Overview

Dataset statistics

Number of variables15
Number of observations94
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory131.4 B

Variable types

Categorical3
Text1
Numeric10
DateTime1

Dataset

Description제주특별자치도 내 주요 관광지점의 입장객 현황 정보입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15084198/fileData.do

Alerts

시도 has constant value ""Constant
데이터기준일자 has constant value ""Constant
2010년 is highly overall correlated with 2011년 and 2 other fieldsHigh correlation
2011년 is highly overall correlated with 2010년 and 4 other fieldsHigh correlation
2012년 is highly overall correlated with 2010년 and 3 other fieldsHigh correlation
2013년 is highly overall correlated with 2010년 and 8 other fieldsHigh correlation
2014년 is highly overall correlated with 2011년 and 7 other fieldsHigh correlation
2015년 is highly overall correlated with 2013년 and 5 other fieldsHigh correlation
2016년 is highly overall correlated with 2013년 and 5 other fieldsHigh correlation
2017년 is highly overall correlated with 2013년 and 5 other fieldsHigh correlation
2018년 is highly overall correlated with 2013년 and 5 other fieldsHigh correlation
2019년 is highly overall correlated with 2013년 and 5 other fieldsHigh correlation
내/외국인구분 is highly overall correlated with 2011년High correlation
2010년 has 19 (20.2%) zerosZeros
2011년 has 19 (20.2%) zerosZeros
2012년 has 19 (20.2%) zerosZeros
2013년 has 34 (36.2%) zerosZeros
2014년 has 36 (38.3%) zerosZeros
2015년 has 37 (39.4%) zerosZeros
2016년 has 37 (39.4%) zerosZeros
2017년 has 37 (39.4%) zerosZeros
2018년 has 33 (35.1%) zerosZeros
2019년 has 30 (31.9%) zerosZeros

Reproduction

Analysis started2023-12-12 09:11:12.023711
Analysis finished2023-12-12 09:11:25.538638
Duration13.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
제주특별자치도
94 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주특별자치도 94
100.0%

Length

2023-12-12T18:11:25.597064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:25.679336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 94
100.0%

행정시
Categorical

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
서귀포시
52 
제주시
42 

Length

Max length4
Median length4
Mean length3.5531915
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서귀포시 52
55.3%
제주시 42
44.7%

Length

2023-12-12T18:11:25.761979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:25.853847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서귀포시 52
55.3%
제주시 42
44.7%
Distinct47
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size884.0 B
2023-12-12T18:11:26.052971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.3829787
Min length3

Characters and Unicode

Total characters600
Distinct characters121
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(주)제주미니미니랜드
2nd row(주)제주미니미니랜드
3rd row국립제주박물관
4th row국립제주박물관
5th row도립미술관
ValueCountFrequency (%)
주)제주미니미니랜드 2
 
2.0%
이중섭미술관 2
 
2.0%
일출랜드 2
 
2.0%
정방폭포 2
 
2.0%
서귀포도립해양공원 2
 
2.0%
서귀포자연휴양림 2
 
2.0%
서복전시관 2
 
2.0%
성산일출봉 2
 
2.0%
소인국테마파크 2
 
2.0%
신영영화박물관 2
 
2.0%
Other values (41) 82
80.4%
2023-12-12T18:11:26.402948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34
 
5.7%
28
 
4.7%
26
 
4.3%
20
 
3.3%
18
 
3.0%
16
 
2.7%
14
 
2.3%
14
 
2.3%
14
 
2.3%
12
 
2.0%
Other values (111) 404
67.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 588
98.0%
Space Separator 8
 
1.3%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
34
 
5.8%
28
 
4.8%
26
 
4.4%
20
 
3.4%
18
 
3.1%
16
 
2.7%
14
 
2.4%
14
 
2.4%
14
 
2.4%
12
 
2.0%
Other values (108) 392
66.7%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 588
98.0%
Common 12
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
34
 
5.8%
28
 
4.8%
26
 
4.4%
20
 
3.4%
18
 
3.1%
16
 
2.7%
14
 
2.4%
14
 
2.4%
14
 
2.4%
12
 
2.0%
Other values (108) 392
66.7%
Common
ValueCountFrequency (%)
8
66.7%
( 2
 
16.7%
) 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 588
98.0%
ASCII 12
 
2.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
34
 
5.8%
28
 
4.8%
26
 
4.4%
20
 
3.4%
18
 
3.1%
16
 
2.7%
14
 
2.4%
14
 
2.4%
14
 
2.4%
12
 
2.0%
Other values (108) 392
66.7%
ASCII
ValueCountFrequency (%)
8
66.7%
( 2
 
16.7%
) 2
 
16.7%

내/외국인구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
내국인
47 
외국인
47 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row내국인
2nd row외국인
3rd row내국인
4th row외국인
5th row내국인

