Overview

Dataset statistics

Number of variables26
Number of observations771
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory168.0 KiB
Average record size in memory223.2 B

Variable types

Categorical8
Text5
Numeric12
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름)
Author지방자치단체
URLhttps://www.data.go.kr/data/15025538/standard.do

Alerts

종량제봉투사용대상 is highly imbalanced (72.3%)Imbalance
1.5ℓ가격 is highly imbalanced (96.1%)Imbalance
2.5ℓ가격 is highly imbalanced (96.8%)Imbalance
120ℓ가격 is highly imbalanced (96.2%)Imbalance
1ℓ가격 has 704 (91.3%) zerosZeros
2ℓ가격 has 678 (87.9%) zerosZeros
3ℓ가격 has 555 (72.0%) zerosZeros
5ℓ가격 has 324 (42.0%) zerosZeros
10ℓ가격 has 184 (23.9%) zerosZeros
20ℓ가격 has 130 (16.9%) zerosZeros
30ℓ가격 has 578 (75.0%) zerosZeros
50ℓ가격 has 319 (41.4%) zerosZeros
60ℓ가격 has 756 (98.1%) zerosZeros
75ℓ가격 has 482 (62.5%) zerosZeros
100ℓ가격 has 686 (89.0%) zerosZeros
125ℓ가격 has 764 (99.1%) zerosZeros

Reproduction

Analysis started2024-05-04 08:05:52.666204
Analysis finished2024-05-04 08:05:53.661721
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

Distinct19
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
경기도
123 
서울특별시
103 
강원도
76 
전라북도
63 
인천광역시
54 
Other values (14)
352 

Length

Max length7
Median length5
Mean length4.3916991
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row부산광역시
2nd row부산광역시
3rd row부산광역시
4th row부산광역시
5th row대전광역시

Common Values

ValueCountFrequency (%)
경기도 123
16.0%
서울특별시 103
13.4%
강원도 76
9.9%
전라북도 63
8.2%
인천광역시 54
7.0%
경상남도 50
 
6.5%
경상북도 45
 
5.8%
전북특별자치도 43
 
5.6%
부산광역시 41
 
5.3%
강원특별자치도 39
 
5.1%
Other values (9) 134
17.4%

Length

2024-05-04T08:05:53.932507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 123
16.0%
서울특별시 103
13.4%
강원도 76
9.9%
전라북도 63
8.2%
인천광역시 54
7.0%
경상남도 50
 
6.5%
경상북도 45
 
5.8%
전북특별자치도 43
 
5.6%
부산광역시 41
 
5.3%
강원특별자치도 39
 
5.1%
Other values (9) 134
17.4%

시군구명
Text

Distinct207
Distinct (%)26.8%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-04T08:05:54.991662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9636835
Min length2

Characters and Unicode

Total characters2285
Distinct characters133
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)7.4%

Sample

1st row사상구
2nd row사상구
3rd row사상구
4th row사상구
5th row서구
ValueCountFrequency (%)
장수군 36
 
4.7%
중구 22
 
2.9%
부안군 18
 
2.3%
동구 17
 
2.2%
안양시 15
 
1.9%
서구 11
 
1.4%
계양구 10
 
1.3%
홍천군 10
 
1.3%
화천군 9
 
1.2%
시흥시 9
 
1.2%
Other values (197) 614
79.6%
2024-05-04T08:05:56.220200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 282
 
12.3%
시 281
 
12.3%
구 246
 
10.8%
양 78
 
3.4%
천 74
 
3.2%
주 60
 
2.6%
동 59
 
2.6%
안 57
 
2.5%
성 54
 
2.4%
수 53
 
2.3%
Other values (123) 1041
45.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2285
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 282
 
12.3%
시 281
 
12.3%
구 246
 
10.8%
양 78
 
3.4%
천 74
 
3.2%
주 60
 
2.6%
동 59
 
2.6%
안 57
 
2.5%
성 54
 
2.4%
수 53
 
2.3%
Other values (123) 1041
45.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2285
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 282
 
12.3%
시 281
 
12.3%
구 246
 
10.8%
양 78
 
3.4%
천 74
 
3.2%
주 60
 
2.6%
동 59
 
2.6%
안 57
 
2.5%
성 54
 
2.4%
수 53
 
2.3%
Other values (123) 1041
45.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2285
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 282
 
12.3%
시 281
 
12.3%
구 246
 
10.8%
양 78
 
3.4%
천 74
 
3.2%
주 60
 
2.6%
동 59
 
2.6%
안 57
 
2.5%
성 54
 
2.4%
수 53
 
2.3%
Other values (123) 1041
45.6%

종량제봉투종류
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
규격봉투
507 
재사용규격봉투
151 
특수규격마대
107 
김장용봉투
 
