Overview

Dataset statistics

Number of variables19
Number of observations480
Missing cells208
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory77.9 KiB
Average record size in memory166.3 B

Variable types

Categorical3
Text2
Numeric14

Dataset

Description시도, 시군, 단지명, 조성상태, 지정면적, 관리면적 등 전국 산업단지현황통계 중 저희 기관이 관할하는 산업단지의 농공산업단지 자료를 분기별로 제공하고 있습니다.
Author한국산업단지공단
URLhttps://www.data.go.kr/data/15085878/fileData.do

Alerts

유형 has constant value ""Constant
지정면적(천제곱미터) is highly overall correlated with 관리면적(천제곱미터) and 3 other fieldsHigh correlation
관리면적(천제곱미터) is highly overall correlated with 지정면적(천제곱미터) and 3 other fieldsHigh correlation
산업시설구역_전체면적(천제곱미터) is highly overall correlated with 지정면적(천제곱미터) and 5 other fieldsHigh correlation
산업시설구역_분양대상(천제곱미터) is highly overall correlated with 지정면적(천제곱미터) and 6 other fieldsHigh correlation
산업시설구역_분양(천제곱미터) is highly overall correlated with 지정면적(천제곱미터) and 9 other fieldsHigh correlation
산업시설구역_미분양(천제곱미터) is highly overall correlated with 산업시설구역_분양률(퍼센트)High correlation
산업시설구역_분양률(퍼센트) is highly overall correlated with 산업시설구역_미분양(천제곱미터) and 1 other fieldsHigh correlation
입주업체(개) is highly overall correlated with 가동업체(개) and 3 other fieldsHigh correlation
가동업체(개) is highly overall correlated with 산업시설구역_분양(천제곱미터) and 4 other fieldsHigh correlation
고용현황(명)_남 is highly overall correlated with 산업시설구역_전체면적(천제곱미터) and 8 other fieldsHigh correlation
고용현황(명)_여 is highly overall correlated with 산업시설구역_분양(천제곱미터) and 6 other fieldsHigh correlation
고용현황(명)_계 is highly overall correlated with 산업시설구역_전체면적(천제곱미터) and 8 other fieldsHigh correlation
누계생산(백만원) is highly overall correlated with 산업시설구역_분양대상(천제곱미터) and 5 other fieldsHigh correlation
누계수출(천달러) is highly overall correlated with 산업시설구역_분양(천제곱미터) and 4 other fieldsHigh correlation
조성상태 is highly overall correlated with 산업시설구역_분양률(퍼센트)High correlation
조성상태 is highly imbalanced (79.7%)Imbalance
고용현황(명)_남 has 47 (9.8%) missing valuesMissing
고용현황(명)_여 has 45 (9.4%) missing valuesMissing
고용현황(명)_계 has 48 (10.0%) missing valuesMissing
누계생산(백만원) has 43 (9.0%) missing valuesMissing
누계수출(천달러) has 25 (5.2%) missing valuesMissing
산업시설구역_분양대상(천제곱미터) has 21 (4.4%) zerosZeros
산업시설구역_분양(천제곱미터) has 23 (4.8%) zerosZeros
산업시설구역_미분양(천제곱미터) has 435 (90.6%) zerosZeros
산업시설구역_분양률(퍼센트) has 23 (4.8%) zerosZeros
입주업체(개) has 25 (5.2%) zerosZeros
가동업체(개) has 31 (6.5%) zerosZeros
고용현황(명)_남 has 31 (6.5%) zerosZeros
고용현황(명)_여 has 33 (6.9%) zerosZeros
고용현황(명)_계 has 30 (6.2%) zerosZeros
누계생산(백만원) has 38 (7.9%) zerosZeros
누계수출(천달러) has 142 (29.6%) zerosZeros

Reproduction

Analysis started2024-04-06 08:42:31.636801
Analysis finished2024-04-06 08:43:37.515997
Duration1 minute and 5.88 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
농공
480 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농공 480
100.0%

Length

2024-04-06T17:43:37.710111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:43:38.002925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농공 480
100.0%

시도
Categorical

Distinct14
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
충남
94 
경남
80 
전남
70 
경북
67 
전북
60 
Other values (9)
109 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)0.6%

Sample

1st row부산
2nd row대구
3rd row대구
4th row대구
5th row대구

Common Values

ValueCountFrequency (%)
충남 94
19.6%
경남 80
16.7%
전남 70
14.6%
경북 67
14.0%
전북 60
12.5%
강원 47
9.8%
충북 44
9.2%
대구 4
 
0.8%
울산 4
 
0.8%
세종 4
 
0.8%
Other values (4) 6
 
1.2%

Length

2024-04-06T17:43:38.301996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충남 94
19.6%
경남 80
16.7%
전남 70
14.6%
경북 67
14.0%
전북 60
12.5%
강원 47
9.8%
충북 44
9.2%
대구 4
 
0.8%
울산 4
 
0.8%
세종 4
 
0.8%
Other values (4) 6
 
1.2%

시군
Text

Distinct122
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-04-06T17:43:39.054927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0125
Min length2

Characters and Unicode

Total characters1446
Distinct characters104
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

