Overview

Dataset statistics

Number of variables8
Number of observations628
Missing cells744
Missing cells (%)14.8%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory41.8 KiB
Average record size in memory68.2 B

Variable types

Numeric4
Text3
Boolean1

Dataset

Description전라남도 여수시 도시계획정보시스템(UPIS) 용도지구 결정조서 현황 데이터 자료로 위치 지구명 등을 제공합니다.
URLhttps://www.data.go.kr/data/15119179/fileData.do

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
면적(변경) is highly overall correlated with 면적(변경후)High correlation
면적(변경후) is highly overall correlated with 면적(변경)High correlation
도면번호 has 149 (23.7%) missing valuesMissing
면적(기정) has 84 (13.4%) missing valuesMissing
면적(변경) has 120 (19.1%) missing valuesMissing
면적(변경후) has 15 (2.4%) missing valuesMissing
비고 has 364 (58.0%) missing valuesMissing
공간도형존재여부 has 7 (1.1%) missing valuesMissing
면적(변경후) is highly skewed (γ1 = 23.87693851)Skewed
면적(기정) has 366 (58.3%) zerosZeros
면적(변경후) has 37 (5.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:28:35.775933
Analysis finished2023-12-12 23:28:37.935602
Duration2.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

도면번호
Real number (ℝ)

MISSING 

Distinct217
Distinct (%)45.3%
Missing149
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean64.824635
Minimum1
Maximum218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-12-13T08:28:38.009984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median35
Q3118.5
95-th percentile196.1
Maximum218
Range217
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation66.558485
Coefficient of variation (CV)1.0267468
Kurtosis-0.67991914
Mean64.824635
Median Absolute Deviation (MAD)31
Skewness0.85985556
Sum31051
Variance4430.0319
MonotonicityNot monotonic
2023-12-13T08:28:38.131736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29
 
4.6%
2 15
 
2.4%
4 13
 
2.1%
7 11
 
1.8%
5 11
 
1.8%
3 11
 
1.8%
8 10
 
1.6%
10 9
 
1.4%
9 9
 
1.4%
6 9
 
1.4%
Other values (207) 352
56.1%
(Missing) 149
23.7%
ValueCountFrequency (%)
1 29
4.6%
2 15
2.4%
3 11
 
1.8%
4 13
2.1%
5 11
 
1.8%
6 9
 
1.4%
7 11
 
1.8%
8 10
 
1.6%
9 9
 
1.4%
10 9
 
1.4%
ValueCountFrequency (%)
218 1
0.2%
217 1
0.2%
216 1
0.2%
215 1
0.2%
214 1
0.2%
213 1
0.2%
212 1
0.2%
211 1
0.2%
210 1
0.2%
209 1
0.2%

위치명
Text

Distinct343
Distinct (%)55.1%
Missing5
Missing (%)0.8%
Memory size5.0 KiB
2023-12-13T08:28:38.399179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length26
Mean length10.303371
Min length3

Characters and Unicode

Total characters6419
Distinct characters178
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)39.5%

Sample

1st row덕충동
2nd row묘도동
3rd row율촌면 산수리
4th row율촌면 산수리
5th row율촌면 산수리
ValueCountFrequency (%)
일원 162
 
10.5%
금오도지구 63
 
4.1%
돌산읍 55
 
3.6%
율촌면 53
 
3.4%
화양면 47
 
3.1%
여천군 37
 
2.4%
남면 35
 
2.3%
소라면 34
 
2.2%
화정면 33
 
2.1%
일대 24
 
1.6%
Other values (393) 996
64.7%
2023-12-13T08:28:38.870396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
916
 
14.3%
리 286
 
4.5%
동 242
 
3.8%
지 239
 
3.7%
면 222
 
3.5%
일 211
 
3.3%
원 180
 
2.8%
산 143
 
2.2%
, 128
 
2.0%
도 126
 
2.0%
Other values (168) 3726
58.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4647
72.4%
Space Separator 916
 
14.3%
Decimal Number 601
 
9.4%
Other Punctuation 128
 
2.0%
Dash Punctuation 77
 
1.2%
Math Symbol 47
 
0.7%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 286
 
6.2%
동 242
 
5.2%
지 239
 
5.1%
면 222
 
4.8%
일 211
 
4.5%
원 180
 
3.9%
산 143
 
3.1%
도 126
 
2.7%
화 121
 
2.6%
구 102
 
2.2%
Other values (151) 2775
59.7%
Decimal Number
ValueCountFrequency (%)
1 120
20.0%
2 83
13.8%
4 61
10.1%
6 55
9.2%
3 55
9.2%
7 51
8.5%
8 49
8.2%
5 47
 
