Overview

Dataset statistics

Number of variables10
Number of observations5046
Missing cells10909
Missing cells (%)21.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory419.0 KiB
Average record size in memory85.0 B

Variable types

Numeric4
Categorical2
Text3
DateTime1

Dataset

Description고유번호,구명,법정동명,산지여부,주지번,부지번,새주소명,생성일,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-365/S/1/datasetView.do

Alerts

X 좌표 is highly overall correlated with 구명High correlation
Y 좌표 is highly overall correlated with 구명High correlation
구명 is highly overall correlated with X 좌표 and 1 other fieldsHigh correlation
산지여부 is highly imbalanced (89.9%)Imbalance
주지번 has 114 (2.3%) missing valuesMissing
부지번 has 699 (13.9%) missing valuesMissing
새주소명 has 5043 (99.9%) missing valuesMissing
생성일 has 5044 (> 99.9%) missing valuesMissing
고유번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 10:20:30.415554
Analysis finished2023-12-11 10:20:34.801563
Duration4.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

UNIQUE 

Distinct5046
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2523.6134
Minimum1
Maximum5448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.5 KiB
2023-12-11T19:20:34.952402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile253.25
Q11262.25
median2523.5
Q33784.75
95-th percentile4793.75
Maximum5448
Range5447
Interquartile range (IQR)2522.5

Descriptive statistics

Standard deviation1457.0073
Coefficient of variation (CV)0.57734966
Kurtosis-1.1990872
Mean2523.6134
Median Absolute Deviation (MAD)1261.5
Skewness0.00055593256
Sum12734153
Variance2122870.3
MonotonicityNot monotonic
2023-12-11T19:20:35.227427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
225 1
 
< 0.1%
4278 1
 
< 0.1%
4288 1
 
< 0.1%
4286 1
 
< 0.1%
4284 1
 
< 0.1%
4283 1
 
< 0.1%
4281 1
 
< 0.1%
4280 1
 
< 0.1%
4279 1
 
< 0.1%
4253 1
 
< 0.1%
Other values (5036) 5036
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5448 1
< 0.1%
5133 1
< 0.1%
5126 1
< 0.1%
5043 1
< 0.1%
5042 1
< 0.1%
5041 1
< 0.1%
5040 1
< 0.1%
5039 1
< 0.1%
5038 1
< 0.1%
5037 1
< 0.1%

구명
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
영등포구
835 
서초구
447 
중구
327 
송파구
 
275
은평구
 
262
Other values (41)
2900 

Length

Max length7
Median length3
Mean length3.148038
Min length2

Unique

Unique15 ?
Unique (%)0.3%

Sample

1st row관악구
2nd row서초구
3rd row서초구
4th row광진구
5th row광진구

Common Values

ValueCountFrequency (%)
영등포구 835
16.5%
서초구 447
 
8.9%
중구 327
 
6.5%
송파구 275
 
5.4%
은평구 262
 
5.2%
강서구 247
 
4.9%
광진구 215
 
4.3%
성북구 205
 
4.1%
강남구 201
 
4.0%
마포구 178
 
3.5%
Other values (36) 1854
36.7%

Length

2023-12-11T19:20:35.565289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영등포구 835
16.5%
서초구 447
 
8.9%
중구 327
 
6.5%
송파구 275
 
5.4%
은평구 263
 
5.2%
강서구 247
 
4.9%
광진구 216
 
4.3%
성북구 205
 
4.1%
강남구 201
 
4.0%
마포구 178
 
3.5%
Other values (32) 1852
36.7%

법정동명
Text

Distinct631
Distinct (%)12.5%
Missing9
Missing (%)0.2%
Memory size39.6 KiB
2023-12-11T19:20:36.121316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.2773476
Min length1

Characters and Unicode

Total characters16508
Distinct characters232
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique242 ?
Unique (%)4.8%

Sample

1st row봉천동
2nd row원지동
3rd row양재동
4th row구의동
5th row광장동
ValueCountFrequency (%)
여의도동 211
 
