Overview

Dataset statistics

Number of variables17
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 KiB
Average record size in memory152.1 B

Variable types

Categorical4
Text1
Numeric12

Dataset

Description과천도시공사 운영 체육프로그램 월별 관내 및 관외 이용객 현황 - 체육프로그램 업장, 종목명, 월별 관내외 이용 인원수
URLhttps://www.data.go.kr/data/15040289/fileData.do

Alerts

센터 has constant value ""Constant
연도 has constant value ""Constant
01월 is highly overall correlated with 02월 and 10 other fieldsHigh correlation
02월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
03월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
04월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
05월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
06월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
07월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
08월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
09월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 01월 and 10 other fieldsHigh correlation
01월 has 7 (16.3%) zerosZeros
02월 has 9 (20.9%) zerosZeros
03월 has 9 (20.9%) zerosZeros
04월 has 9 (20.9%) zerosZeros
05월 has 7 (16.3%) zerosZeros
06월 has 8 (18.6%) zerosZeros
07월 has 3 (7.0%) zerosZeros
08월 has 3 (7.0%) zerosZeros
09월 has 8 (18.6%) zerosZeros
10월 has 8 (18.6%) zerosZeros
11월 has 11 (25.6%) zerosZeros
12월 has 11 (25.6%) zerosZeros

Reproduction

Analysis started2023-12-12 07:54:33.094995
Analysis finished2023-12-12 07:54:48.908822
Duration15.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

센터
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
과천시민회관(체육)
43 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과천시민회관(체육)
2nd row과천시민회관(체육)
3rd row과천시민회관(체육)
4th row과천시민회관(체육)
5th row과천시민회관(체육)

Common Values

ValueCountFrequency (%)
과천시민회관(체육) 43
100.0%

Length

2023-12-12T16:54:48.984920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:54:49.082572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
과천시민회관(체육 43
100.0%

업장
Categorical

Distinct6
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
체육시설
14 
수영장
11 
대체육관
빙상장
골프장

Length

Max length4
Median length3
Mean length3.4651163
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row골프장
2nd row골프장
3rd row골프장
4th row대체육관
5th row대체육관

Common Values

ValueCountFrequency (%)
체육시설 14
32.6%
수영장 11
25.6%
대체육관 6
14.0%
빙상장 6
14.0%
골프장 3
 
7.0%
볼링장 3
 
7.0%

Length

2023-12-12T16:54:49.196620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:54:49.364980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육시설 14
32.6%
수영장 11
25.6%
대체육관 6
14.0%
빙상장 6
14.0%
골프장 3
 
7.0%
볼링장 3
 
7.0%
Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T16:54:49.609134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.9767442
Min length2

Characters and Unicode

Total characters343
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)11.6%

Sample

1st row개인강습사용(골프장)
2nd row실내골프
3rd row실내골프
4th row개인강습사용(대체육관)
5th row기타체육프로그램(대체육)
ValueCountFrequency (%)
개인강습사용(수영장 2
 
4.4%
수영월정기권 2
 
4.4%
유아체능단 2
 
4.4%
요가및필라테스 2
 
4.4%
프로그램(유아체능단 2
 
4.4%
방과후 2
 
4.4%
무도프로그램 2
 
4.4%
댄스프로그램(발레,줌바등 2
 
4.4%
기타체육프로그램(체육시설 2
 
4.4%
수영 2
 
4.4%
Other values (15) 25
55.6%
2023-12-12T16:54:50.040502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 21
 
6.1%
) 21
 
6.1%
16
 
4.7%
15
 
4.4%
14
 
4.1%
12
 
3.5%
12
 
3.5%
12
 
3.5%
11
 
3.2%
9
 
2.6%
Other values (55) 200
58.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 297
86.6%
Open Punctuation 21
 
6.1%
Close Punctuation 21
 
6.1%
Space Separator 2
 
0.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
16
 
5.4%
15
 
5.1%
14
 
4.7%
12
 
4.0%
12
 
4.0%
12
 
4.0%
11
 
3.7%
9
 
3.0%
9
 
3.0%
9
 
3.0%
Other values (51) 178
59.9%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 297
86.6%
Common 46
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
16
 
