Overview

Dataset statistics

Number of variables11
Number of observations38
Missing cells34
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory95.5 B

Variable types

Text3
Categorical3
Numeric4
DateTime1

Dataset

Description도시계획 개발관련 정보입니다(경상북도 택지개발 준공 지구명, 면적, 사업비, 실시계획, 세대수, 수용인구 등의 현황입니다.)
Author경상북도
URLhttps://www.data.go.kr/data/15086236/fileData.do

Alerts

면 적(천제곱미터) is highly overall correlated with 사업비(억원) and 2 other fieldsHigh correlation
사업비(억원) is highly overall correlated with 면 적(천제곱미터) and 3 other fieldsHigh correlation
세대수(호) is highly overall correlated with 면 적(천제곱미터) and 2 other fieldsHigh correlation
수용인구(명) is highly overall correlated with 면 적(천제곱미터) and 2 other fieldsHigh correlation
비 고 is highly overall correlated with 사업비(억원)High correlation
비 고 is highly imbalanced (82.4%)Imbalance
읍면 has 33 (86.8%) missing valuesMissing
실시계획승인 has 1 (2.6%) missing valuesMissing
지 구 명 has unique valuesUnique
면 적(천제곱미터) has unique valuesUnique
사업비(억원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:17:03.089434
Analysis finished2023-12-12 16:17:05.761231
Duration2.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지 구 명
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T01:17:05.939272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.5526316
Min length5

Characters and Unicode

Total characters211
Distinct characters62
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row포항 장성
2nd row포항 환여
3rd row포항 창포
4th row포항 장량
5th row경주 용강
ValueCountFrequency (%)
경산 9
 
11.8%
구미 7
 
9.2%
김천 5
 
6.6%
포항 4
 
5.3%
안동 4
 
5.3%
점촌 3
 
3.9%
선산 2
 
2.6%
영덕 1
 
1.3%
사동2 1
 
1.3%
구평2 1
 
1.3%
Other values (39) 39
51.3%
2023-12-13T01:17:06.375948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
46
21.8%
산 13
 
6.2%
경 10
 
4.7%
구 9
 
4.3%
동 8
 
3.8%
2 8
 
3.8%
천 8
 
3.8%
미 7
 
3.3%
옥 6
 
2.8%
포 5
 
2.4%
Other values (52) 91
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 152
72.0%
Space Separator 46
 
21.8%
Decimal Number 12
 
5.7%
Other Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 13
 
8.6%
경 10
 
6.6%
구 9
 
5.9%
동 8
 
5.3%
천 8
 
5.3%
미 7
 
4.6%
옥 6
 
3.9%
포 5
 
3.3%
안 5
 
3.3%
김 5
 
3.3%
Other values (47) 76
50.0%
Decimal Number
ValueCountFrequency (%)
2 8
66.7%
1 3
 
25.0%
3 1
 
8.3%
Space Separator
ValueCountFrequency (%)
46
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 152
72.0%
Common 59
 
28.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 13
 
8.6%
경 10
 
6.6%
구 9
 
5.9%
동 8
 
5.3%
천 8
 
5.3%
미 7
 
4.6%
옥 6
 
3.9%
포 5
 
3.3%
안 5
 
3.3%
김 5
 
3.3%
Other values (47) 76
50.0%
Common
ValueCountFrequency (%)
46
78.0%
2 8
 
13.6%
1 3
 
5.1%
, 1
 
1.7%
3 1
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 152
72.0%
ASCII 59
 
28.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
46
78.0%
2 8
 
13.6%
1 3
 
5.1%
, 1
 
1.7%
3 1
 
1.7%
Hangul
ValueCountFrequency (%)
산 13
 
8.6%
경 10
 
6.6%
구 9
 
5.9%
동 8
 
5.3%
천 8
 
5.3%
미 7
 
4.6%
옥 6
 
3.9%
포 5
 
3.3%
안 5
 
3.3%
김 5
 
3.3%
Other values (47) 76
50.0%

시군
Categorical

Distinct9
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
구미
9 
경산
9 
김천
5 
포항
4 
안동
4 
Other values (4)
7 

