Overview

Dataset statistics

Number of variables29
Number of observations411
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory101.3 KiB
Average record size in memory252.3 B

Variable types

Categorical11
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/15025451/standard.do

Alerts

납부필증유형 is highly imbalanced (52.5%)Imbalance
4ℓ가격 is highly imbalanced (97.5%)Imbalance
7ℓ가격 is highly imbalanced (96.9%)Imbalance
15ℓ가격 is highly imbalanced (91.1%)Imbalance
22ℓ가격 is highly imbalanced (93.6%)Imbalance
26ℓ가격 is highly imbalanced (97.5%)Imbalance
40ℓ가격 is highly imbalanced (91.8%)Imbalance
70ℓ가격 is highly imbalanced (93.6%)Imbalance
200ℓ가격 is highly imbalanced (96.3%)Imbalance
1ℓ가격 has 398 (96.8%) zerosZeros
2ℓ가격 has 383 (93.2%) zerosZeros
3ℓ가격 has 203 (49.4%) zerosZeros
5ℓ가격 has 146 (35.5%) zerosZeros
6ℓ가격 has 390 (94.9%) zerosZeros
10ℓ가격 has 305 (74.2%) zerosZeros
20ℓ가격 has 206 (50.1%) zerosZeros
25ℓ가격 has 393 (95.6%) zerosZeros
30ℓ가격 has 396 (96.4%) zerosZeros
50ℓ가격 has 391 (95.1%) zerosZeros
60ℓ가격 has 281 (68.4%) zerosZeros
120ℓ가격 has 146 (35.5%) zerosZeros

Reproduction

Analysis started2024-05-11 07:48:36.698744
Analysis finished2024-05-11 07:48:37.734303
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

Distinct18
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
전라북도
57 
전북특별자치도
39 
서울특별시
39 
부산광역시
35 
전라남도
34 
Other values (13)
207 

Length

Max length7
Median length5
Mean length4.5815085
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st row울산광역시
2nd row울산광역시
3rd row서울특별시
4th row전라북도
5th row서울특별시

Common Values

ValueCountFrequency (%)
전라북도 57
13.9%
전북특별자치도 39
9.5%
서울특별시 39
9.5%
부산광역시 35
8.5%
전라남도 34
8.3%
강원도 29
 
7.1%
경상남도 28
 
6.8%
경기도 25
 
6.1%
경상북도 24
 
5.8%
충청남도 20
 
4.9%
Other values (8) 81
19.7%

Length

2024-05-11T16:48:37.913252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라북도 57
13.9%
전북특별자치도 39
9.5%
서울특별시 39
9.5%
부산광역시 35
8.5%
전라남도 34
8.3%
강원도 29
 
7.1%
경상남도 28
 
6.8%
경기도 25
 
6.1%
경상북도 24
 
5.8%
충청남도 20
 
4.9%
Other values (8) 81
19.7%

시군구명
Text

Distinct161
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T16:48:38.776514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.8880779
Min length2

Characters and Unicode

Total characters1187
Distinct characters113
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

Unique71 ?
Unique (%)17.3%

Sample

1st row중구
2nd row중구
3rd row양천구
4th row완주군
5th row중랑구
ValueCountFrequency (%)
장수군 42
 
10.2%
동구 16
 
3.9%
중구 13
 
3.2%
서구 11
 
2.7%
고성군 11
 
2.7%
남구 9
 
2.2%
영월군 8
 
1.9%
김제시 8
 
1.9%
북구 8
 
1.9%
부안군 8
 
1.9%
Other values (151) 277
67.4%
2024-05-11T16:48:39.958261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 174
 
14.7%
구 136
 
11.5%
시 120
 
10.1%
수 54
 
4.5%
장 45
 
3.8%
성 36
 
3.0%
주 29
 
2.4%
산 27
 
2.3%
동 27
 
2.3%
영 25
 
2.1%
Other values (103) 514
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1187
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 174
 
14.7%
구 136
 
11.5%
시 120
 
10.1%
수 54
 
4.5%
장 45
 
3.8%
성 36
 
3.0%
주 29
 
2.4%
산 27
 
2.3%
동 27
 
2.3%
영 25
 
2.1%
Other values (103) 514
43.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1187
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 174
 
14.7%
구 136
 
11.5%
시 120
 
10.1%
수 54
 
4.5%
장 45
 
3.8%
성 36
 
3.0%
주 29
 
2.4%
산 27
 
2.3%
동 27
 
2.3%
영 25
 
2.1%
Other values (103) 514
43.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1187
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 174
 
14.7%
구 136
 
11.5%
시 120
 
10.1%
수 54
 
4.5%
장 45
 
3.8%
성 36
 
3.0%
주 29
 
2.4%
산 27
 
2.3%
동 27
 
2.3%
영 25
 
2.1%
Other values (103) 514
43.3%

납부필증사용대상
Categorical

Distinct31
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
단독주택
134 
사업장용
100 
공동주택
97 
기타
35 
단독주택+공동주택
 
