Overview

Dataset statistics

Number of variables14
Number of observations46
Missing cells97
Missing cells (%)15.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory123.9 B

Variable types

Categorical3
Text2
Numeric9

Dataset

Description전라남도 시군의 테니스장(시설명, 소유기관, 관리주체, 면적, 수용인원 등)에 관한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037310/fileData.do

Alerts

소유기관 is highly overall correlated with 시군구High correlation
시군구 is highly overall correlated with 소유기관High correlation
부지면적 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
건축면적 is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
면적 is highly overall correlated with 부지면적 and 3 other fieldsHigh correlation
코트 면수 is highly overall correlated with 건축면적 and 3 other fieldsHigh correlation
좌석수 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
수용인원 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
준공 연도 is highly overall correlated with 바닥재료High correlation
건설사업비 is highly overall correlated with 부지면적 and 2 other fieldsHigh correlation
바닥재료 is highly overall correlated with 준공 연도High correlation
건축면적 has 13 (28.3%) missing valuesMissing
연면적 has 11 (23.9%) missing valuesMissing
좌석수 has 25 (54.3%) missing valuesMissing
수용인원 has 21 (45.7%) missing valuesMissing
건설사업비 has 27 (58.7%) missing valuesMissing

Reproduction

Analysis started2023-12-13 00:52:10.882578
Analysis finished2023-12-13 00:52:17.361330
Duration6.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
목포시
여수시
순천시
고흥군
영암군
Other values (17)
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.0%

Sample

1st row목포시
2nd row목포시
3rd row목포시
4th row목포시
5th row여수시

Common Values

ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
장성군 2
 
4.3%
강진군 2
 
4.3%
완도군 2
 
4.3%
보성군 2
 
4.3%
Other values (12) 18
39.1%

Length

2023-12-13T09:52:17.418179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
영광군 2
 
4.3%
나주시 2
 
4.3%
광양시 2
 
4.3%
진도군 2
 
4.3%
Other values (12) 18
39.1%
Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:52:17.583870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length8.1304348
Min length6

Characters and Unicode

Total characters374
Distinct characters95
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

Unique44 ?
Unique (%)95.7%

Sample

1st row목포 시립테니스장
2nd row목포 시립정구장
3rd row목포 시민테니스장
4th row부주산테니스장
5th row진남종합테니스장
ValueCountFrequency (%)
테니스장 24
29.6%
목포 3
 
3.7%
팔마 3
 
3.7%
강진 2
 
2.5%
정구장 2
 
2.5%
시립테니스장 2
 
2.5%
스포츠파크 1
 
1.2%
장흥 1
 
1.2%
군립테니스장 1
 
1.2%
우슬 1
 
1.2%
Other values (41) 41
50.6%
2023-12-13T09:52:17.852662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
47
 
12.6%
44
 
11.8%
42
 
11.2%
42
 
11.2%
35
 
9.4%
8
 
2.1%
6
 
1.6%
6
 
1.6%
5
 
1.3%
5
 
1.3%
Other values (85) 134
35.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 334
89.3%
Space Separator 35
 
9.4%
Decimal Number 3
 
0.8%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
47
 
14.1%
44
 
13.2%
42
 
12.6%
42
 
12.6%
8
 
2.4%
6
 
1.8%
6
 
1.8%
5
 
1.5%
5
 
1.5%
5
 
1.5%
Other values (80) 124
37.1%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Space Separator
ValueCountFrequency (%)
35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 334
89.3%
Common 40
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
47
 
14.1%
44
 
13.2%
42
 
12.6%
42
 
12.6%
8
 
2.4%
6
 
1.8%
6
 
1.8%
5
 
1.5%
5
 
1.5%
5
 
1.5%
Other values (80) 124
37.1%
Common
ValueCountFrequency (%)
35
87.5%
1 2
 
5.0%
2 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 334
89.3%
ASCII 40
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
47
 
14.1%
44
 
13.2%
42
 
12.6%
42
 
12.6%
8
 
2.4%
6
 
1.8%
6
 
1.8%
5
 
1.5%
5
 
1.5%
5
 
1.5%
Other values (80) 124
37.1%
ASCII
ValueCountFrequency (%)
35
87.5%
1 2
 
5.0%
2 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%

소유기관
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)47.8%
Missing0
Missing (%)0.0%
Memory size500.0 B
목포시
여수시
순천시
고흥군
영암군
Other values (17)
29 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)13.0%

