Overview

Dataset statistics

Number of variables14
Number of observations2249
Missing cells5492
Missing cells (%)17.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory268.1 KiB
Average record size in memory122.1 B

Variable types

Numeric7
Categorical3
Text2
Unsupported2

Dataset

Description고유번호,녹지대ID,녹지대명,주소한글,녹지대분류,녹지대조성년도,녹지대면적,조경량,구명,구코드,생성일,사진파일명,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-316/S/1/datasetView.do

Alerts

녹지대ID has constant value ""Constant
녹지대조성년도 is highly overall correlated with 녹지대분류 and 1 other fieldsHigh correlation
조경량 is highly overall correlated with 구코드 and 1 other fieldsHigh correlation
구코드 is highly overall correlated with 조경량 and 2 other fieldsHigh correlation
X 좌표 is highly overall correlated with 구명High correlation
Y 좌표 is highly overall correlated with 조경량 and 2 other fieldsHigh correlation
녹지대분류 is highly overall correlated with 녹지대조성년도High correlation
구명 is highly overall correlated with 녹지대조성년도 and 3 other fieldsHigh correlation
주소한글 has 981 (43.6%) missing valuesMissing
생성일 has 2249 (100.0%) missing valuesMissing
사진파일명 has 2249 (100.0%) missing valuesMissing
고유번호 has unique valuesUnique
생성일 is an unsupported type, check if it needs cleaning or further analysisUnsupported
사진파일명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
녹지대조성년도 has 2162 (96.1%) zerosZeros
녹지대면적 has 174 (7.7%) zerosZeros
조경량 has 1719 (76.4%) zerosZeros

Reproduction

Analysis started2023-12-11 09:51:34.104677
Analysis finished2023-12-11 09:51:42.180706
Duration8.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

UNIQUE 

Distinct2249
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125
Minimum1
Maximum2249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:42.268539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile113.4
Q1563
median1125
Q31687
95-th percentile2136.6
Maximum2249
Range2248
Interquartile range (IQR)1124

Descriptive statistics

Standard deviation649.3747
Coefficient of variation (CV)0.57722195
Kurtosis-1.2
Mean1125
Median Absolute Deviation (MAD)562
Skewness0
Sum2530125
Variance421687.5
MonotonicityNot monotonic
2023-12-11T18:51:42.406066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165 1
 
< 0.1%
1613 1
 
< 0.1%
1603 1
 
< 0.1%
1604 1
 
< 0.1%
1605 1
 
< 0.1%
1608 1
 
< 0.1%
1609 1
 
< 0.1%
1611 1
 
< 0.1%
1614 1
 
< 0.1%
1531 1
 
< 0.1%
Other values (2239) 2239
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2249 1
< 0.1%
2248 1
< 0.1%
2247 1
< 0.1%
2246 1
< 0.1%
2245 1
< 0.1%
2244 1
< 0.1%
2243 1
< 0.1%
2242 1
< 0.1%
2241 1
< 0.1%
2240 1
< 0.1%

녹지대ID
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
0
2249 

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 2249
100.0%

Length

2023-12-11T18:51:42.532494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:51:42.647236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2249
100.0%
Distinct935
Distinct (%)41.8%
Missing11
Missing (%)0.5%
Memory size17.7 KiB
2023-12-11T18:51:42.912051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.5214477
Min length2

Characters and Unicode

Total characters16833
Distinct characters357
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique617 ?
Unique (%)27.6%

Sample

1st row합정로변 푸른거리
2nd row마포구청내
3rd row신도림역북측
4th row원효녹지대
5th row송정제방
ValueCountFrequency (%)
녹지 120
 
4.0%
분리대 85
 
2.9%
녹지대 67
 
2.3%
올림픽로 55
 
1.8%
마을마당 50
 
1.7%
강동대로 44
 
1.5%
봉천복개로 39
 
1.3%
테헤란로분리대 37
 
1.2%
합정로변 32
 
1.1%
푸른거리 32
 
1.1%
Other values (1002) 2412
81.1%
2023-12-11T18:51:43.471095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1021
 