4
규격봉투+재사용규격봉투
 
2

Length

Max length12
Median length4
Mean length4.8910506
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row재사용규격봉투
2nd row규격봉투
3rd row규격봉투
4th row규격봉투
5th row규격봉투

Common Values

ValueCountFrequency (%)
규격봉투 507
65.8%
재사용규격봉투 151
 
19.6%
특수규격마대 107
 
13.9%
김장용봉투 4
 
0.5%
규격봉투+재사용규격봉투 2
 
0.3%

Length

2024-05-04T08:05:56.815205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:05:57.239867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
규격봉투 507
65.8%
재사용규격봉투 151
 
19.6%
특수규격마대 107
 
13.9%
김장용봉투 4
 
0.5%
규격봉투+재사용규격봉투 2
 
0.3%

종량제봉투처리방식
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
소각용
456 
매립용
263 
기타
50 
소각용+매립용
 
2

Length

Max length7
Median length3
Mean length2.9455253
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소각용
2nd row소각용
3rd row매립용
4th row매립용
5th row소각용

Common Values

ValueCountFrequency (%)
소각용 456
59.1%
매립용 263
34.1%
기타 50
 
6.5%
소각용+매립용 2
 
0.3%

Length

2024-05-04T08:05:57.661767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:05:58.021832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소각용 456
59.1%
매립용 263
34.1%
기타 50
 
6.5%
소각용+매립용 2
 
0.3%

종량제봉투용도
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
생활쓰레기
647 
음식물쓰레기
124 

Length

Max length6
Median length5
Mean length5.1608301
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
생활쓰레기 647
83.9%
음식물쓰레기 124
 
16.1%

Length

2024-05-04T08:05:58.518291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:05:58.979055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활쓰레기 647
83.9%
음식물쓰레기 124
 
16.1%

종량제봉투사용대상
Categorical

IMBALANCE 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
가정용
632 
사업장용
65 
기타
 
57
가정용or사업용
 
8
가정용+사업장용
 
2
Other values (7)
 
7

Length

Max length13
Median length3
Mean length3.1115435
Min length2

Unique

Unique7 ?
Unique (%)0.9%

Sample

1st row가정용
2nd row사업장용
3rd row기타
4th row기타
5th row기타

Common Values

ValueCountFrequency (%)
가정용 632
82.0%
사업장용 65
 
8.4%
기타 57
 
7.4%
가정용or사업용 8
 
1.0%
가정용+사업장용 2
 
0.3%
영업용 1
 
0.1%
소규모배출사업장 전용 1
 
0.1%
영업장용 1
 
0.1%
범용 1
 
0.1%
기타(영업장용) 1
 
0.1%
Other values (2) 2
 
0.3%

Length

2024-05-04T08:05:59.418688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정용 632
81.9%
사업장용 65
 
8.4%
기타 57
 
7.4%
가정용or사업용 8
 
1.0%
가정용+사업장용 2
 
0.3%
영업용 1
 
0.1%
소규모배출사업장 1
 
0.1%
전용 1
 
0.1%
영업장용 1
 
0.1%
범용 1
 
0.1%
Other values (3) 3
 
0.4%

1ℓ가격
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2399481
Minimum0
Maximum100
Zeros704
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:05:59.798144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile45
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.036213
Coefficient of variation (CV)3.6329011
Kurtosis15.498625
Mean5.2399481
Median Absolute Deviation (MAD)0
Skewness3.9726544
Sum4040
Variance362.37742
MonotonicityNot monotonic
2024-05-04T08:06:00.229133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 704
91.3%
100 21
 
2.7%
40 14
 
1.8%
30 9
 
1.2%
50 9
 
1.2%
60 8
 
1.0%
20 5
 
0.6%
80 1
 
0.1%
ValueCountFrequency (%)
0 704
91.3%
20 5
 
0.6%
30 9
 
1.2%
40 14
 
1.8%
50 9
 
1.2%
60 8
 
1.0%
80 1
 
0.1%
100 21
 
2.7%
ValueCountFrequency (%)
100 21
 
2.7%
80 1
 
0.1%
60 8
 
1.0%
50 9
 
1.2%
40 14
 
1.8%
30 9
 
1.2%
20 5
 
0.6%
0 704
91.3%

1.5ℓ가격
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
765 
50
 
3
40
 
2
70
 
1

Length

Max length2
Median length1
Mean length1.0077821
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 765
99.2%
50 3
 
0.4%
40 2
 
0.3%
70 1
 
0.1%

Length

2024-05-04T08:06:00.863231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:06:01.331313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 765
99.2%
50 3
 
0.4%
40 2
 
0.3%
70 1
 
0.1%

2ℓ가격
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.180285
Minimum0
Maximum200
Zeros678
Zeros (%)87.9%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:01.654364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile80
Maximum200
Range200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.26966
Coefficient of variation (CV)3.2440728
Kurtosis15.325334
Mean11.180285
Median Absolute Deviation (MAD)0
Skewness3.8763179
Sum8620
Variance1315.4882
MonotonicityNot monotonic
2024-05-04T08:06:02.186434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 678
87.9%
60 18
 
