Overview

Dataset statistics

Number of variables10
Number of observations21
Missing cells16
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory92.1 B

Variable types

Text2
Categorical3
Numeric5

Dataset

Description공사에서 조성한 산업단지들의 조성 현황에 대한 데이터로써 소재지 시군, 사업지구명, 시설면적, 분양율 등의 항목을 제공합니다.
Author경기주택도시공사
URLhttps://www.data.go.kr/data/15004460/fileData.do

Alerts

기타유의사항 has constant value ""Constant
진행현황 is highly overall correlated with 지원시설면적 and 1 other fieldsHigh correlation
분양율 is highly overall correlated with 진행현황High correlation
총면적 is highly overall correlated with 산업시설면적 and 3 other fieldsHigh correlation
산업시설면적 is highly overall correlated with 총면적 and 3 other fieldsHigh correlation
지원시설면적 is highly overall correlated with 총면적 and 4 other fieldsHigh correlation
공공시설면적 is highly overall correlated with 총면적 and 3 other fieldsHigh correlation
공원녹지면적 is highly overall correlated with 총면적 and 3 other fieldsHigh correlation
산업시설면적 has 3 (14.3%) missing valuesMissing
지원시설면적 has 4 (19.0%) missing valuesMissing
공공시설면적 has 4 (19.0%) missing valuesMissing
공원녹지면적 has 5 (23.8%) missing valuesMissing
사업지구명 has unique valuesUnique
총면적 has unique valuesUnique

Reproduction

Analysis started2024-04-21 14:47:23.033658
Analysis finished2024-04-21 14:47:30.212002
Duration7.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-04-21T23:47:30.598369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.047619
Min length3

Characters and Unicode

Total characters64
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)23.8%

Sample

1st row성남시
2nd row광명시
3rd row안성시
4th row양주시
5th row양주시
ValueCountFrequency (%)
양주시 4
19.0%
안성시 3
14.3%
파주시 3
14.3%
성남시 2
9.5%
연천군 2
9.5%
평택시 2
9.5%
광명시 1
 
4.8%
김포시 1
 
4.8%
동두천시 1
 
4.8%
오산시 1
 
4.8%
2024-04-21T23:47:31.531188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 19
29.7%
주 7
 
10.9%
성 6
 
9.4%
양 4
 
6.2%
안 3
 
4.7%
파 3
 
4.7%
천 3
 
4.7%
군 2
 
3.1%
택 2
 
3.1%
평 2
 
3.1%
Other values (11) 13
20.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 19
29.7%
주 7
 
10.9%
성 6
 
9.4%
양 4
 
6.2%
안 3
 
4.7%
파 3
 
4.7%
천 3
 
4.7%
군 2
 
3.1%
택 2
 
3.1%
평 2
 
3.1%
Other values (11) 13
20.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 19
29.7%
주 7
 
10.9%
성 6
 
9.4%
양 4
 
6.2%
안 3
 
4.7%
파 3
 
4.7%
천 3
 
4.7%
군 2
 
3.1%
택 2
 
3.1%
평 2
 
3.1%
Other values (11) 13
20.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 19
29.7%
주 7
 
10.9%
성 6
 
9.4%
양 4
 
6.2%
안 3
 
4.7%
파 3
 
4.7%
천 3
 
4.7%
군 2
 
3.1%
택 2
 
3.1%
평 2
 
3.1%
Other values (11) 13
20.3%

사업지구명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size296.0 B
2024-04-21T23:47:32.267659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length8.5238095
Min length4

Characters and Unicode

Total characters179
Distinct characters81
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row판교제2테크노밸리
2nd row광명 시흥 도시첨단 산업단지
3rd row안성제5 일반산업단지
4th row양주 은남일반산업단지
5th row경기양주 테크노밸리 도시첨단산업단지
ValueCountFrequency (%)
판교제2테크노밸리 2
 
