Overview

Dataset statistics

Number of variables17
Number of observations603
Missing cells602
Missing cells (%)5.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory146.2 B

Variable types

Numeric9
Categorical4
Text3
DateTime1

Dataset

Description고유번호,구명,법정동명,산지여부,주지번,부지번,새주소명,학교명,조성년도,조성면적,교목수,관목수,초화류수,생성일,사진파일명,위도,경도
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-1368/S/1/datasetView.do

Alerts

생성일 has constant value ""Constant
사진파일명 has constant value ""Constant
위도 is highly overall correlated with 구명High correlation
경도 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
산지여부 is highly imbalanced (95.5%)Imbalance
새주소명 is highly imbalanced (98.2%)Imbalance
생성일 has 602 (99.8%) missing valuesMissing
고유번호 has unique valuesUnique
교목수 has 24 (4.0%) zerosZeros
관목수 has 14 (2.3%) zerosZeros
초화류수 has 85 (14.1%) zerosZeros

Reproduction

Analysis started2023-12-11 06:17:51.793528
Analysis finished2023-12-11 06:18:02.939466
Duration11.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

UNIQUE 

Distinct603
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean302
Minimum1
Maximum603
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:03.013877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31.1
Q1151.5
median302
Q3452.5
95-th percentile572.9
Maximum603
Range602
Interquartile range (IQR)301

Descriptive statistics

Standard deviation174.21538
Coefficient of variation (CV)0.57687213
Kurtosis-1.2
Mean302
Median Absolute Deviation (MAD)151
Skewness0
Sum182106
Variance30351
MonotonicityNot monotonic
2023-12-11T15:18:03.195754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 1
 
0.2%
451 1
 
0.2%
143 1
 
0.2%
331 1
 
0.2%
455 1
 
0.2%
427 1
 
0.2%
322 1
 
0.2%
416 1
 
0.2%
392 1
 
0.2%
154 1
 
0.2%
Other values (593) 593
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
603 1
0.2%
602 1
0.2%
601 1
0.2%
600 1
0.2%
599 1
0.2%
598 1
0.2%
597 1
0.2%
596 1
0.2%
595 1
0.2%
594 1
0.2%

구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
노원구
44 
강남구
 
39
양천구
 
39
송파구
 
32
강서구
 
31
Other values (20)
418 

Length

Max length4
Median length3
Mean length3.0845771
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광진구
2nd row노원구
3rd row동작구
4th row노원구
5th row금천구

Common Values

ValueCountFrequency (%)
노원구 44
 
7.3%
강남구 39
 
6.5%
양천구 39
 
6.5%
송파구 32
 
5.3%
강서구 31
 
5.1%
중랑구 29
 
4.8%
서초구 27
 
4.5%
관악구 27
 
4.5%
영등포구 27
 
4.5%
마포구 27
 
4.5%
Other values (15) 281
46.6%

Length

2023-12-11T15:18:03.373582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노원구 44
 
7.3%
양천구 39
 
6.5%
강남구 39
 
6.5%
송파구 32
 
5.3%
강서구 31
 
5.1%
중랑구 29
 
4.8%
서초구 27
 
4.5%
관악구 27
 
4.5%
영등포구 27
 
4.5%
마포구 27
 
4.5%
Other values (15) 281
46.6%

법정동명
Text

Distinct189
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T15:18:03.686502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.0912106
Min length2

Characters and Unicode

Total characters1864
Distinct characters165
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)11.3%

Sample

1st row군자동
2nd row상계동
3rd row사당동
4th row월계동
5th row독산동
ValueCountFrequency (%)
상계동 18
 
3.0%
신정동 14
 
2.3%
신림동 14
 
2.3%
신월동 13
 
2.2%
봉천동 13
 
2.2%
목동 12
 
2.0%
중계동 9
 
1.5%
창동 9
 
1.5%
망우동 9
 
1.5%
개포동 9
 
1.5%
Other values (179) 483
80.1%
2023-12-11T15:18:04.180384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 605
32.5%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.2%
산 21
 
1.1%
Other values (155) 948
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1830
98.2%
Decimal Number 34
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
Decimal Number
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
7 2
 
5.9%
6 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1830
98.2%
Common 34
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
Common
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
7 2
 
5.9%
6 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1830
98.2%
ASCII 34
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 605
33.1%
신 70
 
