Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Numeric4
Categorical4

Dataset

Description제주도 장기체류 외국인 유동인구 구성 비율 - 거주인구, 근무인구, 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/567

Alerts

base_year_month is highly overall correlated with quarterHigh correlation
resd_pop_cnt is highly overall correlated with work_pop_cnt and 1 other fieldsHigh correlation
work_pop_cnt is highly overall correlated with resd_pop_cnt and 1 other fieldsHigh correlation
visit_pop_cnt is highly overall correlated with resd_pop_cnt and 1 other fieldsHigh correlation
quarter is highly overall correlated with base_year_monthHigh correlation
work_pop_cnt is highly skewed (γ1 = 28.93353538)Skewed
resd_pop_cnt has 5648 (56.5%) zerosZeros
work_pop_cnt has 7887 (78.9%) zerosZeros
visit_pop_cnt has 193 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-11 20:12:44.564712
Analysis finished2023-12-11 20:12:47.654311
Duration3.09 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

base_year_month
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201804.61
Minimum201801
Maximum201809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:12:47.700074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201801
5-th percentile201801
Q1201803
median201805
Q3201807
95-th percentile201808
Maximum201809
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.310879
Coefficient of variation (CV)1.1451071 × 10-5
Kurtosis-1.2380037
Mean201804.61
Median Absolute Deviation (MAD)2
Skewness-0.038320566
Sum2.0180461 × 109
Variance5.3401615
MonotonicityNot monotonic
2023-12-12T05:12:47.815276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
201808 1347
13.5%
201806 1298
13.0%
201807 1280
12.8%
201802 1224
12.2%
201804 1218
12.2%
201805 1213
12.1%
201803 1212
12.1%
201801 1170
11.7%
201809 38
 
0.4%
ValueCountFrequency (%)
201801 1170
11.7%
201802 1224
12.2%
201803 1212
12.1%
201804 1218
12.2%
201805 1213
12.1%
201806 1298
13.0%
201807 1280
12.8%
201808 1347
13.5%
201809 38
 
0.4%
ValueCountFrequency (%)
201809 38
 
0.4%
201808 1347
13.5%
201807 1280
12.8%
201806 1298
13.0%
201805 1213
12.1%
201804 1218
12.2%
201803 1212
12.1%
201802 1224
12.2%
201801 1170
11.7%

quarter
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2분기
3729 
1분기
3606 
3분기
2665 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1분기
2nd row1분기
3rd row3분기
4th row3분기
5th row2분기

Common Values

ValueCountFrequency (%)
2분기 3729
37.3%
1분기 3606
36.1%
3분기 2665
26.7%

Length

2023-12-12T05:12:48.011554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T05:12:48.174164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2분기 3729
37.3%
1분기 3606
36.1%
3분기 2665
26.7%

nationality
Categorical

Distinct30
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기타
 
460
중국
 
448
인도네시아
 
440
스리랑카
 
433
필리핀
 
433
Other values (25)
7786 

Length

Max length7
Median length6
Mean length3.246
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row파키스탄
2nd row오스트레일리아
3rd row필리핀
4th row파키스탄
5th row캄보디아

Common Values

ValueCountFrequency (%)
기타 460
 
4.6%
중국 448
 
4.5%
인도네시아 440
 
4.4%
스리랑카 433
 
4.3%
필리핀 433
 
4.3%
베트남 423
 
4.2%
미국 419
 
4.2%
캄보디아 408
 
4.1%
미얀마 402
 
4.0%
네팔 399
 
4.0%
Other values (20) 5735
57.4%

Length

2023-12-12T05:12:48.279380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 460
 
4.6%
중국 448
 
4.5%
인도네시아 440
 
4.4%
스리랑카 433
 
4.3%
필리핀 433
 
4.3%
베트남 423
 
4.2%
미국 419
 
4.2%
캄보디아 408
 
4.1%
미얀마 402
 
4.0%
네팔 399
 
4.0%
Other values (20) 5735
57.4%

time_range
Categorical

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
17-18시
906 
19-20시
896 
13-14시
885 
11-12시
879 
21-22시
878 
Other values (7)
5556 

Length

Max length6
Median length6
Mean length5.3169
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3-4시
2nd row3-4시
3rd row21-22시
4th row19-20시
5th row1-2시

Common Values

ValueCountFrequency (%)
17-18시 906
9.1%
19-20시 896
9.0%
13-14시 885
8.8%
11-12시 879
8.8%
21-22시 878
8.8%
15-16시 876
8.8%
23-24시 861
8.6%
9-10시 807
8.1%
7-8시 785
7.8%
1-2시 778
7.8%
Other values (2) 1449
14.5%

