Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows2162
Duplicate rows (%)21.6%
Total size in memory947.3 KiB
Average record size in memory97.0 B

Variable types

Categorical2
Numeric8

Dataset

Description서울특별시 서대문구_상권별 기상현황-유동인구 분석 기초자료(강수없음) 기초자료 데이터로 날짜별 상권별, 시간별, 기온, 습도, 풍향, 풍속, 유동인구에 따른 데이터 항목을 제공합니다
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15097061/fileData.do

Alerts

강수량 has constant value ""Constant
Dataset has 2162 (21.6%) duplicate rowsDuplicates
우편번호 is highly overall correlated with 상권코드High correlation
상권코드 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
습도 is highly overall correlated with 연월일 High correlation
유동인구 is highly overall correlated with 상권코드High correlation
연월일 is highly overall correlated with 습도High correlation

Reproduction

Analysis started2023-12-12 13:48:22.481830
Analysis finished2023-12-12 13:48:32.144764
Duration9.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월일
Categorical

HIGH CORRELATION 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2020-02-01
 
341
2020-01-23
 
338
2020-01-31
 
335
2020-01-11
 
334
2020-01-13
 
330
Other values (31)
8322 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02-01
2nd row2020-01-20
3rd row2020-01-30
4th row2020-01-27
5th row2020-01-13

Common Values

ValueCountFrequency (%)
2020-02-01 341
 
3.4%
2020-01-23 338
 
3.4%
2020-01-31 335
 
3.4%
2020-01-11 334
 
3.3%
2020-01-13 330
 
3.3%
2020-01-10 329
 
3.3%
2020-01-09 326
 
3.3%
2020-01-04 317
 
3.2%
2020-01-29 317
 
3.2%
2020-01-18 316
 
3.2%
Other values (26) 6717
67.2%

Length

2023-12-12T22:48:32.212875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-02-01 341
 
3.4%
2020-01-23 338
 
3.4%
2020-01-31 335
 
3.4%
2020-01-11 334
 
3.3%
2020-01-13 330
 
3.3%
2020-01-10 329
 
3.3%
2020-01-09 326
 
3.3%
2020-01-04 317
 
3.2%
2020-01-29 317
 
3.2%
2020-01-18 316
 
3.2%
Other values (26) 6717
67.2%

시간
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9133
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:32.344369image/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 deviation1.4558829
Coefficient of variation (CV)0.37203458
Kurtosis-0.8805673
Mean3.9133
Median Absolute Deviation (MAD)1
Skewness-0.29381183
Sum39133
Variance2.1195951
MonotonicityNot monotonic
2023-12-12T22:48:32.462765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 2583
25.8%
4 2037
20.4%
3 1978
19.8%
6 1488
14.9%
2 1294
12.9%
1 620
 
6.2%
ValueCountFrequency (%)
1 620
 
6.2%
2 1294
12.9%
3 1978
19.8%
4 2037
20.4%
5 2583
25.8%
6 1488
14.9%
ValueCountFrequency (%)
6 1488
14.9%
5 2583
25.8%
4 2037
20.4%
3 1978
19.8%
2 1294
12.9%
1 620
 
6.2%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3706.3076
Minimum3605
Maximum3789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:32.606983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3605
5-th percentile3624
Q13665
median3711
Q33755
95-th percentile3788
Maximum3789
Range184
Interquartile range (IQR)90

Descriptive statistics

Standard deviation53.908324
Coefficient of variation (CV)0.014545022
Kurtosis-1.1843123
Mean3706.3076
Median Absolute Deviation (MAD)46
Skewness-0.12655462
Sum37063076
Variance2906.1074
MonotonicityNot monotonic
2023-12-12T22:48:32.762362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3712 405
 