2.3%
50 17
 
2.2%
190 16
 
2.1%
40 13
 
1.7%
80 7
 
0.9%
120 6
 
0.8%
200 5
 
0.6%
45 4
 
0.5%
100 4
 
0.5%
ValueCountFrequency (%)
0 678
87.9%
40 13
 
1.7%
45 4
 
0.5%
50 17
 
2.2%
60 18
 
2.3%
80 7
 
0.9%
90 3
 
0.4%
100 4
 
0.5%
120 6
 
0.8%
190 16
 
2.1%
ValueCountFrequency (%)
200 5
 
0.6%
190 16
2.1%
120 6
 
0.8%
100 4
 
0.5%
90 3
 
0.4%
80 7
 
0.9%
60 18
2.3%
50 17
2.2%
45 4
 
0.5%
40 13
1.7%

2.5ℓ가격
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
767 
80
 
3
110
 
1

Length

Max length3
Median length1
Mean length1.0064851
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 767
99.5%
80 3
 
0.4%
110 1
 
0.1%

Length

2024-05-04T08:06:02.920390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:06:03.431871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 767
99.5%
80 3
 
0.4%
110 1
 
0.1%

3ℓ가격
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.642023
Minimum0
Maximum300
Zeros555
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:03.897961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q360
95-th percentile140
Maximum300
Range300
Interquartile range (IQR)60

Descriptive statistics

Standard deviation61.515569
Coefficient of variation (CV)2.0075557
Kurtosis7.9587365
Mean30.642023
Median Absolute Deviation (MAD)0
Skewness2.6645763
Sum23625
Variance3784.1652
MonotonicityNot monotonic
2024-05-04T08:06:04.449885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 555
72.0%
70 37
 
4.8%
90 29
 
3.8%
60 28
 
3.6%
300 21
 
2.7%
80 19
 
2.5%
100 18
 
2.3%
110 13
 
1.7%
50 11
 
1.4%
140 8
 
1.0%
Other values (9) 32
 
4.2%
ValueCountFrequency (%)
0 555
72.0%
30 3
 
0.4%
40 3
 
0.4%
50 11
 
1.4%
60 28
 
3.6%
70 37
 
4.8%
75 1
 
0.1%
80 19
 
2.5%
90 29
 
3.8%
100 18
 
2.3%
ValueCountFrequency (%)
300 21
2.7%
190 1
 
0.1%
180 7
 
0.9%
160 1
 
0.1%
150 5
 
0.6%
140 8
 
1.0%
130 6
 
0.8%
120 5
 
0.6%
110 13
1.7%
100 18
2.3%

5ℓ가격
Real number (ℝ)

ZEROS 

Distinct31
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.692607
Minimum0
Maximum800
Zeros324
Zeros (%)42.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:05.031154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median90
Q3140
95-th percentile235
Maximum800
Range800
Interquartile range (IQR)140

Descriptive statistics

Standard deviation108.96047
Coefficient of variation (CV)1.2014262
Kurtosis7.7077723
Mean90.692607
Median Absolute Deviation (MAD)90
Skewness2.1685761
Sum69924
Variance11872.385
MonotonicityNot monotonic
2024-05-04T08:06:05.545510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 324
42.0%
90 62
 
8.0%
130 51
 
6.6%
100 38
 
4.9%
120 36
 
4.7%
150 35
 
4.5%
140 26
 
3.4%
170 21
 
2.7%
160 21
 
2.7%
500 21
 
2.7%
Other values (21) 136
17.6%
ValueCountFrequency (%)
0 324
42.0%
40 2
 
0.3%
60 5
 
0.6%
65 1
 
0.1%
70 13
 
1.7%
74 1
 
0.1%
80 20
 
2.6%
85 1
 
0.1%
90 62
 
8.0%
100 38
 
4.9%
ValueCountFrequency (%)
800 2
 
0.3%
500 21
2.7%
340 1
 
0.1%
300 7
 
0.9%
290 1
 
0.1%
270 1
 
0.1%
250 5
 
0.6%
240 1
 
0.1%
230 1
 
0.1%
220 19
2.5%

10ℓ가격
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean230.12711
Minimum0
Maximum3000
Zeros184
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:06.064074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1110
median210
Q3300
95-th percentile535
Maximum3000
Range3000
Interquartile range (IQR)190

Descriptive statistics

Standard deviation236.02199
Coefficient of variation (CV)1.0256158
Kurtosis30.884129
Mean230.12711
Median Absolute Deviation (MAD)90
Skewness3.8452578
Sum177428
Variance55706.379
MonotonicityNot monotonic
2024-05-04T08:06:06.620767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 184
23.9%
250 61
 
