Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

DateTime1
Categorical3
Numeric10

Dataset

Description제주도 내 효율적인 교통량 측정을 위한 날씨유동인구 활용 교통량 데이터 매쉬업 결과 정보입니다.
Author제주특별자치도
URLhttps://www.data.go.kr/data/15074777/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
읍면동명 is highly overall correlated with 거주인구 and 6 other fieldsHigh correlation
시도명 is highly overall correlated with 교통량 and 2 other fieldsHigh correlation
거주인구 is highly overall correlated with 근무인구 and 3 other fieldsHigh correlation
근무인구 is highly overall correlated with 거주인구 and 2 other fieldsHigh correlation
방문인구 is highly overall correlated with 거주인구 and 3 other fieldsHigh correlation
총 유동인구 is highly overall correlated with 거주인구 and 3 other fieldsHigh correlation
교통량 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
평균 속도 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
평균 소요 시간 is highly overall correlated with 읍면동명High correlation
거주인구 has unique valuesUnique
방문인구 has unique valuesUnique
총 유동인구 has unique valuesUnique
교통량 has 4151 (41.5%) zerosZeros
일강수량 has 6552 (65.5%) zerosZeros

Reproduction

Analysis started2023-12-12 17:30:08.068555
Analysis finished2023-12-12 17:30:26.273908
Duration18.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct803
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2018-01-01 00:00:00
Maximum2020-04-30 00:00:00
2023-12-13T02:30:26.365922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:26.564155image/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
6026 
서귀포시
3974 

Length

Max length4
Median length3
Mean length3.3974
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 6026
60.3%
서귀포시 3974
39.7%

Length

2023-12-13T02:30:26.753267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:30:26.888323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 6026
60.3%
서귀포시 3974
39.7%

읍면동명
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
건입동
 
274
중앙동
 
267
표선면
 
265
서홍동
 
261
영천동
 
260
Other values (36)
8673 

Length

Max length4
Median length3
Mean length3.1728
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안덕면
2nd row중앙동
3rd row화북동
4th row구좌읍
5th row화북동

Common Values

ValueCountFrequency (%)
건입동 274
 
2.7%
중앙동 267
 
2.7%
표선면 265
 
2.6%
서홍동 261
 
2.6%
영천동 260
 
2.6%
한경면 260
 
2.6%
일도1동 260
 
2.6%
애월읍 259
 
2.6%
용담1동 259
 
2.6%
외도동 258
 
2.6%
Other values (31) 7377
73.8%

Length

2023-12-13T02:30:27.020879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건입동 274
 
2.7%
중앙동 267
 
2.7%
표선면 265
 
2.6%
서홍동 261
 
2.6%
영천동 260
 
2.6%
한경면 260
 
2.6%
일도1동 260
 
2.6%
애월읍 259
 
2.6%
용담1동 259
 
2.6%
예래동 258
 
2.6%
Other values (31) 7377
73.8%

거주인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315149.73
Minimum14605.033
Maximum1353560.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:27.183474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14605.033
5-th percentile34600.955
Q193969.997
median219517.05
Q3408486.02
95-th percentile1062531
Maximum1353560.5
Range1338955.5
Interquartile range (IQR)314516.02

Descriptive statistics

Standard deviation297551.98
Coefficient of variation (CV)0.94416068
Kurtosis1.7566872
Mean315149.73
Median Absolute Deviation (MAD)139701.36
Skewness1.5628828
Sum3.1514973 × 109
Variance8.8537181 × 1010
MonotonicityNot monotonic
2023-12-13T02:30:27.397834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
365838.311 1
 
