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

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 NODE_ID and 3 other fieldsHigh correlation
Unnamed: 7 is highly imbalanced (99.1%)Imbalance
CRDNT_Y is highly skewed (γ1 = 28.80610303)Skewed
STTN_TY has 5509 (55.1%) zerosZeros

Reproduction

Analysis started2024-05-11 09:38:46.241020
Analysis finished2024-05-11 09:39:00.055752
Duration13.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_DE
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210457
Minimum20210101
Maximum20210901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:39:00.285881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210101
5-th percentile20210101
Q120210301
median20210501
Q320210701
95-th percentile20210801
Maximum20210901
Range800
Interquartile range (IQR)400

Descriptive statistics

Standard deviation229.21558
Coefficient of variation (CV)1.1341435 × 10-5
Kurtosis-1.2199859
Mean20210457
Median Absolute Deviation (MAD)200
Skewness-0.020832371
Sum2.0210457 × 1011
Variance52539.782
MonotonicityNot monotonic
2024-05-11T09:39:00.781371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20210601 1297
13.0%
20210401 1278
12.8%
20210801 1266
12.7%
20210701 1264
12.6%
20210301 1244
12.4%
20210501 1223
12.2%
20210101 1208
12.1%
20210201 1192
11.9%
20210901 28
 
0.3%
ValueCountFrequency (%)
20210101 1208
12.1%
20210201 1192
11.9%
20210301 1244
12.4%
20210401 1278
12.8%
20210501 1223
12.2%
20210601 1297
13.0%
20210701 1264
12.6%
20210801 1266
12.7%
20210901 28
 
0.3%
ValueCountFrequency (%)
20210901 28
 
0.3%
20210801 1266
12.7%
20210701 1264
12.6%
20210601 1297
13.0%
20210501 1223
12.2%
20210401 1278
12.8%
20210301 1244
12.4%
20210201 1192
11.9%
20210101 1208
12.1%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct7124
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1306623 × 108
Minimum1 × 108
Maximum1.2900006 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:39:01.446997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100013 × 108
Q11.0790013 × 108
median1.1390002 × 108
Q31.1900011 × 108
95-th percentile1.2300029 × 108
Maximum1.2900006 × 108
Range29000058
Interquartile range (IQR)11099987

Descriptive statistics

Standard deviation6950825.2
Coefficient of variation (CV)0.061475699
Kurtosis-1.0973587
Mean1.1306623 × 108
Median Absolute Deviation (MAD)5999840.5
Skewness-0.17053146
Sum1.1306623 × 1012
Variance4.8313971 × 1013
MonotonicityNot monotonic
2024-05-11T09:39:02.059898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112000197 5
 
0.1%
123000319 5
 
0.1%
118000020 5
 
0.1%
109900170 5
 
0.1%
120000041 5
 
0.1%
118900174 4
 
< 0.1%
120900025 4
 
< 0.1%
109900093 4
 
< 0.1%
107000501 4
 
< 0.1%
104000041 4
 
< 0.1%
Other values (7114) 9955
99.6%
ValueCountFrequency (%)
100000001 1
 
< 0.1%
100000002 1
 
< 0.1%
100000005 2
< 0.1%
100000008 1
 
< 0.1%
100000009 2
< 0.1%
100000010 1
 
< 0.1%
100000011 3
< 0.1%
100000013 1
 
< 0.1%
100000014 3
< 0.1%
100000015 2
< 0.1%
ValueCountFrequency (%)
129000059 1
< 0.1%
129000058 2
< 0.1%
124900130 1
< 0.1%
124900129 1
< 0.1%
124900128 1
< 0.1%
124900127 1
< 0.1%
124900122 1
< 0.1%
124900121 1
< 0.1%
124900120 1
< 0.1%
124900118 1
< 0.1%

STTN_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct7117
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14197.278
Minimum1001
Maximum25999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:39:02.679268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2225.95
Q18725.5
median14551.5
Q320203.25
95-th percentile24345.05
Maximum25999
Range24998
Interquartile range (IQR)11477.75

Descriptive statistics

Standard deviation6959.3377
Coefficient of variation (CV)0.49018818
Kurtosis-1.0977785
Mean14197.278
Median Absolute Deviation (MAD)5767
Skewness-0.1662569
Sum1.4197278 × 108
Variance48432381
MonotonicityNot monotonic
2024-05-11T09:39:03.211186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21142 5
 
0.1%
13280 5
 
0.1%
19105 5
 
0.1%
10700 5
 
0.1%
24499 5
 
0.1%
15531 4
 
< 0.1%
15250 4
 
< 0.1%
4659 4
 
< 0.1%
22378 4
 
< 0.1%
14566 4
 
< 0.1%
Other values (7107) 9955
99.6%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1005 2
< 0.1%
1008 2
< 0.1%
1009 1
< 0.1%
1010 2
< 0.1%
1012 1
< 0.1%
1013 2
< 0.1%
1015 1
< 0.1%
1016 1
< 0.1%
ValueCountFrequency (%)
25999 1
 
