Overview

Dataset statistics

Number of variables9
Number of observations187
Missing cells4
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory77.7 B

Variable types

Numeric5
Text3
Categorical1

Dataset

Description광주광역시 광산구 내 음식물쓰레기 RFID를 설치한 공동주택 현황에 관한 데이터로 공동주택명, 설치대수 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15031934/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 설치대수High correlation
설치대수 is highly overall correlated with 세대수High correlation
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
관리사무소연락처 has 4 (2.1%) missing valuesMissing
연번 has unique valuesUnique
공동주택명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:36:07.243641
Analysis finished2023-12-12 15:36:11.538173
Duration4.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94
Minimum1
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T00:36:11.650394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.3
Q147.5
median94
Q3140.5
95-th percentile177.7
Maximum187
Range186
Interquartile range (IQR)93

Descriptive statistics

Standard deviation54.126395
Coefficient of variation (CV)0.57581272
Kurtosis-1.2
Mean94
Median Absolute Deviation (MAD)47
Skewness0
Sum17578
Variance2929.6667
MonotonicityStrictly increasing
2023-12-13T00:36:11.849000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
130 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
Other values (177) 177
94.7%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%

공동주택명
Text

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T00:36:12.137493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length15
Mean length8.5454545
Min length4

Characters and Unicode

Total characters1598
Distinct characters217
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

Unique187 ?
Unique (%)100.0%

Sample

1st row신가부영아파트
2nd row하남금호타운
3rd row영천마을주공10단지
4th row수완대방노블랜드1차
5th row첨단부영1차아파트
ValueCountFrequency (%)
첨단 3
 
1.4%
더테라스 2
 
1.0%
신가부영아파트 1
 
0.5%
첨단라인6차 1
 
0.5%
소촌모아드림타운2차 1
 
0.5%
첨단부영e그린타운1차 1
 
0.5%
첨단lh1단지 1
 
0.5%
첨단lh2단지 1
 
0.5%
첨단lh3단지 1
 
0.5%
첨단중해마루힐 1
 
0.5%
Other values (196) 196
93.8%
2023-12-13T00:36:12.673669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
차 72
 
4.5%
아 60
 
3.8%
단 57
 
3.6%
트 48
 
3.0%
파 38
 
2.4%
첨 37
 
2.3%
리 36
 
2.3%
수 35
 
2.2%
2 34
 
2.1%
스 33
 
2.1%
Other values (207) 1148
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1426
89.2%
Decimal Number 102
 
6.4%
Uppercase Letter 38
 
2.4%
Space Separator 22
 
1.4%
Dash Punctuation 6
 
0.4%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
차 72
 
5.0%
아 60
 
4.2%
단 57
 
4.0%
트 48
 
3.4%
파 38
 
2.7%
첨 37
 
2.6%
리 36
 
2.5%
수 35
 
2.5%
스 33
 
2.3%
산 30
 
2.1%
Other values (177) 980
68.7%
Uppercase Letter
ValueCountFrequency (%)
H 7
18.4%
L 6
15.8%
S 5
13.2%
E 4
10.5%
G 3
7.9%
B 2
 
5.3%
U 2
 
5.3%
T 2
 
5.3%
M 2
 
5.3%
P 1
 
2.6%
Other values (4) 4
10.5%
Decimal Number
ValueCountFrequency (%)
2 34
33.3%
1 29
28.4%
3 19
18.6%
6 6
 
5.9%
5 5
 
4.9%
7 3
 
2.9%
4 2
 
2.0%
9 2
 
2.0%
0 1
 
1.0%
8 1
 
1.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1426
89.2%
Common 133
 
8.3%
Latin 39
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
차 72
 
5.0%
아 60
 
4.2%
단 57
 
4.0%
트 48
 
3.4%
파 38
 
2.7%
첨 37
 
2.6%
리 36
 
2.5%
수 35
 
2.5%
스 33
 
2.3%
산 30
 
2.1%
Other values (177) 980
68.7%
Common
ValueCountFrequency (%)
2 34
25.6%
1 29
21.8%
22
16.5%
3 19
14.3%
- 6
 