Sample

1st row목포시
2nd row목포시
3rd row목포시
4th row목포시
5th row여수시

Common Values

ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
장성군 2
 
4.3%
강진군 2
 
4.3%
완도군 2
 
4.3%
보성군 2
 
4.3%
Other values (12) 18
39.1%

Length

2023-12-13T09:52:17.971873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포시 4
 
8.7%
여수시 4
 
8.7%
순천시 3
 
6.5%
고흥군 3
 
6.5%
영암군 3
 
6.5%
무안군 3
 
6.5%
영광군 2
 
4.3%
나주시 2
 
4.3%
광양시 2
 
4.3%
진도군 2
 
4.3%
Other values (12) 18
39.1%
Distinct26
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
2023-12-13T09:52:18.109051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.2391304
Min length3

Characters and Unicode

Total characters287
Distinct characters65
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)34.8%

Sample

1st row위탁_테니스클럽
2nd row위탁_소프트테니스클럽
3rd row위탁_테니스클럽
4th row체육시설관리사무소
5th row여수시_체육지원과
ValueCountFrequency (%)
위탁_테니스협회 8
17.0%
체육시설관리사무소 4
 
8.5%
고흥군 3
 
6.4%
무안군 3
 
6.4%
위탁_테니스클럽 3
 
6.4%
나주시 2
 
4.3%
진도군 2
 
4.3%
영광군 2
 
4.3%
보성군 2
 
4.3%
곡성군 2
 
4.3%
Other values (16) 16
34.0%
2023-12-13T09:52:18.343647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
8.0%
_ 17
 
5.9%
16
 
5.6%
16
 
5.6%
) 15
 
5.2%
14
 
4.9%
14
 
4.9%
14
 
4.9%
10
 
3.5%
10
 
3.5%
Other values (55) 138
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 250
87.1%
Connector Punctuation 17
 
5.9%
Close Punctuation 15
 
5.2%
Open Punctuation 2
 
0.7%
Dash Punctuation 1
 
0.3%
Other Symbol 1
 
0.3%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
 
9.2%
16
 
6.4%
16
 
6.4%
14
 
5.6%
14
 
5.6%
14
 
5.6%
10
 
4.0%
10
 
4.0%
9
 
3.6%
7
 
2.8%
Other values (49) 117
46.8%
Connector Punctuation
ValueCountFrequency (%)
_ 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 251
87.5%
Common 36
 
12.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
 
9.2%
16
 
6.4%
16
 
6.4%
14
 
5.6%
14
 
5.6%
14
 
5.6%
10
 
4.0%
10
 
4.0%
9
 
3.6%
7
 
2.8%
Other values (50) 118
47.0%
Common
ValueCountFrequency (%)
_ 17
47.2%
) 15
41.7%
( 2
 
5.6%
- 1
 
2.8%
1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 250
87.1%
ASCII 36
 
12.5%
None 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
23
 
9.2%
16
 
6.4%
16
 
6.4%
14
 
5.6%
14
 
5.6%
14
 
5.6%
10
 
4.0%
10
 
4.0%
9
 
3.6%
7
 
2.8%
Other values (49) 117
46.8%
ASCII
ValueCountFrequency (%)
_ 17
47.2%
) 15
41.7%
( 2
 
5.6%
- 1
 
2.8%
1
 
2.8%
None
ValueCountFrequency (%)
1
100.0%

부지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11927.196
Minimum1394
Maximum93612
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.447700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1394
5-th percentile1959
Q13209
median4999
Q37878
95-th percentile61541.25
Maximum93612
Range92218
Interquartile range (IQR)4669

Descriptive statistics

Standard deviation20592.669
Coefficient of variation (CV)1.7265307
Kurtosis9.7554235
Mean11927.196
Median Absolute Deviation (MAD)2293.5
Skewness3.1921631
Sum548651
Variance4.2405802 × 108
MonotonicityNot monotonic
2023-12-13T09:52:18.546541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
5000 2
 
