Overview

Dataset statistics

Number of variables6
Number of observations80
Missing cells24
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory50.6 B

Variable types

Numeric1
Categorical2
Text3

Dataset

Description경기도 성남시내 폐기물 처리업체 현황에 대한 데이터로 업종, 업체명, 업체 주소,전화번호 등의 항목을 제공하고 있습니다.
Author경기도 성남시
URLhttps://www.data.go.kr/data/15031937/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 업 종High correlation
업 종 is highly overall correlated with 연번High correlation
전화번호 has 24 (30.0%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:10:02.790232
Analysis finished2023-12-12 18:10:03.522803
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct80
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum1
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-13T03:10:03.595642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.95
Q120.75
median40.5
Q360.25
95-th percentile76.05
Maximum80
Range79
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation23.2379
Coefficient of variation (CV)0.57377531
Kurtosis-1.2
Mean40.5
Median Absolute Deviation (MAD)20
Skewness0
Sum3240
Variance540
MonotonicityStrictly increasing
2023-12-13T03:10:03.751103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.2%
42 1
 
1.2%
60 1
 
1.2%
59 1
 
1.2%
58 1
 
1.2%
57 1
 
1.2%
56 1
 
1.2%
55 1
 
1.2%
54 1
 
1.2%
53 1
 
1.2%
Other values (70) 70
87.5%
ValueCountFrequency (%)
1 1
1.2%
2 1
1.2%
3 1
1.2%
4 1
1.2%
5 1
1.2%
6 1
1.2%
7 1
1.2%
8 1
1.2%
9 1
1.2%
10 1
1.2%
ValueCountFrequency (%)
80 1
1.2%
79 1
1.2%
78 1
1.2%
77 1
1.2%
76 1
1.2%
75 1
1.2%
74 1
1.2%
73 1
1.2%
72 1
1.2%
71 1
1.2%

업 종
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
건설
26 
비배출시설계폐기물
23 
배출시설계폐기물
20 
배출시설계/비배출시설계폐기물
10 
중간처리업
 
1

Length

Max length15
Median length9
Mean length7.175
Min length2

Unique

Unique1 ?
Unique (%)1.2%

Sample

1st row배출시설계/비배출시설계폐기물
2nd row배출시설계폐기물
3rd row배출시설계폐기물
4th row배출시설계/비배출시설계폐기물
5th row배출시설계/비배출시설계폐기물

Common Values

ValueCountFrequency (%)
건설 26
32.5%
비배출시설계폐기물 23
28.7%
배출시설계폐기물 20
25.0%
배출시설계/비배출시설계폐기물 10
 
12.5%
중간처리업 1
 
1.2%

Length

2023-12-13T03:10:03.912267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:10:04.035250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설 26
32.5%
비배출시설계폐기물 23
28.7%
배출시설계폐기물 20
25.0%
배출시설계/비배출시설계폐기물 10
 
12.5%
중간처리업 1
 
1.2%

업 체 명
Text

Distinct77
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-13T03:10:04.284997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.325
Min length4

Characters and Unicode

Total characters586
Distinct characters129
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

Unique74 ?
Unique (%)92.5%

Sample

1st row(주)대신환경
2nd row(주)에코로지스
3rd row(주)대성환경
4th row(주)평화환경
5th row대원환경산업(주)
ValueCountFrequency (%)
주식회사 11
 
11.8%
주)이든개발 2
 
2.2%
영광자원 2
 
2.2%
장수환경 2
 
2.2%
주)중원기업 1
 
1.1%
한성산업(주 1
 
1.1%
주)도시개발 1
 
1.1%
주)종합환경 1
 
1.1%
주)성남환경산업 1
 
1.1%
하나기업(주 1
 
1.1%
Other values (70) 70
75.3%
2023-12-13T03:10:04.816283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 59
 
10.1%
( 45
 
7.7%
) 45
 
7.7%
경 36
 
6.1%
환 34
 
5.8%
업 19
 
3.2%
사 16
 
2.7%
산 14
 
2.4%
이 14
 
2.4%
13
 
2.2%
Other values (119) 291
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 478
81.6%
Open Punctuation 46
 
7.8%
Close Punctuation 46
 
7.8%
Space Separator 13
 
2.2%
Uppercase Letter 2
 
0.3%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 59
 
12.3%
경 36
 
7.5%
환 34
 
7.1%
업 19
 
4.0%
사 16
 
3.3%
산 14
 
2.9%
이 14
 
2.9%
식 12
 
2.5%
회 12
 
2.5%
개 11
 
2.3%
Other values (111) 251
52.5%
Open Punctuation
ValueCountFrequency (%)
( 45
97.8%
[ 1
 
