Overview

Dataset statistics

Number of variables13
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory119.1 B

Variable types

Text1
Numeric12

Dataset

Description서울올림픽기념국민체육진흥공단에서 지급중인 종목별 경기력향상연구연금(메달리스트 체육연금 중 월정금) 지급 현황(비장애 체육인) 정보를 제공하는 데이터입니다.
Author서울올림픽기념국민체육진흥공단
URLhttps://www.data.go.kr/data/15054338/fileData.do

Alerts

1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
구분 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:41:58.440180
Analysis finished2023-12-12 14:42:15.040266
Duration16.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-12T23:42:15.213852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.5714286
Min length2

Characters and Unicode

Total characters108
Distinct characters80
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)100.0%

Sample

1st row육상
2nd row수영
3rd row축구
4th row야구
5th row테니스
ValueCountFrequency (%)
육상 1
 
2.4%
골프 1
 
2.4%
봅슬레이 1
 
2.4%
배드민턴 1
 
2.4%
태권도 1
 
2.4%
빙상 1
 
2.4%
스키 1
 
2.4%
롤러스케이팅 1
 
2.4%
요트 1
 
2.4%
볼링 1
 
2.4%
Other values (32) 32
76.2%
2023-12-12T23:42:15.608609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
 
6.5%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (70) 75
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
99.1%
Decimal Number 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7
 
6.5%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (69) 74
69.2%
Decimal Number
ValueCountFrequency (%)
5 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107
99.1%
Common 1
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7
 
6.5%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (69) 74
69.2%
Common
ValueCountFrequency (%)
5 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107
99.1%
ASCII 1
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7
 
6.5%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
2
 
1.9%
2
 
1.9%
2
 
1.9%
Other values (69) 74
69.2%
ASCII
ValueCountFrequency (%)
5 1
100.0%

1월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17524405
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:15.739942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9000000
Q326887500
95-th percentile59826250
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20432114
Coefficient of variation (CV)1.1659234
Kurtosis1.5051854
Mean17524405
Median Absolute Deviation (MAD)6475000
Skewness1.5119398
Sum7.36025 × 108
Variance4.1747126 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:15.903671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
55100000 1
 
2.4%
74025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
74025000 1
2.4%
60075000 1
2.4%
55100000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

2월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17461905
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:16.090150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9000000
Q326887500
95-th percentile59695000
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20318066
Coefficient of variation (CV)1.1635653
Kurtosis1.5498887
Mean17461905
Median Absolute Deviation (MAD)6475000
Skewness1.5156005
Sum7.334 × 108
Variance4.1282382 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:16.224198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
52475000 1
 
2.4%
74025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
74025000 1
2.4%
60075000 1
2.4%
52475000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

3월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17461905
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:16.375479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9000000
Q326887500
95-th percentile59695000
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20318066
Coefficient of variation (CV)1.1635653
Kurtosis1.5498887
Mean17461905
Median Absolute Deviation (MAD)6475000
Skewness1.5156005
Sum7.334 × 108
Variance4.1282382 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:16.530501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
52475000 1
 
2.4%
74025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
74025000 1
2.4%
60075000 1
2.4%
52475000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

4월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17438095
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:16.686969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9000000
Q326887500
95-th percentile59695000
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20250641
Coefficient of variation (CV)1.1612874
Kurtosis1.5033462
Mean17438095
Median Absolute Deviation (MAD)6475000
Skewness1.5054146
Sum7.324 × 108
Variance4.1008845 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:16.834335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
52475000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
60075000 1
2.4%
52475000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

5월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17438095
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:16.984264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9000000
Q326887500
95-th percentile59695000
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20250641
Coefficient of variation (CV)1.1612874
Kurtosis1.5033462
Mean17438095
Median Absolute Deviation (MAD)6475000
Skewness1.5054146
Sum7.324 × 108
Variance4.1008845 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:17.139880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
52475000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
60075000 1
2.4%
52475000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

