Overview

Dataset statistics

Number of variables15
Number of observations3728
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory484.3 KiB
Average record size in memory133.0 B

Variable types

Numeric13
Categorical1
Text1

Dataset

Description연도(2007~2022)별, 공업용 지하수 정보제공를 제공합니다.제공정보- 연도, 시도, 시군구, 총계(개소수, 이용량), 국가공단(개소수, 이용량), 지방공단(개소수, 이용량), 농공단지(개소수, 이용량), 자유입지업체(개소수, 이용량), 기타(개소수, 이용량)
Author한국수자원공사
URLhttps://www.data.go.kr/data/15054542/fileData.do

Alerts

총계-개소수(개소) is highly overall correlated with 총계-이용량(톤) and 8 other fieldsHigh correlation
총계-이용량(톤) is highly overall correlated with 총계-개소수(개소) and 8 other fieldsHigh correlation
국가공단-개소수(개소) is highly overall correlated with 국가공단-이용량(톤)High correlation
국가공단-이용량(톤) is highly overall correlated with 국가공단-개소수(개소)High correlation
지방공단-개소수(개소) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
지방공단-이용량(톤) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
농공단지-개소수(개소) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
농공단지-이용량(톤) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
자유입지업체-개소수(개소) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
자유입지업체-이용량(톤) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
기타-개소수(개소) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
기타-이용량(톤) is highly overall correlated with 총계-개소수(개소) and 2 other fieldsHigh correlation
국가공단-개소수(개소) has 3020 (81.0%) zerosZeros
국가공단-이용량(톤) has 3051 (81.8%) zerosZeros
지방공단-개소수(개소) has 2034 (54.6%) zerosZeros
지방공단-이용량(톤) has 2082 (55.8%) zerosZeros
농공단지-개소수(개소) has 1796 (48.2%) zerosZeros
농공단지-이용량(톤) has 1834 (49.2%) zerosZeros
자유입지업체-개소수(개소) has 584 (15.7%) zerosZeros
자유입지업체-이용량(톤) has 609 (16.3%) zerosZeros
기타-개소수(개소) has 173 (4.6%) zerosZeros
기타-이용량(톤) has 188 (5.0%) zerosZeros

Reproduction

Analysis started2024-05-11 10:10:18.691497
Analysis finished2024-05-11 10:11:25.044567
Duration1 minute and 6.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct17
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.9531
Minimum2007
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:25.291287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12011
median2015
Q32019
95-th percentile2023
Maximum2023
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.9032711
Coefficient of variation (CV)0.0024334418
Kurtosis-1.2107359
Mean2014.9531
Median Absolute Deviation (MAD)4
Skewness0.013048994
Sum7511745
Variance24.042067
MonotonicityIncreasing
2024-05-11T10:11:25.816940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2009 224
 
6.0%
2010 223
 
6.0%
2007 222
 
6.0%
2008 222
 
6.0%
2011 221
 
5.9%
2012 220
 
5.9%
2013 220
 
5.9%
2015 219
 
5.9%
2016 219
 
5.9%
2014 218
 
5.8%
Other values (7) 1520
40.8%
ValueCountFrequency (%)
2007 222
6.0%
2008 222
6.0%
2009 224
6.0%
2010 223
6.0%
2011 221
5.9%
2012 220
5.9%
2013 220
5.9%
2014 218
5.8%
2015 219
5.9%
2016 219
5.9%
ValueCountFrequency (%)
2023 217
5.8%
2022 217
5.8%
2021 217
5.8%
2020 217
5.8%
2019 217
5.8%
2018 217
5.8%
2017 218
5.8%
2016 219
5.9%
2015 219
5.9%
2014 218
5.8%

시도
Categorical

Distinct19
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size29.3 KiB
경기도
514 
경상북도
390 
전라남도
374 
서울특별시
367 
경상남도
311 
Other values (14)
1772 

Length

Max length7
Median length5
Mean length4.1413627
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 514
13.8%
경상북도 390
10.5%
전라남도 374
10.0%
서울특별시 367
9.8%
경상남도 311
8.3%
강원도 288
7.7%
충청남도 260
7.0%
부산광역시 242
6.5%
전라북도 224
 
