Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells191
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.3 KiB
Average record size in memory84.0 B

Variable types

DateTime1
Categorical4
Numeric4

Dataset

Description제주 관광을 위한 날짜별 지역 추천 데이터 매쉬업 결과 정보입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15074779/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
읍면동명 is highly overall correlated with 시도명High correlation
시도명 is highly overall correlated with 읍면동명High correlation
일강수량 has 6761 (67.6%) zerosZeros

Reproduction

Analysis started2023-12-12 04:08:59.483737
Analysis finished2023-12-12 04:09:03.503056
Duration4.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct851
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-01-01 00:00:00
Maximum2020-04-30 00:00:00
2023-12-12T13:09:03.577626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:03.735476image/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
7430 
서귀포시
2570 

Length

Max length4
Median length3
Mean length3.257
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 7430
74.3%
서귀포시 2570
 
25.7%

Length

2023-12-12T13:09:03.919447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:04.038534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 7430
74.3%
서귀포시 2570
 
25.7%

읍면동명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
예래동
2570 
아라동
2527 
조천읍
1843 
삼양동
1578 
화북동
1482 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row삼양동
2nd row예래동
3rd row예래동
4th row조천읍
5th row아라동

Common Values

ValueCountFrequency (%)
예래동 2570
25.7%
아라동 2527
25.3%
조천읍 1843
18.4%
삼양동 1578
15.8%
화북동 1482
14.8%

Length

2023-12-12T13:09:04.170750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:04.302210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
예래동 2570
25.7%
아라동 2527
25.3%
조천읍 1843
18.4%
삼양동 1578
15.8%
화북동 1482
14.8%

평균 기온
Real number (ℝ)

Distinct326
Distinct (%)3.3%
Missing80
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean13.583296
Minimum-6
Maximum29.6
Zeros11
Zeros (%)0.1%
Negative251
Negative (%)2.5%
Memory size166.0 KiB
2023-12-12T13:09:04.484610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile1.4
Q17.4
median13.3
Q319.6
95-th percentile26.8
Maximum29.6
Range35.6
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation7.8080088
Coefficient of variation (CV)0.57482429
Kurtosis-0.8607028
Mean13.583296
Median Absolute Deviation (MAD)6.1
Skewness0.080205896
Sum134746.3
Variance60.965001
MonotonicityNot monotonic
2023-12-12T13:09:04.673174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.8 86
 
0.9%
11.8 82
 
0.8%
12.7 82
 
0.8%
8.7 78
 
0.8%
19.0 77
 
0.8%
13.5 75
 
0.8%
6.9 74
 
0.7%
16.7 74
 
0.7%
9.4 72
 
0.7%
18.2 71
 
0.7%
Other values (316) 9149
91.5%
(Missing) 80
 
0.8%
ValueCountFrequency (%)
-6.0 4
 
< 0.1%
-5.6 3
 
< 0.1%
-5.5 6
 
0.1%
-4.9 2
 
< 0.1%
-4.8 4
 
< 0.1%
-4.4 1
 
< 0.1%
-3.9 8
0.1%
-3.7 3
 
< 0.1%
-3.2 17
0.2%
-3.0 10
0.1%
ValueCountFrequency (%)
29.6 4
 
< 0.1%
29.5 3
 
< 0.1%
29.4 6
 
0.1%
29.2 4
 
< 0.1%
29.1 2
 
< 0.1%
29.0 3
 
< 0.1%
28.9 10
0.1%
28.8 4
 
< 0.1%
28.7 3
 
< 0.1%
28.6 16
0.2%

일강수량
Real number (ℝ)

ZEROS 

Distinct137
Distinct (%)1.4%
Missing27
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean5.0688359
Minimum0
Maximum446
Zeros6761
Zeros (%)67.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:09:04.901008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile24.5
Maximum446
Range446
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.368373
Coefficient of variation (CV)4.4129212
Kurtosis130.935
Mean5.0688359
Median Absolute Deviation (MAD)0
Skewness10.138454
Sum50551.5
Variance500.34411
MonotonicityNot monotonic
2023-12-12T13:09:05.079076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6761
67.6%
0.5 394
 