4.3%
3890 1
 
2.2%
3967 1
 
2.2%
7312 1
 
2.2%
5523 1
 
2.2%
93612 1
 
2.2%
3500 1
 
2.2%
71104 1
 
2.2%
6567 1
 
2.2%
29029 1
 
2.2%
Other values (35) 35
76.1%
ValueCountFrequency (%)
1394 1
2.2%
1405 1
2.2%
1941 1
2.2%
2013 1
2.2%
2154 1
2.2%
2231 1
2.2%
2551 1
2.2%
2562 1
2.2%
2776 1
2.2%
2937 1
2.2%
ValueCountFrequency (%)
93612 1
2.2%
88618 1
2.2%
71104 1
2.2%
32853 1
2.2%
29029 1
2.2%
20000 1
2.2%
18843 1
2.2%
12000 1
2.2%
11820 1
2.2%
11794 1
2.2%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)93.9%
Missing13
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean720.60606
Minimum18
Maximum6375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.636830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile36.8
Q171
median112
Q3198
95-th percentile4908.4
Maximum6375
Range6357
Interquartile range (IQR)127

Descriptive statistics

Standard deviation1693.6672
Coefficient of variation (CV)2.3503371
Kurtosis6.5174963
Mean720.60606
Median Absolute Deviation (MAD)57
Skewness2.7480162
Sum23780
Variance2868508.6
MonotonicityNot monotonic
2023-12-13T09:52:18.729125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
95 2
 
4.3%
215 2
 
4.3%
103 1
 
2.2%
187 1
 
2.2%
104 1
 
2.2%
3517 1
 
2.2%
507 1
 
2.2%
147 1
 
2.2%
85 1
 
2.2%
4010 1
 
2.2%
Other values (21) 21
45.7%
(Missing) 13
28.3%
ValueCountFrequency (%)
18 1
2.2%
32 1
2.2%
40 1
2.2%
43 1
2.2%
52 1
2.2%
55 1
2.2%
56 1
2.2%
66 1
2.2%
71 1
2.2%
83 1
2.2%
ValueCountFrequency (%)
6375 1
2.2%
6256 1
2.2%
4010 1
2.2%
3517 1
2.2%
507 1
2.2%
222 1
2.2%
215 2
4.3%
198 1
2.2%
187 1
2.2%
181 1
2.2%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)97.1%
Missing11
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean821.25714
Minimum18
Maximum6375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:18.849739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile37.6
Q189.5
median147
Q3223.5
95-th percentile4689.2
Maximum6375
Range6357
Interquartile range (IQR)134

Descriptive statistics

Standard deviation1689.8534
Coefficient of variation (CV)2.0576423
Kurtosis5.4209667
Mean821.25714
Median Absolute Deviation (MAD)68
Skewness2.5028981
Sum28744
Variance2855604.7
MonotonicityNot monotonic
2023-12-13T09:52:18.944343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
95 2
 
4.3%
382 1
 
2.2%
181 1
 
2.2%
126 1
 
2.2%
4010 1
 
2.2%
85 1
 
2.2%
147 1
 
2.2%
1340 1
 
2.2%
135 1
 
2.2%
3387 1
 
2.2%
Other values (24) 24
52.2%
(Missing) 11
23.9%
ValueCountFrequency (%)
18 1
2.2%
32 1
2.2%
40 1
2.2%
43 1
2.2%
52 1
2.2%
56 1
2.2%
66 1
2.2%
83 1
2.2%
85 1
2.2%
94 1
2.2%
ValueCountFrequency (%)
6375 1
2.2%
6274 1
2.2%
4010 1
2.2%
3387 1
2.2%
3230 1
2.2%
1340 1
2.2%
532 1
2.2%
382 1
2.2%
225 1
2.2%
222 1
2.2%

바닥재료
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Memory size500.0 B
클레이
10 
토사
앙투카
하드코트
하드
Other values (14)
16 

Length

Max length12
Median length10
Mean length3.7608696
Min length2

Unique

Unique13 ?
Unique (%)28.3%

Sample

1st row클레이
2nd row클레이
3rd row클레이
4th row앙투카
5th row하드

Common Values

ValueCountFrequency (%)
클레이 10
21.7%
토사 8
17.4%
앙투카 5
10.9%
하드코트 4
 
8.7%
하드 3
 
6.5%
마사토 3
 
6.5%
클래이 6캐미칼 4 1
 
2.2%
하드6토사2 1
 
2.2%
마사토,인조잔디 1
 
2.2%
인조잔디 1
 
2.2%
Other values (9) 9
19.6%

Length

2023-12-13T09:52:19.042294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
클레이 10
19.6%
토사 8
15.7%
앙투카 5
9.8%
하드 5
9.8%
하드코트 4
 
7.8%
마사토 3
 
5.9%
4 2
 
3.9%
3 1
 
2.0%
아크릴(2)클레이(3 1
 
2.0%
잔디3 1
 
2.0%
Other values (11) 11
21.6%

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3331.413
Minimum141
Maximum12284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.153651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile1046.25
Q11751.5
median2856
Q33500
95-th percentile7289.75
Maximum12284
Range12143
Interquartile range (IQR)1748.5

