Overview

Dataset statistics

Number of variables5
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory44.1 B

Variable types

Categorical3
Numeric2

Dataset

Description한국공항공사의 공항별 공작물(건수), 토지(㎡), 건물(㎡), 입목(그루) 및 공항시설관리권 출자현황을 제공
URLhttps://www.data.go.kr/data/15002682/fileData.do

Alerts

비고 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 비고High correlation
출자액(백만원) has unique valuesUnique
수량 has 12 (18.8%) zerosZeros

Reproduction

Analysis started2023-12-12 20:03:41.570649
Analysis finished2023-12-12 20:03:42.427634
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size644.0 B
김포
5 
김해
5 
제주
5 
대구
5 
울산
5 
Other values (8)
39 

Length

Max length4
Median length2
Mean length2.15625
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김포 5
 
7.8%
김해 5
 
7.8%
제주 5
 
7.8%
대구 5
 
7.8%
울산 5
 
7.8%
청주 5
 
7.8%
광주 5
 
7.8%
여수 5
 
7.8%
포항경주 5
 
7.8%
사천 5
 
7.8%
Other values (3) 14
21.9%

Length

2023-12-13T05:03:42.524596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김포 5
 
7.8%
김해 5
 
7.8%
제주 5
 
7.8%
대구 5
 
7.8%
울산 5
 
7.8%
청주 5
 
7.8%
광주 5
 
7.8%
여수 5
 
7.8%
포항경주 5
 
7.8%
사천 5
 
7.8%
Other values (3) 14
21.9%

구분(단위)
Categorical

Distinct7
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
공작물(건수)
13 
입목(그루)
13 
공항시설관리권
12 
토지(㎡)
8 
건물(㎡)
8 
Other values (2)
10 

Length

Max length7
Median length6
Mean length6.140625
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row토 지(㎡)
2nd row건 물(㎡)
3rd row공작물(건수)
4th row입목(그루)
5th row공항시설관리권

Common Values

ValueCountFrequency (%)
공작물(건수) 13
20.3%
입목(그루) 13
20.3%
공항시설관리권 12
18.8%
토지(㎡) 8
12.5%
건물(㎡) 8
12.5%
토 지(㎡) 5
 
7.8%
건 물(㎡) 5
 
7.8%

Length

2023-12-13T05:03:42.710992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:03:42.881999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공작물(건수 13
17.6%
입목(그루 13
17.6%
공항시설관리권 12
16.2%
토지(㎡ 8
10.8%
건물(㎡ 8
10.8%
토 5
 
6.8%
지(㎡ 5
 
6.8%
건 5
 
6.8%
물(㎡ 5
 
6.8%

수량
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73286.203
Minimum0
Maximum2219507
Zeros12
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T05:03:43.077578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q156
median3689.5
Q337786.25
95-th percentile286073.3
Maximum2219507
Range2219507
Interquartile range (IQR)37730.25

Descriptive statistics

Standard deviation284427.13
Coefficient of variation (CV)3.8810461
Kurtosis53.516555
Mean73286.203
Median Absolute Deviation (MAD)3689.5
Skewness7.0810364
Sum4690317
Variance8.0898794 × 1010
MonotonicityNot monotonic
2023-12-13T05:03:43.266571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
18.8%
2219507 1
 
1.6%
16333 1
 
1.6%
1958 1
 
1.6%
26 1
 
1.6%
367 1
 
1.6%
161343 1
 
1.6%
30616 1
 
1.6%
51 1
 
1.6%
103474 1
 
1.6%
Other values (43) 43
67.2%
ValueCountFrequency (%)
0 12
18.8%
17 1
 
1.6%
26 1
 
1.6%
51 1
 
1.6%
53 1
 
1.6%
57 1
 
1.6%
115 1
 
1.6%
163 1
 
1.6%
164 1
 
1.6%
170 1
 
1.6%
ValueCountFrequency (%)
2219507 1
1.6%
419616 1
1.6%
330169 1
1.6%
304430 1
1.6%
182052 1
1.6%
161343 1
1.6%
158412 1
1.6%
103474 1
1.6%
85545 1
1.6%
72368 1
1.6%

출자액(백만원)
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34717.125
Minimum5
Maximum866097
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T05:03:43.449872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile49.55
Q1488
median1862
Q314548.5
95-th percentile201303.7
Maximum866097
Range866092
Interquartile range (IQR)14060.5

Descriptive statistics

Standard deviation117542.14
Coefficient of variation (CV)3.3857106
Kurtosis41.034473
Mean34717.125
Median Absolute Deviation (MAD)1799.5
Skewness6.0404361
Sum2221896
Variance1.3816154 × 1010
MonotonicityNot monotonic
2023-12-13T05:03:43.631625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
866097 1
 
