Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.7 KiB
Average record size in memory78.0 B

Variable types

Numeric5
Text2
Categorical1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15067/S/1/datasetView.do

Alerts

STDR_DE is highly overall correlated with Unnamed: 7High correlation
NODE_ID is highly overall correlated with STTN_NO and 2 other fieldsHigh correlation
STTN_NO is highly overall correlated with NODE_ID and 2 other fieldsHigh correlation
CRDNT_Y is highly overall correlated with NODE_ID and 2 other fieldsHigh correlation
STTN_TY is highly overall correlated with Unnamed: 7High correlation
Unnamed: 7 is highly overall correlated with STDR_DE and 4 other fieldsHigh correlation
Unnamed: 7 is highly imbalanced (99.7%)Imbalance
CRDNT_Y is highly skewed (γ1 = 70.49911305)Skewed
STTN_TY has 5412 (54.1%) zerosZeros

Reproduction

Analysis started2024-05-11 09:38:18.512148
Analysis finished2024-05-11 09:38:31.079223
Duration12.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_DE
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20220451
Minimum20220101
Maximum20220801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:31.302488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220101
5-th percentile20220101
Q120220201
median20220501
Q320220601
95-th percentile20220801
Maximum20220801
Range700
Interquartile range (IQR)400

Descriptive statistics

Standard deviation229.96355
Coefficient of variation (CV)1.137282 × 10-5
Kurtosis-1.2411552
Mean20220451
Median Absolute Deviation (MAD)200
Skewness-0.0071386975
Sum2.0220451 × 1011
Variance52883.235
MonotonicityNot monotonic
2024-05-11T09:38:31.650069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
20220101 1295
13.0%
20220601 1268
12.7%
20220501 1259
12.6%
20220801 1257
12.6%
20220701 1238
12.4%
20220301 1235
12.3%
20220401 1228
12.3%
20220201 1220
12.2%
ValueCountFrequency (%)
20220101 1295
13.0%
20220201 1220
12.2%
20220301 1235
12.3%
20220401 1228
12.3%
20220501 1259
12.6%
20220601 1268
12.7%
20220701 1238
12.4%
20220801 1257
12.6%
ValueCountFrequency (%)
20220801 1257
12.6%
20220701 1238
12.4%
20220601 1268
12.7%
20220501 1259
12.6%
20220401 1228
12.3%
20220301 1235
12.3%
20220201 1220
12.2%
20220101 1295
13.0%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct7103
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1316368 × 108
Minimum1 × 108
Maximum1.2900019 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:32.021392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100026 × 108
Q11.0790021 × 108
median1.1390005 × 108
Q31.190001 × 108
95-th percentile1.2300037 × 108
Maximum1.2900019 × 108
Range29000186
Interquartile range (IQR)11099890

Descriptive statistics

Standard deviation6899177.2
Coefficient of variation (CV)0.060966355
Kurtosis-1.0743112
Mean1.1316368 × 108
Median Absolute Deviation (MAD)5999758
Skewness-0.16707303
Sum1.1316368 × 1012
Variance4.7598645 × 1013
MonotonicityNot monotonic
2024-05-11T09:38:32.458835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121000032 5
 
0.1%
123000249 5
 
0.1%
111000928 5
 
0.1%
124000178 5
 
0.1%
112000051 5
 
0.1%
111000560 4
 
< 0.1%
105000236 4
 
< 0.1%
100900002 4
 
< 0.1%
102900006 4
 
< 0.1%
102900058 4
 
< 0.1%
Other values (7093) 9955
99.6%
ValueCountFrequency (%)
100000001 1
< 0.1%
100000002 1
< 0.1%
100000005 2
< 0.1%
100000006 1
< 0.1%
100000007 2
< 0.1%
100000008 1
< 0.1%
100000010 1
< 0.1%
100000011 1
< 0.1%
100000012 1
< 0.1%
100000014 1
< 0.1%
ValueCountFrequency (%)
129000187 1
 
< 0.1%
129000059 1
 
< 0.1%
124900137 1
 
< 0.1%
124900134 1
 
< 0.1%
124900133 2
< 0.1%
124900132 1
 
< 0.1%
124900130 1
 
< 0.1%
124900128 1
 
< 0.1%
124900127 3
< 0.1%
124900126 2
< 0.1%

STTN_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct7098
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14282.92
Minimum1001
Maximum25997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:33.215935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2501
Q18843.5
median14592.5
Q320192.5
95-th percentile24393
Maximum25997
Range24996
Interquartile range (IQR)11349

Descriptive statistics

Standard deviation6914.4413
Coefficient of variation (CV)0.48410558
Kurtosis-1.0776519
Mean14282.92
Median Absolute Deviation (MAD)5667.5
Skewness-0.15907493
Sum1.428292 × 108
Variance47809499
MonotonicityNot monotonic
2024-05-11T09:38:33.746515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13134 5
 
