Overview

Dataset statistics

Number of variables15
Number of observations65
Missing cells276
Missing cells (%)28.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory132.0 B

Variable types

Categorical5
Text3
Numeric7

Dataset

Description상기한 법령에서 분류하지 않은 체육시설(F1 경기장, 클라이밍, 자전거경기장, 족구장 등)에 관한 정보(시설명, 관리주체, 면적, 규모 등)에 관한 데이터를 조회하실 수 있습니다.
Author전라남도
URLhttps://www.data.go.kr/data/15037313/fileData.do

Alerts

시군 is highly overall correlated with 건축면적 and 7 other fieldsHigh correlation
관리주체 is highly overall correlated with 길이_미터 and 7 other fieldsHigh correlation
주로수 is highly overall correlated with 건축면적 and 5 other fieldsHigh correlation
주로폭 is highly overall correlated with 건축면적 and 9 other fieldsHigh correlation
좌석수 is highly overall correlated with 건축면적 and 9 other fieldsHigh correlation
건축면적 is highly overall correlated with 연면적 and 6 other fieldsHigh correlation
연면적 is highly overall correlated with 건축면적 and 2 other fieldsHigh correlation
폭_미터 is highly overall correlated with 주로폭 and 1 other fieldsHigh correlation
길이_미터 is highly overall correlated with 건축면적 and 5 other fieldsHigh correlation
면적 is highly overall correlated with 길이_미터 and 3 other fieldsHigh correlation
준공 연도 is highly overall correlated with 시군 and 4 other fieldsHigh correlation
건설사업비_백만원 is highly overall correlated with 건축면적 and 4 other fieldsHigh correlation
주로수 is highly imbalanced (80.3%)Imbalance
주로폭 is highly imbalanced (85.6%)Imbalance
좌석수 is highly imbalanced (82.9%)Imbalance
부지면적 has 12 (18.5%) missing valuesMissing
건축면적 has 51 (78.5%) missing valuesMissing
연면적 has 43 (66.2%) missing valuesMissing
폭_미터 has 34 (52.3%) missing valuesMissing
길이_미터 has 34 (52.3%) missing valuesMissing
면적 has 21 (32.3%) missing valuesMissing
좌석 형태 has 62 (95.4%) missing valuesMissing
준공 연도 has 3 (4.6%) missing valuesMissing
건설사업비_백만원 has 16 (24.6%) missing valuesMissing
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:47:18.067895
Analysis finished2023-12-12 20:47:26.143069
Duration8.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
광양시
15 
목포시
10 
장성군
9 
무안군
7 
순천시
4 
Other values (11)
20 

Length

Max length3
Median length3
Mean length2.9846154
Min length2

Unique

Unique7 ?
Unique (%)10.8%

Sample

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

Common Values

ValueCountFrequency (%)
광양시 15
23.1%
목포시 10
15.4%
장성군 9
13.8%
무안군 7
10.8%
순천시 4
 
6.2%
장흥군 4
 
6.2%
해남군 3
 
4.6%
함평군 3
 
4.6%
신안군 3
 
4.6%
도청 1
 
1.5%
Other values (6) 6
 
9.2%

Length

2023-12-13T05:47:26.247535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광양시 15
23.1%
목포시 10
15.4%
장성군 9
13.8%
무안군 7
10.8%
순천시 4
 
6.2%
장흥군 4
 
6.2%
해남군 3
 
4.6%
함평군 3
 
4.6%
신안군 3
 
4.6%
도청 1
 
1.5%
Other values (6) 6
 
9.2%

시설명
Text

UNIQUE 

Distinct65
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size652.0 B
2023-12-13T05:47:26.575100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length9.2615385
Min length4

Characters and Unicode

Total characters602
Distinct characters143
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)100.0%

Sample

1st rowF1국제 자동차 경주장
2nd row목포국제스포츠클라이밍센터
3rd row목포파크골프장(부주산국제파크골프장)
4th row서해(연산동)파크골프장
5th row북항수질관리사무소파크골프장
ValueCountFrequency (%)
그라운드골프장 12
 
12.8%
파크골프장 4
 
4.3%
마동 3
 
3.2%
족구장 2
 
2.1%
풋살 2
 
2.1%
광영 2
 
2.1%
풋살장 2
 
2.1%
옥곡면 2
 
2.1%
부주산 2
 
2.1%
우슬전천후육상실내경기장 1
 
1.1%
Other values (62) 62
66.0%
2023-12-13T05:47:27.126861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 67
 