6.1%
824
 
4.9%
818
 
4.9%
735
 
4.4%
709
 
4.2%
596
 
3.5%
591
 
3.5%
304
 
1.8%
291
 
1.7%
271
 
1.6%
Other values (347) 10673
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14656
87.1%
Space Separator 735
 
4.4%
Decimal Number 601
 
3.6%
Lowercase Letter 195
 
1.2%
Uppercase Letter 178
 
1.1%
Open Punctuation 147
 
0.9%
Dash Punctuation 126
 
0.7%
Close Punctuation 108
 
0.6%
Other Punctuation 82
 
0.5%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1021
 
7.0%
824
 
5.6%
818
 
5.6%
709
 
4.8%
596
 
4.1%
591
 
4.0%
304
 
2.1%
291
 
2.0%
271
 
1.8%
246
 
1.7%
Other values (317) 8985
61.3%
Decimal Number
ValueCountFrequency (%)
1 145
24.1%
2 112
18.6%
3 73
12.1%
4 55
 
9.2%
6 49
 
8.2%
5 49
 
8.2%
9 35
 
5.8%
7 35
 
5.8%
0 25
 
4.2%
8 23
 
3.8%
Lowercase Letter
ValueCountFrequency (%)
c 91
46.7%
i 87
44.6%
p 4
 
2.1%
b 3
 
1.5%
a 3
 
1.5%
t 3
 
1.5%
m 3
 
1.5%
j 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
C 86
48.3%
I 86
48.3%
A 2
 
1.1%
R 2
 
1.1%
L 1
 
0.6%
G 1
 
0.6%
Space Separator
ValueCountFrequency (%)
735
100.0%
Open Punctuation
ValueCountFrequency (%)
( 147
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 126
100.0%
Close Punctuation
ValueCountFrequency (%)
) 108
100.0%
Other Punctuation
ValueCountFrequency (%)
. 82
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14656
87.1%
Common 1804
 
10.7%
Latin 373
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1021
 
7.0%
824
 
5.6%
818
 
5.6%
709
 
4.8%
596
 
4.1%
591
 
4.0%
304
 
2.1%
291
 
2.0%
271
 
1.8%
246
 
1.7%
Other values (317) 8985
61.3%
Common
ValueCountFrequency (%)
735
40.7%
( 147
 
8.1%
1 145
 
8.0%
- 126
 
7.0%
2 112
 
6.2%
) 108
 
6.0%
. 82
 
4.5%
3 73
 
4.0%
4 55
 
3.0%
6 49
 
2.7%
Other values (6) 172
 
9.5%
Latin
ValueCountFrequency (%)
c 91
24.4%
i 87
23.3%
C 86
23.1%
I 86
23.1%
p 4
 
1.1%
b 3
 
0.8%
a 3
 
0.8%
t 3
 
0.8%
m 3
 
0.8%
A 2
 
0.5%
Other values (4) 5
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14656
87.1%
ASCII 2177
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1021
 
7.0%
824
 
5.6%
818
 
5.6%
709
 
4.8%
596
 
4.1%
591
 
4.0%
304
 
2.1%
291
 
2.0%
271
 
1.8%
246
 
1.7%
Other values (317) 8985
61.3%
ASCII
ValueCountFrequency (%)
735
33.8%
( 147
 
6.8%
1 145
 
6.7%
- 126
 
5.8%
2 112
 
5.1%
) 108
 
5.0%
c 91
 
4.2%
i 87
 
4.0%
C 86
 
4.0%
I 86
 
4.0%
Other values (20) 454
20.9%

주소한글
Text

MISSING 

Distinct625
Distinct (%)49.3%
Missing981
Missing (%)43.6%
Memory size17.7 KiB
2023-12-11T18:51:43.764328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length13
Mean length8.7736593
Min length3

Characters and Unicode

Total characters11125
Distinct characters248
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique447 ?
Unique (%)35.3%