6.1%
광명 1
 
3.0%
포승 1
 
3.0%
평택 1
 
3.0%
경기경제자유구역 1
 
3.0%
경기기업성장센터 1
 
3.0%
계획지구 1
 
3.0%
고덕국제화 1
 
3.0%
파주월롱 1
 
3.0%
파주선유 1
 
3.0%
Other values (22) 22
66.7%
2024-04-21T23:47:33.426803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
6.7%
단 8
 
4.5%
주 7
 
3.9%
지 7
 
3.9%
제 6
 
3.4%
업 6
 
3.4%
양 6
 
3.4%
산 6
 
3.4%
성 4
 
2.2%
기 4
 
2.2%
Other values (71) 113
63.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
84.4%
Space Separator 12
 
6.7%
Uppercase Letter 9
 
5.0%
Decimal Number 5
 
2.8%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 8
 
5.3%
주 7
 
4.6%
지 7
 
4.6%
제 6
 
4.0%
업 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
성 4
 
2.6%
기 4
 
2.6%
경 4
 
2.6%
Other values (59) 93
61.6%
Uppercase Letter
ValueCountFrequency (%)
X 2
22.2%
I 2
22.2%
B 2
22.2%
C 1
11.1%
D 1
11.1%
L 1
11.1%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
5 1
 
20.0%
4 1
 
20.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
84.4%
Common 19
 
10.6%
Latin 9
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 8
 
5.3%
주 7
 
4.6%
지 7
 
4.6%
제 6
 
4.0%
업 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
성 4
 
2.6%
기 4
 
2.6%
경 4
 
2.6%
Other values (59) 93
61.6%
Common
ValueCountFrequency (%)
12
63.2%
2 3
 
15.8%
( 1
 
5.3%
) 1
 
5.3%
5 1
 
5.3%
4 1
 
5.3%
Latin
ValueCountFrequency (%)
X 2
22.2%
I 2
22.2%
B 2
22.2%
C 1
11.1%
D 1
11.1%
L 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
84.4%
ASCII 28
 
15.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
42.9%
2 3
 
10.7%
X 2
 
7.1%
I 2
 
7.1%
B 2
 
7.1%
C 1
 
3.6%
D 1
 
3.6%
L 1
 
3.6%
( 1
 
3.6%
) 1
 
3.6%
Other values (2) 2
 
7.1%
Hangul
ValueCountFrequency (%)
단 8
 
5.3%
주 7
 
4.6%
지 7
 
4.6%
제 6
 
4.0%
업 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
성 4
 
2.6%
기 4
 
2.6%
경 4
 
2.6%
Other values (59) 93
61.6%

진행현황
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size296.0 B
조성완료
16 
조성중
5 

Length

Max length4
Median length4
Mean length3.7619048
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
조성완료 16
76.2%
조성중 5
 
23.8%

Length

2024-04-21T23:47:33.844839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T23:47:34.173750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
조성완료 16
76.2%
조성중 5
 
23.8%

총면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean951996.38
Minimum11924
Maximum3906000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:47:34.493829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11924
5-th percentile187000
Q1440000
median682000
Q31312000
95-th percentile2046000
Maximum3906000
Range3894076
Interquartile range (IQR)872000

Descriptive statistics

Standard deviation881220.54
Coefficient of variation (CV)0.92565535
Kurtosis5.4963945
Mean951996.38
Median Absolute Deviation (MAD)310000
Skewness2.0654286
Sum19991924
Variance7.7654964 × 1011
MonotonicityNot monotonic
2024-04-21T23:47:34.897395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
430000 1
 
4.8%
493000 1
 
4.8%
2046000 1
 
4.8%
11924 1
 
4.8%
3906000 1
 
4.8%
840000 1
 
4.8%
1312000 1
 
4.8%
1713000 1
 
4.8%
1616000 1
 
4.8%
512000 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
11924 1
4.8%
187000 1
4.8%
206000 1
4.8%
218000 1
4.8%
430000 1
4.8%
440000 1
4.8%
493000 1
4.8%
512000 1
4.8%
586000 1
4.8%
600000 1
4.8%
ValueCountFrequency (%)
3906000 1
4.8%
2046000 1
4.8%
1713000 1
4.8%
1681000 1
4.8%
1616000 1
4.8%
1312000 1
4.8%
992000 1
4.8%
840000 1
4.8%
811000 1
4.8%
709000 1
4.8%