Common Values

ValueCountFrequency (%)
내국인 47
50.0%
외국인 47
50.0%

Length

2023-12-12T18:11:26.523783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:26.601699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내국인 47
50.0%
외국인 47
50.0%

2010년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214035.37
Minimum0
Maximum1495779
Zeros19
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:26.698618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1247.75
median40982
Q3269024
95-th percentile901179.3
Maximum1495779
Range1495779
Interquartile range (IQR)268776.25

Descriptive statistics

Standard deviation343713.16
Coefficient of variation (CV)1.6058708
Kurtosis4.0093593
Mean214035.37
Median Absolute Deviation (MAD)40982
Skewness2.0522648
Sum20119325
Variance1.1813874 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:26.843917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
20.2%
289965 1
 
1.1%
1495779 1
 
1.1%
1193 1
 
1.1%
111413 1
 
1.1%
686419 1
 
1.1%
113509 1
 
1.1%
89095 1
 
1.1%
773008 1
 
1.1%
490395 1
 
1.1%
Other values (66) 66
70.2%
ValueCountFrequency (%)
0 19
20.2%
16 1
 
1.1%
53 1
 
1.1%
90 1
 
1.1%
213 1
 
1.1%
220 1
 
1.1%
331 1
 
1.1%
496 1
 
1.1%
506 1
 
1.1%
1193 1
 
1.1%
ValueCountFrequency (%)
1495779 1
1.1%
1471558 1
1.1%
1354077 1
1.1%
1141632 1
1.1%
961147 1
1.1%
868889 1
1.1%
773008 1
1.1%
743001 1
1.1%
697145 1
1.1%
686419 1
1.1%

2011년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226161.06
Minimum0
Maximum1716917
Zeros19
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:27.015639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1207.75
median60394.5
Q3254384.75
95-th percentile940606.7
Maximum1716917
Range1716917
Interquartile range (IQR)254177

Descriptive statistics

Standard deviation363951.74
Coefficient of variation (CV)1.6092591
Kurtosis4.5805358
Mean226161.06
Median Absolute Deviation (MAD)60394.5
Skewness2.1268683
Sum21259140
Variance1.3246087 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:27.168409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
20.2%
256889 1
 
1.1%
1716917 1
 
1.1%
1050 1
 
1.1%
114971 1
 
1.1%
656463 1
 
1.1%
95199 1
 
1.1%
54553 1
 
1.1%
729794 1
 
1.1%
738103 1
 
1.1%
Other values (66) 66
70.2%
ValueCountFrequency (%)
0 19
20.2%
31 1
 
1.1%
101 1
 
1.1%
122 1
 
1.1%
148 1
 
1.1%
182 1
 
1.1%
285 1
 
1.1%
305 1
 
1.1%
978 1
 
1.1%
1050 1
 
1.1%
ValueCountFrequency (%)
1716917 1
1.1%
1546399 1
1.1%
1418502 1
1.1%
1089383 1
1.1%
942402 1
1.1%
939640 1
1.1%
814477 1
1.1%
764548 1
1.1%
738103 1
1.1%
729794 1
1.1%

2012년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212094.61
Minimum0
Maximum1813015
Zeros19
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:27.371591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1230
median52914.5
Q3288983
95-th percentile904990.85
Maximum1813015
Range1813015
Interquartile range (IQR)288753

Descriptive statistics

Standard deviation351563.11
Coefficient of variation (CV)1.6575769
Kurtosis6.5003429
Mean212094.61
Median Absolute Deviation (MAD)52914.5
Skewness2.4446491
Sum19936893
Variance1.2359662 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:27.561480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
20.2%
207247 1
 
