Overview

Dataset statistics

Number of variables5
Number of observations54
Missing cells11
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory43.4 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description충청북도 농기계임대사업소 현황의 데이터로서, 순번, 시군, 사업소 구분, 연락처 등의 항목 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15103634/fileData.do

Alerts

순번 is highly overall correlated with 시군High correlation
시군 is highly overall correlated with 순번High correlation
연락처 has 11 (20.4%) missing valuesMissing
순번 has unique valuesUnique
사업장주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:43:47.888139
Analysis finished2023-12-12 15:43:48.350742
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.5
Minimum1
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-13T00:43:48.442108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.65
Q114.25
median27.5
Q340.75
95-th percentile51.35
Maximum54
Range53
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation15.732133
Coefficient of variation (CV)0.57207755
Kurtosis-1.2
Mean27.5
Median Absolute Deviation (MAD)13.5
Skewness0
Sum1485
Variance247.5
MonotonicityStrictly increasing
2023-12-13T00:43:48.613322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
42 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
38 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
54 1
1.9%
53 1
1.9%
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%

시군
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size564.0 B
청주시
10 
괴산군
7 
충주시
6 
음성군
6 
제천시
5 
Other values (6)
20 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청주시 10
18.5%
괴산군 7
13.0%
충주시 6
11.1%
음성군 6
11.1%
제천시 5
9.3%
단양군 5
9.3%
옥천군 4
 
7.4%
영동군 4
 
7.4%
진천군 3
 
5.6%
보은군 2
 
3.7%

Length

2023-12-13T00:43:48.758934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청주시 10
18.5%
괴산군 7
13.0%
충주시 6
11.1%
음성군 6
11.1%
제천시 5
9.3%
단양군 5
9.3%
옥천군 4
 
7.4%
영동군 4
 
7.4%
진천군 3
 
5.6%
보은군 2
 
3.7%

사업소구분
Text

Distinct34
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-13T00:43:48.944385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.3333333
Min length3

Characters and Unicode

Total characters288
Distinct characters54
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

Unique31 ?
Unique (%)57.4%

Sample

1st row본 점
2nd row분소(강서)
3rd row분소(낭성)
4th row분소(강내)
5th row분소(남이)
ValueCountFrequency (%)
홈페이지 11
16.9%
본 11
16.9%
점 10
 
15.4%
분소(북부 2
 
3.1%
분소(안남 1
 
1.5%
소 1
 
1.5%
분소(청천 1
 
1.5%
분소(장연 1
 
1.5%
분소(사리 1
 
1.5%
분소(감물 1
 
1.5%
Other values (25) 25
38.5%
2023-12-13T00:43:49.394257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 36
12.5%
) 32
 
11.1%
분 32
 
11.1%
( 32
 
11.1%
이 14
 
4.9%
홈 11
 
3.8%
지 11
 
3.8%
본 11
 
3.8%
11
 
3.8%
페 11
 
3.8%
Other values (44) 87
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 208
72.2%
Close Punctuation 32
 
11.1%
Open Punctuation 32
 
11.1%
Space Separator 11
 
3.8%
Connector Punctuation 5
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 36
17.3%
분 32
15.4%
이 14
 
6.7%
홈 11
 
5.3%
지 11
 
5.3%
본 11
 
5.3%
페 11
 
5.3%
점 10
 
4.8%
부 9
 
4.3%
남 5
 
2.4%
Other values (40) 58
27.9%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Space Separator
ValueCountFrequency (%)
11
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 208
72.2%
Common 80
 
27.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 36
17.3%
분 32
15.4%
이 14
 
6.7%
홈 11
 
5.3%
지 11
 
5.3%
본 11
 
5.3%
페 11
 
5.3%
점 10
 
4.8%
부 9
 
4.3%
남 5
 
2.4%
Other values (40) 58
27.9%
Common
ValueCountFrequency (%)
) 32
40.0%
( 32
40.0%
11
 
13.8%
_ 5
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 208
72.2%
ASCII 80
 
27.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 36
17.3%
분 32
15.4%
이 14
 
6.7%
홈 11
 
5.3%
지 11
 
5.3%
본 11
 
5.3%
페 11
 
5.3%
점 10
 
4.8%
부 9
 
4.3%
남 5
 
2.4%
Other values (40) 58
27.9%
ASCII
ValueCountFrequency (%)
) 32
40.0%
( 32
40.0%
11
 
