Overview

Dataset statistics

Number of variables9
Number of observations59
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory81.2 B

Variable types

Categorical2
Numeric7

Dataset

Description년도,구분,일반이용자,운동시설,자전거,주요행사 및 마라톤,특화공원,기타,합계
Author한강사업본부
URLhttps://data.seoul.go.kr/dataList/OA-12039/S/1/datasetView.do

Alerts

일반이용자 is highly overall correlated with 자전거 and 4 other fieldsHigh correlation
운동시설 is highly overall correlated with 기타 and 1 other fieldsHigh correlation
자전거 is highly overall correlated with 일반이용자 and 4 other fieldsHigh correlation
주요행사 및 마라톤 is highly overall correlated with 일반이용자 and 4 other fieldsHigh correlation
특화공원 is highly overall correlated with 일반이용자 and 4 other fieldsHigh correlation
기타 is highly overall correlated with 일반이용자 and 5 other fieldsHigh correlation
합계 is highly overall correlated with 일반이용자 and 5 other fieldsHigh correlation
일반이용자 has unique valuesUnique
기타 has unique valuesUnique
합계 has unique valuesUnique
운동시설 has 2 (3.4%) zerosZeros
자전거 has 4 (6.8%) zerosZeros
주요행사 및 마라톤 has 3 (5.1%) zerosZeros
특화공원 has 43 (72.9%) zerosZeros

Reproduction

Analysis started2023-12-11 05:33:05.008830
Analysis finished2023-12-11 05:33:10.524484
Duration5.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct5
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size604.0 B
2012년
12 
2011년
12 
2010년
12 
2009년
12 
2013년
11 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013년
2nd row2013년
3rd row2013년
4th row2013년
5th row2013년

Common Values

ValueCountFrequency (%)
2012년 12
20.3%
2011년 12
20.3%
2010년 12
20.3%
2009년 12
20.3%
2013년 11
18.6%

Length

2023-12-11T14:33:10.588273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:33:10.700162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012년 12
20.3%
2011년 12
20.3%
2010년 12
20.3%
2009년 12
20.3%
2013년 11
18.6%

구분
Categorical

Distinct12
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size604.0 B
강서
5 
난지
5 
망원
5 
양화
5 
여의도
5 
Other values (7)
34 

Length

Max length3
Median length2
Mean length2.2372881
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강서
2nd row난지
3rd row망원
4th row양화
5th row여의도

Common Values

ValueCountFrequency (%)
강서 5
8.5%
난지 5
8.5%
망원 5
8.5%
양화 5
8.5%
여의도 5
8.5%
이촌 5
8.5%
반포 5
8.5%
잠원 5
8.5%
뚝섬 5
8.5%
잠실 5
8.5%
Other values (2) 9
15.3%

Length

2023-12-11T14:33:10.820103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강서 5
8.5%
난지 5
8.5%
망원 5
8.5%
양화 5
8.5%
여의도 5
8.5%
이촌 5
8.5%
반포 5
8.5%
잠원 5
8.5%
뚝섬 5
8.5%
잠실 5
8.5%
Other values (2) 9
15.3%

일반이용자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2237658.9
Minimum351032
Maximum11635571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:10.947504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum351032
5-th percentile597722.6
Q11015268.5
median1374424
Q31858239.5
95-th percentile7308455.7
Maximum11635571
Range11284539
Interquartile range (IQR)842971

Descriptive statistics

Standard deviation2408997.5
Coefficient of variation (CV)1.0765705
Kurtosis5.2387003
Mean2237658.9
Median Absolute Deviation (MAD)452493
Skewness2.360914
Sum1.3202187 × 108
Variance5.8032692 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:11.393108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
496357 1
 
1.7%
1594732 1
 
1.7%
921931 1
 
1.7%
3482371 1
 
1.7%
686360 1
 
1.7%
1378248 1
 
1.7%
594524 1
 
1.7%
1284094 1
 
1.7%
974818 1
 
1.7%
3006968 1
 
1.7%
Other values (49) 49
83.1%
ValueCountFrequency (%)
351032 1
1.7%
496357 1
1.7%
594524 1
1.7%
598078 1
1.7%
686360 1
1.7%
730872 1
1.7%
733901 1
1.7%
770420 1
1.7%
831165 1
1.7%
849218 1
1.7%
ValueCountFrequency (%)
11635571 1
1.7%
9545299 1
1.7%
9409674 1
1.7%
7074987 1
1.7%
6864264 1
1.7%
5883009 1
1.7%
5526956 1
1.7%
5161329 1
1.7%
4066798 1
1.7%
3935854 1
1.7%

