Overview

Dataset statistics

Number of variables11
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory103.5 B

Variable types

DateTime1
Numeric9
Categorical1

Dataset

Description경주시시설관리공단에서 운영하고 있는 경주국민체육센터의 월별매출 내역입니다. (2020년 12월부터 2022년 11월까지 내역입니다.)
Author경주시시설관리공단
URLhttps://www.data.go.kr/data/15095659/fileData.do

Alerts

강습수영 is highly overall correlated with 자유수영 and 6 other fieldsHigh correlation
자유수영 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
일일수영 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
헬스 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
일일헬스 is highly overall correlated with 자유수영 and 5 other fieldsHigh correlation
에어로빅 is highly overall correlated with 강습수영 and 6 other fieldsHigh correlation
사 물 함 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
기타수입 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
합계 is highly overall correlated with 강습수영 and 7 other fieldsHigh correlation
요가_필라테스 is highly imbalanced (62.9%)Imbalance
구분 has unique valuesUnique
강습수영 has 16 (66.7%) zerosZeros
자유수영 has 4 (16.7%) zerosZeros
일일수영 has 5 (20.8%) zerosZeros
헬스 has 4 (16.7%) zerosZeros
일일헬스 has 5 (20.8%) zerosZeros
에어로빅 has 18 (75.0%) zerosZeros
사 물 함 has 5 (20.8%) zerosZeros
기타수입 has 5 (20.8%) zerosZeros
합계 has 3 (12.5%) zerosZeros

Reproduction

Analysis started2023-12-12 05:58:46.646784
Analysis finished2023-12-12 05:58:55.995610
Duration9.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
Minimum2020-12-20 00:00:00
Maximum2022-11-22 00:00:00
2023-12-12T14:58:56.047070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:56.182423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)

강습수영
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4565292.1
Minimum-2029040
Maximum20792390
Zeros16
Zeros (%)66.7%
Negative1
Negative (%)4.2%
Memory size348.0 B
2023-12-12T14:58:56.304999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2029040
5-th percentile0
Q10
median0
Q35772975
95-th percentile19524725
Maximum20792390
Range22821430
Interquartile range (IQR)5772975

Descriptive statistics

Standard deviation8091393.3
Coefficient of variation (CV)1.7723714
Kurtosis-0.22667646
Mean4565292.1
Median Absolute Deviation (MAD)0
Skewness1.2815346
Sum1.0956701 × 108
Variance6.5470645 × 1013
MonotonicityNot monotonic
2023-12-12T14:58:56.437051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 16
66.7%
3379600 1
 
4.2%
-2029040 1
 
4.2%
16979300 1
 
4.2%
12953100 1
 
4.2%
19604180 1
 
4.2%
20792390 1
 
4.2%
19074480 1
 
4.2%
18813000 1
 
4.2%
ValueCountFrequency (%)
-2029040 1
 
4.2%
0 16
66.7%
3379600 1
 
4.2%
12953100 1
 
4.2%
16979300 1
 
4.2%
18813000 1
 
4.2%
19074480 1
 
4.2%
19604180 1
 
4.2%
20792390 1
 
4.2%
ValueCountFrequency (%)
20792390 1
 
4.2%
19604180 1
 
4.2%
19074480 1
 
4.2%
18813000 1
 
4.2%
16979300 1
 
4.2%
12953100 1
 
4.2%
3379600 1
 
4.2%
0 16
66.7%
-2029040 1
 
4.2%

자유수영
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11663345
Minimum-62760
Maximum25569180
Zeros4
Zeros (%)16.7%
Negative2
Negative (%)8.3%
Memory size348.0 B
2023-12-12T14:58:56.563756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-62760
5-th percentile-23205
Q12052750
median12767000
Q320756425
95-th percentile24895417
Maximum25569180
Range25631940
Interquartile range (IQR)18703675

Descriptive statistics

Standard deviation9513243
Coefficient of variation (CV)0.81565308
Kurtosis-1.6209118
Mean11663345
Median Absolute Deviation (MAD)9132885
Skewness0.015529528
Sum2.7992027 × 108
Variance9.0501792 × 1013
MonotonicityNot monotonic
2023-12-12T14:58:56.733905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 4
 
