Overview

Dataset statistics

Number of variables9
Number of observations3201
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory244.0 KiB
Average record size in memory78.0 B

Variable types

Categorical2
Numeric5
Text2

Alerts

strd_yr has constant value ""Constant
sopsrt_clsf_cd has constant value ""Constant
exche_gtn is highly skewed (γ1 = 45.76184268)Skewed

Reproduction

Analysis started2023-12-11 22:33:10.423579
Analysis finished2023-12-11 22:33:15.431692
Duration5.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

strd_yr
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2022
3201 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 3201
100.0%

Length

2023-12-12T07:33:15.493973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:33:15.577771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 3201
100.0%

sopsrt_clsf_cd
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
LT
3201 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
LT 3201
100.0%

Length

2023-12-12T07:33:15.677210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:33:15.771651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
lt 3201
100.0%

std_adstrd_cd
Real number (ℝ)

Distinct388
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33034338
Minimum11140570
Maximum50130310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.3 KiB
2023-12-12T07:33:15.867448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11140570
5-th percentile11380510
Q127710259
median41133670
Q341463575
95-th percentile44133590
Maximum50130310
Range38989740
Interquartile range (IQR)13753316

Descriptive statistics

Standard deviation11578875
Coefficient of variation (CV)0.35051028
Kurtosis-0.63896093
Mean33034338
Median Absolute Deviation (MAD)2999890
Skewness-0.86376672
Sum1.0574292 × 1011
Variance1.3407035 × 1014
MonotonicityNot monotonic
2023-12-12T07:33:16.020805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41117600 83
 
2.6%
41630560 53
 
1.7%
41570570 52
 
1.6%
41360570 51
 
1.6%
28260537 50
 
1.6%
28260536 46
 
1.4%
28185830 41
 
1.3%
41281577 39
 
1.2%
28200655 35
 
1.1%
41570550 35
 
1.1%
Other values (378) 2716
84.8%
ValueCountFrequency (%)
11140570 6
0.2%
11140590 7
0.2%
11140650 1
 
< 0.1%
11140670 5
0.2%
11170560 5
0.2%
11170625 6
0.2%
11170685 4
0.1%
11200535 2
 
0.1%
11200615 9
0.3%
11200620 3
 
0.1%
ValueCountFrequency (%)
50130310 2
 
0.1%
50130250 1
 
< 0.1%
50110660 3
 
0.1%
50110650 1
 
< 0.1%
50110610 2
 
0.1%
48330253 13
0.4%
48250540 6
0.2%
48250530 7
0.2%
48250250 3
 
0.1%
48129650 10
0.3%

adstrd_nm
Text

Distinct380
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2023-12-12T07:33:16.313712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.6101218
Min length2

Characters and Unicode

Total characters11556
Distinct characters209
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

Unique26 ?
Unique (%)0.8%

Sample

1st row자양4동
2nd row필동
3rd row필동
4th row필동
5th row필동
ValueCountFrequency (%)
광교1동 83
 
2.6%
회천4동 53
 
1.7%
구래동 52
 
1.6%
별내동 51
 
1.6%
청라2동 50
 
1.6%
청라1동 46
 
1.4%
송도2동 41
 
1.3%
삼송2동 39
 
1.2%
풍산동 35
 
1.1%
다산1동 35
 
1.1%
Other values (370) 2716
84.8%
2023-12-12T07:33:16.776031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3078
26.6%
1 802
 
6.9%
2 548
 
4.7%
산 234
 
2.0%
정 214
 
1.9%
읍 204
 
1.8%
3 189
 
1.6%
도 150
 
1.3%
신 148
 
1.3%
청 147
 
1.3%
Other values (199) 5842
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9797
84.8%
Decimal Number 1726
 
14.9%
Other Punctuation 33
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3078
31.4%
산 234
 
2.4%
정 214
 
2.2%
읍 204
 
2.1%
도 150
 
1.5%
신 148
 
1.5%
청 147
 
1.5%
송 142
 
1.4%
현 125
 
1.3%
라 123
 
1.3%
Other values (189) 5232
53.4%
Decimal Number
ValueCountFrequency (%)
1 802
46.5%
2 548
31.7%
3 189
 
