Overview

Dataset statistics

Number of variables13
Number of observations76
Missing cells16
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.7 KiB
Average record size in memory117.7 B

Variable types

Text1
Numeric12

Dataset

Description광주광역시 생활체육종목 및 동호인 현황으로 자료출처는 광주광역시 체육회임 각 자치구별로 종목별 클럽 수 및 동호인 현황등으로 관리되고 있습니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15107391/fileData.do

Alerts

클럽합계 is highly overall correlated with 동호인합계 and 10 other fieldsHigh correlation
동호인합계 is highly overall correlated with 클럽합계 and 10 other fieldsHigh correlation
동구클럽 is highly overall correlated with 클럽합계 and 6 other fieldsHigh correlation
동구동호인 is highly overall correlated with 클럽합계 and 9 other fieldsHigh correlation
서구클럽 is highly overall correlated with 클럽합계 and 9 other fieldsHigh correlation
서구동호인 is highly overall correlated with 클럽합계 and 9 other fieldsHigh correlation
남구클럽 is highly overall correlated with 클럽합계 and 10 other fieldsHigh correlation
남구동호인 is highly overall correlated with 클럽합계 and 10 other fieldsHigh correlation
북구클럽 is highly overall correlated with 클럽합계 and 9 other fieldsHigh correlation
북구동호인 is highly overall correlated with 클럽합계 and 8 other fieldsHigh correlation
광산구클럽 is highly overall correlated with 클럽합계 and 10 other fieldsHigh correlation
광산구동호인 is highly overall correlated with 클럽합계 and 10 other fieldsHigh correlation
동구클럽 has 2 (2.6%) missing valuesMissing
동구동호인 has 2 (2.6%) missing valuesMissing
서구클럽 has 2 (2.6%) missing valuesMissing
서구동호인 has 2 (2.6%) missing valuesMissing
남구클럽 has 2 (2.6%) missing valuesMissing
남구동호인 has 2 (2.6%) missing valuesMissing
북구클럽 has 2 (2.6%) missing valuesMissing
북구동호인 has 2 (2.6%) missing valuesMissing
종목 has unique valuesUnique
클럽합계 has 14 (18.4%) zerosZeros
동호인합계 has 14 (18.4%) zerosZeros
동구클럽 has 43 (56.6%) zerosZeros
동구동호인 has 43 (56.6%) zerosZeros
서구클럽 has 36 (47.4%) zerosZeros
서구동호인 has 36 (47.4%) zerosZeros
남구클럽 has 40 (52.6%) zerosZeros
남구동호인 has 40 (52.6%) zerosZeros
북구클럽 has 37 (48.7%) zerosZeros
북구동호인 has 37 (48.7%) zerosZeros
광산구클럽 has 35 (46.1%) zerosZeros
광산구동호인 has 35 (46.1%) zerosZeros

Reproduction

Analysis started2023-12-12 17:39:52.376842
Analysis finished2023-12-12 17:40:09.372890
Duration17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

종목
Text

UNIQUE 

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size740.0 B
2023-12-13T02:40:09.614421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.6184211
Min length2

Characters and Unicode

Total characters275
Distinct characters126
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)100.0%

Sample

1st row걷 기
2nd row검 도
3rd row게이트볼
4th row골 프
5th row국무도
ValueCountFrequency (%)
7
 
6.7%
2
 
1.9%
2
 
1.9%
2
 
1.9%
1
 
1.0%
1
 
1.0%
트레킹 1
 
1.0%
테니스 1
 
1.0%
1
 
1.0%
1
 
1.0%
Other values (85) 85
81.7%
2023-12-13T02:40:10.269296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28
 
10.2%
13
 
4.7%
9
 
3.3%
8
 
2.9%
7
 
2.5%
6
 
2.2%
6
 
2.2%
6
 
2.2%
6
 
2.2%
5
 
1.8%
Other values (116) 181
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
89.1%
Space Separator 28
 
