Overview

Dataset statistics

Number of variables15
Number of observations137
Missing cells373
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 KiB
Average record size in memory135.0 B

Variable types

DateTime1
Numeric14

Dataset

Description2011~2022년 10년치 월별 불법주정차 단속통계치가 들어있는 파일입니다 . 각 년의 월별을 기준으로 단속건수가 나와있으며, 단속주체로 종로구와 시청으로 나뉘어져 있습니다.
Author서울특별시 종로구
URLhttps://www.data.go.kr/data/15101868/fileData.do

Alerts

총합계 is highly overall correlated with 구청합계 and 1 other fieldsHigh correlation
구청합계 is highly overall correlated with 총합계 and 2 other fieldsHigh correlation
시청합계 is highly overall correlated with 종로구청 고정형 CCTV and 4 other fieldsHigh correlation
종로구청 고정형 CCTV is highly overall correlated with 구청합계 and 4 other fieldsHigh correlation
종로구청 주행형CCTV is highly overall correlated with 총합계 and 2 other fieldsHigh correlation
종로구청 PDA is highly overall correlated with 시청합계 and 5 other fieldsHigh correlation
종로구청 본관 상황실 외 is highly overall correlated with 종로구 소방서 and 1 other fieldsHigh correlation
종로구 소방서 is highly overall correlated with 종로구청 본관 상황실 외 High correlation
종로구청 시민신고 is highly overall correlated with 시청합계 and 3 other fieldsHigh correlation
시청 고정형 CCTV is highly overall correlated with 시청합계 and 6 other fieldsHigh correlation
시청 주행형 CCTV is highly overall correlated with 시청 고정형 CCTV High correlation
시청 버스 CCTV is highly overall correlated with 종로구청 본관 상황실 외 and 1 other fieldsHigh correlation
시청 PDA is highly overall correlated with 시청합계 and 2 other fieldsHigh correlation
종로구청 주행형CCTV has 31 (22.6%) missing valuesMissing
종로구청 본관 상황실 외 has 124 (90.5%) missing valuesMissing
종로구 소방서 has 68 (49.6%) missing valuesMissing
종로구 경찰서 has 26 (19.0%) missing valuesMissing
종로구청 시민신고 has 31 (22.6%) missing valuesMissing
시청 주행형 CCTV has 12 (8.8%) missing valuesMissing
시청 버스 CCTV has 81 (59.1%) missing valuesMissing
연월 has unique valuesUnique
총합계 has unique valuesUnique
종로구청 PDA has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:43:18.524148
Analysis finished2023-12-12 08:43:41.648392
Duration23.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
Minimum2011-01-01 00:00:00
Maximum2022-05-01 00:00:00
2023-12-12T17:43:41.750761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:41.949124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

총합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14558.409
Minimum9022
Maximum20217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:42.111969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9022
5-th percentile10668.6
Q113221
median14762
Q315909
95-th percentile17583.2
Maximum20217
Range11195
Interquartile range (IQR)2688

Descriptive statistics

Standard deviation2134.2164
Coefficient of variation (CV)0.14659682
Kurtosis0.18981442
Mean14558.409
Median Absolute Deviation (MAD)1401
Skewness-0.17576286
Sum1994502
Variance4554879.8
MonotonicityNot monotonic
2023-12-12T17:43:42.262364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9745 1
 
0.7%
14607 1
 
0.7%
14112 1
 
0.7%
12492 1
 
0.7%
15207 1
 
0.7%
16320 1
 
0.7%
12772 1
 
0.7%
13826 1
 
0.7%
13221 1
 
0.7%
13828 1
 
0.7%
Other values (127) 127
92.7%
ValueCountFrequency (%)
9022 1
0.7%
9415 1
0.7%
9745 1
0.7%
9951 1
0.7%
10112 1
0.7%
10474 1
0.7%
10539 1
0.7%
10701 1
0.7%
10769 1
0.7%
11101 1
0.7%
ValueCountFrequency (%)
20217 1
0.7%
20149 1
0.7%
19266 1
0.7%
18520 1
0.7%
18204 1
0.7%
18118 1
0.7%
17760 1
0.7%
17539 1
0.7%
17506 1
0.7%
17328 1
0.7%