1.1%
1813015 1
 
1.1%
2847 1
 
1.1%
124479 1
 
1.1%
555427 1
 
1.1%
82862 1
 
1.1%
8743 1
 
1.1%
137589 1
 
1.1%
1111386 1
 
1.1%
Other values (66) 66
70.2%
ValueCountFrequency (%)
0 19
20.2%
103 1
 
1.1%
105 1
 
1.1%
132 1
 
1.1%
138 1
 
1.1%
196 1
 
1.1%
332 1
 
1.1%
979 1
 
1.1%
1069 1
 
1.1%
2039 1
 
1.1%
ValueCountFrequency (%)
1813015 1
1.1%
1437537 1
1.1%
1411341 1
1.1%
1134286 1
1.1%
1111386 1
1.1%
793855 1
1.1%
767381 1
1.1%
722635 1
1.1%
654409 1
1.1%
644004 1
1.1%

2013년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)64.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176521.31
Minimum0
Maximum1804413
Zeros34
Zeros (%)36.2%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:27.758660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7468
Q3180953.25
95-th percentile936872.95
Maximum1804413
Range1804413
Interquartile range (IQR)180953.25

Descriptive statistics

Standard deviation349137.26
Coefficient of variation (CV)1.977876
Kurtosis8.0943611
Mean176521.31
Median Absolute Deviation (MAD)7468
Skewness2.7955425
Sum16593003
Variance1.2189683 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:27.937064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
36.2%
20442 1
 
1.1%
141746 1
 
1.1%
92 1
 
1.1%
37152 1
 
1.1%
6205 1
 
1.1%
1804413 1
 
1.1%
1377282 1
 
1.1%
235291 1
 
1.1%
53384 1
 
1.1%
Other values (51) 51
54.3%
ValueCountFrequency (%)
0 34
36.2%
6 1
 
1.1%
50 1
 
1.1%
92 1
 
1.1%
147 1
 
1.1%
276 1
 
1.1%
359 1
 
1.1%
553 1
 
1.1%
683 1
 
1.1%
691 1
 
1.1%
ValueCountFrequency (%)
1804413 1
1.1%
1377282 1
1.1%
1365165 1
1.1%
1349235 1
1.1%
1207661 1
1.1%
791064 1
1.1%
665682 1
1.1%
631449 1
1.1%
582701 1
1.1%
570867 1
1.1%

2014년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178355.53
Minimum0
Maximum1785016
Zeros36
Zeros (%)38.3%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:28.241228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5000.5
Q3147766.75
95-th percentile1190255.7
Maximum1785016
Range1785016
Interquartile range (IQR)147766.75

Descriptive statistics

Standard deviation365640.18
Coefficient of variation (CV)2.0500636
Kurtosis7.5691262
Mean178355.53
Median Absolute Deviation (MAD)5000.5
Skewness2.757237
Sum16765420
Variance1.3369274 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:28.462975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36
38.3%
332779 1
 
1.1%
516768 1
 
1.1%
15516 1
 
1.1%
183202 1
 
1.1%
152204 1
 
1.1%
208 1
 
1.1%
30965 1
 
1.1%
4856 1
 
1.1%
1619935 1
 
1.1%
Other values (49) 49
52.1%
ValueCountFrequency (%)
0 36
38.3%
77 1
 
1.1%
142 1
 
1.1%
161 1
 
1.1%
191 1
 
1.1%
208 1
 
1.1%
392 1
 
1.1%
609 1
 
1.1%
889 1
 
1.1%
1386 1
 
1.1%
ValueCountFrequency (%)
1785016 1
1.1%
1619935 1
1.1%
1348010 1
1.1%
1252181 1
1.1%
1234927 1
1.1%
1166202 1
1.1%
696200 1
1.1%
578780 1
1.1%
576281 1
1.1%
546772 1
1.1%

2015년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean181690.98
Minimum0
Maximum1785361
Zeros37
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:29.004718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1893.5
Q3156586.75
95-th percentile1086422.4
Maximum1785361
Range1785361
Interquartile range (IQR)156586.75

Descriptive statistics

Standard deviation372011.25
Coefficient of variation (CV)2.0474943
Kurtosis7.0245362
Mean181690.98
Median Absolute Deviation (MAD)1893.5
Skewness2.6796054
Sum17078952
Variance1.3839237 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:29.224116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
39.4%
325067 1
 