10.2%
Decimal Number 1
 
0.4%
Lowercase Letter 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
13
 
5.3%
9
 
3.7%
8
 
3.3%
7
 
2.9%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
5
 
2.0%
Other values (113) 174
71.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 245
89.1%
Common 29
 
10.5%
Latin 1
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
13
 
5.3%
9
 
3.7%
8
 
3.3%
7
 
2.9%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
5
 
2.0%
Other values (113) 174
71.0%
Common
ValueCountFrequency (%)
28
96.6%
3 1
 
3.4%
Latin
ValueCountFrequency (%)
e 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
89.1%
ASCII 30
 
10.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28
93.3%
3 1
 
3.3%
e 1
 
3.3%
Hangul
ValueCountFrequency (%)
13
 
5.3%
9
 
3.7%
8
 
3.3%
7
 
2.9%
6
 
2.4%
6
 
2.4%
6
 
2.4%
6
 
2.4%
5
 
2.0%
5
 
2.0%
Other values (113) 174
71.0%

클럽합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.684211
Minimum0
Maximum314
Zeros14
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:10.508408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.75
median13
Q329
95-th percentile122.75
Maximum314
Range314
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation48.650649
Coefficient of variation (CV)1.7573428
Kurtosis17.143094
Mean27.684211
Median Absolute Deviation (MAD)11
Skewness3.7605886
Sum2104
Variance2366.8856
MonotonicityNot monotonic
2023-12-13T02:40:10.689012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 14
18.4%
7 5
 
6.6%
3 5
 
6.6%
14 5
 
6.6%
13 3
 
3.9%
10 3
 
3.9%
29 3
 
3.9%
12 3
 
3.9%
21 2
 
2.6%
32 2
 
2.6%
Other values (27) 31
40.8%
ValueCountFrequency (%)
0 14
18.4%
3 5
 
6.6%
4 2
 
2.6%
5 1
 
1.3%
6 2
 
2.6%
7 5
 
6.6%
8 1
 
1.3%
9 1
 
1.3%
10 3
 
3.9%
12 3
 
3.9%
ValueCountFrequency (%)
314 1
1.3%
176 1
1.3%
162 1
1.3%
152 1
1.3%
113 1
1.3%
92 1
1.3%
79 1
1.3%
56 1
1.3%
55 1
1.3%
51 1
1.3%

동호인합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct60
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1017.0921
Minimum0
Maximum22779
Zeros14
Zeros (%)18.4%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:10.870343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q155.5
median265.5
Q3642.75
95-th percentile3983.25
Maximum22779
Range22779
Interquartile range (IQR)587.25

Descriptive statistics

Standard deviation2859.2092
Coefficient of variation (CV)2.8111605
Kurtosis45.952877
Mean1017.0921
Median Absolute Deviation (MAD)265.5
Skewness6.3101073
Sum77299
Variance8175077.1
MonotonicityNot monotonic
2023-12-13T02:40:11.348144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
18.4%
235 2
 
2.6%
405 2
 
2.6%
51 2
 
2.6%
154 1
 
1.3%
77 1
 
1.3%
1202 1
 
1.3%
574 1
 
1.3%
562 1
 
1.3%
3834 1
 
1.3%
Other values (50) 50
65.8%
ValueCountFrequency (%)
0 14
18.4%
30 1
 
1.3%
42 1
 
1.3%
50 1
 
1.3%
51 2
 
2.6%
57 1
 
1.3%
65 1
 
1.3%
77 1
 
1.3%
81 1
 
1.3%
83 1
 
1.3%
ValueCountFrequency (%)
22779 1
1.3%
7079 1
1.3%
6893 1
1.3%
4431 1
1.3%
3834 1
1.3%
3112 1
1.3%
2037 1
1.3%
2016 1
1.3%
1962 1
1.3%
1894 1
1.3%

동구클럽
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)14.9%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean3.527027
Minimum0
Maximum28
Zeros43
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:11.498517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36.75
95-th percentile14
Maximum28
Range28
Interquartile range (IQR)6.75