Unique17 ?
Unique (%)3.5%

Sample

1st row기장군
2nd row달성군
3rd row달성군
4th row군위군
5th row군위군
ValueCountFrequency (%)
공주시 12
 
2.5%
논산시 10
 
2.1%
함안군 10
 
2.1%
아산시 9
 
1.9%
보령시 8
 
1.7%
예산군 8
 
1.7%
고성군 8
 
1.7%
밀양시 8
 
1.7%
홍성군 8
 
1.7%
김해시 8
 
1.7%
Other values (113) 393
81.5%
2024-04-06T17:43:40.444458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
277
19.2%
203
 
14.0%
65
 
4.5%
61
 
4.2%
54
 
3.7%
43
 
3.0%
37
 
2.6%
33
 
2.3%
31
 
2.1%
29
 
2.0%
Other values (94) 613
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1444
99.9%
Space Separator 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
277
19.2%
203
 
14.1%
65
 
4.5%
61
 
4.2%
54
 
3.7%
43
 
3.0%
37
 
2.6%
33
 
2.3%
31
 
2.1%
29
 
2.0%
Other values (93) 611
42.3%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1444
99.9%
Common 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
277
19.2%
203
 
14.1%
65
 
4.5%
61
 
4.2%
54
 
3.7%
43
 
3.0%
37
 
2.6%
33
 
2.3%
31
 
2.1%
29
 
2.0%
Other values (93) 611
42.3%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1444
99.9%
ASCII 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
277
19.2%
203
 
14.1%
65
 
4.5%
61
 
4.2%
54
 
3.7%
43
 
3.0%
37
 
2.6%
33
 
2.3%
31
 
2.1%
29
 
2.0%
Other values (93) 611
42.3%
ASCII
ValueCountFrequency (%)
2
100.0%
Distinct470
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2024-04-06T17:43:41.366467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length2
Mean length3.2583333
Min length2

Characters and Unicode

Total characters1564
Distinct characters254
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique460 ?
Unique (%)95.8%

Sample

1st row정관
2nd row구지
3rd row옥포
4th row군위
5th row효령
ValueCountFrequency (%)
금성 3
 
0.6%
봉황 2
 
0.4%
동면 2
 
0.4%
장수 2
 
0.4%
군서 2
 
0.4%
수동 2
 
0.4%
신평 2
 
0.4%
풍산 2
 
0.4%
동화 2
 
0.4%
옥천 2
 
0.4%
Other values (475) 475
95.8%
2024-04-06T17:43:42.699222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
52
 
3.3%
2 49
 
3.1%
43
 
2.7%
40
 
2.6%
34
 
2.2%
31
 
2.0%
31
 
2.0%
28
 
1.8%
28
 
1.8%
26
 
1.7%
Other values (244) 1202
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1439
92.0%
Decimal Number 56
 
3.6%
Open Punctuation 23
 
1.5%
Close Punctuation 23
 
1.5%
Space Separator 16
 
1.0%
Other Punctuation 6
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
52
 
3.6%
43
 
3.0%
40
 
2.8%
34
 
2.4%
31
 
2.2%
31
 
2.2%
28
 
1.9%
28
 
1.9%
26
 
1.8%
25
 
1.7%
Other values (236) 1101
76.5%
Decimal Number
ValueCountFrequency (%)
2 49
87.5%
3 4
 
7.1%
1 3
 
5.4%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Other Punctuation
ValueCountFrequency (%)
: 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1439
92.0%
Common 125
 
8.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
52
 
3.6%
43
 
3.0%
40
 
2.8%
34
 
2.4%
31
 
2.2%
31
 
2.2%
28
 
1.9%
28
 
1.9%
26
 
1.8%
25
 
1.7%
Other values (236) 1101
76.5%
Common
ValueCountFrequency (%)
2 49
39.2%
( 23
18.4%
) 23
18.4%
16
 
12.8%
: 6
 
4.8%
3 4
 
3.2%
1 3
 
2.4%
- 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1439
92.0%
ASCII 125
 
8.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
52
 
3.6%
43
 
3.0%
40
 
2.8%
34
 
2.4%
31
 
2.2%
31
 
2.2%
28
 
1.9%
28
 
1.9%
26
 
1.8%
25
 
1.7%
Other values (236) 1101
76.5%
ASCII
ValueCountFrequency (%)
2 49
39.2%
( 23
18.4%
) 23
18.4%
16
 
12.8%
: 6
 
4.8%
3 4
 
3.2%
1 3
 
2.4%
- 1
 
0.8%

조성상태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
완료
451 
조성중
 
16
미개발
 
11
<NA>
 
2

Length

Max length4
Median length2
Mean length2.0645833
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완료 451
94.0%
조성중 16
 
3.3%
미개발 11
 
2.3%
<NA> 2
 
0.4%

Length

2024-04-06T17:43:43.139080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:43:43.414517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완료 451
94.0%
조성중 16
 
3.3%
미개발 11
 
2.3%
na 2
 
0.4%

지정면적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct223
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162.025
Minimum0
Maximum777
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:43.693840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1104.75
median143
Q3193.5
95-th percentile329
Maximum777
Range777
Interquartile range (IQR)88.75