산업시설면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing3
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean629773.56
Minimum11924
Maximum2841000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:47:35.256454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11924
5-th percentile112288.6
Q1249500
median395500
Q3837500
95-th percentile1563450
Maximum2841000
Range2829076
Interquartile range (IQR)588000

Descriptive statistics

Standard deviation663875.65
Coefficient of variation (CV)1.0541498
Kurtosis7.0091612
Mean629773.56
Median Absolute Deviation (MAD)251500
Skewness2.3899789
Sum11335924
Variance4.4073088 × 1011
MonotonicityNot monotonic
2024-04-21T23:47:35.617388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
332000 1
 
4.8%
1338000 1
 
4.8%
11924 1
 
4.8%
2841000 1
 
4.8%
567000 1
 
4.8%
692000 1
 
4.8%
1120000 1
 
4.8%
964000 1
 
4.8%
140000 1
 
4.8%
246000 1
 
4.8%
Other values (8) 8
38.1%
(Missing) 3
 
14.3%
ValueCountFrequency (%)
11924 1
4.8%
130000 1
4.8%
140000 1
4.8%
148000 1
4.8%
246000 1
4.8%
260000 1
4.8%
332000 1
4.8%
352000 1
4.8%
395000 1
4.8%
396000 1
4.8%
ValueCountFrequency (%)
2841000 1
4.8%
1338000 1
4.8%
1120000 1
4.8%
964000 1
4.8%
886000 1
4.8%
692000 1
4.8%
567000 1
4.8%
517000 1
4.8%
396000 1
4.8%
395000 1
4.8%

지원시설면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)88.2%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean45705.882
Minimum3000
Maximum163000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:47:35.954219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile3000
Q113000
median27000
Q352000
95-th percentile143000
Maximum163000
Range160000
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation47964.524
Coefficient of variation (CV)1.0494169
Kurtosis1.3449505
Mean45705.882
Median Absolute Deviation (MAD)23000
Skewness1.4492393
Sum777000
Variance2.3005956 × 109
MonotonicityNot monotonic
2024-04-21T23:47:36.330775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3000 2
 
9.5%
52000 2
 
9.5%
138000 1
 
4.8%
27000 1
 
4.8%
26000 1
 
4.8%
13000 1
 
4.8%
21000 1
 
4.8%
163000 1
 
4.8%
4000 1
 
4.8%
15000 1
 
4.8%
Other values (5) 5
23.8%
(Missing) 4
19.0%
ValueCountFrequency (%)
3000 2
9.5%
4000 1
4.8%
7000 1
4.8%
13000 1
4.8%
15000 1
4.8%
21000 1
4.8%
26000 1
4.8%
27000 1
4.8%
37000 1
4.8%
44000 1
4.8%
ValueCountFrequency (%)
163000 1
4.8%
138000 1
4.8%
107000 1
4.8%
65000 1
4.8%
52000 2
9.5%
44000 1
4.8%
37000 1
4.8%
27000 1
4.8%
26000 1
4.8%
21000 1
4.8%

공공시설면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing4
Missing (%)19.0%
Infinite0
Infinite (%)0.0%
Mean222058.82
Minimum25000
Maximum525000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:47:36.700151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile29000
Q1112000
median148000
Q3346000
95-th percentile513000
Maximum525000
Range500000
Interquartile range (IQR)234000

Descriptive statistics

Standard deviation163049.64
Coefficient of variation (CV)0.73426326
Kurtosis-0.87934916
Mean222058.82
Median Absolute Deviation (MAD)118000
Skewness0.637729
Sum3775000
Variance2.6585184 × 1010
MonotonicityNot monotonic
2024-04-21T23:47:37.070567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
143000 1
 