4.0%
3735 396
 
4.0%
3734 314
 
3.1%
3646 309
 
3.1%
3692 288
 
2.9%
3789 279
 
2.8%
3628 264
 
2.6%
3665 246
 
2.5%
3777 245
 
2.5%
3766 241
 
2.4%
Other values (71) 7013
70.1%
ValueCountFrequency (%)
3605 142
1.4%
3606 3
 
< 0.1%
3607 15
 
0.1%
3611 51
 
0.5%
3612 30
 
0.3%
3615 75
 
0.8%
3616 101
 
1.0%
3624 149
1.5%
3625 80
 
0.8%
3628 264
2.6%
ValueCountFrequency (%)
3789 279
2.8%
3788 225
2.2%
3787 136
1.4%
3780 164
1.6%
3779 227
2.3%
3778 69
 
0.7%
3777 245
2.5%
3776 222
2.2%
3767 141
1.4%
3766 241
2.4%

상권코드
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.1134
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:32.910498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q320
95-th percentile29
Maximum32
Range31
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.1142003
Coefficient of variation (CV)0.75240645
Kurtosis-0.95243685
Mean12.1134
Median Absolute Deviation (MAD)8
Skewness0.43484653
Sum121134
Variance83.068647
MonotonicityNot monotonic
2023-12-12T22:48:33.034434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1567
 
15.7%
7 654
 
6.5%
2 609
 
6.1%
11 538
 
5.4%
16 503
 
5.0%
23 417
 
4.2%
18 417
 
4.2%
6 369
 
3.7%
15 354
 
3.5%
5 354
 
3.5%
Other values (22) 4218
42.2%
ValueCountFrequency (%)
1 1567
15.7%
2 609
 
6.1%
3 345
 
3.5%
4 206
 
2.1%
5 354
 
3.5%
6 369
 
3.7%
7 654
6.5%
8 199
 
2.0%
9 257
 
2.6%
10 301
 
3.0%
ValueCountFrequency (%)
32 154
 
1.5%
31 197
2.0%
30 35
 
0.4%
29 152
 
1.5%
28 160
 
1.6%
27 130
 
1.3%
26 77
 
0.8%
25 166
 
1.7%
24 274
2.7%
23 417
4.2%

기온
Real number (ℝ)

Distinct362
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4924025
Minimum-11.8
Maximum10.875
Zeros48
Zeros (%)0.5%
Negative4592
Negative (%)45.9%
Memory size166.0 KiB
2023-12-12T22:48:33.163274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-11.8
5-th percentile-5.35
Q1-1.675
median0.375
Q32.9
95-th percentile6.625
Maximum10.875
Range22.675
Interquartile range (IQR)4.575

Descriptive statistics

Standard deviation3.7738695
Coefficient of variation (CV)7.6641964
Kurtosis0.59913856
Mean0.4924025
Median Absolute Deviation (MAD)2.3
Skewness-0.27894686
Sum4924.025
Variance14.242091
MonotonicityNot monotonic
2023-12-12T22:48:33.298310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.075 136
 
1.4%
-0.85 119
 
1.2%
1.15 113
 
1.1%
-1.475 104
 
1.0%
-2.925 99
 
1.0%
-1.5 95
 
0.9%
0.375 91
 
0.9%
3.65 87
 
0.9%
0.925 87
 
0.9%
-4.35 87
 
0.9%
Other values (352) 8982
89.8%
ValueCountFrequency (%)
-11.8 32
0.3%
-11.475 50
0.5%
-11.275 4
 
< 0.1%
-10.1 4
 
< 0.1%
-9.975 45
0.4%
-9.825 8
 
0.1%
-9.725 2
 
< 0.1%
-9.375 25
0.2%
-9.225 41
0.4%
-8.975 5
 
0.1%
ValueCountFrequency (%)
10.875 10
0.1%
10.4 8
0.1%
9.75 11
0.1%
9.725 3
 
< 0.1%
9.625 16
0.2%
9.6 8
0.1%
9.575 8
0.1%
9.5 5
 
0.1%
9.475 6
 
0.1%
9.375 14
0.1%

습도
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.96435
Minimum25.5
Maximum96.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:33.426002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25.5
5-th percentile36.5
Q146.75
median58
Q368
95-th percentile79.75
Maximum96.5
Range71
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation13.673126
Coefficient of variation (CV)0.23588855
Kurtosis-0.66083661
Mean57.96435
Median Absolute Deviation (MAD)10.5
Skewness0.11290274
Sum579643.5
Variance186.95439
MonotonicityNot monotonic
2023-12-12T22:48:33.578090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.5 208
 