9
Other values (26)
36 

Length

Max length20
Median length4
Mean length4.6399027
Min length2

Unique

Unique19 ?
Unique (%)4.6%

Sample

1st row공동주택
2nd row사업장용
3rd row사업장용+기타
4th row단독주택
5th row공동주택

Common Values

ValueCountFrequency (%)
단독주택 134
32.6%
사업장용 100
24.3%
공동주택 97
23.6%
기타 35
 
8.5%
단독주택+공동주택 9
 
2.2%
단독주택+공동주택+사업장용 5
 
1.2%
업소용 2
 
0.5%
사업장용+기타 2
 
0.5%
기타(음식물쓰레기 전용 수거함 신청자 2
 
0.5%
종량제지역 내 일반가정 및 사업장 2
 
0.5%
Other values (21) 23
 
5.6%

Length

2024-05-11T16:48:40.420767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단독주택 134
31.3%
사업장용 101
23.6%
공동주택 97
22.7%
기타 35
 
8.2%
단독주택+공동주택 10
 
2.3%
단독주택+공동주택+사업장용 5
 
1.2%
내 2
 
0.5%
사업자용 2
 
0.5%
단독주택+사업장용 2
 
0.5%
단독주택+공동주택+사업장용+기타 2
 
0.5%
Other values (28) 38
 
8.9%

납부필증유형
Categorical

IMBALANCE 

Distinct13
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
납부필증
241 
기타
90 
전용용기
54 
전용용기+납부필증
 
8
납부필증+기타
 
3
Other values (8)
 
15

Length

Max length12
Median length4
Mean length3.7858881
Min length2

Unique

Unique3 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
납부필증 241
58.6%
기타 90
 
21.9%
전용용기 54
 
13.1%
전용용기+납부필증 8
 
1.9%
납부필증+기타 3
 
0.7%
납부필증(칩) 3
 
0.7%
종량제 3
 
0.7%
기타(RFID) 2
 
0.5%
전용용기+납부필증+기타 2
 
0.5%
플라스틱칩 2
 
0.5%
Other values (3) 3
 
0.7%

Length

2024-05-11T16:48:40.922460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
납부필증 243
58.8%
기타 90
 
21.8%
전용용기 55
 
13.3%
전용용기+납부필증 8
 
1.9%
납부필증+기타 3
 
0.7%
납부필증(칩 3
 
0.7%
종량제 3
 
0.7%
기타(rfid 2
 
0.5%
전용용기+납부필증+기타 2
 
0.5%
플라스틱칩 2
 
0.5%
Other values (2) 2
 
0.5%

1ℓ가격
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3284672
Minimum0
Maximum140
Zeros398
Zeros (%)96.8%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:41.252174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.566974
Coefficient of variation (CV)6.2560357
Kurtosis48.017178
Mean2.3284672
Median Absolute Deviation (MAD)0
Skewness6.8394945
Sum957
Variance212.19672
MonotonicityNot monotonic
2024-05-11T16:48:41.601658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 398
96.8%
100 6
 
1.5%
20 2
 
0.5%
60 2
 
0.5%
30 1
 
0.2%
140 1
 
0.2%
27 1
 
0.2%
ValueCountFrequency (%)
0 398
96.8%
20 2
 
0.5%
27 1
 
0.2%
30 1
 
0.2%
60 2
 
0.5%
100 6
 
1.5%
140 1
 
0.2%
ValueCountFrequency (%)
140 1
 
0.2%
100 6
 
1.5%
60 2
 
0.5%
30 1
 
0.2%
27 1
 
0.2%
20 2
 
0.5%
0 398
96.8%

2ℓ가격
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.128954
Minimum0
Maximum4400
Zeros383
Zeros (%)93.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:41.826119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile40
Maximum4400
Range4400
Interquartile range (IQR)0

Descriptive statistics

Standard deviation218.60317
Coefficient of variation (CV)12.762202
Kurtosis396.91212
Mean17.128954
Median Absolute Deviation (MAD)0
Skewness19.762523
Sum7040
Variance47787.347
MonotonicityNot monotonic
2024-05-11T16:48:42.063749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 383
93.2%
40 8
 
1.9%
190 4
 
1.0%
60 3
 
0.7%
120 3
 
0.7%
80 3
 
0.7%
200 2
 
0.5%
70 1
 
0.2%
50 1
 
0.2%
100 1
 
0.2%
Other values (2) 2
 
0.5%
ValueCountFrequency (%)
0 383
93.2%
40 8
 
1.9%
50 1
 
0.2%
60 3
 
0.7%
70 1
 
0.2%
80 3
 
0.7%
100 1
 
0.2%
120 3
 
0.7%
160 1
 
0.2%
190 4
 
1.0%
ValueCountFrequency (%)
4400 1
 
0.2%
200 2
0.5%
190 4
1.0%
160 1
 
0.2%
120 3
0.7%
100 1
 
0.2%
80 3
0.7%
70 1
 
0.2%
60 3
0.7%
50 1
 
0.2%

3ℓ가격
Real number (ℝ)

