Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory644.5 KiB
Average record size in memory66.0 B

Variable types

DateTime1
Categorical4
Numeric2

Dataset

Description제주빅데이터센터 데이터를 활용하여 국가별 외국인 방문지 파악을 위한 유동인구 및 와이파이 이용현황 데이터 제공 - 방문인구는 해당 유동인구 수의 일별 합계 ※ 유동인구는 01~24시 해당 시간 정각 측정 인구 (머문 시간(분)/60분)
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/749

Alerts

데이터기준일자 has constant value ""Constant
시도명 is highly overall correlated with 읍면동명High correlation
읍면동명 is highly overall correlated with 시도명High correlation
방문인구 has 2495 (24.9%) zerosZeros
서비스 사용시간 has 4147 (41.5%) zerosZeros

Reproduction

Analysis started2023-12-11 20:13:00.478127
Analysis finished2023-12-11 20:13:01.506877
Duration1.03 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct233
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-01-01 00:00:00
Maximum2018-12-31 00:00:00
2023-12-12T05:13:01.600326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:13:01.776683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제주시
6170 
서귀포시
3830 

Length

Max length4
Median length3
Mean length3.383
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 6170
61.7%
서귀포시 3830
38.3%

Length

2023-12-12T05:13:01.961814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:13:02.086736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 6170
61.7%
서귀포시 3830
38.3%

읍면동명
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
애월읍
 
274
한림읍
 
257
천지동
 
256
용담2동
 
254
삼양동
 
253
Other values (38)
8706 

Length

Max length4
Median length3
Mean length3.1637
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남원읍
2nd row영천동
3rd row중문동
4th row이도2동
5th row아라동

Common Values

ValueCountFrequency (%)
애월읍 274
 
2.7%
한림읍 257
 
2.6%
천지동 256
 
2.6%
용담2동 254
 
2.5%
삼양동 253
 
2.5%
대정읍 251
 
2.5%
연동 250
 
2.5%
이도1동 247
 
2.5%
남원읍 246
 
2.5%
아라동 246
 
2.5%
Other values (33) 7466
74.7%

Length

2023-12-12T05:13:02.201343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
애월읍 274
 
2.7%
한림읍 257
 
2.6%
천지동 256
 
2.6%
용담2동 254
 
2.5%
삼양동 253
 
2.5%
대정읍 251
 
2.5%
연동 250
 
2.5%
이도1동 247
 
2.5%
남원읍 246
 
2.5%
아라동 246
 
2.5%
Other values (33) 7466
74.7%

국적
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
인도
1057 
기타
1041 
미국
1020 
타이
1007 
중국
999 
Other values (5)
4876 

Length

Max length5
Median length2
Mean length2.5779
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row기타
2nd row베트남
3rd row말레이시아
4th row싱가포르
5th row인도

Common Values

ValueCountFrequency (%)
인도 1057
10.6%
기타 1041
10.4%
미국 1020
10.2%
타이 1007
10.1%
중국 999
10.0%
베트남 990
9.9%
일본 987
9.9%
홍콩 979
9.8%
싱가포르 971
9.7%
말레이시아 949
9.5%

Length

2023-12-12T05:13:02.324921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:13:02.461305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인도 1057
10.6%
기타 1041
10.4%
미국 1020
10.2%
타이 1007
10.1%
중국 999
10.0%
베트남 990
9.9%
일본 987
9.9%
홍콩 979
9.8%
싱가포르 971
9.7%
말레이시아 949
9.5%

방문인구
Real number (ℝ)

ZEROS 

Distinct5468
Distinct (%)54.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.83019
Minimum0
Maximum44923.225
Zeros2495
Zeros (%)24.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:13:02.626444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.132
median65.2215
Q3259.906
95-th percentile1591.944
Maximum44923.225
Range44923.225
Interquartile range (IQR)258.774

Descriptive statistics

Standard deviation2112.8022
Coefficient of variation (CV)4.6657714
Kurtosis218.14506
Mean452.83019
Median Absolute Deviation (MAD)65.2215
Skewness13.334609
Sum4528301.9
Variance4463933
MonotonicityNot monotonic
2023-12-12T05:13:02.751620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2495
 
24.9%
1.737 12
 
0.1%
5.088 10
 
0.1%
1.132 9
 
0.1%
1.61 9
 
0.1%
1.954 9
 
0.1%
24.304 9
 
0.1%
7.379 9
 
0.1%
11.604 9
 
0.1%
2.487 8
 
0.1%
Other values (5458) 7421
74.2%
ValueCountFrequency (%)
0.0 2495
24.9%
0.846 3
 