11.0%
4 96
 
5.6%
5 43
 
2.5%
7 32
 
1.9%
6 14
 
0.8%
9 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 22
66.7%
, 11
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9797
84.8%
Common 1759
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3078
31.4%
산 234
 
2.4%
정 214
 
2.2%
읍 204
 
2.1%
도 150
 
1.5%
신 148
 
1.5%
청 147
 
1.5%
송 142
 
1.4%
현 125
 
1.3%
라 123
 
1.3%
Other values (189) 5232
53.4%
Common
ValueCountFrequency (%)
1 802
45.6%
2 548
31.2%
3 189
 
10.7%
4 96
 
5.5%
5 43
 
2.4%
7 32
 
1.8%
. 22
 
1.3%
6 14
 
0.8%
, 11
 
0.6%
9 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9797
84.8%
ASCII 1759
 
15.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3078
31.4%
산 234
 
2.4%
정 214
 
2.2%
읍 204
 
2.1%
도 150
 
1.5%
신 148
 
1.5%
청 147
 
1.5%
송 142
 
1.4%
현 125
 
1.3%
라 123
 
1.3%
Other values (189) 5232
53.4%
ASCII
ValueCountFrequency (%)
1 802
45.6%
2 548
31.2%
3 189
 
10.7%
4 96
 
5.5%
5 43
 
2.4%
7 32
 
1.8%
. 22
 
1.3%
6 14
 
0.8%
, 11
 
0.6%
9 2
 
0.1%

lnno_adres
Text

Distinct803
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Memory size25.1 KiB
2023-12-12T07:33:17.045685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length19.364574
Min length12

Characters and Unicode

Total characters61986
Distinct characters336
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)2.7%

Sample

1st row서울 광진구 자양동 3-7
2nd row서울 중구 충무로3가 49번지외 7필지
3rd row서울 중구 충무로3가 49번지외 7필지
4th row서울 중구 충무로3가 49번지외 7필지
5th row서울 중구 충무로3가 49번지외 7필지
ValueCountFrequency (%)
경기 1477
 
10.1%
서울 558
 
3.8%
인천 424
 
2.9%
김포시 176
 
1.2%
고양시 171
 
1.2%
부산 169
 
1.2%
서구 162
 
1.1%
수원시 153
 
1.0%
충남 143
 
1.0%
덕양구 139
 
0.9%
Other values (1468) 11113
75.7%
2023-12-12T07:33:17.473516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11484
 
18.5%
동 3290
 
5.3%
1 3067
 
4.9%
- 2646
 
4.3%
구 2356
 
3.8%
2 2055
 
3.3%
시 1905
 
3.1%
경 1618
 
2.6%
기 1580
 
2.5%
3 1426
 
2.3%
Other values (326) 30559
49.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33023
53.3%
Decimal Number 14190
22.9%
Space Separator 11484
 
18.5%
Dash Punctuation 2646
 
4.3%
Other Punctuation 179
 
0.3%
Uppercase Letter 168
 
0.3%
Open Punctuation 125
 
0.2%
Close Punctuation 125
 
0.2%
Lowercase Letter 41
 
0.1%
Modifier Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3290
 
10.0%
구 2356
 
7.1%
시 1905
 
5.8%
경 1618
 
4.9%
기 1580
 
4.8%
지 1157
 
3.5%
서 1134
 
3.4%
남 800
 
2.4%
번 754
 
2.3%
양 688
 
2.1%
Other values (289) 17741
53.7%
Uppercase Letter
ValueCountFrequency (%)
L 54
32.1%
B 32
19.0%
C 30
17.9%
H 17
 
10.1%
M 11
 
6.5%
A 5
 
3.0%
F 5
 
3.0%
I 5
 
3.0%
R 4
 
2.4%
U 4
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 3067
21.6%
2 2055
14.5%
3 1426
10.0%
6 1291
9.1%
4 1135
 
8.0%
5 1131
 
8.0%
9 1065
 
7.5%
7 1049
 
7.4%
8 998
 
7.0%
0 973
 
6.9%
Lowercase Letter
ValueCountFrequency (%)
l 12
29.3%
t 8
19.5%
c 7
17.1%
b 6
14.6%
o 6
14.6%
i 2
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 157
87.7%
. 15
 