ZEROS 

Distinct21
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.15572
Minimum0
Maximum6500
Zeros203
Zeros (%)49.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:42.456949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median50
Q3110
95-th percentile240
Maximum6500
Range6500
Interquartile range (IQR)110

Descriptive statistics

Standard deviation461.09606
Coefficient of variation (CV)4.3435819
Kurtosis126.40471
Mean106.15572
Median Absolute Deviation (MAD)50
Skewness10.790055
Sum43630
Variance212609.58
MonotonicityNot monotonic
2024-05-11T16:48:42.712227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 203
49.4%
70 56
 
13.6%
180 19
 
4.6%
120 19
 
4.6%
100 18
 
4.4%
240 16
 
3.9%
60 11
 
2.7%
110 10
 
2.4%
150 10
 
2.4%
90 9
 
2.2%
Other values (11) 40
 
9.7%
ValueCountFrequency (%)
0 203
49.4%
50 3
 
0.7%
60 11
 
2.7%
70 56
 
13.6%
80 7
 
1.7%
90 9
 
2.2%
100 18
 
4.4%
110 10
 
2.4%
120 19
 
4.6%
130 4
 
1.0%
ValueCountFrequency (%)
6500 1
 
0.2%
4730 1
 
0.2%
3400 2
 
0.5%
300 7
 
1.7%
240 16
3.9%
210 9
2.2%
200 3
 
0.7%
180 19
4.6%
170 1
 
0.2%
150 10
2.4%

4ℓ가격
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
410 
160
 
1

Length

Max length3
Median length1
Mean length1.0048662
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 410
99.8%
160 1
 
0.2%

Length

2024-05-11T16:48:43.057382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:43.471436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 410
99.8%
160 1
 
0.2%

5ℓ가격
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.48905
Minimum0
Maximum7000
Zeros146
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:43.779413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3200
95-th percentile600
Maximum7000
Range7000
Interquartile range (IQR)200

Descriptive statistics

Standard deviation546.685
Coefficient of variation (CV)2.8108781
Kurtosis93.82414
Mean194.48905
Median Absolute Deviation (MAD)100
Skewness9.1315095
Sum79935
Variance298864.49
MonotonicityNot monotonic
2024-05-11T16:48:44.225649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 146
35.5%
90 49
 
11.9%
200 28
 
6.8%
300 21
 
5.1%
150 21
 
5.1%
400 20
 
4.9%
700 17
 
4.1%
100 16
 
3.9%
250 12
 
2.9%
120 10
 
2.4%
Other values (20) 71
17.3%
ValueCountFrequency (%)
0 146
35.5%
70 3
 
0.7%
80 6
 
1.5%
85 1
 
0.2%
90 49
 
11.9%
100 16
 
3.9%
110 10
 
2.4%
120 10
 
2.4%
130 8
 
1.9%
140 5
 
1.2%
ValueCountFrequency (%)
7000 1
 
0.2%
5060 1
 
0.2%
4650 2
 
0.5%
700 17
4.1%
500 7
 
1.7%
400 20
4.9%
350 4
 
1.0%
340 2
 
0.5%
330 1
 
0.2%
300 21
5.1%

6ℓ가격
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.586375
Minimum0
Maximum1000
Zeros390
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:44.509924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile50
Maximum1000
Range1000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation100.69793
Coefficient of variation (CV)5.1412233
Kurtosis41.505039
Mean19.586375
Median Absolute Deviation (MAD)0
Skewness6.1428918
Sum8050
Variance10140.072
MonotonicityNot monotonic
2024-05-11T16:48:44.865156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 390
94.9%
420 5
 
1.2%
660 4
 
1.0%
110 3
 
0.7%
210 3
 
0.7%
260 2
 
0.5%
600 1
 
0.2%
130 1
 
0.2%
100 1
 
0.2%
1000 1
 
0.2%
ValueCountFrequency (%)
0 390
94.9%
100 1
 
0.2%
110 3
 
0.7%
130 1
 
0.2%
210 3
 
0.7%
260 2
 
0.5%
420 5
 
1.2%
600 1
 
0.2%
660 4
 
1.0%
1000 1
 
0.2%
ValueCountFrequency (%)
1000 1
 
0.2%
660 4
 
1.0%
600 1
 
0.2%
420 5
 
1.2%
260 2
 
0.5%
210 3
 
0.7%
130 1
 
0.2%
110 3
 
0.7%
100 1
 
0.2%
0 390
94.9%

7ℓ가격
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
409 
450
 
1
310
 
1

Length

Max length3
Median length1
Mean length1.0097324
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 409
99.5%
450 1
 
0.2%
310 1
 
0.2%

Length

2024-05-11T16:48:45.164878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:45.443687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 409
99.5%
450 1
 
0.2%
310 1
 
0.2%

10ℓ가격
Real number (ℝ)