2.1%
45.75 197
 
2.0%
51.75 156
 
1.6%
58.0 154
 
1.5%
48.5 151
 
1.5%
72.5 147
 
1.5%
45.5 144
 
1.4%
65.25 135
 
1.4%
65.5 133
 
1.3%
73.5 123
 
1.2%
Other values (195) 8452
84.5%
ValueCountFrequency (%)
25.5 16
0.2%
26.25 3
 
< 0.1%
26.75 1
 
< 0.1%
27.0 1
 
< 0.1%
27.75 1
 
< 0.1%
28.0 31
0.3%
28.5 8
 
0.1%
28.75 2
 
< 0.1%
29.75 2
 
< 0.1%
30.0 8
 
0.1%
ValueCountFrequency (%)
96.5 26
 
0.3%
96.25 15
 
0.1%
92.75 5
 
0.1%
89.5 24
 
0.2%
88.5 15
 
0.1%
87.5 54
0.5%
86.5 90
0.9%
86.0 21
 
0.2%
84.25 6
 
0.1%
84.0 26
 
0.3%

강수량
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T22:48:33.705262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:48:33.785793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

풍향
Real number (ℝ)

Distinct589
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213.50264
Minimum0
Maximum357.9133
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:33.902430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.270505
Q169.5609
median287.2374
Q3307.95
95-th percentile350.0094
Maximum357.9133
Range357.9133
Interquartile range (IQR)238.3891

Descriptive statistics

Standard deviation122.22049
Coefficient of variation (CV)0.57245421
Kurtosis-1.466654
Mean213.50264
Median Absolute Deviation (MAD)49.4569
Skewness-0.52776283
Sum2135026.4
Variance14937.847
MonotonicityNot monotonic
2023-12-12T22:48:34.024020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.047 79
 
0.8%
63.5369 66
 
0.7%
57.0916 66
 
0.7%
330.5969 65
 
0.7%
357.9133 64
 
0.6%
42.7846 62
 
0.6%
308.9353 62
 
0.6%
28.9036 59
 
0.6%
98.9056 59
 
0.6%
100.3991 56
 
0.6%
Other values (579) 9362
93.6%
ValueCountFrequency (%)
0.0 11
 
0.1%
0.0117 48
0.5%
1.0771 17
 
0.2%
2.9582 35
0.4%
3.4175 48
0.5%
5.3313 6
 
0.1%
6.6395 6
 
0.1%
7.5704 3
 
< 0.1%
8.2566 42
0.4%
8.9611 4
 
< 0.1%
ValueCountFrequency (%)
357.9133 64
0.6%
356.9265 32
0.3%
355.9743 28
0.3%
355.2805 4
 
< 0.1%
354.0763 3
 
< 0.1%
353.9559 28
0.3%
353.0136 46
0.5%
352.7041 42
0.4%
352.2709 25
 
0.2%
351.7101 51
0.5%

풍속
Real number (ℝ)

Distinct585
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2891918
Minimum0
Maximum5.5416
Zeros11
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:34.156827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2344
Q10.6292
median1.0794
Q31.7432
95-th percentile3.03753
Maximum5.5416
Range5.5416
Interquartile range (IQR)1.114

Descriptive statistics

Standard deviation0.92474758
Coefficient of variation (CV)0.717308
Kurtosis2.1997815
Mean1.2891918
Median Absolute Deviation (MAD)0.5191
Skewness1.3589949
Sum12891.918
Variance0.85515809
MonotonicityNot monotonic
2023-12-12T22:48:34.296104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4678 79
 