구청합계
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11202.015
Minimum5994
Maximum16497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:42.443778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5994
5-th percentile7770
Q19963
median11466
Q312477
95-th percentile14263.6
Maximum16497
Range10503
Interquartile range (IQR)2514

Descriptive statistics

Standard deviation2006.1952
Coefficient of variation (CV)0.17909235
Kurtosis0.25952903
Mean11202.015
Median Absolute Deviation (MAD)1105
Skewness-0.22937523
Sum1534676
Variance4024819
MonotonicityNot monotonic
2023-12-12T17:43:42.600610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12605 2
 
1.5%
6466 1
 
0.7%
12477 1
 
0.7%
11226 1
 
0.7%
9929 1
 
0.7%
12304 1
 
0.7%
12093 1
 
0.7%
10698 1
 
0.7%
12105 1
 
0.7%
11467 1
 
0.7%
Other values (126) 126
92.0%
ValueCountFrequency (%)
5994 1
0.7%
6357 1
0.7%
6466 1
0.7%
6669 1
0.7%
7155 1
0.7%
7714 1
0.7%
7758 1
0.7%
7773 1
0.7%
7864 1
0.7%
7927 1
0.7%
ValueCountFrequency (%)
16497 1
0.7%
16168 1
0.7%
15701 1
0.7%
15599 1
0.7%
14770 1
0.7%
14480 1
0.7%
14446 1
0.7%
14218 1
0.7%
14092 1
0.7%
13651 1
0.7%

시청합계
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3356.3942
Minimum1616
Maximum5592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:42.790425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1616
5-th percentile2000.8
Q12661
median3190
Q34182
95-th percentile4959.2
Maximum5592
Range3976
Interquartile range (IQR)1521

Descriptive statistics

Standard deviation959.37761
Coefficient of variation (CV)0.2858358
Kurtosis-0.97169291
Mean3356.3942
Median Absolute Deviation (MAD)756
Skewness0.22454131
Sum459826
Variance920405.4
MonotonicityNot monotonic
2023-12-12T17:43:43.019766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3580 2
 
1.5%
3279 1
 
0.7%
1754 1
 
0.7%
2563 1
 
0.7%
2903 1
 
0.7%
4227 1
 
0.7%
2074 1
 
0.7%
1721 1
 
0.7%
2130 1
 
0.7%
2404 1
 
0.7%
Other values (126) 126
92.0%
ValueCountFrequency (%)
1616 1
0.7%
1719 1
0.7%
1721 1
0.7%
1754 1
0.7%
1818 1
0.7%
1890 1
0.7%
1912 1
0.7%
2023 1
0.7%
2038 1
0.7%
2045 1
0.7%
ValueCountFrequency (%)
5592 1
0.7%
5221 1
0.7%
5146 1
0.7%
5059 1
0.7%
5042 1
0.7%
4985 1
0.7%
4976 1
0.7%
4955 1
0.7%
4860 1
0.7%
4837 1
0.7%

종로구청 고정형 CCTV
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5340.854
Minimum1160
Maximum12453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:43.192577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1160
5-th percentile2666.2
Q13782
median4505
Q36495
95-th percentile10249
Maximum12453
Range11293
Interquartile range (IQR)2713

Descriptive statistics

Standard deviation2357.5695
Coefficient of variation (CV)0.44142182
Kurtosis0.38446918
Mean5340.854
Median Absolute Deviation (MAD)1289
Skewness0.9806481
Sum731697
Variance5558133.9
MonotonicityNot monotonic
2023-12-12T17:43:43.654094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4252 2
 