3.9%
1.0 331
 
3.3%
1.5 220
 
2.2%
2.0 163
 
1.6%
2.5 136
 
1.4%
5.0 134
 
1.3%
3.5 131
 
1.3%
3.0 105
 
1.1%
4.5 100
 
1.0%
Other values (127) 1498
 
15.0%
ValueCountFrequency (%)
0.0 6761
67.6%
0.5 394
 
3.9%
1.0 331
 
3.3%
1.5 220
 
2.2%
2.0 163
 
1.6%
2.5 136
 
1.4%
3.0 105
 
1.1%
3.5 131
 
1.3%
4.0 56
 
0.6%
4.5 100
 
1.0%
ValueCountFrequency (%)
446.0 1
 
< 0.1%
396.0 3
 
< 0.1%
377.0 4
 
< 0.1%
301.0 7
0.1%
258.0 7
0.1%
242.0 2
 
< 0.1%
239.5 5
 
0.1%
227.0 2
 
< 0.1%
217.5 3
 
< 0.1%
207.5 14
0.1%

최대 풍속
Real number (ℝ)

Distinct76
Distinct (%)0.8%
Missing84
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.6211577
Minimum0.5
Maximum12.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:09:05.230770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.6
median2.2
Q33.3
95-th percentile5.6
Maximum12.2
Range11.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.4039897
Coefficient of variation (CV)0.53563726
Kurtosis2.5856731
Mean2.6211577
Median Absolute Deviation (MAD)0.8
Skewness1.3555355
Sum25991.4
Variance1.9711872
MonotonicityNot monotonic
2023-12-12T13:09:05.746333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.9 458
 
4.6%
1.5 446
 
4.5%
2.0 399
 
4.0%
1.8 373
 
3.7%
1.6 373
 
3.7%
1.7 369
 
3.7%
1.4 365
 
3.6%
2.1 349
 
3.5%
1.3 345
 
3.5%
1.1 336
 
3.4%
Other values (66) 6103
61.0%
ValueCountFrequency (%)
0.5 10
 
0.1%
0.6 38
 
0.4%
0.7 59
 
0.6%
0.8 88
 
0.9%
0.9 162
1.6%
1.0 215
2.1%
1.1 336
3.4%
1.2 252
2.5%
1.3 345
3.5%
1.4 365
3.6%
ValueCountFrequency (%)
12.2 7
 
0.1%
9.1 1
 
< 0.1%
9.0 4
 
< 0.1%
8.4 11
0.1%
8.1 6
 
0.1%
7.8 9
0.1%
7.5 7
 
0.1%
7.4 4
 
< 0.1%
7.3 13
0.1%
7.1 18
0.2%

업종명
Categorical

Distinct39
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중식 음식점업
 
375
육류 소매업
 
375
한식 음식점업
 
374
일식 음식점업
 
362
차량용 주유소 운영업
 
361
Other values (34)
8153 

Length

Max length23
Median length15
Mean length9.2468
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일식 음식점업
2nd row기타음식료품위주종합소매업
3rd row전시 및 행사 대행업
4th row피자, 햄버거, 샌드위치 및 유사 음식점업
5th row슈퍼마켓

Common Values

ValueCountFrequency (%)
중식 음식점업 375
 
3.8%
육류 소매업 375
 
3.8%
한식 음식점업 374
 
3.7%
일식 음식점업 362
 
3.6%
차량용 주유소 운영업 361
 
3.6%
욕탕업 358
 
3.6%
비알콜 음료점업 358
 
3.6%
슈퍼마켓 357
 
3.6%
차량용 가스 충전업 355
 
3.5%
빵 및 과자류 소매업 355
 
3.5%
Other values (29) 6370
63.7%

Length

2023-12-12T13:09:05.945290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소매업 2723
 
10.4%
및 2346
 
9.0%
음식점업 2135
 
8.2%
기타 1166
 
4.5%
운영업 810
 
3.1%
차량용 716
 
2.7%
임대업 443
 
1.7%
종합 430
 
1.6%
주점업 423
 
1.6%
그외 382
 
1.5%
Other values (56) 14621
55.8%

이용금액
Real number (ℝ)

Distinct2991
Distinct (%)29.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5000578 × 108
Minimum5000
Maximum3.949453 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:09:06.107983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5000
5-th percentile439325
Q19523059
median34117651
Q31.3687526 × 108
95-th percentile6.5820366 × 108
Maximum3.949453 × 109
Range3.949448 × 109
Interquartile range (IQR)1.273522 × 108