0.8%
1.1487 66
 
0.7%
0.7557 66
 
0.7%
0.3519 65
 
0.7%
0.6206 64
 
0.6%
0.757 62
 
0.6%
1.5445 62
 
0.6%
0.3565 59
 
0.6%
0.1943 59
 
0.6%
0.694 56
 
0.6%
Other values (575) 9362
93.6%
ValueCountFrequency (%)
0.0 11
 
0.1%
0.0724 35
0.4%
0.075 1
 
< 0.1%
0.118 52
0.5%
0.1191 30
0.3%
0.149 31
0.3%
0.159 40
0.4%
0.1662 26
0.3%
0.1672 35
0.4%
0.1814 6
 
0.1%
ValueCountFrequency (%)
5.5416 6
 
0.1%
5.4713 11
 
0.1%
5.368 1
 
< 0.1%
5.1049 2
 
< 0.1%
5.0387 2
 
< 0.1%
4.7185 20
0.2%
4.662 8
 
0.1%
4.587 9
 
0.1%
4.5408 1
 
< 0.1%
4.4892 45
0.4%

유동인구
Real number (ℝ)

HIGH CORRELATION 

Distinct4246
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22009.456
Minimum530.5
Maximum120282.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:48:34.429111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum530.5
5-th percentile1784.713
Q15937.85
median13987.09
Q320891.177
95-th percentile96534.25
Maximum120282.46
Range119751.96
Interquartile range (IQR)14953.327

Descriptive statistics

Standard deviation26656.675
Coefficient of variation (CV)1.2111465
Kurtosis3.4967451
Mean22009.456
Median Absolute Deviation (MAD)7772.34
Skewness2.0722315
Sum2.2009456 × 108
Variance7.1057833 × 108
MonotonicityNot monotonic
2023-12-12T22:48:34.567150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83585.96 16
 
0.2%
86038.47 16
 
0.2%
73458.94 16
 
0.2%
98897.2 15
 
0.1%
97758.24 15
 
0.1%
96789.22 15
 
0.1%
100785.29 14
 
0.1%
79032.55 14
 
0.1%
97843.24 14
 
0.1%
108608.26 14
 
0.1%
Other values (4236) 9851
98.5%
ValueCountFrequency (%)
530.5 1
< 0.1%
535.63 1
< 0.1%
537.07 1
< 0.1%
543.73 1
< 0.1%
547.92 1
< 0.1%
549.98 1
< 0.1%
565.53 1
< 0.1%
569.36 1
< 0.1%
576.4 1
< 0.1%
579.18 1
< 0.1%
ValueCountFrequency (%)
120282.46 12
0.1%
119357.06 10
0.1%
118475.28 8
0.1%
118426.52 4
 
< 0.1%
118218.57 8
0.1%
117521.14 6
0.1%
116896.56 8
0.1%
115042.86 6
0.1%
114869.4 9
0.1%
114246.86 11
0.1%

