Overview

Dataset statistics

Number of variables9
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory81.1 B

Variable types

Categorical2
Numeric7

Dataset

Description파일 다운로드
Author한강사업본부
URLhttps://data.seoul.go.kr/dataList/OA-12039/S/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 (3.1%) zerosZeros
자전거 has 4 (6.2%) zerosZeros
주요행사 및 마라톤 has 3 (4.7%) zerosZeros
특화공원 has 44 (68.8%) zerosZeros

Reproduction

Analysis started2023-12-11 05:32:54.313529
Analysis finished2023-12-11 05:33:00.328791
Duration6.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct5
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size644.0 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
20.3%
2010년 13
20.3%
2011년 13
20.3%
2012년 13
20.3%
2013년 12
18.8%

Length

2023-12-11T14:33:00.417521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T14:33:00.540017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009년 13
20.3%
2010년 13
20.3%
2011년 13
20.3%
2012년 13
20.3%
2013년 12
18.8%

구분
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size644.0 B
광나루
5 
잠실
5 
뚝섬
5 
잠원
5 
반포
5 
Other values (8)
39 

Length

Max length5
Median length2
Mean length2.453125
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광나루 5
 
7.8%
잠실 5
 
7.8%
뚝섬 5
 
7.8%
잠원 5
 
7.8%
반포 5
 
7.8%
이촌 5
 
7.8%
여의도 5
 
7.8%
양화 5
 
7.8%
망원 5
 
7.8%
난지 5
 
7.8%
Other values (3) 14
21.9%

Length

2023-12-11T14:33:00.710580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광나루 5
 
7.8%
잠실 5
 
7.8%
뚝섬 5
 
7.8%
잠원 5
 
7.8%
반포 5
 
7.8%
이촌 5
 
7.8%
여의도 5
 
7.8%
양화 5
 
7.8%
망원 5
 
7.8%
난지 5
 
7.8%
Other values (3) 14
21.9%

일반이용자
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4125683.6
Minimum351032
Maximum28580342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:00.853028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum351032
5-th percentile611320.3
Q11084000
median1393512
Q33125818.8
95-th percentile26311866
Maximum28580342
Range28229310
Interquartile range (IQR)2041818.8

Descriptive statistics

Standard deviation6993137.8
Coefficient of variation (CV)1.6950253
Kurtosis6.899563
Mean4125683.6
Median Absolute Deviation (MAD)493100.5
Skewness2.7830676
Sum2.6404375 × 108
Variance4.8903977 × 1013
MonotonicityNot monotonic
2023-12-11T14:33:01.037270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1374424 1
 
1.6%
1356008 1
 
1.6%
921931 1
 
1.6%
3482371 1
 
1.6%
686360 1
 
1.6%
28580342 1
 
1.6%
875379 1
 
1.6%
1466935 1
 
1.6%
6864264 1
 
1.6%
831165 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
351032 1
1.6%
496357 1
1.6%
594524 1
1.6%
598078 1
1.6%
686360 1
1.6%
730872 1
1.6%
733901 1
1.6%
770420 1
1.6%
831165 1
1.6%
849218 1
1.6%
ValueCountFrequency (%)
28580342 1
1.6%
28244524 1
1.6%
27787181 1
1.6%
27429132 1
1.6%
19980695 1
1.6%
11635571 1
1.6%
9545299 1
1.6%
9409674 1
1.6%
7074987 1
1.6%
6864264 1
1.6%

운동시설
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean584263.72
Minimum0
Maximum5031256
Zeros2
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:01.254358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1945.6
Q151733
median157054
Q3435736.75
95-th percentile3085658.3
Maximum5031256
Range5031256
Interquartile range (IQR)384003.75

Descriptive statistics

Standard deviation1137387.3
Coefficient of variation (CV)1.9467019
Kurtosis7.7121099
Mean584263.72
Median Absolute Deviation (MAD)125032.5
Skewness2.856531
Sum37392878
Variance1.2936498 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:01.431206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.1%
157913 1
 
1.6%
190825 1
 
1.6%
262 1
 
1.6%
58227 1
 
1.6%
21113 1
 
1.6%
4237842 1
 
1.6%
79789 1
 
1.6%
34040 1
 
1.6%
2726779 1
 
1.6%
Other values (53) 53
82.8%
ValueCountFrequency (%)
0 2
3.1%
210 1
1.6%
262 1
1.6%
11486 1
1.6%
16828 1
1.6%
18414 1
1.6%
21113 1
1.6%
22470 1
1.6%
29580 1
1.6%
30003 1
1.6%
ValueCountFrequency (%)
5031256 1
1.6%
5012205 1
1.6%
4237842 1
1.6%
3148990 1
1.6%
2726779 1
1.6%
2507321 1
1.6%
2169696 1
1.6%
1266146 1
1.6%
1073104 1
1.6%
817038 1
1.6%

자전거
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1637163.4
Minimum0
Maximum12697113
Zeros4
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:01.598944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25496.25
Q1546583.75
median735286
Q31283068
95-th percentile9399470.6
Maximum12697113
Range12697113
Interquartile range (IQR)736484.25