Descriptive statistics

Standard deviation91.417504
Coefficient of variation (CV)0.56421851
Kurtosis6.2329387
Mean162.025
Median Absolute Deviation (MAD)41
Skewness1.8864921
Sum77772
Variance8357.1601
MonotonicityNot monotonic
2024-04-06T17:43:44.673119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149 11
 
2.3%
147 10
 
2.1%
143 9
 
1.9%
150 9
 
1.9%
145 8
 
1.7%
144 8
 
1.7%
146 7
 
1.5%
106 6
 
1.2%
103 6
 
1.2%
132 6
 
1.2%
Other values (213) 400
83.3%
ValueCountFrequency (%)
0 1
0.2%
30 1
0.2%
34 2
0.4%
40 1
0.2%
42 2
0.4%
44 2
0.4%
45 1
0.2%
47 1
0.2%
49 1
0.2%
50 1
0.2%
ValueCountFrequency (%)
777 1
0.2%
568 1
0.2%
551 1
0.2%
530 1
0.2%
522 1
0.2%
501 1
0.2%
480 1
0.2%
427 1
0.2%
408 1
0.2%
405 1
0.2%

관리면적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct225
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.99167
Minimum0
Maximum775
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:45.264368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1103
median142
Q3192.25
95-th percentile327.05
Maximum775
Range775
Interquartile range (IQR)89.25

Descriptive statistics

Standard deviation91.444474
Coefficient of variation (CV)0.5680075
Kurtosis6.22826
Mean160.99167
Median Absolute Deviation (MAD)41.5
Skewness1.8920784
Sum77276
Variance8362.0918
MonotonicityNot monotonic
2024-04-06T17:43:45.707101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149 10
 
2.1%
144 9
 
1.9%
147 8
 
1.7%
145 8
 
1.7%
150 7
 
1.5%
112 7
 
1.5%
146 7
 
1.5%
143 7
 
1.5%
142 6
 
1.2%
102 6
 
1.2%
Other values (215) 405
84.4%
ValueCountFrequency (%)
0 1
0.2%
30 1
0.2%
33 1
0.2%
34 1
0.2%
40 1
0.2%
42 2
0.4%
44 1
0.2%
45 2
0.4%
47 1
0.2%
49 1
0.2%
ValueCountFrequency (%)
775 1
0.2%
568 1
0.2%
550 1
0.2%
529 1
0.2%
522 1
0.2%
501 1
0.2%
480 1
0.2%
427 1
0.2%
408 1
0.2%
405 1
0.2%

산업시설구역_전체면적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct194
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.15208
Minimum0
Maximum597
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:46.093039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43
Q179
median104
Q3145
95-th percentile245.05
Maximum597
Range597
Interquartile range (IQR)66

Descriptive statistics

Standard deviation72.236128
Coefficient of variation (CV)0.59136223
Kurtosis7.6370581
Mean122.15208
Median Absolute Deviation (MAD)31
Skewness2.1296995
Sum58633
Variance5218.0582
MonotonicityNot monotonic
2024-04-06T17:43:46.471733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 9
 
1.9%
98 7
 
1.5%
96 7
 
1.5%
81 7
 
1.5%
105 7
 
1.5%
107 7
 
1.5%
94 6
 
1.2%
83 6
 
1.2%
78 6
 
1.2%
88 6
 
1.2%
Other values (184) 412
85.8%
ValueCountFrequency (%)
0 1
0.2%
14 1
0.2%
15 1
0.2%
28 1
0.2%
29 2
0.4%
30 2
0.4%
31 1
0.2%
34 2
0.4%
35 1
0.2%
37 2
0.4%
ValueCountFrequency (%)
597 1
0.2%
504 1
0.2%
481 1
0.2%
445 1
0.2%
409 1
0.2%
389 1
0.2%
362 1
0.2%
360 1
0.2%
346 1
0.2%
330 1
0.2%

산업시설구역_분양대상(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct194
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.23542
Minimum0
Maximum597
Zeros21
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:46.863820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29
Q173
median102
Q3141
95-th percentile245.05
Maximum597
Range597
Interquartile range (IQR)68

Descriptive statistics

Standard deviation75.266741
Coefficient of variation (CV)0.64201367
Kurtosis6.7486762
Mean117.23542
Median Absolute Deviation (MAD)33
Skewness1.8941277
Sum56273
Variance5665.0823
MonotonicityNot monotonic
2024-04-06T17:43:47.362417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21
 
4.4%
91 9
 
1.9%
81 7
 
1.5%
105 7
 
1.5%
96 7
 
1.5%
98 7
 
1.5%
94 6
 
1.2%
108 6
 
1.2%
43 6
 
1.2%
49 6
 
1.2%
Other values (184) 398
82.9%
ValueCountFrequency (%)
0 21
4.4%
14 1
 
0.2%
28 1
 
0.2%
29 2
 
0.4%
30 1
 
0.2%
31 1
 
0.2%
34 2
 
0.4%
35 1
 
0.2%
37 2
 
0.4%
39 2
 
0.4%
ValueCountFrequency (%)
597 1
0.2%
504 1
0.2%
481 1
0.2%
445 1
0.2%
409 1
0.2%
389 1
0.2%
362 1
0.2%
360 1
0.2%
346 1
0.2%
330 1
0.2%