4.2%
서초동 198
 
3.9%
영등포동 95
 
1.9%
신길동 90
 
1.8%
방배동 82
 
1.6%
양평동 75
 
1.5%
대림동 73
 
1.4%
양재동 69
 
1.4%
상계동 62
 
1.2%
문래동 58
 
1.2%
Other values (621) 4026
79.9%
2023-12-11T19:20:37.005664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 4894
29.6%
가 499
 
3.0%
신 398
 
2.4%
양 318
 
1.9%
도 312
 
1.9%
의 284
 
1.7%
서 259
 
1.6%
여 249
 
1.5%
산 241
 
1.5%
방 237
 
1.4%
Other values (222) 8817
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15863
96.1%
Decimal Number 638
 
3.9%
Space Separator 5
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 4894
30.9%
가 499
 
3.1%
신 398
 
2.5%
양 318
 
2.0%
도 312
 
2.0%
의 284
 
1.8%
서 259
 
1.6%
여 249
 
1.6%
산 241
 
1.5%
방 237
 
1.5%
Other values (210) 8172
51.5%
Decimal Number
ValueCountFrequency (%)
1 188
29.5%
2 169
26.5%
3 98
15.4%
4 58
 
9.1%
5 57
 
8.9%
6 40
 
6.3%
7 16
 
2.5%
8 10
 
1.6%
9 1
 
0.2%
0 1
 
0.2%
Space Separator
ValueCountFrequency (%)
5
100.0%
Other Punctuation
ValueCountFrequency (%)
？ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15863
96.1%
Common 645
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 4894
30.9%
가 499
 
3.1%
신 398
 
2.5%
양 318
 
2.0%
도 312
 
2.0%
의 284
 
1.8%
서 259
 
1.6%
여 249
 
1.6%
산 241
 
1.5%
방 237
 
1.5%
Other values (210) 8172
51.5%
Common
ValueCountFrequency (%)
1 188
29.1%
2 169
26.2%
3 98
15.2%
4 58
 
9.0%
5 57
 
8.8%
6 40
 
6.2%
7 16
 
2.5%
8 10
 
1.6%
5
 
0.8%
？ 2
 
0.3%
Other values (2) 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15857
96.1%
ASCII 643
 
3.9%
Compat Jamo 6
 
< 0.1%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 4894
30.9%
가 499
 
3.1%
신 398
 
2.5%
양 318
 
2.0%
도 312
 
2.0%
의 284
 
1.8%
서 259
 
1.6%
여 249
 
1.6%
산 241
 
1.5%
방 237
 
1.5%
Other values (205) 8166
51.5%
ASCII
ValueCountFrequency (%)
1 188
29.2%
2 169
26.3%
3 98
15.2%
4 58
 
9.0%
5 57
 
8.9%
6 40
 
6.2%
7 16
 
2.5%
8 10
 
1.6%
5
 
0.8%
9 1
 
0.2%
None
ValueCountFrequency (%)
？ 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅎ 2
33.3%
ㅅ 1
16.7%
ㄷ 1
16.7%
ㅇ 1
16.7%
ㅂ 1
16.7%

산지여부
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.6 KiB
1
4938 
2
 
96
<NA>
 
12

Length

Max length4
Median length1
Mean length1.0071344
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 4938
97.9%
2 96
 
1.9%
<NA> 12
 
0.2%

Length

2023-12-11T19:20:37.299827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T19:20:37.511396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4938
97.9%
2 96
 
1.9%
na 12
 
0.2%

주지번
Real number (ℝ)

MISSING 

Distinct1137
Distinct (%)23.1%
Missing114
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean375.92295
Minimum0
Maximum4936
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size44.5 KiB
2023-12-11T19:20:37.757020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q154
median204
Q3539.25
95-th percentile1355
Maximum4936
Range4936
Interquartile range (IQR)485.25

Descriptive statistics

Standard deviation481.7657
Coefficient of variation (CV)1.2815544
Kurtosis19.310825
Mean375.92295
Median Absolute Deviation (MAD)176.5
Skewness3.2495848
Sum1854052
Variance232098.19
MonotonicityNot monotonic
2023-12-11T19:20:38.088237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 80
 