1.5%
1898 1
 
0.7%
6089 1
 
0.7%
4505 1
 
0.7%
6697 1
 
0.7%
6323 1
 
0.7%
4698 1
 
0.7%
5794 1
 
0.7%
6156 1
 
0.7%
6554 1
 
0.7%
Other values (126) 126
92.0%
ValueCountFrequency (%)
1160 1
0.7%
1898 1
0.7%
1915 1
0.7%
2119 1
0.7%
2172 1
0.7%
2346 1
0.7%
2551 1
0.7%
2695 1
0.7%
2718 1
0.7%
2737 1
0.7%
ValueCountFrequency (%)
12453 1
0.7%
11600 1
0.7%
11397 1
0.7%
11095 1
0.7%
10626 1
0.7%
10618 1
0.7%
10297 1
0.7%
10237 1
0.7%
10138 1
0.7%
9692 1
0.7%

종로구청 주행형CCTV
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct105
Distinct (%)99.1%
Missing31
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean800.96226
Minimum14
Maximum2218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:43.807585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile71
Q1352.25
median751
Q31157
95-th percentile1652.25
Maximum2218
Range2204
Interquartile range (IQR)804.75

Descriptive statistics

Standard deviation528.82308
Coefficient of variation (CV)0.6602347
Kurtosis-0.45326881
Mean800.96226
Median Absolute Deviation (MAD)407
Skewness0.49106837
Sum84902
Variance279653.85
MonotonicityNot monotonic
2023-12-12T17:43:43.943276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
208 2
 
1.5%
1165 1
 
0.7%
770 1
 
0.7%
793 1
 
0.7%
831 1
 
0.7%
844 1
 
0.7%
1467 1
 
0.7%
1403 1
 
0.7%
1371 1
 
0.7%
1299 1
 
0.7%
Other values (95) 95
69.3%
(Missing) 31
 
22.6%
ValueCountFrequency (%)
14 1
0.7%
25 1
0.7%
35 1
0.7%
38 1
0.7%
56 1
0.7%
69 1
0.7%
77 1
0.7%
89 1
0.7%
115 1
0.7%
127 1
0.7%
ValueCountFrequency (%)
2218 1
0.7%
2070 1
0.7%
2030 1
0.7%
1838 1
0.7%
1805 1
0.7%
1662 1
0.7%
1623 1
0.7%
1619 1
0.7%
1588 1
0.7%
1587 1
0.7%

종로구청 PDA
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4982.9124
Minimum1923
Maximum9147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:44.072355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1923
5-th percentile2412.4
Q13717
median5058
Q36084
95-th percentile7493.8
Maximum9147
Range7224
Interquartile range (IQR)2367

Descriptive statistics

Standard deviation1609.6406
Coefficient of variation (CV)0.32303208
Kurtosis-0.56556358
Mean4982.9124
Median Absolute Deviation (MAD)1223
Skewness0.081119789
Sum682659
Variance2590942.8
MonotonicityNot monotonic
2023-12-12T17:43:44.193183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4322 1
 
0.7%
5126 1
 
0.7%
5131 1
 
0.7%
4530 1
 
0.7%
4366 1
 
0.7%
4663 1
 
0.7%
4641 1
 
0.7%
4990 1
 
0.7%
4697 1
 
0.7%
3965 1
 
0.7%
Other values (127) 127
92.7%
ValueCountFrequency (%)
1923 1
0.7%
1950 1
0.7%
2093 1
0.7%
2125 1
0.7%
2225 1
0.7%
2288 1
0.7%
2314 1
0.7%
2437 1
0.7%
2474 1
0.7%
2571 1
0.7%
ValueCountFrequency (%)
9147 1
0.7%
8552 1
0.7%
8283 1
0.7%
8223 1
0.7%
8124 1
0.7%
7952 1
0.7%
7621 1
0.7%
7462 1
0.7%
7260 1
0.7%
7237 1
0.7%

