Overview

Dataset statistics

Number of variables8
Number of observations64
Missing cells8
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory72.1 B

Variable types

DateTime2
Numeric6

Dataset

Description제주특별자치도 서귀포시에서 관리하는 직영관광지(천지연폭포, 정방폭포, 주상절리, 천제연폭포, 산방산, 감귤박물관)에 관련한 데이터로 2018년 6월부터 2022년 6월까지의 월별 관람객 현황 정보를 제공합니다.
Author제주특별자치도 서귀포시
URLhttps://www.data.go.kr/data/15048283/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
천지연폭포 is highly overall correlated with 정방폭포 and 3 other fieldsHigh correlation
정방폭포 is highly overall correlated with 천지연폭포 and 3 other fieldsHigh correlation
중문대포 주상절리대 is highly overall correlated with 천지연폭포 and 3 other fieldsHigh correlation
천제연폭포 is highly overall correlated with 천지연폭포 and 3 other fieldsHigh correlation
감귤박물관 is highly overall correlated with 천지연폭포 and 3 other fieldsHigh correlation
기준연월 has 1 (1.6%) missing valuesMissing
천지연폭포 has 1 (1.6%) missing valuesMissing
정방폭포 has 1 (1.6%) missing valuesMissing
중문대포 주상절리대 has 1 (1.6%) missing valuesMissing
천제연폭포 has 1 (1.6%) missing valuesMissing
산방산_용머리 has 1 (1.6%) missing valuesMissing
감귤박물관 has 1 (1.6%) missing valuesMissing
데이터기준일자 has 1 (1.6%) missing valuesMissing
중문대포 주상절리대 has 9 (14.1%) zerosZeros
감귤박물관 has 2 (3.1%) zerosZeros

Reproduction

Analysis started2023-12-12 13:07:46.876134
Analysis finished2023-12-12 13:07:51.245031
Duration4.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월
Date

MISSING 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Memory size644.0 B
Minimum2018-06-01 00:00:00
Maximum2023-08-01 00:00:00
2023-12-12T22:07:51.324278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:51.504186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

천지연폭포
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean85166.825
Minimum22416
Maximum142454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:51.688699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22416
5-th percentile38582.5
Q165181.5
median83224
Q3102802.5
95-th percentile136163.9
Maximum142454
Range120038
Interquartile range (IQR)37621

Descriptive statistics

Standard deviation28547.232
Coefficient of variation (CV)0.33519193
Kurtosis-0.38723951
Mean85166.825
Median Absolute Deviation (MAD)19210
Skewness0.097206879
Sum5365510
Variance8.1494447 × 108
MonotonicityNot monotonic
2023-12-12T22:07:51.877209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127651 1
 
1.6%
118456 1
 
1.6%
84989 1
 
1.6%
89379 1
 
1.6%
79202 1
 
1.6%
75976 1
 
1.6%
69364 1
 
1.6%
61695 1
 
1.6%
86377 1
 
1.6%
82484 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
22416 1
1.6%
31424 1
1.6%
35018 1
1.6%
38163 1
1.6%
42358 1
1.6%
42516 1
1.6%
49432 1
1.6%
54220 1
1.6%
54341 1
1.6%
56104 1
1.6%
ValueCountFrequency (%)
142454 1
1.6%
140915 1
1.6%
138664 1
1.6%
136348 1
1.6%
134507 1
1.6%
128873 1
1.6%
127651 1
1.6%
123038 1
1.6%
118456 1
1.6%
117223 1
1.6%

정방폭포
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean53761.238
Minimum16691
Maximum84634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:52.061414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16691
5-th percentile24344
Q145317.5
median54077
Q364403.5
95-th percentile76246.1
Maximum84634
Range67943
Interquartile range (IQR)19086

Descriptive statistics

Standard deviation15496.275
Coefficient of variation (CV)0.28824252
Kurtosis-0.070016301
Mean53761.238
Median Absolute Deviation (MAD)9886
Skewness-0.31834685
Sum3386958
Variance2.4013453 × 108
MonotonicityNot monotonic
2023-12-12T22:07:52.228828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75037 1
 
1.6%
64844 1
 
1.6%
52664 1
 
1.6%
56131 1
 
1.6%
52299 1
 
1.6%
51083 1
 
1.6%
50424 1
 
1.6%
40998 1
 
1.6%
58548 1
 
1.6%
54139 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
16691 1
1.6%
19637 1
1.6%
21422 1
1.6%
23935 1
1.6%
28025 1
1.6%
29234 1
1.6%
33860 1
1.6%
34853 1
1.6%
37408 1
1.6%
37543 1
1.6%
ValueCountFrequency (%)
84634 1
1.6%
84022 1
1.6%
76416 1
1.6%
76274 1
1.6%
75995 1
1.6%
75412 1
1.6%
75037 1
1.6%
74079 1
1.6%
72860 1
1.6%
70452 1
1.6%