16.7%
3864000 1
 
4.2%
20826400 1
 
4.2%
22129770 1
 
4.2%
20733100 1
 
4.2%
23835680 1
 
4.2%
25082430 1
 
4.2%
25569180 1
 
4.2%
23076350 1
 
4.2%
16641720 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
-62760 1
 
4.2%
-27300 1
 
4.2%
0 4
16.7%
2737000 1
 
4.2%
3864000 1
 
4.2%
4839900 1
 
4.2%
5222700 1
 
4.2%
10267000 1
 
4.2%
12108000 1
 
4.2%
13426000 1
 
4.2%
ValueCountFrequency (%)
25569180 1
4.2%
25082430 1
4.2%
23835680 1
4.2%
23076350 1
4.2%
22129770 1
4.2%
20826400 1
4.2%
20733100 1
4.2%
17896400 1
4.2%
17027000 1
4.2%
16641720 1
4.2%

일일수영
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4425047.5
Minimum0
Maximum11137300
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:58:56.858476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11369842.5
median4054900
Q37392192.5
95-th percentile9298107
Maximum11137300
Range11137300
Interquartile range (IQR)6022350

Descriptive statistics

Standard deviation3586286.2
Coefficient of variation (CV)0.81045147
Kurtosis-1.3175623
Mean4425047.5
Median Absolute Deviation (MAD)3195145
Skewness0.20893512
Sum1.0620114 × 108
Variance1.2861449 × 1013
MonotonicityNot monotonic
2023-12-12T14:58:56.991544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
20.8%
842120 1
 
4.2%
2082260 1
 
4.2%
6682060 1
 
4.2%
7232410 1
 
4.2%
7871540 1
 
4.2%
9198300 1
 
4.2%
9315720 1
 
4.2%
8618200 1
 
4.2%
6511840 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 5
20.8%
842120 1
 
4.2%
1545750 1
 
4.2%
1548100 1
 
4.2%
2082260 1
 
4.2%
2409600 1
 
4.2%
3421110 1
 
4.2%
3672110 1
 
4.2%
4437690 1
 
4.2%
5592380 1
 
4.2%
ValueCountFrequency (%)
11137300 1
4.2%
9315720 1
4.2%
9198300 1
4.2%
8618200 1
4.2%
8415650 1
4.2%
7871540 1
4.2%
7232410 1
4.2%
6682060 1
4.2%
6511840 1
4.2%
5667000 1
4.2%

헬스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1595953.8
Minimum-90090
Maximum3623460
Zeros4
Zeros (%)16.7%
Negative2
Negative (%)8.3%
Memory size348.0 B
2023-12-12T14:58:57.121471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-90090
5-th percentile-54808
Q1218625
median1555550
Q32771905
95-th percentile3562507.5
Maximum3623460
Range3713550
Interquartile range (IQR)2553280

Descriptive statistics

Standard deviation1330468.9
Coefficient of variation (CV)0.83365131
Kurtosis-1.4741072
Mean1595953.8
Median Absolute Deviation (MAD)1301155
Skewness0.11804467
Sum38302890
Variance1.7701476 × 1012
MonotonicityNot monotonic
2023-12-12T14:58:57.268609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 4
 
16.7%
885100 1
 
4.2%
3518520 1
 
4.2%
3186020 1
 
4.2%
3081950 1
 
4.2%
3570270 1
 
4.2%
3623460 1
 
4.2%
2893810 1
 
4.2%
2731270 1
 
4.2%
2082900 1
 
4.2%
Other values (11) 11
45.8%
ValueCountFrequency (%)
-90090 1
 
4.2%
-64480 1
 
4.2%
0 4
16.7%
291500 1
 
4.2%
692560 1
 
4.2%
715500 1
 
4.2%
885100 1
 
4.2%
1475000 1
 
4.2%
1537000 1
 
4.2%
1574100 1
 
4.2%
ValueCountFrequency (%)
3623460 1
4.2%
3570270 1
4.2%
3518520 1
4.2%
3186020 1
4.2%
3081950 1
4.2%
2893810 1
4.2%
2731270 1
4.2%
2597000 1
4.2%
2252500 1
4.2%
2082900 1
4.2%

