Overview

Dataset statistics

Number of variables9
Number of observations75
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.9 KiB
Average record size in memory80.8 B

Variable types

Categorical2
Numeric7

Dataset

Description파일 다운로드
Author한강사업본부
URLhttps://data.seoul.go.kr/dataList/OA-12040/F/1/datasetView.do

Alerts

일반이용자 is highly overall correlated with 운동시설 and 6 other fieldsHigh correlation
운동시설 is highly overall correlated with 일반이용자 and 5 other fieldsHigh correlation
자전거 is highly overall correlated with 일반이용자 and 5 other fieldsHigh correlation
주요행사 및 마라톤 is highly overall correlated with 일반이용자 and 5 other fieldsHigh correlation
특화공원 is highly overall correlated with 일반이용자 and 5 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
구분 is highly overall correlated with 일반이용자High correlation
일반이용자 has unique valuesUnique
기타 has unique valuesUnique
합계 has unique valuesUnique
운동시설 has 2 (2.7%) zerosZeros
자전거 has 4 (5.3%) zerosZeros
주요행사 및 마라톤 has 3 (4.0%) zerosZeros
특화공원 has 51 (68.0%) zerosZeros

Reproduction

Analysis started2023-12-11 05:39:33.618137
Analysis finished2023-12-11 05:39:39.598598
Duration5.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct6
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size732.0 B
2009년
13 
2010년
13 
2011년
13 
2012년
13 
2013년
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2009년
2nd row2009년
3rd row2009년
4th row2009년
5th row2009년

Common Values

ValueCountFrequency (%)
2009년 13
17.3%
2010년 13
17.3%
2011년 13
17.3%
2012년 13
17.3%
2013년 12
16.0%
2017년 11
14.7%

Length

2023-12-11T14:39:39.669172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:39:39.795331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009년 13
17.3%
2010년 13
17.3%
2011년 13
17.3%
2012년 13
17.3%
2013년 12
16.0%
2017년 11
14.7%

구분
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size732.0 B
광나루
6 
잠실
6 
뚝섬
6 
잠원
6 
반포
6 
Other values (8)
45 

Length

Max length5
Median length2
Mean length2.4133333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광나루
2nd row잠실
3rd row뚝섬
4th row잠원
5th row반포

Common Values

ValueCountFrequency (%)
광나루 6
 
8.0%
잠실 6
 
8.0%
뚝섬 6
 
8.0%
잠원 6
 
8.0%
반포 6
 
8.0%
이촌 6
 
8.0%
여의도 6
 
8.0%
양화 6
 
8.0%
망원 6
 
8.0%
난지 6
 
8.0%
Other values (3) 15
20.0%

Length

2023-12-11T14:39:39.915955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광나루 6
 
8.0%
잠실 6
 
8.0%
뚝섬 6
 
8.0%
잠원 6
 
8.0%
반포 6
 
8.0%
이촌 6
 
8.0%
여의도 6
 
8.0%
양화 6
 
8.0%
망원 6
 
8.0%
난지 6
 
8.0%
Other values (3) 15
20.0%

일반이용자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3841300
Minimum351032
Maximum28580342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:40.032499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum351032
5-th percentile659875.4
Q11094723.5
median1399632
Q32519650.5
95-th percentile22215226
Maximum28580342
Range28229310
Interquartile range (IQR)1424927

Descriptive statistics

Standard deviation6533789.4
Coefficient of variation (CV)1.7009318
Kurtosis8.490022
Mean3841300
Median Absolute Deviation (MAD)456485
Skewness3.016652
Sum2.880975 × 108
Variance4.2690403 × 1013
MonotonicityNot monotonic
2023-12-11T14:39:40.151148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1374424 1
 
1.3%
878892 1
 
1.3%
1263542 1
 
1.3%
5161329 1
 
1.3%
1489767 1
 
1.3%
598078 1
 
1.3%
28244524 1
 
1.3%
733901 1
 
1.3%
3935854 1
 
1.3%
1147010 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
351032 1
1.3%
496357 1
1.3%
594524 1
1.3%
598078 1
1.3%
686360 1
1.3%
730872 1
1.3%
733901 1
1.3%
741177 1
1.3%
770420 1
1.3%
831165 1
1.3%
ValueCountFrequency (%)
28580342 1
1.3%
28244524 1
1.3%
27787181 1
1.3%
27429132 1
1.3%
19980695 1
1.3%
11635571 1
1.3%
9545299 1
1.3%
9409674 1
1.3%
7268299 1
1.3%
7074987 1
1.3%