7.9%
180 56
 
7.3%
300 43
 
5.6%
200 37
 
4.8%
230 35
 
4.5%
310 26
 
3.4%
150 25
 
3.2%
260 19
 
2.5%
430 19
 
2.5%
Other values (44) 266
34.5%
ValueCountFrequency (%)
0 184
23.9%
70 2
 
0.3%
100 6
 
0.8%
110 4
 
0.5%
120 10
 
1.3%
130 7
 
0.9%
140 11
 
1.4%
148 1
 
0.1%
150 25
 
3.2%
160 9
 
1.2%
ValueCountFrequency (%)
3000 1
 
0.1%
2000 1
 
0.1%
1400 1
 
0.1%
1020 9
1.2%
1000 15
1.9%
800 1
 
0.1%
790 1
 
0.1%
660 1
 
0.1%
630 1
 
0.1%
600 6
 
0.8%

20ℓ가격
Real number (ℝ)

ZEROS 

Distinct85
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean544.31518
Minimum0
Maximum6000
Zeros130
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:07.276140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1295
median470
Q3660
95-th percentile2000
Maximum6000
Range6000
Interquartile range (IQR)365

Descriptive statistics

Standard deviation529.93495
Coefficient of variation (CV)0.97358108
Kurtosis18.899849
Mean544.31518
Median Absolute Deviation (MAD)180
Skewness3.1273814
Sum419667
Variance280831.06
MonotonicityNot monotonic
2024-05-04T08:06:07.743036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 130
 
16.9%
490 52
 
6.7%
400 44
 
5.7%
360 43
 
5.6%
600 30
 
3.9%
560 22
 
2.9%
700 22
 
2.9%
500 22
 
2.9%
2040 22
 
2.9%
850 20
 
2.6%
Other values (75) 364
47.2%
ValueCountFrequency (%)
0 130
16.9%
140 2
 
0.3%
180 2
 
0.3%
200 6
 
0.8%
210 1
 
0.1%
220 5
 
0.6%
230 7
 
0.9%
240 2
 
0.3%
250 6
 
0.8%
260 3
 
0.4%
ValueCountFrequency (%)
6000 1
 
0.1%
4000 1
 
0.1%
3000 1
 
0.1%
2800 1
 
0.1%
2400 1
 
0.1%
2280 1
 
0.1%
2200 1
 
0.1%
2040 22
2.9%
2000 14
1.8%
1800 2
 
0.3%

30ℓ가격
Real number (ℝ)

ZEROS 

Distinct58
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean247.36706
Minimum0
Maximum20000
Zeros578
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:08.302441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3175
95-th percentile1280
Maximum20000
Range20000
Interquartile range (IQR)175

Descriptive statistics

Standard deviation852.00051
Coefficient of variation (CV)3.4442764
Kurtosis376.31384
Mean247.36706
Median Absolute Deviation (MAD)0
Skewness16.67131
Sum190720
Variance725904.88
MonotonicityNot monotonic
2024-05-04T08:06:09.075431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 578
75.0%
540 39
 
5.1%
740 19
 
2.5%
1280 18
 
2.3%
850 7
 
0.9%
1110 7
 
0.9%
780 7
 
0.9%
430 7
 
0.9%
1000 6
 
0.8%
600 5
 
0.6%
Other values (48) 78
 
10.1%
ValueCountFrequency (%)
0 578
75.0%
350 4
 
0.5%
360 1
 
0.1%
390 1
 
0.1%
400 1
 
0.1%
410 4
 
0.5%
420 1
 
0.1%
430 7
 
0.9%
440 1
 
0.1%
450 3
 
0.4%
ValueCountFrequency (%)
20000 1
0.1%
3600 1
0.1%
3220 1
0.1%
3060 1
0.1%
3000 1
0.1%
2350 1
0.1%
2160 1
0.1%
2110 1
0.1%
2010 2
0.3%
2000 2
0.3%

50ℓ가격
Real number (ℝ)

ZEROS 

Distinct119
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean867.06874
Minimum0
Maximum5290
Zeros319
Zeros (%)41.4%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:09.763075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median790
Q31400
95-th percentile2500
Maximum5290
Range5290
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation1006.431
Coefficient of variation (CV)1.160728
Kurtosis4.5616914
Mean867.06874
Median Absolute Deviation (MAD)790
Skewness1.7518463
Sum668510
Variance1012903.3
MonotonicityNot monotonic
2024-05-04T08:06:10.253297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 319
41.4%
900 48
 