Descriptive statistics

Standard deviation5.5052229
Coefficient of variation (CV)1.5608678
Kurtosis6.98222
Mean3.527027
Median Absolute Deviation (MAD)0
Skewness2.3277811
Sum261
Variance30.307479
MonotonicityNot monotonic
2023-12-13T02:40:11.631912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 43
56.6%
7 8
 
10.5%
5 7
 
9.2%
8 5
 
6.6%
4 3
 
3.9%
6 2
 
2.6%
14 2
 
2.6%
10 1
 
1.3%
15 1
 
1.3%
28 1
 
1.3%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 43
56.6%
4 3
 
3.9%
5 7
 
9.2%
6 2
 
2.6%
7 8
 
10.5%
8 5
 
6.6%
10 1
 
1.3%
14 2
 
2.6%
15 1
 
1.3%
25 1
 
1.3%
ValueCountFrequency (%)
28 1
 
1.3%
25 1
 
1.3%
15 1
 
1.3%
14 2
 
2.6%
10 1
 
1.3%
8 5
6.6%
7 8
10.5%
6 2
 
2.6%
5 7
9.2%
4 3
 
3.9%

동구동호인
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct29
Distinct (%)39.2%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean88.648649
Minimum0
Maximum1300
Zeros43
Zeros (%)56.6%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:11.748018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q384.75
95-th percentile424.9
Maximum1300
Range1300
Interquartile range (IQR)84.75

Descriptive statistics

Standard deviation210.09166
Coefficient of variation (CV)2.3699364
Kurtosis19.768889
Mean88.648649
Median Absolute Deviation (MAD)0
Skewness4.1782864
Sum6560
Variance44138.505
MonotonicityNot monotonic
2023-12-13T02:40:11.894708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 43
56.6%
80 2
 
2.6%
84 2
 
2.6%
100 2
 
2.6%
413 1
 
1.3%
71 1
 
1.3%
175 1
 
1.3%
151 1
 
1.3%
264 1
 
1.3%
122 1
 
1.3%
Other values (19) 19
25.0%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 43
56.6%
30 1
 
1.3%
42 1
 
1.3%
45 1
 
1.3%
51 1
 
1.3%
54 1
 
1.3%
60 1
 
1.3%
66 1
 
1.3%
71 1
 
1.3%
80 2
 
2.6%
ValueCountFrequency (%)
1300 1
1.3%
1028 1
1.3%
520 1
1.3%
447 1
1.3%
413 1
1.3%
283 1
1.3%
264 1
1.3%
208 1
1.3%
175 1
1.3%
151 1
1.3%

서구클럽
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)24.3%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean5.3783784
Minimum0
Maximum38
Zeros36
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:12.044615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q37
95-th percentile22.7
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.8908699
Coefficient of variation (CV)1.4671467
Kurtosis5.3562348
Mean5.3783784
Median Absolute Deviation (MAD)3
Skewness2.1847309
Sum398
Variance62.265827
MonotonicityNot monotonic
2023-12-13T02:40:12.175626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 36
47.4%
7 12
 
15.8%
5 3
 
3.9%
3 3
 
3.9%
9 3
 
3.9%
4 3
 
3.9%
13 2
 
2.6%
12 2
 
2.6%
24 1
 
1.3%
33 1
 
1.3%
Other values (8) 8
 
10.5%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 36
47.4%
3 3
 
3.9%
4 3
 
3.9%
5 3
 
3.9%
6 1
 
1.3%
7 12
 
15.8%
8 1
 
1.3%
9 3
 
3.9%
10 1
 
1.3%
12 2
 
2.6%
ValueCountFrequency (%)
38 1
1.3%
33 1
1.3%
27 1
1.3%
24 1
1.3%
22 1
1.3%
19 1
1.3%
14 1
1.3%
13 2
2.6%
12 2
2.6%
10 1
1.3%