6.0%
충청북도 194
 
5.2%
Other values (9) 564
15.1%

Length

2024-05-11T10:11:26.512107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 514
13.8%
경상북도 390
10.5%
전라남도 374
10.0%
서울특별시 367
9.8%
경상남도 311
8.3%
강원도 288
7.7%
충청남도 260
7.0%
부산광역시 242
6.5%
전라북도 224
 
6.0%
충청북도 194
 
5.2%
Other values (9) 564
15.1%
Distinct209
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size29.3 KiB
2024-05-11T10:11:27.363078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9525215
Min length2

Characters and Unicode

Total characters11007
Distinct characters135
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

Unique0 ?
Unique (%)0.0%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
서구 85
 
2.3%
동구 71
 
1.9%
남구 71
 
1.9%
북구 67
 
1.8%
중구 60
 
1.6%
고성군 34
 
0.9%
강서구 34
 
0.9%
옹진군 17
 
0.5%
나주시 17
 
0.5%
부평구 17
 
0.5%
Other values (199) 3255
87.3%
2024-05-11T10:11:29.108428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1465
 
13.3%
1321
 
12.0%
1095
 
9.9%
361
 
3.3%
340
 
3.1%
306
 
2.8%
306
 
2.8%
275
 
2.5%
241
 
2.2%
221
 
2.0%
Other values (125) 5076
46.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11007
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1465
 
13.3%
1321
 
12.0%
1095
 
9.9%
361
 
3.3%
340
 
3.1%
306
 
2.8%
306
 
2.8%
275
 
2.5%
241
 
2.2%
221
 
2.0%
Other values (125) 5076
46.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11007
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1465
 
13.3%
1321
 
12.0%
1095
 
9.9%
361
 
3.3%
340
 
3.1%
306
 
2.8%
306
 
2.8%
275
 
2.5%
241
 
2.2%
221
 
2.0%
Other values (125) 5076
46.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11007
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1465
 
13.3%
1321
 
12.0%
1095
 
9.9%
361
 
3.3%
340
 
3.1%
306
 
2.8%
306
 
2.8%
275
 
2.5%
241
 
2.2%
221
 
2.0%
Other values (125) 5076
46.1%

총계-개소수(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct309
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.970762
Minimum0
Maximum677
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:29.654562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q113
median36
Q375
95-th percentile207
Maximum677
Range677
Interquartile range (IQR)62

Descriptive statistics

Standard deviation79.395374
Coefficient of variation (CV)1.3021877
Kurtosis19.262426
Mean60.970762
Median Absolute Deviation (MAD)26
Skewness3.5956126
Sum227299
Variance6303.6254
MonotonicityNot monotonic
2024-05-11T10:11:30.131611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 101
 
2.7%
2 95
 
2.5%
1 93
 
2.5%
5 81
 
2.2%
10 78
 
2.1%
7 74
 
2.0%
11 69
 
1.9%
13 69
 
1.9%
9 67
 
1.8%
8 64
 
1.7%
Other values (299) 2937
78.8%
ValueCountFrequency (%)
0 2
 
0.1%
1 93
2.5%
2 95
2.5%
3 51
1.4%
4 101
2.7%
5 81
2.2%
6 58
1.6%
7 74
2.0%
8 64
1.7%
9 67
1.8%
ValueCountFrequency (%)
677 1
 
< 0.1%
652 1
 
< 0.1%
651 1
 
< 0.1%
645 1
 
< 0.1%
643 1
 
< 0.1%
642 1
 
< 0.1%
641 2
0.1%
638 1
 
< 0.1%
637 1
 
< 0.1%
636 3
0.1%

총계-이용량(톤)
Real number (ℝ)

HIGH CORRELATION 

Distinct2575
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean784693.03
Minimum0
Maximum8967617
Zeros15
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:30.584343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5628
Q178434.5
median327040
Q31041038
95-th percentile2926134.6
Maximum8967617
Range8967617
Interquartile range (IQR)962603.5

Descriptive statistics

Standard deviation1183613.6
Coefficient of variation (CV)1.5083778
Kurtosis14.163686
Mean784693.03
Median Absolute Deviation (MAD)307539.5
Skewness3.2545424
Sum2.9253356 × 109
Variance1.4009411 × 1012
MonotonicityNot monotonic
2024-05-11T10:11:31.012909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15
 
