Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows19
Duplicate rows (%)0.2%
Total size in memory859.4 KiB
Average record size in memory88.0 B

Variable types

DateTime1
Numeric8

Dataset

Description서대문구 전체 상권별 업종별 이용률-미세먼지 분석 기초자료 데이터로 날짜별 상권별, 업종별 날짜별, 시간별, 매출건수와 매출금액, 미세먼지, 초미세먼지 데이터 항목을 제공합니다
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15097165/fileData.do

Alerts

Dataset has 19 (0.2%) duplicate rowsDuplicates
우편번호 is highly overall correlated with 상권코드High correlation
상권코드 is highly overall correlated with 우편번호High correlation
매출건수 is highly overall correlated with 매출금액High correlation
매출금액 is highly overall correlated with 매출건수High correlation
미세먼지 is highly overall correlated with 초미세먼지High correlation
초미세먼지 is highly overall correlated with 미세먼지High correlation
매출금액 is highly skewed (γ1 = 31.88829233)Skewed

Reproduction

Analysis started2023-12-12 03:15:15.684431
Analysis finished2023-12-12 03:15:27.390282
Duration11.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2020-01-01 00:00:00
Maximum2020-02-03 00:00:00
2023-12-12T12:15:27.462114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:27.613444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)

시간
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9117
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:27.760433image/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 deviation1.4302824
Coefficient of variation (CV)0.36564214
Kurtosis-0.82967299
Mean3.9117
Median Absolute Deviation (MAD)1
Skewness-0.28408211
Sum39117
Variance2.0457077
MonotonicityNot monotonic
2023-12-12T12:15:27.887526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 2542
25.4%
4 2137
21.4%
3 2076
20.8%
6 1432
14.3%
2 1226
12.3%
1 587
 
5.9%
ValueCountFrequency (%)
1 587
 
5.9%
2 1226
12.3%
3 2076
20.8%
4 2137
21.4%
5 2542
25.4%
6 1432
14.3%
ValueCountFrequency (%)
6 1432
14.3%
5 2542
25.4%
4 2137
21.4%
3 2076
20.8%
2 1226
12.3%
1 587
 
5.9%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3706.9431
Minimum3605
Maximum3789
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:28.041582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3605
5-th percentile3624
Q13665
median3711
Q33757
95-th percentile3788
Maximum3789
Range184
Interquartile range (IQR)92

Descriptive statistics

Standard deviation53.687951
Coefficient of variation (CV)0.014483079
Kurtosis-1.1779145
Mean3706.9431
Median Absolute Deviation (MAD)46
Skewness-0.12362624
Sum37069431
Variance2882.3961
MonotonicityNot monotonic
2023-12-12T12:15:28.198334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3712 406
 
4.1%
3646 322
 
3.2%
3735 322
 
3.2%
3734 321
 
3.2%
3789 297
 
3.0%
3692 285
 
2.9%
3777 260
 
2.6%
3766 257
 
2.6%
3628 255
 
2.5%
3741 240
 
2.4%
Other values (71) 7035
70.3%
ValueCountFrequency (%)
3605 136
1.4%
3606 6
 
0.1%
3607 17
 
0.2%
3611 39
 
0.4%
3612 27
 
0.3%
3615 75
 
0.8%
3616 103
1.0%
3624 131
1.3%
3625 57
 
0.6%
3628 255
2.5%
ValueCountFrequency (%)
3789 297
3.0%
3788 218
2.2%
3787 152
1.5%
3780 182
1.8%
3779 188
1.9%
3778 67
 
0.7%
3777 260
2.6%
3776 216
2.2%
3767 145
1.5%
3766 257
2.6%

업종코드
Real number (ℝ)

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.7897
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:28.339310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median12
Q317
95-th percentile24
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.7156819
Coefficient of variation (CV)0.5696228
Kurtosis-1.2575136
Mean11.7897
Median Absolute Deviation (MAD)6
Skewness0.17117726
Sum117897
Variance45.100384
MonotonicityNot monotonic
2023-12-12T12:15:28.474988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
17 1432
14.3%
5 1140
11.4%
3 1088
10.9%
13 862
8.6%
4 750
 