일일헬스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549596.67
Minimum0
Maximum1456100
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:58:57.417480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1157550
median614300
Q3855480
95-th percentile1254075
Maximum1456100
Range1456100
Interquartile range (IQR)697930

Descriptive statistics

Standard deviation444677.41
Coefficient of variation (CV)0.80909772
Kurtosis-0.92647424
Mean549596.67
Median Absolute Deviation (MAD)344500
Skewness0.33445803
Sum13190320
Variance1.97738 × 1011
MonotonicityNot monotonic
2023-12-12T14:58:57.556382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
20.8%
261800 1
 
4.2%
299200 1
 
4.2%
644700 1
 
4.2%
635300 1
 
4.2%
734100 1
 
4.2%
1200100 1
 
4.2%
1263600 1
 
4.2%
948780 1
 
4.2%
828240 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 5
20.8%
145700 1
 
4.2%
161500 1
 
4.2%
261800 1
 
4.2%
277800 1
 
4.2%
299200 1
 
4.2%
353600 1
 
4.2%
593300 1
 
4.2%
635300 1
 
4.2%
644700 1
 
4.2%
ValueCountFrequency (%)
1456100 1
4.2%
1263600 1
4.2%
1200100 1
4.2%
999600 1
4.2%
948780 1
4.2%
937200 1
4.2%
828240 1
4.2%
787800 1
4.2%
734100 1
4.2%
661900 1
4.2%

에어로빅
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358120
Minimum0
Maximum1684200
Zeros18
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:58:57.716566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3225750
95-th percentile1611960
Maximum1684200
Range1684200
Interquartile range (IQR)225750

Descriptive statistics

Standard deviation647461.17
Coefficient of variation (CV)1.8079447
Kurtosis-0.0072791167
Mean358120
Median Absolute Deviation (MAD)0
Skewness1.3638691
Sum8594880
Variance4.1920597 × 1011
MonotonicityNot monotonic
2023-12-12T14:58:57.847332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 18
75.0%
903000 1
 
4.2%
1351000 1
 
4.2%
1456280 1
 
4.2%
1583400 1
 
4.2%
1684200 1
 
4.2%
1617000 1
 
4.2%
ValueCountFrequency (%)
0 18
75.0%
903000 1
 
4.2%
1351000 1
 
4.2%
1456280 1
 
4.2%
1583400 1
 
4.2%
1617000 1
 
4.2%
1684200 1
 
4.2%
ValueCountFrequency (%)
1684200 1
 
4.2%
1617000 1
 
4.2%
1583400 1
 
4.2%
1456280 1
 
4.2%
1351000 1
 
4.2%
903000 1
 
4.2%
0 18
75.0%

요가_필라테스
Categorical

IMBALANCE 

Distinct4
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
0
21 
482800
 
1
904600
 
1
837770
 
1

Length

Max length6
Median length1
Mean length1.625
Min length1

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
87.5%
482800 1
 
4.2%
904600 1
 
4.2%
837770 1
 
4.2%

Length

2023-12-12T14:58:57.970811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:58:58.074916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
87.5%
482800 1
 
4.2%
904600 1
 
4.2%
837770 1
 
4.2%

사 물 함
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237500
Minimum0
Maximum570000
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:58:58.157106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q158500
median272000
Q3390000
95-th percentile506500
Maximum570000
Range570000
Interquartile range (IQR)331500

Descriptive statistics

Standard deviation193944.19
Coefficient of variation (CV)0.8166071
Kurtosis-1.3991128
Mean237500
Median Absolute Deviation (MAD)198000
Skewness0.17803474
Sum5700000
Variance3.7614348 × 1010
MonotonicityNot monotonic
2023-12-12T14:58:58.524169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 5
20.8%
130000 1
 
4.2%
94000 1
 
4.2%
508000 1
 
4.2%
498000 1
 
4.2%
484000 1
 
4.2%
570000 1
 
4.2%
486000 1
 
4.2%
420000 1
 
4.2%
380000 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 5
20.8%
36000 1
 