Length

2023-12-12T05:12:48.394068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17-18시 906
9.1%
19-20시 896
9.0%
13-14시 885
8.8%
11-12시 879
8.8%
21-22시 878
8.8%
15-16시 876
8.8%
23-24시 861
8.6%
9-10시 807
8.1%
7-8시 785
7.8%
1-2시 778
7.8%
Other values (2) 1449
14.5%

emd_name
Categorical

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제주시 이도2동
 
294
서귀포시 예래동
 
289
제주시 애월읍
 
289
제주시 한림읍
 
278
제주시 용담2동
 
276
Other values (38)
8574 

Length

Max length8
Median length8
Mean length7.5483
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서귀포시 성산읍
2nd row서귀포시 대륜동
3rd row제주시 구좌읍
4th row서귀포시 중앙동
5th row제주시 오라동

Common Values

ValueCountFrequency (%)
제주시 이도2동 294
 
2.9%
서귀포시 예래동 289
 
2.9%
제주시 애월읍 289
 
2.9%
제주시 한림읍 278
 
2.8%
제주시 용담2동 276
 
2.8%
제주시 노형동 269
 
2.7%
서귀포시 안덕면 268
 
2.7%
서귀포시 대정읍 268
 
2.7%
서귀포시 중문동 267
 
2.7%
제주시 연동 260
 
2.6%
Other values (33) 7242
72.4%

Length

2023-12-12T05:12:48.520538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제주시 6124
30.6%
서귀포시 3876
19.4%
이도2동 294
 
1.5%
예래동 289
 
1.4%
애월읍 289
 
1.4%
한림읍 278
 
1.4%
용담2동 276
 
1.4%
노형동 269
 
1.3%
안덕면 268
 
1.3%
대정읍 268
 
1.3%
Other values (35) 7769
38.8%

resd_pop_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4296
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean889.19295
Minimum0
Maximum146927.73
Zeros5648
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:12:48.639721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3467.3425
95-th percentile3293.607
Maximum146927.73
Range146927.73
Interquartile range (IQR)467.3425

Descriptive statistics

Standard deviation4957.5641
Coefficient of variation (CV)5.5753525
Kurtosis435.80179
Mean889.19295
Median Absolute Deviation (MAD)0
Skewness18.599419
Sum8891929.5
Variance24577442
MonotonicityNot monotonic
2023-12-12T05:12:48.767256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5648
56.5%
406.56 4
 
< 0.1%
430.46 3
 
< 0.1%
182.8 2
 
< 0.1%
812.97 2
 
< 0.1%
338.08 2
 
< 0.1%
321.38 2
 
< 0.1%
913.06 2
 
< 0.1%
1035.55 2
 
< 0.1%
364.58 2
 
< 0.1%
Other values (4286) 4331
43.3%
ValueCountFrequency (%)
0.0 5648
56.5%
4.71 1
 
< 0.1%
4.92 1
 
< 0.1%
5.31 1
 
< 0.1%
5.43 1
 
< 0.1%
5.59 1
 
< 0.1%
5.66 1
 
< 0.1%
6.13 1
 
< 0.1%
8.27 1
 
< 0.1%
8.97 1
 
< 0.1%
ValueCountFrequency (%)
146927.73 1
< 0.1%
144690.55 1
< 0.1%
137079.01 1
< 0.1%
131508.93 1
< 0.1%
129926.29 1
< 0.1%
116135.87 1
< 0.1%
114594.64 1
< 0.1%
99959.21 1
< 0.1%
99293.79 1
< 0.1%
95206.99 1
< 0.1%

work_pop_cnt
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2060
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.893041
Minimum0
Maximum34277.44
Zeros7887
Zeros (%)78.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:12:48.938127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile433.474
Maximum34277.44
Range34277.44
Interquartile range (IQR)0

Descriptive statistics

Standard deviation714.14088
Coefficient of variation (CV)7.2213462
Kurtosis1163.143
Mean98.893041
Median Absolute Deviation (MAD)0
Skewness28.933535
Sum988930.41
Variance509997.2
MonotonicityNot monotonic
2023-12-12T05:12:49.090701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7887
78.9%
138.48 2
 
< 0.1%
26.03 2
 
< 0.1%
15.26 2
 
< 0.1%
15.25 2
 
< 0.1%
155.24 2
 
< 0.1%
76.17 2
 
< 0.1%
137.49 2
 
< 0.1%
173.4 2
 
< 0.1%
814.24 2
 
< 0.1%
Other values (2050) 2095
 
20.9%
ValueCountFrequency (%)
0.0 7887
78.9%
3.92 1
 
< 0.1%
3.98 1
 
< 0.1%
4.8 1
 
< 0.1%
4.9 1
 
< 0.1%
4.98 1
 
< 0.1%
5.01 1
 
< 0.1%
5.06 1
 
< 0.1%
5.4 1
 
< 0.1%
5.43 1
 
< 0.1%
ValueCountFrequency (%)
34277.44 1
< 0.1%
33646.5 1
< 0.1%
21367.71 1
< 0.1%
15850.32 1
< 0.1%
13170.68 1
< 0.1%
12003.88 1
< 0.1%
10971.36 1
< 0.1%
10470.34 1
< 0.1%
10407.33 1
< 0.1%
10022.43 1
< 0.1%

visit_pop_cnt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7843
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean473.70274
Minimum0
Maximum36201.66
Zeros193
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T05:12:49.488167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6985
Q118.8575
median66.655
Q3254.085
95-th percentile2264.099
Maximum36201.66
Range36201.66
Interquartile range (IQR)235.2275

