Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells16
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory84.3 B

Variable types

Text2
Categorical2
Numeric5

Dataset

Description경기주택도시공사_산업단지 조성 현황
Author경기주택도시공사
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=FNVPZYP1CFJUB0C6PJU729392132&infSeq=1

Alerts

분양율(%) is highly overall correlated with 진행현황High correlation
진행현황 is highly overall correlated with 지원시설면적(㎡) and 1 other fieldsHigh 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 started2024-03-23 01:41:52.349997
Analysis finished2024-03-23 01:42:05.463042
Duration13.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-23T01:42:05.632475image/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 row광명시
2nd row김포시
3rd row동두천시
4th row성남시
5th row성남시
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%
2024-03-23T01:42:06.485447image/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 size300.0 B
2024-03-23T01:42:06.919349image/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 row광명 시흥 도시첨단 산업단지
2nd row김포양촌
3rd row동두천2
4th row판교제2테크노밸리
5th row판교제2테크노밸리 경기기업성장센터
ValueCountFrequency (%)
판교제2테크노밸리 2
 
6.1%
광명 1
 
3.0%
양주남면 1
 
3.0%
계획지구 1
 
3.0%
고덕국제화 1
 
3.0%
bix 1
 
3.0%
포승 1
 
3.0%
평택 1
 
3.0%
경기경제자유구역 1
 
3.0%
파주월롱 1
 
3.0%
Other values (22) 22
66.7%
2024-03-23T01:42:07.900915image/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%
업 6
 
3.4%
제 6
 
3.4%
양 6
 
3.4%
산 6
 
3.4%
경 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%
업 6
 
4.0%
제 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
경 4
 
2.6%
기 4
 
2.6%
성 4
 
2.6%
Other values (59) 93
61.6%
Uppercase Letter
ValueCountFrequency (%)
X 2
22.2%
I 2
22.2%
B 2
22.2%
L 1
11.1%
C 1
11.1%
D 1
11.1%
Decimal Number
ValueCountFrequency (%)
2 3
60.0%
4 1
 
20.0%
5 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%
업 6
 
4.0%
제 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
경 4
 
2.6%
기 4
 
2.6%
성 4
 
2.6%
Other values (59) 93
61.6%
Common
ValueCountFrequency (%)
12
63.2%
2 3
 
15.8%
4 1
 
5.3%
5 1
 
5.3%
( 1
 
5.3%
) 1
 
5.3%
Latin
ValueCountFrequency (%)
X 2
22.2%
I 2
22.2%
B 2
22.2%
L 1
11.1%
C 1
11.1%
D 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%
L 1
 
3.6%
C 1
 
3.6%
D 1
 
3.6%
4 1
 
3.6%
5 1
 
3.6%
Other values (2) 2
 
7.1%
Hangul
ValueCountFrequency (%)
단 8
 
5.3%
주 7
 
4.6%
지 7
 
4.6%
업 6
 
4.0%
제 6
 
4.0%
양 6
 
4.0%
산 6
 
4.0%
경 4
 
2.6%
기 4
 
2.6%
성 4
 
2.6%
Other values (59) 93
61.6%

진행현황
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
조성완료
16 
조성중
5 

Length

Max length4
Median length4
Mean length3.7619048
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-03-23T01:42:08.299144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:42:08.643880image/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 size321.0 B
2024-03-23T01:42:08.978153image/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
2024-03-23T01:42:09.452174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
493000 1
 
4.8%
1681000 1
 
4.8%
1616000 1
 
4.8%
3906000 1
 
4.8%
2046000 1
 
4.8%
840000 1
 
4.8%
1312000 1
 
4.8%
1713000 1
 
4.8%
512000 1
 
4.8%
440000 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 size321.0 B
2024-03-23T01:42:09.844738image/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
2024-03-23T01:42:10.223819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
260000 1
 
4.8%
964000 1
 
4.8%
2841000 1
 
4.8%
1338000 1
 
4.8%
567000 1
 
4.8%
692000 1
 
4.8%
1120000 1
 
4.8%
332000 1
 
4.8%
246000 1
 
4.8%
886000 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 size321.0 B
2024-03-23T01:42:10.568913image/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
2024-03-23T01:42:10.964466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3000 2
 
9.5%
52000 2
 
9.5%
27000 1
 
4.8%
163000 1
 
4.8%
4000 1
 
4.8%
138000 1
 
4.8%
13000 1
 
4.8%
21000 1
 
4.8%
15000 1
 
4.8%
26000 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 size321.0 B
2024-03-23T01:42:11.327885image/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
2024-03-23T01:42:11.646180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
389000 1
 
