Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

Numeric4
Categorical5

Dataset

Description내국인 유동인구 구성비율 - 방문인구는 해당 유동인구 수의 연월별 합계 ※ 유동인구는 01~24시 해당 시간 정각 측정 인구 (머문 시간(분)/60분)
Author제주데이터허브
URLhttps://www.jejudatahub.net/data/view/data/563

Alerts

residence_population_count is highly overall correlated with work_population_count and 1 other fieldsHigh correlation
work_population_count is highly overall correlated with residence_population_count and 1 other fieldsHigh correlation
visit_population_count is highly overall correlated with residence_population_count and 1 other fieldsHigh correlation
work_population_count has 241 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-11 19:53:19.217821
Analysis finished2023-12-11 19:53:24.013449
Duration4.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

base_year_month
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201846.76
Minimum201801
Maximum201909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:53:24.119472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201802
Q1201806
median201811
Q3201904
95-th percentile201908
Maximum201909
Range108
Interquartile range (IQR)98

Descriptive statistics

Standard deviation48.425745
Coefficient of variation (CV)0.00023991342
Kurtosis-1.8527301
Mean201846.76
Median Absolute Deviation (MAD)9
Skewness0.36207099
Sum2.0184676 × 109
Variance2345.0528
MonotonicityNot monotonic
2023-12-12T04:53:24.323714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
201806 563
 
5.6%
201807 516
 
5.2%
201902 515
 
5.1%
201908 506
 
5.1%
201808 499
 
5.0%
201905 497
 
5.0%
201811 496
 
5.0%
201804 491
 
4.9%
201901 490
 
4.9%
201809 488
 
4.9%
Other values (11) 4939
49.4%
ValueCountFrequency (%)
201801 482
4.8%
201802 473
4.7%
201803 480
4.8%
201804 491
4.9%
201805 476
4.8%
201806 563
5.6%
201807 516
5.2%
201808 499
5.0%
201809 488
4.9%
201810 460
4.6%
ValueCountFrequency (%)
201909 248
2.5%
201908 506
5.1%
201907 468
4.7%
201906 478
4.8%
201905 497
5.0%
201904 451
4.5%
201903 443
4.4%
201902 515
5.1%
201901 490
4.9%
201812 480
4.8%

quarter
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2분기
2956 
1분기
2883 
3분기
2725 
4분기
1436 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4분기
2nd row4분기
3rd row3분기
4th row4분기
5th row2분기

Common Values

ValueCountFrequency (%)
2분기 2956
29.6%
1분기 2883
28.8%
3분기 2725
27.3%
4분기 1436
14.4%

Length

2023-12-12T04:53:24.599861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:53:24.784357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2분기 2956
29.6%
1분기 2883
28.8%
3분기 2725
27.3%
4분기 1436
14.4%

emd_name
Categorical

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제주시 건입동
 
280
서귀포시 남원읍
 
271
제주시 노형동
 
255
서귀포시 중문동
 
251
서귀포시 정방동
 
250
Other values (38)
8693 

Length

Max length8
Median length8
Mean length7.5615
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주시 용담1동
2nd row제주시 이호동
3rd row제주시 연동
4th row제주시 삼도1동
5th row제주시 용담2동

Common Values

ValueCountFrequency (%)
제주시 건입동 280
 
2.8%
서귀포시 남원읍 271
 
2.7%
제주시 노형동 255
 
2.5%
서귀포시 중문동 251
 
2.5%
서귀포시 정방동 250
 
2.5%
제주시 추자면 249
 
2.5%
제주시 용담2동 249
 
2.5%
서귀포시 대천동 248
 
2.5%
서귀포시 중앙동 248
 
2.5%
제주시 애월읍 246
 
2.5%
Other values (33) 7453
74.5%

Length

2023-12-12T04:53:25.030141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제주시 6007
30.0%
서귀포시 3993
20.0%
건입동 280
 
1.4%
남원읍 271
 
1.4%
노형동 255
 
1.3%
중문동 251
 
1.3%
정방동 250
 
1.2%
추자면 249
 
1.2%
용담2동 249
 
1.2%
중앙동 248
 
1.2%
Other values (35) 7947
39.7%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여성
5016 
남성
4984 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row여성
2nd row남성
3rd row남성
4th row여성
5th row남성