4.8%
525000 1
 
4.8%
510000 1
 
4.8%
128000 1
 
4.8%
320000 1
 
4.8%
346000 1
 
4.8%
395000 1
 
4.8%
55000 1
 
4.8%
118000 1
 
4.8%
148000 1
 
4.8%
Other values (7) 7
33.3%
(Missing) 4
19.0%
ValueCountFrequency (%)
25000 1
4.8%
30000 1
4.8%
55000 1
4.8%
108000 1
4.8%
112000 1
4.8%
118000 1
4.8%
128000 1
4.8%
143000 1
4.8%
148000 1
4.8%
150000 1
4.8%
ValueCountFrequency (%)
525000 1
4.8%
510000 1
4.8%
395000 1
4.8%
389000 1
4.8%
346000 1
4.8%
320000 1
4.8%
273000 1
4.8%
150000 1
4.8%
148000 1
4.8%
143000 1
4.8%

공원녹지면적
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)100.0%
Missing5
Missing (%)23.8%
Infinite0
Infinite (%)0.0%
Mean136562.5
Minimum25000
Maximum511000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size317.0 B
2024-04-21T23:47:37.414145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile27250
Q168500
median92500
Q3187750
95-th percentile313750
Maximum511000
Range486000
Interquartile range (IQR)119250

Descriptive statistics

Standard deviation123210.37
Coefficient of variation (CV)0.90222698
Kurtosis5.1366366
Mean136562.5
Median Absolute Deviation (MAD)44500
Skewness2.051459
Sum2185000
Variance1.5180796 × 1010
MonotonicityNot monotonic
2024-04-21T23:47:37.764717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
118000 1
 
4.8%
76000 1
 
4.8%
511000 1
 
4.8%
108000 1
 
4.8%
248000 1
 
4.8%
182000 1
 
4.8%
205000 1
 
4.8%
34000 1
 
4.8%
77000 1
 
4.8%
71000 1
 
4.8%
Other values (6) 6
28.6%
(Missing) 5
23.8%
ValueCountFrequency (%)
25000 1
4.8%
28000 1
4.8%
34000 1
4.8%
67000 1
4.8%
69000 1
4.8%
71000 1
4.8%
76000 1
4.8%
77000 1
4.8%
108000 1
4.8%
118000 1
4.8%
ValueCountFrequency (%)
511000 1
4.8%
248000 1
4.8%
243000 1
4.8%
205000 1
4.8%
182000 1
4.8%
123000 1
4.8%
118000 1
4.8%
108000 1
4.8%
77000 1
4.8%
76000 1
4.8%

분양율
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size296.0 B
100.0
8 
<NA>
7 
99.5
2 
99.2
2 
99.6
1 

Length

Max length5
Median length4
Mean length4.3809524
Min length4

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
100.0 8
38.1%
<NA> 7
33.3%
99.5 2
 
9.5%
99.2 2
 
9.5%
99.6 1
 
4.8%
42.9 1
 
4.8%

Length

2024-04-21T23:47:38.155430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T23:47:38.504458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100.0 8
38.1%
na 7
33.3%
99.5 2
 
9.5%
99.2 2
 
9.5%
99.6 1
 
4.8%
42.9 1
 
4.8%

기타유의사항
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size296.0 B
공란은 데이터 미집계
21 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공란은 데이터 미집계
2nd row공란은 데이터 미집계
3rd row공란은 데이터 미집계
4th row공란은 데이터 미집계
5th row공란은 데이터 미집계

Common Values

ValueCountFrequency (%)
공란은 데이터 미집계 21
100.0%

Length

2024-04-21T23:47:38.887896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T23:47:39.192295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공란은 21
33.3%
데이터 21
33.3%
미집계 21
33.3%