3.8%
가 48
 
2.6%
계 39
 
2.1%
방 31
 
1.7%
상 29
 
1.6%
정 26
 
1.4%
수 24
 
1.3%
양 23
 
1.3%
산 21
 
1.1%
Other values (148) 914
49.9%
ASCII
ValueCountFrequency (%)
2 16
47.1%
3 8
23.5%
1 4
 
11.8%
7 2
 
5.9%
6 2
 
5.9%
4 1
 
2.9%
5 1
 
2.9%

산지여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
1
600 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 600
99.5%
2 3
 
0.5%

Length

2023-12-11T15:18:04.331109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:18:04.449610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 600
99.5%
2 3
 
0.5%

주지번
Real number (ℝ)

Distinct390
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean398.05307
Minimum1
Maximum4656
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:04.588610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.1
Q198
median272
Q3573
95-th percentile1089.9
Maximum4656
Range4655
Interquartile range (IQR)475

Descriptive statistics

Standard deviation440.89091
Coefficient of variation (CV)1.1076184
Kurtosis27.224962
Mean398.05307
Median Absolute Deviation (MAD)202
Skewness3.7242335
Sum240026
Variance194384.79
MonotonicityNot monotonic
2023-12-11T15:18:04.758216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11
 
1.8%
94 6
 
1.0%
150 6
 
1.0%
89 6
 
1.0%
43 5
 
0.8%
90 5
 
0.8%
220 5
 
0.8%
267 5
 
0.8%
7 5
 
0.8%
3 5
 
0.8%
Other values (380) 544
90.2%
ValueCountFrequency (%)
1 11
1.8%
2 3
 
0.5%
3 5
0.8%
4 2
 
0.3%
5 2
 
0.3%
6 2
 
0.3%
7 5
0.8%
8 1
 
0.2%
9 1
 
0.2%
11 2
 
0.3%
ValueCountFrequency (%)
4656 1
0.2%
4482 1
0.2%
2727 1
0.2%
1704 1
0.2%
1694 1
0.2%
1690 1
0.2%
1685 1
0.2%
1647 1
0.2%
1635 1
0.2%
1531 1
0.2%

부지번
Text

Distinct68
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T15:18:04.988970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.2139303
Min length1

Characters and Unicode

Total characters732
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)6.1%

Sample

1st row
2nd row4
3rd row1
4th row
5th row27
ValueCountFrequency (%)
1 110
31.5%
2 34
 
9.7%
3 30
 
8.6%
4 22
 
6.3%
5 17
 
4.9%
6 12
 
3.4%
7 10
 
2.9%
10 7
 
2.0%
19 5
 
1.4%
8 5
 
1.4%
Other values (57) 97
27.8%
2023-12-11T15:18:05.332078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 478
65.3%
Space Separator 254
34.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 162
33.9%
2 78
16.3%
3 55
 
11.5%
6 38
 
7.9%
4 34
 
7.1%
5 32
 
6.7%
7 28
 
5.9%
8 19
 
4.0%
9 18
 
3.8%
0 14
 
2.9%
Space Separator
ValueCountFrequency (%)
254
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
254
34.7%
1 162
22.1%
2 78
 
10.7%
3 55
 
7.5%
6 38
 
5.2%
4 34
 
4.6%
5 32
 
4.4%
7 28
 
3.8%
8 19
 
2.6%
9 18
 
2.5%

새주소명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
602 
현석동 토정로16길 42-4
 
1

Length

Max length15
Median length1
Mean length1.0232172
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
602
99.8%
현석동 토정로16길 42-4 1
 
0.2%

Length

2023-12-11T15:18:05.555884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:18:05.694887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
현석동 1
33.3%
토정로16길 1
33.3%
42-4 1
33.3%

학교명
Text

Distinct577
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
2023-12-11T15:18:05.990305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length6
Mean length5.9369818
Min length3

Characters and Unicode

Total characters3580
Distinct characters200
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique551 ?
Unique (%)91.4%

Sample

1st row세종초등학교
2nd row신상중
3rd row남사초등학교
4th row염광여고
5th row영남초등학교
ValueCountFrequency (%)
성원중학교 2
 
0.3%
동원중학교 2
 
0.3%
성산중학교 2
 
0.3%
서강초등학교 2
 
0.3%
대모초등학교 2
 
0.3%
동구로초등학교 2
 
0.3%
가락중학교 2
 
0.3%
대림초등학교 2
 
0.3%
한남초등학교 2
 
0.3%
송중초등학교 2
 
0.3%
Other values (571) 587
96.7%
2023-12-11T15:18:06.433982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 572
16.0%
교 566
15.8%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.6%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (190) 1204
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3565
99.6%
Space Separator 5
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Decimal Number 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
Space Separator
ValueCountFrequency (%)
5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3565
99.6%
Common 15
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
Common
ValueCountFrequency (%)
5
33.3%
) 4
26.7%
( 4
26.7%
2 1
 