2.2%
Close Punctuation
ValueCountFrequency (%)
) 45
97.8%
] 1
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 478
81.6%
Common 106
 
18.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 59
 
12.3%
경 36
 
7.5%
환 34
 
7.1%
업 19
 
4.0%
사 16
 
3.3%
산 14
 
2.9%
이 14
 
2.9%
식 12
 
2.5%
회 12
 
2.5%
개 11
 
2.3%
Other values (111) 251
52.5%
Common
ValueCountFrequency (%)
( 45
42.5%
) 45
42.5%
13
 
12.3%
] 1
 
0.9%
[ 1
 
0.9%
. 1
 
0.9%
Latin
ValueCountFrequency (%)
S 1
50.0%
D 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 478
81.6%
ASCII 108
 
18.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 59
 
12.3%
경 36
 
7.5%
환 34
 
7.1%
업 19
 
4.0%
사 16
 
3.3%
산 14
 
2.9%
이 14
 
2.9%
식 12
 
2.5%
회 12
 
2.5%
개 11
 
2.3%
Other values (111) 251
52.5%
ASCII
ValueCountFrequency (%)
( 45
41.7%
) 45
41.7%
13
 
12.0%
] 1
 
0.9%
[ 1
 
0.9%
S 1
 
0.9%
. 1
 
0.9%
D 1
 
0.9%

업체 주소
Text

Distinct76
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-12-13T03:10:05.088082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length43
Mean length35.7625
Min length20

Characters and Unicode

Total characters2861
Distinct characters169
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)91.2%

Sample

1st row경기도 성남시 분당구 판교로 790 (야탑동)
2nd row경기도 성남시 분당구 야탑로 243, 부건프라자 2층 203호 (야탑동)
3rd row경기도 성남시 중원구 상대원동 517-14 성남산업단지관리공단 502호
4th row경기도 성남시 분당구 장미로92번길 13-3 (야탑동)
5th row경기도 성남시 중원구 둔촌대로268번길 19, 2층 (하대원동)
ValueCountFrequency (%)
경기도 80
 
13.4%
성남시 80
 
13.4%
중원구 37
 
6.2%
분당구 28
 
4.7%
야탑동 17
 
2.8%
하대원동 16
 
2.7%
수정구 15
 
2.5%
3층 10
 
1.7%
성남동 8
 
1.3%
둔촌대로 8
 
1.3%
Other values (215) 300
50.1%
2023-12-13T03:10:05.522403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
528
 
18.5%
1 107
 
3.7%
성 99
 
3.5%
남 97
 
3.4%
동 95
 
3.3%
도 89
 
3.1%
시 83
 
2.9%
구 82
 
2.9%
기 82
 
2.9%
2 81
 
2.8%
Other values (159) 1518
53.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1608
56.2%
Space Separator 528
 
18.5%
Decimal Number 473
 
16.5%
Other Punctuation 73
 
2.6%
Open Punctuation 71
 
2.5%
Close Punctuation 71
 
2.5%
Dash Punctuation 28
 
1.0%
Uppercase Letter 7
 
0.2%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 99
 
6.2%
남 97
 
6.0%
동 95
 
5.9%
도 89
 
5.5%
시 83
 
5.2%
구 82
 
5.1%
기 82
 
5.1%
경 80
 
5.0%
로 71
 
4.4%
원 65
 
4.0%
Other values (137) 765
47.6%
Decimal Number
ValueCountFrequency (%)
1 107
22.6%
2 81
17.1%
0 58
12.3%
3 52
11.0%
4 48
10.1%
5 34
 
7.2%
8 29
 
6.1%
7 24
 
5.1%
6 23
 
4.9%
9 17
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
A 2
28.6%
B 2
28.6%
K 1
14.3%
S 1
14.3%
N 1
14.3%
Lowercase Letter
ValueCountFrequency (%)
b 1
50.0%
n 1
50.0%
Space Separator
ValueCountFrequency (%)
528
100.0%
Other Punctuation
ValueCountFrequency (%)
, 73
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1608
56.2%
Common 1244
43.5%
Latin 9
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 99
 
6.2%
남 97
 
6.0%
동 95
 
5.9%
도 89
 
5.5%
시 83
 
5.2%
구 82
 
5.1%
기 82
 
5.1%
경 80
 
5.0%
로 71
 
4.4%
원 65
 
4.0%
Other values (137) 765
47.6%
Common
ValueCountFrequency (%)
528
42.4%
1 107
 
8.6%
2 81
 
6.5%
, 73
 
5.9%
( 71
 
5.7%
) 71
 
5.7%
0 58
 
4.7%
3 52
 
4.2%
4 48
 
3.9%
5 34
 
2.7%
Other values (5) 121
 
9.7%
Latin
ValueCountFrequency (%)
A 2
22.2%
B 2
22.2%
b 1
11.1%
K 1
11.1%
S 1
11.1%
N 1
11.1%
n 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1608
56.2%
ASCII 1253
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
528
42.1%
1 107
 
