Overview

Dataset statistics

Number of variables9
Number of observations1715
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory130.8 KiB
Average record size in memory78.1 B

Variable types

Categorical2
Numeric5
Text2

Alerts

strd_yr has constant value ""Constant
sopsrt_clsf_cd has constant value ""Constant
std_adstrd_cd is highly overall correlated with laHigh correlation
la is highly overall correlated with std_adstrd_cdHigh correlation

Reproduction

Analysis started2023-12-11 22:33:08.428398
Analysis finished2023-12-11 22:33:11.147879
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

strd_yr
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2021
1715 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 1715
100.0%

Length

2023-12-12T07:33:11.285407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:33:11.611954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 1715
100.0%

sopsrt_clsf_cd
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
PB
1715 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
PB 1715
100.0%

Length

2023-12-12T07:33:11.824010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:33:12.030702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pb 1715
100.0%

std_adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct201
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14553300
Minimum11110530
Maximum41210610
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-12T07:33:12.205597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110530
5-th percentile11140605
Q111350625
median11500640
Q311680750
95-th percentile26230520
Maximum41210610
Range30100080
Interquartile range (IQR)330125

Descriptive statistics

Standard deviation6526644.7
Coefficient of variation (CV)0.44846492
Kurtosis2.5603395
Mean14553300
Median Absolute Deviation (MAD)180090
Skewness1.8813527
Sum2.495891 × 1010
Variance4.2597091 × 1013
MonotonicityNot monotonic
2023-12-12T07:33:12.345940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500604 74
 
4.3%
26110580 71
 
4.1%
26230510 66
 
3.8%
26230520 61
 
3.6%
11350625 57
 
3.3%
11680750 48
 
2.8%
11680740 43
 
2.5%
11440730 42
 
2.4%
11500641 39
 
2.3%
26110510 36
 
2.1%
Other values (191) 1178
68.7%
ValueCountFrequency (%)
11110530 3
 
0.2%
11110615 26
1.5%
11110630 6
 
0.3%
11110640 2
 
0.1%
11110700 2
 
0.1%
11110710 2
 
0.1%
11140520 8
 
0.5%
11140540 2
 
0.1%
11140550 14
0.8%
11140580 7
 
0.4%
ValueCountFrequency (%)
41210610 2
 
0.1%
41210540 5
 
0.3%
41133540 6
 
0.3%
41131620 1
 
0.1%
41131580 1
 
0.1%
41131570 1
 
0.1%
41131530 1
 
0.1%
41131520 4
 
0.2%
29110655 1
 
0.1%
29110525 24
1.4%

adstrd_nm
Text

Distinct200
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2023-12-12T07:33:12.684107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.9148688
Min length2

Characters and Unicode

Total characters6714
Distinct characters147
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

Unique23 ?
Unique (%)1.3%

Sample

1st row신내1동
2nd row거여1동
3rd row거여1동
4th row거여1동
5th row거여1동
ValueCountFrequency (%)
가양2동 74
 
4.3%
남포동 71
 
4.1%
부전1동 66
 
3.8%
부전2동 61
 
3.6%
중계2.3동 57
 
3.3%
수서동 48
 
2.8%
일원2동 43
 
2.5%
성산2동 42
 
2.4%
중앙동 39
 
2.3%
방화3동 39
 
2.3%
Other values (190) 1175
68.5%
2023-12-12T07:33:13.502322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1734
25.8%
2 519
 
7.7%
1 323
 
4.8%
3 204
 
3.0%
. 161
 
2.4%
가 143
 
2.1%
계 143
 
2.1%
부 136
 
2.0%
중 135
 
2.0%
전 130
 
1.9%
Other values (137) 3086
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5285
78.7%
Decimal Number 1268
 
18.9%
Other Punctuation 161
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1734
32.8%
가 143
 