7.8%
9 41
 
6.8%
0 39
 
6.5%
Space Separator
ValueCountFrequency (%)
916
100.0%
Other Punctuation
ValueCountFrequency (%)
, 128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
~ 47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
R 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4647
72.4%
Common 1771
 
27.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 286
 
6.2%
동 242
 
5.2%
지 239
 
5.1%
면 222
 
4.8%
일 211
 
4.5%
원 180
 
3.9%
산 143
 
3.1%
도 126
 
2.7%
화 121
 
2.6%
구 102
 
2.2%
Other values (151) 2775
59.7%
Common
ValueCountFrequency (%)
916
51.7%
, 128
 
7.2%
1 120
 
6.8%
2 83
 
4.7%
- 77
 
4.3%
4 61
 
3.4%
6 55
 
3.1%
3 55
 
3.1%
7 51
 
2.9%
8 49
 
2.8%
Other values (6) 176
 
9.9%
Latin
ValueCountFrequency (%)
R 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4647
72.4%
ASCII 1772
 
27.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
916
51.7%
, 128
 
7.2%
1 120
 
6.8%
2 83
 
4.7%
- 77
 
4.3%
4 61
 
3.4%
6 55
 
3.1%
3 55
 
3.1%
7 51
 
2.9%
8 49
 
2.8%
Other values (7) 177
 
10.0%
Hangul
ValueCountFrequency (%)
리 286
 
6.2%
동 242
 
5.2%
지 239
 
5.1%
면 222
 
4.8%
일 211
 
4.5%
원 180
 
3.9%
산 143
 
3.1%
도 126
 
2.7%
화 121
 
2.6%
구 102
 
2.2%
Other values (151) 2775
59.7%

지역명
Text

Distinct505
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
2023-12-13T08:28:39.166008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length6.0047771
Min length2

Characters and Unicode

Total characters3771
Distinct characters207
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique431 ?
Unique (%)68.6%

Sample

1st row굴앞지구
2nd row광양포지구
3rd row금산지구
4th row신대지구
5th row평여지구
ValueCountFrequency (%)
연변 15
 
2.2%
고도지구 13
 
1.9%
취락지구 7
 
1.0%
상여 6
 
0.9%
개간촉진지구 6
 
0.9%
봉산지구 5
 
0.7%
대1-1호선 5
 
0.7%
율촌지구 5
 
0.7%
중심시가지 5
 
0.7%
학동지구 4
 
0.6%
Other values (504) 596
89.4%
2023-12-13T08:28:39.593502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 623
 
16.5%
구 610
 
16.2%
) 109
 
2.9%
( 109
 
2.9%
도 75
 
2.0%
연 74
 
2.0%
2 65
 
1.7%
1 64
 
1.7%
산 62
 
1.6%
자 62
 
1.6%
Other values (197) 1918
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3289
87.2%
Decimal Number 194
 
5.1%
Close Punctuation 109
 
2.9%
Open Punctuation 109
 
2.9%
Space Separator 39
 
1.0%
Dash Punctuation 23
 
0.6%
Other Punctuation 6
 
0.2%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 623
 
18.9%
구 610
 
18.5%
도 75
 
2.3%
연 74
 
2.2%
산 62
 
1.9%
자 62
 
1.9%
관 52
 
1.6%
호 51
 
1.6%
동 50
 
1.5%
경 49
 
1.5%
Other values (180) 1581
48.1%
Decimal Number
ValueCountFrequency (%)
2 65
33.5%
1 64
33.0%
3 27
13.9%
6 11
 
5.7%
4 11
 
5.7%
5 9
 
4.6%
8 3
 
1.5%
7 2
 
1.0%
9 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 3
50.0%
· 2
33.3%
. 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 109
100.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3289
87.2%
Common 482
 
12.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 623
 
18.9%
구 610
 
18.5%
도 75
 
2.3%
연 74
 
2.2%
산 62
 
1.9%
자 62
 
1.9%
관 52
 
1.6%
호 51
 
1.6%
동 50
 
1.5%
경 49
 
1.5%
Other values (180) 1581
48.1%
Common
ValueCountFrequency (%)
) 109
22.6%
( 109
22.6%
2 65
13.5%
1 64
13.3%
39
 
8.1%
3 27
 
5.6%
- 23
 
4.8%
6 11
 
2.3%
4 11
 
2.3%
5 9
 
1.9%
Other values (7) 15
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3289
87.2%
ASCII 480
 
