Overview

Dataset statistics

Number of variables7
Number of observations66
Missing cells19
Missing cells (%)4.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory61.0 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description경상남도 김해시 비료생산업 등록현황(연번, 업종구분, 업체명, 업체전화번호 등)에 대한 데이터를 제공합니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15092341

Alerts

연번 is highly overall correlated with 업종구분High correlation
업종구분 is highly overall correlated with 연번High correlation
업체전화번호 has 19 (28.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:21:31.488912
Analysis finished2023-12-11 00:21:33.381183
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct66
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.5
Minimum1
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-11T09:21:33.478807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.25
Q117.25
median33.5
Q349.75
95-th percentile62.75
Maximum66
Range65
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation19.196354
Coefficient of variation (CV)0.57302549
Kurtosis-1.2
Mean33.5
Median Absolute Deviation (MAD)16.5
Skewness0
Sum2211
Variance368.5
MonotonicityStrictly increasing
2023-12-11T09:21:33.626853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
51 1
 
1.5%
37 1
 
1.5%
38 1
 
1.5%
39 1
 
1.5%
40 1
 
1.5%
41 1
 
1.5%
42 1
 
1.5%
43 1
 
1.5%
44 1
 
1.5%
Other values (56) 56
84.8%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%
57 1
1.5%

업종구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size660.0 B
생산업
47 
수입업
19 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생산업 47
71.2%
수입업 19
28.8%

Length

2023-12-11T09:21:33.784488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:21:33.887711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산업 47
71.2%
수입업 19
28.8%

업체명
Text

Distinct60
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-11T09:21:34.136788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length15
Mean length7.5909091
Min length2

Characters and Unicode

Total characters501
Distinct characters114
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)83.3%

Sample

1st row(주)개원산업
2nd row(주)그린포인트지엔씨
3rd row(주)금양소재산업
4th row(주)네오바이오텍
5th row(주)대양케미칼
ValueCountFrequency (%)
농업회사법인 6
 
7.2%
주식회사 4
 
4.8%
주)차세대케미칼 3
 
3.6%
개원산업 3
 
3.6%
주)정토바이오텍 2
 
2.4%
현대특산 2
 
2.4%
주 2
 
2.4%
주)윈터그린 2
 
2.4%
원종축산 1
 
1.2%
한림유기산업 1
 
1.2%
Other values (57) 57
68.7%
2023-12-11T09:21:34.529362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 36
 
7.2%
( 31
 
6.2%
) 31
 
6.2%
산 19
 
3.8%
업 19
 
3.8%
17
 
3.4%
농 16
 
3.2%
사 15
 
3.0%
회 12
 
2.4%
인 10
 
2.0%
Other values (104) 295
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 419
83.6%
Open Punctuation 31
 
6.2%
Close Punctuation 31
 
6.2%
Space Separator 17
 
3.4%
Uppercase Letter 2
 
0.4%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 36
 
8.6%
산 19
 
4.5%
업 19
 
4.5%
농 16
 
3.8%
사 15
 
3.6%
회 12
 
2.9%
인 10
 
2.4%
오 10
 
2.4%
비 10
 
2.4%
이 10
 
2.4%
Other values (98) 262
62.5%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
J 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Space Separator
ValueCountFrequency (%)
17
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 419
83.6%
Common 80
 
16.0%
Latin 2
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 36
 
8.6%
산 19
 
4.5%
업 19
 
4.5%
농 16
 
3.8%
사 15
 
3.6%
회 12
 
2.9%
인 10
 
2.4%
오 10
 
2.4%
비 10
 
2.4%
이 10
 
2.4%
Other values (98) 262
62.5%
Common
ValueCountFrequency (%)
( 31
38.8%
) 31
38.8%
17
21.2%
1 1
 
1.2%
Latin
ValueCountFrequency (%)
H 1
50.0%
J 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 419
83.6%
ASCII 82
 
16.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 36
 
8.6%
산 19
 
4.5%
업 19
 
4.5%
농 16
 
3.8%
사 15
 
3.6%
회 12
 
2.9%
인 10
 
2.4%
오 10
 
2.4%
비 10
 
2.4%
이 10
 
2.4%
Other values (98) 262
62.5%
ASCII
ValueCountFrequency (%)
( 31
37.8%
) 31
37.8%
17
20.7%
1 1
 
