522.615.620.5
442020충북321<NA>0.61.015.729.837.615.2
452020충남430<NA>2.42.728.339.321.46.0
462020전북3751.01.34.022.831.131.38.4
472020전남3601.13.77.414.017.828.627.3
482020경북5571.06.85.113.428.127.817.7
492020경남6601.44.34.228.718.518.424.5
502020제주120<NA>6.59.543.422.611.86.2