Overview

Dataset statistics

Number of variables19
Number of observations139
Missing cells515
Missing cells (%)19.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.7 KiB
Average record size in memory166.9 B

Variable types

Categorical4
Text1
Numeric14

Dataset

Description충청북도 산업단지 현황 데이터를 제공합니다. (산업단지명, 조성상태, 면적, 분양, 입주업체수, 가동업체수, 고용현황, 생산액 등)(단위 : 개, 천㎡, 명,백만원, 천달러, %)
URLhttps://www.data.go.kr/data/15028994/fileData.do

Alerts

시도 has constant value ""Constant
지정면적 is highly overall correlated with 관리면적 and 8 other fieldsHigh correlation
관리면적 is highly overall correlated with 지정면적 and 7 other fieldsHigh correlation
산업시설구역(전체면적) is highly overall correlated with 지정면적 and 9 other fieldsHigh correlation
산업시설구역(분양대상) is highly overall correlated with 지정면적 and 10 other fieldsHigh correlation
산업시설구역(분양) is highly overall correlated with 지정면적 and 10 other fieldsHigh correlation
산업시설구역(미분양) is highly overall correlated with 산업시설구역(분양률) and 2 other fieldsHigh correlation
산업시설구역(분양률) is highly overall correlated with 산업시설구역(미분양)High correlation
입주업체 is highly overall correlated with 지정면적 and 8 other fieldsHigh correlation
가동업체 is highly overall correlated with 산업시설구역(분양대상) and 4 other fieldsHigh correlation
고용현황(명)(남) is highly overall correlated with 지정면적 and 9 other fieldsHigh correlation
고용현황(명)(여) is highly overall correlated with 지정면적 and 11 other fieldsHigh correlation
고용현황(명)(계) is highly overall correlated with 지정면적 and 11 other fieldsHigh correlation
누계생산(백만원) is highly overall correlated with 산업시설구역(전체면적) and 6 other fieldsHigh correlation
누계수출(천달러) is highly overall correlated with 산업시설구역(전체면적) and 6 other fieldsHigh correlation
유형 is highly overall correlated with 지정면적 and 3 other fieldsHigh correlation
조성상태 is highly overall correlated with 산업시설구역(미분양)High correlation
산업시설구역(분양대상) has 24 (17.3%) missing valuesMissing
산업시설구역(분양) has 25 (18.0%) missing valuesMissing
산업시설구역(미분양) has 123 (88.5%) missing valuesMissing
산업시설구역(분양률) has 25 (18.0%) missing valuesMissing
입주업체 has 28 (20.1%) missing valuesMissing
가동업체 has 32 (23.0%) missing valuesMissing
고용현황(명)(남) has 48 (34.5%) missing valuesMissing
고용현황(명)(여) has 48 (34.5%) missing valuesMissing
고용현황(명)(계) has 48 (34.5%) missing valuesMissing
누계생산(백만원) has 50 (36.0%) missing valuesMissing
누계수출(천달러) has 64 (46.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:37:53.151949
Analysis finished2023-12-12 10:38:22.517111
Duration29.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유형
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
일반
92 
농공
43 
국가
 
2
도시첨단
 
2

Length

Max length4
Median length2
Mean length2.028777
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국가
2nd row국가
3rd row일반
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
일반 92
66.2%
농공 43
30.9%
국가 2
 
1.4%
도시첨단 2
 
1.4%

Length

2023-12-12T19:38:22.628184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:22.776663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 92
66.2%
농공 43
30.9%
국가 2
 
1.4%
도시첨단 2
 
1.4%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
충북
139 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충북 139
100.0%

Length

2023-12-12T19:38:22.913924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:23.039764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충북 139
100.0%

시군
Categorical

Distinct12
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
음성군
24 
청주시
23 
진천군
23 
충주시
22 
제천시
9 
Other values (7)
38 

Length

Max length6
Median length3
Mean length3.0431655
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보은군
2nd row청주시
3rd row제천시
4th row제천시
5th row청주시

Common Values

ValueCountFrequency (%)
음성군 24
17.3%
청주시 23
16.5%
진천군 23
16.5%
충주시 22
15.8%
제천시 9
 
6.5%
옥천군 8
 
5.8%
괴산군 7
 
5.0%
보은군 6
 
4.3%
증평군 6
 
4.3%
영동군 5
 
3.6%
Other values (2) 6
 
4.3%

Length

2023-12-12T19:38:23.205445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
음성군 24
17.3%
청주시 23
16.5%
진천군 23
16.5%
충주시 22
15.8%
제천시 9
 
6.5%
옥천군 8
 
5.8%
괴산군 7
 
5.0%
보은군 6
 
4.3%
증평군 6
 
4.3%
영동군 5
 
3.6%
Other values (2) 6
 
4.3%

단지명
Text

Distinct129
Distinct (%)92.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T19:38:23.507066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length5.323741
Min length2

Characters and Unicode

Total characters740
Distinct characters186
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)86.3%

Sample

1st row보은
2nd row오송생명과학
3rd row제천
4th row제천제2
5th row청주[재생사업지구(부분)]
ValueCountFrequency (%)
보은 3
 
