Overview

Dataset statistics

Number of variables8
Number of observations5343
Missing cells8518
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory360.2 KiB
Average record size in memory69.0 B

Variable types

Numeric5
Categorical1
Text2

Dataset

Description고유번호,법정동코드,오염우려지역,지점번호,조사지점명,보고년도(자료기준년도),X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21101/S/1/datasetView.do

Alerts

고유번호 is highly overall correlated with 보고년도(자료기준년도) and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with Y좌표High correlation
보고년도(자료기준년도) is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 법정동코드High correlation
오염우려지역 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
법정동코드 has 1101 (20.6%) missing valuesMissing
지점번호 has 4958 (92.8%) missing valuesMissing
조사지점명 has 2459 (46.0%) missing valuesMissing
고유번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 04:18:25.524964
Analysis finished2023-12-11 04:18:30.241534
Duration4.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct5343
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20089995
Minimum19970001
Maximum20150408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2023-12-11T13:18:30.331011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19970001
5-th percentile20000006
Q120060318
median20100156
Q320130052
95-th percentile20150141
Maximum20150408
Range180407
Interquartile range (IQR)69735

Descriptive statistics

Standard deviation45107.85
Coefficient of variation (CV)0.0022452892
Kurtosis-0.041829629
Mean20089995
Median Absolute Deviation (MAD)30031
Skewness-0.78870286
Sum1.0734084 × 1011
Variance2.0347181 × 109
MonotonicityStrictly increasing
2023-12-11T13:18:30.518778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19970001 1
 
< 0.1%
20120127 1
 
< 0.1%
20120125 1
 
< 0.1%
20120124 1
 
< 0.1%
20120123 1
 
< 0.1%
20120122 1
 
< 0.1%
20120121 1
 
< 0.1%
20120120 1
 
< 0.1%
20120119 1
 
< 0.1%
20120118 1
 
< 0.1%
Other values (5333) 5333
99.8%
ValueCountFrequency (%)
19970001 1
< 0.1%
19970002 1
< 0.1%
19970003 1
< 0.1%
19970004 1
< 0.1%
19970005 1
< 0.1%
19970006 1
< 0.1%
19970007 1
< 0.1%
19970008 1
< 0.1%
19970009 1
< 0.1%
19970010 1
< 0.1%
ValueCountFrequency (%)
20150408 1
< 0.1%
20150407 1
< 0.1%
20150406 1
< 0.1%
20150405 1
< 0.1%
20150404 1
< 0.1%
20150403 1
< 0.1%
20150402 1
< 0.1%
20150401 1
< 0.1%
20150400 1
< 0.1%
20150399 1
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct284
Distinct (%)6.7%
Missing1101
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean11429999
Minimum11110102
Maximum11740110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2023-12-11T13:18:30.715769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110102
5-th percentile11140162
Q111290108
median11440127
Q311560124
95-th percentile11710109
Maximum11740110
Range630008
Interquartile range (IQR)270016.25

Descriptive statistics

Standard deviation180118.93
Coefficient of variation (CV)0.015758438
Kurtosis-1.1636537
Mean11429999
Median Absolute Deviation (MAD)149980
Skewness-0.023943878
Sum4.8486054 × 1010
Variance3.2442827 × 1010
MonotonicityNot monotonic
2023-12-11T13:18:30.926136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11530102 124
 
2.3%
11545101 107
 
2.0%
11440127 91
 
1.7%
11620102 89
 
1.7%
11200115 81
 
1.5%
11350105 79
 
1.5%
11470101 68
 
1.3%
11200114 66
 
1.2%
11320108 64
 
1.2%
11545102 64
 
1.2%
Other values (274) 3409
63.8%
(Missing) 1101
 
20.6%
ValueCountFrequency (%)
11110102 2
 
< 0.1%
11110104 3
0.1%
11110109 2
 
< 0.1%
11110110 1
 
< 0.1%
11110111 5
0.1%
11110115 7
0.1%
11110117 1
 
< 0.1%
11110119 2
 
< 0.1%
11110120 1
 
< 0.1%
11110121 1
 
< 0.1%
ValueCountFrequency (%)
11740110 23
0.4%
11740109 32
0.6%
11740108 25
0.5%
11740107 7
 
0.1%
11740106 19
0.4%
11740105 3
 
0.1%
11740103 8
 
0.1%
11740102 21
0.4%
11740101 8
 
0.1%
11710114 6
 
0.1%

오염우려지역
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size41.9 KiB
교통관련시설 지역
1264 
<NA>
474 
어린이놀이터 지역
432 
공장 및 공업지역
403 
기타 토지개발 등 지역
302 
Other values (33)
2468 