운동시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean585094.84
Minimum0
Maximum5031256
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:40.281213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8118.8
Q174688
median194601
Q3486405.5
95-th percentile2853442.3
Maximum5031256
Range5031256
Interquartile range (IQR)411717.5

Descriptive statistics

Standard deviation1055396
Coefficient of variation (CV)1.8038032
Kurtosis9.1640672
Mean585094.84
Median Absolute Deviation (MAD)165021
Skewness3.0307244
Sum43882113
Variance1.1138607 × 1012
MonotonicityNot monotonic
2023-12-11T14:39:40.406136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
2.7%
172686 1
 
1.3%
156195 1
 
1.3%
2507321 1
 
1.3%
153278 1
 
1.3%
130269 1
 
1.3%
5012205 1
 
1.3%
18414 1
 
1.3%
190825 1
 
1.3%
157913 1
 
1.3%
Other values (64) 64
85.3%
ValueCountFrequency (%)
0 2
2.7%
210 1
1.3%
262 1
1.3%
11486 1
1.3%
16828 1
1.3%
18414 1
1.3%
21113 1
1.3%
22470 1
1.3%
29580 1
1.3%
30003 1
1.3%
ValueCountFrequency (%)
5031256 1
1.3%
5012205 1
1.3%
4237842 1
1.3%
3148990 1
1.3%
2726779 1
1.3%
2507321 1
1.3%
2169696 1
1.3%
1292552 1
1.3%
1266146 1
1.3%
1073104 1
1.3%

자전거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1596233.1
Minimum0
Maximum12697113
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:40.533891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118982.5
Q1555950.5
median834677
Q31346772
95-th percentile7987780.7
Maximum12697113
Range12697113
Interquartile range (IQR)790821.5

Descriptive statistics

Standard deviation2545112.3
Coefficient of variation (CV)1.594449
Kurtosis10.756712
Mean1596233.1
Median Absolute Deviation (MAD)290408
Skewness3.3341141
Sum1.1971748 × 108
Variance6.4775966 × 1012
MonotonicityNot monotonic
2023-12-11T14:39:40.668541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
5.3%
1466451 1
 
1.3%
612845 1
 
1.3%
639723 1
 
1.3%
3596601 1
 
1.3%
794216 1
 
1.3%
651242 1
 
1.3%
12697113 1
 
1.3%
536185 1
 
1.3%
396165 1
 
1.3%
Other values (62) 62
82.7%
ValueCountFrequency (%)
0 4
5.3%
169975 1
 
1.3%
211310 1
 
1.3%
396165 1
 
1.3%
410023 1
 
1.3%
431169 1
 
1.3%
464922 1
 
1.3%
481561 1
 
1.3%
487872 1
 
1.3%
493122 1
 
1.3%
ValueCountFrequency (%)
12697113 1
1.3%
12224030 1
1.3%
10465841 1
1.3%
9784477 1
1.3%
7217768 1
1.3%
3637311 1
1.3%
3614652 1
1.3%
3596601 1
1.3%
2154083 1
1.3%
2040023 1
1.3%

주요행사 및 마라톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean787643.25
Minimum0
Maximum9928606
Zeros3
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:40.829187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1177.6
Q125654
median98029
Q3317385
95-th percentile5147429.9
Maximum9928606
Range9928606
Interquartile range (IQR)291731

Descriptive statistics

Standard deviation2027833.7
Coefficient of variation (CV)2.5745585
Kurtosis11.439501
Mean787643.25
Median Absolute Deviation (MAD)90282
Skewness3.4407017
Sum59073244
Variance4.1121094 × 1012
MonotonicityNot monotonic
2023-12-11T14:39:40.995341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
4.0%
318815 1
 