Length

Max length3
Median length2
Mean length2.0263158
Min length2

Unique

Unique2 ?
Unique (%)5.3%

Sample

1st row포항
2nd row포항
3rd row포항
4th row포항
5th row경주

Common Values

ValueCountFrequency (%)
구미 9
23.7%
경산 9
23.7%
김천 5
13.2%
포항 4
10.5%
안동 4
10.5%
문경 3
 
7.9%
영주 2
 
5.3%
경주 1
 
2.6%
영덕 1
 
2.6%

Length

2023-12-13T01:17:06.545358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:06.708911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
구미 9
23.7%
경산 9
23.7%
김천 5
13.2%
포항 4
10.5%
안동 4
10.5%
문경 3
 
7.9%
영주 2
 
5.3%
경주 1
 
2.6%
영덕 1
 
2.6%

읍면
Text

MISSING 

Distinct5
Distinct (%)100.0%
Missing33
Missing (%)86.8%
Memory size436.0 B
2023-12-13T01:17:06.921571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10
Distinct characters9
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 (%)100.0%

Sample

1st row선산
2nd row고아
3rd row강구
4th row하양
5th row아포
ValueCountFrequency (%)
선산 1
20.0%
고아 1
20.0%
강구 1
20.0%
하양 1
20.0%
아포 1
20.0%
2023-12-13T01:17:07.566569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 2
20.0%
선 1
10.0%
산 1
10.0%
고 1
10.0%
강 1
10.0%
구 1
10.0%
하 1
10.0%
양 1
10.0%
포 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 2
20.0%
선 1
10.0%
산 1
10.0%
고 1
10.0%
강 1
10.0%
구 1
10.0%
하 1
10.0%
양 1
10.0%
포 1
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 2
20.0%
선 1
10.0%
산 1
10.0%
고 1
10.0%
강 1
10.0%
구 1
10.0%
하 1
10.0%
양 1
10.0%
포 1
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 2
20.0%
선 1
10.0%
산 1
10.0%
고 1
10.0%
강 1
10.0%
구 1
10.0%
하 1
10.0%
양 1
10.0%
포 1
10.0%

리동
Text

Distinct30
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T01:17:07.742957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9210526
Min length1

Characters and Unicode

Total characters73
Distinct characters46
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

Unique23 ?
Unique (%)60.5%

Sample

1st row장성
2nd row환여
3rd row창포
4th row장량
5th row용강
ValueCountFrequency (%)
옥계 3
 
7.9%
도량 2
 
5.3%
흥덕 2
 
5.3%
구평 2
 
5.3%
신음 2
 
5.3%
사 2
 
5.3%
옥산 2
 
5.3%
대평 1
 
2.6%
장성 1
 
2.6%
서사 1
 
2.6%
Other values (20) 20
52.6%
2023-12-13T01:17:08.089237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
옥 6
 
8.2%
사 3
 
4.1%
량 3
 
4.1%
계 3
 
4.1%
평 3
 
4.1%
서 2
 
2.7%
장 2
 
2.7%
교 2
 
2.7%
부 2
 
2.7%
상 2
 
2.7%
Other values (36) 45
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
옥 6
 
8.2%
사 3
 
4.1%
량 3
 
4.1%
계 3
 
4.1%
평 3
 
4.1%
서 2
 
2.7%
장 2
 
2.7%
교 2
 
2.7%
부 2
 
2.7%
상 2
 
2.7%
Other values (36) 45
61.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 73
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
옥 6
 
8.2%
사 3
 
4.1%
량 3
 
4.1%
계 3
 
4.1%
평 3
 
4.1%
서 2
 
2.7%
장 2
 
2.7%
교 2
 
2.7%
부 2
 
2.7%
상 2
 
2.7%
Other values (36) 45
61.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
옥 6
 