0.4%
13452 12
 
0.3%
2500 11
 
0.3%
39150 11
 
0.3%
24031 10
 
0.3%
2000 9
 
0.2%
34090 9
 
0.2%
267775 9
 
0.2%
1595 9
 
0.2%
33069 8
 
0.2%
Other values (2565) 3625
97.2%
ValueCountFrequency (%)
0 15
0.4%
10 1
 
< 0.1%
11 1
 
< 0.1%
52 1
 
< 0.1%
81 1
 
< 0.1%
181 1
 
< 0.1%
220 1
 
< 0.1%
241 1
 
< 0.1%
268 1
 
< 0.1%
365 2
 
0.1%
ValueCountFrequency (%)
8967617 1
< 0.1%
8965996 2
0.1%
8938796 1
< 0.1%
8829902 1
< 0.1%
8819482 1
< 0.1%
8479784 1
< 0.1%
8441842 1
< 0.1%
8408349 1
< 0.1%
8378768 1
< 0.1%
8330742 1
< 0.1%

국가공단-개소수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3149142
Minimum0
Maximum161
Zeros3020
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:31.530330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum161
Range161
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.67729
Coefficient of variation (CV)5.8386244
Kurtosis174.84579
Mean1.3149142
Median Absolute Deviation (MAD)0
Skewness11.976126
Sum4902
Variance58.940781
MonotonicityNot monotonic
2024-05-11T10:11:32.025208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0 3020
81.0%
1 307
 
8.2%
2 86
 
2.3%
3 64
 
1.7%
4 45
 
1.2%
5 36
 
1.0%
8 31
 
0.8%
7 25
 
0.7%
6 14
 
0.4%
30 11
 
0.3%
Other values (34) 89
 
2.4%
ValueCountFrequency (%)
0 3020
81.0%
1 307
 
8.2%
2 86
 
2.3%
3 64
 
1.7%
4 45
 
1.2%
5 36
 
1.0%
6 14
 
0.4%
7 25
 
0.7%
8 31
 
0.8%
9 10
 
0.3%
ValueCountFrequency (%)
161 1
< 0.1%
137 1
< 0.1%
130 1
< 0.1%
124 1
< 0.1%
115 1
< 0.1%
109 1
< 0.1%
107 1
< 0.1%
91 1
< 0.1%
81 2
0.1%
79 1
< 0.1%

국가공단-이용량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33824.804
Minimum0
Maximum3678480
Zeros3051
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:32.547836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile69050.3
Maximum3678480
Range3678480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation244571.12
Coefficient of variation (CV)7.2305258
Kurtosis108.78663
Mean33824.804
Median Absolute Deviation (MAD)0
Skewness10.081638
Sum1.2609887 × 108
Variance5.9815033 × 1010
MonotonicityNot monotonic
2024-05-11T10:11:33.145493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3051
81.8%
2500 34
 
0.9%
7300 20
 
0.5%
10800 19
 
0.5%
756 17
 
0.5%
1800 17
 
0.5%
803 16
 
0.4%
23725 16
 
0.4%
10950 15
 
0.4%
12000 15
 
0.4%
Other values (168) 508
 
13.6%
ValueCountFrequency (%)
0 3051
81.8%
18 8
 
0.2%
182 4
 
0.1%
350 1
 
< 0.1%
548 7
 
0.2%
730 4
 
0.1%
756 17
 
0.5%
803 16
 
0.4%
900 2
 
0.1%
1133 1
 
< 0.1%
ValueCountFrequency (%)
3678480 1
 
< 0.1%
3441770 1
 
< 0.1%
3176677 1
 
< 0.1%
3058497 1
 
< 0.1%
3042697 1
 
< 0.1%
2922317 1
 
< 0.1%
2758859 1
 
< 0.1%
2716330 5
0.1%
2676180 1
 
< 0.1%
2657930 1
 
< 0.1%

지방공단-개소수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7658262
Minimum0
Maximum156
Zeros2034
Zeros (%)54.6%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:33.740587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33.25
95-th percentile32
Maximum156
Range156
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation14.936594
Coefficient of variation (CV)2.5905384
Kurtosis32.213222
Mean5.7658262
Median Absolute Deviation (MAD)0
Skewness4.9108012
Sum21495
Variance223.10184
MonotonicityNot monotonic
2024-05-11T10:11:34.312879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2034
54.6%
2 321
 