4.5%
6 6
 
4.5%
5 5
 
3.8%
7 3
 
2.3%
4 2
 
1.5%
9 2
 
1.5%
Other values (5) 5
 
3.8%
Latin
ValueCountFrequency (%)
H 7
17.9%
L 6
15.4%
S 5
12.8%
E 4
10.3%
G 3
7.7%
B 2
 
5.1%
U 2
 
5.1%
T 2
 
5.1%
M 2
 
5.1%
P 1
 
2.6%
Other values (5) 5
12.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1426
89.2%
ASCII 172
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
차 72
 
5.0%
아 60
 
4.2%
단 57
 
4.0%
트 48
 
3.4%
파 38
 
2.7%
첨 37
 
2.6%
리 36
 
2.5%
수 35
 
2.5%
스 33
 
2.3%
산 30
 
2.1%
Other values (177) 980
68.7%
ASCII
ValueCountFrequency (%)
2 34
19.8%
1 29
16.9%
22
12.8%
3 19
11.0%
H 7
 
4.1%
- 6
 
3.5%
6 6
 
3.5%
L 6
 
3.5%
5 5
 
2.9%
S 5
 
2.9%
Other values (20) 33
19.2%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.6631
Minimum24
Maximum1956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T00:36:12.890439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile68.3
Q1188
median346
Q3582.5
95-th percentile1290.4
Maximum1956
Range1932
Interquartile range (IQR)394.5

Descriptive statistics

Standard deviation383.16587
Coefficient of variation (CV)0.85592462
Kurtosis2.5598743
Mean447.6631
Median Absolute Deviation (MAD)181
Skewness1.6164213
Sum83713
Variance146816.08
MonotonicityNot monotonic
2023-12-13T00:36:13.071209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76 3
 
1.6%
237 3
 
1.6%
100 3
 
1.6%
200 3
 
1.6%
78 2
 
1.1%
44 2
 
1.1%
288 2
 
1.1%
169 2
 
1.1%
468 2
 
1.1%
298 2
 
1.1%
Other values (151) 163
87.2%
ValueCountFrequency (%)
24 1
0.5%
31 1
0.5%
32 1
0.5%
33 1
0.5%
44 2
1.1%
64 1
0.5%
65 1
0.5%
66 1
0.5%
68 1
0.5%
69 1
0.5%
ValueCountFrequency (%)
1956 1
0.5%
1792 1
0.5%
1673 1
0.5%
1660 1
0.5%
1511 1
0.5%
1500 1
0.5%
1485 1
0.5%
1344 1
0.5%
1321 1
0.5%
1300 1
0.5%

주소
Text

Distinct185
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-13T00:36:13.450548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length18.770053
Min length16

Characters and Unicode

Total characters3510
Distinct characters72
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

Unique183 ?
Unique (%)97.9%

Sample

1st row광주광역시 광산구 신창동 1108-1
2nd row광주광역시 광산구 월곡동 613-1
3rd row광주광역시 광산구 월곡동 685-1
4th row광주광역시 광산구 신가동 1304
5th row광주광역시 광산구 산월동 882-1
ValueCountFrequency (%)
광주광역시 187
25.0%
광산구 187
25.0%
송정동 16
 
2.1%
소촌동 16
 
2.1%
쌍암동 15
 
2.0%
신가동 14
 
1.9%
도산동 12
 
1.6%
우산동 11
 
1.5%
신창동 10
 
1.3%
산월동 10
 
1.3%
Other values (213) 270
36.1%
2023-12-13T00:36:13.961029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
광 561
16.0%
561
16.0%
산 228
 
6.5%
역 187
 
5.3%
시 187
 
5.3%
구 187
 
5.3%
주 187
 
5.3%
동 171
 
4.9%
1 168
 
4.8%
- 106
 
3.0%
Other values (62) 967
27.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2090
59.5%
Decimal Number 751
 
21.4%
Space Separator 561
 
16.0%
Dash Punctuation 106
 
3.0%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 561
26.8%
산 228
10.9%
역 187
 
8.9%
시 187
 
8.9%
구 187
 
8.9%
주 187
 
8.9%
동 171
 
8.2%
월 28
 
1.3%
신 26
 
1.2%
정 23
 
1.1%
Other values (48) 305
14.6%
Decimal Number
ValueCountFrequency (%)
1 168
22.4%
8 87
11.6%
2 87
11.6%
6 70
9.3%
7 68
9.1%
5 63
 