11.1%
프 31
 
5.1%
골 31
 
5.1%
29
 
4.8%
구 20
 
3.3%
운 18
 
3.0%
파 17
 
2.8%
크 17
 
2.8%
드 16
 
2.7%
라 16
 
2.7%
Other values (133) 340
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 558
92.7%
Space Separator 29
 
4.8%
Close Punctuation 4
 
0.7%
Open Punctuation 4
 
0.7%
Decimal Number 3
 
0.5%
Other Punctuation 2
 
0.3%
Uppercase Letter 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 67
 
12.0%
프 31
 
5.6%
골 31
 
5.6%
구 20
 
3.6%
운 18
 
3.2%
파 17
 
3.0%
크 17
 
3.0%
드 16
 
2.9%
라 16
 
2.9%
그 15
 
2.7%
Other values (125) 310
55.6%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
2 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
F 1
50.0%
X 1
50.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 558
92.7%
Common 42
 
7.0%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 67
 
12.0%
프 31
 
5.6%
골 31
 
5.6%
구 20
 
3.6%
운 18
 
3.2%
파 17
 
3.0%
크 17
 
3.0%
드 16
 
2.9%
라 16
 
2.9%
그 15
 
2.7%
Other values (125) 310
55.6%
Common
ValueCountFrequency (%)
29
69.0%
) 4
 
9.5%
( 4
 
9.5%
. 2
 
4.8%
1 2
 
4.8%
2 1
 
2.4%
Latin
ValueCountFrequency (%)
F 1
50.0%
X 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 558
92.7%
ASCII 44
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 67
 
12.0%
프 31
 
5.6%
골 31
 
5.6%
구 20
 
3.6%
운 18
 
3.2%
파 17
 
3.0%
크 17
 
3.0%
드 16
 
2.9%
라 16
 
2.9%
그 15
 
2.7%
Other values (125) 310
55.6%
ASCII
ValueCountFrequency (%)
29
65.9%
) 4
 
9.1%
( 4
 
9.1%
. 2
 
4.5%
1 2
 
4.5%
F 1
 
2.3%
X 1
 
2.3%
2 1
 
2.3%

관리주체
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Memory size652.0 B
광양시
15 
장성군
9 
목포시
7 
무안군
7 
순천시
4 
Other values (16)
23 

Length

Max length14
Median length3
Mean length3.6307692
Min length3

Unique

Unique12 ?
Unique (%)18.5%

Sample

1st row전남개발공사KIC사업단
2nd row위탁(산악연맹)
3rd row체육시설관리사무소
4th row목포시
5th row목포시

Common Values

ValueCountFrequency (%)
광양시 15
23.1%
장성군 9
13.8%
목포시 7
10.8%
무안군 7
10.8%
순천시 4
 
6.2%
신안군 3
 
4.6%
함평군 3
 
4.6%
해남군 3
 
4.6%
장흥군 2
 
3.1%
담양군 1
 
1.5%
Other values (11) 11
16.9%

Length

2023-12-13T05:47:27.391577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광양시 15
23.1%
장성군 9
13.8%
목포시 7
10.8%
무안군 7
10.8%
순천시 4
 
6.2%
신안군 3
 
4.6%
함평군 3
 
4.6%
해남군 3
 
4.6%
장흥군 2
 
3.1%
위탁(산악연맹 1
 
1.5%
Other values (11) 11
16.9%

부지면적
Text

MISSING 

Distinct48
Distinct (%)90.6%
Missing12
Missing (%)18.5%
Memory size652.0 B
2023-12-13T05:47:27.669689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length4.3773585
Min length3

Characters and Unicode

Total characters232
Distinct characters13
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

Unique43 ?
Unique (%)81.1%

Sample

1st row1698061
2nd row4654
3rd row40000
4th row6600
5th row4708
ValueCountFrequency (%)
19326 2
 
3.8%
5497 2
 
3.8%
4791 2
 
3.8%
3500 2
 
3.8%
1488 2
 
3.8%
32300 1
 
1.9%
115772 1
 
1.9%
1698061 1
 
1.9%
9808 1
 
1.9%
14450 1
 
1.9%
Other values (38) 38
71.7%
2023-12-13T05:47:28.279277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42
18.1%
1 35
15.1%
3 26
11.2%
4 26
11.2%
6 18
7.8%
8 18
7.8%
7 18
7.8%
5 17
7.3%
2 15
 
