Overview

Dataset statistics

Number of variables14
Number of observations2249
Missing cells2249
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory261.5 KiB
Average record size in memory119.1 B

Variable types

Numeric5
Categorical4
Text4
Unsupported1

Dataset

Description고유번호,녹지대ID,녹지대명,주소한글,녹지대분류,녹지대조성년도,녹지대면적,조경량,구명,구코드,생성일,사진파일명,경도,위도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1321/S/1/datasetView.do

Alerts

녹지대ID has constant value ""Constant
사진파일명 has constant value ""Constant
녹지대조성년도 is highly overall correlated with 녹지대분류 and 1 other fieldsHigh correlation
조경량 is highly overall correlated with 구코드High correlation
구코드 is highly overall correlated with 조경량 and 1 other fieldsHigh correlation
녹지대분류 is highly overall correlated with 녹지대조성년도High correlation
구명 is highly overall correlated with 녹지대조성년도 and 1 other fieldsHigh correlation
생성일 has 2249 (100.0%) missing valuesMissing
고유번호 has unique valuesUnique
생성일 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 06:35:30.550310
Analysis finished2023-12-11 06:35:34.331306
Duration3.78 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
2023-12-11T15:35:34.405564image/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
2023-12-11T15:35:34.573500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103 1
 
< 0.1%
1444 1
 
< 0.1%
1454 1
 
< 0.1%
1456 1
 
< 0.1%
1457 1
 
< 0.1%
1435 1
 
< 0.1%
1417 1
 
< 0.1%
1440 1
 
< 0.1%
1458 1
 
< 0.1%
1452 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

2023-12-11T15:35:34.708428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:35:34.801225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2249
100.0%

녹지대명
Text

Distinct936
Distinct (%)41.6%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2023-12-11T15:35:35.046158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.4895509
Min length1

Characters and Unicode

Total characters16844
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 (%)27.4%

Sample

1st row영등포ic
2nd row남부순환로수벽
3rd row가락사거리녹지대
4th row올림픽로 분리대
5th row성내천둔치공원(오금동)
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%
2023-12-11T15:35:35.532485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 1021
 
6.1%
지 824
 
4.9%
로 818
 
4.9%
746
 
4.4%
녹 709
 
4.2%
동 596
 
3.5%
리 591
 
3.5%
마 304
 
1.8%
변 291
 
1.7%
분 271
 
1.6%
Other values (347) 10673
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14656
87.0%
Space Separator 746
 
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 (%)
대 1021
 
7.0%
지 824
 
5.6%
로 818
 
5.6%
녹 709
 
4.8%
동 596
 
4.1%
리 591
 
4.0%
마 304
 
2.1%
변 291
 
2.0%
분 271
 
1.8%
교 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%
5 49
 
8.2%
6 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%
t 3
 
1.5%
a 3
 
1.5%
b 3
 
1.5%
m 3
 
1.5%
j 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
I 86
48.3%
C 86
48.3%
R 2
 
1.1%
A 2
 
1.1%
L 1
 
0.6%
G 1
 
0.6%
Space Separator
ValueCountFrequency (%)
746
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
87.0%
Common 1815
 
10.8%
Latin 373
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1021
 
7.0%
지 824
 
5.6%
로 818
 
5.6%
녹 709
 
4.8%
동 596
 
4.1%
리 591
 
4.0%
마 304
 
2.1%
변 291
 
2.0%
분 271
 
1.8%
교 246
 
1.7%
Other values (317) 8985
61.3%
Common
ValueCountFrequency (%)
746
41.1%
( 147
 
8.1%
1 145
 
8.0%
- 126
 
6.9%
2 112
 
6.2%
) 108
 
6.0%
. 82
 
4.5%
3 73
 
4.0%
4 55
 
3.0%
5 49
 
2.7%
Other values (6) 172
 
9.5%
Latin
ValueCountFrequency (%)
c 91
24.4%
i 87
23.3%
I 86
23.1%
C 86
23.1%
p 4
 
1.1%
t 3
 
0.8%
a 3
 
0.8%
b 3
 
0.8%
m 3
 
0.8%
R 2
 
0.5%
Other values (4) 5
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14656
87.0%
ASCII 2188
 