Sample

1st row2003.하반기 완공예정
2nd row이촌동173-1일대
3rd row송정제방
4th row흑석동 269외 7필지
5th row을지로1가R
ValueCountFrequency (%)
2003.하반기 51
 
2.8%
완공예정 51
 
2.8%
신월동 33
 
1.8%
성내동485-둔촌동337-9 25
 
1.4%
염곡사거리~내곡동 25
 
1.4%
목동 21
 
1.1%
강남역~서초역 20
 
1.1%
1가 20
 
1.1%
중구 19
 
1.0%
태평로 19
 
1.0%
Other values (784) 1557
84.6%
2023-12-11T18:51:44.225725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1053
 
9.5%
1 690
 
6.2%
573
 
5.2%
2 556
 
5.0%
- 521
 
4.7%
3 474
 
4.3%
4 397
 
3.6%
0 307
 
2.8%
5 297
 
2.7%
6 234
 
2.1%
Other values (238) 6023
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6171
55.5%
Decimal Number 3583
32.2%
Space Separator 573
 
5.2%
Dash Punctuation 521
 
4.7%
Math Symbol 74
 
0.7%
Other Punctuation 51
 
0.5%
Open Punctuation 49
 
0.4%
Uppercase Letter 46
 
0.4%
Close Punctuation 29
 
0.3%
Lowercase Letter 28
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1053
 
17.1%
167
 
2.7%
147
 
2.4%
144
 
2.3%
132
 
2.1%
122
 
2.0%
119
 
1.9%
118
 
1.9%
109
 
1.8%
94
 
1.5%
Other values (216) 3966
64.3%
Decimal Number
ValueCountFrequency (%)
1 690
19.3%
2 556
15.5%
3 474
13.2%
4 397
11.1%
0 307
8.6%
5 297
8.3%
6 234
 
6.5%
8 233
 
6.5%
7 210
 
5.9%
9 185
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
R 28
60.9%
A 10
 
21.7%
I 4
 
8.7%
C 4
 
8.7%
Lowercase Letter
ValueCountFrequency (%)
i 14
50.0%
c 14
50.0%
Space Separator
ValueCountFrequency (%)
573
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 521
100.0%
Math Symbol
ValueCountFrequency (%)
~ 74
100.0%
Other Punctuation
ValueCountFrequency (%)
. 51
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6171
55.5%
Common 4880
43.9%
Latin 74
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1053
 
17.1%
167
 
2.7%
147
 
2.4%
144
 
2.3%
132
 
2.1%
122
 
2.0%
119
 
1.9%
118
 
1.9%
109
 
1.8%
94
 
1.5%
Other values (216) 3966
64.3%
Common
ValueCountFrequency (%)
1 690
14.1%
573
11.7%
2 556
11.4%
- 521
10.7%
3 474
9.7%
4 397
8.1%
0 307
6.3%
5 297
6.1%
6 234
 
4.8%
8 233
 
4.8%
Other values (6) 598
12.3%
Latin
ValueCountFrequency (%)
R 28
37.8%
i 14
18.9%
c 14
18.9%
A 10
 
13.5%
I 4
 
5.4%
C 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6171
55.5%
ASCII 4954
44.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1053
 
17.1%
167
 
2.7%
147
 
2.4%
144
 
2.3%
132
 
2.1%
122
 
2.0%
119
 
1.9%
118
 
1.9%
109
 
1.8%
94
 
1.5%
Other values (216) 3966
64.3%
ASCII
ValueCountFrequency (%)
1 690
13.9%
573
11.6%
2 556
11.2%
- 521
10.5%
3 474
9.6%
4 397
8.0%
0 307
6.2%
5 297
6.0%
6 234
 
4.7%
8 233
 
4.7%
Other values (12) 672
13.6%

녹지대분류
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
광장녹지
473 
도로변녹지
416 
중앙분리대
172 
<NA>
124 
수벽
113 
Other values (40)
951 