6.5%
9 14
 
6.0%
Other values (3) 3
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 229
98.7%
Other Letter 3
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42
18.3%
1 35
15.3%
3 26
11.4%
4 26
11.4%
6 18
7.9%
8 18
7.9%
7 18
7.9%
5 17
7.4%
2 15
 
6.6%
9 14
 
6.1%
Other Letter
ValueCountFrequency (%)
순 1
33.3%
천 1
33.3%
시 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 229
98.7%
Hangul 3
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42
18.3%
1 35
15.3%
3 26
11.4%
4 26
11.4%
6 18
7.9%
8 18
7.9%
7 18
7.9%
5 17
7.4%
2 15
 
6.6%
9 14
 
6.1%
Hangul
ValueCountFrequency (%)
순 1
33.3%
천 1
33.3%
시 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
98.7%
Hangul 3
 
1.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42
18.3%
1 35
15.3%
3 26
11.4%
4 26
11.4%
6 18
7.9%
8 18
7.9%
7 18
7.9%
5 17
7.4%
2 15
 
6.6%
9 14
 
6.1%
Hangul
ValueCountFrequency (%)
순 1
33.3%
천 1
33.3%
시 1
33.3%

건축면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing51
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean6313.1357
Minimum50
Maximum79297.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:28.466156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile58.45
Q1118.25
median282
Q31804.5
95-th percentile29325.965
Maximum79297.9
Range79247.9
Interquartile range (IQR)1686.25

Descriptive statistics

Standard deviation21025.615
Coefficient of variation (CV)3.3304552
Kurtosis13.936165
Mean6313.1357
Median Absolute Deviation (MAD)199
Skewness3.7298489
Sum88383.9
Variance4.4207651 × 108
MonotonicityNot monotonic
2023-12-13T05:47:28.652791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
79297.9 1
 
1.5%
300.0 1
 
1.5%
131.0 1
 
1.5%
63.0 1
 
1.5%
2418.0 1
 
1.5%
264.0 1
 
1.5%
114.0 1
 
1.5%
50.0 1
 
1.5%
261.0 1
 
1.5%
2304.0 1
 
1.5%
Other values (4) 4
 
6.2%
(Missing) 51
78.5%
ValueCountFrequency (%)
50.0 1
1.5%
63.0 1
1.5%
97.0 1
1.5%
114.0 1
1.5%
131.0 1
1.5%
261.0 1
1.5%
264.0 1
1.5%
300.0 1
1.5%
348.0 1
1.5%
495.0 1
1.5%
ValueCountFrequency (%)
79297.9 1
1.5%
2418.0 1
1.5%
2304.0 1
1.5%
2241.0 1
1.5%
495.0 1
1.5%
348.0 1
1.5%
300.0 1
1.5%
264.0 1
1.5%
261.0 1
1.5%
131.0 1
1.5%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)95.5%
Missing43
Missing (%)66.2%
Infinite0
Infinite (%)0.0%
Mean5390.7555
Minimum50
Maximum79297.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:28.848400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile64.7
Q1151.35
median531.2
Q32122.285
95-th percentile11830.3
Maximum79297.9
Range79247.9
Interquartile range (IQR)1970.935

Descriptive statistics

Standard deviation16819.503
Coefficient of variation (CV)3.1200641
Kurtosis20.167894
Mean5390.7555
Median Absolute Deviation (MAD)451.2
Skewness4.4275601
Sum118596.62
Variance2.8289568 × 108
MonotonicityNot monotonic
2023-12-13T05:47:29.065498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1488.0 2
 
3.1%
1705.0 1
 
1.5%
525.0 1
 
1.5%
862.74 1
 
1.5%
11874.0 1
 
1.5%
97.0 1
 
1.5%
2304.0 1
 
1.5%
500.0 1
 
1.5%
506.0 1
 
1.5%
3200.0 1
 
1.5%
Other values (11) 11
 
16.9%
(Missing) 43
66.2%
ValueCountFrequency (%)
50.0 1
1.5%
63.0 1
1.5%
97.0 1
1.5%
114.0 1
1.5%
118.8 1
1.5%
131.0 1
1.5%
212.4 1
1.5%
261.0 1
1.5%
500.0 1
1.5%
506.0 1
1.5%
ValueCountFrequency (%)
79297.9 1
1.5%
11874.0 1
1.5%
11000.0 1
1.5%
3200.0 1
1.5%
2304.0 1
1.5%
2261.38 1
1.5%
1705.0 1
1.5%
1488.0 2
3.1%
862.74 1
1.5%
537.4 1
1.5%