서구동호인
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)48.6%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean195.71622
Minimum0
Maximum2209
Zeros36
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:12.321450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median50.5
Q3193.5
95-th percentile918.2
Maximum2209
Range2209
Interquartile range (IQR)193.5

Descriptive statistics

Standard deviation386.04569
Coefficient of variation (CV)1.9724768
Kurtosis13.045238
Mean195.71622
Median Absolute Deviation (MAD)50.5
Skewness3.3561844
Sum14483
Variance149031.27
MonotonicityNot monotonic
2023-12-13T02:40:12.456235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 36
47.4%
120 3
 
3.9%
131 2
 
2.6%
56 1
 
1.3%
109 1
 
1.3%
243 1
 
1.3%
83 1
 
1.3%
45 1
 
1.3%
642 1
 
1.3%
92 1
 
1.3%
Other values (26) 26
34.2%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 36
47.4%
45 1
 
1.3%
56 1
 
1.3%
59 1
 
1.3%
65 1
 
1.3%
83 1
 
1.3%
92 1
 
1.3%
100 1
 
1.3%
107 1
 
1.3%
109 1
 
1.3%
ValueCountFrequency (%)
2209 1
1.3%
1781 1
1.3%
1059 1
1.3%
965 1
1.3%
893 1
1.3%
808 1
1.3%
642 1
1.3%
557 1
1.3%
445 1
1.3%
438 1
1.3%

남구클럽
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)21.6%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean4.8378378
Minimum0
Maximum52
Zeros40
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:12.569961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile24.05
Maximum52
Range52
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.1442429
Coefficient of variation (CV)1.8901508
Kurtosis11.721331
Mean4.8378378
Median Absolute Deviation (MAD)0
Skewness3.1858036
Sum358
Variance83.617179
MonotonicityNot monotonic
2023-12-13T02:40:12.683780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 40
52.6%
5 6
 
7.9%
7 6
 
7.9%
4 5
 
6.6%
3 3
 
3.9%
6 3
 
3.9%
8 2
 
2.6%
10 1
 
1.3%
9 1
 
1.3%
22 1
 
1.3%
Other values (6) 6
 
7.9%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 40
52.6%
3 3
 
3.9%
4 5
 
6.6%
5 6
 
7.9%
6 3
 
3.9%
7 6
 
7.9%
8 2
 
2.6%
9 1
 
1.3%
10 1
 
1.3%
15 1
 
1.3%
ValueCountFrequency (%)
52 1
1.3%
39 1
1.3%
27 1
1.3%
26 1
1.3%
23 1
1.3%
22 1
1.3%
15 1
1.3%
10 1
1.3%
9 1
1.3%
8 2
2.6%

남구동호인
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)44.6%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean181.27027
Minimum0
Maximum3840
Zeros40
Zeros (%)52.6%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:12.810809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3139.5
95-th percentile804.6
Maximum3840
Range3840
Interquartile range (IQR)139.5

Descriptive statistics

Standard deviation507.68209
Coefficient of variation (CV)2.8006914
Kurtosis37.930161
Mean181.27027
Median Absolute Deviation (MAD)0
Skewness5.6653218
Sum13414
Variance257741.1
MonotonicityNot monotonic
2023-12-13T02:40:12.962837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 40
52.6%
113 2
 
2.6%
110 2
 
2.6%
72 1
 
1.3%
355 1
 
1.3%
305 1
 
1.3%
51 1
 
1.3%
169 1
 
1.3%
130 1
 
1.3%
701 1
 
1.3%
Other values (23) 23
30.3%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 40
52.6%
51 1
 
1.3%
55 1
 
1.3%
57 1
 
1.3%
72 1
 
1.3%
80 1
 
1.3%
108 1
 
1.3%
110 2
 
2.6%
113 2
 
2.6%
115 1
 
1.3%
ValueCountFrequency (%)
3840 1
1.3%
1371 1
1.3%
1300 1
1.3%
997 1
1.3%
701 1
1.3%
668 1
1.3%
510 1
1.3%
355 1
1.3%
305 1
1.3%
237 1
1.3%