13.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 1021
 
7.0%
지 824
 
5.6%
로 818
 
5.6%
녹 709
 
4.8%
동 596
 
4.1%
리 591
 
4.0%
마 304
 
2.1%
변 291
 
2.0%
분 271
 
1.8%
교 246
 
1.7%
Other values (317) 8985
61.3%
ASCII
ValueCountFrequency (%)
746
34.1%
( 147
 
6.7%
1 145
 
6.6%
- 126
 
5.8%
2 112
 
5.1%
) 108
 
4.9%
c 91
 
4.2%
i 87
 
4.0%
I 86
 
3.9%
C 86
 
3.9%
Other values (20) 454
20.7%

주소한글
Text

Distinct626
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2023-12-11T15:35:35.848018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length5.3828368
Min length1

Characters and Unicode

Total characters12106
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 (%)19.9%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
2003.하반기 51
 
2.8%
완공예정 51
 
2.8%
신월동 33
 
1.8%
염곡사거리~내곡동 25
 
1.4%
성내동485-둔촌동337-9 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%
2023-12-11T15:35:36.250939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1554
 
12.8%
동 1053
 
8.7%
1 690
 
5.7%
2 556
 
4.6%
- 521
 
4.3%
3 474
 
3.9%
4 397
 
3.3%
0 307
 
2.5%
5 297
 
2.5%
6 234
 
1.9%
Other values (238) 6023
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6171
51.0%
Decimal Number 3583
29.6%
Space Separator 1554
 
12.8%
Dash Punctuation 521
 
4.3%
Math Symbol 74
 
0.6%
Other Punctuation 51
 
0.4%
Open Punctuation 49
 
0.4%
Uppercase Letter 46
 
0.4%
Close Punctuation 29
 
0.2%
Lowercase Letter 28
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1053
 
17.1%
로 167
 
2.7%
대 147
 
2.4%
지 144
 
2.3%
일 132
 
2.1%
가 122
 
2.0%
사 119
 
1.9%
리 118
 
1.9%
성 109
 
1.8%
신 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%
C 4
 
8.7%
I 4
 
8.7%
Lowercase Letter
ValueCountFrequency (%)
i 14
50.0%
c 14
50.0%
Space Separator
ValueCountFrequency (%)
1554
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
51.0%
Common 5861
48.4%
Latin 74
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1053
 
17.1%
로 167
 
2.7%
대 147
 
2.4%
지 144
 
2.3%
일 132
 
2.1%
가 122
 
2.0%
사 119
 
1.9%
리 118
 
1.9%
성 109
 
1.8%
신 94
 
1.5%
Other values (216) 3966
64.3%
Common
ValueCountFrequency (%)
1554
26.5%
1 690
11.8%
2 556
 
9.5%
- 521
 
8.9%
3 474
 
8.1%
4 397
 
6.8%
0 307
 
5.2%
5 297
 
5.1%
6 234
 
4.0%
8 233
 
4.0%
Other values (6) 598
 
10.2%
Latin
ValueCountFrequency (%)
R 28
37.8%
i 14
18.9%
c 14
18.9%
A 10
 
13.5%
C 4
 
5.4%
I 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6171
51.0%
ASCII 5935
49.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1554
26.2%
1 690
11.6%
2 556
 
9.4%
- 521
 
8.8%
3 474
 
8.0%
4 397
 
6.7%
0 307
 
5.2%
5 297
 
5.0%
6 234
 
3.9%
8 233
 
3.9%
Other values (12) 672
11.3%
Hangul
ValueCountFrequency (%)
동 1053
 
17.1%
로 167
 
2.7%
대 147
 
2.4%
지 144
 
2.3%
일 132
 
2.1%
가 122
 
2.0%
사 119
 
1.9%
리 118
 
1.9%
성 109
 
1.8%
신 94
 
1.5%
Other values (216) 3966
64.3%

녹지대분류
Categorical

HIGH CORRELATION 

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

Length

Max length9
Median length8
Mean length4.0644731
Min length1

Unique

Unique10 ?
Unique (%)0.4%

Sample

1st row도로변녹지
2nd row수벽
3rd row도로변녹지
4th row중앙분리대
5th row하천변조경

Common Values

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

Length

2023-12-11T15:35:36.393050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
광장녹지 473
21.8%
도로변녹지 416
19.2%
중앙분리대 172
 
7.9%
수벽 113
 
5.2%
간이휴게소 106
 
4.9%
기타 103
 
4.7%
수림대 103
 
4.7%
휴게소 68
 
3.1%
분리녹지대 54
 
2.5%
가로녹지대 52
 
2.4%
Other values (35) 511
23.5%

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
2023-12-11T15:35:36.524302image/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
2023-12-11T15:35:36.647913image/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%
1982 5
 