1.1%
458342 1
 
1.1%
505425 1
 
1.1%
5233 1
 
1.1%
195644 1
 
1.1%
159535 1
 
1.1%
199 1
 
1.1%
48325 1
 
1.1%
1759 1
 
1.1%
Other values (48) 48
51.1%
ValueCountFrequency (%)
0 37
39.4%
53 1
 
1.1%
152 1
 
1.1%
193 1
 
1.1%
199 1
 
1.1%
376 1
 
1.1%
520 1
 
1.1%
589 1
 
1.1%
754 1
 
1.1%
1160 1
 
1.1%
ValueCountFrequency (%)
1785361 1
1.1%
1628124 1
1.1%
1448984 1
1.1%
1224882 1
1.1%
1178192 1
1.1%
1037008 1
1.1%
933161 1
1.1%
717089 1
1.1%
690838 1
1.1%
630355 1
1.1%

2016년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191035.54
Minimum0
Maximum1823177
Zeros37
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:29.433883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2721.5
Q3167272.5
95-th percentile1092366.5
Maximum1823177
Range1823177
Interquartile range (IQR)167272.5

Descriptive statistics

Standard deviation388804.23
Coefficient of variation (CV)2.0352455
Kurtosis7.0363722
Mean191035.54
Median Absolute Deviation (MAD)2721.5
Skewness2.6818471
Sum17957341
Variance1.5116873 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:29.610783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
39.4%
319939 1
 
1.1%
461563 1
 
1.1%
506775 1
 
1.1%
7696 1
 
1.1%
209301 1
 
1.1%
144581 1
 
1.1%
208 1
 
1.1%
51728 1
 
1.1%
3098 1
 
1.1%
Other values (48) 48
51.1%
ValueCountFrequency (%)
0 37
39.4%
75 1
 
1.1%
208 1
 
1.1%
247 1
 
1.1%
299 1
 
1.1%
393 1
 
1.1%
573 1
 
1.1%
1543 1
 
1.1%
1589 1
 
1.1%
2026 1
 
1.1%
ValueCountFrequency (%)
1823177 1
1.1%
1767374 1
1.1%
1457992 1
1.1%
1402539 1
1.1%
1194878 1
1.1%
1037168 1
1.1%
859410 1
1.1%
805658 1
1.1%
754778 1
1.1%
704648 1
1.1%

2017년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)61.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174313.77
Minimum0
Maximum1758733
Zeros37
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:29.804388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2190
Q3107925.75
95-th percentile916081.55
Maximum1758733
Range1758733
Interquartile range (IQR)107925.75

Descriptive statistics

Standard deviation368082.84
Coefficient of variation (CV)2.1116109
Kurtosis7.5929093
Mean174313.77
Median Absolute Deviation (MAD)2190
Skewness2.7489885
Sum16385494
Variance1.3548498 × 1011
MonotonicityNot monotonic
2023-12-12T18:11:30.011814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
39.4%
337345 1
 
1.1%
685062 1
 
1.1%
633273 1
 
1.1%
8981 1
 
1.1%
168460 1
 
1.1%
486047 1
 
1.1%
898 1
 
1.1%
38722 1
 
1.1%
2080 1
 
1.1%
Other values (48) 48
51.1%
ValueCountFrequency (%)
0 37
39.4%
49 1
 
1.1%
108 1
 
1.1%
407 1
 
1.1%
439 1
 
1.1%
670 1
 
1.1%
898 1
 
1.1%
998 1
 
1.1%
1304 1
 
1.1%
2080 1
 
1.1%
ValueCountFrequency (%)
1758733 1
1.1%
1738680 1
1.1%
1417292 1
1.1%
1184768 1
1.1%
934979 1
1.1%
905906 1
1.1%
894318 1
1.1%
814242 1
1.1%
777643 1
1.1%
685062 1
1.1%

2018년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157596.47
Minimum0
Maximum1498554
Zeros33
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:30.223014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5579.5
Q3135289
95-th percentile829687.4
Maximum1498554
Range1498554
Interquartile range (IQR)135289

Descriptive statistics

Standard deviation307894.49
Coefficient of variation (CV)1.953689
Kurtosis6.4595729
Mean157596.47
Median Absolute Deviation (MAD)5579.5
Skewness2.5541895
Sum14814068
Variance9.4799019 × 1010
MonotonicityNot monotonic
2023-12-12T18:11:30.413787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
35.1%
6138 1
 