5.4%
15
 
5.1%
14
 
4.7%
12
 
4.0%
12
 
4.0%
12
 
4.0%
11
 
3.7%
9
 
3.0%
9
 
3.0%
9
 
3.0%
Other values (51) 178
59.9%
Common
ValueCountFrequency (%)
( 21
45.7%
) 21
45.7%
2
 
4.3%
, 2
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 297
86.6%
ASCII 46
 
13.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 21
45.7%
) 21
45.7%
2
 
4.3%
, 2
 
4.3%
Hangul
ValueCountFrequency (%)
16
 
5.4%
15
 
5.1%
14
 
4.7%
12
 
4.0%
12
 
4.0%
12
 
4.0%
11
 
3.7%
9
 
3.0%
9
 
3.0%
9
 
3.0%
Other values (51) 178
59.9%

관내외구분
Categorical

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
관내
22 
관외
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관외
2nd row관내
3rd row관외
4th row관외
5th row관내

Common Values

ValueCountFrequency (%)
관내 22
51.2%
관외 21
48.8%

Length

2023-12-12T16:54:50.271526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:54:50.396895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관내 22
51.2%
관외 21
48.8%

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2022
43 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 43
100.0%

Length

2023-12-12T16:54:50.882847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:54:51.004214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 43
100.0%

01월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.27907
Minimum0
Maximum278
Zeros7
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:51.109553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q337
95-th percentile212.4
Maximum278
Range278
Interquartile range (IQR)35

Descriptive statistics

Standard deviation71.5443
Coefficient of variation (CV)1.7331859
Kurtosis3.0497421
Mean41.27907
Median Absolute Deviation (MAD)7
Skewness2.0161328
Sum1775
Variance5118.5869
MonotonicityNot monotonic
2023-12-12T16:54:51.239689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 7
16.3%
3 5
 
11.6%
2 4
 
9.3%
4 3
 
7.0%
216 2
 
4.7%
1 2
 
4.7%
12 2
 
4.7%
11 2
 
4.7%
98 1
 
2.3%
39 1
 
2.3%
Other values (14) 14
32.6%
ValueCountFrequency (%)
0 7
16.3%
1 2
 
4.7%
2 4
9.3%
3 5
11.6%
4 3
7.0%
7 1
 
2.3%
9 1
 
2.3%
11 2
 
4.7%
12 2
 
4.7%
15 1
 
2.3%
ValueCountFrequency (%)
278 1
2.3%
216 2
4.7%
180 1
2.3%
177 1
2.3%
160 1
2.3%
98 1
2.3%
91 1
2.3%
55 1
2.3%
43 1
2.3%
39 1
2.3%

02월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.627907
Minimum0
Maximum286
Zeros9
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:51.376222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q338.5
95-th percentile200.4
Maximum286
Range286
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation77.720814
Coefficient of variation (CV)1.7415294
Kurtosis3.265801
Mean44.627907
Median Absolute Deviation (MAD)9
Skewness2.0609143
Sum1919
Variance6040.5249
MonotonicityNot monotonic
2023-12-12T16:54:51.516755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 9
20.9%
3 5
 
11.6%
2 4
 
9.3%
12 2
 
4.7%
15 2
 
4.7%
43 1
 
2.3%
46 1
 
2.3%
201 1
 
2.3%
53 1
 
2.3%
34 1
 
2.3%
Other values (16) 16
37.2%
ValueCountFrequency (%)
0 9
20.9%
2 4
9.3%
3 5
11.6%
4 1
 
2.3%
6 1
 
2.3%
7 1
 
2.3%
9 1
 
2.3%
10 1
 
2.3%
12 2
 
4.7%
15 2
 
4.7%
ValueCountFrequency (%)
286 1
2.3%
284 1
2.3%
201 1
2.3%
195 1
2.3%
194 1
2.3%
169 1
2.3%
106 1
2.3%
98 1
2.3%
53 1
2.3%
46 1
2.3%

03월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.511628
Minimum0
Maximum343
Zeros9
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:51.662362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median9
Q340
95-th percentile214.6
Maximum343
Range343
Interquartile range (IQR)38.5

