Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows25
Duplicate rows (%)0.2%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

DateTime1
Numeric12

Dataset

Description서대문구 전체 업종별 이용률 - 기상현황 - 유동인구 분석 기초자료 - 강수있음 자료 데이터로 날짜별 상권별, 업종별 기온, 습도, 강수량, 풍향, 풍속, 유동인구에 따른 데이터 항목을 제공합니다.
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15097049/fileData.do

Alerts

Dataset has 25 (0.2%) duplicate rowsDuplicates
우편번호 is highly overall correlated with 상권코드High correlation
상권코드 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
평균건수 is highly overall correlated with 평균금액High correlation
평균금액 is highly overall correlated with 평균건수High correlation
유동인구 is highly overall correlated with 상권코드High correlation
평균금액 is highly skewed (γ1 = 37.32003983)Skewed

Reproduction

Analysis started2023-12-12 17:54:45.449993
Analysis finished2023-12-12 17:55:08.206831
Duration22.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct79
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-06 00:00:00
Maximum2020-08-29 00:00:00
2023-12-13T02:55:08.272664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:08.430000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시간
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9847
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:08.555149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4198829
Coefficient of variation (CV)0.35633371
Kurtosis-0.79003264
Mean3.9847
Median Absolute Deviation (MAD)1
Skewness-0.33929518
Sum39847
Variance2.0160675
MonotonicityNot monotonic
2023-12-13T02:55:08.652013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 2756
27.6%
3 2130
21.3%
4 1974
19.7%
6 1511
15.1%
2 1086
 
10.9%
1 543
 
5.4%
ValueCountFrequency (%)
1 543
 
5.4%
2 1086
 
10.9%
3 2130
21.3%
4 1974
19.7%
5 2756
27.6%
6 1511
15.1%
ValueCountFrequency (%)
6 1511
15.1%
5 2756
27.6%
4 1974
19.7%
3 2130
21.3%
2 1086
 
10.9%
1 543
 
5.4%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3702.6767
Minimum3605
Maximum3789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:08.766581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3605
5-th percentile3624
Q13664
median3709
Q33742
95-th percentile3787
Maximum3789
Range184
Interquartile range (IQR)78

Descriptive statistics

Standard deviation53.252322
Coefficient of variation (CV)0.014382115
Kurtosis-1.160695
Mean3702.6767
Median Absolute Deviation (MAD)43
Skewness-0.049349181
Sum37026767
Variance2835.8098
MonotonicityNot monotonic
2023-12-13T02:55:08.921606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3735 385
 
3.9%
3712 376
 
3.8%
3734 343
 
3.4%
3646 335
 
3.4%
3692 286
 
2.9%
3628 279
 
2.8%
3789 244
 
2.4%
3788 232
 
2.3%
3757 231
 
2.3%
3665 224
 
2.2%
Other values (72) 7065
70.7%
ValueCountFrequency (%)
3605 166
1.7%
3606 9
 
0.1%
3607 9
 
0.1%
3611 54
 
0.5%
3612 27
 
0.3%
3615 103
 
1.0%
3616 91
 
0.9%
3624 155
1.6%
3625 69
 
0.7%
3628 279
2.8%
ValueCountFrequency (%)
3789 244
2.4%
3788 232
2.3%
3787 127
1.3%
3780 129
1.3%
3779 167
1.7%
3778 60
 
0.6%
3777 208
2.1%
3776 173
1.7%
3767 110
1.1%
3766 180
1.8%

업종코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.4442
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:09.045280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median11
Q317
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.5813538
Coefficient of variation (CV)0.57508203
Kurtosis-1.2310468
Mean11.4442
Median Absolute Deviation (MAD)6
Skewness0.22196031
Sum114442
Variance43.314218
MonotonicityNot monotonic
2023-12-13T02:55:09.342758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 1388
13.9%
5 1244
12.4%
3 1106
11.1%
13 911
9.1%
4 775
7.8%
9 654
 
