Overview

Dataset statistics

Number of variables15
Number of observations1565
Missing cells495
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory198.8 KiB
Average record size in memory130.1 B

Variable types

Numeric11
Categorical2
Text2

Dataset

Description시설번호,지역코드,시설일련번호,시도명,시군구명,수용시설명,도로명주소코드,법정동코드,행정동코드,상세주소,시설면적,경도,위도,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21063/S/1/datasetView.do

Alerts

시도명 has constant value ""Constant
시설번호 is highly overall correlated with 시군구명High correlation
지역코드 is highly overall correlated with 도로명주소코드 and 5 other fieldsHigh correlation
도로명주소코드 is highly overall correlated with 지역코드 and 4 other fieldsHigh correlation
법정동코드 is highly overall correlated with 지역코드 and 5 other fieldsHigh correlation
행정동코드 is highly overall correlated with 지역코드 and 5 other fieldsHigh correlation
경도 is highly overall correlated with X좌표 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 지역코드 and 5 other fieldsHigh correlation
X좌표 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
Y좌표 is highly overall correlated with 지역코드 and 5 other fieldsHigh correlation
시군구명 is highly overall correlated with 시설번호 and 7 other fieldsHigh correlation
도로명주소코드 has 495 (31.6%) missing valuesMissing
시설번호 has unique valuesUnique
X좌표 has unique valuesUnique
Y좌표 has unique valuesUnique

Reproduction

Analysis started2024-05-18 09:43:31.048902
Analysis finished2024-05-18 09:44:44.520905
Duration1 minute and 13.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1565
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.8096
Minimum80
Maximum11283
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:44.673661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile432.2
Q11591
median7563
Q38000
95-th percentile10938.8
Maximum11283
Range11203
Interquartile range (IQR)6409

Descriptive statistics

Standard deviation3657.1619
Coefficient of variation (CV)0.69982686
Kurtosis-1.5476984
Mean5225.8096
Median Absolute Deviation (MAD)3713
Skewness0.058463009
Sum8178392
Variance13374833
MonotonicityNot monotonic
2024-05-18T18:44:44.938142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1
 
0.1%
7592 1
 
0.1%
7614 1
 
0.1%
7613 1
 
0.1%
7612 1
 
0.1%
7611 1
 
0.1%
7610 1
 
0.1%
7609 1
 
0.1%
7608 1
 
0.1%
7607 1
 
0.1%
Other values (1555) 1555
99.4%
ValueCountFrequency (%)
80 1
0.1%
82 1
0.1%
83 1
0.1%
84 1
0.1%
85 1
0.1%
86 1
0.1%
87 1
0.1%
88 1
0.1%
89 1
0.1%
90 1
0.1%
ValueCountFrequency (%)
11283 1
0.1%
11279 1
0.1%
11278 1
0.1%
11277 1
0.1%
11276 1
0.1%
11275 1
0.1%
11274 1
0.1%
11271 1
0.1%
11270 1
0.1%
11269 1
0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1451243 × 109
Minimum1.111 × 109
Maximum1.174 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:45.201153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111 × 109
5-th percentile1.117 × 109
Q11.1305 × 109
median1.147 × 109
Q31.162 × 109
95-th percentile1.171 × 109
Maximum1.174 × 109
Range63000000
Interquartile range (IQR)31500000

Descriptive statistics

Standard deviation18031136
Coefficient of variation (CV)0.015746008
Kurtosis-1.1424473
Mean1.1451243 × 109
Median Absolute Deviation (MAD)15000000
Skewness-0.1208718
Sum1.7921195 × 1012
Variance3.2512185 × 1014
MonotonicityNot monotonic
2024-05-18T18:44:45.607646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1168000000 120
 
7.7%
1150000000 116
 
7.4%
1171000000 90
 
5.8%
1147000000 90
 
5.8%
1162000000 80
 
5.1%
1135000000 79
 
5.0%
1156000000 72
 
4.6%
1129000000 67
 
4.3%
1144000000 65
 
4.2%
1165000000 64
 
4.1%
Other values (15) 722
46.1%
ValueCountFrequency (%)
1111000000 46
2.9%
1114000000 19
 
1.2%
1117000000 50
3.2%
1120000000 61
3.9%
1121500000 39
2.5%
1123000000 50
3.2%
1126000000 52
3.3%
1129000000 67
4.3%
1130500000 51
3.3%
1132000000 56
3.6%
ValueCountFrequency (%)
1174000000 54
3.5%
1171000000 90
5.8%
1168000000 120
7.7%
1165000000 64
4.1%
1162000000 80
5.1%
1159000000 44
 
2.8%
1156000000 72
4.6%
1154500000 45
 
2.9%
1153000000 48
 
3.1%
1150000000 116
7.4%

시설일련번호
Real number (ℝ)