< 0.1%
139635.134 1
 
< 0.1%
149807.26 1
 
< 0.1%
71253.003 1
 
< 0.1%
720394.908 1
 
< 0.1%
431159.264 1
 
< 0.1%
723820.331 1
 
< 0.1%
1201106.583 1
 
< 0.1%
175201.99 1
 
< 0.1%
195270.133 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
14605.033 1
< 0.1%
14854.496 1
< 0.1%
15332.318 1
< 0.1%
15873.917 1
< 0.1%
16216.554 1
< 0.1%
22571.971 1
< 0.1%
23074.648 1
< 0.1%
23385.263 1
< 0.1%
23673.553 1
< 0.1%
23721.727 1
< 0.1%
ValueCountFrequency (%)
1353560.51 1
< 0.1%
1321818.779 1
< 0.1%
1311308.286 1
< 0.1%
1309750.382 1
< 0.1%
1298452.018 1
< 0.1%
1294326.527 1
< 0.1%
1291794.539 1
< 0.1%
1289917.591 1
< 0.1%
1286022.99 1
< 0.1%
1283918.568 1
< 0.1%

근무인구
Real number (ℝ)

HIGH CORRELATION 

Distinct9998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35451.758
Minimum2214.526
Maximum269482.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:27.586435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2214.526
5-th percentile5320.6797
Q112152.35
median21513.953
Q340017.685
95-th percentile139760.04
Maximum269482.98
Range267268.46
Interquartile range (IQR)27865.335

Descriptive statistics

Standard deviation40625.777
Coefficient of variation (CV)1.1459453
Kurtosis6.6342498
Mean35451.758
Median Absolute Deviation (MAD)12224.87
Skewness2.5324614
Sum3.5451758 × 108
Variance1.6504537 × 109
MonotonicityNot monotonic
2023-12-13T02:30:27.756404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9135.679 2
 
< 0.1%
7187.696 2
 
< 0.1%
24309.915 1
 
< 0.1%
6132.265 1
 
< 0.1%
17745.743 1
 
< 0.1%
7753.982 1
 
< 0.1%
93537.145 1
 
< 0.1%
36684.579 1
 
< 0.1%
90302.036 1
 
< 0.1%
106271.765 1
 
< 0.1%
Other values (9988) 9988
99.9%
ValueCountFrequency (%)
2214.526 1
< 0.1%
2354.551 1
< 0.1%
2421.638 1
< 0.1%
2675.783 1
< 0.1%
2687.593 1
< 0.1%
2702.247 1
< 0.1%
2710.019 1
< 0.1%
2728.161 1
< 0.1%
2739.44 1
< 0.1%
2759.588 1
< 0.1%
ValueCountFrequency (%)
269482.981 1
< 0.1%
264575.667 1
< 0.1%
263476.965 1
< 0.1%
260353.984 1
< 0.1%
247992.595 1
< 0.1%
247636.849 1
< 0.1%
246885.476 1
< 0.1%
246546.995 1
< 0.1%
242194.806 1
< 0.1%
242172.987 1
< 0.1%

방문인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean195287.86
Minimum23696.639
Maximum786387.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:27.912911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23696.639
5-th percentile62729.79
Q1100001.75
median151819.39
Q3235088.25
95-th percentile550146.34
Maximum786387.11
Range762690.47
Interquartile range (IQR)135086.5

Descriptive statistics

Standard deviation140496.46
Coefficient of variation (CV)0.71943263
Kurtosis1.9720549
Mean195287.86
Median Absolute Deviation (MAD)59014.603
Skewness1.6065768
Sum1.9528786 × 109
Variance1.9739254 × 1010
MonotonicityNot monotonic
2023-12-13T02:30:28.115767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157334.627 1
 
< 0.1%
67240.068 1
 
< 0.1%
151255.622 1
 
< 0.1%
66584.578 1
 
< 0.1%
528285.051 1
 
< 0.1%
305184.588 1
 
< 0.1%
498868.101 1
 
< 0.1%
549355.426 1
 
< 0.1%
107798.897 1
 
< 0.1%
127680.389 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
23696.639 1
< 0.1%
24033.742 1
< 0.1%
24265.812 1
< 0.1%
25222.145 1
< 0.1%
26081.43 1
< 0.1%
26177.85 1
< 0.1%
26316.382 1
< 0.1%
27686.892 1
< 0.1%
28055.774 1
< 0.1%
28086.936 1
< 0.1%
ValueCountFrequency (%)
786387.113 1
< 0.1%
784724.994 1
< 0.1%
744016.313 1
< 0.1%
723459.209 1
< 0.1%
719913.93 1
< 0.1%
697153.953 1
< 0.1%
696648.625 1
< 0.1%
689158.1 1
< 0.1%
685949.34 1
< 0.1%
684180.515 1
< 0.1%