1.6%
2 46
 
0.9%
7 39
 
0.8%
44 38
 
0.8%
14 38
 
0.8%
45 37
 
0.7%
23 37
 
0.7%
17 33
 
0.7%
13 32
 
0.6%
19 31
 
0.6%
Other values (1127) 4521
89.6%
(Missing) 114
 
2.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 80
1.6%
2 46
0.9%
3 29
 
0.6%
4 14
 
0.3%
5 28
 
0.6%
6 20
 
0.4%
7 39
0.8%
8 27
 
0.5%
9 22
 
0.4%
ValueCountFrequency (%)
4936 1
< 0.1%
4934 1
< 0.1%
4933 1
< 0.1%
4902 1
< 0.1%
4780 1
< 0.1%
4759 1
< 0.1%
4662 1
< 0.1%
4501 1
< 0.1%
4362 1
< 0.1%
4327 1
< 0.1%

부지번
Text

MISSING 

Distinct251
Distinct (%)5.8%
Missing699
Missing (%)13.9%
Memory size39.6 KiB
2023-12-11T19:20:38.572621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length1
Mean length1.4067173
Min length1

Characters and Unicode

Total characters6115
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)2.7%

Sample

1st row
2nd row362
3rd row
4th row5
5th row1
ValueCountFrequency (%)
1 767
18.3%
2 350
 
8.3%
0 303
 
7.2%
3 287
 
6.8%
4 216
 
5.1%
5 211
 
5.0%
6 156
 
3.7%
7 136
 
3.2%
8 119
 
2.8%
9 99
 
2.4%
Other values (239) 1552
37.0%
2023-12-11T19:20:39.120808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1737
28.4%
2 873
14.3%
3 631
 
10.3%
0 532
 
8.7%
4 510
 
8.3%
5 462
 
7.6%
6 375
 
6.1%
7 336
 
5.5%
8 257
 
4.2%
9 250
 
4.1%
Other values (2) 152
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5963
97.5%
Space Separator 151
 
2.5%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1737
29.1%
2 873
14.6%
3 631
 
10.6%
0 532
 
8.9%
4 510
 
8.6%
5 462
 
7.7%
6 375
 
6.3%
7 336
 
5.6%
8 257
 
4.3%
9 250
 
4.2%
Space Separator
ValueCountFrequency (%)
151
100.0%
Close Punctuation
ValueCountFrequency (%)
] 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6115
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1737
28.4%
2 873
14.3%
3 631
 
10.3%
0 532
 
8.7%
4 510
 
8.3%
5 462
 
7.6%
6 375
 
6.1%
7 336
 
5.5%
8 257
 
4.2%
9 250
 
4.1%
Other values (2) 152
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1737
28.4%
2 873
14.3%
3 631
 
10.3%
0 532
 
8.7%
4 510
 
8.3%
5 462
 
7.6%
6 375
 
6.1%
7 336
 
5.5%
8 257
 
4.2%
9 250
 
4.1%
Other values (2) 152
 
2.5%

새주소명
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing5043
Missing (%)99.9%
Memory size39.6 KiB
2023-12-11T19:20:39.322620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length11
Min length10

Characters and Unicode

Total characters33
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row정동 세종대로 99
2nd row보광동 장문로39길 11
3rd row무교동 무교로 20
ValueCountFrequency (%)
정동 1
11.1%
세종대로 1
11.1%
99 1
11.1%
보광동 1
11.1%
장문로39길 1
11.1%
11 1
11.1%
무교동 1
11.1%
무교로 1
11.1%
20 1
11.1%
2023-12-11T19:20:39.648866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
18.2%
로 3
 
9.1%
9 3
 
9.1%
동 3
 
9.1%
교 2
 
6.1%
무 2
 
6.1%
1 2
 
6.1%
정 1
 
3.0%
3 1
 
3.0%
2 1
 
3.0%
Other values (9) 9
27.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19
57.6%
Decimal Number 8
24.2%
Space Separator 6
 
18.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 3
15.8%
동 3
15.8%
교 2
10.5%
무 2
10.5%
정 1
 
5.3%
길 1
 
5.3%
광 1
 
5.3%
문 1
 
5.3%
장 1
 
5.3%
보 1
 
5.3%
Other values (3) 3
15.8%
Decimal Number
ValueCountFrequency (%)
9 3
37.5%
1 2
25.0%
3 1
 