종로구청 본관 상황실 외
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)84.6%
Missing124
Missing (%)90.5%
Infinite0
Infinite (%)0.0%
Mean11.461538
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:44.331033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8
Q16
median9
Q314
95-th percentile29
Maximum32
Range31
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.996438
Coefficient of variation (CV)0.78492412
Kurtosis1.4626715
Mean11.461538
Median Absolute Deviation (MAD)5
Skewness1.3785246
Sum149
Variance80.935897
MonotonicityNot monotonic
2023-12-12T17:43:44.471020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4 2
 
1.5%
11 2
 
1.5%
8 1
 
0.7%
9 1
 
0.7%
14 1
 
0.7%
32 1
 
0.7%
7 1
 
0.7%
27 1
 
0.7%
6 1
 
0.7%
15 1
 
0.7%
(Missing) 124
90.5%
ValueCountFrequency (%)
1 1
0.7%
4 2
1.5%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
11 2
1.5%
14 1
0.7%
15 1
0.7%
27 1
0.7%
ValueCountFrequency (%)
32 1
0.7%
27 1
0.7%
15 1
0.7%
14 1
0.7%
11 2
1.5%
9 1
0.7%
8 1
0.7%
7 1
0.7%
6 1
0.7%
4 2
1.5%

종로구 소방서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)11.6%
Missing68
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean2.5652174
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:44.572966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5.6
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.434303
Coefficient of variation (CV)0.94896558
Kurtosis10.838121
Mean2.5652174
Median Absolute Deviation (MAD)1
Skewness2.894811
Sum177
Variance5.9258312
MonotonicityNot monotonic
2023-12-12T17:43:44.682330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 31
22.6%
2 14
 
10.2%
3 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
10 2
 
1.5%
15 1
 
0.7%
6 1
 
0.7%
(Missing) 68
49.6%
ValueCountFrequency (%)
1 31
22.6%
2 14
10.2%
3 8
 
5.8%
4 7
 
5.1%
5 5
 
3.6%
6 1
 
0.7%
10 2
 
1.5%
15 1
 
0.7%
ValueCountFrequency (%)
15 1
 
0.7%
10 2
 
1.5%
6 1
 
0.7%
5 5
 
3.6%
4 7
 
5.1%
3 8
 
5.8%
2 14
10.2%
1 31
22.6%

종로구 경찰서
Real number (ℝ)

MISSING 

Distinct76
Distinct (%)68.5%
Missing26
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean53.513514
Minimum1
Maximum401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:44.809265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q18
median35
Q370.5
95-th percentile173.5
Maximum401
Range400
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation62.882844
Coefficient of variation (CV)1.1750835
Kurtosis8.2728686
Mean53.513514
Median Absolute Deviation (MAD)28
Skewness2.3624567
Sum5940
Variance3954.2521
MonotonicityNot monotonic
2023-12-12T17:43:44.950875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6
 
4.4%
8 5
 
3.6%
2 5
 
3.6%
6 4
 
2.9%
28 3
 
2.2%
12 3
 
2.2%
4 3
 
2.2%
5 3
 
2.2%
3 2
 
1.5%
46 2
 
1.5%
Other values (66) 75
54.7%
(Missing) 26
 
19.0%
ValueCountFrequency (%)
1 6
4.4%
2 5
3.6%
3 2
 
1.5%
4 3
2.2%
5 3
2.2%
6 4
2.9%
7 1
 
0.7%
8 5
3.6%
9 2
 
1.5%
10 1
 
0.7%
ValueCountFrequency (%)
401 1
0.7%
250 1
0.7%
205 1
0.7%
189 1
0.7%
182 1
0.7%
176 1
0.7%
171 2
1.5%
167 1
0.7%
153 1
0.7%
135 1
0.7%

종로구청 시민신고
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct86
Distinct (%)81.1%
Missing31
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean275.01887
Minimum2
Maximum1417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:45.081961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5.75
Q138.25
median181
Q3496
95-th percentile733
Maximum1417
Range1415
Interquartile range (IQR)457.75