Descriptive statistics

Standard deviation84.038362
Coefficient of variation (CV)1.7687957
Kurtosis3.861604
Mean47.511628
Median Absolute Deviation (MAD)9
Skewness2.1478151
Sum2043
Variance7062.4463
MonotonicityNot monotonic
2023-12-12T16:54:51.825112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 9
20.9%
2 3
 
7.0%
3 3
 
7.0%
9 3
 
7.0%
1 2
 
4.7%
4 2
 
4.7%
343 1
 
2.3%
52 1
 
2.3%
198 1
 
2.3%
41 1
 
2.3%
Other values (17) 17
39.5%
ValueCountFrequency (%)
0 9
20.9%
1 2
 
4.7%
2 3
 
7.0%
3 3
 
7.0%
4 2
 
4.7%
5 1
 
2.3%
9 3
 
7.0%
12 1
 
2.3%
13 1
 
2.3%
15 1
 
2.3%
ValueCountFrequency (%)
343 1
2.3%
276 1
2.3%
215 1
2.3%
211 1
2.3%
198 1
2.3%
187 1
2.3%
114 1
2.3%
100 1
2.3%
52 1
2.3%
43 1
2.3%

04월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.74419
Minimum0
Maximum772
Zeros9
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:51.991621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median15
Q3126.5
95-th percentile397.3
Maximum772
Range772
Interquartile range (IQR)124

Descriptive statistics

Standard deviation169.6787
Coefficient of variation (CV)1.684253
Kurtosis5.2737813
Mean100.74419
Median Absolute Deviation (MAD)15
Skewness2.2242148
Sum4332
Variance28790.862
MonotonicityNot monotonic
2023-12-12T16:54:52.172704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 9
20.9%
3 3
 
7.0%
4 2
 
4.7%
7 1
 
2.3%
44 1
 
2.3%
5 1
 
2.3%
391 1
 
2.3%
131 1
 
2.3%
29 1
 
2.3%
22 1
 
2.3%
Other values (22) 22
51.2%
ValueCountFrequency (%)
0 9
20.9%
1 1
 
2.3%
2 1
 
2.3%
3 3
 
7.0%
4 2
 
4.7%
5 1
 
2.3%
7 1
 
2.3%
8 1
 
2.3%
10 1
 
2.3%
12 1
 
2.3%
ValueCountFrequency (%)
772 1
2.3%
518 1
2.3%
398 1
2.3%
391 1
2.3%
311 1
2.3%
309 1
2.3%
297 1
2.3%
248 1
2.3%
238 1
2.3%
148 1
2.3%

05월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.16279
Minimum0
Maximum902
Zeros7
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:52.343803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median15
Q3138
95-th percentile530.4
Maximum902
Range902
Interquartile range (IQR)135

Descriptive statistics

Standard deviation205.4776
Coefficient of variation (CV)1.6819983
Kurtosis4.568111
Mean122.16279
Median Absolute Deviation (MAD)15
Skewness2.1541259
Sum5253
Variance42221.044
MonotonicityNot monotonic
2023-12-12T16:54:52.490353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 7
 
16.3%
3 3
 
7.0%
2 2
 
4.7%
15 2
 
4.7%
5 2
 
4.7%
22 1
 
2.3%
440 1
 
2.3%
143 1
 
2.3%
34 1
 
2.3%
6 1
 
2.3%
Other values (22) 22
51.2%
ValueCountFrequency (%)
0 7
16.3%
1 1
 
2.3%
2 2
 
4.7%
3 3
7.0%
4 1
 
2.3%
5 2
 
4.7%
6 1
 
2.3%
7 1
 
2.3%
8 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
902 1
2.3%
631 1
2.3%
534 1
2.3%
498 1
2.3%
440 1
2.3%
347 1
2.3%
316 1
2.3%
306 1
2.3%
258 1
2.3%
149 1
2.3%

06월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.7907
Minimum0
Maximum1045
Zeros8
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:52.642073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median14
Q3168
95-th percentile620.6
Maximum1045
Range1045
Interquartile range (IQR)165