1.2%
H 1
 
1.2%
J 1
 
1.2%

업체주소
Text

Distinct55
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size660.0 B
2023-12-11T09:21:34.882794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length21.863636
Min length18

Characters and Unicode

Total characters1443
Distinct characters95
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)69.7%

Sample

1st row경상남도 김해시 상동면 매리 542-3
2nd row서울특별시 강남구 개포동 1236-2
3rd row경상남도 김해시 한림면 용덕리 278-3
4th row경상남도 김해시 외동 1256-1 정우센터빌딩 1101호
5th row경상남도 김해시 진영읍 본산리 305-1
ValueCountFrequency (%)
경상남도 65
20.2%
김해시 64
19.9%
한림면 20
 
6.2%
상동면 9
 
2.8%
진례면 8
 
2.5%
퇴래리 8
 
2.5%
주촌면 7
 
2.2%
고모리 6
 
1.9%
생림면 6
 
1.9%
매리 4
 
1.2%
Other values (95) 125
38.8%
2023-12-11T09:21:35.382505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
256
17.7%
상 74
 
5.1%
시 68
 
4.7%
남 66
 
4.6%
경 65
 
4.5%
도 65
 
4.5%
김 65
 
4.5%
해 65
 
4.5%
2 57
 
4.0%
리 54
 
3.7%
Other values (85) 608
42.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 872
60.4%
Decimal Number 268
 
18.6%
Space Separator 256
 
17.7%
Dash Punctuation 47
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 74
 
8.5%
시 68
 
7.8%
남 66
 
7.6%
경 65
 
7.5%
도 65
 
7.5%
김 65
 
7.5%
해 65
 
7.5%
리 54
 
6.2%
면 52
 
6.0%
림 30
 
3.4%
Other values (73) 268
30.7%
Decimal Number
ValueCountFrequency (%)
2 57
21.3%
1 51
19.0%
4 33
12.3%
5 29
10.8%
3 24
9.0%
7 24
9.0%
0 22
 
8.2%
8 11
 
4.1%
6 10
 
3.7%
9 7
 
2.6%
Space Separator
ValueCountFrequency (%)
256
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 872
60.4%
Common 571
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 74
 
8.5%
시 68
 
7.8%
남 66
 
7.6%
경 65
 
7.5%
도 65
 
7.5%
김 65
 
7.5%
해 65
 
7.5%
리 54
 
6.2%
면 52
 
6.0%
림 30
 
3.4%
Other values (73) 268
30.7%
Common
ValueCountFrequency (%)
256
44.8%
2 57
 
10.0%
1 51
 
8.9%
- 47
 
8.2%
4 33
 
5.8%
5 29
 
5.1%
3 24
 
4.2%
7 24
 
4.2%
0 22
 
3.9%
8 11
 
1.9%
Other values (2) 17
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 872
60.4%
ASCII 571
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
256
44.8%
2 57
 
10.0%
1 51
 
8.9%
- 47
 
8.2%
4 33
 
5.8%
5 29
 
5.1%
3 24
 
4.2%
7 24
 
4.2%
0 22
 
3.9%
8 11
 
1.9%
Other values (2) 17
 
3.0%
Hangul
ValueCountFrequency (%)
상 74
 
8.5%
시 68
 
7.8%
남 66
 
7.6%
경 65
 
7.5%
도 65
 
7.5%
김 65
 
7.5%
해 65
 
7.5%
리 54
 
6.2%
면 52
 
6.0%
림 30
 
3.4%
Other values (73) 268
30.7%

업체전화번호
Text

MISSING 

Distinct37
Distinct (%)78.7%
Missing19
Missing (%)28.8%
Memory size660.0 B
2023-12-11T09:21:35.630229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters564
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

Unique30 ?
Unique (%)63.8%

Sample

1st row055-323-9779
2nd row055-343-1163
3rd row055-345-8380
4th row055-329-6722
5th row055-339-6464
ValueCountFrequency (%)
055-323-9779 4
 