Common Values

ValueCountFrequency (%)
여성 5016
50.2%
남성 4984
49.8%

Length

2023-12-12T04:53:25.251697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:53:25.398955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여성 5016
50.2%
남성 4984
49.8%

age_range
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
69세 이하
1152 
19세 이하
1139 
79세 이하
1129 
29세 이하
1127 
59세 이하
1105 
Other values (4)
4348 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row80세 이상
2nd row19세 이하
3rd row19세 이하
4th row29세 이하
5th row49세 이하

Common Values

ValueCountFrequency (%)
69세 이하 1152
11.5%
19세 이하 1139
11.4%
79세 이하 1129
11.3%
29세 이하 1127
11.3%
59세 이하 1105
11.1%
39세 이하 1093
10.9%
80세 이상 1089
10.9%
49세 이하 1087
10.9%
10세 미만 1079
10.8%

Length

2023-12-12T04:53:25.567949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:53:25.784906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이하 7832
39.2%
69세 1152
 
5.8%
19세 1139
 
5.7%
79세 1129
 
5.6%
29세 1127
 
5.6%
59세 1105
 
5.5%
39세 1093
 
5.5%
80세 1089
 
5.4%
이상 1089
 
5.4%
49세 1087
 
5.4%
Other values (2) 2158
 
10.8%

time_range
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
17-18시
908 
3-4시
864 
21-22시
862 
1-2시
839 
7-8시
833 
Other values (7)
5694 

Length

Max length6
Median length6
Mean length5.2472
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11-12시
2nd row23-24시
3rd row17-18시
4th row7-8시
5th row3-4시

Common Values

ValueCountFrequency (%)
17-18시 908
9.1%
3-4시 864
8.6%
21-22시 862
8.6%
1-2시 839
8.4%
7-8시 833
8.3%
5-6시 833
8.3%
23-24시 823
8.2%
13-14시 816
8.2%
19-20시 812
8.1%
11-12시 810
8.1%
Other values (2) 1600
16.0%

Length

2023-12-12T04:53:26.035676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17-18시 908
9.1%
3-4시 864
8.6%
21-22시 862
8.6%
1-2시 839
8.4%
7-8시 833
8.3%
5-6시 833
8.3%
23-24시 823
8.2%
13-14시 816
8.2%
19-20시 812
8.1%
11-12시 810
8.1%
Other values (2) 1600
16.0%

residence_population_count
Real number (ℝ)

HIGH CORRELATION 

Distinct9984
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39350.18
Minimum114.66
Maximum393563.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:53:26.227489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum114.66
5-th percentile2028.9715
Q18061.03
median21486.03
Q348088.01
95-th percentile149904.14
Maximum393563.39
Range393448.73
Interquartile range (IQR)40026.98

Descriptive statistics

Standard deviation50038.374
Coefficient of variation (CV)1.2716174
Kurtosis7.3124023
Mean39350.18
Median Absolute Deviation (MAD)15857.555
Skewness2.4885791
Sum3.935018 × 108
Variance2.5038389 × 109
MonotonicityNot monotonic
2023-12-12T04:53:26.455666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28945.57 2
 
< 0.1%
2173.55 2
 
< 0.1%
27128.14 2
 
< 0.1%
7127.94 2
 
< 0.1%
25525.75 2
 
< 0.1%
23301.17 2
 
< 0.1%
5035.61 2
 
< 0.1%
2342.7 2
 
< 0.1%
31613.81 2
 
< 0.1%
1514.54 2
 
< 0.1%
Other values (9974) 9980
99.8%
ValueCountFrequency (%)
114.66 1
< 0.1%
116.31 1
< 0.1%
133.41 1
< 0.1%
160.36 1
< 0.1%
186.35 1
< 0.1%
202.16 1
< 0.1%
233.95 1
< 0.1%
252.58 1
< 0.1%
275.28 1
< 0.1%
280.71 1
< 0.1%
ValueCountFrequency (%)
393563.39 1
< 0.1%
369950.78 1
< 0.1%
348594.52 1
< 0.1%
335762.53 1
< 0.1%
334614.22 1
< 0.1%
328975.61 1
< 0.1%
326571.06 1
< 0.1%
324794.19 1
< 0.1%
319832.04 1
< 0.1%
318974.95 1
< 0.1%

work_population_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9656
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4487.5107
Minimum0
Maximum127362.41
Zeros241
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:53:26.719748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile53.7245
Q1545.9025
median1658.255
Q34484.4325
95-th percentile18254.132
Maximum127362.41
Range127362.41
Interquartile range (IQR)3938.53