총 유동인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean545889.34
Minimum41326.368
Maximum2079277.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:28.351465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41326.368
5-th percentile123904.51
Q1220055.12
median383508.64
Q3637775.08
95-th percentile1768368.9
Maximum2079277.9
Range2037951.5
Interquartile range (IQR)417719.96

Descriptive statistics

Standard deviation460034.53
Coefficient of variation (CV)0.84272488
Kurtosis1.9371481
Mean545889.34
Median Absolute Deviation (MAD)202714.94
Skewness1.6284267
Sum5.4588934 × 109
Variance2.1163177 × 1011
MonotonicityNot monotonic
2023-12-13T02:30:28.544964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
547482.853 1
 
< 0.1%
218057.325 1
 
< 0.1%
318808.625 1
 
< 0.1%
145591.562 1
 
< 0.1%
1342217.104 1
 
< 0.1%
773028.431 1
 
< 0.1%
1312990.468 1
 
< 0.1%
1856733.774 1
 
< 0.1%
297241.743 1
 
< 0.1%
340509.2 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
41326.368 1
< 0.1%
41848.469 1
< 0.1%
41925.107 1
< 0.1%
43653.225 1
< 0.1%
44324.483 1
< 0.1%
63661.887 1
< 0.1%
76564.011 1
< 0.1%
80211.507 1
< 0.1%
80302.216 1
< 0.1%
80539.751 1
< 0.1%
ValueCountFrequency (%)
2079277.89 1
< 0.1%
2074921.837 1
< 0.1%
2066483.867 1
< 0.1%
2039684.911 1
< 0.1%
2027294.424 1
< 0.1%
2026003.987 1
< 0.1%
2024612.727 1
< 0.1%
2020632.094 1
< 0.1%
2018383.839 1
< 0.1%
2017277.955 1
< 0.1%

교통량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct5517
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean377.4355
Minimum0
Maximum2261.111
Zeros4151
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:28.722440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median45.5845
Q3635.552
95-th percentile1548.3879
Maximum2261.111
Range2261.111
Interquartile range (IQR)635.552

Descriptive statistics

Standard deviation535.26257
Coefficient of variation (CV)1.4181564
Kurtosis0.88510487
Mean377.4355
Median Absolute Deviation (MAD)45.5845
Skewness1.3807015
Sum3774355
Variance286506.02
MonotonicityNot monotonic
2023-12-13T02:30:29.176211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4151
41.5%
0.143 14
 
0.1%
0.3329999999999999 11
 
0.1%
0.2 9
 
0.1%
0.1 7
 
0.1%
0.111 7
 
0.1%
1.0 6
 
0.1%
0.053 6
 
0.1%
0.667 6
 
0.1%
0.222 5
 
0.1%
Other values (5507) 5778
57.8%
ValueCountFrequency (%)
0.0 4151
41.5%
0.027 2
 
< 0.1%
0.0289999999999999 1
 
< 0.1%
0.031 5
 
0.1%
0.036 2
 
< 0.1%
0.048 2
 
< 0.1%
0.05 1
 
< 0.1%
0.053 6
 
0.1%
0.054 2
 
< 0.1%
0.059 2
 
< 0.1%
ValueCountFrequency (%)
2261.111 1
< 0.1%
2260.611 1
< 0.1%
2256.5 1
< 0.1%
2237.278 1
< 0.1%
2213.056 1
< 0.1%
2210.667 1
< 0.1%
2210.111 1
< 0.1%
2207.5 1
< 0.1%
2206.444 1
< 0.1%
2196.889 1
< 0.1%