6.5%
16 520
 
5.2%
24 520
 
5.2%
18 494
 
4.9%
19 455
 
4.5%
Other values (15) 1933
19.3%
ValueCountFrequency (%)
1 68
 
0.7%
2 15
 
0.1%
3 1106
11.1%
4 775
7.8%
5 1244
12.4%
6 215
 
2.1%
7 262
 
2.6%
8 315
 
3.1%
9 654
6.5%
10 319
 
3.2%
ValueCountFrequency (%)
25 22
 
0.2%
24 520
 
5.2%
23 58
 
0.6%
22 108
 
1.1%
21 108
 
1.1%
20 158
 
1.6%
19 455
 
4.5%
18 494
 
4.9%
17 1388
13.9%
16 520
 
5.2%

상권코드
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.6236
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:09.532979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median11
Q320
95-th percentile29
Maximum32
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0444405
Coefficient of variation (CV)0.71647077
Kurtosis-1.019499
Mean12.6236
Median Absolute Deviation (MAD)7
Skewness0.36155188
Sum126236
Variance81.801903
MonotonicityNot monotonic
2023-12-13T02:55:09.723879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1340
 
13.4%
7 627
 
6.3%
11 507
 
5.1%
16 507
 
5.1%
2 487
 
4.9%
23 474
 
4.7%
5 423
 
4.2%
18 407
 
4.1%
15 406
 
4.1%
6 379
 
3.8%
Other values (22) 4443
44.4%
ValueCountFrequency (%)
1 1340
13.4%
2 487
 
4.9%
3 346
 
3.5%
4 257
 
2.6%
5 423
 
4.2%
6 379
 
3.8%
7 627
6.3%
8 198
 
2.0%
9 275
 
2.8%
10 312
 
3.1%
ValueCountFrequency (%)
32 131
 
1.3%
31 201
2.0%
30 30
 
0.3%
29 160
 
1.6%
28 184
 
1.8%
27 156
 
1.6%
26 81
 
0.8%
25 185
 
1.8%
24 312
3.1%
23 474
4.7%

평균건수
Real number (ℝ)

HIGH CORRELATION 

Distinct287
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.1664
Minimum0.5
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:09.876941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q12
median6
Q316
95-th percentile67
Maximum1479
Range1478.5
Interquartile range (IQR)14

Descriptive statistics

Standard deviation34.242783
Coefficient of variation (CV)2.1181453
Kurtosis388.06733
Mean16.1664
Median Absolute Deviation (MAD)5
Skewness12.733713
Sum161664
Variance1172.5682
MonotonicityNot monotonic
2023-12-13T02:55:10.023619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 1230
 
12.3%
2.0 742
 
7.4%
3.0 716
 
7.2%
4.0 519
 
5.2%
0.5 465
 
4.7%
5.0 451
 
4.5%
6.0 371
 
3.7%
7.0 359
 
3.6%
1.5 285
 
2.9%
8.0 255
 
2.5%
Other values (277) 4607
46.1%
ValueCountFrequency (%)
0.5 465
 
4.7%
1.0 1230
12.3%
1.5 285
 
2.9%
2.0 742
7.4%
2.5 166
 
1.7%
3.0 716
7.2%
3.5 110
 
1.1%
4.0 519
5.2%
4.5 80
 
0.8%
5.0 451
 
4.5%
ValueCountFrequency (%)
1479.0 1
< 0.1%
746.0 1
< 0.1%
626.0 1
< 0.1%
500.0 1
< 0.1%
459.0 1
< 0.1%
446.0 1
< 0.1%
420.0 1
< 0.1%
378.0 1
< 0.1%
369.0 1
< 0.1%
361.0 1
< 0.1%

평균금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5549
Distinct (%)55.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243023.69
Minimum300
Maximum64928628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:10.164809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile7000
Q128287.5
median76350
Q3207250
95-th percentile931457.5
Maximum64928628
Range64928328
Interquartile range (IQR)178962.5