< 0.1%
25998 2
< 0.1%
25996 1
 
< 0.1%
25990 3
< 0.1%
25989 2
< 0.1%
25988 1
 
< 0.1%
25782 2
< 0.1%
25764 1
 
< 0.1%
25763 1
 
< 0.1%
25762 1
 
< 0.1%

STTN_NM
Text

Distinct5273
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:39:03.926452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length7.5063
Min length2

Characters and Unicode

Total characters75063
Distinct characters641
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

Unique2586 ?
Unique (%)25.9%

Sample

1st row모아빌라
2nd row남강중고등학교입구
3rd row장평중학교앞
4th row버티고개
5th row양재역신한은행앞
ValueCountFrequency (%)
광화문 13
 
0.1%
경남아파트 12
 
0.1%
한신아파트 12
 
0.1%
새마을금고 12
 
0.1%
삼성역 12
 
0.1%
벽산아파트 12
 
0.1%
국민은행 11
 
0.1%
현대홈타운아파트 10
 
0.1%
개봉역 10
 
0.1%
가산디지털단지역 10
 
0.1%
Other values (5267) 9894
98.9%
2024-05-11T09:39:05.123385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2232
 
3.0%
아 2019
 
2.7%
. 2002
 
2.7%
파 1976
 
2.6%
트 1969
 
2.6%
교 1795
 
2.4%
역 1442
 
1.9%
구 1419
 
1.9%
지 1234
 
1.6%
대 1232
 
1.6%
Other values (631) 57743
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69877
93.1%
Decimal Number 2206
 
2.9%
Other Punctuation 2021
 
2.7%
Uppercase Letter 659
 
0.9%
Open Punctuation 134
 
0.2%
Close Punctuation 134
 
0.2%
Lowercase Letter 19
 
< 0.1%
Space Separator 8
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2232
 
3.2%
아 2019
 
2.9%
파 1976
 
2.8%
트 1969
 
2.8%
교 1795
 
2.6%
역 1442
 
2.1%
구 1419
 
2.0%
지 1234
 
1.8%
대 1232
 
1.8%
학 1224
 
1.8%
Other values (588) 53335
76.3%
Uppercase Letter
ValueCountFrequency (%)
T 90
13.7%
K 79
12.0%
S 79
12.0%
C 59
9.0%
A 56
8.5%
G 53
8.0%
P 42
6.4%
L 38
 
5.8%
B 34
 
5.2%
M 29
 
4.4%
Other values (12) 100
15.2%
Decimal Number
ValueCountFrequency (%)
1 626
28.4%
2 435
19.7%
3 307
13.9%
4 191
 
8.7%
5 157
 
7.1%
0 141
 
6.4%
7 114
 
5.2%
6 98
 
4.4%
9 74
 
3.4%
8 63
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 2002
99.1%
· 10
 
0.5%
& 7
 
0.3%
? 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 13
68.4%
t 3
 
15.8%
k 3
 
15.8%
Open Punctuation
ValueCountFrequency (%)
( 134
100.0%
Close Punctuation
ValueCountFrequency (%)
) 134
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69877
93.1%
Common 4508
 
6.0%
Latin 678
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2232
 
3.2%
아 2019
 
2.9%
파 1976
 
2.8%
트 1969
 
2.8%
교 1795
 
2.6%
역 1442
 
2.1%
구 1419
 
2.0%
지 1234
 
1.8%
대 1232
 
1.8%
학 1224
 
1.8%
Other values (588) 53335
76.3%
Latin
ValueCountFrequency (%)
T 90
13.3%
K 79
11.7%
S 79
11.7%
C 59
8.7%
A 56
8.3%
G 53
7.8%
P 42
 
6.2%
L 38
 
5.6%
B 34
 
5.0%
M 29
 
4.3%
Other values (15) 119
17.6%
Common
ValueCountFrequency (%)
. 2002
44.4%
1 626
 
13.9%
2 435
 
9.6%
3 307
 
6.8%
4 191
 
4.2%
5 157
 
3.5%
0 141
 
3.1%
( 134
 
3.0%
) 134
 
3.0%
7 114
 
2.5%
Other values (8) 267
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69877
93.1%
ASCII 5176
 
6.9%
None 10
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2232
 
3.2%
아 2019
 
2.9%
파 1976
 
2.8%
트 1969
 
2.8%
교 1795
 
2.6%
역 1442
 
2.1%
구 1419
 
2.0%
지 1234
 
1.8%
대 1232
 
1.8%
학 1224
 
1.8%
Other values (588) 53335
76.3%
ASCII
ValueCountFrequency (%)
. 2002
38.7%
1 626
 
12.1%
2 435
 
8.4%
3 307
 
5.9%
4 191
 
3.7%
5 157
 
3.0%
0 141
 
2.7%
( 134
 
2.6%
) 134
 
2.6%
7 114
 
2.2%
Other values (32) 935
18.1%
None
ValueCountFrequency (%)
· 10
100.0%

CRDNT_X
Text

Distinct7120
Distinct (%)71.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:39:06.143257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8197
Min length3