1.1%
502999 1
 
1.1%
452848 1
 
1.1%
9964 1
 
1.1%
159140 1
 
1.1%
133585 1
 
1.1%
367 1
 
1.1%
38828 1
 
1.1%
1025 1
 
1.1%
Other values (52) 52
55.3%
ValueCountFrequency (%)
0 33
35.1%
39 1
 
1.1%
45 1
 
1.1%
263 1
 
1.1%
367 1
 
1.1%
503 1
 
1.1%
610 1
 
1.1%
719 1
 
1.1%
1006 1
 
1.1%
1025 1
 
1.1%
ValueCountFrequency (%)
1498554 1
1.1%
1331102 1
1.1%
1182598 1
1.1%
937239 1
1.1%
863646 1
1.1%
811402 1
1.1%
808824 1
1.1%
735522 1
1.1%
678984 1
1.1%
519306 1
1.1%

2019년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165455.26
Minimum0
Maximum1299352
Zeros30
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size978.0 B
2023-12-12T18:11:30.624619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19299
Q3152281.25
95-th percentile807216.65
Maximum1299352
Range1299352
Interquartile range (IQR)152281.25

Descriptive statistics

Standard deviation293286.73
Coefficient of variation (CV)1.7726045
Kurtosis4.2797429
Mean165455.26
Median Absolute Deviation (MAD)19299
Skewness2.1790878
Sum15552794
Variance8.6017107 × 1010
MonotonicityNot monotonic
2023-12-12T18:11:30.880352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
31.9%
12807 1
 
1.1%
432957 1
 
1.1%
27571 1
 
1.1%
480607 1
 
1.1%
12273 1
 
1.1%
206415 1
 
1.1%
128937 1
 
1.1%
357 1
 
1.1%
33320 1
 
1.1%
Other values (55) 55
58.5%
ValueCountFrequency (%)
0 30
31.9%
49 1
 
1.1%
170 1
 
1.1%
235 1
 
1.1%
238 1
 
1.1%
357 1
 
1.1%
514 1
 
1.1%
690 1
 
1.1%
708 1
 
1.1%
921 1
 
1.1%
ValueCountFrequency (%)
1299352 1
1.1%
1265897 1
1.1%
1018635 1
1.1%
876843 1
1.1%
827808 1
1.1%
796129 1
1.1%
757856 1
1.1%
729749 1
1.1%
728623 1
1.1%
627289 1
1.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size884.0 B
Minimum2021-06-30 00:00:00
Maximum2021-06-30 00:00:00
2023-12-12T18:11:31.036500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:31.151059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T18:11:24.248749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.641746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.825990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.887834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.342691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.577549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.787859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.118275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.449589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.054435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.379949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.768762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.934374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.006955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.520256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.697450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.951901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.258443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.602537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.188731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.474589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:12.896558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.028208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.117554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.648097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.812660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.070012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.402828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.710355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.313647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.568993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.002540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.134887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.223705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.769550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.943561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.215723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.545684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.817224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.431661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.659214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.115048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.230177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.325248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.888979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.069152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.348076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.658707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.926951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.531032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.754803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.225714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.354315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.441189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.003848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.188365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.473183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.787781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:22.063246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.658769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.841767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.349366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.453415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.545643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.107157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.313761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.574079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:20.910530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:22.215970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.781384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.932147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.486566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.564547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:15.970582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.231971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.443351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.699262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.045975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:22.354098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:23.906434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:25.027579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.595427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.664829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.080628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.345769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.565232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.839665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.166779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:22.467363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.038247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:25.132132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:13.708100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:14.780739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:16.210564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:17.469916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:18.690452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:19.992050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:21.323617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:22.918684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:24.138967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:11:31.251952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정시관광지내/외국인구분2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년
행정시1.0001.0000.0000.1000.2480.0000.0000.0000.0000.0990.2030.1950.122
관광지1.0001.0000.0000.0000.0000.0000.0000.7940.5710.0000.0000.0000.000
내/외국인구분0.0000.0001.0000.5010.5260.6370.4100.3010.2380.2340.4090.5600.464
2010년0.1000.0000.5011.0000.9790.8770.8050.7700.8950.8390.8490.7440.886
2011년0.2480.0000.5260.9791.0000.8960.8430.7720.9430.9000.9360.8430.950
2012년0.0000.0000.6370.8770.8961.0000.9790.8310.8310.7890.7940.8440.779
2013년0.0000.0000.4100.8050.8430.9791.0000.8580.8730.8750.8190.8590.790
2014년0.0000.7940.3010.7700.7720.8310.8581.0000.8940.8710.8590.8970.797
2015년0.0000.5710.2380.8950.9430.8310.8730.8941.0000.9870.9840.9050.967
2016년0.0990.0000.2340.8390.9000.7890.8750.8710.9871.0000.9810.9360.956
2017년0.2030.0000.4090.8490.9360.7940.8190.8590.9840.9811.0000.9460.971
2018년0.1950.0000.5600.7440.8430.8440.8590.8970.9050.9360.9461.0000.931
2019년0.1220.0000.4640.8860.9500.7790.7900.7970.9670.9560.9710.9311.000
2023-12-12T18:11:31.437550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
내/외국인구분행정시
내/외국인구분1.0000.000
행정시0.0001.000
2023-12-12T18:11:31.585773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년행정시내/외국인구분
2010년1.0000.9870.9620.6180.4930.3960.3880.4020.3260.3080.0910.482
2011년0.9871.0000.9760.6600.5420.4490.4410.4530.3790.3550.2360.507
2012년0.9620.9761.0000.6890.5750.4820.4740.4840.4060.3850.0000.467
2013년0.6180.6600.6891.0000.8660.7710.7620.7710.7100.6250.0000.296
2014년0.4930.5420.5750.8661.0000.9180.9080.9180.8580.7740.0000.313
2015년0.3960.4490.4820.7710.9181.0000.9970.9930.9360.8530.0000.227
2016년0.3880.4410.4740.7620.9080.9971.0000.9900.9330.8510.0900.223
2017년0.4020.4530.4840.7710.9180.9930.9901.0000.9430.8580.1930.392
2018년0.3260.3790.4060.7100.8580.9360.9330.9431.0000.9200.1390.412
2019년0.3080.3550.3850.6250.7740.8530.8510.8580.9201.0000.1130.446
행정시0.0910.2360.0000.0000.0000.0000.0900.1930.1390.1131.0000.000
내/외국인구분0.4820.5070.4670.2960.3130.2270.2230.3920.4120.4460.0001.000