6.2%
1250 35
 
4.5%
1400 19
 
2.5%
700 14
 
1.8%
2070 13
 
1.7%
1540 12
 
1.6%
5100 12
 
1.6%
1500 11
 
1.4%
980 8
 
1.0%
Other values (109) 280
36.3%
ValueCountFrequency (%)
0 319
41.4%
320 2
 
0.3%
450 1
 
0.1%
480 2
 
0.3%
500 3
 
0.4%
530 1
 
0.1%
540 4
 
0.5%
560 4
 
0.5%
570 1
 
0.1%
590 2
 
0.3%
ValueCountFrequency (%)
5290 1
 
0.1%
5100 12
1.6%
5000 2
 
0.3%
4490 1
 
0.1%
4000 1
 
0.1%
3920 1
 
0.1%
3600 1
 
0.1%
3500 1
 
0.1%
3480 1
 
0.1%
3470 1
 
0.1%

60ℓ가격
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.849546
Minimum0
Maximum4500
Zeros756
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:10.603640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4500
Range4500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation481.2887
Coefficient of variation (CV)7.4216202
Kurtosis57.038987
Mean64.849546
Median Absolute Deviation (MAD)0
Skewness7.5879239
Sum49999
Variance231638.82
MonotonicityNot monotonic
2024-05-04T08:06:11.066629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 756
98.1%
3000 3
 
0.4%
3880 2
 
0.3%
3980 2
 
0.3%
4500 1
 
0.1%
4220 1
 
0.1%
3970 1
 
0.1%
3070 1
 
0.1%
849 1
 
0.1%
1300 1
 
0.1%
Other values (2) 2
 
0.3%
ValueCountFrequency (%)
0 756
98.1%
849 1
 
0.1%
1300 1
 
0.1%
3000 3
 
0.4%
3070 1
 
0.1%
3660 1
 
0.1%
3710 1
 
0.1%
3880 2
 
0.3%
3970 1
 
0.1%
3980 2
 
0.3%
ValueCountFrequency (%)
4500 1
 
0.1%
4220 1
 
0.1%
3980 2
0.3%
3970 1
 
0.1%
3880 2
0.3%
3710 1
 
0.1%
3660 1
 
0.1%
3070 1
 
0.1%
3000 3
0.4%
1300 1
 
0.1%

75ℓ가격
Real number (ℝ)

ZEROS 

Distinct97
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean742.52918
Minimum0
Maximum6590
Zeros482
Zeros (%)62.5%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:11.924185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31400
95-th percentile2780
Maximum6590
Range6590
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation1087.8104
Coefficient of variation (CV)1.4650069
Kurtosis1.6984478
Mean742.52918
Median Absolute Deviation (MAD)0
Skewness1.3841304
Sum572490
Variance1183331.4
MonotonicityNot monotonic
2024-05-04T08:06:12.650081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 482
62.5%
1350 47
 
6.1%
1880 24
 
3.1%
3080 11
 
1.4%
1500 9
 
1.2%
2100 9
 
1.2%
2000 9
 
1.2%
2080 8
 
1.0%
2250 7
 
0.9%
1100 6
 
0.8%
Other values (87) 159
 
20.6%
ValueCountFrequency (%)
0 482
62.5%
750 1
 
0.1%
780 1
 
0.1%
790 2
 
0.3%
830 3
 
0.4%
900 2
 
0.3%
930 1
 
0.1%
950 1
 
0.1%
1000 2
 
0.3%
1040 3
 
0.4%
ValueCountFrequency (%)
6590 1
 
0.1%
6000 1
 
0.1%
4730 1
 
0.1%
4230 1
 
0.1%
4200 1
 
0.1%
4000 1
 
0.1%
3920 2
0.3%
3900 1
 
0.1%
3800 3
0.4%
3700 1
 
0.1%

100ℓ가격
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265.07134
Minimum0
Maximum10400
Zeros686
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:13.387758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2405
Maximum10400
Range10400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation886.28927
Coefficient of variation (CV)3.3435877
Kurtosis32.253343
Mean265.07134
Median Absolute Deviation (MAD)0
Skewness4.733614
Sum204370
Variance785508.66
MonotonicityNot monotonic
2024-05-04T08:06:13.994069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 686
89.0%
2500 7
 
0.9%
2700 4
 
0.5%
3060 4
 
0.5%
1350 4
 
0.5%
2000 4
 
0.5%
1200 4
 
0.5%
2770 3
 
0.4%
2470 2
 
0.3%
1110 2
 
0.3%
Other values (35) 51
 
6.6%
ValueCountFrequency (%)
0 686
89.0%
650 2
 
0.3%
830 1
 
0.1%
960 2
 
0.3%
1000 1
 
0.1%
1020 1
 
0.1%
1110 2
 
0.3%
1190 2
 
0.3%
1200 4
 
0.5%
1300 2
 
0.3%
ValueCountFrequency (%)
10400 1
0.1%
6500 2
0.3%
4700 1
0.1%
4400 1
0.1%
4000 2
0.3%
3880 2
0.3%
3870 1
0.1%
3680 1
0.1%
3080 1
0.1%
3070 1
0.1%