ZEROS 

Distinct30
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.14112
Minimum0
Maximum5880
Zeros305
Zeros (%)74.2%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:45.694914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3145
95-th percentile1400
Maximum5880
Range5880
Interquartile range (IQR)145

Descriptive statistics

Standard deviation524.19489
Coefficient of variation (CV)3.0451463
Kurtosis68.267602
Mean172.14112
Median Absolute Deviation (MAD)0
Skewness7.0414532
Sum70750
Variance274780.28
MonotonicityNot monotonic
2024-05-11T16:48:45.983946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 305
74.2%
1400 22
 
5.4%
350 10
 
2.4%
400 7
 
1.7%
270 7
 
1.7%
200 6
 
1.5%
150 6
 
1.5%
600 4
 
1.0%
500 4
 
1.0%
230 4
 
1.0%
Other values (20) 36
 
8.8%
ValueCountFrequency (%)
0 305
74.2%
130 2
 
0.5%
140 1
 
0.2%
150 6
 
1.5%
170 3
 
0.7%
180 2
 
0.5%
200 6
 
1.5%
210 2
 
0.5%
220 2
 
0.5%
230 4
 
1.0%
ValueCountFrequency (%)
5880 2
 
0.5%
1400 22
5.4%
1000 2
 
0.5%
850 1
 
0.2%
700 4
 
1.0%
600 4
 
1.0%
500 4
 
1.0%
490 1
 
0.2%
440 1
 
0.2%
410 1
 
0.2%

15ℓ가격
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
401 
1650
 
5
660
 
3
540
 
1
940
 
1

Length

Max length4
Median length1
Mean length1.0608273
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 401
97.6%
1650 5
 
1.2%
660 3
 
0.7%
540 1
 
0.2%
940 1
 
0.2%

Length

2024-05-11T16:48:46.263190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:46.488135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 401
97.6%
1650 5
 
1.2%
660 3
 
0.7%
540 1
 
0.2%
940 1
 
0.2%

20ℓ가격
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.04623
Minimum0
Maximum16000
Zeros206
Zeros (%)50.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:46.808002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3810
95-th percentile2800
Maximum16000
Range16000
Interquartile range (IQR)810

Descriptive statistics

Standard deviation1546.8907
Coefficient of variation (CV)2.3051925
Kurtosis57.727944
Mean671.04623
Median Absolute Deviation (MAD)0
Skewness6.8088755
Sum275800
Variance2392870.9
MonotonicityNot monotonic
2024-05-11T16:48:47.192648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 206
50.1%
2000 18
 
4.4%
1200 17
 
4.1%
2800 14
 
3.4%
700 13
 
3.2%
810 13
 
3.2%
1000 11
 
2.7%
400 9
 
2.2%
600 8
 
1.9%
350 8
 
1.9%
Other values (43) 94
22.9%
ValueCountFrequency (%)
0 206
50.1%
240 2
 
0.5%
280 2
 
0.5%
300 1
 
0.2%
320 4
 
1.0%
330 1
 
0.2%
340 5
 
1.2%
350 8
 
1.9%
360 1
 
0.2%
370 2
 
0.5%
ValueCountFrequency (%)
16000 1
 
0.2%
15000 1
 
0.2%
12750 2
 
0.5%
3200 4
 
1.0%
2800 14
3.4%
2400 3
 
0.7%
2200 3
 
0.7%
2000 18
4.4%
1680 1
 
0.2%
1600 4
 
1.0%

22ℓ가격
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
405 
300
 
4
1400
 
1
770
 
1

Length

Max length4
Median length1
Mean length1.0316302
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 405
98.5%
300 4
 
1.0%
1400 1
 
0.2%
770 1
 
0.2%

Length

2024-05-11T16:48:47.495226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:47.734406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 405
98.5%
300 4
 
1.0%
1400 1
 
0.2%
770 1
 
0.2%

25ℓ가격
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.38443
Minimum0
Maximum3500
Zeros393
Zeros (%)95.6%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:47.920615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum3500
Range3500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation580.54403
Coefficient of variation (CV)5.1656981
Kurtosis28.288298
Mean112.38443
Median Absolute Deviation (MAD)0
Skewness5.4059772
Sum46190
Variance337031.37
MonotonicityNot monotonic
2024-05-11T16:48:48.354667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 393
95.6%
3500 10
 
2.4%
1100 3
 
0.7%
500 1
 
0.2%
3320 1
 
0.2%
850 1
 
0.2%
1430 1
 
0.2%
1790 1
 
0.2%
ValueCountFrequency (%)
0 393
95.6%
500 1
 
0.2%
850 1
 
0.2%
1100 3
 
0.7%
1430 1
 
0.2%
1790 1
 
0.2%
3320 1
 
0.2%
3500 10
 
2.4%
ValueCountFrequency (%)
3500 10
 
2.4%
3320 1
 
0.2%
1790 1
 
0.2%
1430 1
 
0.2%
1100 3
 
0.7%
850 1
 
0.2%
500 1
 
0.2%
0 393
95.6%

26ℓ가격
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
410 
1660
 
1

Length

Max length4
Median length1
Mean length1.0072993
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
0 410
99.8%
1660 1
 