7.5%
9 643
 
6.4%
24 614
 
6.1%
16 541
 
5.4%
18 487
 
4.9%
19 444
 
4.4%
Other values (15) 1999
20.0%
ValueCountFrequency (%)
1 72
 
0.7%
2 17
 
0.2%
3 1088
10.9%
4 750
7.5%
5 1140
11.4%
6 169
 
1.7%
7 269
 
2.7%
8 348
 
3.5%
9 643
6.4%
10 282
 
2.8%
ValueCountFrequency (%)
25 30
 
0.3%
24 614
6.1%
23 81
 
0.8%
22 140
 
1.4%
21 101
 
1.0%
20 206
 
2.1%
19 444
 
4.4%
18 487
 
4.9%
17 1432
14.3%
16 541
 
5.4%

상권코드
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.1579
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:28.607366image/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 deviation9.1364608
Coefficient of variation (CV)0.75148346
Kurtosis-0.9665326
Mean12.1579
Median Absolute Deviation (MAD)8
Skewness0.42254201
Sum121579
Variance83.474915
MonotonicityNot monotonic
2023-12-12T12:15:28.774542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1580
 
15.8%
2 649
 
6.5%
7 645
 
6.5%
11 489
 
4.9%
16 485
 
4.9%
18 457
 
4.6%
23 432
 
4.3%
6 403
 
4.0%
15 363
 
3.6%
20 346
 
3.5%
Other values (22) 4151
41.5%
ValueCountFrequency (%)
1 1580
15.8%
2 649
6.5%
3 275
 
2.8%
4 207
 
2.1%
5 341
 
3.4%
6 403
 
4.0%
7 645
6.5%
8 212
 
2.1%
9 244
 
2.4%
10 303
 
3.0%
ValueCountFrequency (%)
32 148
 
1.5%
31 206
2.1%
30 30
 
0.3%
29 171
 
1.7%
28 159
 
1.6%
27 128
 
1.3%
26 79
 
0.8%
25 167
 
1.7%
24 248
2.5%
23 432
4.3%

매출건수
Real number (ℝ)

HIGH CORRELATION 

Distinct311
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.96005
Minimum0.5
Maximum1425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:28.915589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q12.5
median6.5
Q318
95-th percentile70.05
Maximum1425
Range1424.5
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation43.902018
Coefficient of variation (CV)2.4444263
Kurtosis343.94448
Mean17.96005
Median Absolute Deviation (MAD)5.5
Skewness14.138817
Sum179600.5
Variance1927.3872
MonotonicityNot monotonic
2023-12-12T12:15:29.399016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 1146
 
11.5%
3.0 690
 
6.9%
2.0 673
 
6.7%
4.0 514
 
5.1%
0.5 446
 
4.5%
5.0 441
 
4.4%
6.0 398
 
4.0%
7.0 333
 
3.3%
8.0 287
 
2.9%
9.0 243
 
2.4%
Other values (301) 4829
48.3%
ValueCountFrequency (%)
0.5 446
 
4.5%
1.0 1146
11.5%
1.5 233
 
2.3%
2.0 673
6.7%
2.5 159
 
1.6%
3.0 690
6.9%
3.5 111
 
1.1%
4.0 514
5.1%
4.5 111
 
1.1%
5.0 441
 
4.4%
ValueCountFrequency (%)
1425.0 1
< 0.1%
1375.0 1
< 0.1%
1302.0 1
< 0.1%
1012.0 1
< 0.1%
880.0 1
< 0.1%
804.0 1
< 0.1%
669.0 1
< 0.1%
666.0 1
< 0.1%
513.0 1
< 0.1%
503.0 1
< 0.1%

매출금액
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5677
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290087.12
Minimum500
Maximum74311243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:29.570108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile7450
Q130000
median82500
Q3220500
95-th percentile1000000
Maximum74311243
Range74310743
Interquartile range (IQR)190500

Descriptive statistics

Standard deviation1671529
Coefficient of variation (CV)5.7621619
Kurtosis1197.4774
Mean290087.12
Median Absolute Deviation (MAD)64565
Skewness31.888292
Sum2.9008712 × 109
Variance2.7940091 × 1012
MonotonicityNot monotonic
2023-12-12T12:15:29.790675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15000.0 65
 