Length

Max length9
Median length8
Mean length4.2298799
Min length2

Unique

Unique10 ?
Unique (%)0.4%

Sample

1st row<NA>
2nd row공공건물
3rd row<NA>
4th row도로변녹지
5th row광장녹지

Common Values

ValueCountFrequency (%)
광장녹지 473
21.0%
도로변녹지 416
18.5%
중앙분리대 172
 
7.6%
<NA> 124
 
5.5%
수벽 113
 
5.0%
간이휴게소 106
 
4.7%
수림대 103
 
4.6%
기타 103
 
4.6%
휴게소 68
 
3.0%
분리녹지대 54
 
2.4%
Other values (35) 517
23.0%

Length

2023-12-11T18:51:44.379575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광장녹지 473
20.6%
도로변녹지 416
18.1%
중앙분리대 172
 
7.5%
na 124
 
5.4%
수벽 113
 
4.9%
간이휴게소 106
 
4.6%
수림대 103
 
4.5%
기타 103
 
4.5%
휴게소 68
 
3.0%
분리녹지대 54
 
2.4%
Other values (36) 563
24.5%

녹지대조성년도
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.29213
Minimum0
Maximum2003
Zeros2162
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:44.504760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2003
Range2003
Interquartile range (IQR)0

Descriptive statistics

Standard deviation382.45861
Coefficient of variation (CV)5.0130807
Kurtosis21.240619
Mean76.29213
Median Absolute Deviation (MAD)0
Skewness4.8187634
Sum171581
Variance146274.59
MonotonicityNot monotonic
2023-12-11T18:51:44.661505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 2162
96.1%
1999 29
 
1.3%
1980 12
 
0.5%
1990 6
 
0.3%
1997 5
 
0.2%
1982 5
 
0.2%
2002 4
 
0.2%
1996 4
 
0.2%
2003 3
 
0.1%
1998 3
 
0.1%
Other values (8) 16
 
0.7%
ValueCountFrequency (%)
0 2162
96.1%
107 1
 
< 0.1%
1980 12
 
0.5%
1982 5
 
0.2%
1987 3
 
0.1%
1990 6
 
0.3%
1991 3
 
0.1%
1993 1
 
< 0.1%
1994 1
 
< 0.1%
1995 2
 
0.1%
ValueCountFrequency (%)
2003 3
 
0.1%
2002 4
 
0.2%
2001 2
 
0.1%
2000 3
 
0.1%
1999 29
1.3%
1998 3
 
0.1%
1997 5
 
0.2%
1996 4
 
0.2%
1995 2
 
0.1%
1994 1
 
< 0.1%

녹지대면적
Real number (ℝ)

ZEROS 

Distinct622
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7853.1285
Minimum0
Maximum249864
Zeros174
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:44.820082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1200
Q38253
95-th percentile35815
Maximum249864
Range249864
Interquartile range (IQR)7953

Descriptive statistics

Standard deviation18099.578
Coefficient of variation (CV)2.3047601
Kurtosis73.338864
Mean7853.1285
Median Absolute Deviation (MAD)1136
Skewness6.7110531
Sum17661686
Variance3.2759471 × 108
MonotonicityNot monotonic
2023-12-11T18:51:44.988611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 174
 
7.7%
9489 39
 
1.7%
8253 37
 
1.6%
200 35
 
1.6%
28907 34
 
1.5%
10363 28
 
1.2%
500 27
 
1.2%
892 26
 
1.2%
667 26
 
1.2%
24286 25
 
1.1%
Other values (612) 1798
79.9%
ValueCountFrequency (%)
0 174
7.7%
10 3
 
0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
15 3
 
0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
18 2
 
0.1%
19 1
 
< 0.1%
20 8
 
0.4%
ValueCountFrequency (%)
249864 5
 
0.2%
104000 4
 
0.2%
98600 2
 
0.1%
77200 10
0.4%
72000 14
0.6%
64800 2
 
0.1%
59915 1
 
< 0.1%
57340 2
 
0.1%
56200 2
 
0.1%
55095 7
0.3%

조경량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct293
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2593.7434
Minimum0
Maximum303764
Zeros1719
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:45.146980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5455.8
Maximum303764
Range303764
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20930.604
Coefficient of variation (CV)8.0696507
Kurtosis188.52183
Mean2593.7434
Median Absolute Deviation (MAD)0
Skewness13.40143
Sum5833329
Variance4.3809017 × 108
MonotonicityNot monotonic
2023-12-11T18:51:45.303047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1719
76.4%
1624 13
 