0.2%
1997 5
 
0.2%
1996 4
 
0.2%
2002 4
 
0.2%
1987 3
 
0.1%
2000 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
2023-12-11T15:35:36.794909image/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
2023-12-11T15:35:36.953699image/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%
667 26
 
1.2%
892 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
2023-12-11T15:35:37.088566image/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
2023-12-11T15:35:37.228703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1719
76.4%
1624 13
 
0.6%
15109 12
 
0.5%
1212 12
 
0.5%
51802 10
 
0.4%
2743 10
 
0.4%
303764 10
 
0.4%
15500 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 row영등포구
2nd row서초구
3rd row송파구
4th row송파구
5th row송파구

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

2023-12-11T15:35:37.377849image/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
2023-12-11T15:35:37.794549image/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
2023-12-11T15:35:37.922644image/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
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2249
100.0%

Length

2023-12-11T15:35:38.058660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:35:38.155566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

경도
Text

Distinct2245
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2023-12-11T15:35:38.461455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.895954
Min length1

Characters and Unicode

Total characters24505
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2241 ?
Unique (%)99.6%

Sample

1st row126.9136206
2nd row126.9931597
3rd row127.1154226
4th row127.1003898
5th row127.137227
ValueCountFrequency (%)
126.8256405 2
 
0.1%
127.0163069 2
 
0.1%
127.0733056 2
 
0.1%
127.0151763 2
 
0.1%
127.0733602 1
 
< 0.1%
126.9136206 1
 
< 0.1%
126.9766614 1
 
< 0.1%
126.9535716 1
 
< 0.1%
127.0407246 1
 
< 0.1%
127.0390926 1
 
< 0.1%
Other values (2234) 2234
99.4%
2023-12-11T15:35:38.965849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3998
16.3%
2 3620
14.8%
7 2543
10.4%
6 2415
9.9%
. 2248
9.2%
9 2056
8.4%
0 1919
7.8%
8 1660
6.8%
3 1400
 
5.7%
4 1339
 
5.5%
Other values (2) 1307
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22256
90.8%
Other Punctuation 2248
 
9.2%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3998
18.0%
2 3620
16.3%
7 2543
11.4%
6 2415
10.9%
9 2056
9.2%
0 1919
8.6%
8 1660
7.5%
3 1400
 
6.3%
4 1339
 
6.0%
5 1306
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 2248
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3998
16.3%
2 3620
14.8%
7 2543
10.4%
6 2415
9.9%
. 2248
9.2%
9 2056
8.4%
0 1919
7.8%
8 1660
6.8%
3 1400
 
5.7%
4 1339
 
5.5%
Other values (2) 1307
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3998
16.3%
2 3620
14.8%
7 2543
10.4%
6 2415
9.9%
. 2248
9.2%
9 2056
8.4%
0 1919
7.8%
8 1660
6.8%
3 1400
 
5.7%
4 1339
 
5.5%
Other values (2) 1307
 
5.3%

위도
Text

Distinct2244
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
2023-12-11T15:35:39.354770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.8892841
Min length1

Characters and Unicode

Total characters22241
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2239 ?
Unique (%)99.6%

Sample

1st row37.520411
2nd row37.4747777
3rd row37.4975797
4th row37.5139019
5th row37.502803
ValueCountFrequency (%)
37.5355061 2
 
0.1%
37.5598642 2
 
0.1%
37.5593636 2
 
0.1%
37.5493282 2
 
0.1%
37.4833019 2
 
0.1%
37.6105219 1
 
< 0.1%
37.5581639 1
 
< 0.1%
37.506085 1
 
< 0.1%
37.559158 1
 
< 0.1%
37.4593155 1
 
< 0.1%
Other values (2233) 2233
99.3%
2023-12-11T15:35:39.845290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 3561
16.0%
3 3514
15.8%
5 3091
13.9%
. 2248
10.1%
4 1777
8.0%
6 1650
7.4%
2 1384
 
6.2%
8 1362
 
6.1%
1 1353
 
6.1%
9 1261
 
5.7%
Other values (2) 1040
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19992
89.9%
Other Punctuation 2248
 
10.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 3561
17.8%
3 3514
17.6%
5 3091
15.5%
4 1777
8.9%
6 1650
8.3%
2 1384
 
6.9%
8 1362
 
6.8%
1 1353
 
6.8%
9 1261
 
6.3%
0 1039
 
5.2%
Other Punctuation
ValueCountFrequency (%)
. 2248
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22241
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 3561
16.0%
3 3514
15.8%
5 3091
13.9%
. 2248
10.1%
4 1777
8.0%
6 1650
7.4%
2 1384
 