산업시설구역_분양(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct196
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.18542
Minimum0
Maximum597
Zeros23
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:47.832301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.75
Q168
median99
Q3139
95-th percentile243.1
Maximum597
Range597
Interquartile range (IQR)71

Descriptive statistics

Standard deviation76.308769
Coefficient of variation (CV)0.67419259
Kurtosis6.6652097
Mean113.18542
Median Absolute Deviation (MAD)35
Skewness1.8827983
Sum54329
Variance5823.0282
MonotonicityNot monotonic
2024-04-06T17:43:48.297087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
4.8%
49 8
 
1.7%
105 7
 
1.5%
108 7
 
1.5%
78 7
 
1.5%
91 7
 
1.5%
96 6
 
1.2%
92 6
 
1.2%
81 6
 
1.2%
94 6
 
1.2%
Other values (186) 397
82.7%
ValueCountFrequency (%)
0 23
4.8%
4 1
 
0.2%
9 3
 
0.6%
11 1
 
0.2%
12 1
 
0.2%
14 1
 
0.2%
19 1
 
0.2%
25 1
 
0.2%
27 1
 
0.2%
28 2
 
0.4%
ValueCountFrequency (%)
597 1
0.2%
504 1
0.2%
481 1
0.2%
445 1
0.2%
409 1
0.2%
389 1
0.2%
362 1
0.2%
360 1
0.2%
346 1
0.2%
330 1
0.2%

산업시설구역_미분양(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.05
Minimum0
Maximum248
Zeros435
Zeros (%)90.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:48.695618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21
Maximum248
Range248
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20.217543
Coefficient of variation (CV)4.991986
Kurtosis69.670683
Mean4.05
Median Absolute Deviation (MAD)0
Skewness7.6434257
Sum1944
Variance408.74906
MonotonicityNot monotonic
2024-04-06T17:43:49.110134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 435
90.6%
17 3
 
0.6%
6 2
 
0.4%
11 2
 
0.4%
3 2
 
0.4%
13 2
 
0.4%
39 2
 
0.4%
2 2
 
0.4%
21 2
 
0.4%
8 2
 
0.4%
Other values (26) 26
 
5.4%
ValueCountFrequency (%)
0 435
90.6%
2 2
 
0.4%
3 2
 
0.4%
4 1
 
0.2%
6 2
 
0.4%
8 2
 
0.4%
11 2
 
0.4%
13 2
 
0.4%
15 1
 
0.2%
16 1
 
0.2%
ValueCountFrequency (%)
248 1
0.2%
183 1
0.2%
146 1
0.2%
144 1
0.2%
126 1
0.2%
86 1
0.2%
85 1
0.2%
77 1
0.2%
70 1
0.2%
60 1
0.2%

산업시설구역_분양률(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.978833
Minimum0
Maximum100
Zeros23
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:49.489620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.451
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.747688
Coefficient of variation (CV)0.26905851
Kurtosis8.2329924
Mean91.978833
Median Absolute Deviation (MAD)0
Skewness-3.1214204
Sum44149.84
Variance612.44805
MonotonicityNot monotonic
2024-04-06T17:43:49.868114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
100.0 414
86.2%
0.0 23
 
4.8%
57.14 1
 
0.2%
22.7 1
 
0.2%
19.74 1
 
0.2%
58.04 1
 
0.2%
22.73 1
 
0.2%
15.79 1
 
0.2%
19.79 1
 
0.2%
9.47 1
 
0.2%
Other values (35) 35
 
7.3%
ValueCountFrequency (%)
0.0 23
4.8%
9.09 1
 
0.2%
9.47 1
 
0.2%
15.79 1
 
0.2%
16.67 1
 
0.2%
19.74 1
 
0.2%
19.79 1
 
0.2%
20.45 1
 
0.2%
22.7 1
 
0.2%
22.73 1
 
0.2%
ValueCountFrequency (%)
100.0 414
86.2%
98.18 1
 
0.2%
97.22 1
 
0.2%
96.39 1
 
0.2%
94.29 1
 
0.2%
93.88 1
 
0.2%
92.59 1
 
0.2%
90.54 1
 
0.2%
90.18 1
 
0.2%
89.76 1
 
0.2%

입주업체(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.260417
Minimum0
Maximum172
Zeros25
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:50.280408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median14
Q323
95-th percentile44.1
Maximum172
Range172
Interquartile range (IQR)18

Descriptive statistics

Standard deviation17.92413
Coefficient of variation (CV)1.0384529
Kurtosis19.479515
Mean17.260417
Median Absolute Deviation (MAD)9
Skewness3.3312598
Sum8285
Variance321.27442
MonotonicityNot monotonic
2024-04-06T17:43:50.645175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
5.2%
1 24
 
5.0%
3 22
 
4.6%
17 20
 
4.2%
2 20
 
4.2%
16 20
 
4.2%
5 18
 
3.8%
12 18
 
3.8%
6 17
 
3.5%
10 17
 
3.5%
Other values (52) 279
58.1%
ValueCountFrequency (%)
0 25
5.2%
1 24
5.0%
2 20
4.2%
3 22
4.6%
4 16
3.3%
5 18
3.8%
6 17
3.5%
7 13
2.7%
8 9
 