북구클럽
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)23.0%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean6.7972973
Minimum0
Maximum120
Zeros37
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:13.104817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q37
95-th percentile24.1
Maximum120
Range120
Interquartile range (IQR)7

Descriptive statistics

Standard deviation15.981136
Coefficient of variation (CV)2.3511015
Kurtosis35.511146
Mean6.7972973
Median Absolute Deviation (MAD)1.5
Skewness5.4379825
Sum503
Variance255.3967
MonotonicityNot monotonic
2023-12-13T02:40:13.222407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 37
48.7%
7 11
 
14.5%
4 4
 
5.3%
10 4
 
5.3%
5 3
 
3.9%
11 2
 
2.6%
3 2
 
2.6%
6 2
 
2.6%
49 1
 
1.3%
14 1
 
1.3%
Other values (7) 7
 
9.2%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 37
48.7%
3 2
 
2.6%
4 4
 
5.3%
5 3
 
3.9%
6 2
 
2.6%
7 11
 
14.5%
9 1
 
1.3%
10 4
 
5.3%
11 2
 
2.6%
13 1
 
1.3%
ValueCountFrequency (%)
120 1
 
1.3%
49 1
 
1.3%
43 1
 
1.3%
28 1
 
1.3%
22 1
 
1.3%
17 1
 
1.3%
14 1
 
1.3%
13 1
 
1.3%
11 2
2.6%
10 4
5.3%

북구동호인
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct33
Distinct (%)44.6%
Missing2
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean327.21622
Minimum0
Maximum11930
Zeros37
Zeros (%)48.7%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:13.340687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21
Q3187.5
95-th percentile1100
Maximum11930
Range11930
Interquartile range (IQR)187.5

Descriptive statistics

Standard deviation1406.727
Coefficient of variation (CV)4.2990748
Kurtosis65.737203
Mean327.21622
Median Absolute Deviation (MAD)21
Skewness7.9227181
Sum24214
Variance1978880.8
MonotonicityNot monotonic
2023-12-13T02:40:13.459645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 37
48.7%
65 2
 
2.6%
300 2
 
2.6%
1100 2
 
2.6%
150 2
 
2.6%
80 2
 
2.6%
174 1
 
1.3%
291 1
 
1.3%
42 1
 
1.3%
235 1
 
1.3%
Other values (23) 23
30.3%
(Missing) 2
 
2.6%
ValueCountFrequency (%)
0 37
48.7%
42 1
 
1.3%
54 1
 
1.3%
65 2
 
2.6%
80 2
 
2.6%
92 1
 
1.3%
100 1
 
1.3%
105 1
 
1.3%
110 1
 
1.3%
119 1
 
1.3%
ValueCountFrequency (%)
11930 1
1.3%
1941 1
1.3%
1200 1
1.3%
1100 2
2.6%
860 1
1.3%
630 1
1.3%
440 1
1.3%
360 1
1.3%
313 1
1.3%
300 2
2.6%

광산구클럽
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6842105
Minimum0
Maximum108
Zeros35
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:13.590380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q37
95-th percentile29.75
Maximum108
Range108
Interquartile range (IQR)7

Descriptive statistics

Standard deviation16.089923
Coefficient of variation (CV)2.0938941
Kurtosis21.857863
Mean7.6842105
Median Absolute Deviation (MAD)4
Skewness4.2760573
Sum584
Variance258.88561
MonotonicityNot monotonic
2023-12-13T02:40:13.696616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 35
46.1%
7 15
19.7%
5 5
 