Distinct143
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.941214
Minimum2
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:46.020945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q125
median45
Q369
95-th percentile113.8
Maximum150
Range148
Interquartile range (IQR)44

Descriptive statistics

Standard deviation31.839598
Coefficient of variation (CV)0.63754152
Kurtosis-0.04910985
Mean49.941214
Median Absolute Deviation (MAD)22
Skewness0.69091696
Sum78158
Variance1013.76
MonotonicityNot monotonic
2024-05-18T18:44:46.460371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 22
 
1.4%
19 22
 
1.4%
41 21
 
1.3%
21 21
 
1.3%
40 21
 
1.3%
20 20
 
1.3%
32 20
 
1.3%
24 20
 
1.3%
54 20
 
1.3%
31 20
 
1.3%
Other values (133) 1358
86.8%
ValueCountFrequency (%)
2 16
1.0%
3 12
0.8%
4 15
1.0%
5 12
0.8%
6 19
1.2%
7 16
1.0%
8 17
1.1%
9 15
1.0%
10 19
1.2%
11 16
1.0%
ValueCountFrequency (%)
150 1
 
0.1%
149 1
 
0.1%
145 1
 
0.1%
143 1
 
0.1%
140 1
 
0.1%
139 2
0.1%
138 2
0.1%
137 2
0.1%
136 2
0.1%
135 3
0.2%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
서울특별시
1565 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 1565
100.0%

Length

2024-05-18T18:44:46.863851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T18:44:47.171555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 1565
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
강남구
120 
강서구
116 
양천구
 
90
송파구
 
90
관악구
 
80
Other values (20)
1069 

Length

Max length4
Median length3
Mean length3.0945687
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강동구
2nd row강동구
3rd row강동구
4th row강서구
5th row강남구

Common Values

ValueCountFrequency (%)
강남구 120
 
7.7%
강서구 116
 
7.4%
양천구 90
 
5.8%
송파구 90
 
5.8%
관악구 80
 
5.1%
노원구 79
 
5.0%
영등포구 72
 
4.6%
성북구 67
 
4.3%
마포구 65
 
4.2%
서초구 64
 
4.1%
Other values (15) 722
46.1%

Length

2024-05-18T18:44:47.564265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 120
 
7.7%
강서구 116
 
7.4%
양천구 90
 
5.8%
송파구 90
 
5.8%
관악구 80
 
5.1%
노원구 79
 
5.0%
영등포구 72
 
4.6%
성북구 67
 
4.3%
마포구 65
 
4.2%
서초구 64
 
4.1%
Other values (15) 722
46.1%
Distinct1540
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2024-05-18T18:44:48.024702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length31
Mean length9.1201278
Min length4

Characters and Unicode

Total characters14273
Distinct characters396
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1519 ?
Unique (%)97.1%

Sample

1st row강동고등학교 운동장
2nd row한영고등학교 운동장
3rd row상일초등학교 운동장
4th row서울공항초등학교 운동장
5th row중동중학교 운동장
ValueCountFrequency (%)
운동장 914
35.5%
공원 9
 
0.3%
어린이공원 5
 
0.2%
어린이 5
 
0.2%
동답초등학교 3
 
0.1%
주차장 3
 
0.1%
공영주차장 3
 
0.1%
사범대학 3
 
0.1%
중앙어린이공원 3
 
0.1%
무지개어린이공원 3
 
0.1%
Other values (1589) 1621
63.0%
2024-05-18T18:44:49.002547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1136
 
8.0%
1079
 
7.6%
1065
 
7.5%
1046
 
7.3%
1037
 
7.3%
1007
 
7.1%
748
 
5.2%
575
 
4.0%
534
 
3.7%
521
 
3.7%
Other values (386) 5525
38.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13153
92.2%
Space Separator 1007
 
7.1%
Open Punctuation 37
 
0.3%
Close Punctuation 37
 
0.3%
Decimal Number 29
 
0.2%
Dash Punctuation 4
 
< 0.1%
Uppercase Letter 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1136
 
8.6%
1079
 
8.2%
1065
 
8.1%
1046
 
8.0%
1037
 
7.9%
748
 
5.7%
575
 
4.4%
534
 
4.1%
521
 
4.0%
336
 
2.6%
Other values (367) 5076
38.6%
Decimal Number
ValueCountFrequency (%)
2 7
24.1%
3 7
24.1%
1 7
24.1%
5 2
 
6.9%
4 2
 
6.9%
6 1
 
3.4%
7 1
 
3.4%
8 1
 
3.4%
9 1
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
M 1
33.3%
D 1
33.3%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
1007
100.0%
Open Punctuation
ValueCountFrequency (%)
( 37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13153
92.2%
Common 1117
 