120ℓ가격
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
0
765 
1700
 
4
1680
 
1
2600
 
1

Length

Max length4
Median length1
Mean length1.0233463
Min length1

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 765
99.2%
1700 4
 
0.5%
1680 1
 
0.1%
2600 1
 
0.1%

Length

2024-05-04T08:06:14.487981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T08:06:14.899609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 765
99.2%
1700 4
 
0.5%
1680 1
 
0.1%
2600 1
 
0.1%

125ℓ가격
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.035019
Minimum0
Maximum8790
Zeros764
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-04T08:06:15.319148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8790
Range8790
Interquartile range (IQR)0

Descriptive statistics

Standard deviation764.33172
Coefficient of variation (CV)10.465277
Kurtosis106.83417
Mean73.035019
Median Absolute Deviation (MAD)0
Skewness10.406707
Sum56310
Variance584202.98
MonotonicityNot monotonic
2024-05-04T08:06:16.028782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 764
99.1%
8070 2
 
0.3%
8790 1
 
0.1%
7870 1
 
0.1%
8250 1
 
0.1%
7660 1
 
0.1%
7600 1
 
0.1%
ValueCountFrequency (%)
0 764
99.1%
7600 1
 
0.1%
7660 1
 
0.1%
7870 1
 
0.1%
8070 2
 
0.3%
8250 1
 
0.1%
8790 1
 
0.1%
ValueCountFrequency (%)
8790 1
 
0.1%
8250 1
 
0.1%
8070 2
 
0.3%
7870 1
 
0.1%
7660 1
 
0.1%
7600 1
 
0.1%
0 764
99.1%

관리부서명
Text

Distinct78
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-04T08:06:16.556010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length5
Mean length5.7496757
Min length3

Characters and Unicode

Total characters4433
Distinct characters103
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)2.7%

Sample

1st row청소행정과
2nd row청소행정과
3rd row청소행정과
4th row청소행정과
5th row자원순환과
ValueCountFrequency (%)
자원순환과 219
23.5%
환경과 117
 
12.5%
청소행정과 97
 
10.4%
환경위생과 81
 
8.7%
환경보호과 32
 
3.4%
청소과 24
 
2.6%
청소자원과 22
 
2.4%
부안군청 18
 
1.9%
경기도 15
 
1.6%
전라북도 13
 
1.4%
Other values (81) 295
31.6%
2024-05-04T08:06:17.746851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 706
15.9%
환 531
12.0%
경 339
 
7.6%
원 300
 
6.8%
자 293
 
6.6%
청 270
 
6.1%
순 219
 
4.9%
소 176
 
4.0%
162
 
3.7%
정 118
 
2.7%
Other values (93) 1319
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4271
96.3%
Space Separator 162
 
3.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 706
16.5%
환 531
12.4%
경 339
 
7.9%
원 300
 
7.0%
자 293
 
6.9%
청 270
 
6.3%
순 219
 
5.1%
소 176
 
4.1%
정 118
 
2.8%
생 111
 
2.6%
Other values (92) 1208
28.3%
Space Separator
ValueCountFrequency (%)
162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4271
96.3%
Common 162
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 706
16.5%
환 531
12.4%
경 339
 
7.9%
원 300
 
7.0%
자 293
 
6.9%
청 270
 
6.3%
순 219
 
5.1%
소 176
 
4.1%
정 118
 
2.8%
생 111
 
2.6%
Other values (92) 1208
28.3%
Common
ValueCountFrequency (%)
162
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4271
96.3%
ASCII 162
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 706
16.5%
환 531
12.4%
경 339
 
7.9%
원 300
 
7.0%
자 293
 
6.9%
청 270
 
6.3%
순 219
 
5.1%
소 176
 
4.1%
정 118
 
2.8%
생 111
 
2.6%
Other values (92) 1208
28.3%
ASCII
ValueCountFrequency (%)
162
100.0%

관리부서전화번호
Text

Distinct248
Distinct (%)32.2%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-04T08:06:18.405621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.006485
Min length11

Characters and Unicode

Total characters9257
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

Unique83 ?
Unique (%)10.8%

Sample

1st row051-310-4335
2nd row051-310-4335
3rd row051-310-4335
4th row051-310-4335
5th row042-288-3572
ValueCountFrequency (%)
063-350-2537 36
 
4.7%
063-580-4357 18
 
2.3%
031-8045-5448 15
 
1.9%
033-430-2633 10
 
1.3%
02-2286-5528 9
 
1.2%
032-760-7410 9
 
1.2%
063-281-2325 8
 
1.0%
033-250-3127 8
 
1.0%
033-670-2184 8
 
1.0%
033-639-2337 8
 
1.0%
Other values (238) 642
83.3%
2024-05-04T08:06:19.707680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1542
16.7%
0 1420
15.3%
3 1362
14.7%
5 928
10.0%
2 869
9.4%
4 784
8.5%
6 669
7.2%
1 532
 