< 0.1%
1.132 9
 
0.1%
1.249 4
 
< 0.1%
1.403 4
 
< 0.1%
1.411 1
 
< 0.1%
1.4469999999999998 5
 
0.1%
1.5530000000000002 5
 
0.1%
1.591 3
 
< 0.1%
1.61 9
 
0.1%
ValueCountFrequency (%)
44923.225 1
< 0.1%
44825.21599999999 1
< 0.1%
44275.473 1
< 0.1%
43607.324 1
< 0.1%
42457.092 1
< 0.1%
42137.86 1
< 0.1%
40725.323 1
< 0.1%
40379.279 1
< 0.1%
38947.215 1
< 0.1%
38402.294 1
< 0.1%

서비스 사용시간
Real number (ℝ)

ZEROS 

Distinct5188
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean494267.04
Minimum0
Maximum54407475
Zeros4147
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:13:03.136692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median813
Q318175.25
95-th percentile2196237.9
Maximum54407475
Range54407475
Interquartile range (IQR)18175.25

Descriptive statistics

Standard deviation2626842
Coefficient of variation (CV)5.3146211
Kurtosis120.64669
Mean494267.04
Median Absolute Deviation (MAD)813
Skewness9.6746101
Sum4.9426704 × 109
Variance6.9002992 × 1012
MonotonicityNot monotonic
2023-12-12T05:13:03.279058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4147
41.5%
1 8
 
0.1%
3 7
 
0.1%
377 7
 
0.1%
378 7
 
0.1%
363 6
 
0.1%
325 6
 
0.1%
385 6
 
0.1%
313 6
 
0.1%
473 5
 
0.1%
Other values (5178) 5795
58.0%
ValueCountFrequency (%)
0 4147
41.5%
1 8
 
0.1%
2 2
 
< 0.1%
3 7
 
0.1%
4 4
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
54407475 1
< 0.1%
51070455 1
< 0.1%
45663322 1
< 0.1%
45363017 1
< 0.1%
40468778 1
< 0.1%
40290757 1
< 0.1%
39685781 1
< 0.1%
39673903 1
< 0.1%
39624273 1
< 0.1%
37190858 1
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-12-15
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-15
2nd row2020-12-15
3rd row2020-12-15
4th row2020-12-15
5th row2020-12-15

Common Values

ValueCountFrequency (%)
2020-12-15 10000
100.0%

Length

2023-12-12T05:13:03.413567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:13:03.531444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-15 10000
100.0%

Interactions

2023-12-12T05:13:01.082891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:13:00.901350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:13:01.167765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:13:00.988265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:13:03.594927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명읍면동명국적방문인구서비스 사용시간
시도명1.0001.0000.0000.0460.092
읍면동명1.0001.0000.0000.2970.188
국적0.0000.0001.0000.3110.441
방문인구0.0460.2970.3111.0000.000
서비스 사용시간0.0920.1880.4410.0001.000
2023-12-12T05:13:03.682103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명읍면동명국적
시도명1.0000.9980.000
읍면동명0.9981.0000.000
국적0.0000.0001.000
2023-12-12T05:13:03.761478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방문인구서비스 사용시간시도명읍면동명국적
방문인구1.0000.4010.0350.1070.100
서비스 사용시간0.4011.0000.0710.0660.149
시도명0.0350.0711.0000.9980.000
읍면동명0.1070.0660.9981.0000.000
국적0.1000.1490.0000.0001.000

Missing values

2023-12-12T05:13:01.307436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:13:01.436197image/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

일자시도명읍면동명국적방문인구서비스 사용시간데이터기준일자
153212018-11-05서귀포시남원읍기타6719.86625339352020-12-15
545492018-02-04서귀포시영천동베트남0.002020-12-15
649152018-02-28서귀포시중문동말레이시아28.34556062020-12-15
400962018-01-02제주시이도2동싱가포르27.72102020-12-15
152432018-11-05제주시아라동인도117.91524322020-12-15
209902018-11-18서귀포시효돈동중국191.89953012020-12-15
18972018-10-05제주시삼양동타이0.002020-12-15
124082018-10-29서귀포시영천동미국0.03852020-12-15
70702018-10-17제주시아라동중국848.578977262020-12-15
361272018-12-24제주시한림읍타이24.41202020-12-15
일자시도명읍면동명국적방문인구서비스 사용시간데이터기준일자
955582018-05-11제주시이도1동미국41.204564732020-12-15
853252018-04-17제주시봉개동말레이시아59.1414203022020-12-15
633882018-02-25제주시삼양동미국0.002020-12-15
832602018-04-12서귀포시남원읍중국3731.3691944732020-12-15
556432018-02-07제주시삼양동인도0.002020-12-15
112382018-10-27제주시추자면미국2.82302020-12-15
56122018-10-14제주시구좌읍홍콩297.8566172020-12-15
408552018-01-04제주시한림읍말레이시아65.63702020-12-15
252052018-11-28서귀포시대정읍말레이시아32.65512422020-12-15
568142018-02-10제주시추자면일본0.002020-12-15