7.8%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1136
 
8.6%
1079
 
8.2%
1065
 
8.1%
1046
 
8.0%
1037
 
7.9%
748
 
5.7%
575
 
4.4%
534
 
4.1%
521
 
4.0%
336
 
2.6%
Other values (367) 5076
38.6%
Common
ValueCountFrequency (%)
1007
90.2%
( 37
 
3.3%
) 37
 
3.3%
2 7
 
0.6%
3 7
 
0.6%
1 7
 
0.6%
- 4
 
0.4%
5 2
 
0.2%
4 2
 
0.2%
6 1
 
0.1%
Other values (6) 6
 
0.5%
Latin
ValueCountFrequency (%)
C 1
33.3%
M 1
33.3%
D 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13153
92.2%
ASCII 1120
 
7.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1136
 
8.6%
1079
 
8.2%
1065
 
8.1%
1046
 
8.0%
1037
 
7.9%
748
 
5.7%
575
 
4.4%
534
 
4.1%
521
 
4.0%
336
 
2.6%
Other values (367) 5076
38.6%
ASCII
ValueCountFrequency (%)
1007
89.9%
( 37
 
3.3%
) 37
 
3.3%
2 7
 
0.6%
3 7
 
0.6%
1 7
 
0.6%
- 4
 
0.4%
5 2
 
0.2%
4 2
 
0.2%
6 1
 
0.1%
Other values (9) 9
 
0.8%

도로명주소코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct965
Distinct (%)90.2%
Missing495
Missing (%)31.6%
Infinite0
Infinite (%)0.0%
Mean1.1468343 × 1024
Minimum1.1110101 × 1024
Maximum1.174011 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:49.422006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 1024
5-th percentile1.1170106 × 1024
Q11.1320106 × 1024
median1.1500103 × 1024
Q31.1620102 × 1024
95-th percentile1.1710112 × 1024
Maximum1.174011 × 1024
Range6.30009 × 1022
Interquartile range (IQR)2.99996 × 1022

Descriptive statistics

Standard deviation1.8187233 × 1022
Coefficient of variation (CV)0.01585864
Kurtosis-1.0628389
Mean1.1468343 × 1024
Median Absolute Deviation (MAD)1.49999 × 1022
Skewness-0.29239159
Sum1.2271128 × 1027
Variance3.3077544 × 1044
MonotonicityNot monotonic
2024-05-18T18:44:49.860904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.17101110010089e+24 6
 
0.4%
1.1470101001033e+24 4
 
0.3%
1.17101110010088e+24 4
 
0.3%
1.17101010010022e+24 3
 
0.2%
1.1560110001004e+24 3
 
0.2%
1.1470101001031e+24 3
 
0.2%
1.12151040010554e+24 3
 
0.2%
1.12151010010176e+24 3
 
0.2%
1.17101010010019e+24 3
 
0.2%
1.1500102001069e+24 3
 
0.2%
Other values (955) 1035
66.1%
(Missing) 495
31.6%
ValueCountFrequency (%)
1.11101010010089e+24 3
0.2%
1.11101010010123e+24 1
 
0.1%
1.11101020010001e+24 1
 
0.1%
1.1110109001015e+24 1
 
0.1%
1.11101130010032e+24 1
 
0.1%
1.11101150010001e+24 1
 
0.1%
1.11101320010114e+24 1
 
0.1%
1.11101340010002e+24 1
 
0.1%
1.11101380010038e+24 1
 
0.1%
1.11101400020002e+24 1
 
0.1%
ValueCountFrequency (%)
1.17401100010737e+24 1
0.1%
1.17401100010685e+24 2
0.1%
1.17401100010347e+24 1
0.1%
1.17401090010447e+24 1
0.1%
1.17401090010306e+24 1
0.1%
1.17401090010233e+24 1
0.1%
1.17401090010038e+24 1
0.1%
1.17401090010023e+24 1
0.1%
1.1740108001053e+24 1
0.1%
1.174010800103e+24 1
0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct274
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1451641 × 109
Minimum1.1110101 × 109
Maximum1.174011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:50.279117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110101 × 109
5-th percentile1.1170113 × 109
Q11.1305101 × 109
median1.1470102 × 109
Q31.1620101 × 109
95-th percentile1.1710111 × 109
Maximum1.174011 × 109
Range63000900
Interquartile range (IQR)31500000

Descriptive statistics

Standard deviation18039588
Coefficient of variation (CV)0.015752842
Kurtosis-1.1451977
Mean1.1451641 × 109
Median Absolute Deviation (MAD)15000000
Skewness-0.1248711
Sum1.7921818 × 1012
Variance3.2542674 × 1014
MonotonicityNot monotonic
2024-05-18T18:44:50.725517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1162010200 41
 