8.4%
: 7
 
3.9%
Open Punctuation
ValueCountFrequency (%)
( 120
96.0%
[ 5
 
4.0%
Close Punctuation
ValueCountFrequency (%)
) 120
96.0%
] 5
 
4.0%
Space Separator
ValueCountFrequency (%)
11484
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2646
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33023
53.3%
Common 28754
46.4%
Latin 209
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3290
 
10.0%
구 2356
 
7.1%
시 1905
 
5.8%
경 1618
 
4.9%
기 1580
 
4.8%
지 1157
 
3.5%
서 1134
 
3.4%
남 800
 
2.4%
번 754
 
2.3%
양 688
 
2.1%
Other values (289) 17741
53.7%
Common
ValueCountFrequency (%)
11484
39.9%
1 3067
 
10.7%
- 2646
 
9.2%
2 2055
 
7.1%
3 1426
 
5.0%
6 1291
 
4.5%
4 1135
 
3.9%
5 1131
 
3.9%
9 1065
 
3.7%
7 1049
 
3.6%
Other values (10) 2405
 
8.4%
Latin
ValueCountFrequency (%)
L 54
25.8%
B 32
15.3%
C 30
14.4%
H 17
 
8.1%
l 12
 
5.7%
M 11
 
5.3%
t 8
 
3.8%
c 7
 
3.3%
b 6
 
2.9%
o 6
 
2.9%
Other values (7) 26
12.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33023
53.3%
ASCII 28963
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11484
39.7%
1 3067
 
10.6%
- 2646
 
9.1%
2 2055
 
7.1%
3 1426
 
4.9%
6 1291
 
4.5%
4 1135
 
3.9%
5 1131
 
3.9%
9 1065
 
3.7%
7 1049
 
3.6%
Other values (27) 2614
 
9.0%
Hangul
ValueCountFrequency (%)
동 3290
 
10.0%
구 2356
 
7.1%
시 1905
 
5.8%
경 1618
 
4.9%
기 1580
 
4.8%
지 1157
 
3.5%
서 1134
 
3.4%
남 800
 
2.4%
번 754
 
2.3%
양 688
 
2.1%
Other values (289) 17741
53.7%

exche_gtn
Real number (ℝ)

SKEWED 

Distinct1188
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88416.212
Minimum3118
Maximum25390909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.3 KiB
2023-12-12T07:33:17.784644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3118
5-th percentile22273
Q130469
median43209
Q393545
95-th percentile182636
Maximum25390909
Range25387791
Interquartile range (IQR)63076

Descriptive statistics

Standard deviation482947.55
Coefficient of variation (CV)5.4622059
Kurtosis2361.6144
Mean88416.212
Median Absolute Deviation (MAD)17373
Skewness45.761843
Sum2.8302029 × 108
Variance2.3323834 × 1011
MonotonicityNot monotonic
2023-12-12T07:33:17.951974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26727 90
 
2.8%
31182 85
 
2.7%
28955 83
 
2.6%
40091 83
 
2.6%
35636 81
 
2.5%
24500 80
 
2.5%
33409 69
 
2.2%
22273 61
 
1.9%
37864 60
 
1.9%
44545 59
 
1.8%
Other values (1178) 2450
76.5%
ValueCountFrequency (%)
3118 1
< 0.1%
5345 1
< 0.1%
8464 1
< 0.1%
8909 1
< 0.1%
9132 1
< 0.1%
9444 1
< 0.1%
11315 1
< 0.1%
13407 1
< 0.1%
13542 1
< 0.1%
13809 1
< 0.1%
ValueCountFrequency (%)
25390909 1
< 0.1%
4367370 1
< 0.1%
4098182 1
< 0.1%
3604173 1
< 0.1%
3273824 1
< 0.1%
2806364 1
< 0.1%
2519313 1
< 0.1%
1832762 1
< 0.1%
1813000 1
< 0.1%
1804091 1
< 0.1%

la
Real number (ℝ)