평균 속도
Real number (ℝ)

HIGH CORRELATION 

Distinct3450
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.072892
Minimum23.667
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:29.345128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.667
5-th percentile27.75
Q134.25
median39.665
Q349.056
95-th percentile55.412
Maximum95
Range71.333
Interquartile range (IQR)14.806

Descriptive statistics

Standard deviation8.7225911
Coefficient of variation (CV)0.21236856
Kurtosis-0.85471271
Mean41.072892
Median Absolute Deviation (MAD)7.416
Skewness0.19986171
Sum410728.92
Variance76.083595
MonotonicityNot monotonic
2023-12-13T02:30:29.504078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.0 49
 
0.5%
35.0 49
 
0.5%
28.0 48
 
0.5%
38.0 45
 
0.4%
31.0 43
 
0.4%
30.0 42
 
0.4%
32.0 41
 
0.4%
34.5 40
 
0.4%
27.0 40
 
0.4%
34.0 40
 
0.4%
Other values (3440) 9563
95.6%
ValueCountFrequency (%)
23.667 1
 
< 0.1%
23.909 1
 
< 0.1%
24.111 2
 
< 0.1%
24.333 1
 
< 0.1%
24.444000000000003 1
 
< 0.1%
24.556 1
 
< 0.1%
24.667 4
< 0.1%
24.778 2
 
< 0.1%
24.889 2
 
< 0.1%
25.0 5
0.1%
ValueCountFrequency (%)
95.0 1
 
< 0.1%
78.0 1
 
< 0.1%
69.0 1
 
< 0.1%
61.5 1
 
< 0.1%
61.25 1
 
< 0.1%
61.143 1
 
< 0.1%
61.0 1
 
< 0.1%
60.75 3
< 0.1%
60.714 1
 
< 0.1%
60.5 4
< 0.1%

평균 소요 시간
Real number (ℝ)

HIGH CORRELATION 

Distinct4118
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.206865
Minimum13.167
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:29.678307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.167
5-th percentile18
Q127.78625
median34.545
Q346.25425
95-th percentile60.725
Maximum112
Range98.833
Interquartile range (IQR)18.468

Descriptive statistics

Standard deviation12.982804
Coefficient of variation (CV)0.34893573
Kurtosis-0.30719996
Mean37.206865
Median Absolute Deviation (MAD)9.545
Skewness0.4376595
Sum372068.65
Variance168.55321
MonotonicityNot monotonic
2023-12-13T02:30:29.822963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 44
 
0.4%
29.0 39
 
0.4%
17.0 31
 
0.3%
17.333 31
 
0.3%
28.0 30
 
0.3%
16.667 29
 
0.3%
23.0 28
 
0.3%
27.0 27
 
0.3%
32.0 27
 
0.3%
22.75 27
 
0.3%
Other values (4108) 9687
96.9%
ValueCountFrequency (%)
13.167 1
 
< 0.1%
13.333 3
 
< 0.1%
13.5 1
 
< 0.1%
13.667 11
0.1%
13.833 1
 
< 0.1%
14.0 7
0.1%
14.167 4
 
< 0.1%
14.25 3
 
< 0.1%
14.333 10
0.1%
14.5 2
 
< 0.1%
ValueCountFrequency (%)
112.0 1
< 0.1%
97.0 1
< 0.1%
91.6 1
< 0.1%
91.1 1
< 0.1%
89.0 1
< 0.1%
83.6 1
< 0.1%
82.6 1
< 0.1%
81.8 1
< 0.1%
81.6 1
< 0.1%
80.8 1
< 0.1%

평균 기온
Real number (ℝ)

Distinct1334
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.574444
Minimum-9.9
Maximum30.4
Zeros25
Zeros (%)0.2%
Negative303
Negative (%)3.0%
Memory size166.0 KiB
2023-12-13T02:30:30.002442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9.9
5-th percentile1.1
Q17.5
median13.3
Q319.8
95-th percentile26.3
Maximum30.4
Range40.3
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation7.8237859
Coefficient of variation (CV)0.57636144
Kurtosis-0.80897956
Mean13.574444
Median Absolute Deviation (MAD)6.2
Skewness-0.02733915
Sum135744.44
Variance61.211627
MonotonicityNot monotonic
2023-12-13T02:30:30.211367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.0 68
 