1.9%
9 13
2.7%
ValueCountFrequency (%)
172 1
0.2%
138 1
0.2%
120 1
0.2%
110 1
0.2%
102 1
0.2%
79 1
0.2%
78 1
0.2%
69 1
0.2%
64 1
0.2%
60 1
0.2%

가동업체(개)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.683333
Minimum0
Maximum162
Zeros31
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:51.046350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median12
Q321
95-th percentile42.05
Maximum162
Range162
Interquartile range (IQR)17

Descriptive statistics

Standard deviation17.208161
Coefficient of variation (CV)1.097226
Kurtosis20.012386
Mean15.683333
Median Absolute Deviation (MAD)8
Skewness3.4485156
Sum7528
Variance296.12081
MonotonicityNot monotonic
2024-04-06T17:43:51.427594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
6.5%
3 26
 
5.4%
2 24
 
5.0%
1 23
 
4.8%
15 21
 
4.4%
7 20
 
4.2%
11 19
 
4.0%
19 18
 
3.8%
6 18
 
3.8%
4 18
 
3.8%
Other values (51) 262
54.6%
ValueCountFrequency (%)
0 31
6.5%
1 23
4.8%
2 24
5.0%
3 26
5.4%
4 18
3.8%
5 16
3.3%
6 18
3.8%
7 20
4.2%
8 10
 
2.1%
9 15
3.1%
ValueCountFrequency (%)
162 1
0.2%
138 1
0.2%
120 1
0.2%
96 1
0.2%
95 1
0.2%
78 1
0.2%
74 1
0.2%
69 1
0.2%
64 1
0.2%
60 1
0.2%

고용현황(명)_남
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct282
Distinct (%)65.1%
Missing47
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean234.23788
Minimum0
Maximum3351
Zeros31
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:51.852439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q182
median154
Q3300
95-th percentile670.2
Maximum3351
Range3351
Interquartile range (IQR)218

Descriptive statistics

Standard deviation295.94018
Coefficient of variation (CV)1.2634173
Kurtosis35.034898
Mean234.23788
Median Absolute Deviation (MAD)90
Skewness4.7081156
Sum101425
Variance87580.589
MonotonicityNot monotonic
2024-04-06T17:43:52.346620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31
 
6.5%
82 6
 
1.2%
70 6
 
1.2%
84 5
 
1.0%
171 5
 
1.0%
178 4
 
0.8%
183 4
 
0.8%
87 4
 
0.8%
149 4
 
0.8%
141 4
 
0.8%
Other values (272) 360
75.0%
(Missing) 47
 
9.8%
ValueCountFrequency (%)
0 31
6.5%
4 1
 
0.2%
19 1
 
0.2%
20 1
 
0.2%
21 1
 
0.2%
22 1
 
0.2%
26 1
 
0.2%
28 1
 
0.2%
30 2
 
0.4%
31 2
 
0.4%
ValueCountFrequency (%)
3351 1
0.2%
2010 1
0.2%
1696 1
0.2%
1537 1
0.2%
1529 1
0.2%
1485 1
0.2%
1421 1
0.2%
1272 1
0.2%
1203 1
0.2%
1186 1
0.2%

고용현황(명)_여
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct195
Distinct (%)44.8%
Missing45
Missing (%)9.4%
Infinite0
Infinite (%)0.0%
Mean95.186207
Minimum0
Maximum1002
Zeros33
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:52.834784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126
median63
Q3118
95-th percentile273.9
Maximum1002
Range1002
Interquartile range (IQR)92

Descriptive statistics

Standard deviation117.02284
Coefficient of variation (CV)1.2294097
Kurtosis16.317445
Mean95.186207
Median Absolute Deviation (MAD)44
Skewness3.3809347
Sum41406
Variance13694.345
MonotonicityNot monotonic
2024-04-06T17:43:53.338100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33
 
6.9%
12 8
 
1.7%
26 7
 
1.5%
16 7
 
1.5%
61 6
 
1.2%
45 6
 
1.2%
74 6
 
1.2%
24 5
 
1.0%
84 5
 
1.0%
29 5
 
1.0%
Other values (185) 347
72.3%
(Missing) 45
 
9.4%
ValueCountFrequency (%)
0 33
6.9%
1 1
 
0.2%
2 3
 
0.6%
4 3
 
0.6%
5 2
 
0.4%
6 3
 
0.6%
7 1
 
0.2%
8 5
 
1.0%
9 2
 
0.4%
10 2
 
0.4%
ValueCountFrequency (%)
1002 1
0.2%
802 1
0.2%
706 1
0.2%
683 1
0.2%
660 1
0.2%
625 1
0.2%
557 1
0.2%
496 1
0.2%
471 1
0.2%
429 1
0.2%

고용현황(명)_계
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct294
Distinct (%)68.1%
Missing48
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean330.62731
Minimum0
Maximum3714
Zeros30
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:53.726081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1123
median231
Q3418.25
95-th percentile835.7
Maximum3714
Range3714
Interquartile range (IQR)295.25

Descriptive statistics

Standard deviation385.96954
Coefficient of variation (CV)1.1673855
Kurtosis21.964028
Mean330.62731
Median Absolute Deviation (MAD)132
Skewness3.8683379
Sum142831
Variance148972.48
MonotonicityNot monotonic
2024-04-06T17:43:54.127660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 30
 