0.2%

Length

2024-05-11T16:48:48.778893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:49.056386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 410
99.8%
1660 1
 
0.2%

30ℓ가격
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.226277
Minimum0
Maximum4200
Zeros396
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:49.281011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4200
Range4200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation309.40319
Coefficient of variation (CV)6.551505
Kurtosis104.46359
Mean47.226277
Median Absolute Deviation (MAD)0
Skewness9.3784605
Sum19410
Variance95730.336
MonotonicityNot monotonic
2024-05-11T16:48:49.496328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 396
96.4%
1000 8
 
1.9%
500 2
 
0.5%
550 1
 
0.2%
560 1
 
0.2%
4200 1
 
0.2%
3000 1
 
0.2%
2100 1
 
0.2%
ValueCountFrequency (%)
0 396
96.4%
500 2
 
0.5%
550 1
 
0.2%
560 1
 
0.2%
1000 8
 
1.9%
2100 1
 
0.2%
3000 1
 
0.2%
4200 1
 
0.2%
ValueCountFrequency (%)
4200 1
 
0.2%
3000 1
 
0.2%
2100 1
 
0.2%
1000 8
 
1.9%
560 1
 
0.2%
550 1
 
0.2%
500 2
 
0.5%
0 396
96.4%

40ℓ가격
Categorical

IMBALANCE 

Distinct5
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
402 
5600
 
4
1600
 
3
2590
 
1
2400
 
1

Length

Max length4
Median length1
Mean length1.0656934
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 402
97.8%
5600 4
 
1.0%
1600 3
 
0.7%
2590 1
 
0.2%
2400 1
 
0.2%

Length

2024-05-11T16:48:49.818727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:50.126921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 402
97.8%
5600 4
 
1.0%
1600 3
 
0.7%
2590 1
 
0.2%
2400 1
 
0.2%

50ℓ가격
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.036496
Minimum0
Maximum1600
Zeros391
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:50.331583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1600
Range1600
Interquartile range (IQR)0

Descriptive statistics

Standard deviation259.60887
Coefficient of variation (CV)4.7170312
Kurtosis24.815911
Mean55.036496
Median Absolute Deviation (MAD)0
Skewness4.9935857
Sum22620
Variance67396.767
MonotonicityNot monotonic
2024-05-11T16:48:50.672010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 391
95.1%
1600 8
 
1.9%
750 4
 
1.0%
830 2
 
0.5%
600 2
 
0.5%
1130 1
 
0.2%
980 1
 
0.2%
1150 1
 
0.2%
700 1
 
0.2%
ValueCountFrequency (%)
0 391
95.1%
600 2
 
0.5%
700 1
 
0.2%
750 4
 
1.0%
830 2
 
0.5%
980 1
 
0.2%
1130 1
 
0.2%
1150 1
 
0.2%
1600 8
 
1.9%
ValueCountFrequency (%)
1600 8
 
1.9%
1150 1
 
0.2%
1130 1
 
0.2%
980 1
 
0.2%
830 2
 
0.5%
750 4
 
1.0%
700 1
 
0.2%
600 2
 
0.5%
0 391
95.1%

60ℓ가격
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1217.3698
Minimum0
Maximum40000
Zeros281
Zeros (%)68.4%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:51.505558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31300
95-th percentile8400
Maximum40000
Range40000
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation2972.6017
Coefficient of variation (CV)2.441823
Kurtosis71.179203
Mean1217.3698
Median Absolute Deviation (MAD)0
Skewness6.4384643
Sum500339
Variance8836360.7
MonotonicityNot monotonic
2024-05-11T16:48:51.882996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 281
68.4%
8400 17
 
4.1%
1400 7
 
1.7%
6000 7
 
1.7%
1200 7
 
1.7%
3600 6
 
1.5%
2400 5
 
1.2%
3000 5
 
1.2%
1800 4
 
1.0%
960 4
 
1.0%
Other values (42) 68
 
16.5%
ValueCountFrequency (%)
0 281
68.4%
849 1
 
0.2%
960 4
 
1.0%
1000 2
 
0.5%
1020 2
 
0.5%
1050 3
 
0.7%
1100 1
 
0.2%
1110 2
 
0.5%
1170 2
 
0.5%
1190 1
 
0.2%
ValueCountFrequency (%)
40000 1
 
0.2%
9600 4
 
1.0%
8400 17
4.1%
7980 1
 
0.2%
7200 3
 
0.7%
6600 3
 
0.7%
6000 7
1.7%
4800 3
 
0.7%
3850 1
 
0.2%
3600 6
 
1.5%

70ℓ가격
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
405 
6888
 
4
7000
 
1
9800
 
1

Length

Max length4
Median length1
Mean length1.0437956
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
0 405
98.5%
6888 4
 
1.0%
7000 1
 
0.2%
9800 1
 
0.2%

Length

2024-05-11T16:48:52.302906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:52.652593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 405
98.5%
6888 4
 