0.7%
10.1 56
 
0.6%
8.8 52
 
0.5%
18.6 50
 
0.5%
23.1 50
 
0.5%
13.9 49
 
0.5%
16.9 48
 
0.5%
15.5 48
 
0.5%
7.6 47
 
0.5%
8.0 47
 
0.5%
Other values (1324) 9485
94.8%
ValueCountFrequency (%)
-9.9 1
< 0.1%
-9.4 1
< 0.1%
-9.25 1
< 0.1%
-9.1 1
< 0.1%
-9.0 1
< 0.1%
-8.9 1
< 0.1%
-8.5 1
< 0.1%
-8.4 1
< 0.1%
-8.2 1
< 0.1%
-7.632999999999999 1
< 0.1%
ValueCountFrequency (%)
30.4 2
< 0.1%
30.2 2
< 0.1%
29.9 1
< 0.1%
29.8 1
< 0.1%
29.6 2
< 0.1%
29.5 1
< 0.1%
29.45 1
< 0.1%
29.3 2
< 0.1%
29.25 1
< 0.1%
29.233 1
< 0.1%

일강수량
Real number (ℝ)

ZEROS 

Distinct501
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2890699
Minimum0
Maximum587
Zeros6552
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:30.414850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile36.5
Maximum587
Range587
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation29.525887
Coefficient of variation (CV)4.0507071
Kurtosis112.53539
Mean7.2890699
Median Absolute Deviation (MAD)0
Skewness9.0402911
Sum72890.699
Variance871.77801
MonotonicityNot monotonic
2023-12-13T02:30:30.563888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6552
65.5%
0.5 356
 
3.6%
1.0 244
 
2.4%
1.5 159
 
1.6%
3.0 112
 
1.1%
2.0 104
 
1.0%
2.5 92
 
0.9%
3.5 71
 
0.7%
4.0 63
 
0.6%
5.0 53
 
0.5%
Other values (491) 2194
 
21.9%
ValueCountFrequency (%)
0.0 6552
65.5%
0.1 4
 
< 0.1%
0.125 15
 
0.1%
0.1669999999999999 42
 
0.4%
0.2 5
 
0.1%
0.25 39
 
0.4%
0.3 2
 
< 0.1%
0.3329999999999999 29
 
0.3%
0.375 7
 
0.1%
0.4 2
 
< 0.1%
ValueCountFrequency (%)
587.0 1
 
< 0.1%
583.5 2
< 0.1%
500.5 1
 
< 0.1%
442.0 1
 
< 0.1%
419.0 1
 
< 0.1%
415.0 1
 
< 0.1%
405.0 4
< 0.1%
382.0 3
< 0.1%
357.0 2
< 0.1%
352.5 1
 
< 0.1%

평균 풍속
Real number (ℝ)

Distinct459
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7439697
Minimum0
Maximum13.633
Zeros57
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:30:30.766663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q11.7
median2.4
Q33.4
95-th percentile5.55
Maximum13.633
Range13.633
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.4701261
Coefficient of variation (CV)0.53576616
Kurtosis3.8015425
Mean2.7439697
Median Absolute Deviation (MAD)0.8
Skewness1.5149985
Sum27439.697
Variance2.1612708
MonotonicityNot monotonic
2023-12-13T02:30:30.968323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 333
 
