Overview

Dataset statistics

Number of variables6
Number of observations37
Missing cells29
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory53.6 B

Variable types

Numeric2
Text3
DateTime1

Dataset

Description광주광역시 서구 관내 위치한 골프연습장업에 대한 상호명, 지번주소, 도로명주소, 시설면적, 전화번호, 최초등록일자 등에 관한 정보입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15034925/fileData.do

Alerts

전화번호 has 22 (59.5%) missing valuesMissing
시설면적 has 7 (18.9%) missing valuesMissing
번호 has unique valuesUnique
최초등록일자 has unique valuesUnique

Reproduction

Analysis started2023-12-23 06:47:48.976773
Analysis finished2023-12-23 06:47:52.401786
Duration3.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-23T06:47:52.704393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8
Q110
median19
Q328
95-th percentile35.2
Maximum37
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.824355
Coefficient of variation (CV)0.56970291
Kurtosis-1.2
Mean19
Median Absolute Deviation (MAD)9
Skewness0
Sum703
Variance117.16667
MonotonicityStrictly increasing
2023-12-23T06:47:53.158645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 1
 
2.7%
29 1
 
2.7%
22 1
 
2.7%
23 1
 
2.7%
24 1
 
2.7%
25 1
 
2.7%
26 1
 
2.7%
27 1
 
2.7%
28 1
 
2.7%
30 1
 
2.7%
Other values (27) 27
73.0%
ValueCountFrequency (%)
1 1
2.7%
2 1
2.7%
3 1
2.7%
4 1
2.7%
5 1
2.7%
6 1
2.7%
7 1
2.7%
8 1
2.7%
9 1
2.7%
10 1
2.7%
ValueCountFrequency (%)
37 1
2.7%
36 1
2.7%
35 1
2.7%
34 1
2.7%
33 1
2.7%
32 1
2.7%
31 1
2.7%
30 1
2.7%
29 1
2.7%
28 1
2.7%

상호
Text

Distinct36
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-23T06:47:53.876050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length8.5945946
Min length2

Characters and Unicode

Total characters318
Distinct characters100
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

Unique35 ?
Unique (%)94.6%

Sample

1st row상무신도심골프연습장
2nd row염주골프센터
3rd row이글골프연습장
4th row이지골프 아카데미
5th row매월골프랜지
ValueCountFrequency (%)
아카데미 4
 
7.4%
골프 4
 
7.4%
골프존 3
 
5.6%
상무스크린골프 2
 
3.7%
골든힐스골프연습장 1
 
1.9%
klgi골프연구소 1
 
1.9%
아탑-휘트니스(골프연습장 1
 
1.9%
카카오g 1
 
1.9%
스크린골프 1
 
1.9%
신세계 1
 
1.9%
Other values (35) 35
64.8%
2023-12-23T06:47:55.292126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
프 33
 
10.4%
골 32
 
10.1%
17
 
5.3%
아 12
 
3.8%
연 12
 
3.8%
장 12
 
3.8%
스 11
 
3.5%
습 11
 
3.5%
카 11
 
3.5%
미 9
 
2.8%
Other values (90) 158
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
84.9%
Uppercase Letter 23
 
7.2%
Space Separator 17
 
5.3%
Close Punctuation 3
 
0.9%
Open Punctuation 3
 
0.9%
Lowercase Letter 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
프 33
 
12.2%
골 32
 
11.9%
아 12
 
4.4%
연 12
 
4.4%
장 12
 
4.4%
스 11
 
4.1%
습 11
 
4.1%
카 11
 
4.1%
미 9
 
3.3%
데 9
 
3.3%
Other values (72) 118
43.7%
Uppercase Letter
ValueCountFrequency (%)
S 4
17.4%
G 2
8.7%
T 2
8.7%
P 2
8.7%
M 2
8.7%
I 2
8.7%
K 2
8.7%
O 2
8.7%
V 1
 
4.3%
A 1
 
4.3%
Other values (3) 3
13.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
84.9%
Common 24
 
7.5%
Latin 24
 
7.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
프 33
 
12.2%
골 32
 
11.9%
아 12
 
4.4%
연 12
 
4.4%
장 12
 
4.4%
스 11
 
4.1%
습 11
 
4.1%
카 11
 
4.1%
미 9
 
3.3%
데 9
 
3.3%
Other values (72) 118
43.7%
Latin
ValueCountFrequency (%)
S 4
16.7%
G 2
8.3%
T 2
8.3%
P 2
8.3%
M 2
8.3%
I 2
8.3%
K 2
8.3%
O 2
8.3%
V 1
 