6월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25513095
Minimum300000
Maximum3.46175 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:17.291358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9012500
Q330775000
95-th percentile72377500
Maximum3.46175 × 108
Range3.45875 × 108
Interquartile range (IQR)27606250

Descriptive statistics

Standard deviation54562061
Coefficient of variation (CV)2.1385904
Kurtosis30.513126
Mean25513095
Median Absolute Deviation (MAD)6637500
Skewness5.208561
Sum1.07155 × 109
Variance2.9770185 × 1015
MonotonicityNot monotonic
2023-12-12T23:42:17.429312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
4800000 1
 
2.4%
52475000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
346175000 1
2.4%
75825000 1
2.4%
73025000 1
2.4%
60075000 1
2.4%
52475000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%

7월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17529762
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:17.578547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9012500
Q326887500
95-th percentile59593750
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20136218
Coefficient of variation (CV)1.1486875
Kurtosis1.5264755
Mean17529762
Median Absolute Deviation (MAD)6637500
Skewness1.4959688
Sum7.3625 × 108
Variance4.0546726 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:17.736515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
5400000 1
 
2.4%
50450000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
1875000 1
2.4%
2100000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
60075000 1
2.4%
50450000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

8월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17536905
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:17.897205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9012500
Q326887500
95-th percentile59593750
Maximum75825000
Range75525000
Interquartile range (IQR)23718750

Descriptive statistics

Standard deviation20130582
Coefficient of variation (CV)1.1478982
Kurtosis1.5286953
Mean17536905
Median Absolute Deviation (MAD)6637500
Skewness1.4967872
Sum7.3655 × 108
Variance4.0524031 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:18.096544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
5400000 1
 
2.4%
50450000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
2100000 1
2.4%
2175000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
60075000 1
2.4%
50450000 1
2.4%
44000000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%
31825000 1
2.4%

9월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20341667
Minimum300000
Maximum1.18875 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:18.242989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9387500
Q331756250
95-th percentile72427500
Maximum1.18875 × 108
Range1.18575 × 108
Interquartile range (IQR)28587500

Descriptive statistics

Standard deviation25578102
Coefficient of variation (CV)1.2574241
Kurtosis4.5404616
Mean20341667
Median Absolute Deviation (MAD)7012500
Skewness1.999668
Sum8.5435 × 108
Variance6.5423932 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:18.398710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
9475000 1
 
2.4%
51675000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
2100000 1
2.4%
2175000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
118875000 1
2.4%
75825000 1
2.4%
73025000 1
2.4%
61075000 1
2.4%
51675000 1
2.4%
44900000 1
2.4%
41325000 1
2.4%
39275000 1
2.4%
36100000 1
2.4%
35725000 1
2.4%

10월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17955952
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:18.557059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9012500
Q329387500
95-th percentile65426250
Maximum75825000
Range75525000
Interquartile range (IQR)26218750

Descriptive statistics

Standard deviation20607955
Coefficient of variation (CV)1.1476949
Kurtosis1.3753871
Mean17955952
Median Absolute Deviation (MAD)6637500
Skewness1.4673167
Sum7.5415 × 108
Variance4.246878 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:18.723658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
5575000 1
 
2.4%
51675000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
2100000 1
2.4%
2175000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
66150000 1
2.4%
51675000 1
2.4%
44900000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36125000 1
2.4%
36100000 1
2.4%
32350000 1
2.4%

11월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18730357
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:19.142862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13168750
median9012500
Q331756250
95-th percentile62688750
Maximum75825000
Range75525000
Interquartile range (IQR)28587500

Descriptive statistics

Standard deviation21170394
Coefficient of variation (CV)1.1302718
Kurtosis0.74554546
Mean18730357
Median Absolute Deviation (MAD)6637500
Skewness1.3089921
Sum7.86675 × 108
Variance4.4818557 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:19.284344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
5575000 1
 