8.6%
3 240
 
6.4%
1 201
 
5.4%
4 111
 
3.0%
5 81
 
2.2%
6 67
 
1.8%
7 64
 
1.7%
9 48
 
1.3%
8 35
 
0.9%
Other values (75) 526
 
14.1%
ValueCountFrequency (%)
0 2034
54.6%
1 201
 
5.4%
2 321
 
8.6%
3 240
 
6.4%
4 111
 
3.0%
5 81
 
2.2%
6 67
 
1.8%
7 64
 
1.7%
8 35
 
0.9%
9 48
 
1.3%
ValueCountFrequency (%)
156 1
 
< 0.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
151 3
0.1%
150 2
0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
134 1
 
< 0.1%
105 2
0.1%
97 1
 
< 0.1%

지방공단-이용량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct625
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84094.653
Minimum0
Maximum1578488
Zeros2082
Zeros (%)55.8%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:34.918689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q348666
95-th percentile582968.45
Maximum1578488
Range1578488
Interquartile range (IQR)48666

Descriptive statistics

Standard deviation208722.29
Coefficient of variation (CV)2.4819924
Kurtosis12.951063
Mean84094.653
Median Absolute Deviation (MAD)0
Skewness3.394139
Sum3.1350487 × 108
Variance4.3564993 × 1010
MonotonicityNot monotonic
2024-05-11T10:11:35.475724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2082
55.8%
21900 20
 
0.5%
18250 19
 
0.5%
48666 17
 
0.5%
2555 17
 
0.5%
5850 17
 
0.5%
60500 17
 
0.5%
50600 17
 
0.5%
2880 17
 
0.5%
13177 15
 
0.4%
Other values (615) 1490
40.0%
ValueCountFrequency (%)
0 2082
55.8%
62 1
 
< 0.1%
300 8
 
0.2%
360 2
 
0.1%
540 1
 
< 0.1%
721 9
 
0.2%
917 10
 
0.3%
994 2
 
0.1%
1095 3
 
0.1%
1569 8
 
0.2%
ValueCountFrequency (%)
1578488 3
0.1%
1555060 4
0.1%
1508912 1
 
< 0.1%
1452925 4
0.1%
1449674 1
 
< 0.1%
1195718 2
0.1%
1145458 1
 
< 0.1%
1096468 1
 
< 0.1%
1080330 2
0.1%
1071755 2
0.1%

농공단지-개소수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1190987
Minimum0
Maximum63
Zeros1796
Zeros (%)48.2%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:36.092812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile18
Maximum63
Range63
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.4026239
Coefficient of variation (CV)1.554375
Kurtosis5.3873414
Mean4.1190987
Median Absolute Deviation (MAD)1
Skewness2.0716044
Sum15356
Variance40.993593
MonotonicityNot monotonic
2024-05-11T10:11:36.683461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 1796
48.2%
1 325
 
8.7%
2 190
 
5.1%
4 153
 
4.1%
8 143
 
3.8%
6 139
 
3.7%
3 128
 
3.4%
7 94
 
2.5%
10 89
 
2.4%
15 87
 
2.3%
Other values (28) 584
 
15.7%
ValueCountFrequency (%)
0 1796
48.2%
1 325
 
8.7%
2 190
 
5.1%
3 128
 
3.4%
4 153
 
4.1%
5 77
 
2.1%
6 139
 
3.7%
7 94
 
2.5%
8 143
 
3.8%
9 57
 
1.5%
ValueCountFrequency (%)
63 1
 
< 0.1%
40 1
 
< 0.1%
37 1
 
< 0.1%
36 4
0.1%
34 4
0.1%
32 2
 
0.1%
31 4
0.1%
30 1
 
< 0.1%
29 1
 
< 0.1%
28 8
0.2%

농공단지-이용량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct562
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98192.454
Minimum0
Maximum7201250
Zeros1834
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:37.204683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median365
Q396125
95-th percentile541480
Maximum7201250
Range7201250
Interquartile range (IQR)96125