4.2%
A 1
 
4.2%
Other values (4) 4
16.7%
Common
ValueCountFrequency (%)
17
70.8%
) 3
 
12.5%
( 3
 
12.5%
- 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
84.9%
ASCII 48
 
15.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
프 33
 
12.2%
골 32
 
11.9%
아 12
 
4.4%
연 12
 
4.4%
장 12
 
4.4%
스 11
 
4.1%
습 11
 
4.1%
카 11
 
4.1%
미 9
 
3.3%
데 9
 
3.3%
Other values (72) 118
43.7%
ASCII
ValueCountFrequency (%)
17
35.4%
S 4
 
8.3%
) 3
 
6.2%
( 3
 
6.2%
G 2
 
4.2%
T 2
 
4.2%
P 2
 
4.2%
M 2
 
4.2%
I 2
 
4.2%
K 2
 
4.2%
Other values (8) 9
18.8%

도로명주소
Text

Distinct36
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-23T06:47:56.173023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length36
Mean length29.243243
Min length22

Characters and Unicode

Total characters1082
Distinct characters104
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)94.6%

Sample

1st row광주광역시 서구 상무자유로 66 (치평동)
2nd row광주광역시 서구 금화로 278 (풍암동)
3rd row광주광역시 서구 회재유통길 77 (매월동)
4th row광주광역시 서구 풍암2로 12, 6층 (풍암동)
5th row광주광역시 서구 풍서좌로 203-1 (매월동)
ValueCountFrequency (%)
광주광역시 37
 
16.7%
서구 37
 
16.7%
치평동 10
 
4.5%
매월동 6
 
2.7%
풍암동 5
 
2.3%
마륵동 4
 
1.8%
2층 4
 
1.8%
금호동 4
 
1.8%
4층 3
 
1.4%
쌍촌동 3
 
1.4%
Other values (88) 109
49.1%
2023-12-23T06:47:57.254318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
185
 
17.1%
광 76
 
7.0%
동 43
 
4.0%
( 41
 
3.8%
서 41
 
3.8%
) 41
 
3.8%
시 39
 
3.6%
주 39
 
3.6%
역 37
 
3.4%
구 37
 
3.4%
Other values (94) 503
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 632
58.4%
Space Separator 185
 
17.1%
Decimal Number 147
 
13.6%
Open Punctuation 41
 
3.8%
Close Punctuation 41
 
3.8%
Other Punctuation 31
 
2.9%
Dash Punctuation 3
 
0.3%
Math Symbol 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 76
 
12.0%
동 43
 
6.8%
서 41
 
6.5%
시 39
 
6.2%
주 39
 
6.2%
역 37
 
5.9%
구 37
 
5.9%
로 36
 
5.7%
층 21
 
3.3%
평 13
 
2.1%
Other values (77) 250
39.6%
Decimal Number
ValueCountFrequency (%)
1 31
21.1%
2 26
17.7%
6 20
13.6%
0 14
9.5%
3 12
 
8.2%
4 11
 
7.5%
9 10
 
6.8%
5 9
 
6.1%
7 8
 
5.4%
8 6
 
4.1%
Math Symbol
ValueCountFrequency (%)
∼ 1
50.0%
~ 1
50.0%
Space Separator
ValueCountFrequency (%)
185
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Other Punctuation
ValueCountFrequency (%)
, 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 632
58.4%
Common 450
41.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 76
 
12.0%
동 43
 
6.8%
서 41
 
6.5%
시 39
 
6.2%
주 39
 
6.2%
역 37
 
5.9%
구 37
 
5.9%
로 36
 
5.7%
층 21
 
3.3%
평 13
 
2.1%
Other values (77) 250
39.6%
Common
ValueCountFrequency (%)
185
41.1%
( 41
 
9.1%
) 41
 
9.1%
1 31
 
6.9%
, 31
 
6.9%
2 26
 
5.8%
6 20
 
4.4%
0 14
 
3.1%
3 12
 
2.7%
4 11
 
2.4%
Other values (7) 38
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 632
58.4%
ASCII 449
41.5%
Math Operators 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
185
41.2%
( 41
 
9.1%
) 41
 
9.1%
1 31
 
6.9%
, 31
 
6.9%
2 26
 
5.8%
6 20
 
4.5%
0 14
 
3.1%
3 12
 
2.7%
4 11
 
2.4%
Other values (6) 37
 
8.2%
Hangul
ValueCountFrequency (%)
광 76
 
12.0%
동 43
 
6.8%
서 41
 
6.5%
시 39
 
6.2%
주 39
 
6.2%
역 37
 
5.9%
구 37
 
5.9%
로 36
 
5.7%
층 21
 
3.3%
평 13
 
2.1%
Other values (77) 250
39.6%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%

전화번호
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing22
Missing (%)59.5%
Memory size428.0 B
2023-12-23T06:47:57.660655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters180
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row062-374-7082
2nd row062-380-6870
3rd row062-373-2121
4th row062-655-0666
5th row062-681-2255
ValueCountFrequency (%)
062-374-7082 1
 