2.7%
계 143
 
2.7%
부 136
 
2.6%
중 135
 
2.6%
전 130
 
2.5%
방 118
 
2.2%
서 98
 
1.9%
포 92
 
1.7%
양 91
 
1.7%
Other values (127) 2465
46.6%
Decimal Number
ValueCountFrequency (%)
2 519
40.9%
1 323
25.5%
3 204
 
16.1%
4 112
 
8.8%
7 42
 
3.3%
6 29
 
2.3%
5 18
 
1.4%
8 13
 
1.0%
9 8
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 161
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5285
78.7%
Common 1429
 
21.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1734
32.8%
가 143
 
2.7%
계 143
 
2.7%
부 136
 
2.6%
중 135
 
2.6%
전 130
 
2.5%
방 118
 
2.2%
서 98
 
1.9%
포 92
 
1.7%
양 91
 
1.7%
Other values (127) 2465
46.6%
Common
ValueCountFrequency (%)
2 519
36.3%
1 323
22.6%
3 204
 
14.3%
. 161
 
11.3%
4 112
 
7.8%
7 42
 
2.9%
6 29
 
2.0%
5 18
 
1.3%
8 13
 
0.9%
9 8
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5285
78.7%
ASCII 1429
 
21.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1734
32.8%
가 143
 
2.7%
계 143
 
2.7%
부 136
 
2.6%
중 135
 
2.6%
전 130
 
2.5%
방 118
 
2.2%
서 98
 
1.9%
포 92
 
1.7%
양 91
 
1.7%
Other values (127) 2465
46.6%
ASCII
ValueCountFrequency (%)
2 519
36.3%
1 323
22.6%
3 204
 
14.3%
. 161
 
11.3%
4 112
 
7.8%
7 42
 
2.9%
6 29
 
2.0%
5 18
 
1.3%
8 13
 
0.9%
9 8
 
0.6%

lnno_adres
Text

Distinct273
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
2023-12-12T07:33:13.756150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length21.912536
Min length14

Characters and Unicode

Total characters37580
Distinct characters208
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

Unique65 ?
Unique (%)3.8%

Sample

1st row서울시 중랑구 신내역로 165
2nd row서울시 송파구 오금로53길 14
3rd row서울시 송파구 오금로53길 14
4th row서울시 송파구 오금로53길 14
5th row서울시 송파구 오금로53길 14
ValueCountFrequency (%)
서울특별시 959
 
13.8%
서울시 370
 
5.3%
부산광역시 272
 
3.9%
중구 205
 
2.9%
강남구 184
 
2.6%
중앙대로 181
 
2.6%
강서구 173
 
2.5%
노원구 162
 
2.3%
부산진구 130
 
1.9%
마포구 121
 
1.7%
Other values (441) 4193
60.3%
2023-12-12T07:33:14.195610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5274
 
14.0%
로 1880
 
5.0%
구 1845
 
4.9%
서 1723
 
4.6%
시 1639
 
4.4%
울 1382
 
3.7%
동 1359
 
3.6%
지 1341
 
3.6%
하 1211
 
3.2%
) 980
 
2.6%
Other values (198) 18946
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24934
66.3%
Decimal Number 5343
 
14.2%
Space Separator 5274
 
14.0%
Close Punctuation 980
 
2.6%
Open Punctuation 980
 
2.6%
Dash Punctuation 69
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1880
 
7.5%
구 1845
 
7.4%
서 1723
 
6.9%
시 1639
 
6.6%
울 1382
 
5.5%
동 1359
 
5.5%
지 1341
 
5.4%
하 1211
 
4.9%
별 959
 
3.8%
특 959
 
3.8%
Other values (184) 10636
42.7%
Decimal Number
ValueCountFrequency (%)
1 912
17.1%
7 829
15.5%
3 664
12.4%
2 594
11.1%
0 581
10.9%
5 534
10.0%
4 472
8.8%
6 326
 
6.1%
9 217
 
4.1%
8 214
 
4.0%
Space Separator
ValueCountFrequency (%)
5274
100.0%
Close Punctuation
ValueCountFrequency (%)
) 980
100.0%
Open Punctuation
ValueCountFrequency (%)
( 980
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24934
66.3%
Common 12646
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1880
 
