Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory47.3 B

Variable types

Text2
Numeric2
Categorical1

Dataset

Description전라북도 골프장 데이터입니다. 업소명, 소재지, 총면적, 홀수, 세부종류 등의 데이터를 포함하여 제공하고 있습니다.
Author전라북도
URLhttps://www.data.go.kr/data/15055705/fileData.do

Alerts

총면적(㎡) is highly overall correlated with 홀수High correlation
홀수 is highly overall correlated with 총면적(㎡)High correlation
총면적(㎡) has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:35:45.708879
Analysis finished2023-12-12 00:35:46.667057
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업소명
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T09:35:46.810463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length6.04
Min length4

Characters and Unicode

Total characters151
Distinct characters70
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)76.0%

Sample

1st row전주월드컵골프장
2nd row군산CC
3rd row군산CC
4th row상떼힐익산CC
5th row상떼힐CC
ValueCountFrequency (%)
베어리버cc 2
 
7.1%
태인cc 2
 
7.1%
cc 2
 
7.1%
군산cc 2
 
7.1%
무주안성cc 1
 
3.6%
전주월드컵골프장 1
 
3.6%
무주덕유산cc 1
 
3.6%
골프존카운티선운 1
 
3.6%
고창cc 1
 
3.6%
순창cc 1
 
3.6%
Other values (14) 14
50.0%
2023-12-12T09:35:47.134607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 41
27.2%
프 5
 
3.3%
골 5
 
3.3%
산 4
 
2.6%
장 4
 
2.6%
주 4
 
2.6%
3
 
2.0%
상 3
 
2.0%
힐 3
 
2.0%
리 3
 
2.0%
Other values (60) 76
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
65.6%
Uppercase Letter 48
31.8%
Space Separator 3
 
2.0%
Lowercase Letter 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
프 5
 
5.1%
골 5
 
5.1%
산 4
 
4.0%
장 4
 
4.0%
주 4
 
4.0%
상 3
 
3.0%
힐 3
 
3.0%
리 3
 
3.0%
트 2
 
2.0%
전 2
 
2.0%
Other values (51) 64
64.6%
Uppercase Letter
ValueCountFrequency (%)
C 41
85.4%
O 2
 
4.2%
T 1
 
2.1%
M 1
 
2.1%
U 1
 
2.1%
N 1
 
2.1%
Y 1
 
2.1%
Space Separator
ValueCountFrequency (%)
3
100.0%
Lowercase Letter
ValueCountFrequency (%)
k 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
65.6%
Latin 49
32.5%
Common 3
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
프 5
 
5.1%
골 5
 
5.1%
산 4
 
4.0%
장 4
 
4.0%
주 4
 
4.0%
상 3
 
3.0%
힐 3
 
3.0%
리 3
 
3.0%
트 2
 
2.0%
전 2
 
2.0%
Other values (51) 64
64.6%
Latin
ValueCountFrequency (%)
C 41
83.7%
O 2
 
4.1%
T 1
 
2.0%
k 1
 
2.0%
M 1
 
2.0%
U 1
 
2.0%
N 1
 
2.0%
Y 1
 
2.0%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
65.6%
ASCII 52
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 41
78.8%
3
 
5.8%
O 2
 
3.8%
T 1
 
1.9%
k 1
 
1.9%
M 1
 
1.9%
U 1
 
1.9%
N 1
 
1.9%
Y 1
 
1.9%
Hangul
ValueCountFrequency (%)
프 5
 
5.1%
골 5
 
5.1%
산 4
 
4.0%
장 4
 
4.0%
주 4
 
4.0%
상 3
 
3.0%
힐 3
 
3.0%
리 3
 
3.0%
트 2
 
2.0%
전 2
 
2.0%
Other values (51) 64
64.6%

소재지
Text

Distinct22
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T09:35:47.396290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length15.64
Min length10

Characters and Unicode

Total characters391
Distinct characters93
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)76.0%

Sample

1st row전주시 덕진구 온고을로 672
2nd row군산시 옥서면 남군산로 1685
3rd row군산시 옥서면 남군산로 1685
4th row익산시 무학로 38길 111(덕기동)
5th row익산시 춘포면 신동리 192
ValueCountFrequency (%)
익산시 4
 