0.1%
22108 5
 
0.1%
25290 5
 
0.1%
12017 5
 
0.1%
24340 5
 
0.1%
23982 4
 
< 0.1%
10542 4
 
< 0.1%
6228 4
 
< 0.1%
4230 4
 
< 0.1%
9216 4
 
< 0.1%
Other values (7088) 9955
99.6%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 1
 
< 0.1%
1005 2
< 0.1%
1006 1
 
< 0.1%
1007 2
< 0.1%
1008 1
 
< 0.1%
1009 2
< 0.1%
1010 3
< 0.1%
1014 2
< 0.1%
1015 1
 
< 0.1%
ValueCountFrequency (%)
25997 2
< 0.1%
25996 1
< 0.1%
25995 1
< 0.1%
25994 1
< 0.1%
25990 2
< 0.1%
25989 1
< 0.1%
25988 1
< 0.1%
25786 2
< 0.1%
25784 1
< 0.1%
25782 1
< 0.1%

STTN_NM
Text

Distinct5269
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:38:34.431934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length7.5259
Min length2

Characters and Unicode

Total characters75259
Distinct characters629
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2600 ?
Unique (%)26.0%

Sample

1st row목동한신청구아파트
2nd row효제초교.연동교회.김마리아활동터
3rd row금용아파트
4th row번3동주민센터
5th row무역센터
ValueCountFrequency (%)
북서울꿈의숲 13
 
0.1%
한신아파트 12
 
0.1%
가산디지털단지역 12
 
0.1%
포스코사거리 11
 
0.1%
금호사거리 11
 
0.1%
현대홈타운아파트 11
 
0.1%
개봉역 10
 
0.1%
벽산아파트 10
 
0.1%
새마을금고 10
 
0.1%
서울역 10
 
0.1%
Other values (5260) 9891
98.9%
2024-05-11T09:38:35.642547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2265
 
3.0%
아 2140
 
2.8%
파 2105
 
2.8%
트 2045
 
2.7%
. 1968
 
2.6%
교 1667
 
2.2%
역 1502
 
2.0%
구 1471
 
2.0%
대 1248
 
1.7%
지 1189
 
1.6%
Other values (619) 57659
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70067
93.1%
Decimal Number 2236
 
3.0%
Other Punctuation 1990
 
2.6%
Uppercase Letter 668
 
0.9%
Open Punctuation 132
 
0.2%
Close Punctuation 132
 
0.2%
Lowercase Letter 23
 
< 0.1%
Dash Punctuation 10
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2265
 
3.2%
아 2140
 
3.1%
파 2105
 
3.0%
트 2045
 
2.9%
교 1667
 
2.4%
역 1502
 
2.1%
구 1471
 
2.1%
대 1248
 
1.8%
지 1189
 
1.7%
학 1122
 
1.6%
Other values (574) 53313
76.1%
Uppercase Letter
ValueCountFrequency (%)
S 85
12.7%
T 84
12.6%
K 79
11.8%
C 66
9.9%
A 50
 
7.5%
P 45
 
6.7%
G 37
 
5.5%
B 34
 
5.1%
M 26
 
3.9%
L 22
 
3.3%
Other values (14) 140
21.0%
Decimal Number
ValueCountFrequency (%)
1 701
31.4%
2 400
17.9%
3 328
14.7%
4 187
 
8.4%
5 148
 
6.6%
0 123
 
5.5%
7 103
 
4.6%
6 102
 
4.6%
9 89
 
4.0%
8 55
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
e 17
73.9%
k 3
 
13.0%
s 2
 
8.7%
t 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 1968
98.9%
· 17
 
0.9%
& 5
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70067
93.1%
Common 4501
 
6.0%
Latin 691
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2265
 
3.2%
아 2140
 
3.1%
파 2105
 
3.0%
트 2045
 
2.9%
교 1667
 
2.4%
역 1502
 
2.1%
구 1471
 
2.1%
대 1248
 
1.8%
지 1189
 
1.7%
학 1122
 
1.6%
Other values (574) 53313
76.1%
Latin
ValueCountFrequency (%)
S 85
12.3%
T 84
12.2%
K 79
11.4%
C 66
9.6%
A 50
 
7.2%
P 45
 
6.5%
G 37
 
5.4%
B 34
 
4.9%
M 26
 
3.8%
L 22
 
3.2%
Other values (18) 163
23.6%
Common
ValueCountFrequency (%)
. 1968
43.7%
1 701
 
15.6%
2 400
 
8.9%
3 328
 
7.3%
4 187
 
4.2%
5 148
 
3.3%
( 132
 
2.9%
) 132
 
2.9%
0 123
 
2.7%
7 103
 
2.3%
Other values (7) 279
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70067
93.1%
ASCII 5175
 