Descriptive statistics

Standard deviation265347.6
Coefficient of variation (CV)2.7023217
Kurtosis282.79013
Mean98192.454
Median Absolute Deviation (MAD)365
Skewness12.18993
Sum3.6606147 × 108
Variance7.040935 × 1010
MonotonicityNot monotonic
2024-05-11T10:11:37.812622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1834
49.2%
10950 24
 
0.6%
10800 24
 
0.6%
24000 20
 
0.5%
7300 20
 
0.5%
103200 17
 
0.5%
365 17
 
0.5%
36000 16
 
0.4%
106835 16
 
0.4%
360 16
 
0.4%
Other values (552) 1724
46.2%
ValueCountFrequency (%)
0 1834
49.2%
360 16
 
0.4%
365 17
 
0.5%
409 15
 
0.4%
513 2
 
0.1%
600 3
 
0.1%
700 7
 
0.2%
1205 14
 
0.4%
1485 2
 
0.1%
1486 1
 
< 0.1%
ValueCountFrequency (%)
7201250 2
0.1%
2054592 1
 
< 0.1%
2052592 3
0.1%
2052591 3
0.1%
2009521 2
0.1%
1329780 1
 
< 0.1%
1297210 1
 
< 0.1%
1237350 1
 
< 0.1%
1202310 1
 
< 0.1%
1147280 1
 
< 0.1%

자유입지업체-개소수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct214
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.517704
Minimum0
Maximum558
Zeros584
Zeros (%)15.7%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:38.372726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q328
95-th percentile121
Maximum558
Range558
Interquartile range (IQR)26

Descriptive statistics

Standard deviation60.001656
Coefficient of variation (CV)2.1040143
Kurtosis41.055489
Mean28.517704
Median Absolute Deviation (MAD)9
Skewness5.6359718
Sum106314
Variance3600.1988
MonotonicityNot monotonic
2024-05-11T10:11:38.950059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 584
 
15.7%
2 199
 
5.3%
1 190
 
5.1%
5 172
 
4.6%
3 154
 
4.1%
4 138
 
3.7%
8 135
 
3.6%
6 122
 
3.3%
10 119
 
3.2%
7 107
 
2.9%
Other values (204) 1808
48.5%
ValueCountFrequency (%)
0 584
15.7%
1 190
 
5.1%
2 199
 
5.3%
3 154
 
4.1%
4 138
 
3.7%
5 172
 
4.6%
6 122
 
3.3%
7 107
 
2.9%
8 135
 
3.6%
9 87
 
2.3%
ValueCountFrequency (%)
558 1
< 0.1%
555 1
< 0.1%
554 1
< 0.1%
553 2
0.1%
551 1
< 0.1%
550 1
< 0.1%
548 1
< 0.1%
546 2
0.1%
544 2
0.1%
542 2
0.1%

자유입지업체-이용량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1473
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342576.69
Minimum0
Maximum6306856
Zeros609
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:39.513741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18978.5
median73684
Q3338500
95-th percentile1559651.5
Maximum6306856
Range6306856
Interquartile range (IQR)329521.5

Descriptive statistics

Standard deviation713110.9
Coefficient of variation (CV)2.0816095
Kurtosis20.227337
Mean342576.69
Median Absolute Deviation (MAD)73684
Skewness4.061509
Sum1.2771259 × 109
Variance5.0852716 × 1011
MonotonicityNot monotonic
2024-05-11T10:11:40.200299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 609
 