운동시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316888.8
Minimum0
Maximum2726779
Zeros2
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:11.541400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile256.8
Q148915.5
median152737
Q3327060.5
95-th percentile1182763.2
Maximum2726779
Range2726779
Interquartile range (IQR)278145

Descriptive statistics

Standard deviation549423.58
Coefficient of variation (CV)1.7338056
Kurtosis11.346289
Mean316888.8
Median Absolute Deviation (MAD)113471
Skewness3.3326243
Sum18696439
Variance3.0186627 × 1011
MonotonicityNot monotonic
2023-12-11T14:33:11.679701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.4%
117064 1
 
1.7%
262 1
 
1.7%
58227 1
 
1.7%
21113 1
 
1.7%
107401 1
 
1.7%
29580 1
 
1.7%
38026 1
 
1.7%
210 1
 
1.7%
473758 1
 
1.7%
Other values (48) 48
81.4%
ValueCountFrequency (%)
0 2
3.4%
210 1
1.7%
262 1
1.7%
11486 1
1.7%
16828 1
1.7%
18414 1
1.7%
21113 1
1.7%
22470 1
1.7%
29580 1
1.7%
30003 1
1.7%
ValueCountFrequency (%)
2726779 1
1.7%
2507321 1
1.7%
2169696 1
1.7%
1073104 1
1.7%
817038 1
1.7%
793541 1
1.7%
512037 1
1.7%
498972 1
1.7%
490877 1
1.7%
473758 1
1.7%

자전거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean887953.03
Minimum0
Maximum3637311
Zeros4
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:11.835661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1533147
median679795
Q31045824.5
95-th percentile2051429
Maximum3637311
Range3637311
Interquartile range (IQR)512677.5

Descriptive statistics

Standard deviation703771
Coefficient of variation (CV)0.79257683
Kurtosis6.2750467
Mean887953.03
Median Absolute Deviation (MAD)214873
Skewness2.175266
Sum52389229
Variance4.9529362 × 1011
MonotonicityNot monotonic
2023-12-11T14:33:11.999536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
6.8%
993015 1
 
1.7%
410023 1
 
1.7%
1601713 1
 
1.7%
169975 1
 
1.7%
967040 1
 
1.7%
211310 1
 
1.7%
2040023 1
 
1.7%
920200 1
 
1.7%
772634 1
 
1.7%
Other values (46) 46
78.0%
ValueCountFrequency (%)
0 4
6.8%
169975 1
 
1.7%
211310 1
 
1.7%
396165 1
 
1.7%
410023 1
 
1.7%
431169 1
 
1.7%
464922 1
 
1.7%
481561 1
 
1.7%
487872 1
 
1.7%
493122 1
 
1.7%
ValueCountFrequency (%)
3637311 1
1.7%
3596601 1
1.7%
2154083 1
1.7%
2040023 1
1.7%
1977511 1
1.7%
1620040 1
1.7%
1601713 1
1.7%
1503655 1
1.7%
1474442 1
1.7%
1466451 1
1.7%

주요행사 및 마라톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean474734.51
Minimum0
Maximum9100608
Zeros3
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:12.173660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile65.7
Q120982
median92030
Q3204427.5
95-th percentile2082759.7
Maximum9100608
Range9100608
Interquartile range (IQR)183445.5

Descriptive statistics

Standard deviation1518898
Coefficient of variation (CV)3.1994683
Kurtosis24.137269
Mean474734.51
Median Absolute Deviation (MAD)82684
Skewness4.852412
Sum28009336
Variance2.3070511 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:12.323292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
5.1%
6180 1
 