Descriptive statistics

Standard deviation2736340.8
Coefficient of variation (CV)1.6713914
Kurtosis9.0238561
Mean1637163.4
Median Absolute Deviation (MAD)250569.5
Skewness3.1151792
Sum1.0477846 × 108
Variance7.4875608 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:01.790441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
6.2%
396165 1
 
1.6%
536185 1
 
1.6%
1601713 1
 
1.6%
169975 1
 
1.6%
9784477 1
 
1.6%
834677 1
 
1.6%
557138 1
 
1.6%
3637311 1
 
1.6%
554763 1
 
1.6%
Other values (51) 51
79.7%
ValueCountFrequency (%)
0 4
6.2%
169975 1
 
1.6%
211310 1
 
1.6%
396165 1
 
1.6%
410023 1
 
1.6%
431169 1
 
1.6%
464922 1
 
1.6%
481561 1
 
1.6%
487872 1
 
1.6%
493122 1
 
1.6%
ValueCountFrequency (%)
12697113 1
1.6%
12224030 1
1.6%
10465841 1
1.6%
9784477 1
1.6%
7217768 1
1.6%
3637311 1
1.6%
3596601 1
1.6%
2154083 1
1.6%
2040023 1
1.6%
1977511 1
1.6%

주요행사 및 마라톤
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean875291.75
Minimum0
Maximum9928606
Zeros3
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:01.939210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile309.7
Q125158.5
median98748
Q3316670
95-th percentile6758464
Maximum9928606
Range9928606
Interquartile range (IQR)291511.5

Descriptive statistics

Standard deviation2178731.3
Coefficient of variation (CV)2.4891486
Kurtosis9.3201122
Mean875291.75
Median Absolute Deviation (MAD)90201.5
Skewness3.1537262
Sum56018672
Variance4.7468699 × 1012
MonotonicityNot monotonic
2023-12-11T14:33:02.161147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3
 
4.7%
120413 1
 
1.6%
318815 1
 
1.6%
17320 1
 
1.6%
256790 1
 
1.6%
3419 1
 
1.6%
9928606 1
 
1.6%
127794 1
 
1.6%
32847 1
 
1.6%
166513 1
 
1.6%
Other values (52) 52
81.2%
ValueCountFrequency (%)
0 3
4.7%
73 1
 
1.6%
1651 1
 
1.6%
2300 1
 
1.6%
3419 1
 
1.6%
3665 1
 
1.6%
6180 1
 
1.6%
7050 1
 
1.6%
7747 1
 
1.6%
9346 1
 
1.6%
ValueCountFrequency (%)
9928606 1
1.6%
9100608 1
1.6%
8279734 1
1.6%
7197837 1
1.6%
4268684 1
1.6%
3168280 1
1.6%
2364032 1
1.6%
2328205 1
1.6%
2055488 1
1.6%
1003833 1
1.6%

특화공원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1719260.9
Minimum0
Maximum18170792
Zeros44
Zeros (%)68.8%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:02.335462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3216576.25
95-th percentile12691144
Maximum18170792
Range18170792
Interquartile range (IQR)216576.25

Descriptive statistics

Standard deviation4221138.8
Coefficient of variation (CV)2.4552054
Kurtosis6.1504558
Mean1719260.9
Median Absolute Deviation (MAD)0
Skewness2.650704
Sum1.100327 × 108
Variance1.7818012 × 1013
MonotonicityNot monotonic
2023-12-11T14:33:02.534289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 44
68.8%
112013 1
 
1.6%
18170792 1
 
1.6%
2136814 1
 
1.6%
12968293 1
 
1.6%
58441 1
 
1.6%
3007244 1
 
1.6%
16498164 1
 
1.6%
1058319 1
 
1.6%
13144506 1
 
1.6%
Other values (11) 11
 
17.2%
ValueCountFrequency (%)
0 44
68.8%
58441 1
 
1.6%
112013 1
 
1.6%
113608 1
 
1.6%
182966 1
 
1.6%
317407 1
 
1.6%
895972 1
 
1.6%
969895 1
 
1.6%
1058319 1
 
1.6%
1362169 1
 
1.6%
ValueCountFrequency (%)
18170792 1
1.6%
16498164 1
1.6%
13144506 1
1.6%
12968293 1
1.6%
11120630 1
1.6%
9226764 1
1.6%
8471159 1
1.6%
8035813 1
1.6%
3007244 1
1.6%
2181731 1
1.6%

기타
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437994.06
Minimum288
Maximum4366892
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:02.722615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum288
5-th percentile1685.95
Q140842.25
median139353.5
Q3385100
95-th percentile2106724.6
Maximum4366892
Range4366604
Interquartile range (IQR)344257.75

Descriptive statistics

Standard deviation786514.4
Coefficient of variation (CV)1.7957193
Kurtosis11.053953
Mean437994.06
Median Absolute Deviation (MAD)112090
Skewness3.1459102
Sum28031620
Variance6.1860491 × 1011
MonotonicityNot monotonic
2023-12-11T14:33:02.907181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42112 1
 