Descriptive statistics

Standard deviation8578.8833
Coefficient of variation (CV)1.9117243
Kurtosis32.928044
Mean4487.5107
Median Absolute Deviation (MAD)1359.53
Skewness4.8039407
Sum44875107
Variance73597238
MonotonicityNot monotonic
2023-12-12T04:53:26.978761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 241
 
2.4%
337.78 3
 
< 0.1%
520.93 3
 
< 0.1%
17.18 3
 
< 0.1%
1249.2 2
 
< 0.1%
350.04 2
 
< 0.1%
759.66 2
 
< 0.1%
8.03 2
 
< 0.1%
1393.74 2
 
< 0.1%
51.71 2
 
< 0.1%
Other values (9646) 9738
97.4%
ValueCountFrequency (%)
0.0 241
2.4%
1.96 1
 
< 0.1%
2.08 1
 
< 0.1%
2.41 1
 
< 0.1%
2.95 1
 
< 0.1%
3.9 1
 
< 0.1%
3.92 1
 
< 0.1%
4.06 1
 
< 0.1%
4.07 1
 
< 0.1%
4.12 1
 
< 0.1%
ValueCountFrequency (%)
127362.41 1
< 0.1%
116718.38 1
< 0.1%
116667.68 1
< 0.1%
101605.25 1
< 0.1%
95185.12 1
< 0.1%
92504.89 1
< 0.1%
89162.35 1
< 0.1%
86769.02 1
< 0.1%
84823.43 1
< 0.1%
83272.84 1
< 0.1%

visit_population_count
Real number (ℝ)

HIGH CORRELATION 

Distinct9994
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24812.655
Minimum147.31
Maximum221903.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T04:53:27.218976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum147.31
5-th percentile2192.761
Q17769.13
median16726.64
Q331663.167
95-th percentile79521.373
Maximum221903.49
Range221756.18
Interquartile range (IQR)23894.037

Descriptive statistics

Standard deviation26032.291
Coefficient of variation (CV)1.0491538
Kurtosis6.8935878
Mean24812.655
Median Absolute Deviation (MAD)10463.515
Skewness2.3248497
Sum2.4812655 × 108
Variance6.7768015 × 108
MonotonicityNot monotonic
2023-12-12T04:53:27.427190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11352.08 2
 
< 0.1%
21048.65 2
 
< 0.1%
2293.61 2
 
< 0.1%
11741.99 2
 
< 0.1%
4266.87 2
 
< 0.1%
8176.13 2
 
< 0.1%
24509.96 1
 
< 0.1%
4964.35 1
 
< 0.1%
12908.2 1
 
< 0.1%
15990.59 1
 
< 0.1%
Other values (9984) 9984
99.8%
ValueCountFrequency (%)
147.31 1
< 0.1%
165.91 1
< 0.1%
177.14 1
< 0.1%
197.84 1
< 0.1%
200.61 1
< 0.1%
226.73 1
< 0.1%
242.94 1
< 0.1%
253.1 1
< 0.1%
253.83 1
< 0.1%
254.33 1
< 0.1%
ValueCountFrequency (%)
221903.49 1
< 0.1%
203208.36 1
< 0.1%
192447.45 1
< 0.1%
182410.16 1
< 0.1%
179438.3 1
< 0.1%
178893.99 1
< 0.1%
178354.29 1
< 0.1%
175434.72 1
< 0.1%
175343.65 1
< 0.1%
173020.83 1
< 0.1%