8.2%
사 3
 
4.1%
량 3
 
4.1%
계 3
 
4.1%
평 3
 
4.1%
서 2
 
2.7%
장 2
 
2.7%
교 2
 
2.7%
부 2
 
2.7%
상 2
 
2.7%
Other values (36) 45
61.6%

면 적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.86842
Minimum10
Maximum936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:17:08.225174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile45.3
Q1153.75
median250.5
Q3411.25
95-th percentile672.8
Maximum936
Range926
Interquartile range (IQR)257.5

Descriptive statistics

Standard deviation216.7779
Coefficient of variation (CV)0.73021542
Kurtosis0.70932553
Mean296.86842
Median Absolute Deviation (MAD)110
Skewness1.0112891
Sum11281
Variance46992.658
MonotonicityNot monotonic
2023-12-13T01:17:08.350319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
195 1
 
2.6%
427 1
 
2.6%
62 1
 
2.6%
186 1
 
2.6%
208 1
 
2.6%
13 1
 
2.6%
160 1
 
2.6%
516 1
 
2.6%
340 1
 
2.6%
605 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
10 1
2.6%
13 1
2.6%
51 1
2.6%
62 1
2.6%
70 1
2.6%
78 1
2.6%
87 1
2.6%
104 1
2.6%
144 1
2.6%
152 1
2.6%
ValueCountFrequency (%)
936 1
2.6%
683 1
2.6%
671 1
2.6%
669 1
2.6%
605 1
2.6%
524 1
2.6%
516 1
2.6%
504 1
2.6%
482 1
2.6%
427 1
2.6%

사업비(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean467.89474
Minimum5
Maximum1905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:17:08.457615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile27.7
Q189.75
median338
Q3576
95-th percentile1477.7
Maximum1905
Range1900
Interquartile range (IQR)486.25

Descriptive statistics

Standard deviation497.0955
Coefficient of variation (CV)1.0624088
Kurtosis1.2294498
Mean467.89474
Median Absolute Deviation (MAD)247.5
Skewness1.4270343
Sum17780
Variance247103.93
MonotonicityNot monotonic
2023-12-13T01:17:08.587868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
26 1
 
2.6%
399 1
 
2.6%
35 1
 
2.6%
79 1
 
2.6%
89 1
 
2.6%
56 1
 
2.6%
92 1
 
2.6%
675 1
 
2.6%
1304 1
 
2.6%
1015 1
 
2.6%
Other values (28) 28
73.7%
ValueCountFrequency (%)
5 1
2.6%
26 1
2.6%
28 1
2.6%
31 1
2.6%
35 1
2.6%
45 1
2.6%
46 1
2.6%
56 1
2.6%
79 1
2.6%
89 1
2.6%
ValueCountFrequency (%)
1905 1
2.6%
1544 1
2.6%
1466 1
2.6%
1447 1
2.6%
1304 1
2.6%
1066 1
2.6%
1015 1
2.6%
675 1
2.6%
601 1
2.6%
577 1
2.6%

실시계획승인
Date

MISSING 

Distinct33
Distinct (%)89.2%
Missing1
Missing (%)2.6%
Memory size436.0 B
Minimum1981-05-22 00:00:00
Maximum2015-12-14 00:00:00
2023-12-13T01:17:08.717503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:08.846790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

세대수(호)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2646.1316
Minimum140
Maximum9688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:17:08.965710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum140
5-th percentile180.95
Q11021.25
median2202.5
Q33859.25
95-th percentile6076.75
Maximum9688
Range9548
Interquartile range (IQR)2838

Descriptive statistics

Standard deviation2136.998
Coefficient of variation (CV)0.80759326
Kurtosis1.5864299
Mean2646.1316
Median Absolute Deviation (MAD)1454
Skewness1.1068545
Sum100553
Variance4566760.5
MonotonicityNot monotonic
2023-12-13T01:17:09.075939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1200 2
 