1.7%
256790 1
 
1.7%
3419 1
 
1.7%
95084 1
 
1.7%
7747 1
 
1.7%
200460 1
 
1.7%
73 1
 
1.7%
25330 1
 
1.7%
29195 1
 
1.7%
Other values (47) 47
79.7%
ValueCountFrequency (%)
0 3
5.1%
73 1
 
1.7%
1651 1
 
1.7%
2300 1
 
1.7%
3419 1
 
1.7%
3665 1
 
1.7%
6180 1
 
1.7%
7050 1
 
1.7%
7747 1
 
1.7%
9346 1
 
1.7%
ValueCountFrequency (%)
9100608 1
1.7%
7197837 1
1.7%
2328205 1
1.7%
2055488 1
1.7%
1003833 1
1.7%
578031 1
1.7%
529510 1
1.7%
443660 1
1.7%
423041 1
1.7%
411248 1
1.7%

특화공원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean932480.51
Minimum0
Maximum13144506
Zeros43
Zeros (%)72.9%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:12.440005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q385227
95-th percentile8079347.6
Maximum13144506
Range13144506
Interquartile range (IQR)85227

Descriptive statistics

Standard deviation2783074.9
Coefficient of variation (CV)2.9845931
Kurtosis12.474494
Mean932480.51
Median Absolute Deviation (MAD)0
Skewness3.5823052
Sum55016350
Variance7.7455061 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:12.568783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 43
72.9%
2136814 1
 
1.7%
112013 1
 
1.7%
182966 1
 
1.7%
8035813 1
 
1.7%
895972 1
 
1.7%
1362169 1
 
1.7%
8471159 1
 
1.7%
317407 1
 
1.7%
969895 1
 
1.7%
Other values (7) 7
 
11.9%
ValueCountFrequency (%)
0 43
72.9%
58441 1
 
1.7%
112013 1
 
1.7%
113608 1
 
1.7%
182966 1
 
1.7%
317407 1
 
1.7%
895972 1
 
1.7%
969895 1
 
1.7%
1058319 1
 
1.7%
1362169 1
 
1.7%
ValueCountFrequency (%)
13144506 1
1.7%
12968293 1
1.7%
8471159 1
1.7%
8035813 1
1.7%
3007244 1
1.7%
2181731 1
1.7%
2136814 1
1.7%
1362169 1
1.7%
1058319 1
1.7%
969895 1
1.7%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237556.1
Minimum288
Maximum1459475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:12.706185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum288
5-th percentile1160.5
Q134581.5
median128196
Q3248359
95-th percentile887402.4
Maximum1459475
Range1459187
Interquartile range (IQR)213777.5

Descriptive statistics

Standard deviation298052.94
Coefficient of variation (CV)1.2546634
Kurtosis4.4704829
Mean237556.1
Median Absolute Deviation (MAD)99165
Skewness2.0414575
Sum14015810
Variance8.8835554 × 1010
MonotonicityNot monotonic
2023-12-11T14:33:12.853568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4767 1
 
1.7%
167150 1
 
1.7%
4349 1
 
1.7%
227163 1
 
1.7%
661 1
 
1.7%
266760 1
 
1.7%
1216 1
 
1.7%
326052 1
 
1.7%
9018 1
 
1.7%
123628 1
 
1.7%
Other values (49) 49
83.1%
ValueCountFrequency (%)
288 1
1.7%
624 1
1.7%
661 1
1.7%
1216 1
1.7%
4349 1
1.7%
4659 1
1.7%
4767 1
1.7%
9018 1
1.7%
10041 1
1.7%
12748 1
1.7%
ValueCountFrequency (%)
1459475 1
1.7%
963836 1
1.7%
890799 1
1.7%
887025 1
1.7%
787613 1
1.7%
733652 1
1.7%
709741 1
1.7%
572727 1
1.7%
548508 1
1.7%
531672 1
1.7%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct59
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5087271.8
Minimum844377
Maximum29638880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size663.0 B
2023-12-11T14:33:13.043839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum844377
5-th percentile883348.7
Q11976682
median2657865
Q33687859.5
95-th percentile23229247
Maximum29638880
Range28794503
Interquartile range (IQR)1711177.5

Descriptive statistics

Standard deviation6775529.8
Coefficient of variation (CV)1.3318592
Kurtosis5.9054782
Mean5087271.8
Median Absolute Deviation (MAD)776207
Skewness2.5545982
Sum3.0014904 × 108
Variance4.5907803 × 1013
MonotonicityNot monotonic
2023-12-11T14:33:13.181948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1548880 1
 