6.2%
184 6
 
1.2%
123 5
 
1.0%
70 5
 
1.0%
67 4
 
0.8%
483 3
 
0.6%
152 3
 
0.6%
442 3
 
0.6%
147 3
 
0.6%
202 3
 
0.6%
Other values (284) 367
76.5%
(Missing) 48
 
10.0%
ValueCountFrequency (%)
0 30
6.2%
5 1
 
0.2%
21 1
 
0.2%
24 1
 
0.2%
27 1
 
0.2%
34 2
 
0.4%
35 1
 
0.2%
38 2
 
0.4%
40 1
 
0.2%
42 1
 
0.2%
ValueCountFrequency (%)
3714 1
0.2%
2716 1
0.2%
2487 1
0.2%
2253 1
0.2%
1928 1
0.2%
1886 1
0.2%
1798 1
0.2%
1787 1
0.2%
1618 1
0.2%
1602 1
0.2%

누계생산(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct396
Distinct (%)90.6%
Missing43
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean124559.37
Minimum0
Maximum2420000
Zeros38
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:55.186094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119989
median52145
Q3130392
95-th percentile447277.6
Maximum2420000
Range2420000
Interquartile range (IQR)110403

Descriptive statistics

Standard deviation238816.19
Coefficient of variation (CV)1.9172881
Kurtosis35.638064
Mean124559.37
Median Absolute Deviation (MAD)42265
Skewness5.2491428
Sum54432445
Variance5.7033174 × 1010
MonotonicityNot monotonic
2024-04-06T17:43:55.577638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38
 
7.9%
60000 2
 
0.4%
6000 2
 
0.4%
63000 2
 
0.4%
3200 2
 
0.4%
56360 1
 
0.2%
235324 1
 
0.2%
342672 1
 
0.2%
348000 1
 
0.2%
10800 1
 
0.2%
Other values (386) 386
80.4%
(Missing) 43
 
9.0%
ValueCountFrequency (%)
0 38
7.9%
356 1
 
0.2%
840 1
 
0.2%
1212 1
 
0.2%
1334 1
 
0.2%
1600 1
 
0.2%
2100 1
 
0.2%
2404 1
 
0.2%
2435 1
 
0.2%
2775 1
 
0.2%
ValueCountFrequency (%)
2420000 1
0.2%
1905510 1
0.2%
1651528 1
0.2%
1541876 1
0.2%
1277998 1
0.2%
1261791 1
0.2%
1250976 1
0.2%
1075918 1
0.2%
758353 1
0.2%
731548 1
0.2%

누계수출(천달러)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct307
Distinct (%)67.5%
Missing25
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean19597.103
Minimum0
Maximum570160
Zeros142
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size4.3 KiB
2024-04-06T17:43:56.063710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1350
Q311138
95-th percentile107822.4
Maximum570160
Range570160
Interquartile range (IQR)11138

Descriptive statistics

Standard deviation56921.177
Coefficient of variation (CV)2.904571
Kurtosis40.084494
Mean19597.103
Median Absolute Deviation (MAD)1350
Skewness5.6680614
Sum8916682
Variance3.2400204 × 109
MonotonicityNot monotonic
2024-04-06T17:43:56.528181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 142
29.6%
240 2
 
0.4%
8000 2
 
0.4%
60 2
 
0.4%
2000 2
 
0.4%
1248 2
 
0.4%
615 2
 
0.4%
116 2
 
0.4%
4400 1
 
0.2%
67200 1
 
0.2%
Other values (297) 297
61.9%
(Missing) 25
 
5.2%
ValueCountFrequency (%)
0 142
29.6%
12 1
 
0.2%
32 1
 
0.2%
33 1
 
0.2%
35 1
 
0.2%
37 1
 
0.2%
40 1
 
0.2%
41 1
 
0.2%
60 2
 
0.4%
61 1
 
0.2%
ValueCountFrequency (%)
570160 1
0.2%
474812 1
0.2%
459400 1
0.2%
366842 1
0.2%
266730 1
0.2%
241200 1
0.2%
239199 1
0.2%
204537 1
0.2%
201737 1
0.2%
201164 1
0.2%