5.3%
1237 1
 
2.6%
491 1
 
2.6%
592 1
 
2.6%
680 1
 
2.6%
185 1
 
2.6%
1582 1
 
2.6%
4237 1
 
2.6%
3479 1
 
2.6%
3869 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
140 1
2.6%
158 1
2.6%
185 1
2.6%
330 1
2.6%
408 1
2.6%
491 1
2.6%
592 1
2.6%
680 1
2.6%
840 1
2.6%
989 1
2.6%
ValueCountFrequency (%)
9688 1
2.6%
6251 1
2.6%
6046 1
2.6%
5403 1
2.6%
4993 1
2.6%
4905 1
2.6%
4716 1
2.6%
4301 1
2.6%
4237 1
2.6%
3869 1
2.6%

수용인구(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8236.5
Minimum320
Maximum22382
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T01:17:09.184763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320
5-th percentile625.45
Q13100
median7878
Q311893.5
95-th percentile18181.8
Maximum22382
Range22062
Interquartile range (IQR)8793.5

Descriptive statistics

Standard deviation6058.2412
Coefficient of variation (CV)0.73553588
Kurtosis-0.65863884
Mean8236.5
Median Absolute Deviation (MAD)4639
Skewness0.52908582
Sum312987
Variance36702287
MonotonicityNot monotonic
2023-12-13T01:17:09.288028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3100 2
 
5.3%
5400 1
 
2.6%
12378 1
 
2.6%
1964 1
 
2.6%
2368 1
 
2.6%
925 1
 
2.6%
6328 1
 
2.6%
16948 1
 
2.6%
13904 1
 
2.6%
5316 1
 
2.6%
Other values (27) 27
71.1%
ValueCountFrequency (%)
320 1
2.6%
560 1
2.6%
637 1
2.6%
925 1
2.6%
1400 1
2.6%
1508 1
2.6%
1964 1
2.6%
2264 1
2.6%
2368 1
2.6%
3100 2
5.3%
ValueCountFrequency (%)
22382 1
2.6%
19620 1
2.6%
17928 1
2.6%
17204 1
2.6%
16948 1
2.6%
16750 1
2.6%
13904 1
2.6%
13290 1
2.6%
12378 1
2.6%
11900 1
2.6%

시 행 자
Categorical

Distinct9
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size436.0 B
한국토지공사
14 
경북개발공사
8 
대한주택공사
7 
한국토지주택공사
3 
점촌시
2 
Other values (4)
4 

Length

Max length8
Median length6
Mean length5.6842105
Min length3

Unique

Unique4 ?
Unique (%)10.5%

Sample

1st row한국토지공사
2nd row한국토지공사
3rd row대한주택공사
4th row한국토지주택공사
5th row경북개발공사

Common Values

ValueCountFrequency (%)
한국토지공사 14
36.8%
경북개발공사 8
21.1%
대한주택공사 7
18.4%
한국토지주택공사 3
 
7.9%
점촌시 2
 
5.3%
김천시 1
 
2.6%
선산군 1
 
2.6%
영주시 1
 
2.6%
영덕군 1
 
2.6%

Length

2023-12-13T01:17:09.390696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:09.503700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한국토지공사 14
36.8%
경북개발공사 8
21.1%
대한주택공사 7
18.4%
한국토지주택공사 3
 
7.9%
점촌시 2
 
5.3%
김천시 1
 
2.6%
선산군 1
 
2.6%
영주시 1
 
2.6%
영덕군 1
 
2.6%

비 고
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size436.0 B
준공
37 
09.09 지정
 
1

Length

Max length8
Median length2
Mean length2.1578947
Min length2

Unique

Unique1 ?
Unique (%)2.6%

Sample

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

Common Values

ValueCountFrequency (%)
준공 37
97.4%
09.09 지정 1
 
2.6%

Length

2023-12-13T01:17:09.621162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:17:09.709290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준공 37
94.9%
09.09 1
 