Distinct786
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.149835
Minimum33.233835
Maximum38.202767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.3 KiB
2023-12-12T07:33:18.104603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.233835
5-th percentile35.226143
Q137.167838
median37.44847
Q337.565376
95-th percentile37.71523
Maximum38.202767
Range4.9689322
Interquartile range (IQR)0.3975386

Descriptive statistics

Standard deviation0.73883335
Coefficient of variation (CV)0.019887931
Kurtosis3.1058531
Mean37.149835
Median Absolute Deviation (MAD)0.1717034
Skewness-1.9273467
Sum118916.62
Variance0.54587472
MonotonicityNot monotonic
2023-12-12T07:33:18.265744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4903092 22
 
0.7%
37.5469085 20
 
0.6%
37.519129 16
 
0.5%
35.1909365 15
 
0.5%
37.3994636 15
 
0.5%
37.1156437 14
 
0.4%
37.3738585 14
 
0.4%
37.3246771 12
 
0.4%
37.3809327 12
 
0.4%
37.5320881 11
 
0.3%
Other values (776) 3050
95.3%
ValueCountFrequency (%)
33.2338346 2
 
0.1%
33.2852116 1
 
< 0.1%
33.48532 3
0.1%
33.4912263 1
 
< 0.1%
33.5162722 2
 
0.1%
35.0530056 5
0.2%
35.080922 4
0.1%
35.0884236 4
0.1%
35.0906007 6
0.2%
35.0913128 3
0.1%
ValueCountFrequency (%)
38.2027668 5
0.2%
38.1881607 2
 
0.1%
37.8670922 1
 
< 0.1%
37.8666601 2
 
0.1%
37.8648827 2
 
0.1%
37.8633261 3
 
0.1%
37.8334079 5
0.2%
37.8232087 8
0.2%
37.822991 5
0.2%
37.821212 4
0.1%

lo
Real number (ℝ)

Distinct786
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.19395
Minimum126.27586
Maximum129.43816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.3 KiB
2023-12-12T07:33:18.378556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.27586
5-th percentile126.63294
Q1126.77909
median127.03072
Q3127.14044
95-th percentile129.06123
Maximum129.43816
Range3.1623007
Interquartile range (IQR)0.3613545

Descriptive statistics

Standard deviation0.70040357
Coefficient of variation (CV)0.0055065791
Kurtosis2.5370089
Mean127.19395
Median Absolute Deviation (MAD)0.1829465
Skewness1.9241643
Sum407147.85
Variance0.49056517
MonotonicityNot monotonic
2023-12-12T07:33:18.486309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1322479 22
 
0.7%
127.0516712 20
 
0.6%
126.8866805 16
 
0.5%
128.9849047 15
 
0.5%
126.9682866 15
 
0.5%
126.9138315 14
 
0.4%
126.7198955 14
 
0.4%
126.7893343 12
 
0.4%
126.8616563 12
 
0.4%
126.6449864 11
 
0.3%
Other values (776) 3050
95.3%
ValueCountFrequency (%)
126.2758567 1
 
< 0.1%
126.3104109 2
0.1%
126.4499634 2
0.1%
126.4691047 3
0.1%
126.4693751 2
0.1%
126.4695035 3
0.1%
126.469575 3
0.1%
126.4697958 3
0.1%
126.4698044 3
0.1%
126.4698452 3
0.1%
ValueCountFrequency (%)
129.4381574 2
 
0.1%
129.367018 2
 
0.1%
129.3515151 4
0.1%
129.3414467 6
0.2%
129.3405069 3
0.1%
129.3398749 5
0.2%
129.3397276 5
0.2%
129.329348 1
 
< 0.1%
129.3272526 2
 
0.1%
129.3264093 2
 
0.1%

data_no
Real number (ℝ)

Distinct22
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2796001
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.3 KiB
2023-12-12T07:33:18.595072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum22
Range21
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4361059
Coefficient of variation (CV)0.74280576
Kurtosis8.1309398
Mean3.2796001
Median Absolute Deviation (MAD)1
Skewness2.1657286
Sum10498
Variance5.9346118
MonotonicityNot monotonic
2023-12-12T07:33:18.683389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 786
24.6%
2 705
22.0%
3 549
17.2%
4 415
13.0%
5 301
 