16.3%
12045 20
 
0.5%
32850 19
 
0.5%
592485 17
 
0.5%
10000 17
 
0.5%
39018 17
 
0.5%
11250 17
 
0.5%
107540 16
 
0.4%
88560 16
 
0.4%
125925 16
 
0.4%
Other values (1463) 2964
79.5%
ValueCountFrequency (%)
0 609
16.3%
52 1
 
< 0.1%
76 1
 
< 0.1%
120 11
 
0.3%
124 8
 
0.2%
197 1
 
< 0.1%
236 2
 
0.1%
237 1
 
< 0.1%
255 4
 
0.1%
317 5
 
0.1%
ValueCountFrequency (%)
6306856 3
0.1%
5312126 1
 
< 0.1%
5299442 1
 
< 0.1%
5296976 1
 
< 0.1%
5201926 1
 
< 0.1%
5164426 1
 
< 0.1%
5087615 1
 
< 0.1%
5031990 1
 
< 0.1%
5004780 2
0.1%
4988620 1
 
< 0.1%

기타-개소수(개소)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct146
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.253219
Minimum0
Maximum230
Zeros173
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:40.913573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median12
Q327
95-th percentile77
Maximum230
Range230
Interquartile range (IQR)22

Descriptive statistics

Standard deviation27.515222
Coefficient of variation (CV)1.2946379
Kurtosis12.530641
Mean21.253219
Median Absolute Deviation (MAD)9
Skewness3.0772704
Sum79232
Variance757.08746
MonotonicityNot monotonic
2024-05-11T10:11:41.571923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 221
 
5.9%
0 173
 
4.6%
2 165
 
4.4%
6 161
 
4.3%
7 161
 
4.3%
4 153
 
4.1%
12 141
 
3.8%
8 133
 
3.6%
9 132
 
3.5%
3 124
 
3.3%
Other values (136) 2164
58.0%
ValueCountFrequency (%)
0 173
4.6%
1 221
5.9%
2 165
4.4%
3 124
3.3%
4 153
4.1%
5 118
3.2%
6 161
4.3%
7 161
4.3%
8 133
3.6%
9 132
3.5%
ValueCountFrequency (%)
230 1
< 0.1%
226 1
< 0.1%
222 1
< 0.1%
218 1
< 0.1%
195 1
< 0.1%
193 2
0.1%
192 2
0.1%
190 2
0.1%
189 2
0.1%
182 1
< 0.1%

기타-이용량(톤)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2065
Distinct (%)55.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226004.43
Minimum0
Maximum5794232
Zeros188
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size32.9 KiB
2024-05-11T10:11:42.013390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120282.75
median79820
Q3232905
95-th percentile974200
Maximum5794232
Range5794232
Interquartile range (IQR)212622.25

Descriptive statistics

Standard deviation436596.47
Coefficient of variation (CV)1.931805
Kurtosis50.615317
Mean226004.43
Median Absolute Deviation (MAD)71511
Skewness5.7377476
Sum8.4254451 × 108
Variance1.9061648 × 1011
MonotonicityNot monotonic
2024-05-11T10:11:42.834378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 188
 
5.0%
1800 16
 
0.4%
82124 14
 
0.4%
4380 13
 
0.3%
2000 13
 
0.3%
197579 12
 
0.3%
3650 11
 
0.3%
1452 11
 
0.3%
2361 11
 
0.3%
94850 10
 
0.3%
Other values (2055) 3429
92.0%
ValueCountFrequency (%)
0 188
5.0%
1 1
 
< 0.1%
7 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
81 1
 
< 0.1%
144 2
 
0.1%
180 2
 
0.1%
181 1
 
< 0.1%
241 1
 
< 0.1%
ValueCountFrequency (%)
5794232 1
< 0.1%
5631878 1
< 0.1%
5631778 1
< 0.1%
5209793 1
< 0.1%
4966064 1
< 0.1%
4947814 1
< 0.1%
4889814 1
< 0.1%
4729642 1
< 0.1%
4646459 1
< 0.1%
4600067 1
< 0.1%