Interactions

2023-12-12T04:53:22.578010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:20.846584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.438348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:22.026394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:22.759247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.007643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.590420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:22.159467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:23.320831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.191964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.742574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:22.313398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:23.471842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.329659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:21.877012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:53:22.449283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:53:27.597398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_year_monthquarteremd_namesexage_rangetime_rangeresidence_population_countwork_population_countvisit_population_count
base_year_month1.0000.4430.1170.0000.0370.0000.0000.0640.000
quarter0.4431.0000.0510.0000.0130.0000.0000.0460.025
emd_name0.1170.0511.0000.0000.0000.0000.7180.5040.631
sex0.0000.0000.0001.0000.0250.0000.0340.0590.081
age_range0.0370.0130.0000.0251.0000.0000.2840.2570.355
time_range0.0000.0000.0000.0000.0001.0000.1400.1960.191
residence_population_count0.0000.0000.7180.0340.2840.1401.0000.4190.651
work_population_count0.0640.0460.5040.0590.2570.1960.4191.0000.579
visit_population_count0.0000.0250.6310.0810.3550.1910.6510.5791.000
2023-12-12T04:53:27.802428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
age_rangeemd_namequartersextime_range
age_range1.0000.0000.0080.0250.000
emd_name0.0001.0000.0260.0000.000
quarter0.0080.0261.0000.0000.000
sex0.0250.0000.0001.0000.000
time_range0.0000.0000.0000.0001.000
2023-12-12T04:53:27.955014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_year_monthresidence_population_countwork_population_countvisit_population_countquarteremd_namesexage_rangetime_range
base_year_month1.0000.0160.0430.0330.4250.0610.0000.0190.000
residence_population_count0.0161.0000.7140.7070.0000.3390.0260.1330.059
work_population_count0.0430.7141.0000.8880.0290.2070.0590.0850.084
visit_population_count0.0330.7070.8881.0000.0150.2720.0620.1700.081
quarter0.4250.0000.0290.0151.0000.0260.0000.0080.000
emd_name0.0610.3390.2070.2720.0261.0000.0000.0000.000
sex0.0000.0260.0590.0620.0000.0001.0000.0250.000
age_range0.0190.1330.0850.1700.0080.0000.0251.0000.000
time_range0.0000.0590.0840.0810.0000.0000.0000.0001.000

Missing values

2023-12-12T04:53:23.674949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:53:23.917322image/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

base_year_monthquarteremd_namesexage_rangetime_rangeresidence_population_countwork_population_countvisit_population_count
454232018104분기제주시 용담1동여성80세 이상11-12시8147.94428.976518.82
551202018124분기제주시 이호동남성19세 이하23-24시14796.7637.985698.83
313462018073분기제주시 연동남성19세 이하17-18시123525.1826605.45106719.32
538752018124분기제주시 삼도1동여성29세 이하7-8시33935.981863.6817126.99
729832019042분기제주시 용담2동남성49세 이하3-4시65668.563471.425106.53
185182018042분기제주시 조천읍여성69세 이하17-18시47471.263456.2752067.64
498722018114분기제주시 애월읍여성39세 이하5-6시155653.794735.4959793.87
807972019062분기제주시 건입동여성59세 이하7-8시39526.443035.2924981.25
895102019083분기서귀포시 정방동여성29세 이하19-20시2957.96914.9724593.59
716502019042분기제주시 구좌읍여성79세 이하23-24시25530.16614.068225.78
base_year_monthquarteremd_namesexage_rangetime_rangeresidence_population_countwork_population_countvisit_population_count
632232019021분기제주시 삼도2동남성69세 이하15-16시8066.992262.4225462.94
572572019011분기서귀포시 중문동여성39세 이하11-12시25224.446245.127538.26
207202018052분기제주시 구좌읍여성59세 이하17-18시50085.559356.2346898.32
648472019021분기제주시 일도2동여성39세 이하3-4시117685.862866.7523041.03
796132019062분기서귀포시 서홍동남성69세 이하11-12시9881.081130.313474.09
246402018062분기서귀포시 예래동남성39세 이하23-24시12502.743691.2259939.23
599972019011분기제주시 조천읍여성10세 미만15-16시41253.22884.7925922.86
86782018021분기제주시 조천읍남성10세 미만13-14시47492.782280.7223179.61
470562018114분기서귀포시 대정읍남성19세 이하23-24시60093.431800.4915115.26
502882018114분기제주시 용담1동남성69세 이하17-18시10135.061516.5618842.79