2.2%
용산 2
 
1.4%
금왕 2
 
1.4%
청산 2
 
1.4%
영동 2
 
1.4%
현도 2
 
1.4%
산수 2
 
1.4%
이월 2
 
1.4%
오창과학 2
 
1.4%
청주도시첨단문화 1
 
0.7%
Other values (119) 119
85.6%
2023-12-12T19:38:24.143592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 25
 
3.4%
) 25
 
3.4%
구 24
 
3.2%
주 23
 
3.1%
산 21
 
2.8%
크 19
 
2.6%
리 17
 
2.3%
테 17
 
2.3%
제 17
 
2.3%
노 16
 
2.2%
Other values (176) 536
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 655
88.5%
Open Punctuation 26
 
3.5%
Close Punctuation 26
 
3.5%
Decimal Number 16
 
2.2%
Other Punctuation 9
 
1.2%
Uppercase Letter 8
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 24
 
3.7%
주 23
 
3.5%
산 21
 
3.2%
크 19
 
2.9%
리 17
 
2.6%
테 17
 
2.6%
제 17
 
2.6%
노 16
 
2.4%
성 14
 
2.1%
충 14
 
2.1%
Other values (158) 473
72.2%
Uppercase Letter
ValueCountFrequency (%)
G 2
25.0%
K 2
25.0%
H 1
12.5%
D 1
12.5%
T 1
12.5%
C 1
12.5%
Decimal Number
ValueCountFrequency (%)
2 10
62.5%
3 3
 
18.8%
4 1
 
6.2%
1 1
 
6.2%
5 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
: 7
77.8%
· 1
 
11.1%
& 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 25
96.2%
[ 1
 
3.8%
Close Punctuation
ValueCountFrequency (%)
) 25
96.2%
] 1
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 655
88.5%
Common 77
 
10.4%
Latin 8
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 24
 
3.7%
주 23
 
3.5%
산 21
 
3.2%
크 19
 
2.9%
리 17
 
2.6%
테 17
 
2.6%
제 17
 
2.6%
노 16
 
2.4%
성 14
 
2.1%
충 14
 
2.1%
Other values (158) 473
72.2%
Common
ValueCountFrequency (%)
( 25
32.5%
) 25
32.5%
2 10
 
13.0%
: 7
 
9.1%
3 3
 
3.9%
· 1
 
1.3%
4 1
 
1.3%
1 1
 
1.3%
] 1
 
1.3%
[ 1
 
1.3%
Other values (2) 2
 
2.6%
Latin
ValueCountFrequency (%)
G 2
25.0%
K 2
25.0%
H 1
12.5%
D 1
12.5%
T 1
12.5%
C 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 655
88.5%
ASCII 84
 
11.4%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 25
29.8%
) 25
29.8%
2 10
 
11.9%
: 7
 
8.3%
3 3
 
3.6%
G 2
 
2.4%
K 2
 
2.4%
4 1
 
1.2%
1 1
 
1.2%
H 1
 
1.2%
Other values (7) 7
 
8.3%
Hangul
ValueCountFrequency (%)
구 24
 
3.7%
주 23
 
3.5%
산 21
 
3.2%
크 19
 
2.9%
리 17
 
2.6%
테 17
 
2.6%
제 17
 
2.6%
노 16
 
2.4%
성 14
 
2.1%
충 14
 
2.1%
Other values (158) 473
72.2%
None
ValueCountFrequency (%)
· 1
100.0%

조성상태
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
완료
103 
조성중
22 
미개발
14 

Length

Max length3
Median length2
Mean length2.2589928
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
완료 103
74.1%
조성중 22
 
15.8%
미개발 14
 
10.1%

Length

2023-12-12T19:38:24.347064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:38:24.498216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
완료 103
74.1%
조성중 22
 
15.8%
미개발 14
 
10.1%

지정면적
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean731.02158
Minimum47
Maximum9450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:24.648057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile64.8
Q1132
median299
Q3840.5
95-th percentile2130.2
Maximum9450
Range9403
Interquartile range (IQR)708.5

Descriptive statistics

Standard deviation1303.1557
Coefficient of variation (CV)1.78265
Kurtosis24.238726
Mean731.02158
Median Absolute Deviation (MAD)210
Skewness4.4730884
Sum101612
Variance1698214.7
MonotonicityNot monotonic
2023-12-12T19:38:24.826364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149 3
 
2.2%
132 3
 
2.2%
136 2
 
1.4%
124 2
 
1.4%
145 2
 
1.4%
806 2
 
1.4%
82 2
 
1.4%
150 2
 
1.4%
142 2
 
1.4%
301 1
 
0.7%
Other values (118) 118
84.9%
ValueCountFrequency (%)
47 1
0.7%
49 1
0.7%
50 1
0.7%
55 1
0.7%
58 1
0.7%
60 1
0.7%
63 1
0.7%
65 1
0.7%
67 1
0.7%
68 1
0.7%
ValueCountFrequency (%)
9450 1
0.7%
8644 1
0.7%
4833 1
0.7%
4178 1
0.7%
4098 1
0.7%
3804 1
0.7%
3284 1
0.7%
2002 1
0.7%
1992 1
0.7%
1811 1
0.7%