0.6%
1212 12
 
0.5%
15109 12
 
0.5%
2743 10
 
0.4%
15500 10
 
0.4%
51802 10
 
0.4%
303764 10
 
0.4%
655 9
 
0.4%
5688 8
 
0.4%
Other values (283) 436
 
19.4%
ValueCountFrequency (%)
0 1719
76.4%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 2
 
0.1%
15 2
 
0.1%
23 2
 
0.1%
25 1
 
< 0.1%
ValueCountFrequency (%)
303764 10
0.4%
77869 1
 
< 0.1%
51802 10
0.4%
51331 4
 
0.2%
49995 3
 
0.1%
35000 4
 
0.2%
32851 5
0.2%
29287 2
 
0.1%
28060 1
 
< 0.1%
26876 4
 
0.2%

구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
마포구
299 
송파구
170 
강동구
168 
영등포구
135 
관악구
 
132
Other values (20)
1345 

Length

Max length4
Median length3
Mean length3.0653624
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마포구
2nd row마포구
3rd row영등포구
4th row구로구
5th row용산구

Common Values

ValueCountFrequency (%)
마포구 299
 
13.3%
송파구 170
 
7.6%
강동구 168
 
7.5%
영등포구 135
 
6.0%
관악구 132
 
5.9%
성동구 125
 
5.6%
서초구 114
 
5.1%
강남구 113
 
5.0%
광진구 104
 
4.6%
중구 99
 
4.4%
Other values (15) 790
35.1%

Length

2023-12-11T18:51:45.470137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마포구 299
 
13.3%
송파구 170
 
7.6%
강동구 168
 
7.5%
영등포구 135
 
6.0%
관악구 132
 
5.9%
성동구 125
 
5.6%
서초구 114
 
5.1%
강남구 113
 
5.0%
광진구 104
 
4.6%
중구 99
 
4.4%
Other values (15) 790
35.1%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.08493
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:45.592456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1260
median440
Q3650
95-th percentile740
Maximum740
Range630
Interquartile range (IQR)390

Descriptive statistics

Standard deviation199.73576
Coefficient of variation (CV)0.4427897
Kurtosis-1.2931172
Mean451.08493
Median Absolute Deviation (MAD)180
Skewness-0.13572698
Sum1014490
Variance39894.374
MonotonicityNot monotonic
2023-12-11T18:51:45.737538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
440 299
 
13.3%
710 170
 
7.6%
740 168
 
7.5%
560 135
 
6.0%
620 132
 
5.9%
200 125
 
5.6%
650 114
 
5.1%
680 113
 
5.0%
210 104
 
4.6%
140 99
 
4.4%
Other values (15) 790
35.1%
ValueCountFrequency (%)
110 75
3.3%
140 99
4.4%
170 66
2.9%
200 125
5.6%
210 104
4.6%
230 86
3.8%
260 33
 
1.5%
290 72
3.2%
300 43
 
1.9%
320 37
 
1.6%
ValueCountFrequency (%)
740 168
7.5%
710 170
7.6%
680 113
5.0%
650 114
5.1%
620 132
5.9%
590 30
 
1.3%
560 135
6.0%
540 27
 
1.2%
530 41
 
1.8%
500 76
3.4%

생성일
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2249
Missing (%)100.0%
Memory size19.9 KiB

사진파일명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2249
Missing (%)100.0%
Memory size19.9 KiB