2.4%
51675000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
27625000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
2100000 1
2.4%
2175000 1
2.4%
2250000 1
2.4%
2500000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
63150000 1
2.4%
53925000 1
2.4%
51675000 1
2.4%
44900000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35425000 1
2.4%

12월
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17999405
Minimum300000
Maximum75825000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-12T23:42:19.430291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile620000
Q13343750
median9012500
Q330950000
95-th percentile62576250
Maximum75825000
Range75525000
Interquartile range (IQR)27606250

Descriptive statistics

Standard deviation20455408
Coefficient of variation (CV)1.1364491
Kurtosis1.3022025
Mean17999405
Median Absolute Deviation (MAD)6600000
Skewness1.439712
Sum7.55975 × 108
Variance4.184237 × 1014
MonotonicityNot monotonic
2023-12-12T23:42:19.571606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
300000 2
 
4.8%
5150000 1
 
2.4%
5575000 1
 
2.4%
51675000 1
 
2.4%
73025000 1
 
2.4%
3225000 1
 
2.4%
3025000 1
 
2.4%
2250000 1
 
2.4%
31275000 1
 
2.4%
8650000 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
300000 2
4.8%
600000 1
2.4%
1000000 1
2.4%
2100000 1
2.4%
2175000 1
2.4%
2250000 1
2.4%
3000000 1
2.4%
3025000 1
2.4%
3150000 1
2.4%
3225000 1
2.4%
ValueCountFrequency (%)
75825000 1
2.4%
73025000 1
2.4%
63150000 1
2.4%
51675000 1
2.4%
44900000 1
2.4%
41325000 1
2.4%
38675000 1
2.4%
36100000 1
2.4%
35425000 1
2.4%
32350000 1
2.4%

Interactions

2023-12-12T23:42:13.084323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:58.861660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.054021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.436648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.098014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.396368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.633217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.823309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.184003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.291554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.355592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.752354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.171700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:58.948997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.166904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.560084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.193899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.514196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.723989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.901562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.295038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.379096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.448454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.869869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.264529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.032758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.303566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.009137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.299349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.608854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.839756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.987769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.386519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.477009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.573566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.971655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.672699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.140856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.412854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.104220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.410124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.709839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.966256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.077738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.473709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.560922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.705072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.078457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.782082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.239130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.524149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.219286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.523368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.808100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.057735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.164901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.563027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.641249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.831849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.206957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.877363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.335288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.660166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.327437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.650017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.913168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.163230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.248459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.647942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.722876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.930193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.326515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:13.987482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.432885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.778791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.424546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.758090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.033598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.269005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.334368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.747262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.808471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.033118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.435533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:14.104595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.533147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:00.887572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.531491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.858885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.131139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.371813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.419148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.851228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.894914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.138875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.553077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:14.232706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.631932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.012255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.629816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:03.968408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.225818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.454887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.825262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.940950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.992720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.267875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.674786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:14.342549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.742053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.130005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.755683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.076913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.334912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.553443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:07.922058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.026243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.078960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.389664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.775236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:14.455605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.853439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.234449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.870631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.178353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.434435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.659067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.005844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.119712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.167532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.496528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.884523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:14.551283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:41:59.953811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:01.334925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:02.967329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:04.285968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:05.542858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:06.740694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:08.089576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:09.208405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:10.262461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:11.610901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:42:12.988395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:42:19.687517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분1월2월3월4월5월6월7월8월9월10월11월12월
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
1월1.0001.0001.0001.0001.0001.0000.9680.9780.9780.9720.9780.9980.962
2월1.0001.0001.0001.0001.0001.0000.8600.9990.9990.9450.9990.9950.997
3월1.0001.0001.0001.0001.0001.0000.8600.9990.9990.9450.9990.9950.997
4월1.0001.0001.0001.0001.0001.0000.8600.9990.9990.9450.9990.9950.997
5월1.0001.0001.0001.0001.0001.0000.8600.9990.9990.9450.9990.9950.997
6월1.0000.9680.8600.8600.8600.8601.0000.9180.9181.0000.9180.9970.911
7월1.0000.9780.9990.9990.9990.9990.9181.0001.0000.9521.0001.0000.999
8월1.0000.9780.9990.9990.9990.9990.9181.0001.0000.9521.0001.0000.999
9월1.0000.9720.9450.9450.9450.9451.0000.9520.9521.0000.9520.9770.951
10월1.0000.9780.9990.9990.9990.9990.9181.0001.0000.9521.0001.0000.999
11월1.0000.9980.9950.9950.9950.9950.9971.0001.0000.9771.0001.0000.988
12월1.0000.9620.9970.9970.9970.9970.9110.9990.9990.9510.9990.9881.000
2023-12-12T23:42:19.850819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0001.0001.0001.0001.0000.9660.9950.9940.9590.9940.9740.993
2월1.0001.0001.0001.0001.0000.9660.9950.9940.9590.9940.9740.993
3월1.0001.0001.0001.0001.0000.9660.9950.9940.9590.9940.9740.993
4월1.0001.0001.0001.0001.0000.9660.9950.9940.9590.9940.9740.993
5월1.0001.0001.0001.0001.0000.9660.9950.9940.9590.9940.9740.993
6월0.9660.9660.9660.9660.9661.0000.9840.9840.9930.9840.9980.982
7월0.9950.9950.9950.9950.9950.9841.0001.0000.9810.9990.9910.998
8월0.9940.9940.9940.9940.9940.9841.0001.0000.9821.0000.9910.998
9월0.9590.9590.9590.9590.9590.9930.9810.9821.0000.9820.9960.980
10월0.9940.9940.9940.9940.9940.9840.9991.0000.9821.0000.9910.998
11월0.9740.9740.9740.9740.9740.9980.9910.9910.9960.9911.0000.989
12월0.9930.9930.9930.9930.9930.9820.9980.9980.9800.9980.9891.000

