Overview

Dataset statistics

Number of variables12
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory109.3 B

Variable types

Numeric6
Categorical5
DateTime1

Dataset

Description경기도 포천시 도시계획위원회전자심의시스템에서 제공한 도시계획위원회심의현황 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15061857/fileData.do

Alerts

수정수용 has constant value ""Constant
데이터기준일 has constant value ""Constant
심의회 is highly overall correlated with 총안건 and 5 other fieldsHigh correlation
총안건 is highly overall correlated with 심의회 and 5 other fieldsHigh correlation
원안수용 is highly overall correlated with 심의회 and 4 other fieldsHigh correlation
조건부수용 is highly overall correlated with 심의회 and 4 other fieldsHigh correlation
재심의결정 is highly overall correlated with 심의회 and 5 other fieldsHigh correlation
위원회 is highly overall correlated with 자문High correlation
자문 is highly overall correlated with 위원회High correlation
유보 is highly overall correlated with 심의회 and 5 other fieldsHigh correlation
부결 is highly overall correlated with 심의회 and 3 other fieldsHigh correlation
자문 is highly imbalanced (61.6%)Imbalance
유보 is highly imbalanced (78.8%)Imbalance
부결 is highly imbalanced (74.8%)Imbalance
심의회 has 15 (37.5%) zerosZeros
총안건 has 15 (37.5%) zerosZeros
원안수용 has 22 (55.0%) zerosZeros
조건부수용 has 18 (45.0%) zerosZeros
재심의결정 has 27 (67.5%) zerosZeros

Reproduction

Analysis started2023-12-12 03:13:29.927722
Analysis finished2023-12-12 03:13:35.815103
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct8
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.5
Minimum2013
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:35.880893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2013
5-th percentile2013
Q12014.75
median2016.5
Q32018.25
95-th percentile2020
Maximum2020
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3204774
Coefficient of variation (CV)0.0011507451
Kurtosis-1.2416176
Mean2016.5
Median Absolute Deviation (MAD)2
Skewness0
Sum80660
Variance5.3846154
MonotonicityIncreasing
2023-12-12T12:13:36.071837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2013 5
12.5%
2014 5
12.5%
2015 5
12.5%
2016 5
12.5%
2017 5
12.5%
2018 5
12.5%
2019 5
12.5%
2020 5
12.5%
ValueCountFrequency (%)
2013 5
12.5%
2014 5
12.5%
2015 5
12.5%
2016 5
12.5%
2017 5
12.5%
2018 5
12.5%
2019 5
12.5%
2020 5
12.5%
ValueCountFrequency (%)
2020 5
12.5%
2019 5
12.5%
2018 5
12.5%
2017 5
12.5%
2016 5
12.5%
2015 5
12.5%
2014 5
12.5%
2013 5
12.5%

위원회
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
1분과
8 
2분과
8 
3분과
8 
도시건축
8 
본위원회
7 

Length

Max length4
Median length3
Mean length3.375
Min length3

Unique

Unique1 ?
Unique (%)2.5%

Sample

1st row본위원
2nd row1분과
3rd row2분과
4th row3분과
5th row도시건축

Common Values

ValueCountFrequency (%)
1분과 8
20.0%
2분과 8
20.0%
3분과 8
20.0%
도시건축 8
20.0%
본위원회 7
17.5%
본위원 1
 
2.5%

Length

2023-12-12T12:13:36.265874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:36.447808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1분과 8
20.0%
2분과 8
20.0%
3분과 8
20.0%
도시건축 8
20.0%
본위원회 7
17.5%
본위원 1
 
2.5%

심의회
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.475
Minimum0
Maximum24
Zeros15
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:36.634709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q312.25
95-th percentile18
Maximum24
Range24
Interquartile range (IQR)12.25

Descriptive statistics

Standard deviation7.295898
Coefficient of variation (CV)1.3325841
Kurtosis-0.48837734
Mean5.475
Median Absolute Deviation (MAD)1
Skewness1.0167944
Sum219
Variance53.230128
MonotonicityNot monotonic
2023-12-12T12:13:36.787798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15
37.5%
1 7
17.5%
2 3
 
7.5%
18 3
 
7.5%
15 3
 
7.5%
3 2
 
5.0%
12 2
 
5.0%
13 2
 
5.0%
10 1
 
2.5%
24 1
 
2.5%
ValueCountFrequency (%)
0 15
37.5%
1 7
17.5%
2 3
 
7.5%
3 2
 
5.0%
10 1
 
2.5%
12 2
 
5.0%
13 2
 
5.0%
15 3
 
7.5%
17 1
 
2.5%
18 3
 
7.5%
ValueCountFrequency (%)
24 1
 
2.5%
18 3
7.5%
17 1
 
2.5%
15 3
7.5%
13 2
 
5.0%
12 2
 
5.0%
10 1
 
2.5%
3 2
 
5.0%
2 3
7.5%
1 7
17.5%

총안건
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.075
Minimum0
Maximum101
Zeros15
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:36.945809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q348.25
95-th percentile86
Maximum101
Range101
Interquartile range (IQR)48.25