중문대포 주상절리대
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct55
Distinct (%)87.3%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean63174.429
Minimum0
Maximum142106
Zeros9
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:52.359101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q138079.5
median67593
Q384131.5
95-th percentile130380.9
Maximum142106
Range142106
Interquartile range (IQR)46052

Descriptive statistics

Standard deviation38198.148
Coefficient of variation (CV)0.60464573
Kurtosis-0.4882647
Mean63174.429
Median Absolute Deviation (MAD)25782
Skewness-0.025008514
Sum3979989
Variance1.4590985 × 109
MonotonicityNot monotonic
2023-12-12T22:07:52.489201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
14.1%
113730 1
 
1.6%
74360 1
 
1.6%
30284 1
 
1.6%
21627 1
 
1.6%
39632 1
 
1.6%
49526 1
 
1.6%
64703 1
 
1.6%
66385 1
 
1.6%
57167 1
 
1.6%
Other values (45) 45
70.3%
ValueCountFrequency (%)
0 9
14.1%
21627 1
 
1.6%
22874 1
 
1.6%
25181 1
 
1.6%
30284 1
 
1.6%
34499 1
 
1.6%
34814 1
 
1.6%
36527 1
 
1.6%
39632 1
 
1.6%
41529 1
 
1.6%
ValueCountFrequency (%)
142106 1
1.6%
140289 1
1.6%
139784 1
1.6%
132231 1
1.6%
113730 1
1.6%
112449 1
1.6%
107474 1
1.6%
106959 1
1.6%
104415 1
1.6%
102362 1
1.6%

천제연폭포
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean35137.159
Minimum9607
Maximum62051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:52.635067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9607
5-th percentile14115.1
Q128298.5
median35130
Q343240.5
95-th percentile52807.4
Maximum62051
Range52444
Interquartile range (IQR)14942

Descriptive statistics

Standard deviation11672.669
Coefficient of variation (CV)0.33220298
Kurtosis-0.29267005
Mean35137.159
Median Absolute Deviation (MAD)7390
Skewness-0.091581817
Sum2213641
Variance1.362512 × 108
MonotonicityNot monotonic
2023-12-12T22:07:52.775028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52837 1
 
1.6%
48264 1
 
1.6%
32717 1
 
1.6%
35130 1
 
1.6%
32946 1
 
1.6%
34007 1
 
1.6%
31354 1
 
1.6%
28496 1
 
1.6%
38233 1
 
1.6%
36915 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
9607 1
1.6%
12444 1
1.6%
13054 1
1.6%
13976 1
1.6%
15367 1
1.6%
16934 1
1.6%
17106 1
1.6%
21884 1
1.6%
22472 1
1.6%
22968 1
1.6%
ValueCountFrequency (%)
62051 1
1.6%
55238 1
1.6%
54191 1
1.6%
52837 1
1.6%
52541 1
1.6%
52012 1
1.6%
50957 1
1.6%
49565 1
1.6%
48979 1
1.6%
48699 1
1.6%

산방산_용머리
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)100.0%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean35511.206
Minimum6843
Maximum87288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:52.935997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6843
5-th percentile11786.2
Q125200.5
median34297
Q342886
95-th percentile64392.7
Maximum87288
Range80445
Interquartile range (IQR)17685.5

Descriptive statistics

Standard deviation16613.03
Coefficient of variation (CV)0.46782501
Kurtosis0.41933104
Mean35511.206
Median Absolute Deviation (MAD)8691
Skewness0.56707738
Sum2237206
Variance2.7599278 × 108
MonotonicityNot monotonic
2023-12-12T22:07:53.079722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35403 1
 
1.6%
20456 1
 
1.6%
23745 1
 
1.6%
33659 1
 
1.6%
24690 1
 
1.6%
12724 1
 
1.6%
10305 1
 
1.6%
13891 1
 
1.6%
18944 1
 
1.6%
41527 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
6843 1
1.6%
6918 1
1.6%
10305 1
1.6%
11682 1
1.6%
12724 1
1.6%
12730 1
1.6%
13856 1
1.6%
13891 1
1.6%
16137 1
1.6%
16888 1
1.6%
ValueCountFrequency (%)
87288 1
1.6%
65919 1
1.6%
64611 1
1.6%
64403 1
1.6%
64300 1
1.6%
62879 1
1.6%
58181 1
1.6%
56327 1
1.6%
56151 1
1.6%
55176 1
1.6%