12.5%
2 1
 
12.5%
0 1
 
12.5%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19
57.6%
Common 14
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 3
15.8%
동 3
15.8%
교 2
10.5%
무 2
10.5%
정 1
 
5.3%
길 1
 
5.3%
광 1
 
5.3%
문 1
 
5.3%
장 1
 
5.3%
보 1
 
5.3%
Other values (3) 3
15.8%
Common
ValueCountFrequency (%)
6
42.9%
9 3
21.4%
1 2
 
14.3%
3 1
 
7.1%
2 1
 
7.1%
0 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19
57.6%
ASCII 14
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6
42.9%
9 3
21.4%
1 2
 
14.3%
3 1
 
7.1%
2 1
 
7.1%
0 1
 
7.1%
Hangul
ValueCountFrequency (%)
로 3
15.8%
동 3
15.8%
교 2
10.5%
무 2
10.5%
정 1
 
5.3%
길 1
 
5.3%
광 1
 
5.3%
문 1
 
5.3%
장 1
 
5.3%
보 1
 
5.3%
Other values (3) 3
15.8%

생성일
Date

MISSING 

Distinct2
Distinct (%)100.0%
Missing5044
Missing (%)> 99.9%
Memory size39.6 KiB
Minimum2011-01-06 00:00:00
Maximum2011-02-01 00:00:00
2023-12-11T19:20:39.768123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:39.877925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)

X 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct5025
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198457.2
Minimum179513.18
Maximum215262.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.5 KiB
2023-12-11T19:20:39.988652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum179513.18
5-th percentile186992.91
Q1192214.46
median198760.38
Q3204104.14
95-th percentile210773.23
Maximum215262.17
Range35748.981
Interquartile range (IQR)11889.675

Descriptive statistics

Standard deviation7285.6841
Coefficient of variation (CV)0.036711614
Kurtosis-0.96417713
Mean198457.2
Median Absolute Deviation (MAD)6050.174
Skewness0.049376303
Sum1.001415 × 109
Variance53081193
MonotonicityNot monotonic
2023-12-11T19:20:40.127595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186366.064 4
 
0.1%
202753.275 3
 
0.1%
186177.211 3
 
0.1%
196479.369 2
 
< 0.1%
197894.116 2
 
< 0.1%
206159.709 2
 
< 0.1%
204096.467 2
 
< 0.1%
204774.405 2
 
< 0.1%
214051.485 2
 
< 0.1%
202652.047 2
 
< 0.1%
Other values (5015) 5022
99.5%
ValueCountFrequency (%)
179513.184 1
< 0.1%
179572.844 1
< 0.1%
182116.759 1
< 0.1%
182147.625 1
< 0.1%
182448.673 1
< 0.1%
182487.107 1
< 0.1%
182615.86 1
< 0.1%
182711.2 1
< 0.1%
182858.876 1
< 0.1%
182865.739 1
< 0.1%
ValueCountFrequency (%)
215262.165 1
< 0.1%
215194.147 1
< 0.1%
215044.861 1
< 0.1%
214988.361 1
< 0.1%
214802.598 1
< 0.1%
214655.889 1
< 0.1%
214627.919 1
< 0.1%
214160.633 1
< 0.1%
214051.485 2
< 0.1%
214028.179 1
< 0.1%

Y 좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct5026
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449463.23
Minimum437534.28
Maximum465507.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.5 KiB
2023-12-11T19:20:40.327037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437534.28
5-th percentile442357.02
Q1445495.96
median448810.62
Q3452284.02
95-th percentile459730.55
Maximum465507.02
Range27972.737
Interquartile range (IQR)6788.062

Descriptive statistics

Standard deviation5207.6082
Coefficient of variation (CV)0.011586283
Kurtosis-0.088496678
Mean449463.23
Median Absolute Deviation (MAD)3416.441
Skewness0.56909834
Sum2.2679914 × 109
Variance27119183
MonotonicityNot monotonic
2023-12-11T19:20:40.480412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446096.048 4
 