Interactions

2024-04-06T17:43:31.503994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:34.334072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.746903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.653486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:46.702147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:51.402365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:55.717395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.892014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.977733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.304477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:13.341926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:17.959744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:22.111477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:27.284773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:31.857337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:34.670152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.054685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.901602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:46.994798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:51.740959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:56.001268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:00.212555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.286226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.637371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:13.649543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:18.432053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:22.458319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:27.606525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:32.157158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:34.977256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.364103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.175552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:47.284274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:52.253093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:56.285033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:00.571344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.584558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.978458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:13.963032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:18.730285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:22.723358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:27.934575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:32.427164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:35.331825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.653054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.434598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:47.558796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:52.550570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:56.540440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:00.904890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:04.870278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:10.252989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:14.284208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:19.080179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:22.977710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:28.218918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:32.680377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:35.681869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:39.932917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:43.736056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.447923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:52.832261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:56.834015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:01.195031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:05.236236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:10.510304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:14.655682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:19.386225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:24.013462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:28.512271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:32.971516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:36.007716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:40.204774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:44.047259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:48.765227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.123577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:57.135585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:01.469456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:05.601925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:10.801676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:14.947655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:19.673173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:24.315524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:28.798830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:33.229382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:36.333106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:40.472248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:44.299428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.022929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.425169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:57.384253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:01.713893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:06.577979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:11.070570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:15.344270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:19.935532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:24.634174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:29.087479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:33.526813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:36.683897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:40.754225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:44.552949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.287918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.691353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:57.633333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:01.965171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:06.867765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:11.324627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:15.642250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:20.172352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:25.017839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:29.416258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:33.780912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:36.989284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:41.019328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:44.819325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.607435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:53.980983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:57.987208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:02.268839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:07.226229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:11.592576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:15.976273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:20.444240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:25.340632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:29.728891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:34.070815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:37.276989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:41.301418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:45.155051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:49.903623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:54.330416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.312693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:02.666928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:07.613655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:11.865299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:16.309003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:20.707705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:25.720974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:30.029745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:34.342853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:37.575280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:41.606888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:45.468169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:50.211583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:54.592449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:58.621397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:02.909850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:07.984304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:12.286585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:16.627904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:20.956198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:26.044360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:30.328854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:34.759470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:37.871303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:41.868634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:45.795831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:50.456224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:54.872155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.016174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.167100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:08.330822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:12.547438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:16.940698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:21.212706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:26.417599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:30.614990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:35.144824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.179366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.165999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:46.136700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:50.749187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:55.139856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.328320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.446693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:08.697660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:12.793825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:17.269416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:21.533179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:26.688277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:30.942282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:35.450177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:38.461042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:42.419634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:46.453475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:51.148798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:55.410214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:42:59.596690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:03.739696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:09.014970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:13.046602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:17.537197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:21.835779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:26.973012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:43:31.233141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:43:56.883321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도조성상태지정면적(천제곱미터)관리면적(천제곱미터)산업시설구역_전체면적(천제곱미터)산업시설구역_분양대상(천제곱미터)산업시설구역_분양(천제곱미터)산업시설구역_미분양(천제곱미터)산업시설구역_분양률(퍼센트)입주업체(개)가동업체(개)고용현황(명)_남고용현황(명)_여고용현황(명)_계누계생산(백만원)누계수출(천달러)
시도1.0000.0000.0000.0000.0000.0000.0000.0000.0000.4980.3990.6000.3870.4840.2550.228
조성상태0.0001.0000.0000.0000.0000.3990.3920.2470.7230.0000.0000.0000.0000.0000.0000.108
지정면적(천제곱미터)0.0000.0001.0001.0000.9320.9250.9170.0820.0000.6120.6440.7770.5950.8720.8630.482
관리면적(천제곱미터)0.0000.0001.0001.0000.9330.9260.9180.1080.0000.6190.6510.7780.5930.8720.8630.481
산업시설구역_전체면적(천제곱미터)0.0000.0000.9320.9331.0001.0000.9990.0000.0000.6960.6890.8300.8220.7870.7660.533
산업시설구역_분양대상(천제곱미터)0.0000.3990.9250.9261.0001.0001.0000.0460.3500.7030.6950.8320.8260.7890.7670.537
산업시설구역_분양(천제곱미터)0.0000.3920.9170.9180.9991.0001.0000.0000.4410.7080.7010.8340.8280.7910.7690.542
산업시설구역_미분양(천제곱미터)0.0000.2470.0820.1080.0000.0460.0001.0000.7700.0000.0000.0000.0000.0000.0000.000
산업시설구역_분양률(퍼센트)0.0000.7230.0000.0000.0000.3500.4410.7701.0000.0000.0000.0000.0000.0000.0000.000
입주업체(개)0.4980.0000.6120.6190.6960.7030.7080.0000.0001.0000.9950.5810.8250.8550.5550.293
가동업체(개)0.3990.0000.6440.6510.6890.6950.7010.0000.0000.9951.0000.6280.8530.8880.5720.293
고용현황(명)_남0.6000.0000.7770.7780.8300.8320.8340.0000.0000.5810.6281.0000.8130.9220.7960.595
고용현황(명)_여0.3870.0000.5950.5930.8220.8260.8280.0000.0000.8250.8530.8131.0000.8900.5440.573
고용현황(명)_계0.4840.0000.8720.8720.7870.7890.7910.0000.0000.8550.8880.9220.8901.0000.8980.651
누계생산(백만원)0.2550.0000.8630.8630.7660.7670.7690.0000.0000.5550.5720.7960.5440.8981.0000.813
누계수출(천달러)0.2280.1080.4820.4810.5330.5370.5420.0000.0000.2930.2930.5950.5730.6510.8131.000
2024-04-06T17:43:57.462550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도조성상태
시도1.0000.000
조성상태0.0001.000
2024-04-06T17:43:57.920106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정면적(천제곱미터)관리면적(천제곱미터)산업시설구역_전체면적(천제곱미터)산업시설구역_분양대상(천제곱미터)산업시설구역_분양(천제곱미터)산업시설구역_미분양(천제곱미터)산업시설구역_분양률(퍼센트)입주업체(개)가동업체(개)고용현황(명)_남고용현황(명)_여고용현황(명)_계누계생산(백만원)누계수출(천달러)시도조성상태
지정면적(천제곱미터)1.0000.9940.9480.8610.7780.057-0.0380.3380.3350.4450.3470.4440.3840.2800.0000.000
관리면적(천제곱미터)0.9941.0000.9520.8630.7800.058-0.0400.3390.3420.4520.3450.4450.3820.2800.0000.000
산업시설구역_전체면적(천제곱미터)0.9480.9521.0000.9280.863-0.0130.0600.3550.3660.5350.3820.5140.4750.3720.0000.000
산업시설구역_분양대상(천제곱미터)0.8610.8630.9281.0000.941-0.0120.2440.4470.4550.6240.4780.6050.5740.4450.0000.260
산업시설구역_분양(천제곱미터)0.7780.7800.8630.9411.000-0.2390.4250.4840.5020.6730.5240.6530.6270.5090.0000.254
산업시설구역_미분양(천제곱미터)0.0570.058-0.013-0.012-0.2391.000-0.765-0.107-0.124-0.216-0.132-0.197-0.215-0.2490.0000.170
산업시설구역_분양률(퍼센트)-0.038-0.0400.0600.2440.425-0.7651.0000.3040.3160.4160.3490.4040.4120.3760.0000.568
입주업체(개)0.3380.3390.3550.4470.484-0.1070.3041.0000.9790.5140.5490.5340.3470.3390.2350.000
가동업체(개)0.3350.3420.3660.4550.502-0.1240.3160.9791.0000.5460.5770.5630.3690.3520.1790.000
고용현황(명)_남0.4450.4520.5350.6240.673-0.2160.4160.5140.5461.0000.7300.9690.7970.6910.2630.000
고용현황(명)_여0.3470.3450.3820.4780.524-0.1320.3490.5490.5770.7301.0000.8640.6770.5620.1660.000
고용현황(명)_계0.4440.4450.5140.6050.653-0.1970.4040.5340.5630.9690.8641.0000.8010.6860.2260.000
누계생산(백만원)0.3840.3820.4750.5740.627-0.2150.4120.3470.3690.7970.6770.8011.0000.7080.1090.000
누계수출(천달러)0.2800.2800.3720.4450.509-0.2490.3760.3390.3520.6910.5620.6860.7081.0000.1010.067
시도0.0000.0000.0000.0000.0000.0000.0000.2350.1790.2630.1660.2260.1090.1011.0000.000
조성상태0.0000.0000.0000.2600.2540.1700.5680.0000.0000.0000.0000.0000.0000.0670.0001.000