12.7%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 623
 
18.9%
구 610
 
18.5%
도 75
 
2.3%
연 74
 
2.2%
산 62
 
1.9%
자 62
 
1.9%
관 52
 
1.6%
호 51
 
1.6%
동 50
 
1.5%
경 49
 
1.5%
Other values (180) 1581
48.1%
ASCII
ValueCountFrequency (%)
) 109
22.7%
( 109
22.7%
2 65
13.5%
1 64
13.3%
39
 
8.1%
3 27
 
5.6%
- 23
 
4.8%
6 11
 
2.3%
4 11
 
2.3%
5 9
 
1.9%
Other values (6) 13
 
2.7%
None
ValueCountFrequency (%)
· 2
100.0%

면적(기정)
Real number (ℝ)

MISSING  ZEROS 

Distinct170
Distinct (%)31.2%
Missing84
Missing (%)13.4%
Infinite0
Infinite (%)0.0%
Mean174393.59
Minimum0
Maximum28186000
Zeros366
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-12-13T08:28:39.733218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q327012.5
95-th percentile426000
Maximum28186000
Range28186000
Interquartile range (IQR)27012.5

Descriptive statistics

Standard deviation1474721.5
Coefficient of variation (CV)8.4562827
Kurtosis252.94194
Mean174393.59
Median Absolute Deviation (MAD)0
Skewness14.736606
Sum94870111
Variance2.1748034 × 1012
MonotonicityNot monotonic
2023-12-13T08:28:39.855058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 366
58.3%
629226.0 2
 
0.3%
365330.0 2
 
0.3%
147660.0 2
 
0.3%
426000.0 2
 
0.3%
23500.0 2
 
0.3%
63900.0 2
 
0.3%
32500.0 2
 
0.3%
58000.0 2
 
0.3%
64543.0 2
 
0.3%
Other values (160) 160
25.5%
(Missing) 84
 
13.4%
ValueCountFrequency (%)
0.0 366
58.3%
2041.6 1
 
0.2%
3420.0 1
 
0.2%
5850.0 1
 
0.2%
6139.0 1
 
0.2%
6550.0 1
 
0.2%
7980.0 1
 
0.2%
8320.0 1
 
0.2%
8750.0 1
 
0.2%
9850.0 1
 
0.2%
ValueCountFrequency (%)
28186000.0 1
0.2%
9992000.0 1
0.2%
9989691.0 1
0.2%
9988589.0 1
0.2%
9167491.0 1
0.2%
3156600.0 1
0.2%
753390.0 1
0.2%
710342.0 1
0.2%
692000.0 1
0.2%
673725.0 1
0.2%

면적(변경)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct477
Distinct (%)93.9%
Missing120
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean108294.73
Minimum2
Maximum4803000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-12-13T08:28:40.006155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1537.9
Q112632.5
median30823
Q378454.25
95-th percentile413699.95
Maximum4803000
Range4802998
Interquartile range (IQR)65821.75

Descriptive statistics

Standard deviation359435.4
Coefficient of variation (CV)3.319048
Kurtosis91.48267
Mean108294.73
Median Absolute Deviation (MAD)24114.5
Skewness8.8673329
Sum55013723
Variance1.2919381 × 1011
MonotonicityNot monotonic
2023-12-13T08:28:40.170365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6000.0 3
 
0.5%
334000.0 3
 
0.5%
123000.0 3
 
0.5%
385.0 2
 
0.3%
8320.0 2
 
0.3%
63420.0 2
 
0.3%
63080.0 2
 
0.3%
15570.0 2
 
0.3%
19030.0 2
 
0.3%
34020.0 2
 
0.3%
Other values (467) 485
77.2%
(Missing) 120
 
19.1%
ValueCountFrequency (%)
2.0 1
0.2%
40.0 2
0.3%
57.0 1
0.2%
104.3 1
0.2%
217.0 1
0.2%
270.0 1
0.2%
350.0 1
0.2%
385.0 2
0.3%
437.0 1
0.2%
524.0 1
0.2%
ValueCountFrequency (%)
4803000.0 1
0.2%
3265000.0 1
0.2%
3156600.0 2
0.3%
2447764.0 1
0.2%
1016513.0 1
0.2%
981879.0 1
0.2%
822200.0 1
0.2%
778030.0 1
0.2%
753390.0 1
0.2%
703310.0 1
0.2%

면적(변경후)
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct536
Distinct (%)87.4%
Missing15
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean626844.16
Minimum0
Maximum2.38029 × 108
Zeros37
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size5.6 KiB
2023-12-13T08:28:40.307010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117750
median39647
Q3106840
95-th percentile565946
Maximum2.38029 × 108
Range2.38029 × 108
Interquartile range (IQR)89090