3.3%
1.8 321
 
3.2%
1.5 316
 
3.2%
2.1 315
 
3.1%
2.2 309
 
3.1%
2.4 306
 
3.1%
2.6 294
 
2.9%
2.0 290
 
2.9%
1.9 286
 
2.9%
1.4 279
 
2.8%
Other values (449) 6951
69.5%
ValueCountFrequency (%)
0.0 57
0.6%
0.1 2
 
< 0.1%
0.2 3
 
< 0.1%
0.3 6
 
0.1%
0.4 22
 
0.2%
0.5 22
 
0.2%
0.6 30
 
0.3%
0.7 45
0.4%
0.75 2
 
< 0.1%
0.8 87
0.9%
ValueCountFrequency (%)
13.633 1
 
< 0.1%
11.8 4
< 0.1%
11.633 1
 
< 0.1%
11.533 1
 
< 0.1%
11.367 1
 
< 0.1%
11.3 1
 
< 0.1%
11.233 1
 
< 0.1%
11.033 1
 
< 0.1%
10.7 1
 
< 0.1%
10.667 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

2023-12-13T02:30:31.145366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:30:31.262797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-15 10000
100.0%

Interactions

2023-12-13T02:30:24.505354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.012663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.249957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:14.762720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.300774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.785599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.166923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.536658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.658839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.207961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.637031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.143527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.352406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:14.954592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.450822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.958261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.326830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.640301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.790810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.328055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.756329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.285504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.450811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.105712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.576266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.073729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.470423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.741407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.953602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.440519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.903867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.438223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.560221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.284552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.759474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.217420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.624381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.852991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.099070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.543440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.044986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.568921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.669179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.457372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.917527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.358217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.746668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.966107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.223367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.684998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.179556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.671256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.758981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.599211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.038862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.479049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.866010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.061749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.332480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.824658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.329082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.791768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.856513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.741000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.200346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.609151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.991965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.188891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.466971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.971386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.456828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:12.893914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.945396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:15.866014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.358002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.744232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.112988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.306275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.842943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.092892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.579561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.032991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:14.441535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.012978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.515149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:18.900819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.271543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.438331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:22.966251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.255560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:25.715088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:13.141062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:14.587082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:16.155141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:17.664423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:19.053866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:20.400322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:21.542636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:23.096884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:30:24.367642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:30:31.348562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명읍면동명거주인구근무인구방문인구총 유동인구교통량평균 속도평균 소요 시간평균 기온일강수량평균 풍속
시도명1.0001.0000.6090.4520.4710.5780.6790.5940.3020.1910.0620.195
읍면동명1.0001.0000.9720.8450.8910.9690.9020.8840.8990.3210.0700.531
거주인구0.6090.9721.0000.8500.8580.9620.6640.3850.7280.2490.1380.334
근무인구0.4520.8450.8501.0000.8150.8670.5170.2400.5280.2230.0690.192
방문인구0.4710.8910.8580.8151.0000.8810.4710.2300.5540.2080.0890.209
총 유동인구0.5780.9690.9620.8670.8811.0000.6530.4210.6480.2480.0760.322
교통량0.6790.9020.6640.5170.4710.6531.0000.3760.5390.1110.0070.165
평균 속도0.5940.8840.3850.2400.2300.4210.3761.0000.8140.0870.0000.212
평균 소요 시간0.3020.8990.7280.5280.5540.6480.5390.8141.0000.0960.0000.245
평균 기온0.1910.3210.2490.2230.2080.2480.1110.0870.0961.0000.2120.183
일강수량0.0620.0700.1380.0690.0890.0760.0070.0000.0000.2121.0000.459
평균 풍속0.1950.5310.3340.1920.2090.3220.1650.2120.2450.1830.4591.000
2023-12-13T02:30:31.536985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명시도명
읍면동명1.0000.998
시도명0.9981.000
2023-12-13T02:30:31.665347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거주인구근무인구방문인구총 유동인구교통량평균 속도평균 소요 시간평균 기온일강수량평균 풍속시도명읍면동명
거주인구1.0000.8250.7910.9670.312-0.0080.337-0.1060.0490.1030.4710.808
근무인구0.8251.0000.8750.8980.2810.0080.417-0.0490.0340.0290.3470.485
방문인구0.7910.8751.0000.9070.297-0.0010.460-0.0450.0020.0260.3620.566
총 유동인구0.9670.8980.9071.0000.338-0.0200.387-0.0820.0320.0790.4460.795
교통량0.3120.2810.2970.3381.000-0.2530.106-0.0690.0030.0110.5290.588
평균 속도-0.0080.008-0.001-0.020-0.2531.0000.3900.051-0.0150.0400.5980.574
평균 소요 시간0.3370.4170.4600.3870.1060.3901.000-0.0060.0310.1070.2320.582
평균 기온-0.106-0.049-0.045-0.082-0.0690.051-0.0061.0000.134-0.1060.1460.116
일강수량0.0490.0340.0020.0320.003-0.0150.0310.1341.0000.1830.0470.024
평균 풍속0.1030.0290.0260.0790.0110.0400.107-0.1060.1831.0000.1490.211
시도명0.4710.3470.3620.4460.5290.5980.2320.1460.0470.1491.0000.998
읍면동명0.8080.4850.5660.7950.5880.5740.5820.1160.0240.2110.9981.000