1.6%
30427 1
 
1.6%
4349 1
 
1.6%
227163 1
 
1.6%
661 1
 
1.6%
2851894 1
 
1.6%
123892 1
 
1.6%
96855 1
 
1.6%
548508 1
 
1.6%
531672 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
288 1
1.6%
624 1
1.6%
661 1
1.6%
1216 1
1.6%
4349 1
1.6%
4659 1
1.6%
4767 1
1.6%
9018 1
1.6%
10041 1
1.6%
12748 1
1.6%
ValueCountFrequency (%)
4366892 1
1.6%
2851894 1
1.6%
2597049 1
1.6%
2109612 1
1.6%
2090363 1
1.6%
1459475 1
1.6%
963836 1
1.6%
890799 1
1.6%
887025 1
1.6%
787613 1
1.6%

합계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9379657.4
Minimum844377
Maximum68620539
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-11T14:33:03.095844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum844377
5-th percentile889999.65
Q12041288.8
median2719563.5
Q35663997.5
95-th percentile56199905
Maximum68620539
Range67776162
Interquartile range (IQR)3622708.8

Descriptive statistics

Standard deviation16518376
Coefficient of variation (CV)1.7610852
Kurtosis6.5663914
Mean9379657.4
Median Absolute Deviation (MAD)854184
Skewness2.6906653
Sum6.0029808 × 108
Variance2.7285675 × 1014
MonotonicityNot monotonic
2023-12-11T14:33:03.268395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2091027 1
 
1.6%
2359336 1
 
1.6%
926542 1
 
1.6%
6988433 1
 
1.6%
881528 1
 
1.6%
66503791 1
 
1.6%
2041531 1
 
1.6%
2187815 1
 
1.6%
16125106 1
 
1.6%
2286607 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
844377 1
1.6%
870913 1
1.6%
881528 1
1.6%
883551 1
1.6%
926542 1
1.6%
984119 1
1.6%
1114142 1
1.6%
1290439 1
1.6%
1548880 1
1.6%
1551218 1
1.6%
ValueCountFrequency (%)
68620539 1
1.6%
66925650 1
1.6%
66503791 1
1.6%
59264352 1
1.6%
38834706 1
1.6%
29638880 1
1.6%
29458908 1
1.6%
24379191 1
1.6%
23101475 1
1.6%
16125106 1
1.6%

Interactions

2023-12-11T14:32:59.202008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:54.664462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.325298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.051040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.728738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.456633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.433256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.293643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:54.757320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.459969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.146454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.827526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.547856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.518270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.410186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:54.842707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.578983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.234543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.922671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.642182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.607725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.541204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:54.942056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.673827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.335522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.028353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.738799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.716365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.672260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.034011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.760739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.420709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.126513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.831154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.806502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.776101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.135828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.855952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.517740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.252249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.933836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.997116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.877822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.226030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:55.942796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:56.615841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:57.364779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:58.037892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T14:32:59.097324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T14:33:03.370733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계
년도1.0000.0000.0000.0000.0000.1500.0000.0000.000
구분0.0001.0000.8000.6740.7200.4070.6690.6320.743
일반이용자0.0000.8001.0000.8810.8640.8780.7970.7790.937
운동시설0.0000.6740.8811.0000.9450.9450.9310.9600.977
자전거0.0000.7200.8640.9451.0000.8780.9830.8970.926
주요행사 및 마라톤0.1500.4070.8780.9450.8781.0000.8670.9620.970
특화공원0.0000.6690.7970.9310.9830.8671.0000.8870.879
기타0.0000.6320.7790.9600.8970.9620.8871.0000.958
합계0.0000.7430.9370.9770.9260.9700.8790.9581.000
2023-12-11T14:33:03.511398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분년도
구분1.0000.000
년도0.0001.000
2023-12-11T14:33:03.616896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일반이용자운동시설자전거주요행사 및 마라톤특화공원기타합계년도구분
일반이용자1.0000.5240.6730.8020.6410.6760.9070.0000.526
운동시설0.5241.0000.5670.5850.5730.6350.6560.0000.365
자전거0.6730.5671.0000.6750.6410.6850.8370.0000.413
주요행사 및 마라톤0.8020.5850.6751.0000.6360.7600.8780.0790.179
특화공원0.6410.5730.6410.6361.0000.6170.7180.0000.365
기타0.6760.6350.6850.7600.6171.0000.8320.0000.334
합계0.9070.6560.8370.8780.7180.8321.0000.0000.436
년도0.0000.0000.0000.0790.0000.0000.0001.0000.000
구분0.5260.3650.4130.1790.3650.3340.4360.0001.000

Missing values

2023-12-11T14:33:00.085143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T14:33:00.264223image/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
542013년뚝섬516132925073213596601215757300724445212014940372
552013년잠원12635421561956397231558502244042299449
562013년반포105571936128761284524644584411196762232612
572013년이촌153350749087785919341124801389583433783
582013년여의도11635571512037150365520554881296829396383629638880
592013년양화1127128253118953476875240370332458279
602013년망원14734021816497109582733401281962521539
612013년난지159473223666490910617805321368141671505222519
622013년강서496357485619930156180047671548880
632013년total27429132503125612224030316828018170792259704968620539