Descriptive statistics

Standard deviation1066656.5
Coefficient of variation (CV)4.3891051
Kurtosis1875.267
Mean243023.69
Median Absolute Deviation (MAD)59350
Skewness37.32004
Sum2.4302369 × 109
Variance1.1377561 × 1012
MonotonicityNot monotonic
2023-12-13T02:55:10.293727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000.0 71
 
0.7%
10000.0 62
 
0.6%
20000.0 52
 
0.5%
7000.0 50
 
0.5%
8000.0 47
 
0.5%
12000.0 44
 
0.4%
6000.0 40
 
0.4%
24000.0 38
 
0.4%
18000.0 35
 
0.4%
35000.0 35
 
0.4%
Other values (5539) 9526
95.3%
ValueCountFrequency (%)
300.0 1
 
< 0.1%
500.0 4
 
< 0.1%
650.0 1
 
< 0.1%
750.0 1
 
< 0.1%
875.0 1
 
< 0.1%
900.0 1
 
< 0.1%
950.0 1
 
< 0.1%
1000.0 11
0.1%
1100.0 1
 
< 0.1%
1150.0 1
 
< 0.1%
ValueCountFrequency (%)
64928628.0 1
< 0.1%
46150641.0 1
< 0.1%
34974811.0 1
< 0.1%
28401842.0 1
< 0.1%
17992791.0 1
< 0.1%
16451550.0 1
< 0.1%
14354250.0 1
< 0.1%
10365400.0 1
< 0.1%
9654540.0 1
< 0.1%
9527020.0 1
< 0.1%

기온
Real number (ℝ)

Distinct434
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.95407
Minimum-12.975
Maximum29.725
Zeros57
Zeros (%)0.6%
Negative185
Negative (%)1.8%
Memory size166.0 KiB
2023-12-13T02:55:10.466790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12.975
5-th percentile3.9
Q113
median20.325
Q324.35
95-th percentile26.725
Maximum29.725
Range42.7
Interquartile range (IQR)11.35

Descriptive statistics

Standard deviation7.8920246
Coefficient of variation (CV)0.43956744
Kurtosis0.21706816
Mean17.95407
Median Absolute Deviation (MAD)4.875
Skewness-0.9356142
Sum179540.7
Variance62.284053
MonotonicityNot monotonic
2023-12-13T02:55:10.651847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.575 119
 
1.2%
25.55 106
 
1.1%
12.825 104
 
1.0%
21.625 99
 
1.0%
23.1 96
 
1.0%
24.875 87
 
0.9%
24.6 85
 
0.9%
13.7 82
 
0.8%
16.85 81
 
0.8%
21.55 79
 
0.8%
Other values (424) 9062
90.6%
ValueCountFrequency (%)
-12.975 27
0.3%
-9.275 32
0.3%
-5.55 1
 
< 0.1%
-4.125 6
 
0.1%
-3.825 8
 
0.1%
-3.4 4
 
< 0.1%
-3.075 31
0.3%
-2.15 40
0.4%
-1.05 1
 
< 0.1%
-1.025 15
 
0.1%
ValueCountFrequency (%)
29.725 19
0.2%
29.3 3
 
< 0.1%
29.05 1
 
< 0.1%
28.975 5
 
0.1%
28.65 3
 
< 0.1%
28.625 4
 
< 0.1%
28.475 7
 
0.1%
28.375 7
 
0.1%
28.275 2
 
< 0.1%
28.1 37
0.4%

습도
Real number (ℝ)

Distinct140
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.030625
Minimum17.75
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:10.802856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.75
5-th percentile64
Q188.25
median94.25
Q396
95-th percentile99
Maximum100
Range82.25
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation11.298411
Coefficient of variation (CV)0.12549519
Kurtosis7.5396922
Mean90.030625
Median Absolute Deviation (MAD)2.75
Skewness-2.5573624
Sum900306.25
Variance127.65408
MonotonicityNot monotonic
2023-12-13T02:55:10.950398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.0 1890
 