관리면적
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean683.41727
Minimum47
Maximum9139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:25.004523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile62.9
Q1130.5
median296
Q3840.5
95-th percentile1921.7
Maximum9139
Range9092
Interquartile range (IQR)710

Descriptive statistics

Standard deviation1208.6959
Coefficient of variation (CV)1.768606
Kurtosis30.438858
Mean683.41727
Median Absolute Deviation (MAD)207
Skewness4.9992603
Sum94995
Variance1460945.8
MonotonicityNot monotonic
2023-12-12T19:38:25.181233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132 4
 
2.9%
149 3
 
2.2%
145 3
 
2.2%
89 2
 
1.4%
283 2
 
1.4%
413 2
 
1.4%
148 2
 
1.4%
124 2
 
1.4%
136 2
 
1.4%
298 1
 
0.7%
Other values (116) 116
83.5%
ValueCountFrequency (%)
47 1
0.7%
49 1
0.7%
50 1
0.7%
55 1
0.7%
58 1
0.7%
60 1
0.7%
62 1
0.7%
63 1
0.7%
65 1
0.7%
66 1
0.7%
ValueCountFrequency (%)
9139 1
0.7%
8644 1
0.7%
4179 1
0.7%
4098 1
0.7%
2595 1
0.7%
2002 1
0.7%
1937 1
0.7%
1920 1
0.7%
1802 1
0.7%
1729 1
0.7%

산업시설구역(전체면적)
Real number (ℝ)

HIGH CORRELATION 

Distinct123
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean386.93525
Minimum28
Maximum3580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:25.333176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile44.9
Q193
median189
Q3511
95-th percentile1157.3
Maximum3580
Range3552
Interquartile range (IQR)418

Descriptive statistics

Standard deviation527.60706
Coefficient of variation (CV)1.3635539
Kurtosis16.677815
Mean386.93525
Median Absolute Deviation (MAD)124
Skewness3.5986334
Sum53784
Variance278369.21
MonotonicityNot monotonic
2023-12-12T19:38:25.512856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 3
 
2.2%
69 2
 
1.4%
274 2
 
1.4%
44 2
 
1.4%
243 2
 
1.4%
108 2
 
1.4%
98 2
 
1.4%
59 2
 
1.4%
52 2
 
1.4%
99 2
 
1.4%
Other values (113) 118
84.9%
ValueCountFrequency (%)
28 1
0.7%
30 1
0.7%
40 2
1.4%
43 1
0.7%
44 2
1.4%
45 1
0.7%
48 1
0.7%
52 2
1.4%
53 1
0.7%
54 1
0.7%
ValueCountFrequency (%)
3580 1
0.7%
3085 1
0.7%
2906 1
0.7%
1431 1
0.7%
1384 1
0.7%
1236 1
0.7%
1169 1
0.7%
1156 1
0.7%
1064 1
0.7%
1025 1
0.7%

산업시설구역(분양대상)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct102
Distinct (%)88.7%
Missing24
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean366.34783
Minimum28
Maximum3580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:26.010291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile47.1
Q189.5
median156
Q3410.5
95-th percentile1159.9
Maximum3580
Range3552
Interquartile range (IQR)321

Descriptive statistics

Standard deviation555.01808
Coefficient of variation (CV)1.5150031
Kurtosis17.026838
Mean366.34783
Median Absolute Deviation (MAD)93
Skewness3.7773499
Sum42130
Variance308045.07
MonotonicityNot monotonic
2023-12-12T19:38:26.183987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 3
 
2.2%
274 2
 
1.4%
108 2
 
1.4%
56 2
 
1.4%
83 2
 
1.4%
98 2
 
1.4%
243 2
 
1.4%
99 2
 
1.4%
115 2
 
1.4%
313 2
 
1.4%
Other values (92) 94
67.6%
(Missing) 24
 
17.3%
ValueCountFrequency (%)
28 1
0.7%
30 1
0.7%
40 1
0.7%
43 1
0.7%
44 1
0.7%
45 1
0.7%
48 1
0.7%
52 2
1.4%
53 1
0.7%
54 1
0.7%
ValueCountFrequency (%)
3580 1
0.7%
3085 1
0.7%
2906 1
0.7%
1384 1
0.7%
1236 1
0.7%
1169 1
0.7%
1156 1
0.7%
1025 1
0.7%
916 1
0.7%
868 1
0.7%

산업시설구역(분양)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct101
Distinct (%)88.6%
Missing25
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean360.5
Minimum6
Maximum3556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:26.392059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile44.65
Q183
median147
Q3407
95-th percentile1133.9
Maximum3556
Range3550
Interquartile range (IQR)324

Descriptive statistics

Standard deviation554.61289
Coefficient of variation (CV)1.5384546
Kurtosis17.100901
Mean360.5
Median Absolute Deviation (MAD)94.5
Skewness3.7917193
Sum41097
Variance307595.46
MonotonicityNot monotonic
2023-12-12T19:38:26.606812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 3
 