Length

Max length20
Median length19
Mean length11.2347
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
교통관련시설 지역 1264
23.7%
<NA> 474
 
8.9%
어린이놀이터 지역 432
 
8.1%
공장 및 공업지역 403
 
7.5%
기타 토지개발 등 지역 302
 
5.7%
산업단지 및 공장지역 222
 
4.2%
토지개발 등 지역 220
 
4.1%
폐기물 적치?매립?소각 등 지역 209
 
3.9%
교통관련시설지역 178
 
3.3%
교통관련시설 지역(세차장 정비소) 149
 
2.8%
Other values (28) 1490
27.9%

Length

2023-12-11T13:18:31.123046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
지역 2858
20.1%
교통관련시설 1735
 
12.2%
등 1160
 
8.1%
및 924
 
6.5%
토지개발 533
 
3.7%
na 474
 
3.3%
어린이놀이터 432
 
3.0%
공장 403
 
2.8%
공업지역 403
 
2.8%
폐기물 396
 
2.8%
Other values (50) 4926
34.6%

지점번호
Text

MISSING 

Distinct204
Distinct (%)53.0%
Missing4958
Missing (%)92.8%
Memory size41.9 KiB
2023-12-11T13:18:31.501297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8701299
Min length4

Characters and Unicode

Total characters1875
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)25.2%

Sample

1st rowAD-1
2nd rowAD-2
3rd rowAD-4
4th rowAD-6
5th rowAD-9
ValueCountFrequency (%)
ad-16 4
 
1.0%
au-25 4
 
1.0%
au-24 4
 
1.0%
ad-37 4
 
1.0%
ad-38 4
 
1.0%
ad-39 4
 
1.0%
ad-41 4
 
1.0%
au-11 4
 
1.0%
au-14 4
 
1.0%
au-15 4
 
1.0%
Other values (194) 345
89.6%
2023-12-11T13:18:32.106755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 385
20.5%
- 385
20.5%
1 148
 
7.9%
D 131
 
7.0%
2 117
 
6.2%
0 116
 
6.2%
U 108
 
5.8%
3 88
 
4.7%
5 49
 
2.6%
4 49
 
2.6%
Other values (14) 299
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 770
41.1%
Decimal Number 720
38.4%
Dash Punctuation 385
20.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 385
50.0%
D 131
 
17.0%
U 108
 
14.0%
C 36
 
4.7%
F 32
 
4.2%
N 30
 
3.9%
P 17
 
2.2%
H 15
 
1.9%
O 9
 
1.2%
L 2
 
0.3%
Other values (3) 5
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 148
20.6%
2 117
16.2%
0 116
16.1%
3 88
12.2%
5 49
 
6.8%
4 49
 
6.8%
6 43
 
6.0%
9 42
 
5.8%
7 38
 
5.3%
8 30
 
4.2%
Dash Punctuation
ValueCountFrequency (%)
- 385
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1105
58.9%
Latin 770
41.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 385
50.0%
D 131
 
17.0%
U 108
 
14.0%
C 36
 
4.7%
F 32
 
4.2%
N 30
 
3.9%
P 17
 
2.2%
H 15
 
1.9%
O 9
 
1.2%
L 2
 
0.3%
Other values (3) 5
 
0.6%
Common
ValueCountFrequency (%)
- 385
34.8%
1 148
 
13.4%
2 117
 
10.6%
0 116
 
10.5%
3 88
 
8.0%
5 49
 
4.4%
4 49
 
4.4%
6 43
 
3.9%
9 42
 
3.8%
7 38
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 385
20.5%
- 385
20.5%
1 148
 
7.9%
D 131
 
7.0%
2 117
 
6.2%
0 116
 
6.2%
U 108
 
5.8%
3 88
 
4.7%
5 49
 
2.6%
4 49
 
2.6%
Other values (14) 299
15.9%

조사지점명
Text

MISSING 

Distinct1559
Distinct (%)54.1%
Missing2459
Missing (%)46.0%
Memory size41.9 KiB
2023-12-11T13:18:32.433461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length7.3172677
Min length1

Characters and Unicode

Total characters21103
Distinct characters505
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique920 ?
Unique (%)31.9%

Sample

1st row자하문주유소
2nd row자하문주유소
3rd row자하문주유소
4th row자하문주유소
5th row자하문주유소
ValueCountFrequency (%)
tkp송유관시설 32
 