Descriptive statistics

Standard deviation232.69708
Coefficient of variation (CV)1.7263586
Kurtosis5.3921865
Mean134.7907
Median Absolute Deviation (MAD)14
Skewness2.2881803
Sum5796
Variance54147.931
MonotonicityNot monotonic
2023-12-12T16:54:52.792769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 8
 
18.6%
5 3
 
7.0%
3 3
 
7.0%
36 2
 
4.7%
4 2
 
4.7%
70 1
 
2.3%
254 1
 
2.3%
40 1
 
2.3%
180 1
 
2.3%
82 1
 
2.3%
Other values (20) 20
46.5%
ValueCountFrequency (%)
0 8
18.6%
1 1
 
2.3%
2 1
 
2.3%
3 3
 
7.0%
4 2
 
4.7%
5 3
 
7.0%
6 1
 
2.3%
12 1
 
2.3%
13 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
1045 1
2.3%
730 1
2.3%
633 1
2.3%
509 1
2.3%
480 1
2.3%
361 1
2.3%
338 1
2.3%
316 1
2.3%
254 1
2.3%
185 1
2.3%

07월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.74419
Minimum0
Maximum1179
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:52.953743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q15
median34
Q3174.5
95-th percentile617.9
Maximum1179
Range1179
Interquartile range (IQR)169.5

Descriptive statistics

Standard deviation249.28642
Coefficient of variation (CV)1.6759406
Kurtosis6.6433943
Mean148.74419
Median Absolute Deviation (MAD)33
Skewness2.4472281
Sum6396
Variance62143.719
MonotonicityNot monotonic
2023-12-12T16:54:53.115976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3 4
 
9.3%
5 3
 
7.0%
0 3
 
7.0%
1 2
 
4.7%
12 2
 
4.7%
527 2
 
4.7%
22 1
 
2.3%
57 1
 
2.3%
9 1
 
2.3%
157 1
 
2.3%
Other values (23) 23
53.5%
ValueCountFrequency (%)
0 3
7.0%
1 2
4.7%
3 4
9.3%
4 1
 
2.3%
5 3
7.0%
6 1
 
2.3%
9 1
 
2.3%
12 2
4.7%
14 1
 
2.3%
22 1
 
2.3%
ValueCountFrequency (%)
1179 1
2.3%
774 1
2.3%
628 1
2.3%
527 2
4.7%
366 1
2.3%
352 1
2.3%
324 1
2.3%
297 1
2.3%
237 1
2.3%
192 1
2.3%

08월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)79.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.09302
Minimum0
Maximum1233
Zeros3
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:53.275267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q14
median34
Q3181.5
95-th percentile646.1
Maximum1233
Range1233
Interquartile range (IQR)177.5

Descriptive statistics

Standard deviation253.16584
Coefficient of variation (CV)1.6218908
Kurtosis7.3349078
Mean156.09302
Median Absolute Deviation (MAD)34
Skewness2.5074829
Sum6712
Variance64092.944
MonotonicityNot monotonic
2023-12-12T16:54:53.468610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
3 3
 
7.0%
4 3
 
7.0%
0 3
 
7.0%
1 2
 
4.7%
12 2
 
4.7%
83 2
 
4.7%
162 1
 
2.3%
72 1
 
2.3%
159 1
 
2.3%
76 1
 
2.3%
Other values (24) 24
55.8%
ValueCountFrequency (%)
0 3
7.0%
1 2
4.7%
2 1
 
2.3%
3 3
7.0%
4 3
7.0%
7 1
 
2.3%
12 2
4.7%
13 1
 
2.3%
15 1
 
2.3%
17 1
 
2.3%
ValueCountFrequency (%)
1233 1
2.3%
745 1
2.3%
660 1
2.3%
521 1
2.3%
508 1
2.3%
377 1
2.3%
375 1
2.3%
330 1
2.3%
293 1
2.3%
246 1
2.3%

09월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.18605
Minimum0
Maximum1478
Zeros8
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:53.595919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median16
Q3197
95-th percentile608.8
Maximum1478
Range1478
Interquartile range (IQR)194.5