7.5%
구 1845
 
7.4%
서 1723
 
6.9%
시 1639
 
6.6%
울 1382
 
5.5%
동 1359
 
5.5%
지 1341
 
5.4%
하 1211
 
4.9%
별 959
 
3.8%
특 959
 
3.8%
Other values (184) 10636
42.7%
Common
ValueCountFrequency (%)
5274
41.7%
) 980
 
7.7%
( 980
 
7.7%
1 912
 
7.2%
7 829
 
6.6%
3 664
 
5.3%
2 594
 
4.7%
0 581
 
4.6%
5 534
 
4.2%
4 472
 
3.7%
Other values (4) 826
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24934
66.3%
ASCII 12646
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5274
41.7%
) 980
 
7.7%
( 980
 
7.7%
1 912
 
7.2%
7 829
 
6.6%
3 664
 
5.3%
2 594
 
4.7%
0 581
 
4.6%
5 534
 
4.2%
4 472
 
3.7%
Other values (4) 826
 
6.5%
Hangul
ValueCountFrequency (%)
로 1880
 
7.5%
구 1845
 
7.4%
서 1723
 
6.9%
시 1639
 
6.6%
울 1382
 
5.5%
동 1359
 
5.5%
지 1341
 
5.4%
하 1211
 
4.9%
별 959
 
3.8%
특 959
 
3.8%
Other values (184) 10636
42.7%

exche_gtn
Real number (ℝ)

Distinct1549
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10010987
Minimum0
Maximum2.7099647 × 108
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-12T07:33:14.371669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile418482
Q11023200
median3764706
Q313780794
95-th percentile35875733
Maximum2.7099647 × 108
Range2.7099647 × 108
Interquartile range (IQR)12757594

Descriptive statistics

Standard deviation16142111
Coefficient of variation (CV)1.6124396
Kurtosis57.890708
Mean10010987
Median Absolute Deviation (MAD)3231373
Skewness5.4159744
Sum1.7168842 × 1010
Variance2.6056774 × 1014
MonotonicityNot monotonic
2023-12-12T07:33:14.559839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2177368 14
 
0.8%
1023200 12
 
0.7%
1716834 10
 
0.6%
1034300 9
 
0.5%
1084900 8
 
0.5%
1013400 8
 
0.5%
1054100 7
 
0.4%
1044200 6
 
0.3%
1075000 5
 
0.3%
3291000 5
 
0.3%
Other values (1539) 1631
95.1%
ValueCountFrequency (%)
0 1
0.1%
42702 1
0.1%
49688 1
0.1%
55178 1
0.1%
65512 1
0.1%
70804 1
0.1%
94124 1
0.1%
100106 1
0.1%
104837 1
0.1%
109825 1
0.1%
ValueCountFrequency (%)
270996469 1
0.1%
202750000 1
0.1%
119969819 1
0.1%
117228555 1
0.1%
109413628 1
0.1%
103711132 1
0.1%
100708333 1
0.1%
89825581 1
0.1%
87168605 1
0.1%
86826347 1
0.1%

la
Real number (ℝ)

HIGH CORRELATION 

Distinct359
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.130058
Minimum35.097673
Maximum37.677718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-12T07:33:14.744663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.097673
5-th percentile35.09898
Q137.486942
median37.534721
Q337.566287
95-th percentile37.642038
Maximum37.677718
Range2.5800454
Interquartile range (IQR)0.079344957

Descriptive statistics

Standard deviation0.91682095
Coefficient of variation (CV)0.02469215
Kurtosis0.97676511
Mean37.130058
Median Absolute Deviation (MAD)0.041677355
Skewness-1.7194773
Sum63678.049
Variance0.84056066
MonotonicityNot monotonic
2023-12-12T07:33:14.922711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5635136398 43
 