2.6%
지정 1
 
2.6%

Interactions

2023-12-13T01:17:04.964135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:03.671990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.097089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.556049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:05.060789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:03.770301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.201858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.654990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:05.174461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:03.891613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.323106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.783144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:05.271152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:03.999597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.452525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:17:04.874636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:17:09.779914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지 구 명시군읍면리동면 적(천제곱미터)사업비(억원)실시계획승인세대수(호)수용인구(명)시 행 자비 고
지 구 명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군1.0001.0001.0001.0000.0000.0000.9510.0000.0000.8820.000
읍면1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
리동1.0001.0001.0001.0000.0000.0000.9570.6520.0000.9351.000
면 적(천제곱미터)1.0000.0001.0000.0001.0000.9360.7840.7240.5180.0000.364
사업비(억원)1.0000.0001.0000.0000.9361.0000.9590.7300.7120.4230.587
실시계획승인1.0000.9511.0000.9570.7840.9591.0000.8020.9581.000NaN
세대수(호)1.0000.0001.0000.6520.7240.7300.8021.0000.7840.0000.296
수용인구(명)1.0000.0001.0000.0000.5180.7120.9580.7841.0000.0000.000
시 행 자1.0000.8821.0000.9350.0000.4231.0000.0000.0001.0000.364
비 고1.0000.0001.0001.0000.3640.587NaN0.2960.0000.3641.000
2023-12-13T01:17:09.889879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비 고시 행 자시군
비 고1.0000.3190.000
시 행 자0.3191.0000.473
시군0.0000.4731.000
2023-12-13T01:17:09.964314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면 적(천제곱미터)사업비(억원)세대수(호)수용인구(명)시군시 행 자비 고
면 적(천제곱미터)1.0000.8880.8990.7870.0000.0000.319
사업비(억원)0.8881.0000.8450.7440.0000.1210.527
세대수(호)0.8990.8451.0000.8490.0000.0000.204
수용인구(명)0.7870.7440.8491.0000.0000.0000.000
시군0.0000.0000.0000.0001.0000.4730.000
시 행 자0.0000.1210.0000.0000.4731.0000.319
비 고0.3190.5270.2040.0000.0000.3191.000

Missing values

2023-12-13T01:17:05.394390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:17:05.589815image/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.
2023-12-13T01:17:05.710972image/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

지 구 명시군읍면리동면 적(천제곱미터)사업비(억원)실시계획승인세대수(호)수용인구(명)시 행 자비 고
0포항 장성포항<NA>장성195261981-05-2212005400한국토지공사준공
1포항 환여포항<NA>환여144451987-02-053301400한국토지공사준공
2포항 창포포항<NA>창포2663921991-09-07430117204대한주택공사준공
3포항 장량포항<NA>장량67114662004-12-11604617928한국토지주택공사준공
4경주 용강경주<NA>용강2492611990-06-07278611144경북개발공사준공
5김천 신음김천<NA>신음1521181991-05-038403100김천시준공
6김천 신음2김천<NA>신음1042121998-08-1011183689대한주택공사준공
7김천 교동김천<NA>교동2523601995-06-3015268112한국토지공사준공
8김천 부곡김천<NA>부곡2133741995-06-3019567644한국토지공사준공
9안동 송현안동<NA>송현78461989-02-289893956대한주택공사준공
지 구 명시군읍면리동면 적(천제곱미터)사업비(억원)실시계획승인세대수(호)수용인구(명)시 행 자비 고
28경산 옥산2경산<NA>옥산34013041994-03-24347913904경북개발공사준공
29경산 임당경산<NA>임당4273991991-12-3012375316한국토지공사준공
30경산 사동경산<NA>사60510151997-01-03386912378한국토지공사준공
31경산 백천경산<NA>백천3285772001-08-2829239059한국토지공사준공
32경산 서부경산<NA>서부2994902001-04-1831599789한국토지공사준공
33경산 대평경산<NA>대평1594041999-12-0114074362대한주택공사준공
34경산 사동2경산<NA>사93615442004-12-13540316750한국토지공사준공
35영덕 금진영덕강구금진51281991-01-15158637영덕군준공
36경산 하양경산하양서사48219052015-12-14499311796한국토지주택공사준공
37김천 송천김천아포송천6831447<NA>47169903한국토지주택공사09.09 지정