X 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct2245
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean199757.45
Minimum182052.99
Maximum215314.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:45.950094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182052.99
5-th percentile186949.85
Q1193096.34
median200279.8
Q3206135.69
95-th percentile211753.28
Maximum215314.69
Range33261.7
Interquartile range (IQR)13039.354

Descriptive statistics

Standard deviation7708.4083
Coefficient of variation (CV)0.038588841
Kurtosis-0.95669435
Mean199757.45
Median Absolute Deviation (MAD)6449.7275
Skewness-0.10808935
Sum4.4905474 × 108
Variance59419559
MonotonicityNot monotonic
2023-12-11T18:51:46.113360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206476.949 2
 
0.1%
201341.09 2
 
0.1%
184589.483 2
 
0.1%
191555.193 1
 
< 0.1%
206860.723 1
 
< 0.1%
190196.178 1
 
< 0.1%
192653.724 1
 
< 0.1%
191049.708 1
 
< 0.1%
193566.631 1
 
< 0.1%
199727.882 1
 
< 0.1%
Other values (2235) 2235
99.4%
ValueCountFrequency (%)
182052.988 1
< 0.1%
182066.367 1
< 0.1%
182069.292 1
< 0.1%
182139.648 1
< 0.1%
182168.594 1
< 0.1%
182345.859 1
< 0.1%
182382.368 1
< 0.1%
182427.796 1
< 0.1%
182429.148 1
< 0.1%
182472.339 1
< 0.1%
ValueCountFrequency (%)
215314.688 1
< 0.1%
215194.69 1
< 0.1%
215152.995 1
< 0.1%
215152.306 1
< 0.1%
214716.988 1
< 0.1%
214645.3 1
< 0.1%
214447.191 1
< 0.1%
214383.634 1
< 0.1%
214277.086 1
< 0.1%
214005.878 1
< 0.1%

Y 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct2245
Distinct (%)99.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean449377.62
Minimum439087.12
Maximum465785.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.9 KiB
2023-12-11T18:51:46.287606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439087.12
5-th percentile441775.36
Q1446011.42
median449624.06
Q3451880.5
95-th percentile458323.95
Maximum465785.12
Range26698.001
Interquartile range (IQR)5869.0813

Descriptive statistics

Standard deviation4931.9213
Coefficient of variation (CV)0.010975004
Kurtosis0.39162326
Mean449377.62
Median Absolute Deviation (MAD)2893.3015
Skewness0.46023543
Sum1.0102009 × 109
Variance24323847
MonotonicityNot monotonic
2023-12-11T18:51:46.463530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451095.264 2
 
0.1%
449979.049 2
 
0.1%
448459.141 2
 
0.1%
451439.591 1
 
< 0.1%
448245.323 1
 
< 0.1%
439939.105 1
 
< 0.1%
451151.553 1
 
< 0.1%
442282.494 1
 
< 0.1%
450797.293 1
 
< 0.1%
452941.589 1
 
< 0.1%
Other values (2235) 2235
99.4%
ValueCountFrequency (%)
439087.119 1
< 0.1%
439239.268 1
< 0.1%
439496.975 1
< 0.1%
439605.988 1
< 0.1%
439616.926 1
< 0.1%
439630.898 1
< 0.1%
439660.104 1
< 0.1%
439707.591 1
< 0.1%
439714.905 1
< 0.1%
439731.552 1
< 0.1%
ValueCountFrequency (%)
465785.12 1
< 0.1%
465591.121 1
< 0.1%
465426.308 1
< 0.1%
465364.51 1
< 0.1%
465360.048 1
< 0.1%
465317.248 1
< 0.1%
465313.598 1
< 0.1%
465304.975 1
< 0.1%
465153.567 1
< 0.1%
465144.118 1
< 0.1%