2.6%
1150010300 36
 
2.3%
1162010100 35
 
2.2%
1147010300 31
 
2.0%
1135010500 30
 
1.9%
1147010100 30
 
1.9%
1154510300 25
 
1.6%
1147010200 23
 
1.5%
1154510200 23
 
1.5%
1132010700 22
 
1.4%
Other values (264) 1269
81.1%
ValueCountFrequency (%)
1111010100 4
0.3%
1111010200 1
 
0.1%
1111011300 2
0.1%
1111011500 1
 
0.1%
1111011800 2
0.1%
1111013200 1
 
0.1%
1111013400 1
 
0.1%
1111013800 1
 
0.1%
1111014000 1
 
0.1%
1111014100 1
 
0.1%
ValueCountFrequency (%)
1174011000 3
 
0.2%
1174010900 7
0.4%
1174010800 4
0.3%
1174010700 7
0.4%
1174010600 8
0.5%
1174010500 4
0.3%
1174010300 8
0.5%
1174010200 9
0.6%
1174010100 4
0.3%
1171011400 3
 
0.2%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct538
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1452019 × 109
Minimum1.1110141 × 109
Maximum1.17407 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:51.007312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110141 × 109
5-th percentile1.1170131 × 109
Q11.1305102 × 109
median1.147058 × 109
Q31.1620525 × 109
95-th percentile1.1710646 × 109
Maximum1.17407 × 109
Range63055900
Interquartile range (IQR)31542300

Descriptive statistics

Standard deviation18042219
Coefficient of variation (CV)0.015754619
Kurtosis-1.1457104
Mean1.1452019 × 109
Median Absolute Deviation (MAD)15047300
Skewness-0.12485721
Sum1.792241 × 1012
Variance3.2552168 × 1014
MonotonicityNot monotonic
2024-05-18T18:44:51.286447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150010300 20
 
1.3%
1132010700 14
 
0.9%
1150053500 11
 
0.7%
1171056600 10
 
0.6%
1168067000 10
 
0.6%
1171057000 10
 
0.6%
1156054000 9
 
0.6%
1154561000 9
 
0.6%
1138010400 9
 
0.6%
1123010600 9
 
0.6%
Other values (528) 1454
92.9%
ValueCountFrequency (%)
1111014100 1
 
0.1%
1111014900 1
 
0.1%
1111016000 1
 
0.1%
1111016400 1
 
0.1%
1111017400 1
 
0.1%
1111017500 1
 
0.1%
1111017800 1
 
0.1%
1111018100 1
 
0.1%
1111018300 2
 
0.1%
1111051500 6
0.4%
ValueCountFrequency (%)
1174070000 6
0.4%
1174069000 1
 
0.1%
1174068500 4
0.3%
1174064000 2
 
0.1%
1174062000 1
 
0.1%
1174061000 1
 
0.1%
1174060000 3
0.2%
1174059000 3
0.2%
1174058000 2
 
0.1%
1174056000 4
0.3%
Distinct1530
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2024-05-18T18:44:51.818667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length19.133546
Min length14

Characters and Unicode

Total characters29944
Distinct characters190
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1496 ?
Unique (%)95.6%

Sample

1st row서울특별시 강동구 상일동 267-0
2nd row서울특별시 강동구 상일동 166-0
3rd row서울특별시 강동구 상일동 325-1
4th row서울특별시 강서구 마곡동 743-5
5th row서울특별시 강남구 일원동 692-0
ValueCountFrequency (%)
서울특별시 1565
 
25.0%
강남구 120
 
1.9%
강서구 116
 
1.9%
송파구 90
 
1.4%
양천구 84
 
1.3%
관악구 80
 
1.3%
노원구 79
 
1.3%
영등포구 72
 
1.2%
성북구 67
 
1.1%
마포구 65
 
1.0%
Other values (1680) 3922
62.7%
2024-05-18T18:44:52.566262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4695
15.7%
1822
 
6.1%
1768
 
5.9%
1647
 
5.5%
1590
 
5.3%
1565
 
5.2%
1565
 
5.2%
1565
 
5.2%
- 1440
 
4.8%
1 1177
 
3.9%
Other values (180) 11110
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17507
58.5%
Decimal Number 6302
 
21.0%
Space Separator 4695
 
15.7%
Dash Punctuation 1440
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1822
 
10.4%
1768
 
10.1%
1647
 
9.4%
1590
 
9.1%
1565
 
8.9%
1565
 
8.9%
1565
 
8.9%
349
 
2.0%
192
 
1.1%
177
 
1.0%
Other values (168) 5267
30.1%
Decimal Number
ValueCountFrequency (%)
1 1177
18.7%
2 821
13.0%
0 820
13.0%
3 637
10.1%
4 585
9.3%
6 535
8.5%
5 512
8.1%
7 438
 