1.0%
7000 1
 
0.2%
9800 1
 
0.2%

120ℓ가격
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4784.2044
Minimum0
Maximum80000
Zeros146
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size3.7 KiB
2024-05-11T16:48:53.060502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2800
Q37200
95-th percentile16800
Maximum80000
Range80000
Interquartile range (IQR)7200

Descriptive statistics

Standard deviation6730.5005
Coefficient of variation (CV)1.4068171
Kurtosis44.220436
Mean4784.2044
Median Absolute Deviation (MAD)2800
Skewness4.8326187
Sum1966308
Variance45299636
MonotonicityNot monotonic
2024-05-11T16:48:53.671811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 146
35.5%
12000 28
 
6.8%
16800 21
 
5.1%
7200 20
 
4.9%
4880 14
 
3.4%
6000 13
 
3.2%
3600 12
 
2.9%
2400 11
 
2.7%
4800 8
 
1.9%
8400 7
 
1.7%
Other values (67) 131
31.9%
ValueCountFrequency (%)
0 146
35.5%
1500 2
 
0.5%
1600 1
 
0.2%
1680 2
 
0.5%
1700 4
 
1.0%
1800 2
 
0.5%
1920 4
 
1.0%
2000 1
 
0.2%
2040 2
 
0.5%
2100 3
 
0.7%
ValueCountFrequency (%)
80000 1
 
0.2%
55000 1
 
0.2%
19200 3
 
0.7%
16800 21
5.1%
15950 1
 
0.2%
14400 3
 
0.7%
13200 4
 
1.0%
12000 28
6.8%
11808 4
 
1.0%
11040 1
 
0.2%

200ℓ가격
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
0
408 
3400
 
1
6050
 
1
20000
 
1

Length

Max length5
Median length1
Mean length1.0243309
Min length1

Unique

Unique3 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
0 408
99.3%
3400 1
 
0.2%
6050 1
 
0.2%
20000 1
 
0.2%

Length

2024-05-11T16:48:54.186735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:48:54.577126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 408
99.3%
3400 1
 
0.2%
6050 1
 
0.2%
20000 1
 
0.2%

관리부서명
Text

Distinct72
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T16:48:55.171911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length5
Mean length6.0656934
Min length3

Characters and Unicode

Total characters2493
Distinct characters94
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

Unique22 ?
Unique (%)5.4%

Sample

1st row경영사업팀
2nd row경영사업팀
3rd row청소행정과
4th row전라북도 완주군청 자원순환과
5th row서울특별시 중랑구청 청소행정과
ValueCountFrequency (%)
자원순환과 112
21.3%
환경과 74
 
14.1%
청소행정과 50
 
9.5%
환경위생과 18
 
3.4%
청소자원과 18
 
3.4%
전라북도 18
 
3.4%
환경보호과 12
 
2.3%
전북특별자치도 12
 
2.3%
청소과 9
 
1.7%
부안군청 8
 
1.5%
Other values (74) 194
37.0%
2024-05-11T16:48:56.350998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 346
 
13.9%
환 256
 
10.3%
원 164
 
6.6%
자 156
 
6.3%
청 155
 
6.2%
경 149
 
6.0%
순 115
 
4.6%
114
 
4.6%
소 112
 
4.5%
정 61
 
2.4%
Other values (84) 865
34.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2379
95.4%
Space Separator 114
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 346
14.5%
환 256
 
10.8%
원 164
 
6.9%
자 156
 
6.6%
청 155
 
6.5%
경 149
 
6.3%
순 115
 
4.8%
소 112
 
4.7%
정 61
 
2.6%
행 57
 
2.4%
Other values (83) 808
34.0%
Space Separator
ValueCountFrequency (%)
114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2379
95.4%
Common 114
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 346
14.5%
환 256
 
10.8%
원 164
 
6.9%
자 156
 
6.6%
청 155
 
6.5%
경 149
 
6.3%
순 115
 
4.8%
소 112
 
4.7%
정 61
 
2.6%
행 57
 
2.4%
Other values (83) 808
34.0%
Common
ValueCountFrequency (%)
114
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2379
95.4%
ASCII 114
 
4.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 346
14.5%
환 256
 
10.8%
원 164
 
6.9%
자 156
 
6.6%
청 155
 
6.5%
경 149
 
6.3%
순 115
 
4.8%
소 112
 
4.7%
정 61
 
2.6%
행 57
 
2.4%
Other values (83) 808
34.0%
ASCII
ValueCountFrequency (%)
114
100.0%

관리부서전화번호
Text

Distinct199
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T16:48:56.839587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.961071
Min length9

Characters and Unicode

Total characters4916
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

Unique87 ?
Unique (%)21.2%

Sample

1st row052-290-7657
2nd row052-290-7657
3rd row02-2620-3420
4th row063-290-2673
5th row2094-1930
ValueCountFrequency (%)
033-680-3982 8
 