폭_미터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)67.7%
Missing34
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean30.193548
Minimum1
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:29.304741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q120
median25
Q338.5
95-th percentile70
Maximum70
Range69
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation17.292425
Coefficient of variation (CV)0.5727192
Kurtosis0.87950367
Mean30.193548
Median Absolute Deviation (MAD)6
Skewness1.014399
Sum936
Variance299.02796
MonotonicityNot monotonic
2023-12-13T05:47:29.555358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
20 5
 
7.7%
70 3
 
4.6%
22 3
 
4.6%
30 2
 
3.1%
39 2
 
3.1%
26 1
 
1.5%
25 1
 
1.5%
45 1
 
1.5%
36 1
 
1.5%
21 1
 
1.5%
Other values (11) 11
 
16.9%
(Missing) 34
52.3%
ValueCountFrequency (%)
1 1
 
1.5%
7 1
 
1.5%
11 1
 
1.5%
12 1
 
1.5%
19 1
 
1.5%
20 5
7.7%
21 1
 
1.5%
22 3
4.6%
23 1
 
1.5%
25 1
 
1.5%
ValueCountFrequency (%)
70 3
4.6%
53 1
 
1.5%
45 1
 
1.5%
40 1
 
1.5%
39 2
3.1%
38 1
 
1.5%
36 1
 
1.5%
34 1
 
1.5%
31 1
 
1.5%
30 2
3.1%

길이_미터
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)54.8%
Missing34
Missing (%)52.3%
Infinite0
Infinite (%)0.0%
Mean489
Minimum13
Maximum7470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:29.747023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q140
median42
Q363
95-th percentile3168.5
Maximum7470
Range7457
Interquartile range (IQR)23

Descriptive statistics

Standard deviation1693.3204
Coefficient of variation (CV)3.4628229
Kurtosis13.434622
Mean489
Median Absolute Deviation (MAD)14
Skewness3.7911826
Sum15159
Variance2867334
MonotonicityNot monotonic
2023-12-13T05:47:29.920573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
40 7
 
10.8%
50 4
 
6.2%
42 3
 
4.6%
63 2
 
3.1%
16 2
 
3.1%
70 2
 
3.1%
7470 1
 
1.5%
116 1
 
1.5%
180 1
 
1.5%
214 1
 
1.5%
Other values (7) 7
 
10.8%
(Missing) 34
52.3%
ValueCountFrequency (%)
13 1
 
1.5%
16 2
 
3.1%
21 1
 
1.5%
23 1
 
1.5%
24 1
 
1.5%
28 1
 
1.5%
40 7
10.8%
42 3
4.6%
43 1
 
1.5%
50 4
6.2%
ValueCountFrequency (%)
7470 1
 
1.5%
6123 1
 
1.5%
214 1
 
1.5%
180 1
 
1.5%
116 1
 
1.5%
70 2
3.1%
63 2
3.1%
50 4
6.2%
43 1
 
1.5%
42 3
4.6%

주로수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
<NA>
62 
2
 
2
4
 
1

Length

Max length4
Median length4
Mean length3.8615385
Min length1

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row2
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 62
95.4%
2 2
 
3.1%
4 1
 
1.5%

Length

2023-12-13T05:47:30.115489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:47:30.273449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 62
95.4%
2 2
 
3.1%
4 1
 
1.5%

주로폭
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size652.0 B
<NA>
63 
12
 
1
5
 
1

Length

Max length4
Median length4
Mean length3.9230769
Min length1

Unique

Unique2 ?
Unique (%)3.1%

Sample

1st row12
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 63
96.9%
12 1
 
1.5%
5 1
 
1.5%

Length

2023-12-13T05:47:30.445497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:47:30.636806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 63
96.9%
12 1
 
1.5%
5 1
 
1.5%

면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)95.5%
Missing21
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean8335.25
Minimum112
Maximum99498
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:30.798741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum112
5-th percentile262.15
Q11371
median3409.5
Q36700
95-th percentile19946.5
Maximum99498
Range99386
Interquartile range (IQR)5329