7.0%
8 417
 
6.6%
9 360
 
5.7%
Space Separator
ValueCountFrequency (%)
4695
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17507
58.5%
Common 12437
41.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1822
 
10.4%
1768
 
10.1%
1647
 
9.4%
1590
 
9.1%
1565
 
8.9%
1565
 
8.9%
1565
 
8.9%
349
 
2.0%
192
 
1.1%
177
 
1.0%
Other values (168) 5267
30.1%
Common
ValueCountFrequency (%)
4695
37.8%
- 1440
 
11.6%
1 1177
 
9.5%
2 821
 
6.6%
0 820
 
6.6%
3 637
 
5.1%
4 585
 
4.7%
6 535
 
4.3%
5 512
 
4.1%
7 438
 
3.5%
Other values (2) 777
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17507
58.5%
ASCII 12437
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4695
37.8%
- 1440
 
11.6%
1 1177
 
9.5%
2 821
 
6.6%
0 820
 
6.6%
3 637
 
5.1%
4 585
 
4.7%
6 535
 
4.3%
5 512
 
4.1%
7 438
 
3.5%
Other values (2) 777
 
6.2%
Hangul
ValueCountFrequency (%)
1822
 
10.4%
1768
 
10.1%
1647
 
9.4%
1590
 
9.1%
1565
 
8.9%
1565
 
8.9%
1565
 
8.9%
349
 
2.0%
192
 
1.1%
177
 
1.0%
Other values (168) 5267
30.1%

시설면적
Real number (ℝ)

Distinct1349
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5907.7898
Minimum100
Maximum378440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:52.838748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile652.2
Q11642
median3104
Q35210
95-th percentile15143.2
Maximum378440
Range378340
Interquartile range (IQR)3568

Descriptive statistics

Standard deviation17176.859
Coefficient of variation (CV)2.9074933
Kurtosis229.54413
Mean5907.7898
Median Absolute Deviation (MAD)1648
Skewness13.529215
Sum9245691
Variance2.950445 × 108
MonotonicityNot monotonic
2024-05-18T18:44:53.122732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183 6
 
0.4%
1500 6
 
0.4%
2000 5
 
0.3%
4500 4
 
0.3%
100 4
 
0.3%
2850 4
 
0.3%
3000 4
 
0.3%
1200 4
 
0.3%
1000 4
 
0.3%
2600 4
 
0.3%
Other values (1339) 1520
97.1%
ValueCountFrequency (%)
100 4
0.3%
165 1
 
0.1%
183 6
0.4%
215 2
 
0.1%
230 1
 
0.1%
241 1
 
0.1%
245 1
 
0.1%
248 1
 
0.1%
298 2
 
0.1%
300 2
 
0.1%
ValueCountFrequency (%)
378440 1
0.1%
284421 1
0.1%
225000 1
0.1%
219167 1
0.1%
195308 1
0.1%
128954 1
0.1%
109653 1
0.1%
103713 1
0.1%
80595 1
0.1%
77499 1
0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct1560
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98695
Minimum126.79887
Maximum127.17742
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:53.550310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.79887
5-th percentile126.83851
Q1126.91168
median127.00198
Q3127.05842
95-th percentile127.12925
Maximum127.17742
Range0.378551
Interquartile range (IQR)0.146739

Descriptive statistics

Standard deviation0.090461681
Coefficient of variation (CV)0.00071236992
Kurtosis-1.0303218
Mean126.98695
Median Absolute Deviation (MAD)0.073578
Skewness-0.11316727
Sum198734.57
Variance0.0081833158
MonotonicityNot monotonic
2024-05-18T18:44:54.092860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.834761 2
 
0.1%
126.900072 2
 
0.1%
126.859337 2
 
0.1%
126.907121 2
 
0.1%
127.084164 2
 
0.1%
126.87255 1
 
0.1%
126.927082 1
 
0.1%
126.927102 1
 
0.1%
126.830476 1
 
0.1%
126.848128 1
 
0.1%
Other values (1550) 1550
99.0%
ValueCountFrequency (%)
126.798865 1
0.1%
126.80137 1
0.1%
126.804478 1
0.1%
126.806735 1
0.1%
126.806736 1
0.1%
126.806881 1
0.1%
126.808421 1
0.1%
126.808814 1
0.1%
126.809396 1
0.1%
126.810246 1
0.1%
ValueCountFrequency (%)
127.177416 1
0.1%
127.177246 1
0.1%
127.175666 1
0.1%
127.174988 1
0.1%
127.173488 1
0.1%
127.172364 1
0.1%
127.170177 1
0.1%
127.1666 1
0.1%
127.165573 1
0.1%
127.164949 1
0.1%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct1560
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.548815
Minimum37.4348
Maximum37.688944
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:54.559295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.4348
5-th percentile37.471451
Q137.503732
median37.544541
Q337.584591
95-th percentile37.65008
Maximum37.688944
Range0.254144
Interquartile range (IQR)0.080859