1.0%
수색차량사업소 20
 
0.6%
세차장 20
 
0.6%
알파색채주식회사 14
 
0.4%
세원주유소 13
 
0.4%
농경지 12
 
0.4%
현대자동차 11
 
0.3%
쌍용자동차 11
 
0.3%
롯데알미늄 11
 
0.3%
금호주유소 10
 
0.3%
Other values (1663) 3052
95.2%
2023-12-11T13:18:32.887122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 720
 
3.4%
차 630
 
3.0%
동 539
 
2.6%
자 464
 
2.2%
소 462
 
2.2%
유 437
 
2.1%
사 378
 
1.8%
원 370
 
1.8%
업 363
 
1.7%
공 357
 
1.7%
Other values (495) 16383
77.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18779
89.0%
Decimal Number 476
 
2.3%
Uppercase Letter 388
 
1.8%
Space Separator 322
 
1.5%
Open Punctuation 306
 
1.5%
Close Punctuation 304
 
1.4%
Other Symbol 280
 
1.3%
Dash Punctuation 149
 
0.7%
Other Punctuation 48
 
0.2%
Lowercase Letter 39
 
0.2%
Other values (2) 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 720
 
3.8%
차 630
 
3.4%
동 539
 
2.9%
자 464
 
2.5%
소 462
 
2.5%
유 437
 
2.3%
사 378
 
2.0%
원 370
 
2.0%
업 363
 
1.9%
공 357
 
1.9%
Other values (433) 14059
74.9%
Uppercase Letter
ValueCountFrequency (%)
K 95
24.5%
S 76
19.6%
T 54
13.9%
P 40
10.3%
E 21
 
5.4%
G 16
 
4.1%
N 15
 
3.9%
C 12
 
3.1%
J 9
 
2.3%
A 6
 
1.5%
Other values (13) 44
11.3%
Lowercase Letter
ValueCountFrequency (%)
o 6
15.4%
k 6
15.4%
s 6
15.4%
t 4
10.3%
n 3
7.7%
b 2
 
5.1%
e 2
 
5.1%
i 2
 
5.1%
a 2
 
5.1%
c 2
 
5.1%
Other values (2) 4
10.3%
Decimal Number
ValueCountFrequency (%)
1 163
34.2%
2 142
29.8%
3 57
 
12.0%
4 47
 
9.9%
6 18
 
3.8%
0 15
 
3.2%
5 11
 
2.3%
8 10
 
2.1%
9 9
 
1.9%
7 4
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 24
50.0%
？ 18
37.5%
, 3
 
6.2%
& 2
 
4.2%
/ 1
 
2.1%
Math Symbol
ValueCountFrequency (%)
~ 2
25.0%
< 2
25.0%
> 2
25.0%
⇒ 2
25.0%
Open Punctuation
ValueCountFrequency (%)
( 300
98.0%
[ 6
 
2.0%
Close Punctuation
ValueCountFrequency (%)
) 298
98.0%
] 6
 
2.0%
Space Separator
ValueCountFrequency (%)
322
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 280
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 149
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19059
90.3%
Common 1617
 
7.7%
Latin 427
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 720
 
3.8%
차 630
 
3.3%
동 539
 
2.8%
자 464
 
2.4%
소 462
 
2.4%
유 437
 
2.3%
사 378
 
2.0%
원 370
 
1.9%
업 363
 
1.9%
공 357
 
1.9%
Other values (434) 14339
75.2%
Latin
ValueCountFrequency (%)
K 95
22.2%
S 76
17.8%
T 54
12.6%
P 40
9.4%
E 21
 
4.9%
G 16
 
3.7%
N 15
 
3.5%
C 12
 
2.8%
J 9
 
2.1%
A 6
 
1.4%
Other values (25) 83
19.4%
Common
ValueCountFrequency (%)
322
19.9%
( 300
18.6%
) 298
18.4%
1 163
10.1%
- 149
9.2%
2 142
8.8%
3 57
 
3.5%
4 47
 
2.9%
. 24
 
1.5%
？ 18
 
1.1%
Other values (16) 97
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18779
89.0%
ASCII 2024
 
9.6%
None 298
 
1.4%
Arrows 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 720
 
3.8%
차 630
 
3.4%
동 539
 
2.9%
자 464
 
2.5%
소 462
 
2.5%
유 437
 
2.3%
사 378
 
2.0%
원 370
 
2.0%
업 363
 
1.9%
공 357
 
1.9%
Other values (433) 14059
74.9%
ASCII
ValueCountFrequency (%)
322
15.9%
( 300
14.8%
) 298
14.7%
1 163
8.1%
- 149
7.4%
2 142
 