6.2%
8 1362
 
6.1%
1 1353
 
6.1%
9 1261
 
5.7%
Other values (2) 1040
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22241
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 3561
16.0%
3 3514
15.8%
5 3091
13.9%
. 2248
10.1%
4 1777
8.0%
6 1650
7.4%
2 1384
 
6.2%
8 1362
 
6.1%
1 1353
 
6.1%
9 1261
 
5.7%
Other values (2) 1040
 
4.7%

Interactions

2023-12-11T15:35:33.605612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:31.514365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.083721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.590085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.054189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.697285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:31.654744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.188879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.672791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.146996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.789464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:31.771047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.292003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.781332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.278454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.874132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:31.879749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.382742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.868814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.395653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.963074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:31.988275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.475003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:32.963517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:35:33.499241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:35:39.974252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호녹지대분류녹지대조성년도녹지대면적조경량구명구코드
고유번호1.0000.2740.1010.0000.0220.4090.306
녹지대분류0.2741.0000.6610.4590.7420.9300.838
녹지대조성년도0.1010.6611.0000.0490.3540.9610.627
녹지대면적0.0000.4590.0491.0000.6950.5300.312
조경량0.0220.7420.3540.6951.0000.6030.391
구명0.4090.9300.9610.5300.6031.0001.000
구코드0.3060.8380.6270.3120.3911.0001.000
2023-12-11T15:35:40.081738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
녹지대분류구명
녹지대분류1.0000.487
구명0.4871.000
2023-12-11T15:35:40.170905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호녹지대조성년도녹지대면적조경량구코드녹지대분류구명
고유번호1.0000.0340.0130.0510.0120.0950.156
녹지대조성년도0.0341.0000.0240.188-0.0640.5550.926
녹지대면적0.0130.0241.000-0.1410.2500.2120.268
조경량0.0510.188-0.1411.000-0.5350.4720.363
구코드0.012-0.0640.250-0.5351.0000.4640.997
녹지대분류0.0950.5550.2120.4720.4641.0000.487
구명0.1560.9260.2680.3630.9970.4871.000

Missing values

2023-12-11T15:35:34.094033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:35:34.254411image/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

고유번호녹지대ID녹지대명주소한글녹지대분류녹지대조성년도녹지대면적조경량구명구코드생성일사진파일명경도위도
01030영등포ic도로변녹지0161450영등포구560<NA>126.913620637.520411
11050남부순환로수벽수벽010000서초구650<NA>126.993159737.4747777
21060가락사거리녹지대도로변녹지0370800송파구710<NA>127.115422637.4975797
31070올림픽로 분리대중앙분리대0289070송파구710<NA>127.100389837.5139019
41080성내천둔치공원(오금동)하천변조경0263900송파구710<NA>127.13722737.502803
51090풍납마을마당휴게소011920송파구710<NA>127.113634137.5275546
61100신암중학교암사동444건물조경012000강동구740<NA>127.132879337.5560708
71110일원가로공원일원동가로녹지대0107710강남구680<NA>127.072494537.4956874
81120수서ic녹지대수서동가로녹지대0331280강남구680<NA>127.098082637.4932576
91130봉천복개로노변분리094890관악구620<NA>126.940390737.483809
고유번호녹지대ID녹지대명주소한글녹지대분류녹지대조성년도녹지대면적조경량구명구코드생성일사진파일명경도위도
223922390미아1동녹지미아동835외10필지도로변녹지06122894강북구300<NA>127.017289337.6223303
224022400화양동마을마당모진동 199-107마을마당0990광진구210<NA>127.075412637.5459168
224122410양화ic양평2동184-4일대광장녹지0358150영등포구560<NA>126.900488637.537339
224222430세종로 녹지광장녹지034711829종로구110<NA>126.976433237.5713184
224322440세곡동녹지대세곡동가로녹지대0233920강남구680<NA>127.108547737.464287
224422450올림픽로 분리대중앙분리대0289070송파구710<NA>127.074576737.5110778
224522460mbc옆분리녹지대여의도중앙분리대018990영등포구560<NA>126.927120337.5257821
224622480개봉동삼호A옆녹지도로변녹지0700구로구530<NA>126.857317337.4946448
224722490용답초등학교내 방음벽용답동27-1기타0600성동구200<NA>127.053155137.5607171
22484280구청사주변녹지노량진동 47-2공공건물012000동작구590<NA>126.939194437.5120113