Descriptive statistics

Standard deviation0.05466848
Coefficient of variation (CV)0.0014559309
Kurtosis-0.61964633
Mean37.548815
Median Absolute Deviation (MAD)0.040356
Skewness0.38150369
Sum58763.895
Variance0.0029886427
MonotonicityNot monotonic
2024-05-18T18:44:55.017345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.565066 2
 
0.1%
37.554635 2
 
0.1%
37.651115 2
 
0.1%
37.585572 2
 
0.1%
37.540505 2
 
0.1%
37.592259 1
 
0.1%
37.58535 1
 
0.1%
37.581559 1
 
0.1%
37.584076 1
 
0.1%
37.58402 1
 
0.1%
Other values (1550) 1550
99.0%
ValueCountFrequency (%)
37.4348 1
0.1%
37.4396 1
0.1%
37.4415 1
0.1%
37.441878 1
0.1%
37.4432 1
0.1%
37.444908 1
0.1%
37.447082 1
0.1%
37.447159 1
0.1%
37.4472 1
0.1%
37.447424 1
0.1%
ValueCountFrequency (%)
37.688944 1
0.1%
37.685617 1
0.1%
37.684918 1
0.1%
37.683826 1
0.1%
37.683796 1
0.1%
37.68264 1
0.1%
37.681102 1
0.1%
37.68059 1
0.1%
37.680387 1
0.1%
37.674342 1
0.1%

X좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1565
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198845.34
Minimum182234.23
Maximum215674.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:55.453524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182234.23
5-th percentile185723.69
Q1192199.27
median200174.91
Q3205164.13
95-th percentile211426.52
Maximum215674.66
Range33440.425
Interquartile range (IQR)12964.862

Descriptive statistics

Standard deviation7994.947
Coefficient of variation (CV)0.040206861
Kurtosis-1.0302671
Mean198845.34
Median Absolute Deviation (MAD)6505.9373
Skewness-0.11291547
Sum3.1119296 × 108
Variance63919178
MonotonicityNot monotonic
2024-05-18T18:44:56.065200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214228.676957 1
 
0.1%
192863.435027 1
 
0.1%
193906.718463 1
 
0.1%
193559.456512 1
 
0.1%
193561.158595 1
 
0.1%
185015.685311 1
 
0.1%
186574.965851 1
 
0.1%
186698.355091 1
 
0.1%
186547.446133 1
 
0.1%
186006.16008 1
 
0.1%
Other values (1555) 1555
99.4%
ValueCountFrequency (%)
182234.234223 1
0.1%
182454.215907 1
0.1%
182727.854058 1
0.1%
182925.277944 1
0.1%
182925.825241 1
0.1%
182938.96747 1
0.1%
183076.339264 1
0.1%
183111.367626 1
0.1%
183161.444186 1
0.1%
183238.782762 1
0.1%
ValueCountFrequency (%)
215674.658945 1
0.1%
215659.398125 1
0.1%
215518.956102 1
0.1%
215461.943504 1
0.1%
215329.631494 1
0.1%
215231.803084 1
0.1%
215038.125827 1
0.1%
214719.733721 1
0.1%
214631.326307 1
0.1%
214573.695414 1
0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1565
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean549925.9
Minimum537272.06
Maximum565475.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.9 KiB
2024-05-18T18:44:56.502142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum537272.06
5-th percentile541338.12
Q1544919.03
median549460.44
Q3553897.86
95-th percentile561163.21
Maximum565475.62
Range28203.563
Interquartile range (IQR)8978.8257

Descriptive statistics

Standard deviation6066.758
Coefficient of variation (CV)0.011031955
Kurtosis-0.61974897
Mean549925.9
Median Absolute Deviation (MAD)4485.4459
Skewness0.38098696
Sum8.6063404 × 108
Variance36805553
MonotonicityNot monotonic
2024-05-18T18:44:56.967398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
550093.516071 1
 
0.1%
560297.284718 1
 
0.1%
553558.395587 1
 
0.1%
553838.063366 1
 
0.1%
553831.867664 1
 
0.1%
547808.619601 1
 
0.1%
547175.54715 1
 
0.1%
547212.740128 1
 
0.1%
546780.388618 1
 
0.1%
547386.801464 1
 
0.1%
Other values (1555) 1555
99.4%
ValueCountFrequency (%)
537272.058854 1
0.1%
537804.782223 1
0.1%
538015.549173 1
0.1%
538057.290349 1
0.1%
538204.251823 1
0.1%
538393.820104 1
0.1%
538635.216046 1
0.1%
538644.009565 1
0.1%
538648.836314 1
0.1%
538672.195858 1
0.1%
ValueCountFrequency (%)
565475.62227 1
0.1%
565106.516516 1
0.1%
565029.10332 1
0.1%
564907.59844 1
0.1%
564904.86212 1
0.1%
564776.552909 1
0.1%
564605.298683 1
0.1%
564548.418838 1
0.1%
564526.215832 1
0.1%
563855.112486 1
0.1%