4.2%
66000 1
 
4.2%
82000 1
 
4.2%
94000 1
 
4.2%
130000 1
 
4.2%
176000 1
 
4.2%
270000 1
 
4.2%
274000 1
 
4.2%
278000 1
 
4.2%
ValueCountFrequency (%)
570000 1
4.2%
508000 1
4.2%
498000 1
4.2%
486000 1
4.2%
484000 1
4.2%
420000 1
4.2%
380000 1
4.2%
354000 1
4.2%
314000 1
4.2%
280000 1
4.2%

기타수입
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21583.333
Minimum0
Maximum75000
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T14:58:58.632374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12500
median13000
Q331750
95-th percentile60400
Maximum75000
Range75000
Interquartile range (IQR)29250

Descriptive statistics

Standard deviation22795.722
Coefficient of variation (CV)1.0561724
Kurtosis-0.18146106
Mean21583.333
Median Absolute Deviation (MAD)13000
Skewness0.96194974
Sum518000
Variance5.1964493 × 108
MonotonicityNot monotonic
2023-12-12T14:58:58.745775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 5
20.8%
31000 2
 
8.3%
8000 1
 
4.2%
5000 1
 
4.2%
56000 1
 
4.2%
25000 1
 
4.2%
57000 1
 
4.2%
47000 1
 
4.2%
61000 1
 
4.2%
75000 1
 
4.2%
Other values (9) 9
37.5%
ValueCountFrequency (%)
0 5
20.8%
1000 1
 
4.2%
3000 1
 
4.2%
5000 1
 
4.2%
7000 1
 
4.2%
8000 1
 
4.2%
10000 1
 
4.2%
12000 1
 
4.2%
14000 1
 
4.2%
15000 1
 
4.2%
ValueCountFrequency (%)
75000 1
4.2%
61000 1
4.2%
57000 1
4.2%
56000 1
4.2%
47000 1
4.2%
34000 1
4.2%
31000 2
8.3%
26000 1
4.2%
25000 1
4.2%
15000 1
4.2%

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23509153
Minimum-152850
Maximum59481810
Zeros3
Zeros (%)12.5%
Negative2
Negative (%)8.3%
Memory size348.0 B
2023-12-12T14:58:58.844929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-152850
5-th percentile-78013
Q14284662.5
median22683905
Q338544888
95-th percentile56319222
Maximum59481810
Range59634660
Interquartile range (IQR)34260225

Descriptive statistics

Standard deviation21765390
Coefficient of variation (CV)0.92582621
Kurtosis-1.2287912
Mean23509153
Median Absolute Deviation (MAD)18919330
Skewness0.5022873
Sum5.6421968 × 108
Variance4.7373221 × 1014
MonotonicityNot monotonic
2023-12-12T14:58:58.985223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 3
 
12.5%
5991020 1
 
4.2%
-152850 1
 
4.2%
53503450 1
 
4.2%
55369780 1
 
4.2%
55820280 1
 
4.2%
59481810 1
 
4.2%
54136310 1
 
4.2%
56407270 1
 
4.2%
33558700 1
 
4.2%
Other values (12) 12
50.0%
ValueCountFrequency (%)
-152850 1
 
4.2%
-91780 1
 
4.2%
0 3
12.5%
2724400 1
 
4.2%
4804750 1
 
4.2%
5983880 1
 
4.2%
5991020 1
 
4.2%
7726000 1
 
4.2%
16321010 1
 
4.2%
22554480 1
 
4.2%
ValueCountFrequency (%)
59481810 1
4.2%
56407270 1
4.2%
55820280 1
4.2%
55369780 1
4.2%
54136310 1
4.2%
53503450 1
4.2%
33558700 1
4.2%
28234850 1
4.2%
27293200 1
4.2%
26560500 1
4.2%