1.3%
24644 1
 
1.3%
15585 1
 
1.3%
215757 1
 
1.3%
114587 1
 
1.3%
31880 1
 
1.3%
2364032 1
 
1.3%
1651 1
 
1.3%
120413 1
 
1.3%
Other values (63) 63
84.0%
ValueCountFrequency (%)
0 3
4.0%
73 1
 
1.3%
1651 1
 
1.3%
2300 1
 
1.3%
3419 1
 
1.3%
3665 1
 
1.3%
6180 1
 
1.3%
7050 1
 
1.3%
7442 1
 
1.3%
7747 1
 
1.3%
ValueCountFrequency (%)
9928606 1
1.3%
9100608 1
1.3%
8279734 1
1.3%
7197837 1
1.3%
4268684 1
1.3%
3168280 1
1.3%
2364032 1
1.3%
2328205 1
1.3%
2055488 1
1.3%
1454523 1
1.3%

특화공원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1686241.8
Minimum0
Maximum18170792
Zeros51
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:41.149958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3606689.5
95-th percentile12048210
Maximum18170792
Range18170792
Interquartile range (IQR)606689.5

Descriptive statistics

Standard deviation4099912.1
Coefficient of variation (CV)2.4313904
Kurtosis6.243249
Mean1686241.8
Median Absolute Deviation (MAD)0
Skewness2.6665002
Sum1.2646813 × 108
Variance1.6809279 × 1013
MonotonicityNot monotonic
2023-12-11T14:39:41.305928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 51
68.0%
112013 1
 
1.3%
1096684 1
 
1.3%
11653889 1
 
1.3%
940254 1
 
1.3%
2744605 1
 
1.3%
18170792 1
 
1.3%
2136814 1
 
1.3%
12968293 1
 
1.3%
58441 1
 
1.3%
Other values (15) 15
 
20.0%
ValueCountFrequency (%)
0 51
68.0%
58441 1
 
1.3%
112013 1
 
1.3%
113608 1
 
1.3%
182966 1
 
1.3%
317407 1
 
1.3%
895972 1
 
1.3%
940254 1
 
1.3%
969895 1
 
1.3%
1058319 1
 
1.3%
ValueCountFrequency (%)
18170792 1
1.3%
16498164 1
1.3%
13144506 1
1.3%
12968293 1
1.3%
11653889 1
1.3%
11120630 1
1.3%
9226764 1
1.3%
8471159 1
1.3%
8035813 1
1.3%
3007244 1
1.3%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean403322.37
Minimum288
Maximum4366892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:41.477606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum288
5-th percentile3409.1
Q149184.5
median128196
Q3344406
95-th percentile2096137.7
Maximum4366892
Range4366604
Interquartile range (IQR)295221.5

Descriptive statistics

Standard deviation739479.62
Coefficient of variation (CV)1.8334704
Kurtosis12.794907
Mean403322.37
Median Absolute Deviation (MAD)101789
Skewness3.3464933
Sum30249178
Variance5.4683011 × 1011
MonotonicityNot monotonic
2023-12-11T14:39:41.641533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42112 1
 
1.3%
4659 1
 
1.3%
224404 1
 
1.3%
452120 1
 
1.3%
221160 1
 
1.3%
139749 1
 
1.3%
2109612 1
 
1.3%
288 1
 
1.3%
247962 1
 
1.3%
66628 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
288 1
1.3%
624 1
1.3%
661 1
1.3%
1216 1
1.3%
4349 1
1.3%
4659 1
1.3%
4767 1
1.3%
6016 1
1.3%
9018 1
1.3%
10041 1
1.3%
ValueCountFrequency (%)
4366892 1
1.3%
2851894 1
1.3%
2597049 1
1.3%
2109612 1
1.3%
2090363 1
1.3%
1459475 1
1.3%
1084341 1
1.3%
963836 1
1.3%
890799 1
1.3%
887025 1
1.3%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct75
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8899835.3
Minimum844377
Maximum68620539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size807.0 B
2023-12-11T14:39:41.817006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum844377
5-th percentile913644.7
Q12072443
median2773008
Q35003573
95-th percentile44963600
Maximum68620539
Range67776162
Interquartile range (IQR)2931130

Descriptive statistics

Standard deviation15468038
Coefficient of variation (CV)1.7380139
Kurtosis7.8592104
Mean8899835.3
Median Absolute Deviation (MAD)915285
Skewness2.8720433
Sum6.6748765 × 108
Variance2.3926019 × 1014
MonotonicityNot monotonic
2023-12-11T14:39:41.983218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2091027 1
 