Missing values

2023-12-12T18:11:25.278299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:11:25.463496image/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

시도행정시관광지내/외국인구분2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년데이터기준일자
0제주특별자치도제주시(주)제주미니미니랜드내국인28996525688920724700000002021-06-30
1제주특별자치도제주시(주)제주미니미니랜드외국인683494301027000000002021-06-30
2제주특별자치도제주시국립제주박물관내국인3784954499584073453704213327793495762736632194633379303581612021-06-30
3제주특별자치도제주시국립제주박물관외국인68751135016135460371315413572328069778502153562021-06-30
4제주특별자치도제주시도립미술관내국인000087322142200109270912561250931260852021-06-30
5제주특별자치도제주시도립미술관외국인0000194020283148214310067082021-06-30
6제주특별자치도제주시만장굴관광지내국인5888556673486440046656825762816908387046487776436789845657722021-06-30
7제주특별자치도제주시만장굴관광지외국인978411182511156321121599849263740851605522678488767612021-06-30
8제주특별자치도제주시민속자연사박물관내국인8688899424027226355827014224943763823451493168024088723071682021-06-30
9제주특별자치도제주시민속자연사박물관외국인1239031137811713202632643742133809335310258887741727842032021-06-30
시도행정시관광지내/외국인구분2010년2011년2012년2013년2014년2015년2016년2017년2018년2019년데이터기준일자
84제주특별자치도서귀포시천지연폭포내국인14715581546399143753713651651348010162812418231771738680133110212658972021-06-30
85제주특별자치도서귀포시천지연폭포외국인1670622020683078593372483583291847741661676899163840576482021-06-30
86제주특별자치도서귀포시카멜리아힐내국인000000005193066192102021-06-30
87제주특별자치도서귀포시카멜리아힐외국인0000000015941995022021-06-30
88제주특별자치도서귀포시테디베어뮤지엄내국인564182509585416450420390117294000002021-06-30
89제주특별자치도서귀포시테디베어뮤지엄외국인1587451303971496517087486943000002021-06-30
90제주특별자치도서귀포시퍼시픽랜드내국인5563515254564724960000006272892021-06-30
91제주특별자치도서귀포시퍼시픽랜드외국인00000000002021-06-30
92제주특별자치도서귀포시한화아쿠아플라넷제주내국인000012349271178192119487811847689372398278082021-06-30
93제주특별자치도서귀포시한화아쿠아플라넷제주외국인00000604252219374546758319758792021-06-30