Descriptive statistics

Standard deviation9729959.1
Coefficient of variation (CV)15.522134
Kurtosis581.98329
Mean626844.16
Median Absolute Deviation (MAD)29844
Skewness23.876939
Sum3.8425547 × 108
Variance9.4672104 × 1013
MonotonicityNot monotonic
2023-12-13T08:28:40.814774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 37
 
5.9%
19650.0 3
 
0.5%
334000.0 3
 
0.5%
30500.0 3
 
0.5%
3156600.0 3
 
0.5%
123000.0 3
 
0.5%
86205.0 3
 
0.5%
5850.0 2
 
0.3%
485670.0 2
 
0.3%
629611.0 2
 
0.3%
Other values (526) 552
87.9%
(Missing) 15
 
2.4%
ValueCountFrequency (%)
0.0 37
5.9%
1429.0 1
 
0.2%
1430.0 1
 
0.2%
1980.0 1
 
0.2%
1990.0 1
 
0.2%
2041.6 1
 
0.2%
2192.0 1
 
0.2%
2420.0 2
 
0.3%
2760.0 1
 
0.2%
2950.0 1
 
0.2%
ValueCountFrequency (%)
238029000.0 1
 
0.2%
32989000.0 1
 
0.2%
9989691.0 1
 
0.2%
9988589.0 1
 
0.2%
9167491.0 1
 
0.2%
8973525.0 1
 
0.2%
3265000.0 1
 
0.2%
3156600.0 3
0.5%
2447764.0 1
 
0.2%
1726855.0 1
 
0.2%

비고
Text

MISSING 

Distinct115
Distinct (%)43.6%
Missing364
Missing (%)58.0%
Memory size5.0 KiB
2023-12-13T08:28:41.068349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length64
Mean length20.625
Min length2

Characters and Unicode

Total characters5445
Distinct characters159
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)29.5%

Sample

1st row28호
2nd row29호
3rd row26호
4th row61호
5th row37호
ValueCountFrequency (%)
국계법 60
 
6.4%
지정 58
 
6.2%
시행(2018.4.19.)에 58
 
6.2%
따른 58
 
6.2%
명칭변경 58
 
6.2%
31조(용도지구의 58
 
6.2%
변경 30
 
3.2%
자연환경지구로 22
 
2.4%
건축물의 17
 
1.8%
건축높이 17
 
1.8%
Other values (179) 495
53.2%
2023-12-13T08:28:41.505531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
667
 
12.2%
1 250
 
4.6%
지 192
 
3.5%
. 187
 
3.4%
) 177
 
3.3%
( 177
 
3.3%
경 134
 
2.5%
2 133
 
2.4%
구 116
 
2.1%
변 114
 
2.1%
Other values (149) 3298
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3109
57.1%
Decimal Number 964
 
17.7%
Space Separator 667
 
12.2%
Other Punctuation 274
 
5.0%
Close Punctuation 185
 
3.4%
Open Punctuation 185
 
3.4%
Math Symbol 21
 
0.4%
Lowercase Letter 20
 
0.4%
Dash Punctuation 14
 
0.3%
Other Symbol 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 192
 
6.2%
경 134
 
4.3%
구 116
 
3.7%
변 114
 
3.7%
에 108
 
3.5%
의 91
 
2.9%
용 90
 
2.9%
호 89
 
2.9%
정 88
 
2.8%
법 85
 
2.7%
Other values (124) 2002
64.4%
Decimal Number
ValueCountFrequency (%)
1 250
25.9%
2 133
13.8%
0 111
11.5%
4 101
10.5%
3 100
 
10.4%
8 84
 
8.7%
9 67
 
7.0%
5 62
 
6.4%
7 32
 
3.3%
6 24
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 187
68.2%
, 57
 
20.8%
: 20
 
7.3%
% 8
 
2.9%
' 2
 
0.7%
Close Punctuation
ValueCountFrequency (%)
) 177
95.7%
」 8
 
4.3%
Open Punctuation
ValueCountFrequency (%)
( 177
95.7%
「 8
 
4.3%
Space Separator
ValueCountFrequency (%)
667
100.0%
Math Symbol
ValueCountFrequency (%)
→ 21
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 5
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3109
57.1%
Common 2316
42.5%
Latin 20
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 192
 
6.2%
경 134
 
4.3%
구 116
 
3.7%
변 114
 
3.7%
에 108
 
3.5%
의 91
 
2.9%
용 90
 
2.9%
호 89
 
2.9%
정 88
 
2.8%
법 85
 
2.7%
Other values (124) 2002
64.4%
Common
ValueCountFrequency (%)
667
28.8%
1 250
 