Missing values

2023-12-12T23:42:14.718067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:42:14.953612image/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

구분1월2월3월4월5월6월7월8월9월10월11월12월
0육상515000051500005150000515000051500005150000515000051500005150000515000051500005150000
1수영250000025000002500000250000025000002500000250000025000002500000250000025000003700000
2축구103500001035000010350000103500001035000010350000103500001035000010350000103500001035000010350000
3야구413250004132500041325000413250004132500041325000413250004132500041325000413250004132500041325000
4테니스532500053250005325000532500053250005325000487500048750004875000487500048750004875000
5정구386750003867500038675000386750003867500038675000386750003867500039275000386750003867500038675000
6농구937500093750009375000937500093750009375000937500093750009375000937500093750009375000
7배구9750000975000097500009750000975000097500009750000975000010500000105000001050000010500000
8탁구238750002387500023875000238750002387500023875000238750002387500024250000242500002425000024250000
9핸드볼758250007582500075825000758250007582500075825000758250007582500075825000758250007582500075825000
구분1월2월3월4월5월6월7월8월9월10월11월12월
32카누300000300000300000300000300000300000300000300000300000300000300000300000
33우슈940000094000009400000940000094000009400000940000094000009400000940000094000009400000
34세팍타크로154500001545000015450000154500001545000015450000154500001545000015450000154500001545000015450000
35보디빌딩412500041250004125000412500041250004125000412500041250004125000412500041250004125000
36수중187500018750001875000187500018750001875000187500021750002175000217500021750002175000
37컬링375000037500003750000375000037500003750000375000037500003750000375000037500003750000
38당구210000021000002100000210000021000002100000210000021000002100000210000021000002100000
39바둑600000600000600000600000600000600000600000600000600000600000600000600000
40봅슬레이300000030000003000000300000030000003000000300000030000003000000300000030000003000000
41스켈레톤100000010000001000000100000010000001000000100000010000001000000100000010000001000000