Descriptive statistics

Standard deviation295.78741
Coefficient of variation (CV)1.0755168
Kurtosis2.2146468
Mean275.01887
Median Absolute Deviation (MAD)153
Skewness1.3828911
Sum29152
Variance87490.19
MonotonicityNot monotonic
2023-12-12T17:43:45.207924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44 4
 
2.9%
48 3
 
2.2%
35 3
 
2.2%
29 3
 
2.2%
2 3
 
2.2%
4 2
 
1.5%
668 2
 
1.5%
733 2
 
1.5%
616 2
 
1.5%
83 2
 
1.5%
Other values (76) 80
58.4%
(Missing) 31
 
22.6%
ValueCountFrequency (%)
2 3
2.2%
4 2
1.5%
5 1
 
0.7%
8 1
 
0.7%
9 1
 
0.7%
10 1
 
0.7%
12 1
 
0.7%
15 1
 
0.7%
16 1
 
0.7%
17 1
 
0.7%
ValueCountFrequency (%)
1417 1
0.7%
1269 1
0.7%
1199 1
0.7%
851 1
0.7%
747 1
0.7%
733 2
1.5%
668 2
1.5%
645 1
0.7%
618 1
0.7%
616 2
1.5%

시청 고정형 CCTV
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2391.7883
Minimum868
Maximum4201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:45.339614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum868
5-th percentile1192.2
Q11665
median2216
Q33186
95-th percentile3830.4
Maximum4201
Range3333
Interquartile range (IQR)1521

Descriptive statistics

Standard deviation882.87301
Coefficient of variation (CV)0.36912673
Kurtosis-1.0499211
Mean2391.7883
Median Absolute Deviation (MAD)699
Skewness0.33629807
Sum327675
Variance779464.76
MonotonicityNot monotonic
2023-12-12T17:43:45.478734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2447 2
 
1.5%
1789 2
 
1.5%
3406 2
 
1.5%
2022 2
 
1.5%
1025 1
 
0.7%
1542 1
 
0.7%
2314 1
 
0.7%
2614 1
 
0.7%
2056 1
 
0.7%
2218 1
 
0.7%
Other values (123) 123
89.8%
ValueCountFrequency (%)
868 1
0.7%
898 1
0.7%
1025 1
0.7%
1043 1
0.7%
1062 1
0.7%
1074 1
0.7%
1141 1
0.7%
1205 1
0.7%
1225 1
0.7%
1233 1
0.7%
ValueCountFrequency (%)
4201 1
0.7%
4188 1
0.7%
4123 1
0.7%
4051 1
0.7%
3969 1
0.7%
3931 1
0.7%
3856 1
0.7%
3824 1
0.7%
3797 1
0.7%
3753 1
0.7%

시청 주행형 CCTV
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct112
Distinct (%)89.6%
Missing12
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean361.096
Minimum1
Maximum1102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:45.632353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q1106
median287
Q3568
95-th percentile984
Maximum1102
Range1101
Interquartile range (IQR)462

Descriptive statistics

Standard deviation308.95353
Coefficient of variation (CV)0.85559941
Kurtosis-0.38841437
Mean361.096
Median Absolute Deviation (MAD)196
Skewness0.8237464
Sum45137
Variance95452.281
MonotonicityNot monotonic
2023-12-12T17:43:45.787813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181 3
 
2.2%
96 3
 
2.2%
1 3
 
2.2%
168 2
 
1.5%
113 2
 
1.5%
30 2
 
1.5%
106 2
 
1.5%
69 2
 
1.5%
3 2
 
1.5%
314 2
 
1.5%
Other values (102) 102
74.5%
(Missing) 12
 
8.8%
ValueCountFrequency (%)
1 3
2.2%
3 2
1.5%
4 1
 
0.7%
5 1
 
0.7%
20 1
 
0.7%
24 1
 
0.7%
25 1
 
0.7%
28 1
 
0.7%
30 2
1.5%
36 1
 
0.7%
ValueCountFrequency (%)
1102 1
0.7%
1077 1
0.7%
1071 1
0.7%
1034 1
0.7%
1032 1
0.7%
999 1
0.7%
986 1
0.7%
976 1
0.7%
964 1
0.7%
961 1
0.7%