Descriptive statistics

Standard deviation18617.843
Coefficient of variation (CV)2.2336274
Kurtosis17.821376
Mean8335.25
Median Absolute Deviation (MAD)2586.5
Skewness4.189736
Sum366751
Variance3.4662407 × 108
MonotonicityNot monotonic
2023-12-13T05:47:31.063724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
112 2
 
3.1%
1500 2
 
3.1%
2304 1
 
1.5%
2860 1
 
1.5%
3366 1
 
1.5%
8000 1
 
1.5%
1560 1
 
1.5%
506 1
 
1.5%
3453 1
 
1.5%
5215 1
 
1.5%
Other values (32) 32
49.2%
(Missing) 21
32.3%
ValueCountFrequency (%)
112 2
3.1%
262 1
1.5%
263 1
1.5%
406 1
1.5%
506 1
1.5%
800 1
1.5%
816 1
1.5%
830 1
1.5%
924 1
1.5%
984 1
1.5%
ValueCountFrequency (%)
99498 1
1.5%
79319 1
1.5%
20224 1
1.5%
18374 1
1.5%
14450 1
1.5%
13466 1
1.5%
8751 1
1.5%
8000 1
1.5%
7750 1
1.5%
7470 1
1.5%

좌석수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size652.0 B
<NA>
62 
118351
 
1
500
 
1
100
 
1

Length

Max length6
Median length4
Mean length4
Min length3

Unique

Unique3 ?
Unique (%)4.6%

Sample

1st row118351
2nd row500
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 62
95.4%
118351 1
 
1.5%
500 1
 
1.5%
100 1
 
1.5%

Length

2023-12-13T05:47:31.269381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:47:31.487042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 62
95.4%
118351 1
 
1.5%
500 1
 
1.5%
100 1
 
1.5%

좌석 형태
Text

MISSING 

Distinct2
Distinct (%)66.7%
Missing62
Missing (%)95.4%
Memory size652.0 B
2023-12-13T05:47:31.681653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.3333333
Min length6

Characters and Unicode

Total characters19
Distinct characters6
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

Unique1 ?
Unique (%)33.3%

Sample

1st row계단식좌석
2nd row계단식좌석
3rd row 계단식좌석
ValueCountFrequency (%)
계단식좌석 3
100.0%
2023-12-13T05:47:32.098711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
21.1%
계 3
15.8%
단 3
15.8%
식 3
15.8%
좌 3
15.8%
석 3
15.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
78.9%
Space Separator 4
 
21.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 3
20.0%
단 3
20.0%
식 3
20.0%
좌 3
20.0%
석 3
20.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
78.9%
Common 4
 
21.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 3
20.0%
단 3
20.0%
식 3
20.0%
좌 3
20.0%
석 3
20.0%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
78.9%
ASCII 4
 
21.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
100.0%
Hangul
ValueCountFrequency (%)
계 3
20.0%
단 3
20.0%
식 3
20.0%
좌 3
20.0%
석 3
20.0%

준공 연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)33.9%
Missing3
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean325561.66
Minimum2001
Maximum20062012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:32.347873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2003.05
Q12010
median2013.5
Q32018.75
95-th percentile2021
Maximum20062012
Range20060011
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation2547622.4
Coefficient of variation (CV)7.8253145
Kurtosis62
Mean325561.66
Median Absolute Deviation (MAD)4.5
Skewness7.8740079
Sum20184823
Variance6.4903798 × 1012
MonotonicityNot monotonic
2023-12-13T05:47:32.569362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2019 8
12.3%
2018 5
 
7.7%
2013 5
 
7.7%
2021 4
 
6.2%
2009 4
 
6.2%
2012 4
 
6.2%
2017 4
 
6.2%
2011 4
 
6.2%
2004 4
 
6.2%
2010 3
 
4.6%
Other values (11) 17
26.2%
ValueCountFrequency (%)
2001 1
 
1.5%
2002 1
 
1.5%
2003 2
3.1%
2004 4
6.2%
2005 1
 
1.5%
2006 1
 
1.5%
2008 1
 
1.5%
2009 4
6.2%
2010 3
4.6%
2011 4
6.2%
ValueCountFrequency (%)
20062012 1
 
1.5%
2021 4
6.2%
2020 3
 
4.6%
2019 8
12.3%
2018 5
7.7%
2017 4
6.2%
2016 2
 
3.1%
2015 2
 
3.1%
2014 2
 
3.1%
2013 5
7.7%

건설사업비_백만원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)79.6%
Missing16
Missing (%)24.6%
Infinite0
Infinite (%)0.0%
Mean9133.1837
Minimum13
Maximum422500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size717.0 B
2023-12-13T05:47:32.759600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile45
Q1104
median300
Q3630
95-th percentile1910
Maximum422500
Range422487
Interquartile range (IQR)526