2.2%
407 2
 
1.4%
108 2
 
1.4%
56 2
 
1.4%
83 2
 
1.4%
52 2
 
1.4%
99 2
 
1.4%
115 2
 
1.4%
313 2
 
1.4%
81 2
 
1.4%
Other values (91) 93
66.9%
(Missing) 25
 
18.0%
ValueCountFrequency (%)
6 1
0.7%
25 1
0.7%
28 1
0.7%
40 1
0.7%
43 1
0.7%
44 1
0.7%
45 1
0.7%
48 1
0.7%
49 1
0.7%
50 1
0.7%
ValueCountFrequency (%)
3556 1
0.7%
3085 1
0.7%
2906 1
0.7%
1384 1
0.7%
1167 1
0.7%
1156 1
0.7%
1122 1
0.7%
1025 1
0.7%
916 1
0.7%
868 1
0.7%

산업시설구역(미분양)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)87.5%
Missing123
Missing (%)88.5%
Infinite0
Infinite (%)0.0%
Mean64.5625
Minimum1
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:26.794626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.75
Q112.25
median27.5
Q3119.25
95-th percentile188
Maximum197
Range196
Interquartile range (IQR)107

Descriptive statistics

Standard deviation68.779812
Coefficient of variation (CV)1.0653214
Kurtosis-0.67695267
Mean64.5625
Median Absolute Deviation (MAD)24
Skewness0.92661353
Sum1033
Variance4730.6625
MonotonicityNot monotonic
2023-12-12T19:38:26.969316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
24 3
 
2.2%
14 1
 
0.7%
31 1
 
0.7%
7 1
 
0.7%
1 1
 
0.7%
135 1
 
0.7%
2 1
 
0.7%
197 1
 
0.7%
68 1
 
0.7%
185 1
 
0.7%
Other values (4) 4
 
2.9%
(Missing) 123
88.5%
ValueCountFrequency (%)
1 1
 
0.7%
2 1
 
0.7%
5 1
 
0.7%
7 1
 
0.7%
14 1
 
0.7%
24 3
2.2%
31 1
 
0.7%
49 1
 
0.7%
68 1
 
0.7%
114 1
 
0.7%
ValueCountFrequency (%)
197 1
 
0.7%
185 1
 
0.7%
153 1
 
0.7%
135 1
 
0.7%
114 1
 
0.7%
68 1
 
0.7%
49 1
 
0.7%
31 1
 
0.7%
24 3
2.2%
14 1
 
0.7%

산업시설구역(분양률)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)10.5%
Missing25
Missing (%)18.0%
Infinite0
Infinite (%)0.0%
Mean97.245614
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:27.138520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile87.65
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.532126
Coefficient of variation (CV)0.12887086
Kurtosis37.967807
Mean97.245614
Median Absolute Deviation (MAD)0
Skewness-5.9582573
Sum11086
Variance157.05418
MonotonicityNot monotonic
2023-12-12T19:38:27.339360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
100 101
72.7%
98 2
 
1.4%
91 2
 
1.4%
88 1
 
0.7%
27 1
 
0.7%
87 1
 
0.7%
99 1
 
0.7%
95 1
 
0.7%
3 1
 
0.7%
76 1
 
0.7%
Other values (2) 2
 
1.4%
(Missing) 25
 
18.0%
ValueCountFrequency (%)
3 1
0.7%
27 1
0.7%
50 1
0.7%
76 1
0.7%
83 1
0.7%
87 1
0.7%
88 1
0.7%
91 2
1.4%
95 1
0.7%
98 2
1.4%
ValueCountFrequency (%)
100 101
72.7%
99 1
 
0.7%
98 2
 
1.4%
95 1
 
0.7%
91 2
 
1.4%
88 1
 
0.7%
87 1
 
0.7%
83 1
 
0.7%
76 1
 
0.7%
50 1
 
0.7%

입주업체
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct45
Distinct (%)40.5%
Missing28
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean27.648649
Minimum1
Maximum700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:27.604470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q322.5
95-th percentile76
Maximum700
Range699
Interquartile range (IQR)18.5

Descriptive statistics

Standard deviation70.77784
Coefficient of variation (CV)2.5599023
Kurtosis75.425189
Mean27.648649
Median Absolute Deviation (MAD)7
Skewness8.0935512
Sum3069
Variance5009.5027
MonotonicityNot monotonic
2023-12-12T19:38:27.776382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
3 10
 
7.2%
1 9
 
6.5%
2 8
 
5.8%
5 7
 
5.0%
8 7
 
5.0%
14 6
 
4.3%
9 5
 
3.6%
16 4
 
2.9%
10 4
 
2.9%
21 3
 
2.2%
Other values (35) 48
34.5%
(Missing) 28
20.1%
ValueCountFrequency (%)
1 9
6.5%
2 8
5.8%
3 10
7.2%
4 3
 