4.1%
고창군 3
 
3.1%
김제시 3
 
3.1%
상증길 2
 
2.1%
무주군 2
 
2.1%
순창군 2
 
2.1%
웅포면 2
 
2.1%
대산면 2
 
2.1%
192 2
 
2.1%
28 2
 
2.1%
Other values (64) 73
75.3%
2023-12-12T09:35:47.772002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
18.4%
면 20
 
5.1%
1 19
 
4.9%
로 18
 
4.6%
산 16
 
4.1%
군 15
 
3.8%
시 14
 
3.6%
8 11
 
2.8%
2 8
 
2.0%
5 8
 
2.0%
Other values (83) 190
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
59.1%
Decimal Number 82
 
21.0%
Space Separator 72
 
18.4%
Dash Punctuation 4
 
1.0%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 20
 
8.7%
로 18
 
7.8%
산 16
 
6.9%
군 15
 
6.5%
시 14
 
6.1%
창 7
 
3.0%
길 6
 
2.6%
고 5
 
2.2%
남 5
 
2.2%
무 4
 
1.7%
Other values (69) 121
52.4%
Decimal Number
ValueCountFrequency (%)
1 19
23.2%
8 11
13.4%
2 8
9.8%
5 8
9.8%
9 8
9.8%
3 8
9.8%
0 8
9.8%
6 5
 
6.1%
7 5
 
6.1%
4 2
 
2.4%
Space Separator
ValueCountFrequency (%)
72
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 231
59.1%
Common 160
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 20
 
8.7%
로 18
 
7.8%
산 16
 
6.9%
군 15
 
6.5%
시 14
 
6.1%
창 7
 
3.0%
길 6
 
2.6%
고 5
 
2.2%
남 5
 
2.2%
무 4
 
1.7%
Other values (69) 121
52.4%
Common
ValueCountFrequency (%)
72
45.0%
1 19
 
11.9%
8 11
 
6.9%
2 8
 
5.0%
5 8
 
5.0%
9 8
 
5.0%
3 8
 
5.0%
0 8
 
5.0%
6 5
 
3.1%
7 5
 
3.1%
Other values (4) 8
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 231
59.1%
ASCII 160
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
45.0%
1 19
 
11.9%
8 11
 
6.9%
2 8
 
5.0%
5 8
 
5.0%
9 8
 
5.0%
3 8
 
5.0%
0 8
 
5.0%
6 5
 
3.1%
7 5
 
3.1%
Other values (4) 8
 
5.0%
Hangul
ValueCountFrequency (%)
면 20
 
8.7%
로 18
 
7.8%
산 16
 
6.9%
군 15
 
6.5%
시 14
 
6.1%
창 7
 
3.0%
길 6
 
2.6%
고 5
 
2.2%
남 5
 
2.2%
무 4
 
1.7%
Other values (69) 121
52.4%

총면적(㎡)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808722.08
Minimum74258
Maximum3570489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T09:35:47.899794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74258
5-th percentile149115
Q1299986
median797168
Q31003506
95-th percentile1372734
Maximum3570489
Range3496231
Interquartile range (IQR)703520

Descriptive statistics

Standard deviation701485.07
Coefficient of variation (CV)0.86739943
Kurtosis9.7634955
Mean808722.08
Median Absolute Deviation (MAD)360032
Skewness2.5712391
Sum20218052
Variance4.920813 × 1011
MonotonicityNot monotonic
2023-12-12T09:35:48.013934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
156851 1
 
4.0%
670293 1
 
4.0%
610250 1
 
4.0%
930788 1
 
4.0%
760515 1
 
4.0%
203636 1
 
4.0%
74258 1
 
4.0%
1221618 1
 
4.0%
1246070 1
 
4.0%
895465 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
74258 1
4.0%
147181 1
4.0%
156851 1
4.0%
163423 1
4.0%
203636 1
4.0%
208797 1
4.0%
299986 1
4.0%
341398 1
4.0%
559779 1
4.0%
610250 1
4.0%
ValueCountFrequency (%)
3570489 1
4.0%
1404400 1
4.0%
1246070 1
4.0%
1221618 1
4.0%
1157200 1
4.0%
1153533 1
4.0%
1003506 1
4.0%
939327 1
4.0%
930788 1
4.0%
895465 1
4.0%