6.6%
4 3
 
3.9%
10 3
 
3.9%
21 2
 
2.6%
3 2
 
2.6%
6 1
 
1.3%
8 1
 
1.3%
27 1
 
1.3%
Other values (8) 8
 
10.5%
ValueCountFrequency (%)
0 35
46.1%
3 2
 
2.6%
4 3
 
3.9%
5 5
 
6.6%
6 1
 
1.3%
7 15
19.7%
8 1
 
1.3%
10 3
 
3.9%
11 1
 
1.3%
12 1
 
1.3%
ValueCountFrequency (%)
108 1
1.3%
60 1
1.3%
57 1
1.3%
38 1
1.3%
27 1
1.3%
24 1
1.3%
21 2
2.6%
13 1
1.3%
12 1
1.3%
11 1
1.3%

광산구동호인
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.10526
Minimum0
Maximum3500
Zeros35
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size816.0 B
2023-12-13T02:40:13.802951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q3172.25
95-th percentile1420
Maximum3500
Range3500
Interquartile range (IQR)172.25

Descriptive statistics

Standard deviation577.60525
Coefficient of variation (CV)2.35656
Kurtosis16.12218
Mean245.10526
Median Absolute Deviation (MAD)60
Skewness3.8100202
Sum18628
Variance333627.83
MonotonicityNot monotonic
2023-12-13T02:40:13.931225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 35
46.1%
97 2
 
2.6%
1840 2
 
2.6%
185 2
 
2.6%
77 2
 
2.6%
230 2
 
2.6%
424 1
 
1.3%
750 1
 
1.3%
3500 1
 
1.3%
710 1
 
1.3%
Other values (27) 27
35.5%
ValueCountFrequency (%)
0 35
46.1%
42 1
 
1.3%
50 1
 
1.3%
54 1
 
1.3%
66 1
 
1.3%
77 2
 
2.6%
81 1
 
1.3%
92 1
 
1.3%
94 1
 
1.3%
96 1
 
1.3%
ValueCountFrequency (%)
3500 1
1.3%
2401 1
1.3%
1840 2
2.6%
1280 1
1.3%
825 1
1.3%
750 1
1.3%
710 1
1.3%
580 1
1.3%
500 1
1.3%
424 1
1.3%