Descriptive statistics

Standard deviation281.91634
Coefficient of variation (CV)1.8524454
Kurtosis11.172589
Mean152.18605
Median Absolute Deviation (MAD)16
Skewness3.0043515
Sum6544
Variance79476.822
MonotonicityNot monotonic
2023-12-12T16:54:53.725088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 8
 
18.6%
3 4
 
9.3%
1 2
 
4.7%
13 2
 
4.7%
380 1
 
2.3%
539 1
 
2.3%
175 1
 
2.3%
47 1
 
2.3%
6 1
 
2.3%
22 1
 
2.3%
Other values (21) 21
48.8%
ValueCountFrequency (%)
0 8
18.6%
1 2
 
4.7%
2 1
 
2.3%
3 4
9.3%
4 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
9 1
 
2.3%
13 2
 
4.7%
16 1
 
2.3%
ValueCountFrequency (%)
1478 1
2.3%
712 1
2.3%
610 1
2.3%
598 1
2.3%
539 1
2.3%
380 1
2.3%
376 1
2.3%
289 1
2.3%
254 1
2.3%
250 1
2.3%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.5814
Minimum0
Maximum1451
Zeros8
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:53.855487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median16
Q3202
95-th percentile582.9
Maximum1451
Range1451
Interquartile range (IQR)199.5

Descriptive statistics

Standard deviation277.09524
Coefficient of variation (CV)1.8160487
Kurtosis10.881904
Mean152.5814
Median Absolute Deviation (MAD)16
Skewness2.9429358
Sum6561
Variance76781.773
MonotonicityNot monotonic
2023-12-12T16:54:53.987980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 8
 
18.6%
3 3
 
7.0%
2 2
 
4.7%
22 2
 
4.7%
5 2
 
4.7%
416 1
 
2.3%
8 1
 
2.3%
569 1
 
2.3%
178 1
 
2.3%
54 1
 
2.3%
Other values (21) 21
48.8%
ValueCountFrequency (%)
0 8
18.6%
1 1
 
2.3%
2 2
 
4.7%
3 3
 
7.0%
4 1
 
2.3%
5 2
 
4.7%
6 1
 
2.3%
8 1
 
2.3%
13 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
1451 1
2.3%
663 1
2.3%
583 1
2.3%
582 1
2.3%
569 1
2.3%
416 1
2.3%
368 1
2.3%
297 1
2.3%
287 1
2.3%
256 1
2.3%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.62791
Minimum0
Maximum1414
Zeros11
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:54.119027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median14
Q3198.5
95-th percentile588.6
Maximum1414
Range1414
Interquartile range (IQR)198

Descriptive statistics

Standard deviation262.42205
Coefficient of variation (CV)1.8528979
Kurtosis12.730719
Mean141.62791
Median Absolute Deviation (MAD)14
Skewness3.1718274
Sum6090
Variance68865.334
MonotonicityNot monotonic
2023-12-12T16:54:54.247539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
25.6%
2 3
 
7.0%
73 1
 
2.3%
82 1
 
2.3%
276 1
 
2.3%
4 1
 
2.3%
39 1
 
2.3%
74 1
 
2.3%
383 1
 
2.3%
21 1
 
2.3%
Other values (21) 21
48.8%
ValueCountFrequency (%)
0 11
25.6%
1 1
 
2.3%
2 3
 
7.0%
3 1
 
2.3%
4 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
7 1
 
2.3%
12 1
 
2.3%
14 1
 
2.3%
ValueCountFrequency (%)
1414 1
2.3%
610 1
2.3%
593 1
2.3%
549 1
2.3%
383 1
2.3%
371 1
2.3%
329 1
2.3%
276 1
2.3%
259 1
2.3%
252 1
2.3%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.62791
Minimum0
Maximum1412
Zeros11
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-12T16:54:54.410587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.5
median13
Q3199
95-th percentile560.6
Maximum1412
Range1412
Interquartile range (IQR)198.5