18.9%
97.0 658
 
6.6%
94.0 437
 
4.4%
93.0 261
 
2.6%
100.0 252
 
2.5%
95.5 248
 
2.5%
96.75 226
 
2.3%
93.5 210
 
2.1%
91.5 187
 
1.9%
96.25 167
 
1.7%
Other values (130) 5464
54.6%
ValueCountFrequency (%)
17.75 7
 
0.1%
21.75 4
 
< 0.1%
33.75 31
0.3%
35.0 32
0.3%
40.25 8
 
0.1%
41.0 4
 
< 0.1%
45.5 9
 
0.1%
46.0 27
0.3%
47.75 5
 
0.1%
48.5 6
 
0.1%
ValueCountFrequency (%)
100.0 252
2.5%
99.75 40
 
0.4%
99.5 85
 
0.9%
99.25 108
1.1%
99.0 65
 
0.7%
98.75 74
 
0.7%
98.5 45
 
0.4%
98.25 99
 
1.0%
98.0 60
 
0.6%
97.75 33
 
0.3%

강수량
Real number (ℝ)

Distinct129
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.55669
Minimum-0.25
Maximum18.125
Zeros0
Zeros (%)0.0%
Negative135
Negative (%)1.4%
Memory size166.0 KiB
2023-12-13T02:55:11.114041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.25
5-th percentile0.125
Q10.25
median0.5
Q31.625
95-th percentile7.75
Maximum18.125
Range18.375
Interquartile range (IQR)1.375

Descriptive statistics

Standard deviation2.6444804
Coefficient of variation (CV)1.6987842
Kurtosis12.481176
Mean1.55669
Median Absolute Deviation (MAD)0.375
Skewness3.2300029
Sum15566.9
Variance6.9932764
MonotonicityNot monotonic
2023-12-13T02:55:11.264411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.125 2043
20.4%
0.25 1178
 
11.8%
0.375 1091
 
10.9%
0.5 499
 
5.0%
0.75 430
 
4.3%
0.625 347
 
3.5%
1.0 338
 
3.4%
1.875 264
 
2.6%
0.875 257
 
2.6%
1.5 197
 
2.0%
Other values (119) 3356
33.6%
ValueCountFrequency (%)
-0.25 124
 
1.2%
-0.125 11
 
0.1%
0.025 52
 
0.5%
0.05 24
 
0.2%
0.075 32
 
0.3%
0.1 34
 
0.3%
0.125 2043
20.4%
0.15 16
 
0.2%
0.175 14
 
0.1%
0.2 21
 
0.2%
ValueCountFrequency (%)
18.125 20
 
0.2%
17.25 53
0.5%
16.975 6
 
0.1%
13.875 20
 
0.2%
13.225 2
 
< 0.1%
13.125 44
0.4%
10.5 8
 
0.1%
9.625 31
0.3%
9.5 20
 
0.2%
8.875 46
0.5%

풍향
Real number (ℝ)

Distinct589
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168.93944
Minimum0
Maximum357.833
Zeros40
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:11.412324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.6016
Q191.836
median166.2428
Q3229.5765
95-th percentile325.8897
Maximum357.833
Range357.833
Interquartile range (IQR)137.7405

Descriptive statistics

Standard deviation89.731249
Coefficient of variation (CV)0.53114447
Kurtosis-0.82697314
Mean168.93944
Median Absolute Deviation (MAD)70.8425
Skewness0.20018941
Sum1689394.4
Variance8051.697
MonotonicityNot monotonic
2023-12-13T02:55:11.562734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.1248 66
 
0.7%
89.6131 64
 
0.6%
159.6931 62
 
0.6%
194.7114 61
 
0.6%
100.7341 60
 
0.6%
323.4571 59
 
0.6%
285.4114 59
 
0.6%
102.1996 58
 
0.6%
172.7431 58
 
0.6%
281.8179 57
 
0.6%
Other values (579) 9396
94.0%
ValueCountFrequency (%)
0.0 40
0.4%
3.0577 46
0.5%
3.121 13
 