8.4%
9 56
 
7.5%
0 55
 
7.3%
3 52
 
6.9%
4 45
 
6.0%
Space Separator
ValueCountFrequency (%)
561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2090
59.5%
Common 1420
40.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 561
26.8%
산 228
10.9%
역 187
 
8.9%
시 187
 
8.9%
구 187
 
8.9%
주 187
 
8.9%
동 171
 
8.2%
월 28
 
1.3%
신 26
 
1.2%
정 23
 
1.1%
Other values (48) 305
14.6%
Common
ValueCountFrequency (%)
561
39.5%
1 168
 
11.8%
- 106
 
7.5%
8 87
 
6.1%
2 87
 
6.1%
6 70
 
4.9%
7 68
 
4.8%
5 63
 
4.4%
9 56
 
3.9%
0 55
 
3.9%
Other values (4) 99
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2090
59.5%
ASCII 1420
40.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
광 561
26.8%
산 228
10.9%
역 187
 
8.9%
시 187
 
8.9%
구 187
 
8.9%
주 187
 
8.9%
동 171
 
8.2%
월 28
 
1.3%
신 26
 
1.2%
정 23
 
1.1%
Other values (48) 305
14.6%
ASCII
ValueCountFrequency (%)
561
39.5%
1 168
 
11.8%
- 106
 
7.5%
8 87
 
6.1%
2 87
 
6.1%
6 70
 
4.9%
7 68
 
4.8%
5 63
 
4.4%
9 56
 
3.9%
0 55
 
3.9%
Other values (4) 99
 
7.0%

설치대수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9197861
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T00:36:14.088301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12.5
median5
Q39
95-th percentile20
Maximum32
Range31
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation6.0034922
Coefficient of variation (CV)0.86758349
Kurtosis3.6859114
Mean6.9197861
Median Absolute Deviation (MAD)3
Skewness1.8225528
Sum1294
Variance36.041918
MonotonicityNot monotonic
2023-12-13T00:36:14.233523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2 38
20.3%
3 22
11.8%
6 18
9.6%
5 15
 
8.0%
8 15
 
8.0%
4 13
 
7.0%
1 9
 
4.8%
9 8
 
4.3%
7 8
 
4.3%
10 8
 
4.3%
Other values (13) 33
17.6%
ValueCountFrequency (%)
1 9
 
4.8%
2 38
20.3%
3 22
11.8%
4 13
 
7.0%
5 15
 
8.0%
6 18
9.6%
7 8
 
4.3%
8 15
 
8.0%
9 8
 
4.3%
10 8
 
4.3%
ValueCountFrequency (%)
32 2
 
1.1%
28 1
 
0.5%
25 1
 
0.5%
24 2
 
1.1%
20 5
2.7%
19 2
 
1.1%
18 3
1.6%
16 1
 
0.5%
15 4
2.1%
14 1
 
0.5%

관리사무소연락처
Text

MISSING 

Distinct176
Distinct (%)96.2%
Missing4
Missing (%)2.1%
Memory size1.6 KiB
2023-12-13T00:36:14.473897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2196
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

Unique172 ?
Unique (%)94.0%

Sample

1st row062-961-0771
2nd row062-951-2233
3rd row062-961-2056
4th row062-953-4020
5th row062-973-0256
ValueCountFrequency (%)
062-942-0058 4
 
2.2%
062-974-4564 3
 
1.6%
062-962-8552 2
 
1.1%
062-945-7677 2
 
1.1%
062-225-1050 1
 
0.5%
062-944-9496 1
 
0.5%
062-961-0771 1
 
0.5%
062-945-5060 1
 
0.5%
062-971-0551 1
 
0.5%
062-971-5355 1
 
0.5%
Other values (166) 166
90.7%
2023-12-13T00:36:14.873759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 366
16.7%
2 309
14.1%
0 303
13.8%
6 284
12.9%
9 243
11.1%
5 149
6.8%
4 132
 