9.4%
6 186
 
5.8%
7 99
 
3.1%
8 49
 
1.5%
9 35
 
1.1%
10 22
 
0.7%
Other values (12) 54
 
1.7%
ValueCountFrequency (%)
1 786
24.6%
2 705
22.0%
3 549
17.2%
4 415
13.0%
5 301
 
9.4%
6 186
 
5.8%
7 99
 
3.1%
8 49
 
1.5%
9 35
 
1.1%
10 22
 
0.7%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 1
 
< 0.1%
20 2
 
0.1%
19 2
 
0.1%
18 2
 
0.1%
17 2
 
0.1%
16 3
0.1%
15 5
0.2%
14 7
0.2%
13 7
0.2%

Interactions

2023-12-12T07:33:14.588924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:12.485201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.131061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.576769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.042089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.732197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:12.635530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.221944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.669655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.176315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.834401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:12.740107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.309785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.748613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.289143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.940551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:12.924900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.405654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.845627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.369829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:15.042498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.040035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.498975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:13.950920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:33:14.475417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:33:18.746479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
std_adstrd_cdexche_gtnlalodata_no
std_adstrd_cd1.0000.0000.7770.7670.127
exche_gtn0.0001.0000.1590.1580.000
la0.7770.1591.0000.8580.000
lo0.7670.1580.8581.0000.016
data_no0.1270.0000.0000.0161.000
2023-12-12T07:33:18.822141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
std_adstrd_cdexche_gtnlalodata_no
std_adstrd_cd1.000-0.169-0.034-0.008-0.025
exche_gtn-0.1691.0000.058-0.0040.338
la-0.0340.0581.000-0.4660.013
lo-0.008-0.004-0.4661.0000.015
data_no-0.0250.3380.0130.0151.000

Missing values

2023-12-12T07:33:15.210120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:33:15.374137image/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
02022LT11215847자양4동서울 광진구 자양동 3-74900037.537968127.0689111
12022LT11140570필동서울 중구 충무로3가 49번지외 7필지6230837.562373126.9926341
22022LT11140570필동서울 중구 충무로3가 49번지외 7필지7343437.562373126.9926342
32022LT11140570필동서울 중구 충무로3가 49번지외 7필지7565937.562373126.9926343
42022LT11140570필동서울 중구 충무로3가 49번지외 7필지10903837.562373126.9926344
52022LT11140570필동서울 중구 충무로3가 49번지외 7필지13129137.562373126.9926345
62022LT11140570필동서울 중구 충무로3가 49번지외 7필지25145537.562373126.9926346
72022LT11140590광희동서울 중구 을지로6가 21-3144737337.565767127.0070011
82022LT11140590광희동서울 중구 을지로6가 21-3149210937.565767127.0070012
92022LT11140590광희동서울 중구 을지로6가 21-3153684637.565767127.0070013
strd_yrsopsrt_clsf_cdstd_adstrd_cdadstrd_nmlnno_adresexche_gtnlalodata_no
31912022LT48330253물금읍경남 양산시 물금읍 범어리 2711-411136435.328768129.014326
31922022LT50110610삼양동제주 제주시 도련1동 1938-51131533.516272126.5802941
31932022LT50110610삼양동제주 제주시 도련1동 1938-52695033.516272126.5802942
31942022LT50110650연동제주 제주시 연동 2325-6 타워프로빌 단지내상가 2층2986633.491226126.4879311
31952022LT50110660노형동제주 제주시 노형동 9257578033.48532126.4814671
31962022LT50110660노형동제주 제주시 노형동 9258023833.48532126.4814672
31972022LT50110660노형동제주 제주시 노형동 9258469533.48532126.4814673
31982022LT50130250대정읍제주 서귀포시 대정읍 보성리 2424 삼정G에듀 단지내 상가327382433.285212126.2758571
31992022LT50130310안덕면제주 서귀포시 안덕면 사계리 126-15790933.233835126.3104111
32002022LT50130310안덕면제주 서귀포시 안덕면 사계리 126-16681833.233835126.3104112