13.8%
_ 5
 
6.2%

사업장주소
Text

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-13T00:43:49.685203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length33
Mean length18.240741
Min length11

Characters and Unicode

Total characters985
Distinct characters147
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique54 ?
Unique (%)100.0%

Sample

1st row청주시 청원군 남일면 단재로480
2nd row청주시 흥덕구 강서로3
3rd row청주시 낭성면 관정길215
4th row청주시 강내면 황탄리길185
5th row청주시 남이면 시목외천로473
ValueCountFrequency (%)
청주시 9
 
6.4%
괴산군 6
 
4.3%
충주시 5
 
3.6%
음성군 5
 
3.6%
제천시 4
 
2.9%
단양군 3
 
2.1%
영동군 3
 
2.1%
옥천군 3
 
2.1%
진천군 2
 
1.4%
흥덕구 2
 
1.4%
Other values (98) 98
70.0%
2023-12-13T00:43:50.134334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
 
8.7%
/ 37
 
3.8%
로 32
 
3.2%
. 31
 
3.1%
면 27
 
2.7%
1 26
 
2.6%
군 25
 
2.5%
t 24
 
2.4%
w 21
 
2.1%
시 19
 
1.9%
Other values (137) 657
66.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 425
43.1%
Lowercase Letter 233
23.7%
Decimal Number 150
 
15.2%
Space Separator 86
 
8.7%
Other Punctuation 78
 
7.9%
Dash Punctuation 11
 
1.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 32
 
7.5%
면 27
 
6.4%
군 25
 
5.9%
시 19
 
4.5%
천 16
 
3.8%
주 15
 
3.5%
산 14
 
3.3%
청 14
 
3.3%
길 13
 
3.1%
성 12
 
2.8%
Other values (97) 238
56.0%
Lowercase Letter
ValueCountFrequency (%)
t 24
 
10.3%
w 21
 
9.0%
o 16
 
6.9%
a 16
 
6.9%
n 15
 
6.4%
r 14
 
6.0%
h 14
 
6.0%
g 13
 
5.6%
e 13
 
5.6%
p 12
 
5.2%
Other values (13) 75
32.2%
Decimal Number
ValueCountFrequency (%)
1 26
17.3%
4 18
12.0%
5 17
11.3%
3 17
11.3%
2 16
10.7%
0 14
9.3%
8 13
8.7%
6 11
7.3%
7 9
 
6.0%
9 9
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/ 37
47.4%
. 31
39.7%
: 10
 
12.8%
Space Separator
ValueCountFrequency (%)
86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 425
43.1%
Common 327
33.2%
Latin 233
23.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 32
 
7.5%
면 27
 
6.4%
군 25
 
5.9%
시 19
 
4.5%
천 16
 
3.8%
주 15
 
3.5%
산 14
 
3.3%
청 14
 
3.3%
길 13
 
3.1%
성 12
 
2.8%
Other values (97) 238
56.0%
Latin
ValueCountFrequency (%)
t 24
 
10.3%
w 21
 
9.0%
o 16
 
6.9%
a 16
 
6.9%
n 15
 
6.4%
r 14
 
6.0%
h 14
 
6.0%
g 13
 
5.6%
e 13
 
5.6%
p 12
 
5.2%
Other values (13) 75
32.2%
Common
ValueCountFrequency (%)
86
26.3%
/ 37
11.3%
. 31
 
9.5%
1 26
 
8.0%
4 18
 
5.5%
5 17
 
5.2%
3 17
 
5.2%
2 16
 
4.9%
0 14
 
4.3%
8 13
 
4.0%
Other values (7) 52
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 560
56.9%
Hangul 425
43.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
 
15.4%
/ 37
 
6.6%
. 31
 
5.5%
1 26
 
4.6%
t 24
 
4.3%
w 21
 
3.8%
4 18
 
3.2%
5 17
 
3.0%
3 17
 
3.0%
2 16
 
2.9%
Other values (30) 267
47.7%
Hangul
ValueCountFrequency (%)
로 32
 
7.5%
면 27
 
6.4%
군 25
 
5.9%
시 19
 
4.5%
천 16
 
3.8%
주 15
 
3.5%
산 14
 
3.3%
청 14
 
3.3%
길 13
 
3.1%
성 12
 
2.8%
Other values (97) 238
56.0%

연락처
Text

MISSING 

Distinct43
Distinct (%)100.0%
Missing11
Missing (%)20.4%
Memory size564.0 B
2023-12-13T00:43:50.428126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters516
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