6.0%
1 132
 
6.0%
7 118
 
5.4%
3 85
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1830
83.3%
Dash Punctuation 366
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 309
16.9%
0 303
16.6%
6 284
15.5%
9 243
13.3%
5 149
8.1%
4 132
7.2%
1 132
7.2%
7 118
 
6.4%
3 85
 
4.6%
8 75
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 366
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2196
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 366
16.7%
2 309
14.1%
0 303
13.8%
6 284
12.9%
9 243
11.1%
5 149
6.8%
4 132
 
6.0%
1 132
 
6.0%
7 118
 
5.4%
3 85
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 366
16.7%
2 309
14.1%
0 303
13.8%
6 284
12.9%
9 243
11.1%
5 149
6.8%
4 132
 
6.0%
1 132
 
6.0%
7 118
 
5.4%
3 85
 
3.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.176972
Minimum35.125429
Maximum35.226157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T00:36:15.050892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.125429
5-th percentile35.131069
Q135.150767
median35.178374
Q335.201353
95-th percentile35.220918
Maximum35.226157
Range0.1007275
Interquartile range (IQR)0.050586624

Descriptive statistics

Standard deviation0.02989006
Coefficient of variation (CV)0.00084970531
Kurtosis-1.2445949
Mean35.176972
Median Absolute Deviation (MAD)0.027354211
Skewness-0.049893777
Sum6578.0937
Variance0.00089341566
MonotonicityNot monotonic
2023-12-13T00:36:15.219778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.2166886301251 2
 
1.1%
35.177975760142 2
 
1.1%
35.1867230091551 1
 
0.5%
35.1510194487418 1
 
0.5%
35.217130583298 1
 
0.5%
35.1983921404977 1
 
0.5%
35.1519031587896 1
 
0.5%
35.2095962039319 1
 
0.5%
35.2182552786953 1
 
0.5%
35.2182298279099 1
 
0.5%
Other values (175) 175
93.6%
ValueCountFrequency (%)
35.1254294579442 1
0.5%
35.126295381556 1
0.5%
35.1282557899083 1
0.5%
35.1286441157887 1
0.5%
35.1287489930999 1
0.5%
35.1289003790567 1
0.5%
35.129792230616 1
0.5%
35.1301073051155 1
0.5%
35.1301993027363 1
0.5%
35.1310354847016 1
0.5%
ValueCountFrequency (%)
35.2261569562483 1
0.5%
35.2251957709846 1
0.5%
35.2238746977201 1
0.5%
35.221595538251 1
0.5%
35.2215170161503 1
0.5%
35.2213436969713 1
0.5%
35.22110109472 1
0.5%
35.221090671042 1
0.5%
35.2209763822172 1
0.5%
35.2209624733493 1
0.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.8162
Minimum126.7516
Maximum126.85415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-13T00:36:15.436109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.7516
5-th percentile126.78552
Q1126.79707
median126.81532
Q3126.83586
95-th percentile126.84983
Maximum126.85415
Range0.10255399
Interquartile range (IQR)0.03878889

Descriptive statistics

Standard deviation0.022337818
Coefficient of variation (CV)0.00017614326
Kurtosis-0.58246013
Mean126.8162
Median Absolute Deviation (MAD)0.019056631
Skewness-0.1776471
Sum23714.629
Variance0.00049897811
MonotonicityNot monotonic
2023-12-13T00:36:15.617418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.854154168135 2
 
1.1%
126.82676188333 2
 
1.1%
126.839557598204 1
 
0.5%
126.812620879964 1
 
0.5%
126.830307536433 1
 
0.5%
126.836545221529 1
 
0.5%
126.796214540183 1
 
0.5%
126.846239325886 1
 
0.5%
126.842577336818 1
 
0.5%
126.848341882075 1
 
0.5%
Other values (175) 175
93.6%
ValueCountFrequency (%)
126.751600174048 1
0.5%
126.756359335704 1
0.5%
126.757520578877 1
0.5%
126.771196350923 1
0.5%
126.773099747551 1
0.5%
126.775876450024 1
0.5%
126.776250892536 1
0.5%
126.777986529605 1
0.5%
126.782928898998 1
0.5%
126.784685138749 1
0.5%
ValueCountFrequency (%)
126.854154168135 2
1.1%
126.852183868795 1
0.5%
126.851709174167 1
0.5%
126.851139978885 1
0.5%
126.850848366559 1
0.5%
126.850557376537 1
0.5%
126.850409071475 1
0.5%
126.850103988369 1
0.5%
126.849879490002 1
0.5%
126.849727014232 1
0.5%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2022-12-31
187 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 187
100.0%