Descriptive statistics

Standard deviation60285.489
Coefficient of variation (CV)6.6007092
Kurtosis48.990217
Mean9133.1837
Median Absolute Deviation (MAD)200
Skewness6.9989743
Sum447526
Variance3.6343402 × 109
MonotonicityNot monotonic
2023-12-13T05:47:32.998712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
300 7
 
10.8%
100 3
 
4.6%
80 2
 
3.1%
70 2
 
3.1%
1026 1
 
1.5%
140 1
 
1.5%
435 1
 
1.5%
630 1
 
1.5%
1800 1
 
1.5%
1500 1
 
1.5%
Other values (29) 29
44.6%
(Missing) 16
24.6%
ValueCountFrequency (%)
13 1
 
1.5%
25 1
 
1.5%
35 1
 
1.5%
60 1
 
1.5%
70 2
3.1%
75 1
 
1.5%
80 2
3.1%
100 3
4.6%
104 1
 
1.5%
140 1
 
1.5%
ValueCountFrequency (%)
422500 1
1.5%
2255 1
1.5%
1950 1
1.5%
1850 1
1.5%
1800 1
1.5%
1660 1
1.5%
1500 1
1.5%
1300 1
1.5%
1200 1
1.5%
1026 1
1.5%

Interactions

2023-12-13T05:47:24.019139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:18.843731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.687375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.451872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.272746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.143329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.051049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.164508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:18.973540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.814256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.560793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.398941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.295253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.201404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.299089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.115283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.942444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.682097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.525569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.434256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.350210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.418680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.216141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.062078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.795636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.647026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.551406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.467228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.539161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.315410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.155571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.926610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.788098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.667888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.621193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.690084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.437481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.249065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.049044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.914761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.773004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.757107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:24.828523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:19.574308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:20.358964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:21.162781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.027226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:22.922664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:47:23.901469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:47:33.159287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군시설명관리주체부지면적건축면적연면적폭_미터길이_미터주로수주로폭면적좌석수좌석 형태준공 연도건설사업비_백만원
시군1.0001.0001.0001.0001.0000.7490.6660.8171.0000.0000.7031.0001.0001.0001.000
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
관리주체1.0001.0001.0001.0001.0000.9640.6790.9181.0000.0000.9421.0001.0001.0001.000
부지면적1.0001.0001.0001.0001.0001.0000.9481.0001.0000.0001.0001.0001.0001.0001.000
건축면적1.0001.0001.0001.0001.0000.5620.0000.0000.0000.0001.0000.000NaNNaN0.523
연면적0.7491.0000.9641.0000.5621.0000.0000.4940.0000.0000.6041.0000.000NaN1.000
폭_미터0.6661.0000.6790.9480.0000.0001.0000.8380.000NaN0.9590.0000.000NaN0.656
길이_미터0.8171.0000.9181.0000.0000.4940.8381.0000.0000.0000.9270.0000.000NaN1.000
주로수1.0001.0001.0001.0000.0000.0000.0000.0001.0000.0000.000NaNNaNNaN0.000
주로폭0.0000.0000.0000.0000.0000.000NaN0.0000.0001.0000.000NaNNaNNaN0.000
면적0.7031.0000.9421.0001.0000.6040.9590.9270.0000.0001.0000.0000.000NaN1.000
좌석수1.0001.0001.0001.0000.0001.0000.0000.000NaNNaN0.0001.0001.000NaN1.000
좌석 형태1.0001.0001.0001.000NaN0.0000.0000.000NaNNaN0.0001.0001.000NaN0.000
준공 연도1.0001.0001.0001.000NaNNaNNaNNaNNaNNaNNaNNaNNaN1.0000.000
건설사업비_백만원1.0001.0001.0001.0000.5231.0000.6561.0000.0000.0001.0001.0000.0000.0001.000
2023-12-13T05:47:33.409070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군관리주체주로수주로폭좌석수
시군1.0000.9481.0001.0001.000
관리주체0.9481.0001.0001.0001.000
주로수1.0001.0001.0001.0001.000
주로폭1.0001.0001.0001.000NaN
좌석수1.0001.0001.000NaN1.000
2023-12-13T05:47:33.591819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축면적연면적폭_미터길이_미터면적준공 연도건설사업비_백만원시군관리주체주로수주로폭좌석수
건축면적1.0000.9620.0290.6000.2000.1980.7360.6450.5001.0001.0001.000
연면적0.9621.000-0.0430.4040.224-0.0310.1990.4440.4640.0001.0001.000
폭_미터0.029-0.0431.0000.3440.4030.380-0.0660.3430.3220.0001.0001.000
길이_미터0.6000.4040.3441.0000.939-0.2200.3310.5870.5560.0001.0001.000
면적0.2000.2240.4030.9391.000-0.2590.0030.4400.5930.0001.0001.000
준공 연도0.198-0.0310.380-0.220-0.2591.0000.0230.8760.8271.0001.0001.000
건설사업비_백만원0.7360.199-0.0660.3310.0030.0231.0000.8630.7990.0001.0001.000
시군0.6450.4440.3430.5870.4400.8760.8631.0000.9481.0001.0001.000
관리주체0.5000.4640.3220.5560.5930.8270.7990.9481.0001.0001.0001.000
주로수1.0000.0000.0000.0000.0001.0000.0001.0001.0001.0001.0001.000
주로폭1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN
좌석수1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.000