2.5%
37.4930432493 41
 
2.4%
37.4874596524 39
 
2.3%
37.5643044991 38
 
2.2%
37.5098596536 36
 
2.1%
37.5650038928 31
 
1.8%
37.6462246366 30
 
1.7%
37.5746879675 24
 
1.4%
37.6420380401 23
 
1.3%
37.4775163131 22
 
1.3%
Other values (349) 1388
80.9%
ValueCountFrequency (%)
35.097673085 6
0.3%
35.097686536 2
 
0.1%
35.0977001705 12
0.7%
35.0977616955 2
 
0.1%
35.0978519671 9
0.5%
35.0979508451 3
 
0.2%
35.0979742274 2
 
0.1%
35.0979765878 5
0.3%
35.0980299367 3
 
0.2%
35.0980941211 10
0.6%
ValueCountFrequency (%)
37.6777184351 5
 
0.3%
37.6701426233 5
 
0.3%
37.6653397392 8
 
0.5%
37.6606631986 9
 
0.5%
37.656327036 2
 
0.1%
37.6546283237 5
 
0.3%
37.6532018777 2
 
0.1%
37.6462246366 30
1.7%
37.6452066157 4
 
0.2%
37.6420380401 23
1.3%

lo
Real number (ℝ)

Distinct359
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.3132
Minimum126.62866
Maximum129.06234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-12T07:33:15.062155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62866
5-th percentile126.81926
Q1126.92253
median127.02944
Q3127.09456
95-th percentile129.05914
Maximum129.06234
Range2.4336805
Interquartile range (IQR)0.17203172

Descriptive statistics

Standard deviation0.75789822
Coefficient of variation (CV)0.0059530215
Kurtosis1.3954871
Mean127.3132
Median Absolute Deviation (MAD)0.07771933
Skewness1.8127635
Sum218342.14
Variance0.57440971
MonotonicityNot monotonic
2023-12-12T07:33:15.246932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8545074448 43
 
2.5%
127.0738690637 41
 
2.4%
127.1051493711 39
 
2.3%
126.9011979771 38
 
2.2%
126.8629204026 36
 
2.1%
126.8520224373 31
 
1.8%
127.0691086638 30
 
1.7%
126.819262622 24
 
1.4%
127.064294996 23
 
1.3%
126.981704041 22
 
1.3%
Other values (349) 1388
80.9%
ValueCountFrequency (%)
126.6286551218 1
0.1%
126.6298348992 1
0.1%
126.630951635 1
0.1%
126.6315241019 1
0.1%
126.6316962138 1
0.1%
126.6363879041 1
0.1%
126.6567828965 1
0.1%
126.6799379969 1
0.1%
126.6802611017 1
0.1%
126.6900073612 1
0.1%
ValueCountFrequency (%)
129.062335598 1
 
0.1%
129.0622907015 3
0.2%
129.0620834157 7
0.4%
129.0618879628 4
0.2%
129.0616888855 4
0.2%
129.061615155 5
0.3%
129.0614125198 3
0.2%
129.0611780568 5
0.3%
129.061051783 7
0.4%
129.0605933566 3
0.2%

data_no
Real number (ℝ)

Distinct43
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4956268
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-12T07:33:15.376907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile25
Maximum43
Range42
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.7047591
Coefficient of variation (CV)1.1861456
Kurtosis5.1085256
Mean6.4956268
Median Absolute Deviation (MAD)2
Skewness2.2700834
Sum11140
Variance59.363312
MonotonicityNot monotonic
2023-12-12T07:33:15.499253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 359
20.9%
2 266
15.5%
3 212
12.4%
4 166
9.7%
5 128
 
7.5%
6 90
 
5.2%
7 66
 
3.8%
8 50
 
2.9%
9 43
 
2.5%
10 35
 
2.0%
Other values (33) 300
17.5%
ValueCountFrequency (%)
1 359
20.9%
2 266
15.5%
3 212
12.4%
4 166
9.7%
5 128
 