10.8%
. 187
 
8.1%
) 177
 
7.6%
( 177
 
7.6%
2 133
 
5.7%
0 111
 
4.8%
4 101
 
4.4%
3 100
 
4.3%
8 84
 
3.6%
Other values (14) 329
14.2%
Latin
ValueCountFrequency (%)
m 20
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3109
57.1%
ASCII 2293
42.1%
Arrows 21
 
0.4%
None 17
 
0.3%
CJK Compat 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
667
29.1%
1 250
 
10.9%
. 187
 
8.2%
) 177
 
7.7%
( 177
 
7.7%
2 133
 
5.8%
0 111
 
4.8%
4 101
 
4.4%
3 100
 
4.4%
8 84
 
3.7%
Other values (10) 306
13.3%
Hangul
ValueCountFrequency (%)
지 192
 
6.2%
경 134
 
4.3%
구 116
 
3.7%
변 114
 
3.7%
에 108
 
3.5%
의 91
 
2.9%
용 90
 
2.9%
호 89
 
2.9%
정 88
 
2.8%
법 85
 
2.7%
Other values (124) 2002
64.4%
Arrows
ValueCountFrequency (%)
→ 21
100.0%
None
ValueCountFrequency (%)
「 8
47.1%
」 8
47.1%
² 1
 
5.9%
CJK Compat
ValueCountFrequency (%)
㎡ 5
100.0%

공간도형존재여부
Boolean

MISSING 

Distinct2
Distinct (%)0.3%
Missing7
Missing (%)1.1%
Memory size1.4 KiB
True
436 
False
185 
(Missing)
 
7
ValueCountFrequency (%)
True 436
69.4%
False 185
29.5%
(Missing) 7
 
1.1%
2023-12-13T08:28:41.623417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-13T08:28:37.310940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.279942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.637647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.967999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.382873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.357473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.716199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.057528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.462896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.457574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.798898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.138999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.547528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.545544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:36.882284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:28:37.220571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:28:41.684552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도면번호면적(기정)면적(변경)면적(변경후)공간도형존재여부
도면번호1.0000.0000.000NaN0.383
면적(기정)0.0001.0000.9421.0000.087
면적(변경)0.0000.9421.0001.0000.178
면적(변경후)NaN1.0001.0001.0000.041
공간도형존재여부0.3830.0870.1780.0411.000
2023-12-13T08:28:41.789880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도면번호면적(기정)면적(변경)면적(변경후)공간도형존재여부
도면번호1.000-0.318-0.115-0.2020.291
면적(기정)-0.3181.000-0.2380.1120.057
면적(변경)-0.115-0.2381.0000.5290.128
면적(변경후)-0.2020.1120.5291.0000.068
공간도형존재여부0.2910.0570.1280.0681.000

Missing values

2023-12-13T08:28:37.648476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:28:37.759732image/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.
2023-12-13T08:28:37.864372image/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

도면번호위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
066덕충동굴앞지구0.016240.016240.028호Y
167묘도동광양포지구0.011550.011550.029호Y
268율촌면 산수리금산지구0.014370.014370.026호Y
369율촌면 산수리신대지구0.027950.027950.061호Y
470율촌면 산수리평여지구0.022020.022020.037호Y
574율촌면 산수리행정지구0.022280.022280.044호Y
672율촌면 월산리관음지구0.011020.011020.020호Y
773율촌면 월산리신촌지구0.028460.028460.055호Y
874율촌면 월산리대초지구0.020270.020270.028호Y
975율촌면 산수리수전지구0.012710.012710.020호Y
도면번호위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부
61839해산동해산지구0.028420.028420.077호Y
61940주삼동대평지구0.09460.09460.028호Y
62041소라면 덕양리성재지구0.063420.063420.0153호N
62142소라면 덕양리통천지구0.019470.019470.044호Y
62243소라면 덕양리흑산지구0.023710.023710.068호Y
62344화장동군장지구0.015800.015800.051호Y
62445소라면 현천리마륜지구0.014400.014400.046호Y
62546화장동대마지구0.023910.023910.080호Y
62647화장동월앙지구0.063080.063080.0161호N
62748여천동안골지구0.038790.038790.095호Y

Duplicate rows

Most frequently occurring

도면번호위치명지역명면적(기정)면적(변경)면적(변경후)비고공간도형존재여부# duplicates
0<NA>여수역 ~ 중로1류6호종점고도지구<NA><NA><NA><NA>N2