Missing values

2024-04-06T17:43:36.006057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:43:36.757693image/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.
2024-04-06T17:43:37.236062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

유형시도시군단지명조성상태지정면적(천제곱미터)관리면적(천제곱미터)산업시설구역_전체면적(천제곱미터)산업시설구역_분양대상(천제곱미터)산업시설구역_분양(천제곱미터)산업시설구역_미분양(천제곱미터)산업시설구역_분양률(퍼센트)입주업체(개)가동업체(개)고용현황(명)_남고용현황(명)_여고용현황(명)_계누계생산(백만원)누계수출(천달러)
0농공부산기장군정관완료2582581891891890100.035351272346161861398878040
1농공대구달성군구지완료1931931601601600100.02222490745642612988682
2농공대구달성군옥포완료1621621301301300100.0494932019351310254322670
3농공대구군위군군위완료3013012222222220100.0333056118874919069713596
4농공대구군위군효령완료1121129999990100.01413152321849000010003
5농공광주광산구소촌완료3243242622622620100.0646411864161602509764121016
6농공울산북구달천완료2602601941941940100.010295849380122941149511850
7농공울산울주군두동완료70705656560100.0444151843312493624848
8농공울산울주군두서완료1231231011011010100.0151446610356918094014984
9농공울산울주군상북완료1391391071071070100.012126787475220049244928
유형시도시군단지명조성상태지정면적(천제곱미터)관리면적(천제곱미터)산업시설구역_전체면적(천제곱미터)산업시설구역_분양대상(천제곱미터)산업시설구역_분양(천제곱미터)산업시설구역_미분양(천제곱미터)산업시설구역_분양률(퍼센트)입주업체(개)가동업체(개)고용현황(명)_남고용현황(명)_여고용현황(명)_계누계생산(백만원)누계수출(천달러)
470농공경남창원시 마산합포구진북완료1331331111111110100.021213651735381216266552
471농공경남고성군제일완료44452828280100.043511263177600
472농공경남거창군승강기전문완료3083081881881880100.0151513945184146850
473농공경남함양군안의제2전문완료2752722082082080100.0171114317160285633353
474농공경남산청군화현완료88876060441673.3372<NA><NA><NA><NA>0
475농공경남함양군함양중방전문완료99996161610100.062<NA><NA><NA><NA>0
476농공경남함양군인산죽염항노화조성중210206890000.01000000
477농공제주서귀포시대정완료1151159494940100.022201603419475501504
478농공제주제주시구좌완료67674949490100.0181810350153292450
479농공제주제주시금능완료1301309797970100.0191926712138869844237