6.7%
. 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3565
99.6%
ASCII 15
 
0.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 572
16.0%
교 566
15.9%
등 410
 
11.5%
초 336
 
9.4%
중 203
 
5.7%
고 99
 
2.8%
신 59
 
1.7%
동 47
 
1.3%
여 45
 
1.3%
서 39
 
1.1%
Other values (185) 1189
33.4%
ASCII
ValueCountFrequency (%)
5
33.3%
) 4
26.7%
( 4
26.7%
2 1
 
6.7%
. 1
 
6.7%

조성년도
Real number (ℝ)

Distinct7
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2803
Minimum2001
Maximum2007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:06.544970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12003
median2005
Q32006
95-th percentile2007
Maximum2007
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8922974
Coefficient of variation (CV)0.00094412812
Kurtosis-1.1402046
Mean2004.2803
Median Absolute Deviation (MAD)2
Skewness-0.19055718
Sum1208581
Variance3.5807893
MonotonicityDecreasing
2023-12-11T15:18:06.887945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2005 118
19.6%
2006 102
16.9%
2002 87
14.4%
2007 86
14.3%
2004 79
13.1%
2003 78
12.9%
2001 53
8.8%
ValueCountFrequency (%)
2001 53
8.8%
2002 87
14.4%
2003 78
12.9%
2004 79
13.1%
2005 118
19.6%
2006 102
16.9%
2007 86
14.3%
ValueCountFrequency (%)
2007 86
14.3%
2006 102
16.9%
2005 118
19.6%
2004 79
13.1%
2003 78
12.9%
2002 87
14.4%
2001 53
8.8%

조성면적
Real number (ℝ)

Distinct213
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1107.3847
Minimum0
Maximum7246
Zeros6
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:07.001324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile213.1
Q1610
median1000
Q31324.5
95-th percentile2500
Maximum7246
Range7246
Interquartile range (IQR)714.5

Descriptive statistics

Standard deviation805.02733
Coefficient of variation (CV)0.72696263
Kurtosis15.783714
Mean1107.3847
Median Absolute Deviation (MAD)367
Skewness2.9485128
Sum667753
Variance648069
MonotonicityNot monotonic
2023-12-11T15:18:07.115669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 43
 
7.1%
1200 42
 
7.0%
1500 31
 
5.1%
500 28
 
4.6%
800 22
 
3.6%
1100 17
 
2.8%
2000 16
 
2.7%
600 16
 
2.7%
900 15
 
2.5%
700 14
 
2.3%
Other values (203) 359
59.5%
ValueCountFrequency (%)
0 6
1.0%
26 1
 
0.2%
49 1
 
0.2%
73 1
 
0.2%
75 1
 
0.2%
87 1
 
0.2%
100 6
1.0%
107 1
 
0.2%
120 1
 
0.2%
140 1
 
0.2%
ValueCountFrequency (%)
7246 1
0.2%
7000 1
0.2%
6320 1
0.2%
6265 1
0.2%
4500 1
0.2%
3700 1
0.2%
3480 1
0.2%
3448 1
0.2%
3324 1
0.2%
3300 2
0.3%

교목수
Real number (ℝ)

ZEROS 

Distinct172
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.303483
Minimum0
Maximum378
Zeros24
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:07.227656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q131
median55
Q391
95-th percentile154.9
Maximum378
Range378
Interquartile range (IQR)60

Descriptive statistics

Standard deviation53.368049
Coefficient of variation (CV)0.79294632
Kurtosis5.8047647
Mean67.303483
Median Absolute Deviation (MAD)29
Skewness1.8372299
Sum40584
Variance2848.1486
MonotonicityNot monotonic
2023-12-11T15:18:07.344534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
4.0%
41 11
 
1.8%
48 11
 
1.8%
38 11
 
1.8%
28 9
 
1.5%
21 9
 
1.5%
24 9
 
1.5%
54 8
 
1.3%
36 8
 
1.3%
43 8
 
1.3%
Other values (162) 495
82.1%
ValueCountFrequency (%)
0 24
4.0%
2 1
 
0.2%
3 2
 
0.3%
4 3
 
0.5%
5 3
 
0.5%
6 5
 
0.8%
7 5
 
0.8%
8 6
 
1.0%
9 4
 
0.7%
10 3
 
0.5%
ValueCountFrequency (%)
378 1
0.2%
373 1
0.2%
306 2
0.3%
303 1
0.2%
292 1
0.2%
271 1
0.2%
262 1
0.2%
246 1
0.2%
240 1
0.2%
228 1
0.2%