4.8%
395000 1
 
4.8%
510000 1
 
4.8%
525000 1
 
4.8%
128000 1
 
4.8%
320000 1
 
4.8%
346000 1
 
4.8%
55000 1
 
4.8%
143000 1
 
4.8%
112000 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 size321.0 B
2024-03-23T01:42:11.891529image/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
2024-03-23T01:42:12.327861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
118000 1
 
4.8%
205000 1
 
4.8%
511000 1
 
4.8%
76000 1
 
4.8%
108000 1
 
4.8%
248000 1
 
4.8%
182000 1
 
4.8%
77000 1
 
4.8%
243000 1
 
4.8%
67000 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 size300.0 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 row99.5
3rd row99.2
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

2024-03-23T01:42:12.684616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:42:13.070439image/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%

Interactions

2024-03-23T01:42:02.923931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:57.008988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:58.738730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:00.060594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:01.415006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:03.198456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:57.383852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:59.079400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:00.343496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:01.658621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:03.439754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:57.756014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:59.328215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:00.597141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:01.922730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:03.752916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:58.058021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:59.610716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:00.887997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:02.186663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:03.976093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:58.371211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:41:59.835946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:01.138845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:42:02.687687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:42:13.359932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업지구명진행현황총면적(㎡)산업시설면적(㎡)지원시설면적(㎡)공공시설면적(㎡)공원녹지면적(㎡)분양율(%)
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
총면적(㎡)0.0001.0000.0001.0000.9480.8230.7950.7800.263
산업시설면적(㎡)0.0001.0000.2660.9481.0000.7100.7930.8960.214
지원시설면적(㎡)0.7061.0000.5570.8230.7101.0000.0000.4460.463
공공시설면적(㎡)0.6991.0000.0000.7950.7930.0001.0000.6270.390
공원녹지면적(㎡)0.5911.0000.0000.7800.8960.4460.6271.0000.000
분양율(%)0.5001.000NaN0.2630.2140.4630.3900.0001.000
2024-03-23T01:42:13.670822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분양율(%)진행현황
분양율(%)1.0001.000
진행현황1.0001.000
2024-03-23T01:42:13.956001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총면적(㎡)산업시설면적(㎡)지원시설면적(㎡)공공시설면적(㎡)공원녹지면적(㎡)진행현황분양율(%)
총면적(㎡)1.0000.9940.6640.9140.8000.0000.000
산업시설면적(㎡)0.9941.0000.5990.8970.7820.0000.000
지원시설면적(㎡)0.6640.5991.0000.7180.5100.5140.338
공공시설면적(㎡)0.9140.8970.7181.0000.7410.0000.000
공원녹지면적(㎡)0.8000.7820.5100.7411.0000.0000.000
진행현황0.0000.0000.5140.0000.0001.0001.000
분양율(%)0.0000.0000.3380.0000.0001.0001.000

Missing values

2024-03-23T01:42:04.314368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:42:04.889102image/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.
2024-03-23T01:42:05.260929image/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

시군명사업지구명진행현황총면적(㎡)산업시설면적(㎡)지원시설면적(㎡)공공시설면적(㎡)공원녹지면적(㎡)분양율(%)
0광명시광명 시흥 도시첨단 산업단지조성중4930002460002700014300077000<NA>
1김포시김포양촌조성완료168100088600016300038900024300099.5
2동두천시동두천2조성완료1870001300004000250002800099.2
3성남시판교제2테크노밸리조성중43000014000013800011800034000<NA>
4성남시판교제2테크노밸리 경기기업성장센터조성완료1192411924<NA><NA><NA><NA>
5안성시안성원곡물류단지조성완료68200039600013000273000<NA>99.6
6안성시안성제4조성완료81100051700021000150000123000100.0
7안성시안성제5 일반산업단지조성중709000<NA><NA><NA><NA><NA>
8양주시경기양주 테크노밸리 도시첨단산업단지조성중218000<NA><NA><NA><NA><NA>
9양주시양주 은남일반산업단지조성중992000<NA><NA><NA><NA><NA>
시군명사업지구명진행현황총면적(㎡)산업시설면적(㎡)지원시설면적(㎡)공공시설면적(㎡)공원녹지면적(㎡)분양율(%)
11양주시양주홍죽조성완료5860003520001500014800071000100.0
12연천군연천BIX(은통일반산업단지)조성완료6000003950002600011200067000<NA>
13연천군연천백학조성완료440000260000300010800069000100.0
14오산시오산가장조성완료512000332000700055000118000100.0
15파주시파주LCD조성완료1713000112000065000346000182000100.0
16파주시파주선유조성완료131200069200052000320000248000100.0
17파주시파주월롱조성완료8400005670003700012800010800099.5
18평택시경기경제자유구역 평택 포승 BIX조성완료204600013380001070005250007600042.9
19평택시고덕국제화 계획지구조성완료3906000284100044000510000511000100.0
20화성시전곡해양조성완료16160009640005200039500020500099.2