Interactions

2024-04-21T23:47:27.684064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:23.566652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.341543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:25.174505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:26.464881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:28.140752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:23.736799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.499888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:25.446503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:26.718174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:28.378370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:23.886900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.640177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:25.699792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:26.961683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:28.635667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.054408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.797027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:25.969303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:27.218616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:28.866921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.198837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:24.939672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:26.217025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T23:47:27.454286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T23:47:39.394052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업지구명진행현황총면적산업시설면적지원시설면적공공시설면적공원녹지면적분양율
시군명1.0001.0000.0000.0000.0000.7060.6990.5910.500
사업지구명1.0001.0001.0001.0001.0001.0001.0001.0001.000
진행현황0.0001.0001.0000.0000.2660.5570.0000.000NaN
총면적0.0001.0000.0001.0000.9480.8230.7950.7800.263
산업시설면적0.0001.0000.2660.9481.0000.7100.7930.8960.214
지원시설면적0.7061.0000.5570.8230.7101.0000.0000.4460.463
공공시설면적0.6991.0000.0000.7950.7930.0001.0000.6270.390
공원녹지면적0.5911.0000.0000.7800.8960.4460.6271.0000.000
분양율0.5001.000NaN0.2630.2140.4630.3900.0001.000
2024-04-21T23:47:39.697545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
진행현황분양율
진행현황1.0001.000
분양율1.0001.000
2024-04-21T23:47:39.941264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총면적산업시설면적지원시설면적공공시설면적공원녹지면적진행현황분양율
총면적1.0000.9940.6640.9140.8000.0000.000
산업시설면적0.9941.0000.5990.8970.7820.0000.000
지원시설면적0.6640.5991.0000.7180.5100.5140.338
공공시설면적0.9140.8970.7181.0000.7410.0000.000
공원녹지면적0.8000.7820.5100.7411.0000.0000.000
진행현황0.0000.0000.5140.0000.0001.0001.000
분양율0.0000.0000.3380.0000.0001.0001.000

Missing values

2024-04-21T23:47:29.198157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T23:47:29.673290image/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.
2024-04-21T23:47:30.023718image/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

시군명사업지구명진행현황총면적산업시설면적지원시설면적공공시설면적공원녹지면적분양율기타유의사항
0성남시판교제2테크노밸리조성중43000014000013800011800034000<NA>공란은 데이터 미집계
1광명시광명 시흥 도시첨단 산업단지조성중4930002460002700014300077000<NA>공란은 데이터 미집계
2안성시안성제5 일반산업단지조성중709000<NA><NA><NA><NA><NA>공란은 데이터 미집계
3양주시양주 은남일반산업단지조성중992000<NA><NA><NA><NA><NA>공란은 데이터 미집계
4양주시경기양주 테크노밸리 도시첨단산업단지조성중218000<NA><NA><NA><NA><NA>공란은 데이터 미집계
5연천군연천BIX(은통일반산업단지)조성완료6000003950002600011200067000<NA>공란은 데이터 미집계
6안성시안성원곡물류단지조성완료68200039600013000273000<NA>99.6공란은 데이터 미집계
7안성시안성제4조성완료81100051700021000150000123000100.0공란은 데이터 미집계
8김포시김포양촌조성완료168100088600016300038900024300099.5공란은 데이터 미집계
9동두천시동두천2조성완료1870001300004000250002800099.2공란은 데이터 미집계
시군명사업지구명진행현황총면적산업시설면적지원시설면적공공시설면적공원녹지면적분양율기타유의사항
11양주시양주홍죽조성완료5860003520001500014800071000100.0공란은 데이터 미집계
12연천군연천백학조성완료440000260000300010800069000100.0공란은 데이터 미집계
13오산시오산가장조성완료512000332000700055000118000100.0공란은 데이터 미집계
14화성시전곡해양조성완료16160009640005200039500020500099.2공란은 데이터 미집계
15파주시파주LCD조성완료1713000112000065000346000182000100.0공란은 데이터 미집계
16파주시파주선유조성완료131200069200052000320000248000100.0공란은 데이터 미집계
17파주시파주월롱조성완료8400005670003700012800010800099.5공란은 데이터 미집계
18평택시고덕국제화 계획지구조성완료3906000284100044000510000511000100.0공란은 데이터 미집계
19성남시판교제2테크노밸리 경기기업성장센터조성완료1192411924<NA><NA><NA><NA>공란은 데이터 미집계
20평택시경기경제자유구역 평택 포승 BIX조성완료204600013380001070005250007600042.9공란은 데이터 미집계