관목수
Real number (ℝ)

ZEROS 

Distinct529
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3456.7629
Minimum0
Maximum27540
Zeros14
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:07.469293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile508.2
Q11631
median3050
Q34741.5
95-th percentile8177.1
Maximum27540
Range27540
Interquartile range (IQR)3110.5

Descriptive statistics

Standard deviation2535.0766
Coefficient of variation (CV)0.73336723
Kurtosis13.836653
Mean3456.7629
Median Absolute Deviation (MAD)1474
Skewness2.2477583
Sum2084428
Variance6426613.3
MonotonicityNot monotonic
2023-12-11T15:18:07.585029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
2.3%
1800 4
 
0.7%
2570 3
 
0.5%
1442 3
 
0.5%
3430 3
 
0.5%
2950 3
 
0.5%
1580 3
 
0.5%
4190 3
 
0.5%
1300 2
 
0.3%
6295 2
 
0.3%
Other values (519) 563
93.4%
ValueCountFrequency (%)
0 14
2.3%
30 1
 
0.2%
80 1
 
0.2%
141 1
 
0.2%
150 2
 
0.3%
210 1
 
0.2%
244 1
 
0.2%
295 1
 
0.2%
310 1
 
0.2%
313 1
 
0.2%
ValueCountFrequency (%)
27540 1
0.2%
13621 1
0.2%
13235 1
0.2%
11800 1
0.2%
11470 1
0.2%
10864 1
0.2%
10778 1
0.2%
10639 1
0.2%
10612 1
0.2%
9785 1
0.2%

초화류수
Real number (ℝ)

ZEROS 

Distinct396
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3517.9502
Minimum0
Maximum40034
Zeros85
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:07.712032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1790
median2350
Q34900
95-th percentile10770
Maximum40034
Range40034
Interquartile range (IQR)4110

Descriptive statistics

Standard deviation4117.5565
Coefficient of variation (CV)1.1704419
Kurtosis15.560306
Mean3517.9502
Median Absolute Deviation (MAD)1810
Skewness2.9681263
Sum2121324
Variance16954272
MonotonicityNot monotonic
2023-12-11T15:18:07.856667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
14.1%
1500 9
 
1.5%
750 5
 
0.8%
600 5
 
0.8%
1400 5
 
0.8%
3250 5
 
0.8%
1000 5
 
0.8%
800 5
 
0.8%
950 4
 
0.7%
2000 4
 
0.7%
Other values (386) 471
78.1%
ValueCountFrequency (%)
0 85
14.1%
35 1
 
0.2%
75 1
 
0.2%
76 1
 
0.2%
89 1
 
0.2%
100 1
 
0.2%
120 2
 
0.3%
180 1
 
0.2%
190 1
 
0.2%
200 2
 
0.3%
ValueCountFrequency (%)
40034 1
0.2%
27905 1
0.2%
26434 1
0.2%
25540 1
0.2%
22860 1
0.2%
17490 1
0.2%
17200 1
0.2%
17000 2
0.3%
16150 1
0.2%
15700 1
0.2%

생성일
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing602
Missing (%)99.8%
Memory size4.8 KiB
Minimum2012-02-02 00:00:00
Maximum2012-02-02 00:00:00
2023-12-11T15:18:07.963582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:08.069469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

사진파일명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 KiB
603 

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 (%)
603
100.0%

Length

2023-12-11T15:18:08.196818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T15:18:08.292506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct561
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.553444
Minimum37.449425
Maximum37.686735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:08.390239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.449425
5-th percentile37.480496
Q137.507165
median37.547592
Q337.59014
95-th percentile37.651338
Maximum37.686735
Range0.2373103
Interquartile range (IQR)0.08297515

Descriptive statistics

Standard deviation0.053827338
Coefficient of variation (CV)0.0014333529
Kurtosis-0.7161858
Mean37.553444
Median Absolute Deviation (MAD)0.0417293
Skewness0.39563011
Sum22644.726
Variance0.0028973823
MonotonicityNot monotonic
2023-12-11T15:18:08.528150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5445388 2
 