Interactions

2024-05-18T18:44:39.074024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:33.468531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:37.987047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:42.900772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:47.831892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:09.969076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:15.103473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:19.416765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:24.536364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:29.256083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:34.351665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:39.338944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:33.724321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:38.256515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:43.083998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:50.069906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:10.144476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:15.370648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:19.598583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:24.815896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:29.524684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:34.530429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:39.612906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:34.002925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:38.443074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:43.283628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:51.879866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:10.398787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:15.646737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:19.823415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:25.166928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:29.812800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:34.719422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:39.896990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:34.305475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:38.726773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:43.494931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:53.710683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:10.687997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:15.940816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:20.030326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:25.446677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:30.122324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:34.956464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:42.063813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:36.383565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:40.908914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:45.783926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:57.555455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:12.882553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:18.005621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:22.470593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:27.627723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:32.450669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:37.234130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:42.260022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:36.568135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:41.190315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:46.064513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:59.198273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:13.200144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:18.200917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:22.771737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:27.870925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:32.726322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:37.535425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:42.523307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:36.784478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:41.470090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:46.348094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:01.115979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:13.459513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:18.396111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:23.067622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:28.068373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:33.011297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:37.814723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:42.710119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:37.075677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:41.753841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:46.634479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:02.893224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:13.742601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:18.636724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:23.422195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:28.270384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:33.340048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:38.097587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:42.999771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:37.274628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:42.039603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:46.966646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:04.825963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:14.029233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:18.834675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:23.716723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:28.467766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:33.588103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:38.384062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:43.201817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:37.450675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:42.309932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:47.253002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:06.552675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:14.298692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:19.018346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:23.986479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:28.667549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:33.849354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:38.654633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:43.397089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:37.721741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:42.589344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:43:47.549614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:08.173887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:14.812240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:19.205973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:24.266827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:28.906834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:34.131683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T18:44:38.860276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T18:44:57.268342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설번호지역코드시설일련번호시군구명도로명주소코드법정동코드행정동코드시설면적경도위도X좌표Y좌표
시설번호1.0000.7130.3740.8420.8020.7140.7140.0560.5810.5280.5810.526
지역코드0.7131.0000.4051.0001.0001.0001.0000.0250.8970.9010.8970.901
시설일련번호0.3740.4051.0000.5310.4470.4170.4150.0000.4950.3320.4980.329
시군구명0.8421.0000.5311.0001.0001.0001.0000.0750.9360.9200.9360.920
도로명주소코드0.8021.0000.4471.0001.0001.0001.0000.0670.9050.9020.9050.902
법정동코드0.7141.0000.4171.0001.0001.0001.0000.0530.9000.8990.9010.899
행정동코드0.7141.0000.4151.0001.0001.0001.0000.0520.9010.8990.9010.899
시설면적0.0560.0250.0000.0750.0670.0530.0521.0000.0000.0210.0000.020
경도0.5810.8970.4950.9360.9050.9000.9010.0001.0000.6201.0000.620
위도0.5280.9010.3320.9200.9020.8990.8990.0210.6201.0000.6221.000
X좌표0.5810.8970.4980.9360.9050.9010.9010.0001.0000.6221.0000.622
Y좌표0.5260.9010.3290.9200.9020.8990.8990.0200.6201.0000.6221.000
2024-05-18T18:44:57.969035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설번호지역코드시설일련번호도로명주소코드법정동코드행정동코드시설면적경도위도X좌표Y좌표시군구명
시설번호1.0000.323-0.1150.3020.3210.3240.1950.333-0.2180.333-0.2180.532
지역코드0.3231.0000.1570.9990.9980.9980.1530.018-0.6790.018-0.6790.995
시설일련번호-0.1150.1571.0000.1390.1650.166-0.086-0.105-0.116-0.105-0.1160.216
도로명주소코드0.3020.9990.1391.0001.0000.9980.2010.095-0.6810.095-0.6810.152
법정동코드0.3210.9980.1651.0001.0000.9980.1520.019-0.6810.020-0.6810.992
행정동코드0.3240.9980.1660.9980.9981.0000.1550.019-0.6820.019-0.6820.992
시설면적0.1950.153-0.0860.2010.1520.1551.0000.171-0.0780.171-0.0780.032
경도0.3330.018-0.1050.0950.0190.0190.1711.0000.1731.0000.1730.682
위도-0.218-0.679-0.116-0.681-0.681-0.682-0.0780.1731.0000.1731.0000.638
X좌표0.3330.018-0.1050.0950.0200.0190.1711.0000.1731.0000.1730.682
Y좌표-0.218-0.679-0.116-0.681-0.681-0.682-0.0780.1731.0000.1731.0000.639
시군구명0.5320.9950.2160.1520.9920.9920.0320.6820.6380.6820.6391.000