2.2%
5 7
5.0%
6 1
 
0.7%
7 2
 
1.4%
8 7
5.0%
9 5
3.6%
10 4
 
2.9%
ValueCountFrequency (%)
700 1
0.7%
173 1
0.7%
161 1
0.7%
103 1
0.7%
98 1
0.7%
79 1
0.7%
73 1
0.7%
71 1
0.7%
68 1
0.7%
61 2
1.4%

가동업체
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)40.2%
Missing32
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean24.943925
Minimum1
Maximum682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:27.951985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median10
Q321
95-th percentile63
Maximum682
Range681
Interquartile range (IQR)17

Descriptive statistics

Standard deviation69.580391
Coefficient of variation (CV)2.7894724
Kurtosis76.666118
Mean24.943925
Median Absolute Deviation (MAD)7
Skewness8.261889
Sum2669
Variance4841.4308
MonotonicityNot monotonic
2023-12-12T19:38:28.121584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 10
 
7.2%
2 9
 
6.5%
1 7
 
5.0%
5 6
 
4.3%
7 5
 
3.6%
10 5
 
3.6%
4 5
 
3.6%
15 5
 
3.6%
14 4
 
2.9%
6 4
 
2.9%
Other values (33) 47
33.8%
(Missing) 32
23.0%
ValueCountFrequency (%)
1 7
5.0%
2 9
6.5%
3 10
7.2%
4 5
3.6%
5 6
4.3%
6 4
 
2.9%
7 5
3.6%
8 3
 
2.2%
9 2
 
1.4%
10 5
3.6%
ValueCountFrequency (%)
682 1
0.7%
173 1
0.7%
161 1
0.7%
89 1
0.7%
65 1
0.7%
63 2
1.4%
61 1
0.7%
59 1
0.7%
53 1
0.7%
52 1
0.7%

고용현황(명)(남)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct86
Distinct (%)94.5%
Missing48
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean1213.3736
Minimum7
Maximum20112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:28.322917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile66
Q1129
median324
Q3920.5
95-th percentile2862.5
Maximum20112
Range20105
Interquartile range (IQR)791.5

Descriptive statistics

Standard deviation3300.0445
Coefficient of variation (CV)2.7197265
Kurtosis24.469236
Mean1213.3736
Median Absolute Deviation (MAD)225
Skewness4.953801
Sum110417
Variance10890293
MonotonicityNot monotonic
2023-12-12T19:38:28.519160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 2
 
1.4%
67 2
 
1.4%
109 2
 
1.4%
61 2
 
1.4%
221 2
 
1.4%
252 1
 
0.7%
161 1
 
0.7%
483 1
 
0.7%
99 1
 
0.7%
260 1
 
0.7%
Other values (76) 76
54.7%
(Missing) 48
34.5%
ValueCountFrequency (%)
7 1
0.7%
53 1
0.7%
61 2
1.4%
65 1
0.7%
67 2
1.4%
74 1
0.7%
84 2
1.4%
86 1
0.7%
88 1
0.7%
90 1
0.7%
ValueCountFrequency (%)
20112 1
0.7%
17732 1
0.7%
17661 1
0.7%
3451 1
0.7%
2877 1
0.7%
2848 1
0.7%
2767 1
0.7%
2380 1
0.7%
2315 1
0.7%
1989 1
0.7%

고용현황(명)(여)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)87.9%
Missing48
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean400.72527
Minimum5
Maximum8164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:28.775962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13.5
Q156
median116
Q3327.5
95-th percentile912.5
Maximum8164
Range8159
Interquartile range (IQR)271.5

Descriptive statistics

Standard deviation1045.0523
Coefficient of variation (CV)2.6079022
Kurtosis37.069478
Mean400.72527
Median Absolute Deviation (MAD)80
Skewness5.7190641
Sum36466
Variance1092134.3
MonotonicityNot monotonic
2023-12-12T19:38:28.999243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 2
 
1.4%
100 2
 
1.4%
49 2
 
1.4%
125 2
 
1.4%
30 2
 
1.4%
114 2
 
1.4%
51 2
 
1.4%
11 2
 
1.4%
5 2
 
1.4%
118 2
 
1.4%
Other values (70) 71
51.1%
(Missing) 48
34.5%
ValueCountFrequency (%)
5 2
1.4%
8 1
0.7%
11 2
1.4%
16 1
0.7%
18 1
0.7%
21 1
0.7%
23 1
0.7%
24 1
0.7%
30 2
1.4%
33 2
1.4%
ValueCountFrequency (%)
8164 1
0.7%
4307 1
0.7%
3981 1
0.7%
1801 1
0.7%
930 1
0.7%
895 1
0.7%
808 1
0.7%
789 1
0.7%
782 1
0.7%
770 1
0.7%

고용현황(명)(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)97.8%
Missing48
Missing (%)34.5%
Infinite0
Infinite (%)0.0%
Mean1614.0989
Minimum18
Maximum25825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:29.201549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile104.5
Q1199.5
median450
Q31219
95-th percentile3648
Maximum25825
Range25807
Interquartile range (IQR)1019.5