Interactions

2023-12-12T14:58:54.756861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:46.982414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.877563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.808908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.909906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.807142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.620374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.924487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.829896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.845165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.062979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.958149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.899160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.017619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.894036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.725752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.026765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.911858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.944968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.146428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.050390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.998635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.098087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.986217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.134981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.132699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.016107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.032542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.232148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.155747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.124150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.189579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.065489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.263862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.210943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.121366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.146747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.345619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.262103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.244495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.321790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.153691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.365012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.303971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.232632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.258487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.450085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.379513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.383223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.431172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.250617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.457057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.407802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.355593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.377568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.558924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.471933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.517436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.519285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.357057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.574227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.514317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.466433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.478925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.665629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.569760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.649153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.610107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.441059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.686044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.617659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.551715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:55.590417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:47.779145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:48.673479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:49.790678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:50.699338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:51.532428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:52.821723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:53.726319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:58:54.658993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:58:59.069298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분강습수영자유수영일일수영헬스일일헬스에어로빅요가_필라테스사 물 함기타수입합계
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
강습수영1.0001.0000.5140.4110.2960.4070.8750.3330.8660.8850.163
자유수영1.0000.5141.0000.7670.9260.7150.7400.0000.8630.6190.758
일일수영1.0000.4110.7671.0000.8790.8690.7740.6760.7120.6060.477
헬스1.0000.2960.9260.8791.0000.7820.6800.0000.7740.6450.884
일일헬스1.0000.4070.7150.8690.7821.0000.9020.0000.6780.8040.733
에어로빅1.0000.8750.7400.7740.6800.9021.0000.8380.9190.9990.694
요가_필라테스1.0000.3330.0000.6760.0000.0000.8381.0000.0000.8550.786
사 물 함1.0000.8660.8630.7120.7740.6780.9190.0001.0000.8500.847
기타수입1.0000.8850.6190.6060.6450.8040.9990.8550.8501.0000.658
합계1.0000.1630.7580.4770.8840.7330.6940.7860.8470.6581.000
2023-12-12T14:58:59.182593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
강습수영자유수영일일수영헬스일일헬스에어로빅사 물 함기타수입합계요가_필라테스
강습수영1.0000.6640.5890.7210.4140.8940.7230.6570.7330.272
자유수영0.6641.0000.8790.9600.8230.6800.9610.9500.9730.000
일일수영0.5890.8791.0000.8550.9460.5990.8800.8670.9200.426
헬스0.7210.9600.8551.0000.8100.7430.9670.9230.9510.000
일일헬스0.4140.8230.9460.8101.0000.4160.8010.7930.8360.000
에어로빅0.8940.6800.5990.7430.4161.0000.7540.6610.7330.483
사 물 함0.7230.9610.8800.9670.8010.7541.0000.9340.9710.000
기타수입0.6570.9500.8670.9230.7930.6610.9341.0000.9450.465
합계0.7330.9730.9200.9510.8360.7330.9710.9451.0000.406
요가_필라테스0.2720.0000.4260.0000.0000.4830.0000.4650.4061.000

Missing values

2023-12-12T14:58:55.730942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:58:55.921851image/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

구분강습수영자유수영일일수영헬스일일헬스에어로빅요가_필라테스사 물 함기타수입합계
02020-12-20038640008421208851002618000013000080005991020
12021-01-210000000000
22021-02-210024096000277800003600010002724400
32021-03-210121080001113730015741001456100002700001500026560500
42021-04-2101342600055923802252500999600002740001000022554480
52021-05-210102670003672110153700066190000176000700016321010
62021-06-2101789640056670002597000787800003140003100027293200
72021-07-2101702700084156501475000937200003540002600028234850
82021-08-21027370001545750291500161500006600030004804750
92021-09-210000000000
구분강습수영자유수영일일수영헬스일일헬스에어로빅요가_필라테스사 물 함기타수입합계
142022-02-220-273000-6448000000-91780
152022-03-220000000000
162022-04-2201664172034211102082900353600002800003400022813330
172022-05-2202307635065118402731270828240003800003100033558700
182022-06-2216979300255691808618200289381094878090300004200007500056407270
192022-07-221295310025082430931572036234601263600135100004860006100054136310
202022-08-221960418023835680919830035702701200100145628005700004700059481810
212022-09-2220792390207331007871540308195073410015834004828004840005700055820280
222022-10-2219074480221297707232410318602063530016842009046004980002500055369780
232022-11-2218813000208264006682060351852064470016170008377705080005600053503450