Descriptive statistics

Standard deviation2283.5642
Coefficient of variation (CV)0.68546414
Kurtosis4.3858511
Mean3331.413
Median Absolute Deviation (MAD)1041.5
Skewness1.7960141
Sum153245
Variance5214665.3
MonotonicityNot monotonic
2023-12-13T09:52:19.255809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3500 4
 
8.7%
1575 1
 
2.2%
1043 1
 
2.2%
2198 1
 
2.2%
7450 1
 
2.2%
1227 1
 
2.2%
2500 1
 
2.2%
2774 1
 
2.2%
2900 1
 
2.2%
2610 1
 
2.2%
Other values (33) 33
71.7%
ValueCountFrequency (%)
141 1
2.2%
782 1
2.2%
1043 1
2.2%
1056 1
2.2%
1155 1
2.2%
1227 1
2.2%
1296 1
2.2%
1394 1
2.2%
1499 1
2.2%
1575 1
2.2%
ValueCountFrequency (%)
12284 1
2.2%
8370 1
2.2%
7450 1
2.2%
6809 1
2.2%
6672 1
2.2%
6472 1
2.2%
6375 1
2.2%
4600 1
2.2%
4455 1
2.2%
4320 1
2.2%

코트 면수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3695652
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.339845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median5
Q36
95-th percentile11.5
Maximum17
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.9466267
Coefficient of variation (CV)0.54876448
Kurtosis4.8896416
Mean5.3695652
Median Absolute Deviation (MAD)1
Skewness1.9627764
Sum247
Variance8.6826087
MonotonicityNot monotonic
2023-12-13T09:52:19.418995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 11
23.9%
5 9
19.6%
6 9
19.6%
3 6
13.0%
2 4
 
8.7%
12 2
 
4.3%
10 2
 
4.3%
17 1
 
2.2%
9 1
 
2.2%
8 1
 
2.2%
ValueCountFrequency (%)
2 4
 
8.7%
3 6
13.0%
4 11
23.9%
5 9
19.6%
6 9
19.6%
8 1
 
2.2%
9 1
 
2.2%
10 2
 
4.3%
12 2
 
4.3%
17 1
 
2.2%
ValueCountFrequency (%)
17 1
 
2.2%
12 2
 
4.3%
10 2
 
4.3%
9 1
 
2.2%
8 1
 
2.2%
6 9
19.6%
5 9
19.6%
4 11
23.9%
3 6
13.0%
2 4
 
8.7%

좌석수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)57.1%
Missing25
Missing (%)54.3%
Infinite0
Infinite (%)0.0%
Mean448.80952
Minimum100
Maximum1845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.497842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median200
Q3500
95-th percentile1675
Maximum1845
Range1745
Interquartile range (IQR)300

Descriptive statistics

Standard deviation498.72193
Coefficient of variation (CV)1.1112107
Kurtosis3.5391375
Mean448.80952
Median Absolute Deviation (MAD)100
Skewness2.1057871
Sum9425
Variance248723.56
MonotonicityNot monotonic
2023-12-13T09:52:19.574716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
200 7
 
15.2%
350 2
 
4.3%
500 2
 
4.3%
100 2
 
4.3%
1675 1
 
2.2%
1845 1
 
2.2%
540 1
 
2.2%
1200 1
 
2.2%
159 1
 
2.2%
369 1
 
2.2%
Other values (2) 2
 
4.3%
(Missing) 25
54.3%
ValueCountFrequency (%)
100 2
 
4.3%
159 1
 
2.2%
165 1
 
2.2%
172 1
 
2.2%
200 7
15.2%
350 2
 
4.3%
369 1
 
2.2%
500 2
 
4.3%
540 1
 
2.2%
1200 1
 
2.2%
ValueCountFrequency (%)
1845 1
 
2.2%
1675 1
 
2.2%
1200 1
 
2.2%
540 1
 
2.2%
500 2
 
4.3%
369 1
 
2.2%
350 2
 
4.3%
200 7
15.2%
172 1
 
2.2%
165 1
 
2.2%

수용인원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)44.0%
Missing21
Missing (%)45.7%
Infinite0
Infinite (%)0.0%
Mean530.84
Minimum100
Maximum2500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:19.654032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1200
median200
Q3500
95-th percentile2400
Maximum2500
Range2400
Interquartile range (IQR)300