Descriptive statistics

Standard deviation31.455616
Coefficient of variation (CV)1.424943
Kurtosis-0.16307263
Mean22.075
Median Absolute Deviation (MAD)2
Skewness1.1312453
Sum883
Variance989.45577
MonotonicityNot monotonic
2023-12-12T12:13:37.109448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 15
37.5%
2 5
 
12.5%
1 2
 
5.0%
4 2
 
5.0%
86 2
 
5.0%
48 2
 
5.0%
58 2
 
5.0%
6 1
 
2.5%
79 1
 
2.5%
49 1
 
2.5%
Other values (7) 7
17.5%
ValueCountFrequency (%)
0 15
37.5%
1 2
 
5.0%
2 5
 
12.5%
4 2
 
5.0%
6 1
 
2.5%
9 1
 
2.5%
14 1
 
2.5%
39 1
 
2.5%
48 2
 
5.0%
49 1
 
2.5%
ValueCountFrequency (%)
101 1
2.5%
86 2
5.0%
79 1
2.5%
70 1
2.5%
61 1
2.5%
58 2
5.0%
51 1
2.5%
49 1
2.5%
48 2
5.0%
39 1
2.5%

원안수용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7
Minimum0
Maximum12
Zeros22
Zeros (%)55.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:37.272783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile7.1
Maximum12
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.901812
Coefficient of variation (CV)1.7069482
Kurtosis3.7465063
Mean1.7
Median Absolute Deviation (MAD)0
Skewness2.0375844
Sum68
Variance8.4205128
MonotonicityNot monotonic
2023-12-12T12:13:37.434050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 22
55.0%
1 7
 
17.5%
2 2
 
5.0%
7 2
 
5.0%
3 2
 
5.0%
6 2
 
5.0%
12 1
 
2.5%
4 1
 
2.5%
9 1
 
2.5%
ValueCountFrequency (%)
0 22
55.0%
1 7
 
17.5%
2 2
 
5.0%
3 2
 
5.0%
4 1
 
2.5%
6 2
 
5.0%
7 2
 
5.0%
9 1
 
2.5%
12 1
 
2.5%
ValueCountFrequency (%)
12 1
 
2.5%
9 1
 
2.5%
7 2
 
5.0%
6 2
 
5.0%
4 1
 
2.5%
3 2
 
5.0%
2 2
 
5.0%
1 7
 
17.5%
0 22
55.0%

조건부수용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.275
Minimum0
Maximum75
Zeros18
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:37.601089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q342
95-th percentile69.2
Maximum75
Range75
Interquartile range (IQR)42

Descriptive statistics

Standard deviation26.668898
Coefficient of variation (CV)1.4593104
Kurtosis-0.58573947
Mean18.275
Median Absolute Deviation (MAD)1
Skewness1.054207
Sum731
Variance711.23013
MonotonicityNot monotonic
2023-12-12T12:13:37.760042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 18
45.0%
1 4
 
10.0%
6 2
 
5.0%
45 2
 
5.0%
2 1
 
2.5%
69 1
 
2.5%
67 1
 
2.5%
75 1
 
2.5%
4 1
 
2.5%
41 1
 
2.5%
Other values (8) 8
20.0%
ValueCountFrequency (%)
0 18
45.0%
1 4
 
10.0%
2 1
 
2.5%
3 1
 
2.5%
4 1
 
2.5%
6 2
 
5.0%
26 1
 
2.5%
39 1
 
2.5%
41 1
 
2.5%
45 2
 
5.0%
ValueCountFrequency (%)
75 1
2.5%
73 1
2.5%
69 1
2.5%
67 1
2.5%
63 1
2.5%
59 1
2.5%
53 1
2.5%
51 1
2.5%
45 2
5.0%
41 1
2.5%

수정수용
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
40 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40
100.0%

Length

2023-12-12T12:13:37.941937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:38.097099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40
100.0%

재심의결정
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.725
Minimum0
Maximum19
Zeros27
Zeros (%)67.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T12:13:38.232835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile9.05
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.7688841
Coefficient of variation (CV)2.1848603
Kurtosis11.267505
Mean1.725
Median Absolute Deviation (MAD)0
Skewness3.1262627
Sum69
Variance14.204487
MonotonicityNot monotonic
2023-12-12T12:13:38.414556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 27
67.5%
3 4
 
10.0%
1 2
 
5.0%
2 2
 
5.0%
10 1
 
2.5%
19 1
 
2.5%
6 1
 
2.5%
7 1
 
2.5%
9 1
 
2.5%
ValueCountFrequency (%)
0 27
67.5%
1 2
 
5.0%
2 2
 
5.0%
3 4
 
10.0%
6 1
 
2.5%
7 1
 
2.5%
9 1
 
2.5%
10 1
 
2.5%
19 1
 
2.5%
ValueCountFrequency (%)
19 1
 
2.5%
10 1
 
2.5%
9 1
 
2.5%
7 1
 
2.5%
6 1
 
2.5%
3 4
 
10.0%
2 2
 
5.0%
1 2
 
5.0%
0 27
67.5%

자문
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
37 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37
92.5%
1 3
 