Interactions

2023-12-12T22:48:30.679060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:24.677120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.539405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.448770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.212236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.104354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.944293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.813749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.787005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:24.774886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.657992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.543588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.307350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.217592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.029329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.913571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.210069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:24.872532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.809599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.633605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.419491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.315348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.132123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.016716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.310183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:24.970066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.910961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.727230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.536754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.429316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.249390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.145844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.409465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.062964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.013508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.813427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.627077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.529317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.385846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.267877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.531317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.183187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.125380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.922208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.731526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.637149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.499374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.366992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.637208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.309389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.231662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.017423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.838273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.755702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.593041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.493294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:31.741274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:25.416550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:26.337004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.106619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:27.947896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:28.850043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:29.694662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:48:30.580571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:48:34.662895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월일시간우편번호상권코드기온습도풍향풍속유동인구
연월일1.0000.2240.0470.0610.8320.8600.7790.7320.354
시간0.2241.0000.1060.0710.4440.5150.4360.4030.377
우편번호0.0470.1061.0000.9360.2480.1260.3920.4510.821
상권코드0.0610.0710.9361.0000.2110.0850.3060.3550.735
기온0.8320.4440.2480.2111.0000.6080.5630.6320.288
습도0.8600.5150.1260.0850.6081.0000.4650.5040.282
풍향0.7790.4360.3920.3060.5630.4651.0000.6110.363
풍속0.7320.4030.4510.3550.6320.5040.6111.0000.446
유동인구0.3540.3770.8210.7350.2880.2820.3630.4461.000
2023-12-12T22:48:34.818300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간우편번호상권코드기온습도풍향풍속유동인구연월일
시간1.000-0.0230.022-0.3100.320-0.196-0.264-0.0300.092
우편번호-0.0231.000-0.5990.1280.012-0.0020.3580.3980.018
상권코드0.022-0.5991.000-0.136-0.027-0.004-0.285-0.5820.021
기온-0.3100.128-0.1361.000-0.141-0.1960.1790.1090.473
습도0.3200.012-0.027-0.1411.000-0.000-0.224-0.0040.517
풍향-0.196-0.002-0.004-0.196-0.0001.0000.0530.0180.405
풍속-0.2640.358-0.2850.179-0.2240.0531.0000.1650.356
유동인구-0.0300.398-0.5820.109-0.0040.0180.1651.0000.131
연월일0.0920.0180.0210.4730.5170.4050.3560.1311.000

Missing values

2023-12-12T22:48:31.894666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:48:32.063337image/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

연월일시간우편번호상권코드기온습도강수량풍향풍속유동인구
827602020-02-0123696255.17558.250286.40081.46544902.77
522162020-01-20537455-5.0552.25034.85231.15792296.93
789672020-01-3053734222.37562.0055.12081.83921649.69
696852020-01-2743714124.0573.750356.92650.84585382.66
323182020-01-136371231-2.151.5020.35250.6755657.45
678812020-01-2653691185.42547.75016.92861.559318255.12
893622020-02-034363010-4.02542.50316.84390.73667733.16
132322020-01-0553636242.67541.0065.07481.59856512.38
463352020-01-185377811.967.00158.98370.730697758.24
831462020-02-0133662161.6574.250323.58430.58137385.13
연월일시간우편번호상권코드기온습도강수량풍향풍속유동인구
564322020-01-223375784.6554.750109.67180.96532664.88
879742020-02-026372514-0.751.750304.90961.73495785.68
69712020-01-034367915-1.47560.75014.9990.4499195.13
373032020-01-15537383-5.3557.250348.77270.618436489.25
263002020-01-11536756-2.92575.250314.5120.92035204.86
775372020-01-303377616.061.50311.21560.8774101298.54
361572020-01-153366516-2.87549.50342.21890.76176613.82
99572020-01-044367117-1.67556.250351.71010.56223327.9
87842020-01-042376424.82544.50289.92722.41810774.98
625232020-01-243361691.82561.00353.95590.18616880.53

Duplicate rows

Most frequently occurring

연월일시간우편번호상권코드기온습도강수량풍향풍속유동인구# duplicates
5692020-01-114364623-1.72572.50301.29871.247120231.426
6512020-01-12437771-1.62568.750274.98991.531486038.476
18522020-01-315364623-1.07573.50330.59690.351920204.256
20002020-02-02537881-0.27558.50292.51463.545873458.946
2542020-01-0543646232.07542.75063.08511.900920940.135
3272020-01-083367560.12582.50282.35821.94724699.05
3562020-01-08536924-1.776.250308.93531.544516842.45
5552020-01-113364623-0.8562.50315.14341.259121158.945
5852020-01-115366516-2.92575.250314.5120.92037247.065
5972020-01-11537761-1.62572.50294.5052.5225100119.65