5.7%
7 430
 
4.6%
8 426
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7715
83.3%
Dash Punctuation 1542
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1420
18.4%
3 1362
17.7%
5 928
12.0%
2 869
11.3%
4 784
10.2%
6 669
8.7%
1 532
 
6.9%
7 430
 
5.6%
8 426
 
5.5%
9 295
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 1542
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9257
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1542
16.7%
0 1420
15.3%
3 1362
14.7%
5 928
10.0%
2 869
9.4%
4 784
8.5%
6 669
7.2%
1 532
 
5.7%
7 430
 
4.6%
8 426
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1542
16.7%
0 1420
15.3%
3 1362
14.7%
5 928
10.0%
2 869
9.4%
4 784
8.5%
6 669
7.2%
1 532
 
5.7%
7 430
 
4.6%
8 426
 
4.6%

데이터기준일자
Date

Distinct162
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
Minimum2020-07-07 00:00:00
Maximum2024-04-02 00:00:00
2024-05-04T08:06:20.183913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T08:06:20.708360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Text

Distinct267
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-04T08:06:21.588768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5397
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)11.5%

Sample

1st row3390000
2nd row3390000
3rd row3390000
4th row3390000
5th row3660000
ValueCountFrequency (%)
4750000 18
 
2.3%
4751000 18
 
2.3%
3830000 15
 
1.9%
3030000 9
 
1.2%
4790000 9
 
1.2%
3490000 9
 
1.2%
4791000 9
 
1.2%
3560000 6
 
0.8%
3820000 6
 
0.8%
3230000 6
 
0.8%
Other values (257) 666
86.4%
2024-05-04T08:06:22.864264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3055
56.6%
4 486
 
9.0%
3 475
 
8.8%
5 357
 
6.6%
1 282
 
5.2%
7 172
 
3.2%
2 165
 
3.1%
6 139
 
2.6%
8 123
 
2.3%
9 123
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5377
99.6%
Uppercase Letter 20
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3055
56.8%
4 486
 
9.0%
3 475
 
8.8%
5 357
 
6.6%
1 282
 
5.2%
7 172
 
3.2%
2 165
 
3.1%
6 139
 
2.6%
8 123
 
2.3%
9 123
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
B 20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5377
99.6%
Latin 20
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3055
56.8%
4 486
 
9.0%
3 475
 
8.8%
5 357
 
6.6%
1 282
 
5.2%
7 172
 
3.2%
2 165
 
3.1%
6 139
 
2.6%
8 123
 
2.3%
9 123
 
2.3%
Latin
ValueCountFrequency (%)
B 20
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5397
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3055
56.6%
4 486
 
9.0%
3 475
 
8.8%
5 357
 
6.6%
1 282
 
5.2%
7 172
 
3.2%
2 165
 
3.1%
6 139
 
2.6%
8 123
 
2.3%
9 123
 
2.3%

제공기관명
Text

Distinct267
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Memory size6.2 KiB
2024-05-04T08:06:23.599991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.5732815
Min length5

Characters and Unicode

Total characters6610
Distinct characters141
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)11.5%

Sample

1st row부산광역시 사상구
2nd row부산광역시 사상구
3rd row부산광역시 사상구
4th row부산광역시 사상구
5th row대전광역시 서구
ValueCountFrequency (%)
경기도 118
 
7.8%
서울특별시 103
 
6.8%
강원특별자치도 59
 
3.9%
강원도 56
 
3.7%
전라북도 53
 
3.5%
전북특별자치도 53
 
3.5%
경상남도 50
 
3.3%
인천광역시 45
 
3.0%
경상북도 44
 
2.9%
부산광역시 41
 
2.7%
Other values (219) 898
59.1%
2024-05-04T08:06:24.607937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
749
 
11.3%
도 537
 
8.1%
시 535
 
8.1%
군 282
 
4.3%
구 257
 
3.9%
특 221
 
3.3%
별 221
 
3.3%
경 217
 
3.3%
북 191
 
2.9%
전 158
 
2.4%
Other values (131) 3242
49.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5861
88.7%
Space Separator 749
 
11.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 537
 
9.2%
시 535
 
9.1%
군 282
 
4.8%
구 257
 
4.4%
특 221
 
3.8%
별 221
 
3.8%
경 217
 
3.7%
북 191
 
3.3%
전 158
 
2.7%
광 156
 
2.7%
Other values (130) 3086
52.7%
Space Separator
ValueCountFrequency (%)
749
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5861
88.7%
Common 749
 