0.7%
10000.0 60
 
0.6%
6000.0 44
 
0.4%
20000.0 44
 
0.4%
18000.0 40
 
0.4%
12000.0 38
 
0.4%
5000.0 37
 
0.4%
25000.0 33
 
0.3%
17000.0 32
 
0.3%
40000.0 32
 
0.3%
Other values (5667) 9575
95.8%
ValueCountFrequency (%)
500.0 1
 
< 0.1%
625.0 1
 
< 0.1%
700.0 1
 
< 0.1%
750.0 2
 
< 0.1%
825.0 1
 
< 0.1%
900.0 2
 
< 0.1%
950.0 1
 
< 0.1%
1000.0 9
0.1%
1150.0 1
 
< 0.1%
1200.0 3
 
< 0.1%
ValueCountFrequency (%)
74311243.0 1
< 0.1%
70466805.0 1
< 0.1%
65646380.0 1
< 0.1%
58516614.0 1
< 0.1%
44352677.0 1
< 0.1%
39286942.0 1
< 0.1%
37907238.0 1
< 0.1%
33388383.0 1
< 0.1%
20930948.0 1
< 0.1%
15992664.0 1
< 0.1%

미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.9843
Minimum3
Maximum94.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:30.010858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6.75
Q126
median43.25
Q358.25
95-th percentile78.25
Maximum94.75
Range91.75
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation22.678912
Coefficient of variation (CV)0.52760919
Kurtosis-0.7062694
Mean42.9843
Median Absolute Deviation (MAD)15.75
Skewness0.071155654
Sum429843
Variance514.33304
MonotonicityNot monotonic
2023-12-12T12:15:30.236977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64.5 287
 
2.9%
40.25 249
 
2.5%
3.0 221
 
2.2%
13.75 198
 
2.0%
34.5 163
 
1.6%
39.0 162
 
1.6%
26.0 160
 
1.6%
57.25 152
 
1.5%
47.5 143
 
1.4%
71.5 137
 
1.4%
Other values (134) 8128
81.3%
ValueCountFrequency (%)
3.0 221
2.2%
3.25 71
 
0.7%
3.5 15
 
0.1%
4.25 79
 
0.8%
5.25 57
 
0.6%
5.5 14
 
0.1%
6.75 82
 
0.8%
7.0 92
0.9%
7.25 76
 
0.8%
7.5 96
1.0%
ValueCountFrequency (%)
94.75 35
 
0.4%
92.75 88
0.9%
92.25 49
0.5%
91.0 72
0.7%
87.25 67
0.7%
84.5 75
0.8%
83.75 50
0.5%
80.5 62
0.6%
78.25 62
0.6%
77.5 46
0.5%

초미세먼지
Real number (ℝ)

HIGH CORRELATION 

Distinct135
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.021725
Minimum1
Maximum76.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T12:15:30.427706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q115
median26.5
Q334.25
95-th percentile60.25
Maximum76.75
Range75.75
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation16.503637
Coefficient of variation (CV)0.6107544
Kurtosis0.29568202
Mean27.021725
Median Absolute Deviation (MAD)9
Skewness0.65311317
Sum270217.25
Variance272.37005
MonotonicityNot monotonic
2023-12-12T12:15:30.625622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.0 228
 