7.5%
6 90
 
5.2%
7 66
 
3.8%
8 50
 
2.9%
9 43
 
2.5%
10 35
 
2.0%
ValueCountFrequency (%)
43 1
 
0.1%
42 1
 
0.1%
41 2
 
0.1%
40 2
 
0.1%
39 3
0.2%
38 4
0.2%
37 4
0.2%
36 5
0.3%
35 5
0.3%
34 5
0.3%

Interactions

2023-12-12T07:33:10.553467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:08.826792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.311110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.736856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.108667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.633925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:08.921551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.403283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.812883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.186928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.707595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.037560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.501704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.885314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.281034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.779712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.144300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.598172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.956367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.391566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.857193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.229109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:09.667351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.031811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:10.475121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:33:15.596821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
std_adstrd_cdexche_gtnlalodata_no
std_adstrd_cd1.0000.1000.9990.8940.181
exche_gtn0.1001.0000.1280.1240.177
la0.9990.1281.0000.9970.245
lo0.8940.1240.9971.0000.315
data_no0.1810.1770.2450.3151.000
2023-12-12T07:33:15.698936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
std_adstrd_cdexche_gtnlalodata_no
std_adstrd_cd1.000-0.256-0.8180.375-0.053
exche_gtn-0.2561.0000.113-0.200-0.056
la-0.8180.1131.000-0.3700.140
lo0.375-0.200-0.3701.000-0.139
data_no-0.053-0.0560.140-0.1391.000

Missing values

2023-12-12T07:33:10.978287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:33:11.094878image/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

strd_yrsopsrt_clsf_cdstd_adstrd_cdadstrd_nmlnno_adresexche_gtnlalodata_no
02021PB11260680신내1동서울시 중랑구 신내역로 16589142937.615342127.1099741
12021PB11710531거여1동서울시 송파구 오금로53길 14106430237.495799127.1421011
22021PB11710531거여1동서울시 송파구 오금로53길 14106480837.495799127.1421012
32021PB11710531거여1동서울시 송파구 오금로53길 14109986537.495799127.1421013
42021PB11710531거여1동서울시 송파구 오금로53길 14110077937.495799127.1421014
52021PB11440660서교동서울특별시 마포구 양화로 지하55(서교동)7193041237.549861126.91438711
62021PB11560535영등포동서울특별시 영등포구 경인로114가길 지하9(영등포동1가)790950837.517679126.914831
72021PB11560535영등포동서울특별시 영등포구 경인로114가길 지하9(영등포동1가)7800000037.517679126.914832
82021PB11380631신사1동서울특별시 은평구 증산로 지하477(신사동)714481937.59837126.9154991
92021PB11380631신사1동서울특별시 은평구 증산로 지하477(신사동)2292428437.59837126.9154992
strd_yrsopsrt_clsf_cdstd_adstrd_cdadstrd_nmlnno_adresexche_gtnlalodata_no
17052021PB28140640금창동인천광역시 동구 송림로 지하44270237.473091126.6363881
17062021PB28177600도화1동인천광역시 미추홀구 경인로 지하12510483737.466811126.6567831
17072021PB28177620주안1동인천광역시 미추홀구 경인로 지하3435517837.465057126.6799381
17082021PB28177620주안1동인천광역시 미추홀구 주안로 지하8613431937.464065126.6802611
17092021PB28177670주안6동인천광역시 미추홀구 경원대로 지하8487080437.457834126.6900071
17102021PB28237550부평5동인천광역시 부평구 부평대로 지하3029300537.49358126.7229451
17112021PB28237550부평5동인천광역시 부평구 시장로 지하1025050737.491637126.7239061
17122021PB28237510부평1동인천광역시 부평구 광장로 지하3016934037.490521126.7242051
17132021PB28237510부평1동인천광역시 부평구 부평대로 지하729326637.49141126.7232471
17142021PB28237550부평5동인천광역시 부평구 시장로 지하424968837.494353126.7265071