Interactions

2024-05-11T10:11:19.137129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:23.815497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:28.782989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:34.126262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:38.619587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:42.862039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:46.682637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:51.698965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:56.457311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:01.081907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:05.062118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:10.023097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:14.175597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:19.511698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:24.183770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:29.187783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:34.692801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:38.899834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:43.132711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:47.010610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:52.098680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:56.739774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:01.380292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:05.255793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:10.313512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:14.493384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:19.912583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:24.567192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:29.491190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:35.151992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:39.135936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:43.399942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:47.476781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:52.423626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:57.111614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:01.755064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:05.543740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:10.609704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:14.865807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:20.213568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:24.841478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:29.818388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:35.562784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:39.459308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:43.673157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:47.800133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:52.715472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:57.566744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:02.123610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:05.944548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:10.867872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:15.163032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:20.533731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:25.271779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:30.137258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:35.954664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:39.845134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:43.988399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:48.190677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:53.112435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:57.894472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:02.534180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:06.495166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:11.261858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:15.553903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:20.896424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:25.665480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:30.478323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:36.270813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:40.139802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:44.283166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:48.957388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:53.553453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:58.260341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:02.839556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:06.866534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:11.561213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:16.150709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:21.237732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:26.111436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:31.173137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:36.600967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:40.452128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:44.571320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:49.324547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:53.871014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:58.690275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:03.103374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:07.359014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:11.864000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:16.522772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:21.566200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:26.548976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:31.575756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:36.940656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:40.810855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:44.878151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:49.730213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:54.197140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:59.036044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:03.424829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:08.134746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:12.170501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:16.976257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:21.866191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:27.016599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:31.975760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:37.326776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:41.228248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:45.177125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:50.041488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:54.550796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:59.378142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:03.699440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:08.449682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:12.463669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:17.324238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:22.198596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:27.405805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:32.440397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:37.613651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:41.535432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:45.550337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:50.336876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:54.904719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:59.762820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:03.961303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:08.775814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:12.755385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:17.715242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:22.585813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:27.794286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:32.886100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:37.871583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:41.851247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:45.846277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:50.619344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:55.194144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:00.086742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:04.244963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:09.107313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:13.023071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:18.115523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:22.949884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:28.090663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:33.310584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:38.144123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:42.163691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:46.135565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:50.937613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:55.792558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:00.527675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:04.523575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:09.428232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:13.511661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:18.419458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:23.296672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:28.398199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:33.680745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:38.387338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:42.471612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:46.409480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:51.296105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:10:56.088399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:00.791488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:04.786282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:09.733092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:13.830625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T10:11:18.790820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T10:11:43.314953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도시도총계-개소수(개소)총계-이용량(톤)국가공단-개소수(개소)국가공단-이용량(톤)지방공단-개소수(개소)지방공단-이용량(톤)농공단지-개소수(개소)농공단지-이용량(톤)자유입지업체-개소수(개소)자유입지업체-이용량(톤)기타-개소수(개소)기타-이용량(톤)
연도1.0000.1380.0000.0450.0170.0000.0000.0000.0000.0000.0000.0000.0000.000
시도0.1381.0000.4610.4750.2110.2230.4420.4170.5250.3050.4460.5120.4700.463
총계-개소수(개소)0.0000.4611.0000.6590.1950.1750.5140.4790.4480.6990.9400.6980.5670.402
총계-이용량(톤)0.0450.4750.6591.0000.3600.4880.4260.5820.3420.6070.5970.8980.6130.837
국가공단-개소수(개소)0.0170.2110.1950.3601.0000.9470.2340.3110.0760.0000.0000.0000.0000.000
국가공단-이용량(톤)0.0000.2230.1750.4880.9471.0000.0000.1920.0000.0000.0920.0000.0000.000
지방공단-개소수(개소)0.0000.4420.5140.4260.2340.0001.0000.8240.1690.1820.3370.3250.3540.278
지방공단-이용량(톤)0.0000.4170.4790.5820.3110.1920.8241.0000.2420.2630.3330.4320.3850.525
농공단지-개소수(개소)0.0000.5250.4480.3420.0760.0000.1690.2421.0000.4130.3390.2100.2380.227
농공단지-이용량(톤)0.0000.3050.6990.6070.0000.0000.1820.2630.4131.0000.6870.6390.1130.233
자유입지업체-개소수(개소)0.0000.4460.9400.5970.0000.0920.3370.3330.3390.6871.0000.7120.3070.119
자유입지업체-이용량(톤)0.0000.5120.6980.8980.0000.0000.3250.4320.2100.6390.7121.0000.4210.740
기타-개소수(개소)0.0000.4700.5670.6130.0000.0000.3540.3850.2380.1130.3070.4211.0000.827
기타-이용량(톤)0.0000.4630.4020.8370.0000.0000.2780.5250.2270.2330.1190.7400.8271.000
2024-05-11T10:11:43.953346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도총계-개소수(개소)총계-이용량(톤)국가공단-개소수(개소)국가공단-이용량(톤)지방공단-개소수(개소)지방공단-이용량(톤)농공단지-개소수(개소)농공단지-이용량(톤)자유입지업체-개소수(개소)자유입지업체-이용량(톤)기타-개소수(개소)기타-이용량(톤)시도
연도1.0000.0080.0110.0200.0270.0320.0420.0350.0430.0100.0230.0340.0130.055
총계-개소수(개소)0.0081.0000.8580.2350.2350.5780.5670.5400.5060.8120.7520.7720.6890.213
총계-이용량(톤)0.0110.8581.0000.1910.1960.5680.5950.5560.5900.7180.8320.6350.7800.198
국가공단-개소수(개소)0.0200.2350.1911.0000.9750.2610.2390.009-0.0020.1620.1240.1460.1460.080
국가공단-이용량(톤)0.0270.2350.1960.9751.0000.2770.2490.0170.0060.1640.1290.1530.1550.085
지방공단-개소수(개소)0.0320.5780.5680.2610.2771.0000.9600.2610.2420.4320.4500.3880.4190.191
지방공단-이용량(톤)0.0420.5670.5950.2390.2490.9601.0000.2670.2560.4420.4790.3750.4270.177
농공단지-개소수(개소)0.0350.5400.5560.0090.0170.2610.2671.0000.9580.4430.4610.3780.4240.251
농공단지-이용량(톤)0.0430.5060.590-0.0020.0060.2420.2560.9581.0000.4210.4720.3450.4460.170
자유입지업체-개소수(개소)0.0100.8120.7180.1620.1640.4320.4420.4430.4211.0000.8960.4270.4000.204
자유입지업체-이용량(톤)0.0230.7520.8320.1240.1290.4500.4790.4610.4720.8961.0000.4210.4980.219
기타-개소수(개소)0.0340.7720.6350.1460.1530.3880.3750.3780.3450.4270.4211.0000.8340.195
기타-이용량(톤)0.0130.6890.7800.1460.1550.4190.4270.4240.4460.4000.4980.8341.0000.192
시도0.0550.2130.1980.0800.0850.1910.1770.2510.1700.2040.2190.1950.1921.000