0.1%
6.1221 37
0.4%
7.3247 11
 
0.1%
8.1593 21
 
0.2%
9.821 56
0.6%
10.8497 32
0.3%
12.742 6
 
0.1%
13.4494 5
 
0.1%
ValueCountFrequency (%)
357.833 52
0.5%
357.7509 3
 
< 0.1%
356.5859 5
 
0.1%
354.7356 39
0.4%
353.4841 45
0.4%
351.7413 55
0.5%
351.4076 39
0.4%
350.1614 27
0.3%
348.6819 40
0.4%
344.6707 4
 
< 0.1%

풍속
Real number (ℝ)

Distinct588
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6106834
Minimum0
Maximum7.8494
Zeros40
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:11.830033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2542
Q10.8009
median1.319
Q32.1963
95-th percentile3.847
Maximum7.8494
Range7.8494
Interquartile range (IQR)1.3954

Descriptive statistics

Standard deviation1.1524731
Coefficient of variation (CV)0.71551808
Kurtosis2.209312
Mean1.6106834
Median Absolute Deviation (MAD)0.6724
Skewness1.3091234
Sum16106.834
Variance1.3281943
MonotonicityNot monotonic
2023-12-13T02:55:12.089632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0078 67
 
0.7%
4.7198 66
 
0.7%
0.2542 64
 
0.6%
0.8009 62
 
0.6%
4.1678 61
 
0.6%
0.1363 60
 
0.6%
2.7514 59
 
0.6%
0.841 59
 
0.6%
1.7749 58
 
0.6%
2.7185 58
 
0.6%
Other values (578) 9386
93.9%
ValueCountFrequency (%)
0.0 40
0.4%
0.0259 1
 
< 0.1%
0.0871 47
0.5%
0.0976 5
 
0.1%
0.103 14
 
0.1%
0.1254 36
0.4%
0.1363 60
0.6%
0.1488 33
0.3%
0.1746 3
 
< 0.1%
0.1869 39
0.4%
ValueCountFrequency (%)
7.8494 5
 
0.1%
6.9171 17
0.2%
6.7699 3
 
< 0.1%
6.4989 19
0.2%
6.0891 4
 
< 0.1%
5.9878 14
0.1%
5.8119 8
0.1%
5.5354 5
 
0.1%
5.5221 2
 
< 0.1%
5.4247 13
0.1%

유동인구
Real number (ℝ)

HIGH CORRELATION 

Distinct4283
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17929.14
Minimum541.12
Maximum117806.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T02:55:12.283513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum541.12
5-th percentile1840.943
Q15818.3025
median12642.7
Q319073.69
95-th percentile65520.36
Maximum117806.23
Range117265.11
Interquartile range (IQR)13255.387

Descriptive statistics

Standard deviation19598.537
Coefficient of variation (CV)1.0931108
Kurtosis4.5559613
Mean17929.14
Median Absolute Deviation (MAD)6755.65
Skewness2.1647713
Sum1.792914 × 108
Variance3.8410263 × 108
MonotonicityNot monotonic
2023-12-13T02:55:12.933346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63953.7 21
 
0.2%
63252.39 18
 
0.2%
64261.76 16
 
0.2%
77350.76 16
 
0.2%
64715.06 15
 
0.1%
59345.1 15
 
0.1%
59624.25 15
 
0.1%
80968.66 14
 
0.1%
74440.41 14
 
0.1%
76839.85 14
 
0.1%
Other values (4273) 9842
98.4%
ValueCountFrequency (%)
541.12 1
 
< 0.1%
604.51 2
< 0.1%
620.15 1
 
< 0.1%
653.8 1
 
< 0.1%
655.45 2
< 0.1%
687.89 2
< 0.1%
713.7 3
< 0.1%
718.29 2
< 0.1%
728.63 1
 
< 0.1%
734.17 1
 
< 0.1%
ValueCountFrequency (%)
117806.23 10
0.1%
115588.49 13
0.1%
109816.38 10
0.1%
101616.79 8
0.1%
94630.98 13
0.1%
93400.91 9
0.1%
92396.13 9
0.1%
89624.74 10
0.1%
85147.18 4
 