1.6%
182 1
 
1.6%
296 1
 
1.6%
927 1
 
1.6%
92 1
 
1.6%
5 1
 
1.6%
177 1
 
1.6%
7502 1
 
1.6%
56977 1
 
1.6%
25268 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
5 1
1.6%
21 1
1.6%
39 1
1.6%
44 1
1.6%
81 1
1.6%
92 1
1.6%
95 1
1.6%
97 1
1.6%
101 1
1.6%
108 1
1.6%
ValueCountFrequency (%)
866097 1
1.6%
248257 1
1.6%
242541 1
1.6%
224806 1
1.6%
68124 1
1.6%
68067 1
1.6%
57567 1
1.6%
56977 1
1.6%
55171 1
1.6%
43461 1
1.6%

비고
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size644.0 B
<NA>
49 
안양, 양주, 강원 무선표지소 포함
5 
부산, 대구, 포항, 예천 무선표지소 포함
5 
제주 무선표지소 포함
5 

Length

Max length23
Median length4
Mean length7.203125
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안양, 양주, 강원 무선표지소 포함
2nd row안양, 양주, 강원 무선표지소 포함
3rd row안양, 양주, 강원 무선표지소 포함
4th row안양, 양주, 강원 무선표지소 포함
5th row안양, 양주, 강원 무선표지소 포함

Common Values

ValueCountFrequency (%)
<NA> 49
76.6%
안양, 양주, 강원 무선표지소 포함 5
 
7.8%
부산, 대구, 포항, 예천 무선표지소 포함 5
 
7.8%
제주 무선표지소 포함 5
 
7.8%

Length

2023-12-13T05:03:43.837774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:03:43.987322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
41.2%
무선표지소 15
 
12.6%
포함 15
 
12.6%
안양 5
 
4.2%
양주 5
 
4.2%
강원 5
 
4.2%
부산 5
 
4.2%
대구 5
 
4.2%
포항 5
 
4.2%
예천 5
 
4.2%

Interactions

2023-12-13T05:03:42.039156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:41.837435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:42.137897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:03:41.926810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:03:44.081370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분구분(단위)수량출자액(백만원)비고
구분1.0000.0000.0000.0001.000
구분(단위)0.0001.0000.3810.3760.000
수량0.0000.3811.0000.9510.000
출자액(백만원)0.0000.3760.9511.0000.000
비고1.0000.0000.0000.0001.000
2023-12-13T05:03:44.208403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분(단위)비고구분
구분(단위)1.0000.0000.000
비고0.0001.0001.000
구분0.0001.0001.000
2023-12-13T05:03:44.313525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수량출자액(백만원)구분구분(단위)비고
수량1.0000.3930.0000.2660.000
출자액(백만원)0.3931.0000.0000.2620.000
구분0.0000.0001.0000.0001.000
구분(단위)0.2660.2620.0001.0000.000
비고0.0000.0001.0000.0001.000

Missing values

2023-12-13T05:03:42.266107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:03:42.381737image/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

구분구분(단위)수량출자액(백만원)비고
0김포토 지(㎡)2219507866097안양, 양주, 강원 무선표지소 포함
1김포건 물(㎡)419616248257안양, 양주, 강원 무선표지소 포함
2김포공작물(건수)11568067안양, 양주, 강원 무선표지소 포함
3김포입목(그루)1820523037안양, 양주, 강원 무선표지소 포함
4김포공항시설관리권055171안양, 양주, 강원 무선표지소 포함
5김해토 지(㎡)30443043461부산, 대구, 포항, 예천 무선표지소 포함
6김해건 물(㎡)7236868124부산, 대구, 포항, 예천 무선표지소 포함
7김해공작물(건수)34213377부산, 대구, 포항, 예천 무선표지소 포함
8김해입목(그루)41567641부산, 대구, 포항, 예천 무선표지소 포함
9김해공항시설관리권0224806부산, 대구, 포항, 예천 무선표지소 포함
구분구분(단위)수량출자액(백만원)비고
54군산토지(㎡)292101821<NA>
55군산건물(㎡)31092170<NA>
56군산공작물(건수)57816<NA>
57군산입목(그루)4416108<NA>
58군산공항시설관리권095<NA>
59원주토지(㎡)9612990<NA>
60원주건물(㎡)16181334<NA>
61원주공작물(건수)17976<NA>
62원주입목(그루)194439<NA>
63원주공항시설관리권021<NA>