Descriptive statistics

Standard deviation705.97708
Coefficient of variation (CV)1.3299244
Kurtosis4.1282583
Mean530.84
Median Absolute Deviation (MAD)100
Skewness2.2831073
Sum13271
Variance498403.64
MonotonicityNot monotonic
2023-12-13T09:52:19.971075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
200 8
 
17.4%
100 4
 
8.7%
500 3
 
6.5%
2500 2
 
4.3%
400 2
 
4.3%
540 1
 
2.2%
2000 1
 
2.2%
300 1
 
2.2%
159 1
 
2.2%
800 1
 
2.2%
(Missing) 21
45.7%
ValueCountFrequency (%)
100 4
8.7%
159 1
 
2.2%
172 1
 
2.2%
200 8
17.4%
300 1
 
2.2%
400 2
 
4.3%
500 3
 
6.5%
540 1
 
2.2%
800 1
 
2.2%
2000 1
 
2.2%
ValueCountFrequency (%)
2500 2
 
4.3%
2000 1
 
2.2%
800 1
 
2.2%
540 1
 
2.2%
500 3
 
6.5%
400 2
 
4.3%
300 1
 
2.2%
200 8
17.4%
172 1
 
2.2%
159 1
 
2.2%

준공 연도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean437874.37
Minimum1981
Maximum20052016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:20.058537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1981
5-th percentile1987.5
Q12000.5
median2006
Q32013
95-th percentile2018
Maximum20052016
Range20050035
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation2956212.9
Coefficient of variation (CV)6.751281
Kurtosis46
Mean437874.37
Median Absolute Deviation (MAD)7
Skewness6.78233
Sum20142221
Variance8.7391948 × 1012
MonotonicityNot monotonic
2023-12-13T09:52:20.153797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2003 4
 
8.7%
2006 4
 
8.7%
2002 4
 
8.7%
2016 3
 
6.5%
1981 2
 
4.3%
2018 2
 
4.3%
2005 2
 
4.3%
2010 2
 
4.3%
1995 2
 
4.3%
2009 2
 
4.3%
Other values (16) 19
41.3%
ValueCountFrequency (%)
1981 2
4.3%
1987 1
 
2.2%
1989 2
4.3%
1990 1
 
2.2%
1992 1
 
2.2%
1994 1
 
2.2%
1995 2
4.3%
1999 1
 
2.2%
2000 1
 
2.2%
2002 4
8.7%
ValueCountFrequency (%)
20052016 1
 
2.2%
2021 1
 
2.2%
2018 2
4.3%
2017 2
4.3%
2016 3
6.5%
2015 1
 
2.2%
2014 1
 
2.2%
2013 2
4.3%
2012 1
 
2.2%
2010 2
4.3%

건설사업비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)73.7%
Missing27
Missing (%)58.7%
Infinite0
Infinite (%)0.0%
Mean133.73684
Minimum12
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size546.0 B
2023-12-13T09:52:20.243867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12
Q157.5
median80
Q3171.5
95-th percentile388.4
Maximum500
Range488
Interquartile range (IQR)114

Descriptive statistics

Standard deviation128.14438
Coefficient of variation (CV)0.9581831
Kurtosis2.8935958
Mean133.73684
Median Absolute Deviation (MAD)55
Skewness1.7157161
Sum2541
Variance16420.982
MonotonicityNot monotonic
2023-12-13T09:52:20.330696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
60 4
 
8.7%
12 2
 
4.3%
150 2
 
4.3%
55 1
 
2.2%
193 1
 
2.2%
257 1
 
2.2%
34 1
 
2.2%
376 1
 
2.2%
199 1
 
2.2%
80 1
 
2.2%
Other values (4) 4
 
8.7%
(Missing) 27
58.7%
ValueCountFrequency (%)
12 2
4.3%
34 1
 
2.2%
45 1
 
2.2%
55 1
 
2.2%
60 4
8.7%
80 1
 
2.2%
103 1
 
2.2%
135 1
 
2.2%
150 2
4.3%
193 1
 
2.2%
ValueCountFrequency (%)
500 1
 
2.2%
376 1
 
2.2%
257 1
 
2.2%
199 1
 
2.2%
193 1
 
2.2%
150 2
4.3%
135 1
 
2.2%
103 1
 
2.2%
80 1
 
2.2%
60 4
8.7%

Interactions

2023-12-13T09:52:16.382166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:52:11.416726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/