0.1%
440983.217 3
 
0.1%
450109.538 3
 
0.1%
441304.758 2
 
< 0.1%
451182.1 2
 
< 0.1%
450502.066 2
 
< 0.1%
444755.038 2
 
< 0.1%
463234.359 2
 
< 0.1%
452718.202 2
 
< 0.1%
464519.848 2
 
< 0.1%
Other values (5016) 5022
99.5%
ValueCountFrequency (%)
437534.283 1
< 0.1%
437586.772 1
< 0.1%
437774.052 1
< 0.1%
438166.187 1
< 0.1%
438209.138 1
< 0.1%
438350.554 1
< 0.1%
438404.53 1
< 0.1%
438449.356 1
< 0.1%
438726.718 1
< 0.1%
438765.837 1
< 0.1%
ValueCountFrequency (%)
465507.02 1
< 0.1%
465408.813 1
< 0.1%
465331.605 1
< 0.1%
465327.261 1
< 0.1%
465323.036 1
< 0.1%
465268.462 1
< 0.1%
465218.958 1
< 0.1%
465166.375 1
< 0.1%
464966.354 1
< 0.1%
464519.848 2
< 0.1%

Interactions

2023-12-11T19:20:33.249542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.372079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.889065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:32.568925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:33.417748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.477570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.996096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:32.692218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:33.593899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.598079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:32.091819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:32.873707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:33.772145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:31.757881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:32.462824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T19:20:33.060437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T19:20:40.586311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구명산지여부주지번새주소명생성일X 좌표Y 좌표
고유번호1.0000.7010.3080.396NaNNaN0.5030.539
구명0.7011.0000.1410.5591.000NaN0.9290.923
산지여부0.3080.1411.0000.098NaNNaN0.0860.144
주지번0.3960.5590.0981.000NaNNaN0.3630.456
새주소명NaN1.000NaNNaN1.0000.0001.0001.000
생성일NaNNaNNaNNaN0.0001.0000.0000.000
X 좌표0.5030.9290.0860.3631.0000.0001.0000.577
Y 좌표0.5390.9230.1440.4561.0000.0000.5771.000
2023-12-11T19:20:40.737185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산지여부구명
산지여부1.0000.112
구명0.1121.000
2023-12-11T19:20:40.839329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호주지번X 좌표Y 좌표구명산지여부
고유번호1.000-0.065-0.135-0.2480.3220.236
주지번-0.0651.000-0.029-0.1620.2270.075
X 좌표-0.135-0.0291.0000.1410.6550.066
Y 좌표-0.248-0.1620.1411.0000.6390.109
구명0.3220.2270.6550.6391.0000.112
산지여부0.2360.0750.0660.1090.1121.000

Missing values

2023-12-11T19:20:34.035389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T19:20:34.409467image/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.
2023-12-11T19:20:34.656659image/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

고유번호구명법정동명산지여부주지번부지번새주소명생성일X 좌표Y 좌표
0225관악구봉천동1228<NA><NA>196479.369441304.758
1227서초구원지동11362<NA><NA>205050.117438449.356
2229서초구양재동1200<NA><NA>202753.275440983.217
31광진구구의동12165<NA><NA>208165.405448729.346
43광진구광장동12421<NA><NA>209182.485449606.48
5217관악구신림동116775<NA><NA>191447.809442820.837
64광진구자양동1630<NA><NA>207265.473448318.178
75광진구자양동180410<NA><NA>206867.885448478.203
86광진구자양동165150<NA><NA>207286.938447859.495
98광진구화양동1321<NA><NA>206001.899449654.919
고유번호구명법정동명산지여부주지번부지번새주소명생성일X 좌표Y 좌표
50363106서초구서초동117163<NA><NA>201011.565443762.985
50373111서초구서초동1130334<NA><NA>202173.217444854.474
50383116서초구방배동1102114<NA><NA>200007.565442198.442
50393121서초구서초동117063<NA><NA>200818.57443859.625
50403126서초구서초동1169417<NA><NA>201213.895443915.246
50413131서초구서초동115728<NA><NA>201106.029443697.254
50423527영등포구대림빌딩16680<NA><NA>191096.157444839.627
50433187서초구방배동176419<NA><NA>198515.575444045.902
50443175서초구서초동117101<NA><NA>200872.544443774.911
50453194서초구서초동114512<NA><NA>201077.205442643.372