8.5%
2 81
 
6.5%
, 73
 
5.8%
( 71
 
5.7%
) 71
 
5.7%
0 58
 
4.6%
3 52
 
4.2%
4 48
 
3.8%
5 34
 
2.7%
Other values (12) 130
 
10.4%
Hangul
ValueCountFrequency (%)
성 99
 
6.2%
남 97
 
6.0%
동 95
 
5.9%
도 89
 
5.5%
시 83
 
5.2%
구 82
 
5.1%
기 82
 
5.1%
경 80
 
5.0%
로 71
 
4.4%
원 65
 
4.0%
Other values (137) 765
47.6%

전화번호
Text

MISSING 

Distinct49
Distinct (%)87.5%
Missing24
Missing (%)30.0%
Memory size772.0 B
2023-12-13T03:10:05.832052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.053571
Min length11

Characters and Unicode

Total characters675
Distinct characters12
Distinct categories3 ?
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 (%)76.8%

Sample

1st row031-745-4695
2nd row031-373-3773
3rd row031-752-5432
4th row031-756-2762
5th row031-704-8997
ValueCountFrequency (%)
031-759-0078 3
 
5.4%
031-757-4106 2
 
3.6%
031-722-0405 2
 
3.6%
031-781-6767 2
 
3.6%
031-322-0189 2
 
3.6%
031-753-4978 2
 
3.6%
031-745-4695 1
 
1.8%
031-721-5454 1
 
1.8%
031-758-1962 1
 
1.8%
031-704-2321 1
 
1.8%
Other values (39) 39
69.6%
2023-12-13T03:10:06.320581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 112
16.6%
0 105
15.6%
7 88
13.0%
1 83
12.3%
3 81
12.0%
5 50
7.4%
2 50
7.4%
4 37
 
5.5%
8 23
 
3.4%
6 23
 
3.4%
Other values (2) 23
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 561
83.1%
Dash Punctuation 112
 
16.6%
Space Separator 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105
18.7%
7 88
15.7%
1 83
14.8%
3 81
14.4%
5 50
8.9%
2 50
8.9%
4 37
 
6.6%
8 23
 
4.1%
6 23
 
4.1%
9 21
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 112
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 112
16.6%
0 105
15.6%
7 88
13.0%
1 83
12.3%
3 81
12.0%
5 50
7.4%
2 50
7.4%
4 37
 
5.5%
8 23
 
3.4%
6 23
 
3.4%
Other values (2) 23
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 112
16.6%
0 105
15.6%
7 88
13.0%
1 83
12.3%
3 81
12.0%
5 50
7.4%
2 50
7.4%
4 37
 
5.5%
8 23
 
3.4%
6 23
 
3.4%
Other values (2) 23
 
3.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
2023-10-17
80 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-17
2nd row2023-10-17
3rd row2023-10-17
4th row2023-10-17
5th row2023-10-17

Common Values

ValueCountFrequency (%)
2023-10-17 80
100.0%

Length

2023-12-13T03:10:06.495066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:10:06.596944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-17 80
100.0%

Interactions

2023-12-13T03:10:03.256495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:10:06.662656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업 종업 체 명업체 주소전화번호
연번1.0000.9370.7960.7220.687
업 종0.9371.0000.7880.9410.816
업 체 명0.7960.7881.0000.9991.000
업체 주소0.7220.9410.9991.0000.998
전화번호0.6870.8161.0000.9981.000
2023-12-13T03:10:06.766040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업 종
연번1.0000.640
업 종0.6401.000

Missing values

2023-12-13T03:10:03.378497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:10:03.482116image/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

연번업 종업 체 명업체 주소전화번호데이터기준일자
01배출시설계/비배출시설계폐기물(주)대신환경경기도 성남시 분당구 판교로 790 (야탑동)031-745-46952023-10-17
12배출시설계폐기물(주)에코로지스경기도 성남시 분당구 야탑로 243, 부건프라자 2층 203호 (야탑동)031-373-37732023-10-17
23배출시설계폐기물(주)대성환경경기도 성남시 중원구 상대원동 517-14 성남산업단지관리공단 502호<NA>2023-10-17
34배출시설계/비배출시설계폐기물(주)평화환경경기도 성남시 분당구 장미로92번길 13-3 (야탑동)031-752-54322023-10-17
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