1.9%
063-540-3165 8
 
1.9%
063-580-4357 8
 
1.9%
033-370-2335 8
 
1.9%
063-454-3463 6
 
1.5%
063-350-1513 6
 
1.5%
063-281-8409 6
 
1.5%
063-620-6917 6
 
1.5%
063-350-1411 6
 
1.5%
063-350-1462 6
 
1.5%
Other values (189) 343
83.5%
2024-05-11T16:48:57.705294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 820
16.7%
0 756
15.4%
3 652
13.3%
5 488
9.9%
6 447
9.1%
4 438
8.9%
2 415
8.4%
1 355
7.2%
7 204
 
4.1%
8 171
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4096
83.3%
Dash Punctuation 820
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 756
18.5%
3 652
15.9%
5 488
11.9%
6 447
10.9%
4 438
10.7%
2 415
10.1%
1 355
8.7%
7 204
 
5.0%
8 171
 
4.2%
9 170
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 820
16.7%
0 756
15.4%
3 652
13.3%
5 488
9.9%
6 447
9.1%
4 438
8.9%
2 415
8.4%
1 355
7.2%
7 204
 
4.1%
8 171
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 820
16.7%
0 756
15.4%
3 652
13.3%
5 488
9.9%
6 447
9.1%
4 438
8.9%
2 415
8.4%
1 355
7.2%
7 204
 
4.1%
8 171
 
3.5%

데이터기준일자
Date

Distinct140
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2020-07-07 00:00:00
Maximum2024-04-26 00:00:00
2024-05-11T16:48:57.982248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:48:58.334713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

제공기관코드
Text

Distinct213
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T16:48:58.949914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2877
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

Unique113 ?
Unique (%)27.5%

Sample

1st rowB553013
2nd rowB553013
3rd row3140000
4th row4720000
5th row3060000
ValueCountFrequency (%)
4750000 21
 
5.1%
4751000 21
 
5.1%
3260000 5
 
1.2%
4900000 4
 
1.0%
4271000 4
 
1.0%
3670000 4
 
1.0%
4341000 4
 
1.0%
4270000 4
 
1.0%
4020000 4
 
1.0%
4990000 4
 
1.0%
Other values (203) 336
81.8%
2024-05-11T16:49:00.065550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1637
56.9%
4 287
 
10.0%
3 226
 
7.9%
5 173
 
6.0%
7 135
 
4.7%
1 135
 
4.7%
2 88
 
3.1%
6 83
 
2.9%
9 63
 
2.2%
8 45
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2872
99.8%
Uppercase Letter 5
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1637
57.0%
4 287
 
10.0%
3 226
 
7.9%
5 173
 
6.0%
7 135
 
4.7%
1 135
 
4.7%
2 88
 
3.1%
6 83
 
2.9%
9 63
 
2.2%
8 45
 
1.6%
Uppercase Letter
ValueCountFrequency (%)
B 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2872
99.8%
Latin 5
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1637
57.0%
4 287
 
10.0%
3 226
 
7.9%
5 173
 
6.0%
7 135
 
4.7%
1 135
 
4.7%
2 88
 
3.1%
6 83
 
2.9%
9 63
 
2.2%
8 45
 
1.6%
Latin
ValueCountFrequency (%)
B 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2877
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1637
56.9%
4 287
 
10.0%
3 226
 
7.9%
5 173
 
6.0%
7 135
 
4.7%
1 135
 
4.7%
2 88
 
3.1%
6 83
 
2.9%
9 63
 
2.2%
8 45
 
1.6%

제공기관명
Text

Distinct213
Distinct (%)51.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2024-05-11T16:49:00.577605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.6350365
Min length6

Characters and Unicode

Total characters3549
Distinct characters121
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

Unique113 ?
Unique (%)27.5%

Sample

1st row울산광역시중구도시관리공단
2nd row울산광역시중구도시관리공단
3rd row서울특별시 양천구
4th row전라북도 완주군
5th row서울특별시 중랑구
ValueCountFrequency (%)
전라북도 48
 
5.9%
전북특별자치도 48
 
5.9%
장수군 42
 
5.1%
서울특별시 39
 
4.8%
부산광역시 35
 
4.3%
전라남도 34
 
4.2%
경상남도 28
 
3.4%
경기도 23
 
2.8%
경상북도 23
 
2.8%
강원특별자치도 21
 
2.6%
Other values (171) 475
58.2%
2024-05-11T16:49:01.387259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
405
 
11.4%
도 279
 
7.9%
시 262
 
7.4%
군 174
 
4.9%
구 151
 
4.3%
전 149
 
4.2%
북 136
 
3.8%
광 120
 
3.4%
특 109
 
3.1%
별 109
 
3.1%
Other values (111) 1655
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3144
88.6%
Space Separator 405
 
11.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 279
 
8.9%
시 262
 
8.3%
군 174
 
5.5%
구 151
 
4.8%
전 149
 
4.7%
북 136
 
4.3%
광 120
 
3.8%
특 109
 
3.5%
별 109
 
3.5%
남 105
 
3.3%
Other values (110) 1550
49.3%
Space Separator
ValueCountFrequency (%)
405
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3144
88.6%
Common 405
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 279
 