0.3%
37.548251 2
 
0.3%
37.4854525 2
 
0.3%
37.5666211 2
 
0.3%
37.5426355 2
 
0.3%
37.5472114 2
 
0.3%
37.4811427 2
 
0.3%
37.6128981 2
 
0.3%
37.4948942 2
 
0.3%
37.6529775 2
 
0.3%
Other values (551) 583
96.7%
ValueCountFrequency (%)
37.4494246 1
0.2%
37.454794 1
0.2%
37.458197 1
0.2%
37.4592136 1
0.2%
37.4621534 1
0.2%
37.462837 1
0.2%
37.4638001 1
0.2%
37.4669867 1
0.2%
37.4671739 1
0.2%
37.4678483 1
0.2%
ValueCountFrequency (%)
37.6867349 1
0.2%
37.6840112 2
0.3%
37.6730088 1
0.2%
37.6729902 1
0.2%
37.6716558 1
0.2%
37.6711309 1
0.2%
37.6703561 1
0.2%
37.6676057 1
0.2%
37.6674043 1
0.2%
37.6660106 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct561
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99408
Minimum126.80704
Maximum127.15332
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2023-12-11T15:18:08.669703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80704
5-th percentile126.84233
Q1126.91953
median127.0112
Q3127.06476
95-th percentile127.12904
Maximum127.15332
Range0.3462799
Interquartile range (IQR)0.1452287

Descriptive statistics

Standard deviation0.089139846
Coefficient of variation (CV)0.00070192127
Kurtosis-1.0011132
Mean126.99408
Median Absolute Deviation (MAD)0.0692954
Skewness-0.2414486
Sum76577.43
Variance0.0079459122
MonotonicityNot monotonic
2023-12-11T15:18:08.815346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0983294 2
 
0.3%
126.9267693 2
 
0.3%
126.9492131 2
 
0.3%
126.9114108 2
 
0.3%
127.0868425 2
 
0.3%
126.9158543 2
 
0.3%
127.0862513 2
 
0.3%
126.9195924 2
 
0.3%
126.8926266 2
 
0.3%
127.0318138 2
 
0.3%
Other values (551) 583
96.7%
ValueCountFrequency (%)
126.8070368 1
0.2%
126.8084206 1
0.2%
126.8088551 1
0.2%
126.8103658 1
0.2%
126.8107677 1
0.2%
126.8145937 1
0.2%
126.8154336 1
0.2%
126.8175267 1
0.2%
126.8189772 1
0.2%
126.8210902 1
0.2%
ValueCountFrequency (%)
127.1533167 1
0.2%
127.1532741 1
0.2%
127.1523464 1
0.2%
127.1509915 1
0.2%
127.150827 1
0.2%
127.1504659 1
0.2%
127.1495388 1
0.2%
127.1479575 1
0.2%
127.1462257 1
0.2%
127.1460559 1
0.2%