Interactions

2023-12-13T02:40:06.927323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:52.923846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.334494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.558593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.864191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.604172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.927711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.907728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.863621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.949980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.250508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.425104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.058000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.024016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.455802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.652426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.345577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.728559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.013948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.983218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.941059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.041816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.330052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.544051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.208359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.144775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.564018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.755959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.463073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.848358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.090512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.050258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.010112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.408302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.407911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.647329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.363080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.307186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.659528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.849931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.562954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.960333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.167958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.120266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.086544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.486755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.496823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.754552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.498523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.416371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.761847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.966317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.678449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.057739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.252486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.195613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.170928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.576443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.584376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.863220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.658503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.537865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.860991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.069242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.809323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.172708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.334486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.278474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.264096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.676184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.697579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.007824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.792975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.648992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.942378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.167970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:57.896157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.278966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.412073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.356082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.348976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.765677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.799575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.092061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:07.962913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.759452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.023621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.276426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.004095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.370189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.492350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.438745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.441282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.846887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.890000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.192135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:08.097146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.876321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.126633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.408781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.145243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.485951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.575764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.551392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.541505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:03.927792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.990508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.340493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:08.228839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:53.987388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.236179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.541267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.265887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.604906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.658055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.634256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.663362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.007637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.084869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.461456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:08.344499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.104028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.346578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.648522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.373931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.718746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.747975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.712275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.762780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.092823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.190673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.617118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:08.440419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:54.212534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:55.447326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:56.749472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:58.489842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:39:59.827238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:00.831002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:01.787600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:02.855244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:04.166599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:05.316618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:40:06.760433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:40:14.033332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종목클럽합계동호인합계동구클럽동구동호인서구클럽서구동호인남구클럽남구동호인북구클럽북구동호인광산구클럽광산구동호인
종목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
클럽합계1.0001.0000.9000.9690.8650.9090.9680.9670.8990.9090.9170.9430.950
동호인합계1.0000.9001.0000.9200.9010.8290.9200.8430.8580.9911.0000.9350.910
동구클럽1.0000.9690.9201.0000.8830.8240.9520.9500.9480.9101.0000.8740.951
동구동호인1.0000.8650.9010.8831.0000.8370.8080.8570.8500.9650.9940.9470.885
서구클럽1.0000.9090.8290.8240.8371.0000.8860.8260.8690.9370.8980.9240.792
서구동호인1.0000.9680.9200.9520.8080.8861.0000.9600.8850.9270.9000.8800.951
남구클럽1.0000.9670.8430.9500.8570.8260.9601.0000.8970.8540.8650.8130.944
남구동호인1.0000.8990.8580.9480.8500.8690.8850.8971.0000.8970.8830.8650.828
북구클럽1.0000.9090.9910.9100.9650.9370.9270.8540.8971.0001.0000.9900.928
북구동호인1.0000.9171.0001.0000.9940.8980.9000.8650.8831.0001.0000.9970.917
광산구클럽1.0000.9430.9350.8740.9470.9240.8800.8130.8650.9900.9971.0000.908
광산구동호인1.0000.9500.9100.9510.8850.7920.9510.9440.8280.9280.9170.9081.000
2023-12-13T02:40:14.162306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
클럽합계동호인합계동구클럽동구동호인서구클럽서구동호인남구클럽남구동호인북구클럽북구동호인광산구클럽광산구동호인
클럽합계1.0000.9560.7010.7430.8100.8120.7650.7590.7990.7510.8270.802
동호인합계0.9561.0000.6100.6870.7630.8060.7390.7690.8000.8010.8030.822
동구클럽0.7010.6101.0000.9580.4480.4810.5750.5480.4740.4250.5660.556
동구동호인0.7430.6870.9581.0000.5270.5830.6270.6310.5180.4810.6140.603
서구클럽0.8100.7630.4480.5271.0000.9550.6000.6090.5990.5550.6410.609
서구동호인0.8120.8060.4810.5830.9551.0000.6210.6650.5750.5430.6770.677
남구클럽0.7650.7390.5750.6270.6000.6211.0000.9680.6470.5720.5820.529
남구동호인0.7590.7690.5480.6310.6090.6650.9681.0000.6470.5920.5830.560
북구클럽0.7990.8000.4740.5180.5990.5750.6470.6471.0000.9650.6560.632
북구동호인0.7510.8010.4250.4810.5550.5430.5720.5920.9651.0000.6200.622
광산구클럽0.8270.8030.5660.6140.6410.6770.5820.5830.6560.6201.0000.953
광산구동호인0.8020.8220.5560.6030.6090.6770.5290.5600.6320.6220.9531.000

Missing values

2023-12-13T02:40:08.617458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:40:08.936289image/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.
2023-12-13T02:40:09.202601image/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

종목클럽합계동호인합계동구클럽동구동호인서구클럽서구동호인남구클럽남구동호인북구클럽북구동호인광산구클럽광산구동호인
0걷 기131548420000005112
1검 도32963720871984178101924187
2게이트볼485425661010091081316711101
3골 프194050045641134807156
4국무도000000000000
5국선도55155100000000
6국학기공2149658051310041007185
7궁 도10126554005720000
8그라운드골프326340055910355101107110
9낚 시000000000000
종목클럽합계동호인합계동구클럽동구동호인서구클럽서구동호인남구클럽남구동호인북구클럽북구동호인광산구클럽광산구동호인
66합기도342037517576423305963010285
67해동검도78478400000000
68수중핀수영132125713450000596
69아이스하키38300383000000
70오리엔티어링142430014243000000
71삼보7135<NA><NA><NA><NA><NA><NA><NA><NA>7135
72드론축구350<NA><NA><NA><NA><NA><NA><NA><NA>350
73격투기6235000000623500
74다트44200000044200
75킥복싱36500000036500