8.9%
시 262
 
8.3%
군 174
 
5.5%
구 151
 
4.8%
전 149
 
4.7%
북 136
 
4.3%
광 120
 
3.8%
특 109
 
3.5%
별 109
 
3.5%
남 105
 
3.3%
Other values (110) 1550
49.3%
Common
ValueCountFrequency (%)
405
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3144
88.6%
ASCII 405
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
405
100.0%
Hangul
ValueCountFrequency (%)
도 279
 
8.9%
시 262
 
8.3%
군 174
 
5.5%
구 151
 
4.8%
전 149
 
4.7%
북 136
 
4.3%
광 120
 
3.8%
특 109
 
3.5%
별 109
 
3.5%
남 105
 
3.3%
Other values (110) 1550
49.3%

Sample

시도명시군구명납부필증사용대상납부필증유형1ℓ가격2ℓ가격3ℓ가격4ℓ가격5ℓ가격6ℓ가격7ℓ가격10ℓ가격15ℓ가격20ℓ가격22ℓ가격25ℓ가격26ℓ가격30ℓ가격40ℓ가격50ℓ가격60ℓ가격70ℓ가격120ℓ가격200ℓ가격관리부서명관리부서전화번호데이터기준일자제공기관코드제공기관명
0울산광역시중구공동주택납부필증00003000000240000000072000144000경영사업팀052-290-76572020-07-07B553013울산광역시중구도시관리공단
1울산광역시중구사업장용납부필증000000000240000000072000144000경영사업팀052-290-76572020-07-07B553013울산광역시중구도시관리공단
2서울특별시양천구사업장용+기타납부필증00007000014000003500000084000168000청소행정과02-2620-34202023-08-153140000서울특별시 양천구
3전라북도완주군단독주택납부필증001300026000000000000000전라북도 완주군청 자원순환과063-290-26732023-08-174720000전라북도 완주군
4서울특별시중랑구공동주택납부필증000000000000000000120000서울특별시 중랑구청 청소행정과2094-19302023-08-163060000서울특별시 중랑구
5서울특별시중랑구사업장용납부필증00007000014000003500000000168000서울특별시 중랑구청 청소행정과2094-19302023-08-163060000서울특별시 중랑구
6전라남도강진군단독주택+공동주택 +사업자용전용용기+납부필증000001100000000550001100022000환경축산과061-430-39672023-08-114920000전라남도 강진군
7충청남도아산시기타기타20406001000018003500000001050021000충청남도 아산시 자원순환과041-540-20702023-08-014520000충청남도 아산시
8강원특별자치도양양군공동주택기타00009000170034000050008300000환경과033-670-21842023-08-104351000강원특별자치도 양양군
9서울특별시성동구공동주택납부필증000000000000000000120000청소행정과02-2286-55422023-08-083030000서울특별시 성동구
시도명시군구명납부필증사용대상납부필증유형1ℓ가격2ℓ가격3ℓ가격4ℓ가격5ℓ가격6ℓ가격7ℓ가격10ℓ가격15ℓ가격20ℓ가격22ℓ가격25ℓ가격26ℓ가격30ℓ가격40ℓ가격50ℓ가격60ℓ가격70ℓ가격120ℓ가격200ℓ가격관리부서명관리부서전화번호데이터기준일자제공기관코드제공기관명
401서울특별시송파구사업장용납부필증00007000014000280000000009800168000자원순환과02-2147-28402023-06-303230000서울특별시 송파구
402경상남도창녕군단독주택+공동주택+사업장용납부필증0090015000006000000001800036000환경위생과055-530-16242023-07-215410000경상남도 창녕군
403전라북도전주시단독주택전용용기002100350007000140000000006888118080전라북도 전주시청 청소지원과063-281-84092023-08-294640000전라북도 전주시
404전라북도전주시사업장용전용용기002100350007000140000000006888118080전라북도 전주시청 청소지원과063-281-84092023-08-294640000전라북도 전주시
405전라북도남원시공동주택납부필증0010001500027005100000001400028000전라북도 남원시청 환경과063-620-69172023-12-294701000전북특별자치도 남원시
406전라북도남원시사업장용납부필증0010001500027005100000001400028000전라북도 남원시청 환경과063-620-69172023-12-294701000전북특별자치도 남원시
407인천광역시연수구단독주택전용용기00180030000600000143000000068300청소행정과032-749-78632024-04-023520000인천광역시 연수구
408인천광역시연수구사업장용전용용기00000000000179000000085300청소행정과032-749-78632024-04-023520000인천광역시 연수구
409서울특별시동대문구공동주택납부필증000000000000000000120000서울특별시 동대문구청02-2127-47312024-03-223050000서울특별시 동대문구
410서울특별시동대문구사업장용납부필증00007000014000003500000084000168000서울특별시 동대문구청02-2127-47312024-03-223050000서울특별시 동대문구