Descriptive statistics

Standard deviation4281.2433
Coefficient of variation (CV)2.6524046
Kurtosis24.39082
Mean1614.0989
Median Absolute Deviation (MAD)309
Skewness4.9372969
Sum146883
Variance18329044
MonotonicityNot monotonic
2023-12-12T19:38:29.417608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 2
 
1.4%
578 2
 
1.4%
132 1
 
0.7%
366 1
 
0.7%
164 1
 
0.7%
608 1
 
0.7%
122 1
 
0.7%
360 1
 
0.7%
436 1
 
0.7%
104 1
 
0.7%
Other values (79) 79
56.8%
(Missing) 48
34.5%
ValueCountFrequency (%)
18 1
0.7%
64 1
0.7%
66 1
0.7%
103 1
0.7%
104 1
0.7%
105 1
0.7%
114 1
0.7%
121 1
0.7%
122 1
0.7%
127 1
0.7%
ValueCountFrequency (%)
25825 1
0.7%
24419 1
0.7%
21713 1
0.7%
5252 1
0.7%
3666 1
0.7%
3630 1
0.7%
3245 1
0.7%
3209 1
0.7%
2706 1
0.7%
2359 1
0.7%

누계생산(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct89
Distinct (%)100.0%
Missing50
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean254953.11
Minimum500
Maximum5088039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:29.615596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2760
Q111630
median45373
Q3144936
95-th percentile703597
Maximum5088039
Range5087539
Interquartile range (IQR)133306

Descriptive statistics

Standard deviation826430.91
Coefficient of variation (CV)3.2415015
Kurtosis26.223416
Mean254953.11
Median Absolute Deviation (MAD)40223
Skewness5.0989336
Sum22690827
Variance6.8298804 × 1011
MonotonicityNot monotonic
2023-12-12T19:38:29.781758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7433 1
 
0.7%
16407 1
 
0.7%
18600 1
 
0.7%
21750 1
 
0.7%
10328 1
 
0.7%
113000 1
 
0.7%
10600 1
 
0.7%
51714 1
 
0.7%
76727 1
 
0.7%
38246 1
 
0.7%
Other values (79) 79
56.8%
(Missing) 50
36.0%
ValueCountFrequency (%)
500 1
0.7%
800 1
0.7%
1111 1
0.7%
1400 1
0.7%
2450 1
0.7%
3225 1
0.7%
3288 1
0.7%
3412 1
0.7%
3910 1
0.7%
4031 1
0.7%
ValueCountFrequency (%)
5088039 1
0.7%
4910297 1
0.7%
3480559 1
0.7%
1180850 1
0.7%
748067 1
0.7%
636892 1
0.7%
444511 1
0.7%
412882 1
0.7%
398670 1
0.7%
342245 1
0.7%

누계수출(천달러)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct75
Distinct (%)100.0%
Missing64
Missing (%)46.0%
Infinite0
Infinite (%)0.0%
Mean138272.12
Minimum7
Maximum2639013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-12T19:38:29.964476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile34.6
Q1650.5
median9166
Q341121
95-th percentile509021.3
Maximum2639013
Range2639006
Interquartile range (IQR)40470.5

Descriptive statistics

Standard deviation468488.46
Coefficient of variation (CV)3.3881628
Kurtosis21.337728
Mean138272.12
Median Absolute Deviation (MAD)9101
Skewness4.6220938
Sum10370409
Variance2.1948144 × 1011
MonotonicityNot monotonic
2023-12-12T19:38:30.145985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2678 1
 
0.7%
382 1
 
0.7%
1320 1
 
0.7%
18300 1
 
0.7%
29 1
 
0.7%
265 1
 
0.7%
20594 1
 
0.7%
10377 1
 
0.7%
535 1
 
0.7%
39010 1
 
0.7%
Other values (65) 65
46.8%
(Missing) 64
46.0%
ValueCountFrequency (%)
7 1
0.7%
21 1
0.7%
28 1
0.7%
29 1
0.7%
37 1
0.7%
50 1
0.7%
62 1
0.7%
65 1
0.7%
88 1
0.7%
100 1
0.7%
ValueCountFrequency (%)
2639013 1
0.7%
2556220 1
0.7%
1758165 1
0.7%
820459 1
0.7%
375548 1
0.7%
253851 1
0.7%
202370 1
0.7%
176987 1
0.7%
138564 1
0.7%
138445 1
0.7%