6.9%
None 17
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2265
 
3.2%
아 2140
 
3.1%
파 2105
 
3.0%
트 2045
 
2.9%
교 1667
 
2.4%
역 1502
 
2.1%
구 1471
 
2.1%
대 1248
 
1.8%
지 1189
 
1.7%
학 1122
 
1.6%
Other values (574) 53313
76.1%
ASCII
ValueCountFrequency (%)
. 1968
38.0%
1 701
 
13.5%
2 400
 
7.7%
3 328
 
6.3%
4 187
 
3.6%
5 148
 
2.9%
( 132
 
2.6%
) 132
 
2.6%
0 123
 
2.4%
7 103
 
2.0%
Other values (34) 953
18.4%
None
ValueCountFrequency (%)
· 17
100.0%

CRDNT_X
Text

Distinct7109
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:38:36.515508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8333
Min length4

Characters and Unicode

Total characters108333
Distinct characters13
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

Unique4782 ?
Unique (%)47.8%

Sample

1st row126.8803505
2nd row127.0022088
3rd row127.0351771
4th row127.046604
5th row127.0623552
ValueCountFrequency (%)
127.1263367 5
 
< 0.1%
126.9027079 5
 
< 0.1%
127.1518429 5
 
< 0.1%
126.9218398 5
 
< 0.1%
126.964326 5
 
< 0.1%
127.016373 5
 
< 0.1%
127.0305026 4
 
< 0.1%
126.9475662 4
 
< 0.1%
126.9188889 4
 
< 0.1%
127.0315279 4
 
< 0.1%
Other values (7099) 9954
99.5%
2024-05-11T09:38:37.716930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16929
15.6%
2 16201
15.0%
6 10968
10.1%
7 10630
9.8%
. 9998
9.2%
9 9021
8.3%
0 8703
8.0%
8 7541
7.0%
3 6163
 
5.7%
5 6115
 
5.6%
Other values (3) 6064
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98331
90.8%
Other Punctuation 9998
 
9.2%
Other Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16929
17.2%
2 16201
16.5%
6 10968
11.2%
7 10630
10.8%
9 9021
9.2%
0 8703
8.9%
8 7541
7.7%
3 6163
 
6.3%
5 6115
 
6.2%
4 6060
 
6.2%
Other Letter
ValueCountFrequency (%)
단 2
50.0%
지 2
50.0%
Other Punctuation
ValueCountFrequency (%)
. 9998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108329
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16929
15.6%
2 16201
15.0%
6 10968
10.1%
7 10630
9.8%
. 9998
9.2%
9 9021
8.3%
0 8703
8.0%
8 7541
7.0%
3 6163
 
5.7%
5 6115
 
5.6%
Hangul
ValueCountFrequency (%)
단 2
50.0%
지 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108329
> 99.9%
Hangul 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16929
15.6%
2 16201
15.0%
6 10968
10.1%
7 10630
9.8%
. 9998
9.2%
9 9021
8.3%
0 8703
8.0%
8 7541
7.0%
3 6163
 
5.7%
5 6115
 
5.6%
Hangul
ValueCountFrequency (%)
단 2
50.0%
지 2
50.0%

CRDNT_Y
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7130
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.568861
Minimum37.430712
Maximum126.8158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:38.299991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.430712
5-th percentile37.471074
Q137.503606
median37.550746
Q337.591217
95-th percentile37.648893
Maximum126.8158
Range89.385089
Interquartile range (IQR)0.087611245

Descriptive statistics

Standard deviation1.2635296
Coefficient of variation (CV)0.033632364
Kurtosis4978.5628
Mean37.568861
Median Absolute Deviation (MAD)0.04438992
Skewness70.499113
Sum375688.61
Variance1.596507
MonotonicityNot monotonic
2024-05-11T09:38:38.753602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.46914722 5
 
0.1%
37.5545613 5
 
0.1%
37.6183599 5
 
0.1%
37.56790142 5
 
0.1%
37.488798 5
 
0.1%
37.50739076 4
 
< 0.1%
37.49346179 4
 
< 0.1%
37.57696318 4
 
< 0.1%
37.66526601 4
 
< 0.1%
37.47019926 4
 
< 0.1%
Other values (7120) 9955
99.6%
ValueCountFrequency (%)
37.43071168 3
< 0.1%
37.4332116 1
 
< 0.1%
37.43371743 1
 
< 0.1%
37.43437898 1
 
< 0.1%
37.43464329 1
 
< 0.1%
37.43498304 3
< 0.1%
37.43500421 1
 
< 0.1%
37.43686295 2
< 0.1%
37.43732107 1
 
< 0.1%
37.43795943 2
< 0.1%
ValueCountFrequency (%)
126.8158008 2
< 0.1%
37.68987622 3
< 0.1%
37.68948783 1
 