Missing values

2024-05-18T18:44:43.834406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T18:44:44.352796image/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

시설번호지역코드시설일련번호시도명시군구명수용시설명도로명주소코드법정동코드행정동코드상세주소시설면적경도위도X좌표Y좌표
080117400000051서울특별시강동구강동고등학교 운동장117401030010267000002019011740103001174052000서울특별시 강동구 상일동 267-04560127.16101937.550251214228.676957550093.516071
182117400000052서울특별시강동구한영고등학교 운동장117401030010166000002024211740103001174052000서울특별시 강동구 상일동 166-05380127.15735437.548818213905.009707549933.921501
28311740000005서울특별시강동구상일초등학교 운동장117401030010358000202046211740103001174052000서울특별시 강동구 상일동 325-12180127.17236437.546988215231.803084549733.199102
3841150000000132서울특별시강서구서울공항초등학교 운동장115001050010399000000000111500105001150060000서울특별시 강서구 마곡동 743-52316126.82175337.562688184251.606884551476.642573
4851168000000123서울특별시강남구중동중학교 운동장116801140010692000000279711680114001168074000서울특별시 강남구 일원동 692-04641127.07779337.488896206879.937089543274.55405
586113050000042서울특별시강북구혜화여자고등학교 운동장113051030010468004301373811305103001130561000서울특별시 강북구 수유동 468-432951127.01317737.630624201163.159007559001.981358
687113050000026서울특별시강북구강북중학교운동장113051030010694000000860911305103001130563000서울특별시 강북구 수유동 6943312127.02597237.644769202292.142448560572.107689
788113050000022서울특별시강북구번동초등학교운동장113051020010236000001476511305102001130560600서울특별시 강북구 번동 2363329127.0407537.628963203597.169886558818.262339
889113050000023서울특별시강북구오현초등학교운동장113051020010248000001478211305102001130560600서울특별시 강북구 번동 2484045127.04688137.623176204138.693408558176.190286
990113050000019서울특별시강북구번동중학교운동장113051020010231000001589411305102001130560000서울특별시 강북구 번동 2314356127.03684837.632117203252.550465559168.171608
시설번호지역코드시설일련번호시도명시군구명수용시설명도로명주소코드법정동코드행정동코드상세주소시설면적경도위도X좌표Y좌표
155510983113050000069서울특별시강북구우이초등학교 운동장113051030010409000600367811305103001130566000서울특별시 강북구 수유동 409-61105127.01435837.638359201267.237748559860.445767
155610334112150000045서울특별시광진구서울특별시 광진구 광나루로36길 47(구의동) 238-1112151030010238000102443811215103001121585000서울특별시 광진구 구의동 238-1400127.08631437.542344207628.03921549207.312557
155710335112900000069서울특별시성북구여의도 순복음 성북교회112901360010080000205494811290136001129068500서울특별시 성북구 하월곡동 80-22827127.03499837.610088203090.193353556723.113181
155810336114100000031서울특별시서대문구신촌역 동측광장 공영주차장<NA>11410112001141055500서울특별시 서대문구 대현동 121-72000126.94310337.559288194972.818194551085.899831
155910337115300000034서울특별시구로구구로구시설관리공단115301020010741002802099011530102001153055000서울특별시 구로구 구로동 741-271574126.88695437.489721190002.442884543369.326462
156010395116800000073서울특별시강남구강남구보건소 주차장116801050010008000001513911680105001168059000서울특별시 강남구 삼성동 8-01973127.04200437.516207203713.444704546303.703606
156110396116800000072서울특별시강남구강남구청 주차장116801050010016000101499611680105001168059000서울특별시 강남구 삼성동 16-14320127.04724537.517782204176.627475546478.709463
156210412111700000071서울특별시용산구한남동공영주차장111701310010685004600000111170131001117068500서울특별시 용산구 한남동 685-201051127.00024237.534831200021.385207548369.899688
156310413112000000021서울특별시성동구성동구보건소 주차장112001030010016000102380411200103001120053500서울특별시 성동구 홍익동 16-15570127.03295237.567087202911.221091551950.454032
156410612113050000070서울특별시강북구강북구민운동장113051020020023000101577111305102001130560000서울특별시 강북구 번동 317-06514127.03727937.629316203290.738761558857.359073