Interactions

2023-12-12T19:38:19.981028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:54.484689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.200654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.952256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.792414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.775743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.151064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.063224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.932064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.890869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.125151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.931774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.884677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.776182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.107209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:54.591131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.326664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.064643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.941201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.880944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.295782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.180629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.065235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.011310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.278050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.047789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.014837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.870579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.214158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:54.699112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.434143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.166582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.115938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:02.012395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.427843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.302341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.196598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.132706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.399070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.148626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.144885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.994329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.318256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:54.836607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.562433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.293173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.275716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:02.162742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.560450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.438867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.313675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.270409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.535303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.337143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.296098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:18.132470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.453777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:54.990569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.689394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.411832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.415700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:02.311217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.697199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.566025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.433326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.389410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.671700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.457785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.426402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:18.257492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.601923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.113479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.806721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.547879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.569246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:02.852382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.825461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.780542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.576536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.540699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.786410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.631275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.567603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:18.751443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.742423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.252288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.945205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.705793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.721883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.020699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:04.971134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:06.910162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.712805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:10.681382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:12.916034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.771273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.706395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:18.891297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:20.862125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.359492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.063662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.815362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:00.861302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.131811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.110472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.034828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.836998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.142354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.036535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:14.913018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.829124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.025141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.019940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.481291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.209497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:58.944555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.003846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.259271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.248598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.174659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:08.991172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.279146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.169144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.036121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:16.980206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.173892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.144863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.611306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.361869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.072962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.159749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.417938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.404450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.312454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.129600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.429585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.326164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.170443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.123615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.304645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.288908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.733665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.492938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.218382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.289290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.554627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.553618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.465405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.275290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.577804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.451819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.318462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.266547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.445981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.415285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.850455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.614037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.339009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.419261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.702792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.668868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.606968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.453088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.713665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.576778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.485298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.419915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.594746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.526833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:55.948373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.727342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.490119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.549469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.847854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.796954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.702166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.608845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.845552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.683684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.616016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.532977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.716933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:21.637266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:56.062626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:57.837315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:37:59.658390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:01.657785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:03.976621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:05.927623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:07.807928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:09.752130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:11.976285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:13.799561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:15.749515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:17.656153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:38:19.848638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:38:30.286195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군조성상태지정면적관리면적산업시설구역(전체면적)산업시설구역(분양대상)산업시설구역(분양)산업시설구역(미분양)산업시설구역(분양률)입주업체가동업체고용현황(명)(남)고용현황(명)(여)고용현황(명)(계)누계생산(백만원)누계수출(천달러)
유형1.0000.3970.2460.6510.4440.5780.6140.6201.0000.0000.3190.0000.4950.7190.6440.4430.000
시군0.3971.0000.3400.2610.0000.1790.3410.4190.6200.1280.2600.0000.4710.0000.2030.0000.000
조성상태0.2460.3401.0000.1370.1810.3470.0000.0000.9460.4470.0000.0000.0000.1820.0000.0000.000
지정면적0.6510.2610.1371.0000.9750.8300.8000.8010.0000.3660.8530.8010.8000.8960.8520.8680.805
관리면적0.4440.0000.1810.9751.0000.7970.7810.7820.0000.2130.7880.7580.7390.8450.9230.9630.954
산업시설구역(전체면적)0.5780.1790.3470.8300.7971.0000.9990.9970.0000.0000.8430.7990.9510.9480.7410.7100.686
산업시설구역(분양대상)0.6140.3410.0000.8000.7810.9991.0000.9990.0000.0000.8470.7970.9450.9470.7610.7170.689
산업시설구역(분양)0.6200.4190.0000.8010.7820.9970.9991.0000.0000.0000.8470.7970.9440.9470.7600.7160.686
산업시설구역(미분양)1.0000.6200.9460.0000.0000.0000.0000.0001.0000.7930.0000.0000.0000.0000.0000.0000.000
산업시설구역(분양률)0.0000.1280.4470.3660.2130.0000.0000.0000.7931.0000.0000.0000.0000.0000.0000.0000.000
입주업체0.3190.2600.0000.8530.7880.8430.8470.8470.0000.0001.0000.9880.9340.9450.7390.8890.834
가동업체0.0000.0000.0000.8010.7580.7990.7970.7970.0000.0000.9881.0000.9220.9160.6900.9030.833
고용현황(명)(남)0.4950.4710.0000.8000.7390.9510.9450.9440.0000.0000.9340.9221.0000.9500.8710.7410.722
고용현황(명)(여)0.7190.0000.1820.8960.8450.9480.9470.9470.0000.0000.9450.9160.9501.0000.9160.8590.795
고용현황(명)(계)0.6440.2030.0000.8520.9230.7410.7610.7600.0000.0000.7390.6900.8710.9161.0000.9480.901
누계생산(백만원)0.4430.0000.0000.8680.9630.7100.7170.7160.0000.0000.8890.9030.7410.8590.9481.0000.990
누계수출(천달러)0.0000.0000.0000.8050.9540.6860.6890.6860.0000.0000.8340.8330.7220.7950.9010.9901.000
2023-12-12T19:38:30.480077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조성상태시군유형
조성상태1.0000.1550.234
시군0.1551.0000.186
유형0.2340.1861.000
2023-12-12T19:38:30.619093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정면적관리면적산업시설구역(전체면적)산업시설구역(분양대상)산업시설구역(분양)산업시설구역(미분양)산업시설구역(분양률)입주업체가동업체고용현황(명)(남)고용현황(명)(여)고용현황(명)(계)누계생산(백만원)누계수출(천달러)유형시군조성상태
지정면적1.0000.9990.9690.9390.9060.041-0.2250.5500.4750.7330.5880.7060.4530.4970.5060.1250.089
관리면적0.9991.0000.9680.9370.9040.145-0.2240.5440.4700.7300.5830.7010.4510.4980.3740.0000.136
산업시설구역(전체면적)0.9690.9681.0000.9620.942-0.024-0.1800.5580.4820.7670.6160.7370.5060.5060.4070.0650.151
산업시설구역(분양대상)0.9390.9370.9621.0000.968-0.177-0.1910.5890.5250.7780.6280.7500.5340.5280.4390.1330.000
산업시설구역(분양)0.9060.9040.9420.9681.000-0.276-0.0470.5680.5080.7700.6270.7410.5320.5130.4450.1690.000
산업시설구역(미분양)0.0410.145-0.024-0.177-0.2761.000-0.753-0.099-0.197-0.0790.025-0.072-0.272-0.0960.7560.1860.593
산업시설구역(분양률)-0.225-0.224-0.180-0.191-0.047-0.7531.000-0.131-0.099-0.0030.056-0.001-0.028-0.1740.0000.0390.316
입주업체0.5500.5440.5580.5890.568-0.099-0.1311.0000.9670.5070.5660.5260.2670.2290.1280.1150.000
가동업체0.4750.4700.4820.5250.508-0.197-0.0990.9671.0000.4910.5610.5140.3160.2450.0000.0000.000
고용현황(명)(남)0.7330.7300.7670.7780.770-0.079-0.0030.5070.4911.0000.8480.9850.8120.7900.2100.2200.000
고용현황(명)(여)0.5880.5830.6160.6280.6270.0250.0560.5660.5610.8481.0000.9110.6940.6570.5450.0000.125
고용현황(명)(계)0.7060.7010.7370.7500.741-0.072-0.0010.5260.5140.9850.9111.0000.8170.7860.5710.1020.000
누계생산(백만원)0.4530.4510.5060.5340.532-0.272-0.0280.2670.3160.8120.6940.8171.0000.8800.3720.0000.000
누계수출(천달러)0.4970.4980.5060.5280.513-0.096-0.1740.2290.2450.7900.6570.7860.8801.0000.0000.0000.000
유형0.5060.3740.4070.4390.4450.7560.0000.1280.0000.2100.5450.5710.3720.0001.0000.1860.234
시군0.1250.0000.0650.1330.1690.1860.0390.1150.0000.2200.0000.1020.0000.0000.1861.0000.155
조성상태0.0890.1360.1510.0000.0000.5930.3160.0000.0000.0000.1250.0000.0000.0000.2340.1551.000