Length

2023-12-13T00:36:15.803174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:36:15.926346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 187
100.0%

Interactions

2023-12-13T00:36:10.456969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.684430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.327057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.069708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.776621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.596756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.826175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.450925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.195679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.915592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.751117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:07.956800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.608163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.362773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.061100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.890090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.107456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.763500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.490739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.185315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:11.045561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.227506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:08.922108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:09.631249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:36:10.320133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:36:15.992903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수설치대수위도경도
연번1.0000.2680.2630.0000.278
세대수0.2681.0000.9240.4030.000
설치대수0.2630.9241.0000.4590.000
위도0.0000.4030.4591.0000.834
경도0.2780.0000.0000.8341.000
2023-12-13T00:36:16.424578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번세대수설치대수위도경도
연번1.000-0.178-0.2440.1800.106
세대수-0.1781.0000.9320.1350.188
설치대수-0.2440.9321.0000.0860.132
위도0.1800.1350.0861.0000.890
경도0.1060.1880.1320.8901.000

Missing values

2023-12-13T00:36:11.223975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:36:11.454629image/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

연번공동주택명세대수주소설치대수관리사무소연락처위도경도데이터기준일자
01신가부영아파트1268광주광역시 광산구 신창동 1108-120062-961-077135.186723126.8395582022-12-31
12하남금호타운1500광주광역시 광산구 월곡동 613-128062-951-223335.164724126.8103532022-12-31
23영천마을주공10단지1300광주광역시 광산구 월곡동 685-115062-961-205635.17368126.8105992022-12-31
34수완대방노블랜드1차707광주광역시 광산구 신가동 130412062-953-402035.183118126.8164522022-12-31
45첨단부영1차아파트1198광주광역시 광산구 산월동 882-120062-973-025635.212346126.8451192022-12-31
56송정대덕9차237광주광역시 광산구 송정동 230-83062-942-908035.135314126.7991082022-12-31
67도산대주1차414광주광역시 광산구 도산동 7906062-946-112235.128749126.7957782022-12-31
78수완영무예다음2차236광주광역시 광산구 수완동 14904062-954-149035.187193126.8252612022-12-31
89첨단7차부영644광주광역시 광산구 산월동 886-210062-973-717435.207702126.8459622022-12-31
910수완중흥에스클래스3단지209광주광역시 광산구 흑석동 5033062-959-005835.184921126.8130672022-12-31
연번공동주택명세대수주소설치대수관리사무소연락처위도경도데이터기준일자
177178신창2차남양휴튼아파트190광주광역시 광산구 신창동 12722062-953-668035.191223126.8358672022-12-31
178179송정동 마이다스빌152광주광역시 광산구 송정동 948-42062-942-556535.136039126.7881662022-12-31
179180첨단호반2차아파트603광주광역시 광산구 월계로 596062-971-307535.21276126.8323662022-12-31
180181첨단비버리힐즈32광주광역시 광산구 첨단과기로 93-312<NA>35.225196126.8360682022-12-31
181182평동역 광신프로그레스(BM)193광주광역시 광산구 평동로752번길 84062-942-896535.125429126.7563592022-12-31
182183수완예미지612광주광역시 광산구 하남동 506-1110062-952-145535.186171126.801832022-12-31
183184수완영무예다음1차179광주광역시 광산구 장덕로95번길 152062-952-005335.19575126.8190532022-12-31
184185윤슬의아침수완2차147광주광역시 광산구 북문대로 575-222062-954-114335.202321126.8350892022-12-31
185186우산빛여울채1485광주광역시 광산구 우산로 1712062-942-501435.159824126.8041472022-12-31
186187한국아델리움메트로시티288광주광역시 광산구 쌍암동 657-51062-971-620335.218253126.8431542022-12-31