시청 버스 CCTV
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)53.6%
Missing81
Missing (%)59.1%
Infinite0
Infinite (%)0.0%
Mean19
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:45.908926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median12
Q324
95-th percentile62.25
Maximum85
Range84
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.390729
Coefficient of variation (CV)1.0731962
Kurtosis2.4472353
Mean19
Median Absolute Deviation (MAD)8
Skewness1.7350879
Sum1064
Variance415.78182
MonotonicityNot monotonic
2023-12-12T17:43:46.034614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2 5
 
3.6%
3 4
 
2.9%
11 4
 
2.9%
24 3
 
2.2%
5 3
 
2.2%
8 3
 
2.2%
7 3
 
2.2%
18 3
 
2.2%
13 3
 
2.2%
1 2
 
1.5%
Other values (20) 23
 
16.8%
(Missing) 81
59.1%
ValueCountFrequency (%)
1 2
 
1.5%
2 5
3.6%
3 4
2.9%
4 1
 
0.7%
5 3
2.2%
6 2
 
1.5%
7 3
2.2%
8 3
2.2%
10 1
 
0.7%
11 4
2.9%
ValueCountFrequency (%)
85 1
0.7%
78 1
0.7%
72 1
0.7%
59 1
0.7%
55 1
0.7%
54 1
0.7%
53 1
0.7%
43 1
0.7%
41 1
0.7%
30 1
0.7%

시청 PDA
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean627.37226
Minimum140
Maximum1446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T17:43:46.148448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile205.8
Q1445
median629
Q3814
95-th percentile1043.6
Maximum1446
Range1306
Interquartile range (IQR)369

Descriptive statistics

Standard deviation258.38164
Coefficient of variation (CV)0.41184741
Kurtosis-0.082374669
Mean627.37226
Median Absolute Deviation (MAD)185
Skewness0.26360224
Sum85950
Variance66761.074
MonotonicityNot monotonic
2023-12-12T17:43:46.319690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
894 2
 
1.5%
541 2
 
1.5%
668 2
 
1.5%
445 2
 
1.5%
452 2
 
1.5%
486 2
 
1.5%
710 2
 
1.5%
951 2
 
1.5%
538 2
 
1.5%
709 1
 
0.7%
Other values (118) 118
86.1%
ValueCountFrequency (%)
140 1
0.7%
149 1
0.7%
168 1
0.7%
183 1
0.7%
186 1
0.7%
189 1
0.7%
205 1
0.7%
206 1
0.7%
223 1
0.7%
234 1
0.7%
ValueCountFrequency (%)
1446 1
0.7%
1346 1
0.7%
1174 1
0.7%
1092 1
0.7%
1073 1
0.7%
1056 1
0.7%
1050 1
0.7%
1042 1
0.7%
1009 1
0.7%
996 1
0.7%