Missing values

2023-12-12T19:38:21.813340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:38:22.118965image/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.
2023-12-12T19:38:22.335232image/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

유형시도시군단지명조성상태지정면적관리면적산업시설구역(전체면적)산업시설구역(분양대상)산업시설구역(분양)산업시설구역(미분양)산업시설구역(분양률)입주업체가동업체고용현황(명)(남)고용현황(명)(여)고용현황(명)(계)누계생산(백만원)누계수출(천달러)
0국가충북보은군보은조성중41784179115611561156<NA>10011<NA><NA><NA><NA><NA>
1국가충북청주시오송생명과학완료48332595138413841384<NA>1007363345118015252748067176987
2일반충북제천시제천완료14721472729729729<NA>10045421632708234014520039010
3일반충북제천시제천제2완료13071307788788788<NA>1005347127964219213020015075
4일반충북청주시청주[재생사업지구(부분)]완료40984098290629062906<NA>1007006821766181642582534805591758165
5일반충북청주시청주테크노폴리스조성중380419371431757757<NA>100191523159303245444511253851
6일반충북충주시중원완료375375204204204<NA>10023234671185859802724455
7일반충북충주시충주DH완료7777737373<NA>10022<NA><NA><NA><NA><NA>
8일반충북충주시만정완료5050454545<NA>10022<NA><NA><NA><NA><NA>
9일반충북충주시충주제1완료12861286847847847<NA>10042381649370201919120927196
유형시도시군단지명조성상태지정면적관리면적산업시설구역(전체면적)산업시설구역(분양대상)산업시설구역(분양)산업시설구역(미분양)산업시설구역(분양률)입주업체가동업체고용현황(명)(남)고용현황(명)(여)고용현황(명)(계)누계생산(백만원)누계수출(천달러)
129농공충북진천군이월전기전자완료329329215215215<NA>10016156361387741212766020
130농공충북진천군진천완료5858484848<NA>1001717130113243556562
131농공충북진천군초평완료136136120120120<NA>10033247963432019317185
132농공충북단양군적성완료124124979797<NA>1001612131511823910600
133농공충북청주시청원구내수완료107107989898<NA>10033909218211009413
134농공충북청주시현도완료7272535353<NA>10011<NA><NA><NA><NA><NA>
135농공충북진천군광혜원제2완료339335241241241<NA>100532107528528212<NA>
136농공충북괴산군괴산발효식품완료321321196196196<NA>10021212489133913400<NA>
137농공충북옥천군옥천의료기기완료145145107107107<NA>10077678014722833174
138농공충북증평군도안2테크노배리(구:도안2농공)미개발133133101<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>