Missing values

2024-05-11T10:11:23.836878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T10:11:24.731030image/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

연도시도시군구총계-개소수(개소)총계-이용량(톤)국가공단-개소수(개소)국가공단-이용량(톤)지방공단-개소수(개소)지방공단-이용량(톤)농공단지-개소수(개소)농공단지-이용량(톤)자유입지업체-개소수(개소)자유입지업체-이용량(톤)기타-개소수(개소)기타-이용량(톤)
02007강원도강릉시8123760030041162302757040673279351578924
12007강원도고성군1275572000033000219000753572
22007강원도동해시5397701319000000046943253624760
32007강원도삼척시257157000000001414870011567000
42007강원도속초시7336860010238193296871180
52007강원도양구군51635000000041455011800
62007강원도양양군201261490000249001611616525084
72007강원도영월군131395650000234500755140449925
82007강원도원주시128132850000195165361219309290563672755200
92007강원도인제군51391000001300000410910
연도시도시군구총계-개소수(개소)총계-이용량(톤)국가공단-개소수(개소)국가공단-이용량(톤)지방공단-개소수(개소)지방공단-이용량(톤)농공단지-개소수(개소)농공단지-이용량(톤)자유입지업체-개소수(개소)자유입지업체-이용량(톤)기타-개소수(개소)기타-이용량(톤)
37182023충청북도단양군411396963002146004748252289785813409680
37192023충청북도보은군6221953768760000112447123473512260115
37202023충청북도영동군981312857004894254103200739018831081214
37212023충청북도옥천군9712763770000154724254452970338274249
37222023충청북도음성군677729435600349035409239930555529944279851444
37232023충청북도제천시94850249006175500141304435538252419161782
37242023충청북도증평군5447054600146631148898223275662089419
37252023충청북도진천군207269956400230335215740182229803618182755
37262023충청북도청주시2562422862003160633000152134021273476320
37272023충청북도충주시17622646710011349525151708217382982277914503