< 0.1%
84961.71 6
0.1%

Interactions

2023-12-13T02:55:06.413847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:49.876399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.110391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.861266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.247769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.785251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.502182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.006725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.635877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.938300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.458583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.952174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.536044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:49.970275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.230933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.959347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.372323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.904508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.604588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.103691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.730597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.047939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.575128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.063490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.637521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.072531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.360453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.082325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.503050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.069187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.724569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.247303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.847191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.169277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.706766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.210078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.751080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.179045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.502565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.178726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.618168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.188766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.836437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.353576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.949156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.272204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.826768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.360485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.859508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.275857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.693205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.274248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.739694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.315966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.967430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.451201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.068251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.382958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.979856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.492845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.964989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.390214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.821939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.385745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.861611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.463751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.097054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.547390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.187933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.500622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.115098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.622979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.066262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.491520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.923452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.494271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.976466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.651562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.230240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.668028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.292873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.661116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.230516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.745163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.148026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.592278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.014897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.611674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.091434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.787866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.338617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.766571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.394470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.769769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.341400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.866119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.237491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.710558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.105578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.730325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.232684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:56.942884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.460423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:59.857651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.502862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:02.893477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.460717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:05.971844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.334060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.813007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.205755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.854803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.354130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.087466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.624769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.005899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.614935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.050573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.583128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.084818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.726919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:50.910375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.321300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:53.984587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.503276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.238017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.768298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.111606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.729436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.194361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.709195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.192008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:07.842034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:51.005677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:52.742560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:54.123436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:55.626601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:57.366828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:54:58.899798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:00.519633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:01.838514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:03.333897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:04.839572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:55:06.303568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:55:13.075921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월일시간우편번호업종코드상권코드평균건수평균금액기온습도강수량풍향풍속유동인구
연월일1.0000.7690.1390.1880.1190.0000.0000.9680.9620.8230.8930.8190.523
시간0.7691.0000.0830.2630.0780.0590.0140.3000.3070.2660.2740.2120.351
우편번호0.1390.0831.0000.2750.9320.1090.0480.1240.0990.0950.3750.3480.816
업종코드0.1880.2630.2751.0000.2590.0540.0160.0640.0510.0200.0730.0800.215
상권코드0.1190.0780.9320.2591.0000.0730.0000.0620.0490.0720.2880.2390.707
평균건수0.0000.0590.1090.0540.0731.0000.9840.0000.0000.0310.0230.0000.194
평균금액0.0000.0140.0480.0160.0000.9841.0000.0000.0000.0000.0230.0000.137
기온0.9680.3000.1240.0640.0620.0000.0001.0000.7680.3070.5900.4120.308
습도0.9620.3070.0990.0510.0490.0000.0000.7681.0000.2640.4890.4180.163
강수량0.8230.2660.0950.0200.0720.0310.0000.3070.2641.0000.3920.3410.162
풍향0.8930.2740.3750.0730.2880.0230.0230.5900.4890.3921.0000.4750.357
풍속0.8190.2120.3480.0800.2390.0000.0000.4120.4180.3410.4751.0000.373
유동인구0.5230.3510.8160.2150.7070.1940.1370.3080.1630.1620.3570.3731.000
2023-12-13T02:55:13.270113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간우편번호업종코드상권코드평균건수평균금액기온습도강수량풍향풍속유동인구
시간1.000-0.016-0.0490.0180.0300.1630.0290.1050.071-0.0360.099-0.022
우편번호-0.0161.000-0.053-0.5640.1210.1150.0430.0840.002-0.0080.2390.342
업종코드-0.049-0.0531.0000.047-0.034-0.127-0.015-0.010-0.013-0.002-0.018-0.034
상권코드0.018-0.5640.0471.000-0.201-0.170-0.044-0.078-0.0010.009-0.157-0.562
평균건수0.0300.121-0.034-0.2011.0000.813-0.0160.045-0.0110.0440.0170.187
평균금액0.1630.115-0.127-0.1700.8131.0000.0010.068-0.0040.0480.0290.190
기온0.0290.043-0.015-0.044-0.0160.0011.0000.0410.095-0.014-0.0440.023
습도0.1050.084-0.010-0.0780.0450.0680.0411.0000.336-0.0370.0110.067
강수량0.0710.002-0.013-0.001-0.011-0.0040.0950.3361.000-0.0970.1770.020
풍향-0.036-0.008-0.0020.0090.0440.048-0.014-0.037-0.0971.000-0.045-0.018
풍속0.0990.239-0.018-0.1570.0170.029-0.0440.0110.177-0.0451.0000.026
유동인구-0.0220.342-0.034-0.5620.1870.1900.0230.0670.020-0.0180.0261.000