Descriptive statistics

Standard deviation260.63832
Coefficient of variation (CV)1.8533897
Kurtosis13.051448
Mean140.62791
Median Absolute Deviation (MAD)13
Skewness3.2044366
Sum6047
Variance67932.334
MonotonicityNot monotonic
2023-12-12T16:54:54.546464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 11
25.6%
13 2
 
4.7%
3 2
 
4.7%
2 2
 
4.7%
382 1
 
2.3%
609 1
 
2.3%
175 1
 
2.3%
57 1
 
2.3%
6 1
 
2.3%
22 1
 
2.3%
Other values (20) 20
46.5%
ValueCountFrequency (%)
0 11
25.6%
1 1
 
2.3%
2 2
 
4.7%
3 2
 
4.7%
4 1
 
2.3%
5 1
 
2.3%
6 1
 
2.3%
8 1
 
2.3%
13 2
 
4.7%
18 1
 
2.3%
ValueCountFrequency (%)
1412 1
2.3%
609 1
2.3%
563 1
2.3%
539 1
2.3%
382 1
2.3%
375 1
2.3%
328 1
2.3%
278 1
2.3%
257 1
2.3%
251 1
2.3%

Interactions

2023-12-12T16:54:47.180208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.636408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.626106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.737989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.996862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.679206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.871388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.921335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.171575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.228732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.524302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.723428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.317692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.711861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.704096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.828764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.097295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.776218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.971836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.989779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.263332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.305543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.629166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.805513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.451575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.800730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.777751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.909021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.194091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.877574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.068394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.077052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.358788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.388977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.771271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.903857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.588585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.899381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.852941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.010957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.334220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.975816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.156921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.187052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.465829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.492151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.898319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.005325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.721513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:33.995213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.943767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.122165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.433858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.069296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.264728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.319566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.565755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.593823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.989069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.112650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.821494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.064393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.036377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.218042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.536708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.158011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.362553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.406650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.641211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.661583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.058419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.190285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.926664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.138371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.118340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.348278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.661680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.259796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.450847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.501549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.724050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.736515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.133624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.311949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:48.037652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.238348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.195799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.447048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:37.805374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.370178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.532845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.613723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.813650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.809928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.220958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.476660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:48.136320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.317484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.284239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.554871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.225455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.466789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.606776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.734548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.896155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.189061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.340807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.647622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:48.235473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.393051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.382117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.665036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.335901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.558146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.686635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.855366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.986983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.267014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.447376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.777834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:48.327809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.470608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.485934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.779495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.445934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.680681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.761979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:41.976983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.077233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.352340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.533995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:46.927094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:48.438509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:34.543543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:35.586852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:36.892009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:38.569682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:39.760713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:40.845334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:42.079068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:43.154296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:44.437000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:45.632962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:54:47.056762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:54:54.666046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업장종목명관내외구분01월02월03월04월05월06월07월08월09월10월11월12월
업장1.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
종목명1.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.4580.4580.0000.667
관내외구분0.0000.0001.0000.0000.0000.0000.2240.0000.0000.0000.2270.0000.2860.3540.155
01월0.0000.0000.0001.0000.9880.9590.9290.8490.9110.8020.7860.8140.8040.7680.803
02월0.0000.0000.0000.9881.0000.9240.9520.8490.9560.8420.8410.7580.7560.8160.744
03월0.0000.0000.0000.9590.9241.0000.7960.9510.8140.9100.9260.8670.8510.8320.859
04월0.0000.0000.2240.9290.9520.7961.0000.9480.9900.9670.9290.8550.8760.9390.886
05월0.0000.0000.0000.8490.8490.9510.9481.0000.9770.9870.9860.9700.9730.9510.951
06월0.0000.0000.0000.9110.9560.8140.9900.9771.0000.9470.9460.9190.9220.9270.890
07월0.0000.0000.0000.8020.8420.9100.9670.9870.9471.0000.9920.9240.9180.9760.927
08월0.0000.0000.2270.7860.8410.9260.9290.9860.9460.9921.0000.9510.9280.9930.946
09월0.0000.4580.0000.8140.7580.8670.8550.9700.9190.9240.9511.0000.9990.9900.998
10월0.0000.4580.2860.8040.7560.8510.8760.9730.9220.9180.9280.9991.0000.9830.995
11월0.0000.0000.3540.7680.8160.8320.9390.9510.9270.9760.9930.9900.9831.0000.997
12월0.0000.6670.1550.8030.7440.8590.8860.9510.8900.9270.9460.9980.9950.9971.000
2023-12-12T16:54:54.817871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업장관내외구분
업장1.0000.000
관내외구분0.0001.000
2023-12-12T16:54:54.927786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
01월02월03월04월05월06월07월08월09월10월11월12월업장관내외구분
01월1.0000.9740.9670.8640.8390.8370.6450.6170.8480.8450.8430.8450.0000.000
02월0.9741.0000.9900.8870.8700.8680.7030.6750.8780.8730.8680.8700.0000.000
03월0.9670.9901.0000.8910.8710.8680.7030.6750.8770.8710.8710.8730.0000.000
04월0.8640.8870.8911.0000.9790.9840.8340.7980.9720.9730.9770.9750.0000.217
05월0.8390.8700.8710.9791.0000.9870.8260.7920.9790.9780.9690.9680.0000.000
06월0.8370.8680.8680.9840.9871.0000.8420.8070.9920.9940.9800.9790.0000.000
07월0.6450.7030.7030.8340.8260.8421.0000.9940.8350.8350.8490.8480.0000.000
08월0.6170.6750.6750.7980.7920.8070.9941.0000.8040.8040.8180.8180.0000.145
09월0.8480.8780.8770.9720.9790.9920.8350.8041.0000.9990.9800.9820.0000.000
10월0.8450.8730.8710.9730.9780.9940.8350.8040.9991.0000.9830.9840.0000.188
11월0.8430.8680.8710.9770.9690.9800.8490.8180.9800.9831.0000.9990.0000.237
12월0.8450.8700.8730.9750.9680.9790.8480.8180.9820.9840.9991.0000.0000.091
업장0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
관내외구분0.0000.0000.0000.2170.0000.0000.0000.1450.0000.1880.2370.0910.0001.000