Descriptive statistics

Standard deviation1628.6692
Coefficient of variation (CV)3.4381671
Kurtosis121.84368
Mean473.70274
Median Absolute Deviation (MAD)57.805
Skewness9.2651513
Sum4737027.4
Variance2652563.4
MonotonicityNot monotonic
2023-12-12T05:12:49.613594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 193
 
1.9%
12.09 10
 
0.1%
8.45 9
 
0.1%
4.06 8
 
0.1%
7.11 8
 
0.1%
15.16 7
 
0.1%
7.06 6
 
0.1%
5.05 6
 
0.1%
11.38 6
 
0.1%
17.33 6
 
0.1%
Other values (7833) 9741
97.4%
ValueCountFrequency (%)
0.0 193
1.9%
2.79 1
 
< 0.1%
2.8 1
 
< 0.1%
2.83 1
 
< 0.1%
2.84 2
 
< 0.1%
2.87 1
 
< 0.1%
2.88 1
 
< 0.1%
2.89 1
 
< 0.1%
2.9 1
 
< 0.1%
2.98 1
 
< 0.1%
ValueCountFrequency (%)
36201.66 1
< 0.1%
30182.62 1
< 0.1%
29126.07 1
< 0.1%
28799.61 1
< 0.1%
28088.52 1
< 0.1%
27940.33 1
< 0.1%
27103.02 1
< 0.1%
25718.96 1
< 0.1%
24098.37 1
< 0.1%
23279.88 1
< 0.1%

Interactions

2023-12-12T05:12:46.960782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:45.582850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.052546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.531680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:47.051784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:45.704490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.167781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.671332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:47.151419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:45.816930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.290998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.785694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:47.284474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:45.909068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.414361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T05:12:46.871847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T05:12:49.733386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_year_monthquarternationalitytime_rangeemd_nameresd_pop_cntwork_pop_cntvisit_pop_cnt
base_year_month1.0001.0000.2870.0000.0000.0000.0000.044
quarter1.0001.0000.1200.0000.0000.0000.0150.000
nationality0.2870.1201.0000.0000.1900.3300.1510.467
time_range0.0000.0000.0001.0000.0000.0200.0120.032
emd_name0.0000.0000.1900.0001.0000.2760.1380.260
resd_pop_cnt0.0000.0000.3300.0200.2761.0000.8140.922
work_pop_cnt0.0000.0150.1510.0120.1380.8141.0000.787
visit_pop_cnt0.0440.0000.4670.0320.2600.9220.7871.000
2023-12-12T05:12:49.869974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
nationalityemd_nametime_rangequarter
nationality1.0000.0420.0000.055
emd_name0.0421.0000.0000.000
time_range0.0000.0001.0000.000
quarter0.0550.0000.0001.000
2023-12-12T05:12:49.986472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_year_monthresd_pop_cntwork_pop_cntvisit_pop_cntquarternationalitytime_rangeemd_name
base_year_month1.000-0.0180.0020.0331.0000.1030.0000.000
resd_pop_cnt-0.0181.0000.5310.5890.0000.1110.0080.098
work_pop_cnt0.0020.5311.0000.5120.0100.0630.0060.056
visit_pop_cnt0.0330.5890.5121.0000.0000.1660.0130.092
quarter1.0000.0000.0100.0001.0000.0550.0000.000
nationality0.1030.1110.0630.1660.0551.0000.0000.042
time_range0.0000.0080.0060.0130.0000.0001.0000.000
emd_name0.0000.0980.0560.0920.0000.0420.0001.000

Missing values

2023-12-12T05:12:47.450742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T05:12:47.590636image/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_monthquarternationalitytime_rangeemd_nameresd_pop_cntwork_pop_cntvisit_pop_cnt
331482018031분기파키스탄3-4시서귀포시 성산읍0.00.048.44
54952018011분기오스트레일리아3-4시서귀포시 대륜동475.21141.990.0
947222018083분기필리핀21-22시제주시 구좌읍965.28106.98192.47
813672018073분기파키스탄19-20시서귀포시 중앙동0.00.07.42
424672018042분기캄보디아1-2시제주시 오라동0.00.024.77
891632018083분기인도15-16시제주시 노형동0.00.0317.85
204932018021분기키르기스스탄7-8시제주시 아라동0.00.015.21
717872018073분기독일17-18시제주시 애월읍0.015.1454.47
103022018011분기파키스탄7-8시서귀포시 송산동0.00.018.09
317342018031분기캐나다9-10시제주시 일도2동84.260.032.47
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837192018083분기뉴질랜드11-12시제주시 아라동0.00.051.76
320002018031분기타이완1-2시서귀포시 영천동0.00.018.94
735822018073분기미국5-6시서귀포시 송산동812.970.0246.89
726502018073분기말레이시아5-6시서귀포시 대정읍0.00.0259.53