2.3%
3.0 223
 
2.2%
31.0 196
 
2.0%
34.25 184
 
1.8%
31.5 184
 
1.8%
34.0 168
 
1.7%
29.5 161
 
1.6%
18.75 161
 
1.6%
22.5 161
 
1.6%
1.0 158
 
1.6%
Other values (125) 8176
81.8%
ValueCountFrequency (%)
1.0 158
1.6%
1.25 43
 
0.4%
1.5 114
1.1%
1.75 71
 
0.7%
2.25 57
 
0.6%
2.5 17
 
0.2%
3.0 223
2.2%
3.75 77
 
0.8%
4.0 60
 
0.6%
4.25 57
 
0.6%
ValueCountFrequency (%)
76.75 49
0.5%
74.5 35
 
0.4%
72.25 88
0.9%
67.0 50
0.5%
66.5 32
 
0.3%
65.75 75
0.8%
63.0 14
 
0.1%
62.0 77
0.8%
61.25 59
0.6%
60.25 65
0.7%

Interactions

2023-12-12T12:15:25.963116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.055579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.294423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.557874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.651396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.221753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.230702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.069798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.099610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.159747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.455865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.698242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.805639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.347304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.321763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.161525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.240436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.297157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.613234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.836649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.958114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.492365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.421452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.261568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.384061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.430342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.756634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.964073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:22.430713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.607862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.519241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.364732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.528098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.627015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.936526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.100251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:22.583239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.745272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.625675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.464674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.672942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.794323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.088567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.242352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:22.735304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.867928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.746188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.594559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.810157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:18.975294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.236810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.394002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:22.898526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.029139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.853315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.740050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:26.947511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:19.124563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:20.378052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:21.507249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:23.050383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.135201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:24.945747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:15:25.851610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:15:30.776275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월일시간우편번호업종코드상권코드매출건수매출금액미세먼지초미세먼지
연월일1.0000.0810.0420.0370.0490.0000.0490.9190.907
시간0.0811.0000.0990.2540.0750.0800.0000.3330.318
우편번호0.0420.0991.0000.2640.9370.1250.0320.0290.000
업종코드0.0370.2540.2641.0000.2450.0830.0490.0660.064
상권코드0.0490.0750.9370.2451.0000.0820.0000.0000.000
매출건수0.0000.0800.1250.0830.0821.0000.9020.0240.010
매출금액0.0490.0000.0320.0490.0000.9021.0000.0000.000
미세먼지0.9190.3330.0290.0660.0000.0240.0001.0000.942
초미세먼지0.9070.3180.0000.0640.0000.0100.0000.9421.000
2023-12-12T12:15:30.912853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간우편번호업종코드상권코드매출건수매출금액미세먼지초미세먼지
시간1.000-0.016-0.0600.0120.0510.165-0.035-0.044
우편번호-0.0161.000-0.058-0.5990.1520.150-0.010-0.007
업종코드-0.060-0.0581.0000.040-0.028-0.1240.0160.018
상권코드0.012-0.5990.0401.000-0.221-0.198-0.007-0.009
매출건수0.0510.152-0.028-0.2211.0000.802-0.006-0.020
매출금액0.1650.150-0.124-0.1980.8021.0000.008-0.006
미세먼지-0.035-0.0100.016-0.007-0.0060.0081.0000.946
초미세먼지-0.044-0.0070.018-0.009-0.020-0.0060.9461.000

Missing values

2023-12-12T12:15:27.113793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:15:27.323215image/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

연월일시간우편번호업종코드상권코드매출건수매출금액미세먼지초미세먼지
389132020-01-14537643221.0175100.022.012.25
47382020-01-025375716811.575845.073.539.5
648692020-01-23436825184.0196000.070.7548.0
2942020-01-0123611594.036100.022.09.75
589662020-01-21436287272.028250.044.7526.75
430422020-01-1613713171213.074200.027.524.0
785082020-01-28637351734.013020.08.254.25
895172020-02-015378013146.0404800.092.7572.25
524692020-01-19237111872.025700.040.2531.75
311962020-01-11637343224.0105100.058.032.5
연월일시간우편번호업종코드상권코드매출건수매출금액미세먼지초미세먼지
474742020-01-174369216461.51315265.068.2540.0
376972020-01-14237892411.017000.029.021.75
579692020-01-2123665131623.0194300.036.023.0
230002020-01-092369113181.06500.050.7530.75
134602020-01-055371016722.0296360.057.2531.0
414052020-01-1543672865.021300.027.7517.5
912682020-02-02336921842.049000.083.7567.0
853082020-01-3133628161138.0955900.026.018.75
81192020-01-03636059288.058100.056.2531.5
212632020-01-08437571786.535635.037.7522.5

Duplicate rows

Most frequently occurring

연월일시간우편번호업종코드상권코드매출건수매출금액미세먼지초미세먼지# duplicates
02020-01-03236465234.028500.077.043.752
12020-01-0443646132320.0147845.041.7520.252
22020-01-054364632315.5181700.056.030.02
32020-01-08236464234.032500.04.251.52
42020-01-08536461623105.51112885.054.7532.52
52020-01-1053646182310.0179145.052.526.252
62020-01-10636463236.092800.050.030.02
72020-01-11436468231.524650.053.2527.752
82020-01-13236463237.571950.034.2516.252
92020-01-176364610232.044000.059.2540.252