7.5%

Length

2023-12-12T12:13:38.563071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:38.692592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37
92.5%
1 3
 
7.5%

유보
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
38 
4
 
1
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)5.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38
95.0%
4 1
 
2.5%
1 1
 
2.5%

Length

2023-12-12T12:13:38.849083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:38.992120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38
95.0%
4 1
 
2.5%
1 1
 
2.5%

부결
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
37 
2
 
1
1
 
1
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique3 ?
Unique (%)7.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37
92.5%
2 1
 
2.5%
1 1
 
2.5%
3 1
 
2.5%

Length

2023-12-12T12:13:39.170642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:13:39.319174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37
92.5%
2 1
 
2.5%
1 1
 
2.5%
3 1
 
2.5%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
Minimum2020-06-30 00:00:00
Maximum2020-06-30 00:00:00
2023-12-12T12:13:39.430821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:39.904022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:13:34.548894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:30.392434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.608436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.290218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.104237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.796576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.685431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:30.521577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.722492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.438479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.234278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.923492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.802387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:30.652705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.819494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.582505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.355108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.027086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.923876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.229023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.921112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.710387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.454825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.147712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:35.144868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.337031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.020726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.846702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.562056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.299944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:35.270218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:31.470729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.163434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:32.965296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:33.669076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:34.429796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:13:40.025070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도위원회심의회총안건원안수용조건부수용재심의결정자문유보부결
연도1.0000.0000.2710.0000.0000.0000.2860.0000.1080.140
위원회0.0001.0000.4550.5290.5990.3840.5000.8360.0000.000
심의회0.2710.4551.0000.8980.9070.9430.8430.0000.7920.770
총안건0.0000.5290.8981.0000.7890.9220.9000.0000.9790.933
원안수용0.0000.5990.9070.7891.0000.8910.7140.0000.7920.161
조건부수용0.0000.3840.9430.9220.8911.0000.8290.0000.6430.657
재심의결정0.2860.5000.8430.9000.7140.8291.0000.0001.0000.928
자문0.0000.8360.0000.0000.0000.0000.0001.0000.0000.000
유보0.1080.0000.7920.9790.7920.6431.0000.0001.0000.653
부결0.1400.0000.7700.9330.1610.6570.9280.0000.6531.000
2023-12-12T12:13:40.186670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위원회유보자문부결
위원회1.0000.0000.6050.000
유보0.0001.0000.0000.668
자문0.6050.0001.0000.000
부결0.0000.6680.0001.000
2023-12-12T12:13:40.316077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도심의회총안건원안수용조건부수용재심의결정위원회자문유보부결
연도1.0000.1040.115-0.1300.0910.1120.0000.0000.0000.052
심의회0.1041.0000.9770.6950.9410.8190.2880.0000.6970.696
총안건0.1150.9771.0000.6990.9720.8070.2730.0000.7530.822
원안수용-0.1300.6950.6991.0000.5990.5760.3620.0000.7710.400
조건부수용0.0910.9410.9720.5991.0000.8060.2280.0000.5110.493
재심의결정0.1120.8190.8070.5760.8061.0000.1910.0000.9590.797
위원회0.0000.2880.2730.3620.2280.1911.0000.6050.0000.000
자문0.0000.0000.0000.0000.0000.0000.6051.0000.0000.000
유보0.0000.6970.7530.7710.5110.9590.0000.0001.0000.668
부결0.0520.6960.8220.4000.4930.7970.0000.0000.6681.000

Missing values

2023-12-12T12:13:35.472294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:13:35.728756image/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.

Sample

연도위원회심의회총안건원안수용조건부수용수정수용재심의결정자문유보부결데이터기준일
02013본위원2622001002020-06-30
120131분과10391226010002020-06-30
220132분과0000000002020-06-30
320133분과31476010002020-06-30
42013도시건축0000000002020-06-30
52014본위원회1201000002020-06-30
620141분과3936000002020-06-30
720142분과0000000002020-06-30
820143분과1413000002020-06-30
92014도시건축0000000002020-06-30
연도위원회심의회총안건원안수용조건부수용수정수용재심의결정자문유보부결데이터기준일
302019본위원회1110000002020-06-30
3120191분과1348045030002020-06-30
3220192분과0000000002020-06-30
3320193분과1249641020002020-06-30
342019도시건축1210000002020-06-30
352020본위원회1404000002020-06-30
3620201분과1379075030012020-06-30
3720202분과0000000002020-06-30
3820203분과1586767090032020-06-30
392020도시건축0000000002020-06-30