감귤박물관
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct62
Distinct (%)98.4%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean6229.3175
Minimum0
Maximum23154
Zeros2
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-12T22:07:53.235342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile259.8
Q13204.5
median5550
Q37992.5
95-th percentile15774.6
Maximum23154
Range23154
Interquartile range (IQR)4788

Descriptive statistics

Standard deviation4480.8325
Coefficient of variation (CV)0.71931355
Kurtosis2.8149589
Mean6229.3175
Median Absolute Deviation (MAD)2445
Skewness1.3506999
Sum392447
Variance20077860
MonotonicityNot monotonic
2023-12-12T22:07:53.383740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.1%
8075 1
 
1.6%
1650 1
 
1.6%
3909 1
 
1.6%
4096 1
 
1.6%
6958 1
 
1.6%
5894 1
 
1.6%
3587 1
 
1.6%
5338 1
 
1.6%
5550 1
 
1.6%
Other values (52) 52
81.2%
ValueCountFrequency (%)
0 2
3.1%
91 1
1.6%
236 1
1.6%
474 1
1.6%
1508 1
1.6%
1523 1
1.6%
1583 1
1.6%
1650 1
1.6%
1706 1
1.6%
1741 1
1.6%
ValueCountFrequency (%)
23154 1
1.6%
17762 1
1.6%
16540 1
1.6%
16155 1
1.6%
12351 1
1.6%
11474 1
1.6%
11098 1
1.6%
10549 1
1.6%
10496 1
1.6%
10246 1
1.6%

데이터기준일자
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)1.6%
Missing1
Missing (%)1.6%
Memory size644.0 B
Minimum2023-08-31 00:00:00
Maximum2023-08-31 00:00:00
2023-12-12T22:07:53.489823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:53.576135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T22:07:49.901364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.127607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.769208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.375851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.927984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.403845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.983543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.233222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.896415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.464657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.016498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.489839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:50.066865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.348729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.011632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.582247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.105059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.567326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:50.173854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.438040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.118843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.670753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.181612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.651230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:50.286122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.532803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.207462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.766329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.250013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.740860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:50.709018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:47.663888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.298241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:48.851737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.336747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:07:49.830876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:07:53.662563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준연월천지연폭포정방폭포중문대포 주상절리대천제연폭포산방산_용머리감귤박물관
기준연월1.0001.0001.0001.0001.0001.0001.000
천지연폭포1.0001.0000.9280.8620.9210.1470.535
정방폭포1.0000.9281.0000.9150.9220.0000.430
중문대포 주상절리대1.0000.8620.9151.0000.8860.0000.637
천제연폭포1.0000.9210.9220.8861.0000.0000.702
산방산_용머리1.0000.1470.0000.0000.0001.0000.000
감귤박물관1.0000.5350.4300.6370.7020.0001.000
2023-12-12T22:07:53.791911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
천지연폭포정방폭포중문대포 주상절리대천제연폭포산방산_용머리감귤박물관
천지연폭포1.0000.9500.7130.9580.3360.641
정방폭포0.9501.0000.6340.9430.3290.530
중문대포 주상절리대0.7130.6341.0000.6820.3340.665
천제연폭포0.9580.9430.6821.0000.3420.639
산방산_용머리0.3360.3290.3340.3421.0000.255
감귤박물관0.6410.5300.6650.6390.2551.000

Missing values

2023-12-12T22:07:50.887238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:07:51.016282image/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.
2023-12-12T22:07:51.143105image/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

기준연월천지연폭포정방폭포중문대포 주상절리대천제연폭포산방산_용머리감귤박물관데이터기준일자
02018-0612765175037113730528373540380752023-08-31
12018-071184566484491021482642045686852023-08-31
22018-08138664846341023625201216137102462023-08-31
32018-091230387286072860486112628190532023-08-31
42018-10136348762741421065419152686104962023-08-31
52018-1110372554077104415400853885092782023-08-31
62018-127685146205738093014028962231542023-08-31
72019-017929050018765903268542705114742023-08-31
82019-028601951423820223511535030105492023-08-31
92019-0310125970452107474418625818179102023-08-31
기준연월천지연폭포정방폭포중문대포 주상절리대천제연폭포산방산_용머리감귤박물관데이터기준일자
542022-1254220407700252651168266592023-08-31
552023-0159325433930229683747559982023-08-31
562023-0274214516820292765171353142023-08-31
572023-0378946533020381996461144912023-08-31
582023-04108046620580451024571351372023-08-31
592023-05112068649630445076430057892023-08-31
602023-0691492593790388413191446712023-08-31
612023-077570856869035900691815232023-08-31
622023-0892116652130381733052731052023-08-31
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