8.5%
055-342-2921 3
 
6.4%
055-343-8141 2
 
4.3%
055-323-9160 2
 
4.3%
055-345-1367 2
 
4.3%
055-345-8141 2
 
4.3%
055-345-8714 2
 
4.3%
055-339-1213 1
 
2.1%
055-328-9700 1
 
2.1%
055-338-8118 1
 
2.1%
Other values (27) 27
57.4%
2023-12-11T09:21:35.949002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 113
20.0%
- 94
16.7%
3 87
15.4%
0 60
10.6%
2 40
 
7.1%
4 34
 
6.0%
1 33
 
5.9%
7 29
 
5.1%
9 27
 
4.8%
6 24
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 470
83.3%
Dash Punctuation 94
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 113
24.0%
3 87
18.5%
0 60
12.8%
2 40
 
8.5%
4 34
 
7.2%
1 33
 
7.0%
7 29
 
6.2%
9 27
 
5.7%
6 24
 
5.1%
8 23
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 94
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 564
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 113
20.0%
- 94
16.7%
3 87
15.4%
0 60
10.6%
2 40
 
7.1%
4 34
 
6.0%
1 33
 
5.9%
7 29
 
5.1%
9 27
 
4.8%
6 24
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 113
20.0%
- 94
16.7%
3 87
15.4%
0 60
10.6%
2 40
 
7.1%
4 34
 
6.0%
1 33
 
5.9%
7 29
 
5.1%
9 27
 
4.8%
6 24
 
4.3%

위도
Real number (ℝ)

Distinct53
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.318265
Minimum35.197908
Maximum37.477427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-11T09:21:36.098053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.197908
5-th percentile35.215176
Q135.25217
median35.300708
Q335.309832
95-th percentile35.359573
Maximum37.477427
Range2.2795188
Interquartile range (IQR)0.057661803

Descriptive statistics

Standard deviation0.27331138
Coefficient of variation (CV)0.0077385278
Kurtosis62.580601
Mean35.318265
Median Absolute Deviation (MAD)0.02485666
Skewness7.8121935
Sum2331.0055
Variance0.07469911
MonotonicityNot monotonic
2023-12-11T09:21:36.233811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.30325158 3
 
4.5%
35.27585153 3
 
4.5%
35.30070819 3
 
4.5%
35.25688437 2
 
3.0%
35.30237577 2
 
3.0%
35.28471976 2
 
3.0%
35.3042452 2
 
3.0%
35.32067718 2
 
3.0%
35.30313325 2
 
3.0%
35.31957946 2
 
3.0%
Other values (43) 43
65.2%
ValueCountFrequency (%)
35.19790845 1
1.5%
35.20629367 1
1.5%
35.21240023 1
1.5%
35.21345806 1
1.5%
35.22032855 1
1.5%
35.22569054 1
1.5%
35.22779775 1
1.5%
35.23174098 1
1.5%
35.23210499 1
1.5%
35.23294633 1
1.5%
ValueCountFrequency (%)
37.47742721 1
1.5%
35.43094128 1
1.5%
35.37433469 1
1.5%
35.36432942 1
1.5%
35.34530252 1
1.5%
35.334499 1
1.5%
35.32821682 1
1.5%
35.32565651 1
1.5%
35.32478983 1
1.5%
35.32233065 1
1.5%

경도
Real number (ℝ)

Distinct53
Distinct (%)80.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81227
Minimum127.04586
Maximum128.97847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size726.0 B
2023-12-11T09:21:36.369526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.04586
5-th percentile128.77383
Q1128.78405
median128.82685
Q3128.86636
95-th percentile128.95997
Maximum128.97847
Range1.9326132
Interquartile range (IQR)0.08230965

Descriptive statistics

Standard deviation0.22814634
Coefficient of variation (CV)0.0017711538
Kurtosis57.512732
Mean128.81227
Median Absolute Deviation (MAD)0.0416674
Skewness-7.3259213
Sum8501.61
Variance0.052050754
MonotonicityNot monotonic
2023-12-11T09:21:36.527619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.9600383 3
 