홀수
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.48
Minimum6
Maximum63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T09:35:48.159094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile6.2
Q19
median18
Q318
95-th percentile27
Maximum63
Range57
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.15467
Coefficient of variation (CV)0.63813903
Kurtosis11.729848
Mean17.48
Median Absolute Deviation (MAD)0
Skewness2.9060229
Sum437
Variance124.42667
MonotonicityNot monotonic
2023-12-12T09:35:48.248422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
18 13
52.0%
9 4
 
16.0%
6 2
 
8.0%
27 2
 
8.0%
63 1
 
4.0%
7 1
 
4.0%
10 1
 
4.0%
21 1
 
4.0%
ValueCountFrequency (%)
6 2
 
8.0%
7 1
 
4.0%
9 4
 
16.0%
10 1
 
4.0%
18 13
52.0%
21 1
 
4.0%
27 2
 
8.0%
63 1
 
4.0%
ValueCountFrequency (%)
63 1
 
4.0%
27 2
 
8.0%
21 1
 
4.0%
18 13
52.0%
10 1
 
4.0%
9 4
 
16.0%
7 1
 
4.0%
6 2
 
8.0%

세부종류
Categorical

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
대중
19 
회원제
6 

Length

Max length3
Median length2
Mean length2.24
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대중
2nd row회원제
3rd row대중
4th row회원제
5th row대중

Common Values

ValueCountFrequency (%)
대중 19
76.0%
회원제 6
 
24.0%

Length

2023-12-12T09:35:48.362516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:35:48.453140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대중 19
76.0%
회원제 6
 
24.0%

Interactions

2023-12-12T09:35:46.303743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:35:46.024467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:35:46.447833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:35:46.174194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:35:48.514097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업소명소재지총면적(㎡)홀수세부종류
업소명1.0001.0000.0000.0000.000
소재지1.0001.0000.0000.0000.000
총면적(㎡)0.0000.0001.0000.8660.124
홀수0.0000.0000.8661.0000.406
세부종류0.0000.0000.1240.4061.000
2023-12-12T09:35:48.597372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총면적(㎡)홀수세부종류
총면적(㎡)1.0000.8340.176
홀수0.8341.0000.251
세부종류0.1760.2511.000

Missing values

2023-12-12T09:35:46.551967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:35:46.633922image/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

업소명소재지총면적(㎡)홀수세부종류
0전주월드컵골프장전주시 덕진구 온고을로 6721568519대중
1군산CC군산시 옥서면 남군산로 168567029318회원제
2군산CC군산시 옥서면 남군산로 1685357048963대중
3상떼힐익산CC익산시 무학로 38길 111(덕기동)79716818회원제
4상떼힐CC익산시 춘포면 신동리 1921634236대중
5베어리버CC익산시 웅포면 강변로 130115353318회원제
6베어리버CC익산시 웅포면 강변로 130100350618대중
7태인CC정읍시 태인면 상증길 2885412118회원제
8태인CC정읍시 태인면 상증길 282087977대중
9남원상록골프장남원시 대산면 월계길11115720018대중
업소명소재지총면적(㎡)홀수세부종류
15써미트CC진안군 부귀면 봉암리 산 98-1140440027대중
16무주덕유산CC무주군 설천면 만선로 18584800018회원제
17무주안성CC무주군 안성면 장무로 153589546518대중
18장수골프리조트장수군 계남면 장안산로 303124607018대중
19전주샹그릴라CC임실군 신덕면 수지로 559-90122161827회원제
20금과골프장순창군 금과면 담순로 716742586대중
21순창CC순창군 순창읍 금산로 1522036369대중
22고창CC고창군 심원면 예향갯벌로 7076051521대중
23골프존카운티선운고창군 아산면 운곡로 41893078818대중
24석정힐CC고창군 고창읍 석정2로 19261025018대중