< 0.1%
37.68935007 1
 
< 0.1%
37.68933105 1
 
< 0.1%
37.68919465 2
< 0.1%
37.68901186 1
 
< 0.1%
37.688568 1
 
< 0.1%
37.68793977 1
 
< 0.1%
37.68724438 2
< 0.1%

STTN_TY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1226121
Minimum0
Maximum37.560725
Zeros5412
Zeros (%)54.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:38:39.131475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile5
Maximum37.560725
Range37.560725
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4643836
Coefficient of variation (CV)1.1610146
Kurtosis6.6120765
Mean2.1226121
Median Absolute Deviation (MAD)0
Skewness0.88772616
Sum21226.121
Variance6.0731866
MonotonicityNot monotonic
2024-05-11T09:38:39.480417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 5412
54.1%
5.0 3779
37.8%
1.0 326
 
3.3%
4.0 277
 
2.8%
3.0 134
 
1.3%
6.0 70
 
0.7%
37.56072469 2
 
< 0.1%
ValueCountFrequency (%)
0.0 5412
54.1%
1.0 326
 
3.3%
3.0 134
 
1.3%
4.0 277
 
2.8%
5.0 3779
37.8%
6.0 70
 
0.7%
37.56072469 2
 
< 0.1%
ValueCountFrequency (%)
37.56072469 2
 
< 0.1%
6.0 70
 
0.7%
5.0 3779
37.8%
4.0 277
 
2.8%
3.0 134
 
1.3%
1.0 326
 
3.3%
0.0 5412
54.1%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9998 
1
 
2

Length

Max length4
Median length4
Mean length3.9994
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9998
> 99.9%
1 2
 
< 0.1%

Length

2024-05-11T09:38:39.937146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:38:40.281431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9998
> 99.9%
1 2
 
< 0.1%

Interactions

2024-05-11T09:38:29.021934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:22.006046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:23.911155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:25.865281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:27.534515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:29.324140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:22.435113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:24.315540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:26.243221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:27.821430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:29.617163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:22.797782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:24.755168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:26.650718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:28.196654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:29.896604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:23.184676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:25.152549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:26.953322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:28.463032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:30.165303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:23.585797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:25.482044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:27.252357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:28.739439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:38:40.568277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TY
STDR_DE1.0000.0360.0000.0000.000
NODE_ID0.0361.0000.9850.0280.158
STTN_NO0.0000.9851.0000.0320.327
CRDNT_Y0.0000.0280.0321.0001.000
STTN_TY0.0000.1580.3271.0001.000
2024-05-11T09:38:40.943346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TYUnnamed: 7
STDR_DE1.0000.0010.0010.002-0.0021.000
NODE_ID0.0011.0000.997-0.671-0.0021.000
STTN_NO0.0010.9971.000-0.671-0.0051.000
CRDNT_Y0.002-0.671-0.6711.000-0.0021.000
STTN_TY-0.002-0.002-0.005-0.0021.0001.000
Unnamed: 71.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T09:38:30.586253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:38:30.944786image/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

STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
690752022060111400004415146목동한신청구아파트126.880350537.5407290.0<NA>
75480202207011009000331879효제초교.연동교회.김마리아활동터127.002208837.5736415.0<NA>
40002022010110900022510313금용아파트127.035177137.6377370.0<NA>
28590202203011089001109578번3동주민센터127.04660437.6261595.0<NA>
485712022040112200009623199무역센터127.062355237.5093330.0<NA>
974422022080112000013521237동아아파트105동앞126.955358937.4903440.0<NA>
248682022020112490009725532둔촌청구아파트127.134212637.5295055.0<NA>
75495202207011000004091904창덕궁126.990177637.577530.0<NA>
444682022040111500001316109등촌주공5단지아파트126.843905237.5581654.0<NA>
996162022080112300055824766오금역127.127947337.5014280.0<NA>
STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
39431202204011050000076007서울시동부병원127.030953637.5739291.0<NA>
46062022010111000039711500유원극동아파트127.046239137.6296730.0<NA>
728812022060112100010122177롯데캐슬아파트127.015535237.5196940.0<NA>
64732022010111390019914952망원역입구126.907282437.5579025.0<NA>
967952022080111800052919963공군호텔126.924128237.5100460.0<NA>
51404202205011039000934597왕약국127.065547337.5483415.0<NA>
357062022030112190011322566방배(백석예술대)역126.998094737.4809795.0<NA>
667722022060111000002311123현대성우아파트127.077795837.6274440.0<NA>
483192022040112100092922825교대역127.015724437.4940830.0<NA>
686382022060111300019614290상암DMC입구126.882612437.5834310.0<NA>