Interactions

2023-12-12T17:43:39.304565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:18.942102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.541639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.121690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.738424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.148278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.691296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.366308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.065527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.963964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.431171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.709338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.223882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.062873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.421008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.053034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.660181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.223247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.824735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.244797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.814049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.484971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.182103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.058788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.518214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.795107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.349600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.137938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.528673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.185867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.796736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.317621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.924751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.338386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.936920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.610422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.281311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.149163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.599939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.882654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.459366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.223457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.639385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.302679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.927774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.428570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.057365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.435091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.072605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.732089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.421000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.262582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.687298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.979722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.554975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.321179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.776036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.411622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.028925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.523672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.215055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.536404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.191499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.830288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.537005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.377065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.768583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.060260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.658617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.407423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.881845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.527185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.139102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.606488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.310138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.623231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.291046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.968590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.640809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.466142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.846983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.168528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.756106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.491609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.988485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.667861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.280017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.711005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.400444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.731928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.419164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.089417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:30.759754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.569702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.960760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.277388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.880369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.568146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.110608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.768074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.391476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.810442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.495098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.840312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.529795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.217933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.187202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.670839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.052256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.392434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.004753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.650817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.226469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:19.887970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.498781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.897922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.589523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.943607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.681463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.337512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.291341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.783291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.157678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.506517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.116962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.756708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.380150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.017796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.629629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.994295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.709449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.066550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.816826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.467895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.398345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:32.941541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.253116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.658867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.493299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.849554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.505947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.129200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.733035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.093212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.797819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.227613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:27.924397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.575461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.520947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.047748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.346328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.766914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.618488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:38.979000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.623115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.230068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.826584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.182218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.891679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.350402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.040174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.697443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.653353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.163153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.442508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.883389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.726327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.058095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.718155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.330430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:21.923913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.272391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:24.970409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.486719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.142783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.816552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.762491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.244323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.539122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:35.989842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.845743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.140552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:40.840821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:20.413833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:22.015897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:23.345304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:25.050106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:26.584388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:28.251705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:29.928481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:31.874579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:33.318310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:34.620961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:36.094406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:37.943848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:43:39.220638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:43:46.428511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총합계구청합계시청합계종로구청 고정형 CCTV종로구청 주행형CCTV종로구청 PDA종로구청 본관 상황실 외종로구 소방서종로구 경찰서종로구청 시민신고시청 고정형 CCTV시청 주행형 CCTV시청 버스 CCTV시청 PDA
총합계1.0000.8900.1490.6360.5240.6570.5040.0000.6400.4080.4530.2770.2240.391
구청합계0.8901.0000.0000.8240.5790.6170.6490.0000.4600.3560.2780.0000.1150.290
시청합계0.1490.0001.0000.5020.0850.6670.0000.0000.1500.4090.8840.3410.0000.597
종로구청 고정형 CCTV0.6360.8240.5021.0000.2270.7230.3690.1240.4260.6410.6130.5210.5960.362
종로구청 주행형CCTV0.5240.5790.0850.2271.0000.4320.5680.0000.0000.3170.2720.3410.0000.357
종로구청 PDA0.6570.6170.6670.7230.4321.0000.0000.1990.0000.7470.7170.2260.0000.496
종로구청 본관 상황실 외0.5040.6490.0000.3690.5680.0001.0001.000NaNNaN0.6710.0000.6190.547
종로구 소방서0.0000.0000.0000.1240.0000.1991.0001.0000.0000.0000.3230.0000.8480.000
종로구 경찰서0.6400.4600.1500.4260.0000.000NaN0.0001.0000.6640.0000.0000.0000.000
종로구청 시민신고0.4080.3560.4090.6410.3170.747NaN0.0000.6641.0000.3840.454NaN0.212
시청 고정형 CCTV0.4530.2780.8840.6130.2720.7170.6710.3230.0000.3841.0000.4940.0000.433
시청 주행형 CCTV0.2770.0000.3410.5210.3410.2260.0000.0000.0000.4540.4941.0000.1740.284
시청 버스 CCTV0.2240.1150.0000.5960.0000.0000.6190.8480.000NaN0.0000.1741.0000.000
시청 PDA0.3910.2900.5970.3620.3570.4960.5470.0000.0000.2120.4330.2840.0001.000
2023-12-12T17:43:46.608966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총합계구청합계시청합계종로구청 고정형 CCTV종로구청 주행형CCTV종로구청 PDA종로구청 본관 상황실 외종로구 소방서종로구 경찰서종로구청 시민신고시청 고정형 CCTV시청 주행형 CCTV시청 버스 CCTV시청 PDA
총합계1.0000.8650.3640.2920.5950.4210.455-0.248-0.092-0.0470.2120.076-0.1650.344
구청합계0.8651.000-0.1040.6240.5600.1100.499-0.1760.0740.283-0.2160.180-0.3470.030
시청합계0.364-0.1041.000-0.6330.1470.6850.410-0.106-0.351-0.6060.916-0.2910.4960.676
종로구청 고정형 CCTV0.2920.624-0.6331.0000.118-0.6000.2980.0860.3070.812-0.7200.438-0.446-0.390
종로구청 주행형CCTV0.5950.5600.1470.1181.0000.5730.350-0.255-0.048-0.4210.064-0.166-0.4280.320
종로구청 PDA0.4210.1100.685-0.6000.5731.0000.353-0.233-0.275-0.7500.688-0.406-0.1430.614
종로구청 본관 상황실 외0.4550.4990.4100.2980.3500.3531.000-0.949-0.101NaN0.287-0.008-0.6240.441
종로구 소방서-0.248-0.176-0.1060.086-0.255-0.233-0.9491.000-0.0400.154-0.0960.0460.205-0.139
종로구 경찰서-0.0920.074-0.3510.307-0.048-0.275-0.101-0.0401.0000.350-0.2850.051-0.484-0.191
종로구청 시민신고-0.0470.283-0.6060.812-0.421-0.750NaN0.1540.3501.000-0.7030.457-0.173-0.428
시청 고정형 CCTV0.212-0.2160.916-0.7200.0640.6880.287-0.096-0.285-0.7031.000-0.5300.5720.517
시청 주행형 CCTV0.0760.180-0.2910.438-0.166-0.406-0.0080.0460.0510.457-0.5301.000-0.022-0.334
시청 버스 CCTV-0.165-0.3470.496-0.446-0.428-0.143-0.6240.205-0.484-0.1730.572-0.0221.0000.064
시청 PDA0.3440.0300.676-0.3900.3200.6140.441-0.139-0.191-0.4280.517-0.3340.0641.000