7.0%
K 95
 
4.7%
S 76
 
3.8%
3 57
 
2.8%
T 54
 
2.7%
Other values (49) 368
18.2%
None
ValueCountFrequency (%)
㈜ 280
94.0%
？ 18
 
6.0%
Arrows
ValueCountFrequency (%)
⇒ 2
100.0%

보고년도(자료기준년도)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.9794
Minimum1997
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2023-12-11T13:18:33.045138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile2000
Q12006
median2010
Q32013
95-th percentile2015
Maximum2015
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5048278
Coefficient of variation (CV)0.0022423464
Kurtosis-0.042021867
Mean2008.9794
Median Absolute Deviation (MAD)3
Skewness-0.78707444
Sum10733977
Variance20.293473
MonotonicityIncreasing
2023-12-11T13:18:33.199688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2010 578
10.8%
2013 574
10.7%
2012 509
9.5%
2008 418
 
7.8%
2009 409
 
7.7%
2015 408
 
7.6%
2014 406
 
7.6%
2011 350
 
6.6%
2007 336
 
6.3%
2006 306
 
5.7%
Other values (9) 1049
19.6%
ValueCountFrequency (%)
1997 79
 
1.5%
1998 79
 
1.5%
1999 104
 
1.9%
2000 104
 
1.9%
2001 108
 
2.0%
2002 111
 
2.1%
2003 130
2.4%
2004 131
2.5%
2005 203
3.8%
2006 306
5.7%
ValueCountFrequency (%)
2015 408
7.6%
2014 406
7.6%
2013 574
10.7%
2012 509
9.5%
2011 350
6.6%
2010 578
10.8%
2009 409
7.7%
2008 418
7.8%
2007 336
6.3%
2006 306
5.7%

X좌표
Real number (ℝ)

Distinct3053
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198629.31
Minimum180857.63
Maximum215714.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2023-12-11T13:18:33.399293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180857.63
5-th percentile185422.61
Q1190951.77
median199874.12
Q3205319.86
95-th percentile210870.76
Maximum215714.92
Range34857.29
Interquartile range (IQR)14368.091

Descriptive statistics

Standard deviation8185.9394
Coefficient of variation (CV)0.041212143
Kurtosis-1.1715103
Mean198629.31
Median Absolute Deviation (MAD)6866.1916
Skewness-0.095589364
Sum1.0612764 × 109
Variance67009604
MonotonicityNot monotonic
2023-12-11T13:18:33.615367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190951.768095 30
 
0.6%
192872.842224 24
 
0.4%
184832.891745 22
 
0.4%
189727.065879 22
 
0.4%
210629.705388 20
 
0.4%
190940.440802 20
 
0.4%
188083.786617 18
 
0.3%
184098.969875 17
 
0.3%
183487.705436 17
 
0.3%
190468.29799 15
 
0.3%
Other values (3043) 5138
96.2%
ValueCountFrequency (%)
180857.6339 1
< 0.1%
180871.1833 1
< 0.1%
181374.396 1
< 0.1%
181735.646 1
< 0.1%
181874.615 1
< 0.1%
182112.7877 1
< 0.1%
182127.6309 1
< 0.1%
182132.3068 1
< 0.1%
182149.3939 1
< 0.1%
182222.53 1
< 0.1%
ValueCountFrequency (%)
215714.923516 4
0.1%
215608.982638 1
 
< 0.1%
215479.011 1
 
< 0.1%
215475.688 1
 
< 0.1%
215475.5208 1
 
< 0.1%
215453.1236 1
 
< 0.1%
215447.7753 1
 
< 0.1%
215429.8404 1
 
< 0.1%
215424.5627 1
 
< 0.1%
215414.7702 1
 
< 0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct3053
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450062.2
Minimum437833.19
Maximum465967.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.1 KiB
2023-12-11T13:18:33.794204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437833.19
5-th percentile441121.11
Q1444747.67
median449686.17
Q3453926.05
95-th percentile461725.75
Maximum465967.63
Range28134.44
Interquartile range (IQR)9178.381

Descriptive statistics

Standard deviation6238.1153
Coefficient of variation (CV)0.013860562
Kurtosis-0.61068629
Mean450062.2
Median Absolute Deviation (MAD)4574.786
Skewness0.33852288
Sum2.4046823 × 109
Variance38914082
MonotonicityNot monotonic
2023-12-11T13:18:33.972639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453159.185225 30
 