Missing values

2023-12-12T16:54:48.590835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:54:48.819347image/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

센터업장종목명관내외구분연도01월02월03월04월05월06월07월08월09월10월11월12월
0과천시민회관(체육)골프장개인강습사용(골프장)관외2022332033333300
1과천시민회관(체육)골프장실내골프관내20221199148133131120118119128129131
2과천시민회관(체육)골프장실내골프관외2022222875541221
3과천시민회관(체육)대체육관개인강습사용(대체육관)관외2022222222111100
4과천시민회관(체육)대체육관기타체육프로그램(대체육)관내2022216194211297316338366375376368371375
5과천시민회관(체육)대체육관기타체육프로그램(대체육)관외2022222021283136323225221918
6과천시민회관(체육)대체육관무도프로그램(대체육관)관내2022111213121412121213131413
7과천시민회관(체육)대체육관무도프로그램(대체육관)관외2022100324323533
8과천시민회관(체육)대체육관방학특강(대체육관)관내202200000034340000
9과천시민회관(체육)볼링장볼링관내20221269101513121313141213
센터업장종목명관내외구분연도01월02월03월04월05월06월07월08월09월10월11월12월
33과천시민회관(체육)체육시설무도프로그램관내2022212630293436417747545957
34과천시민회관(체육)체육시설무도프로그램관외2022475765576666
35과천시민회관(체육)체육시설방과후 프로그램(유아체능단)관내2022351522222222222222222122
36과천시민회관(체육)체육시설방과후 프로그램(유아체능단)관외2022100000000000
37과천시민회관(체육)체육시설요가및필라테스관내2022160169215311347361352377380416383382
38과천시민회관(체육)체육시설요가및필라테스관외2022303439638082788382817472
39과천시민회관(체육)체육시설유아체능단관내2022555341414140404039393940
40과천시민회관(체육)체육시설유아체능단관외2022224444444444
41과천시민회관(체육)체육시설헬스관내2022180201198248258254297330289297276278
42과천시민회관(체육)체육시설헬스관외2022394652586670868377808281