Missing values

2023-12-13T05:47:25.316553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:47:25.612772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T05:47:25.917216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군시설명관리주체부지면적건축면적연면적폭_미터길이_미터주로수주로폭면적좌석수좌석 형태준공 연도건설사업비_백만원
0도청F1국제 자동차 경주장전남개발공사KIC사업단169806179297.979297.912612321299498118351계단식좌석2011422500
1목포시목포국제스포츠클라이밍센터위탁(산악연맹)4654300.0537.4<NA><NA><NA><NA><NA>500계단식좌석20122255
2목포시목포파크골프장(부주산국제파크골프장)체육시설관리사무소40000131.0131.0<NA><NA><NA><NA>20224<NA><NA>20041300
3목포시서해(연산동)파크골프장목포시6600<NA><NA><NA><NA><NA><NA>6600<NA><NA>200370
4목포시북항수질관리사무소파크골프장목포시4708<NA><NA>22214<NA><NA>4708<NA><NA>2004<NA>
5목포시상동파크골프장목포시79319<NA><NA><NA><NA><NA><NA>79319<NA><NA>2006250
6목포시하당근린공원파크골프장목포시18374<NA><NA><NA><NA><NA><NA>18374<NA><NA>2001150
7목포시삼학도파크골프장위탁(목포시파크골프연합회)11000<NA>11000.0<NA><NA><NA><NA>7000<NA><NA>2013104
8목포시남해수질관리사무소파크골프장목포시4331<NA><NA>38116<NA><NA>4331<NA><NA>2004<NA>
9목포시부주산 산악자전거경기장목포시148000<NA><NA>17470<NA><NA>7470<NA><NA>2005872
시군시설명관리주체부지면적건축면적연면적폭_미터길이_미터주로수주로폭면적좌석수좌석 형태준공 연도건설사업비_백만원
55장성군황룡강변생활체육공원배구장장성군1200<NA><NA>1928<NA><NA><NA><NA><NA><NA>153
56장성군삼계풋살장장성군1600<NA><NA>2040<NA><NA><NA><NA><NA><NA>80
57장성군홍길동테마파크 풋살장장성군2170<NA><NA>2343<NA><NA><NA><NA><NA>20091026
58장성군장성파크골프장장성군9978<NA><NA><NA><NA><NA><NA><NA><NA><NA>2013<NA>
59장성군장성그라운드골프장장성군3500<NA><NA>7050<NA><NA><NA><NA><NA>2013<NA>
60장성군삼계면 그라운드골프장장성군3500<NA><NA>7050<NA><NA><NA><NA><NA>2019<NA>
61장성군삼서면 그라운드골프장장성군1428<NA><NA>3442<NA><NA><NA><NA><NA>2019<NA>
62신안군비금다목적체육관신안군<NA>495.0525.0<NA><NA><NA><NA><NA><NA><NA>2019<NA>
63신안군자은그라운드골프장신안군2530<NA><NA>3963<NA><NA>2457<NA><NA>2015<NA>
64신안군지도그라운드골프장신안군3710<NA><NA>5370<NA><NA>3710<NA><NA>2021<NA>