Missing values

2023-12-13T02:30:25.908641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:30:26.165043image/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

일자시도명읍면동명거주인구근무인구방문인구총 유동인구교통량평균 속도평균 소요 시간평균 기온일강수량평균 풍속데이터기준일자
64272020-04-19서귀포시안덕면365838.31124309.915157334.627547482.8530.02748.08147.67613.08.54.92020-12-15
98942018-10-09서귀포시중앙동45492.85211389.334100785.225157667.4110.058.033.14318.60.02.12020-12-15
318492019-09-22제주시화북동666375.21624081.487149296.557839753.259524.02938.17628.70617.267263.1678.02020-12-15
141502019-07-11제주시구좌읍288862.20443177.851195022.821527062.8764.82450.32460.61822.41.3335.12020-12-15
317682019-07-03제주시화북동554090.31678208.745208997.367841296.4281007.35336.88232.29419.90.01.1332020-12-15
301752019-07-15제주시한경면137281.25616285.97584568.925238136.157612.81849.51554.69722.40.00.752020-12-15
88072020-03-23서귀포시정방동43788.8856963.96662746.435113499.2860.056.7531.62514.10.01.42020-12-15
283812018-12-17제주시일도2동637278.35442381.026238349.709918009.088349.81829.27327.3647.60.04.62020-12-15
190032019-09-04제주시삼양동444452.69519260.5792439.005556152.2710.028.28621.71422.645.1672.7672020-12-15
153852018-06-06제주시도두동30459.6664947.29791236.455126643.4171959.537.77831.16720.20.01.12020-12-15
일자시도명읍면동명거주인구근무인구방문인구총 유동인구교통량평균 속도평균 소요 시간평균 기온일강수량평균 풍속데이터기준일자
243712018-12-17제주시용담2동227816.15669893.818293932.127591642.1021253.45527.90932.7277.60.04.62020-12-15
54082019-09-13서귀포시송산동195060.01510535.7129732.172335327.887115.85743.95931.26523.10.04.42020-12-15
11232018-12-14서귀포시대륜동295350.75133555.044170569.992499475.7870.036.92634.2965.20.01.12020-12-15
78272019-09-25서귀포시예래동66773.11620010.994159795.861246579.97292.46946.87550.520.60.01.52020-12-15
21212019-07-17서귀포시대정읍367286.39835700.877137309.191540296.467766.83354.44450.72223.94.06.2332020-12-15
176102020-04-04제주시삼도1동233694.61915056.573118217.803366968.9950.036.12525.08.00.03.42020-12-15
160332020-04-22제주시도두동29672.3934434.30663321.70397428.4021314.68439.10532.7379.60.04.62020-12-15
98282018-07-28서귀포시중앙동43806.7759902.019101433.84155142.6330.059.28632.14327.20.01.12020-12-15
189612019-07-24제주시삼양동449211.54718855.764128768.215596835.5260.028.71421.025.0670.02.2672020-12-15
131682018-12-22제주시건입동155649.22315980.791175777.428347407.442521.430.248.07.55.51.72020-12-15