Unique43 ?
Unique (%)100.0%

Sample

1st row043-201-3844
2nd row043-201-3995
3rd row043-201-3992
4th row043-201-3994
5th row043-201-3993
ValueCountFrequency (%)
043-201-3844 1
 
2.3%
043-833-4551 1
 
2.3%
043-833-2763 1
 
2.3%
043-740-5917 1
 
2.3%
043-835-3675 1
 
2.3%
043-539-7574 1
 
2.3%
043-539-7577 1
 
2.3%
043-830-2738 1
 
2.3%
043-832-0981 1
 
2.3%
043-833-2738 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T00:43:50.865376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 95
18.4%
- 86
16.7%
4 72
14.0%
0 71
13.8%
2 40
7.8%
7 28
 
5.4%
8 27
 
5.2%
5 27
 
5.2%
1 25
 
4.8%
9 25
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 430
83.3%
Dash Punctuation 86
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 95
22.1%
4 72
16.7%
0 71
16.5%
2 40
9.3%
7 28
 
6.5%
8 27
 
6.3%
5 27
 
6.3%
1 25
 
5.8%
9 25
 
5.8%
6 20
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 516
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 95
18.4%
- 86
16.7%
4 72
14.0%
0 71
13.8%
2 40
7.8%
7 28
 
5.4%
8 27
 
5.2%
5 27
 
5.2%
1 25
 
4.8%
9 25
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 95
18.4%
- 86
16.7%
4 72
14.0%
0 71
13.8%
2 40
7.8%
7 28
 
5.4%
8 27
 
5.2%
5 27
 
5.2%
1 25
 
4.8%
9 25
 
4.8%

Interactions

2023-12-13T00:43:48.137413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:43:51.029524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군사업소구분사업장주소연락처
순번1.0000.9540.2501.0001.000
시군0.9541.0000.0001.0001.000
사업소구분0.2500.0001.0001.0001.000
사업장주소1.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.000
2023-12-13T00:43:51.170279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번시군
순번1.0000.809
시군0.8091.000

Missing values

2023-12-13T00:43:48.231119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:43:48.315408image/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

순번시군사업소구분사업장주소연락처
01청주시본 점청주시 청원군 남일면 단재로480043-201-3844
12청주시분소(강서)청주시 흥덕구 강서로3043-201-3995
23청주시분소(낭성)청주시 낭성면 관정길215043-201-3992
34청주시분소(강내)청주시 강내면 황탄리길185043-201-3994
45청주시분소(남이)청주시 남이면 시목외천로473043-201-3993
56청주시분소(오창)청주시 오창읍 두릉유리로1405043-201-3996
67청주시분소(오근장)청주시 상당구 팔결로167(외남동)043-201-3997
78청주시분소(북이)청주시 청원구 북이면 신대석성로16-26043-201-3966
89청주시분소(원평)청주시 흥덕구 미호로53-17043-201-3998
910청주시홈페이지http://www.cheongju.go.kr/nongup/index.do<NA>
순번시군사업소구분사업장주소연락처
4445음성군분소(금왕)음성군 금왕읍 멍심이길209043-871-2372
4546음성군분소(생극)음성군 생극면 음성로1985-3043-871-2382
4647음성군분소(소이)음성군 소이면 한불로584-1043-871-2392
4748음성군분소(대소)음성군 대소면 청천로38043-871-4102
4849음성군홈페이지https://lease.eumseong.go.kr/aml/<NA>
4950단양군본 점단양군 단양읍 중앙1로20043-420-3428
5051단양군분소(가곡)단양군 가곡면 남한강로1093043-420-3416
5152단양군분소(단성)단양군 단성면 월악로4541043-420-3436
5253단양군분소(매포)매포읍 단양산업단지2로90-14043-420-3476
5354단양군홈페이지https://www.danyang.go.kr/atec/587<NA>