0.6%
449407.523268 24
 
0.4%
448967.162426 22
 
0.4%
453435.9147 22
 
0.4%
453167.358911 20
 
0.4%
440839.91992 20
 
0.4%
445734.27473 18
 
0.3%
454048.434088 17
 
0.3%
452481.296563 17
 
0.3%
440601.006534 15
 
0.3%
Other values (3043) 5138
96.2%
ValueCountFrequency (%)
437833.186 1
 
< 0.1%
438017.808908 1
 
< 0.1%
438105.076566 2
< 0.1%
438120.9962 1
 
< 0.1%
438228.268494 4
0.1%
438376.015766 4
0.1%
438484.463957 4
0.1%
438544.714306 4
0.1%
438602.228946 1
 
< 0.1%
438673.497995 1
 
< 0.1%
ValueCountFrequency (%)
465967.6259 1
 
< 0.1%
465965.8926 1
 
< 0.1%
465963.8367 1
 
< 0.1%
465952.369921 1
 
< 0.1%
465892.259206 2
< 0.1%
465841.404603 1
 
< 0.1%
465835.527 1
 
< 0.1%
465827.741 1
 
< 0.1%
465813.831119 1
 
< 0.1%
465809.718144 3
0.1%

Interactions

2023-12-11T13:18:28.861737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:26.452271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.122712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.755038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.295060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.972042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:26.600007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.254302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.868404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.416827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:29.098459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:26.717893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.384704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.975595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.526178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:29.206016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:26.852673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.537569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.085015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.645622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:29.322767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:26.983067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:27.653304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.194130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T13:18:28.756532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T13:18:34.116276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드오염우려지역보고년도(자료기준년도)X좌표Y좌표
고유번호1.0000.1460.8511.0000.1850.156
법정동코드0.1461.0000.4900.0870.9090.895
오염우려지역0.8510.4901.0000.8520.4960.403
보고년도(자료기준년도)1.0000.0870.8521.0000.1650.077
X좌표0.1850.9090.4960.1651.0000.597
Y좌표0.1560.8950.4030.0770.5971.000
2023-12-11T13:18:34.237663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호법정동코드보고년도(자료기준년도)X좌표Y좌표오염우려지역
고유번호1.0000.0900.9970.019-0.0580.537
법정동코드0.0901.0000.030-0.097-0.6220.199
보고년도(자료기준년도)0.9970.0301.0000.025-0.0200.538
X좌표0.019-0.0970.0251.0000.2040.196
Y좌표-0.058-0.622-0.0200.2041.0000.152
오염우려지역0.5370.1990.5380.1960.1521.000

Missing values

2023-12-11T13:18:29.458805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:18:29.654447image/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.
2023-12-11T13:18:30.147032image/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

고유번호법정동코드오염우려지역지점번호조사지점명보고년도(자료기준년도)X좌표Y좌표
01997000111650101<NA>AD-1<NA>1997198763.027509441630.49991
11997000211680104<NA>AD-2<NA>1997203739.627891446791.757749
21997000311710101<NA><NA><NA>1997206489.608511445697.081934
31997000411740109<NA>AD-4<NA>1997211757.355531449400.103839
41997000511545103<NA><NA><NA>1997191539.074193438376.015766
51997000611545101<NA>AD-6<NA>1997189159.011994442463.765636
61997000711560121<NA><NA><NA>1997190877.422316446300.94138
71997000811560101<NA><NA><NA>1997191799.1644446344.109374
81997000911590102<NA>AD-9<NA>1997195769.663695445329.426505
91997001011590104<NA>AD-10<NA>1997195897.017515446028.320133
고유번호법정동코드오염우려지역지점번호조사지점명보고년도(자료기준년도)X좌표Y좌표
53332015039911545102교통관련시설지역<NA>대진자동차공업사2015190434.1846440857.9292
53342015040011545102교통관련시설지역<NA>남부세차장2015190696.0915441617.7804
53352015040111545103교통관련시설지역<NA>VIP손세차장2015191585.2349440197.2054
53362015040211545103산업단지 주변 등의 주거지역<NA>남서울건영아파트-12015191033.9525439276.2602
53372015040311545103산업단지 주변 등의 주거지역<NA>남서울건영아파트-22015191033.9525439276.2602
53382015040411545103산업단지 주변 등의 주거지역<NA>남서울럭키아파트-12015191073.8889438809.742
53392015040511545103산업단지 주변 등의 주거지역<NA>남서울럭키아파트-22015191073.8889438809.742
53402015040611215103사고?민원 등 발생지역<NA>선인에너지㈜구의동주유소-12015208369.12448281.9069
53412015040711215103사고?민원 등 발생지역<NA>선인에너지㈜구의동주유소-22015208369.12448281.9069
53422015040811215103사고?민원 등 발생지역<NA>선인에너지㈜구의동주유소-32015208369.12448281.9069