Interactions

2023-12-11T18:51:40.556744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:35.362091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.263623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.179258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.022952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.855288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.693799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:40.650897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:35.465969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.360047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.320755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.127076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.984370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.791248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:41.056920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:35.626883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.473427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.436350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.277470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.130322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.904185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:41.176232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:35.747297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.612668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.537961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.384101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.232814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:40.046373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:41.302503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:35.865311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.747794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.686928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.521371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.328490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:40.180829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:41.464669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.003466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.896381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.831146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.634081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.436513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:40.305773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:41.582069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:36.127376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.045802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:37.924104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:38.745322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:39.563454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:51:40.437146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:51:46.563917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호녹지대분류녹지대조성년도녹지대면적조경량구명구코드X 좌표Y 좌표
고유번호1.0000.3710.1410.0460.1210.5500.3780.3700.279
녹지대분류0.3711.0000.6930.4480.7670.9290.8350.7230.769
녹지대조성년도0.1410.6931.0000.0490.3540.9610.6270.2360.561
녹지대면적0.0460.4480.0491.0000.6950.5300.3120.3270.264
조경량0.1210.7670.3540.6951.0000.6030.3910.1800.377
구명0.5500.9290.9610.5300.6031.0001.0000.9210.910
구코드0.3780.8350.6270.3120.3911.0001.0000.8970.892
X 좌표0.3700.7230.2360.3270.1800.9210.8971.0000.501
Y 좌표0.2790.7690.5610.2640.3770.9100.8920.5011.000
2023-12-11T18:51:46.668322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명녹지대분류
구명1.0000.489
녹지대분류0.4891.000
2023-12-11T18:51:46.758651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호녹지대조성년도녹지대면적조경량구코드X 좌표Y 좌표녹지대분류구명
고유번호1.0000.0280.0150.068-0.0040.0470.0180.1350.226
녹지대조성년도0.0281.0000.0240.188-0.064-0.0660.1280.5570.926
녹지대면적0.0150.0241.000-0.1410.250-0.116-0.2920.2080.268
조경량0.0680.188-0.1411.000-0.5350.0940.6790.4710.363
구코드-0.004-0.0640.250-0.5351.0000.198-0.6440.4670.997
X 좌표0.047-0.066-0.1160.0940.1981.000-0.0490.3400.641
Y 좌표0.0180.128-0.2920.679-0.644-0.0491.0000.3850.617
녹지대분류0.1350.5570.2080.4710.4670.3400.3851.0000.489
구명0.2260.9260.2680.3630.9970.6410.6170.4891.000

Missing values

2023-12-11T18:51:41.743656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:51:41.931669image/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.
2023-12-11T18:51:42.093256image/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

고유번호녹지대ID녹지대명주소한글녹지대분류녹지대조성년도녹지대면적조경량구명구코드생성일사진파일명X 좌표Y 좌표
01650합정로변 푸른거리2003.하반기 완공예정<NA>000마포구440<NA><NA>191555.193451439.591
11690마포구청내<NA>공공건물019250마포구440<NA><NA>191914.811451615.256
21760<NA><NA><NA>000영등포구560<NA><NA>194069.346446711.708
31790신도림역북측<NA>도로변녹지012000구로구530<NA><NA>190312.611445597.231
41810원효녹지대이촌동173-1일대광장녹지0191920용산구170<NA><NA>195799.583447931.742
51880송정제방송정제방수림대0179870성동구200<NA><NA>204283.587450223.432
62000성산대교I.C<NA>광장녹지0355440마포구440<NA><NA>190914.051450616.148
72020구청앞사거리<NA>도로변녹지03040구로구530<NA><NA>190139.285443981.64
82030흑석2구역녹지흑석동 269외 7필지경관녹지02920동작구590<NA><NA>197053.125445089.091
91150을지로입구녹지대을지로1가R광장녹지014873316중구140<NA><NA>198504.522451862.643
고유번호녹지대ID녹지대명주소한글녹지대분류녹지대조성년도녹지대면적조경량구명구코드생성일사진파일명X 좌표Y 좌표
223913560자유로변녹지대성산대교-고양시계(난지도)도로변녹지0573400마포구440<NA><NA>188795.426452410.872
224016310탄천제방수림대<NA>수림대0720000송파구710<NA><NA>209674.324443288.197
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