Descriptive statistics

Standard deviation3.3145483 × 108
Coefficient of variation (CV)2.2096137
Kurtosis34.340052
Mean1.5000578 × 108
Median Absolute Deviation (MAD)31129293
Skewness5.0403715
Sum1.5000578 × 1012
Variance1.098623 × 1017
MonotonicityNot monotonic
2023-12-12T13:09:06.256933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000000 25
 
0.2%
400000 17
 
0.2%
200000 14
 
0.1%
300000 12
 
0.1%
40458800 12
 
0.1%
41642500 12
 
0.1%
210000 12
 
0.1%
90000 11
 
0.1%
2050000 10
 
0.1%
150000 10
 
0.1%
Other values (2981) 9865
98.7%
ValueCountFrequency (%)
5000 7
0.1%
8000 8
0.1%
10000 3
 
< 0.1%
10370 5
0.1%
14000 4
< 0.1%
28500 4
< 0.1%
30000 2
 
< 0.1%
32400 4
< 0.1%
32500 3
 
< 0.1%
32800 5
0.1%
ValueCountFrequency (%)
3949453004 3
< 0.1%
3694513854 5
0.1%
3690087220 3
< 0.1%
3071502452 3
< 0.1%
3026341667 2
 
< 0.1%
2922570364 2
 
< 0.1%
2896639344 2
 
< 0.1%
2738203580 3
< 0.1%
2708190368 2
 
< 0.1%
2691636612 2
 
< 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

2023-12-12T13:09:06.436945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:06.595826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-15 10000
100.0%

Interactions

2023-12-12T13:09:02.480261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:00.797740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.406956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.907534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.667780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:00.958066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.538218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.041661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.810920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.113031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.660549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.158484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.948225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.249673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:01.789535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:09:02.302827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:09:06.678289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명읍면동명평균 기온일강수량최대 풍속업종명이용금액
시도명1.0001.0000.1950.0640.3400.3990.200
읍면동명1.0001.0000.2810.0970.3540.5060.204
평균 기온0.1950.2811.0000.1800.3360.0820.088
일강수량0.0640.0970.1801.0000.8590.0000.035
최대 풍속0.3400.3540.3360.8591.0000.1230.000
업종명0.3990.5060.0820.0000.1231.0000.772
이용금액0.2000.2040.0880.0350.0000.7721.000
2023-12-12T13:09:06.841946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명시도명업종명
읍면동명1.0001.0000.261
시도명1.0001.0000.335
업종명0.2610.3351.000
2023-12-12T13:09:06.981119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균 기온일강수량최대 풍속이용금액시도명읍면동명업종명
평균 기온1.0000.091-0.2910.0140.1480.1190.028
일강수량0.0911.0000.087-0.0010.0630.0560.000
최대 풍속-0.2910.0871.0000.0090.3390.2130.045
이용금액0.014-0.0010.0091.0000.2000.1180.408
시도명0.1480.0630.3390.2001.0001.0000.335
읍면동명0.1190.0560.2130.1181.0001.0000.261
업종명0.0280.0000.0450.4080.3350.2611.000

Missing values

2023-12-12T13:09:03.125699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:09:03.310181image/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.
2023-12-12T13:09:03.440933image/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

일자시도명읍면동명평균 기온일강수량최대 풍속업종명이용금액데이터기준일자
764042018-09-01제주시삼양동23.85.02.2일식 음식점업353226002020-12-15
217012020-01-23서귀포시예래동12.80.00.9기타음식료품위주종합소매업2778502020-12-15
38762018-02-11서귀포시예래동1.50.03.9전시 및 행사 대행업927871502020-12-15
551322018-12-23제주시조천읍7.79.04.6피자, 햄버거, 샌드위치 및 유사 음식점업351648102020-12-15
323102018-07-18제주시아라동24.50.02.0슈퍼마켓6883613032020-12-15
167642019-05-02서귀포시예래동19.40.03.0과실 및 채소 소매업135753702020-12-15
570782018-01-15제주시화북동9.30.02.8그외 기타 종합 소매업10000002020-12-15
52822018-04-01서귀포시예래동18.00.01.0기타 갬블링 및 베팅업20000002020-12-15
376792019-01-27제주시아라동1.30.01.5차량용 가스 충전업306963082020-12-15
274452018-01-18제주시아라동4.50.01.9육류 소매업985859862020-12-15
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