4.5%
128.7753134 3
 
4.5%
128.8896166 3
 
4.5%
128.8205958 2
 
3.0%
128.7829009 2
 
3.0%
128.775552 2
 
3.0%
128.7837436 2
 
3.0%
128.8177765 2
 
3.0%
128.7828532 2
 
3.0%
128.8577538 2
 
3.0%
Other values (43) 43
65.2%
ValueCountFrequency (%)
127.0458609 1
 
1.5%
128.7487834 1
 
1.5%
128.7521032 1
 
1.5%
128.7733367 1
 
1.5%
128.7753134 3
4.5%
128.775552 2
3.0%
128.7773731 1
 
1.5%
128.7828532 2
3.0%
128.7829009 2
3.0%
128.7837436 2
3.0%
ValueCountFrequency (%)
128.9784741 1
 
1.5%
128.9600383 3
4.5%
128.9597457 1
 
1.5%
128.9491141 1
 
1.5%
128.9398461 1
 
1.5%
128.9270245 1
 
1.5%
128.9259414 1
 
1.5%
128.9179003 1
 
1.5%
128.8896166 3
4.5%
128.8703856 1
 
1.5%

Interactions

2023-12-11T09:21:32.487664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:31.908211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.209663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.606345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.015163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.306566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:33.045980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.101880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:21:32.396281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:21:36.692080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종구분업체명업체주소업체전화번호위도경도
연번1.0000.9980.7740.8450.8620.1300.000
업종구분0.9981.0000.0000.0000.0000.0000.000
업체명0.7740.0001.0001.0001.0001.0001.000
업체주소0.8450.0001.0001.0001.0001.0001.000
업체전화번호0.8620.0001.0001.0001.0001.0001.000
위도0.1300.0001.0001.0001.0001.0000.939
경도0.0000.0001.0001.0001.0000.9391.000
2023-12-11T09:21:36.796076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도업종구분
연번1.000-0.2060.1320.900
위도-0.2061.000-0.1050.000
경도0.132-0.1051.0000.000
업종구분0.9000.0000.0001.000

Missing values

2023-12-11T09:21:33.197029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:21:33.331220image/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

연번업종구분업체명업체주소업체전화번호위도경도
01생산업(주)개원산업경상남도 김해시 상동면 매리 542-3055-323-977935.303252128.960038
12생산업(주)그린포인트지엔씨서울특별시 강남구 개포동 1236-2055-343-116337.477427127.045861
23생산업(주)금양소재산업경상남도 김해시 한림면 용덕리 278-3055-345-838035.30191128.819171
34생산업(주)네오바이오텍경상남도 김해시 외동 1256-1 정우센터빌딩 1101호<NA>35.234323128.866972
45생산업(주)대양케미칼경상남도 김해시 진영읍 본산리 305-1<NA>35.31569128.748783
56생산업(주)비비테크노경상남도 김해시 생림면 생림리 764-1055-329-672235.32479128.828014
67생산업(주)사토야마코리아경상남도 김해시 상동면 우계리 1125-5055-339-646435.300708128.889617
78생산업(주)삼림경상남도 김해시 생림면 봉림리 33번지055-323-916035.319579128.857754
89생산업(주)상록케미칼경상남도 김해시 주촌면 내삼리 1110-14<NA>35.227798128.809668
910생산업(주)신풍년바이오경상남도 김해시 상동면 대감리 163-1055-329-456335.325657128.939846
연번업종구분업체명업체주소업체전화번호위도경도
5657수입업삼원물산경상남도 김해시 생림면 안양리 637-4055-338-677135.364329128.851922
5758수입업상록(주)경상남도 김해시 불암동 220-34055-339-865535.220329128.925941
5859수입업아그리테크노코리아경상남도 김해시 지내동 277-1055-335-310335.232946128.9179
5960수입업엔티코리아경상남도 김해시 생림면 봉림리 821-5055-338-811835.334499128.847319
6061수입업유기코리아경상남도 김해시 외동 1249-4 한국2차아파트<NA>35.232105128.864532
6162수입업제이바이오경상남도 김해시 한림면 신천리 745-1<NA>35.276825128.825678
6263수입업주식회사 개원산업 농업회사법인경상남도 김해시 상동면 매리 542-3055-323-977935.303252128.960038
6364수입업진성농산경상남도 김해시 화목동 492-11055-327-679935.206294128.852429
6465수입업한림무역(주)경상남도 김해시 진례면 청천리 371-1055-329-336635.266894128.752103
6566수입업현대특산경상남도 김해시 진례면 고모리 25번지055-345-871435.28472128.775552