Interactions

2023-12-11T15:18:01.412810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.096300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.315892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.460008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.356093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.148174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.120624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.407447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.383956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.546951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.300159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.426470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.562919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.438036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.249543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.233417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.501846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.482364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.646840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.426688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.541011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.661339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.514560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.340265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.342261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.638902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.590799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.784519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.547257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.667063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.761885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.616179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.436069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.474030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.760587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.708776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.913193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.691148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.807632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.860630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.713051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.533859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.609218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.861617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.849435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:02.039885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.827740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.923912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.967657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.796537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.641544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.714694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.957388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.975183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:02.168793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:53.936205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.066890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.075200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.875301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.735716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:58.818710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.053090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.072288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:02.292978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.068105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.204189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.182906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.979324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.843488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.214805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.154982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.185457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:02.405123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:54.193125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:55.324517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:56.269456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.058674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:57.952585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:17:59.309441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:00.272167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T15:18:01.294764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T15:18:08.941589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구명산지여부주지번부지번새주소명조성년도조성면적교목수관목수초화류수위도경도
고유번호1.0000.7150.0000.1950.0000.0000.0000.1080.0000.1400.0000.4840.565
구명0.7151.0000.0680.5630.6770.0000.0000.3920.2210.1250.2420.9180.939
산지여부0.0000.0681.0000.0000.6330.0000.0120.0000.0000.0000.0000.0000.000
주지번0.1950.5630.0001.0000.0000.0000.2030.0000.0000.2130.0000.3340.263
부지번0.0000.6770.6330.0001.0000.4910.1200.4820.4940.0000.0000.0000.209
새주소명0.0000.0000.0000.0000.4911.0000.0000.0000.0000.0880.0000.0000.000
조성년도0.0000.0000.0120.2030.1200.0001.0000.0000.0590.4000.0590.0000.000
조성면적0.1080.3920.0000.0000.4820.0000.0001.0000.5710.3500.2310.1120.132
교목수0.0000.2210.0000.0000.4940.0000.0590.5711.0000.4700.0000.0000.202
관목수0.1400.1250.0000.2130.0000.0880.4000.3500.4701.0000.1340.0670.084
초화류수0.0000.2420.0000.0000.0000.0000.0590.2310.0000.1341.0000.1300.000
위도0.4840.9180.0000.3340.0000.0000.0000.1120.0000.0670.1301.0000.599
경도0.5650.9390.0000.2630.2090.0000.0000.1320.2020.0840.0000.5991.000
2023-12-11T15:18:09.089217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명새주소명산지여부
구명1.0000.0000.057
새주소명0.0001.0000.000
산지여부0.0570.0001.000
2023-12-11T15:18:09.252865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호주지번조성년도조성면적교목수관목수초화류수위도경도구명산지여부새주소명
고유번호1.000-0.011-0.049-0.0240.017-0.0620.008-0.114-0.1120.3390.0000.000
주지번-0.0111.000-0.0030.015-0.0370.006-0.003-0.084-0.0950.2870.0000.000
조성년도-0.049-0.0031.0000.077-0.0580.3330.1700.0200.0400.0000.0000.000
조성면적-0.0240.0150.0771.0000.3970.3660.159-0.0350.0060.1580.0000.000
교목수0.017-0.037-0.0580.3971.0000.3250.1190.0150.0110.0780.0000.000
관목수-0.0620.0060.3330.3660.3251.0000.213-0.0280.1270.0550.0000.063
초화류수0.008-0.0030.1700.1590.1190.2131.0000.022-0.0120.0960.0000.000
위도-0.114-0.0840.020-0.0350.015-0.0280.0221.0000.1930.6290.0000.000
경도-0.112-0.0950.0400.0060.0110.127-0.0120.1931.0000.6850.0000.000
구명0.3390.2870.0000.1580.0780.0550.0960.6290.6851.0000.0570.000
산지여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0571.0000.000
새주소명0.0000.0000.0000.0000.0000.0630.0000.0000.0000.0000.0001.000

Missing values

2023-12-11T15:18:02.580526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T15:18:02.853531image/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

고유번호구명법정동명산지여부주지번부지번새주소명학교명조성년도조성면적교목수관목수초화류수생성일사진파일명위도경도
0448광진구군자동198세종초등학교20073006238804890<NA>37.550697127.074136
1238노원구상계동17544신상중200710005444600<NA>37.65131127.063652
2150동작구사당동110111남사초등학교2007200018000<NA>37.482348126.978881
3564노원구월계동1820염광여고2007300030627540950<NA>37.630797127.050508
4437금천구독산동15927영남초등학교200710508424643450<NA>37.475679126.909698
5450동대문구휘경동149295전동중학교20079209758427062<NA>37.585276127.071094
6453중랑구망우동12391송곡고등학교200711008419691550<NA>37.605676127.10755
7239노원구하계동1271중평초2007200037839708650<NA>37.63731127.064184
8194관악구신림동12201삼성중학교20071500818500190<NA>37.467848126.943091
9242노원구상계동1638온곡초200713007224521380<NA>37.667404127.066017
고유번호구명법정동명산지여부주지번부지번새주소명학교명조성년도조성면적교목수관목수초화류수생성일사진파일명위도경도
593596마포구성산동1941중동초등학교2001394581500<NA>37.566621126.911411
594370중랑구면목동1551면동초등학교2001480881300700<NA>37.585275127.086691
595590강서구화곡동124266화곡초등학교200101510620<NA>37.54323126.846087
596259마포구합정동14272성산초등학교200112038331082172<NA>37.55329126.911097
597267양천구신정동12812신목초등학교20011000291440200<NA>37.516927126.872875
598268양천구목동1929목원초등학교2001300011312351240<NA>37.541469126.881666
599269양천구신정동11001양목초등학교200110004134291020<NA>37.523699126.861235
600211강남구대치동192410도곡초등학교200112007014970<NA>37.499067127.054691
601321영등포구신길동11843영등포여자고등학교2001105011512102350<NA>37.515241126.915306
602197서초구반포동121원촌초등학교200113001451412210<NA>37.505949127.012831