11.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 537
 
9.2%
시 535
 
9.1%
군 282
 
4.8%
구 257
 
4.4%
특 221
 
3.8%
별 221
 
3.8%
경 217
 
3.7%
북 191
 
3.3%
전 158
 
2.7%
광 156
 
2.7%
Other values (130) 3086
52.7%
Common
ValueCountFrequency (%)
749
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5861
88.7%
ASCII 749
 
11.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
749
100.0%
Hangul
ValueCountFrequency (%)
도 537
 
9.2%
시 535
 
9.1%
군 282
 
4.8%
구 257
 
4.4%
특 221
 
3.8%
별 221
 
3.8%
경 217
 
3.7%
북 191
 
3.3%
전 158
 
2.7%
광 156
 
2.7%
Other values (130) 3086
52.7%

Sample

시도명시군구명종량제봉투종류종량제봉투처리방식종량제봉투용도종량제봉투사용대상1ℓ가격1.5ℓ가격2ℓ가격2.5ℓ가격3ℓ가격5ℓ가격10ℓ가격20ℓ가격30ℓ가격50ℓ가격60ℓ가격75ℓ가격100ℓ가격120ℓ가격125ℓ가격관리부서명관리부서전화번호데이터기준일자제공기관코드제공기관명
0부산광역시사상구재사용규격봉투소각용생활쓰레기가정용000001703406800000000청소행정과051-310-43352023-06-273390000부산광역시 사상구
1부산광역시사상구규격봉투소각용생활쓰레기사업장용000000001460237003530000청소행정과051-310-43352023-06-273390000부산광역시 사상구
2부산광역시사상구규격봉투매립용생활쓰레기기타000000002000330000000청소행정과051-310-43352023-06-273390000부산광역시 사상구
3부산광역시사상구규격봉투매립용생활쓰레기기타000000001280000000청소행정과051-310-43352023-06-273390000부산광역시 사상구
4대전광역시서구규격봉투소각용생활쓰레기기타00001001703306600165002480000자원순환과042-288-35722023-06-273660000대전광역시 서구
5경상남도거창군규격봉투소각용생활쓰레기가정용00000100200400090001350000환경과055-940-35002023-06-275470000경상남도 거창군
6경상남도거창군재사용규격봉투소각용생활쓰레기가정용0000002004000000000환경과055-940-35002023-06-275470000경상남도 거창군
7경상남도거창군특수규격마대소각용생활쓰레기가정용000000000106000000환경과055-940-35002023-06-275470000경상남도 거창군
8울산광역시북구규격봉투소각용생활쓰레기가정용000001603106000149002220000자원순환과052-241-78042023-06-213720000울산광역시 북구
9경기도동두천시규격봉투소각용생활쓰레기가정용000001202505500125002000000경기도 동두천시청 환경보호과031-860-21002023-06-263920000경기도 동두천시
시도명시군구명종량제봉투종류종량제봉투처리방식종량제봉투용도종량제봉투사용대상1ℓ가격1.5ℓ가격2ℓ가격2.5ℓ가격3ℓ가격5ℓ가격10ℓ가격20ℓ가격30ℓ가격50ℓ가격60ℓ가격75ℓ가격100ℓ가격120ℓ가격125ℓ가격관리부서명관리부서전화번호데이터기준일자제공기관코드제공기관명
761서울특별시금천구특수규격마대매립용생활쓰레기가정용000000000510000000청소행정과02-2627-14932023-08-303170000서울특별시 금천구
762경상북도청도군재사용규격봉투소각용생활쓰레기가정용000008015029043071001060140000새마을환경과054-370-21932023-08-295190000경상북도 청도군
763강원특별자치도고성군규격봉투소각용생활쓰레기가정용000001402605000122000241000환경과033-680-39822023-08-294341000강원특별자치도 고성군
764강원특별자치도고성군규격봉투소각용음식물쓰레기가정용0060080120000000000환경과033-680-39822023-08-294341000강원특별자치도 고성군
765강원특별자치도고성군특수규격마대매립용생활쓰레기가정용000000000154000000환경과033-680-39822023-08-294341000강원특별자치도 고성군
766전라남도진도군규격봉투소각용생활쓰레기가정용000007014026039065000000환경수질과061-540-37242023-08-285000000전라남도 진도군
767전라북도전주시규격봉투소각용생활쓰레기가정용000001202304600114000000청소지원과063-281-23252023-08-224640000전라북도 전주시
768전라북도전주시규격봉투소각용음식물쓰레기가정용000000014000000000청소지원과063-281-23252023-08-224640000전라북도 전주시
769경기도화성시규격봉투소각용생활쓰레기가정용000001503006000150002250000자원순환과031-5189-68972023-10-195530000경기도 화성시
770경기도화성시규격봉투기타음식물쓰레기가정용6000018029058011700000000자원순환과031-5189-68972023-10-195530000경기도 화성시