Missing values

2023-12-13T02:55:07.973335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:55:08.122096image/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

연월일시간우편번호업종코드상권코드평균건수평균금액기온습도강수량풍향풍속유동인구
130352020-02-282369616259.075950.04.192.00.12574.24550.49244453.22
839592020-08-10437894154.0442700.024.375100.05.625206.74060.974980455.54
430082020-06-25436285119.5206250.019.27594.00.375216.47061.264710086.31
461932020-06-29637881117.0180899.020.393.50.5310.59673.03143394.31
30002020-01-0733616996.056850.07.797.02.62558.88413.571812680.73
837902020-08-1033734182919.0386900.025.92595.52.1212.89651.19385720.04
710322020-08-0263744354.082500.024.92598.08.525222.43323.02312884.18
623472020-07-28437369310.039010.023.195.50.87540.60161.610233565.0
674102020-08-013377619117.0380950.025.7589.750.125233.83281.504356225.68
444532020-06-293372616143.021800.021.62585.251.875190.09641.3695530.07
연월일시간우편번호업종코드상권코드평균건수평균금액기온습도강수량풍향풍속유동인구
58522020-02-116377924128.0185400.07.07564.00.25108.36922.444656636.96
114872020-02-2453767428.0112860.06.7579.750.592.14273.517114568.68
947602020-08-29237351830.54500.026.9587.250.5153.24540.569919201.87
548312020-07-194362820271.07000.025.5584.00.375164.34152.56627309.84
626212020-07-284364619233.093450.023.195.50.87540.60161.610213318.73
659492020-07-31536467232.527700.023.92596.00.594.56910.568414965.97
263122020-05-145366512165.0149500.017.27551.750.2589.57220.89197742.1
256842020-05-1143628132711.078800.09.7587.50.75180.35310.23746332.34
865732020-08-145366591624.0152100.025.07596.06.5229.57650.81497353.71
567352020-07-222374013214.019300.022.87597.00.375166.24280.53887889.83

Duplicate rows

Most frequently occurring

연월일시간우편번호업종코드상권코드평균건수평균금액기온습도강수량풍향풍속유동인구# duplicates
02020-01-06336468231.539900.03.8595.250.12575.87042.737517194.822
12020-01-0753646162393.5828220.01.897.00.75285.41142.751419561.772
22020-02-155364642312.5214700.00.096.00.875281.81792.012321196.892
32020-02-15536468231.517000.00.096.00.875281.81792.012321196.892
42020-04-11636465232.583350.0-3.07533.75-0.25180.99981.007818977.862
52020-05-09436464231.024000.012.52597.00.125188.11831.039316954.812
62020-05-114364618237.5209600.09.7587.50.75180.35310.237415514.332
72020-05-1543646162318.0236365.014.92596.00.12571.89181.145315577.752
82020-05-18536463233.0100800.011.486.250.375163.31081.120216689.812
92020-06-24536467232.047350.020.894.01.0159.69310.800914738.042