Missing values

2023-12-12T17:43:41.052371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:43:41.329542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T17:43:41.536412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연월총합계구청합계시청합계종로구청 고정형 CCTV종로구청 주행형CCTV종로구청 PDA종로구청 본관 상황실 외종로구 소방서종로구 경찰서종로구청 시민신고시청 고정형 CCTV시청 주행형 CCTV시청 버스 CCTV시청 PDA
02011-0197456466327918982464322<NA><NA><NA><NA>24473566470
12011-0290225994302811601484686<NA><NA><NA><NA>223833411445
22011-03125408359418119154216023<NA><NA><NA><NA>349510627553
32011-04132108255495530371595059<NA><NA><NA><NA>396917078738
42011-0512356786444922737695058<NA><NA><NA><NA>349210672822
52011-06126637947471630021274817<NA><NA>1<NA>365116854843
62011-0710539635741822119254211<NA><NA>2<NA>329217685629
72011-08131958563463229562605347<NA><NA><NA><NA>375321755607
82011-09120707773429727181764879<NA><NA><NA><NA>343011443710
92011-10151669574559223461997025<NA><NA>4<NA>412342053996
연월총합계구청합계시청합계종로구청 고정형 CCTV종로구청 주행형CCTV종로구청 PDA종로구청 본관 상황실 외종로구 소방서종로구 경찰서종로구청 시민신고시청 고정형 CCTV시청 주행형 CCTV시청 버스 CCTV시청 PDA
1272021-08140891217719129192<NA>2314<NA><NA>36681141426<NA>345
1282021-09123381061917197798<NA>2125<NA><NA>286681043390<NA>286
1292021-10152431309821459105<NA>3213<NA>5287471074731<NA>340
1302021-11161761328328939625<NA>3189<NA>4845714141071<NA>408
1312021-12137361099427427769<NA>2580<NA><NA><NA>6451330986<NA>426
1322022-0111272876625066017<NA>2225<NA><NA>85161253937<NA>316
1332022-029951813318185782<NA>2093<NA><NA>1257898686<NA>234
1342022-03122521022920237008<NA>2664<NA>3894651025830<NA>168
1352022-04123101042018906495<NA>3237<NA><NA>905981062623<NA>205
1362022-0510112849616165073<NA>2849<NA>423547868599<NA>149