1.7%
5222519 1
 
1.7%
926542 1
 
1.7%
6988433 1
 
1.7%
881528 1
 
1.7%
2814533 1
 
1.7%
844377 1
 
1.7%
4784627 1
 
1.7%
984119 1
 
1.7%
4549884 1
 
1.7%
Other values (49) 49
83.1%
ValueCountFrequency (%)
844377 1
1.7%
870913 1
1.7%
881528 1
1.7%
883551 1
1.7%
926542 1
1.7%
984119 1
1.7%
1114142 1
1.7%
1290439 1
1.7%
1548880 1
1.7%
1551218 1
1.7%
ValueCountFrequency (%)
29638880 1
1.7%
29458908 1
1.7%
24379191 1
1.7%
23101475 1
1.7%
16125106 1
1.7%
14940372 1
1.7%
14782210 1
1.7%
11562194 1
1.7%
7864575 1
1.7%
7218226 1
1.7%

Interactions

2023-12-11T14:33:09.682116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.379873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.130978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.799721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.538589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.309076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.971593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.767162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.481426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.238959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.894338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.652696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.416273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.084664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.852879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.588625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.334269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.993994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.768175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.501246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.184332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.943381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.711673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.438869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.100674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.884418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.612479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.301718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:10.022909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.816142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.531853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.192895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.983275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.689855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.395923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:10.112728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:05.912368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.619979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.300880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.103217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.771841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.482273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:10.197204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.000208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:06.710441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:07.422570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.202560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:08.883553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:33:09.573243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:33:13.301162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
년도1.0000.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0000.6080.7390.7430.4660.6180.6450.552
일반이용자0.0000.6081.0000.7380.6060.8080.8520.8560.905
운동시설0.0000.7390.7381.0000.7330.5750.6220.8050.900
자전거0.0000.7430.6060.7331.0000.5910.6160.6410.758
주요행사 및 마라톤0.0000.4660.8080.5750.5911.0000.9030.6940.902
특화공원0.0000.6180.8520.6220.6160.9031.0000.6590.877
기타0.0000.6450.8560.8050.6410.6940.6591.0000.864
합계0.0000.5520.9050.9000.7580.9020.8770.8641.000
2023-12-11T14:33:13.445112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분년도
구분1.0000.000
년도0.0001.000
2023-12-11T14:33:13.563172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계년도구분
일반이용자1.0000.3930.5830.7500.5820.5870.8820.0000.298
운동시설0.3931.0000.4480.4850.4860.5370.5620.0000.357
자전거0.5830.4481.0000.5930.5830.5980.7920.0000.458
주요행사 및 마라톤0.7500.4850.5931.0000.5920.7000.8470.0000.255
특화공원0.5820.4860.5830.5921.0000.5530.6840.0000.371
기타0.5870.5370.5980.7000.5531.0000.7860.0000.325
합계0.8820.5620.7920.8470.6840.7861.0000.0000.264
년도0.0000.0000.0000.0000.0000.0000.0001.0000.000
구분0.2980.3570.4580.2550.3710.3250.2640.0001.000

Missing values

2023-12-11T14:33:10.311977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:33:10.468737image/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

년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
02013년강서496357485619930156180047671548880
12013년난지159473223666490910617805321368141671505222519
22013년망원14734021816497109582733401281962521539
32013년양화1127128253118953476875240370332458279
42013년여의도11635571512037150365520554881296829396383629638880
52013년이촌153350749087785919341124801389583433783
62013년반포105571936128761284524644584411196762232612
72013년잠원12635421561956397231558502244042299449
82013년뚝섬516132925073213596601215757300724445212014940372
92013년잠실148976715327879421611458702211602773008
년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
492009년난지138739216828147444210588601068053091353
502009년선유도11013940000127481114142
512009년망원20323331009665500509203001236502899029
522009년양화12299051148659694593460100411857723
532009년광나루13744241579133961651204130421122091027
542009년여의도58830093000310986347197837057272714782210
552009년이촌860607394224550963351120321301873036
562009년반포10934271477564649221821210245761912802
572009년잠원8492182656434311695396001464661746456
582009년잠실18603626958748156116340901314592706378