1.3%
883551 1
 
1.3%
2299449 1
 
1.3%
14940372 1
 
1.3%
2773008 1
 
1.3%
1551218 1
 
1.3%
66925650 1
 
1.3%
1290439 1
 
1.3%
7218226 1
 
1.3%
2040562 1
 
1.3%
Other values (65) 65
86.7%
ValueCountFrequency (%)
844377 1
1.3%
870913 1
1.3%
881528 1
1.3%
883551 1
1.3%
926542 1
1.3%
984119 1
1.3%
1114142 1
1.3%
1290439 1
1.3%
1548880 1
1.3%
1551218 1
1.3%
ValueCountFrequency (%)
68620539 1
1.3%
66925650 1
1.3%
66503791 1
1.3%
59264352 1
1.3%
38834706 1
1.3%
29638880 1
1.3%
29458908 1
1.3%
24379191 1
1.3%
23101475 1
1.3%
21601256 1
1.3%

Interactions

2023-12-11T14:39:38.455505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:33.990135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.744975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.551608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.376382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.121001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.824059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.533647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.096884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.861222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.693229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.488533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.242913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.920106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.621516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.184945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.976616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.817815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.598040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.350114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.001318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.713648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.278993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.099596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.928924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.704235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.438346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.082052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:39.071855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.376268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.226573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.052619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.822768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.528053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.186473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:39.166222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.506804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.330099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.156905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.943830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.628718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.279636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:39.260920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:34.613256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:35.426416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:36.265342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.026911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:37.731113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:39:38.378808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:39:42.118958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
년도1.0000.0000.0000.1620.0000.1140.0000.0000.000
구분0.0001.0000.8030.6650.7360.5370.6890.6430.774
일반이용자0.0000.8031.0000.8180.8720.8560.8030.7890.932
운동시설0.1620.6650.8181.0000.9340.9300.8760.9570.961
자전거0.0000.7360.8720.9341.0000.8820.9730.8940.928
주요행사 및 마라톤0.1140.5370.8560.9300.8821.0000.8510.9660.972
특화공원0.0000.6890.8030.8760.9730.8511.0000.8560.879
기타0.0000.6430.7890.9570.8940.9660.8561.0000.950
합계0.0000.7740.9320.9610.9280.9720.8790.9501.000
2023-12-11T14:39:42.278850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분
년도1.0000.000
구분0.0001.000
2023-12-11T14:39:42.375619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계년도구분
일반이용자1.0000.5140.6650.8050.6600.6870.9050.0000.535
운동시설0.5141.0000.6250.5450.5370.5430.6650.0810.361
자전거0.6650.6251.0000.6450.6230.6140.8420.0000.435
주요행사 및 마라톤0.8050.5450.6451.0000.6600.7620.8650.0510.263
특화공원0.6600.5370.6230.6601.0000.6190.7310.0000.388
기타0.6870.5430.6140.7620.6191.0000.7980.0000.345
합계0.9050.6650.8420.8650.7310.7981.0000.0000.476
년도0.0000.0810.0000.0510.0000.0000.0001.0000.000
구분0.5350.3610.4350.2630.3880.3450.4760.0001.000

Missing values

2023-12-11T14:39:39.390077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:39:39.542105image/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
02009년광나루13744241579133961651204130421122091027
12009년잠실18603626958748156116340901314592706378
22009년뚝섬19575924927067979531595508870253889637
32009년잠원8492182656434311695396001464661746456
42009년반포10934271477564649221821210245761912802
52009년이촌860607394224550963351120321301873036
62009년여의도58830093000310986347197837057272714782210
72009년양화12299051148659694593460100411857723
82009년망원20323331009665500509203001236502899029
92009년선유도11013940000127481114142
년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
652017년잠실153140069236313958752820301002393748080
662017년뚝섬726829912925523614652601798274460539834515920251
672017년잠원98021634673363151774420728392038747
682017년반포14951954819341005796199810940254562574179246
692017년이촌1273070891760108405025090001069643606744
702017년여의도51853876335931589523145452311653889108434121601256
712017년양화1235118429981984859259780264072702343
722017년망원11302394105178747723881701033002557645
732017년난지176881221497990308439171710966841834984558774
742017년강서74117738084996319810795060162102035