6.7%
062-380-6870 1
 
6.7%
062-373-2121 1
 
6.7%
062-655-0666 1
 
6.7%
062-681-2255 1
 
6.7%
062-681-0001 1
 
6.7%
062-655-8011 1
 
6.7%
062-352-1300 1
 
6.7%
062-371-0880 1
 
6.7%
062-513-8207 1
 
6.7%
Other values (5) 5
33.3%
2023-12-23T06:47:58.575306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 30
16.7%
- 30
16.7%
2 26
14.4%
6 25
13.9%
5 17
9.4%
3 15
8.3%
8 13
7.2%
1 12
 
6.7%
7 8
 
4.4%
4 2
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 150
83.3%
Dash Punctuation 30
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 30
20.0%
2 26
17.3%
6 25
16.7%
5 17
11.3%
3 15
10.0%
8 13
8.7%
1 12
 
8.0%
7 8
 
5.3%
4 2
 
1.3%
9 2
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 30
16.7%
- 30
16.7%
2 26
14.4%
6 25
13.9%
5 17
9.4%
3 15
8.3%
8 13
7.2%
1 12
 
6.7%
7 8
 
4.4%
4 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 30
16.7%
- 30
16.7%
2 26
14.4%
6 25
13.9%
5 17
9.4%
3 15
8.3%
8 13
7.2%
1 12
 
6.7%
7 8
 
4.4%
4 2
 
1.1%

시설면적
Real number (ℝ)

MISSING 

Distinct30
Distinct (%)100.0%
Missing7
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean822.287
Minimum97
Maximum9990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-23T06:47:59.112355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97
5-th percentile140.55
Q1199.105
median307.835
Q3602.7025
95-th percentile2046.7075
Maximum9990
Range9893
Interquartile range (IQR)403.5975

Descriptive statistics

Standard deviation1805.0838
Coefficient of variation (CV)2.1951993
Kurtosis24.938825
Mean822.287
Median Absolute Deviation (MAD)146.285
Skewness4.8536193
Sum24668.61
Variance3258327.6
MonotonicityNot monotonic
2023-12-23T06:47:59.575173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
829.9 1
 
2.7%
132.0 1
 
2.7%
818.29 1
 
2.7%
216.82 1
 
2.7%
248.51 1
 
2.7%
182.52 1
 
2.7%
565.81 1
 
2.7%
433.63 1
 
2.7%
282.66 1
 
2.7%
390.0 1
 
2.7%
Other values (20) 20
54.1%
(Missing) 7
 
18.9%
ValueCountFrequency (%)
97.0 1
2.7%
132.0 1
2.7%
151.0 1
2.7%
160.9 1
2.7%
162.2 1
2.7%
181.19 1
2.7%
182.52 1
2.7%
193.2 1
2.7%
216.82 1
2.7%
244.0 1
2.7%
ValueCountFrequency (%)
9990.0 1
2.7%
2623.45 1
2.7%
1341.8 1
2.7%
1308.12 1
2.7%
829.9 1
2.7%
818.29 1
2.7%
763.92 1
2.7%
615.0 1
2.7%
565.81 1
2.7%
546.02 1
2.7%

최초등록일자
Date

UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
Minimum1996-10-29 00:00:00
Maximum2022-10-04 00:00:00
2023-12-23T06:47:59.945690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:48:00.308519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)

Interactions

2023-12-23T06:47:50.315470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:47:49.732063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:47:50.778222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T06:47:50.043007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T06:48:00.578905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호상호도로명주소전화번호시설면적최초등록일자
번호1.0001.0000.9391.0000.0001.000
상호1.0001.0000.9971.0001.0001.000
도로명주소0.9390.9971.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
시설면적0.0001.0001.0001.0001.0001.000
최초등록일자1.0001.0001.0001.0001.0001.000
2023-12-23T06:48:01.075430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호시설면적
번호1.000-0.153
시설면적-0.1531.000

Missing values

2023-12-23T06:47:51.211006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T06:47:51.693586image/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.
2023-12-23T06:47:52.146167image/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

번호상호도로명주소전화번호시설면적최초등록일자
01상무신도심골프연습장광주광역시 서구 상무자유로 66 (치평동)062-374-7082<NA>1996-10-29
12염주골프센터광주광역시 서구 금화로 278 (풍암동)062-380-6870<NA>1998-06-25
23이글골프연습장광주광역시 서구 회재유통길 77 (매월동)062-373-2121<NA>2004-03-30
34이지골프 아카데미광주광역시 서구 풍암2로 12, 6층 (풍암동)062-655-0666303.212004-08-25
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