Characters and Unicode

Total characters108197
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4803 ?
Unique (%)48.0%

Sample

1st row127.0074921
2nd row126.9214522
3rd row127.0703444
4th row127.0060242
5th row127.0352378
ValueCountFrequency (%)
127.1257548 5
 
< 0.1%
126.9451299 5
 
< 0.1%
126.9067867 5
 
< 0.1%
금호여중 5
 
< 0.1%
127.0505137 5
 
< 0.1%
126.9461248 5
 
< 0.1%
126.8352735 4
 
< 0.1%
127.0127098 4
 
< 0.1%
127.0643171 4
 
< 0.1%
127.0315251 4
 
< 0.1%
Other values (7111) 9955
99.5%
2024-05-11T09:39:08.454041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16727
15.5%
2 16223
15.0%
6 11231
10.4%
7 10628
9.8%
. 9988
9.2%
9 9036
8.4%
0 8556
7.9%
8 7624
7.0%
3 6198
 
5.7%
5 5965
 
5.5%
Other values (25) 6021
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98151
90.7%
Other Punctuation 9988
 
9.2%
Other Letter 54
 
< 0.1%
Space Separator 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
금 5
 
9.3%
호 5
 
9.3%
여 5
 
9.3%
중 5
 
9.3%
서 5
 
9.3%
관 3
 
5.6%
역 3
 
5.6%
지 2
 
3.7%
울 2
 
3.7%
용 2
 
3.7%
Other values (13) 17
31.5%
Decimal Number
ValueCountFrequency (%)
1 16727
17.0%
2 16223
16.5%
6 11231
11.4%
7 10628
10.8%
9 9036
9.2%
0 8556
8.7%
8 7624
7.8%
3 6198
 
6.3%
5 5965
 
6.1%
4 5963
 
6.1%
Other Punctuation
ValueCountFrequency (%)
. 9988
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108143
> 99.9%
Hangul 54
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
금 5
 
9.3%
호 5
 
9.3%
여 5
 
9.3%
중 5
 
9.3%
서 5
 
9.3%
관 3
 
5.6%
역 3
 
5.6%
지 2
 
3.7%
울 2
 
3.7%
용 2
 
3.7%
Other values (13) 17
31.5%
Common
ValueCountFrequency (%)
1 16727
15.5%
2 16223
15.0%
6 11231
10.4%
7 10628
9.8%
. 9988
9.2%
9 9036
8.4%
0 8556
7.9%
8 7624
7.0%
3 6198
 
5.7%
5 5965
 
5.5%
Other values (2) 5967
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108143
> 99.9%
Hangul 54
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16727
15.5%
2 16223
15.0%
6 11231
10.4%
7 10628
9.8%
. 9988
9.2%
9 9036
8.4%
0 8556
7.9%
8 7624
7.0%
3 6198
 
5.7%
5 5965
 
5.5%
Other values (2) 5967
 
5.5%
Hangul
ValueCountFrequency (%)
금 5
 
9.3%
호 5
 
9.3%
여 5
 
9.3%
중 5
 
9.3%
서 5
 
9.3%
관 3
 
5.6%
역 3
 
5.6%
지 2
 
3.7%
울 2
 
3.7%
용 2
 
3.7%
Other values (13) 17
31.5%

CRDNT_Y
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7134
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.657689
Minimum37.430712
Maximum127.01919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:39:09.221765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.430712
5-th percentile37.471174
Q137.502813
median37.549251
Q337.590524
95-th percentile37.649733
Maximum127.01919
Range89.588477
Interquartile range (IQR)0.087710958

Descriptive statistics

Standard deviation3.0965931
Coefficient of variation (CV)0.082230035
Kurtosis828.22223
Mean37.657689
Median Absolute Deviation (MAD)0.0442443
Skewness28.806103
Sum376576.89
Variance9.5888888
MonotonicityNot monotonic
2024-05-11T09:39:09.656507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.46985299 5
 
0.1%
37.47006516 5
 
0.1%
37.51968302 5
 
0.1%
37.64558155 5
 
0.1%
37.56976518 5
 
0.1%
37.51042245 4
 
< 0.1%
37.61968904 4
 
< 0.1%
37.57925518 4
 
< 0.1%
37.48707357 4
 
< 0.1%
37.54524221 4
 
< 0.1%
Other values (7124) 9955
99.6%
ValueCountFrequency (%)
37.43071168 1
< 0.1%
37.43290817 1
< 0.1%
37.4332116 1
< 0.1%
37.43464329 1
< 0.1%
37.43479642 1
< 0.1%
37.43498304 1
< 0.1%
37.43500421 1
< 0.1%
37.43552416 2
< 0.1%
37.43686295 1
< 0.1%
37.43795943 1
< 0.1%
ValueCountFrequency (%)
127.0191882 2
< 0.1%
127.0187299 3
< 0.1%
126.981134 1
 
< 0.1%
126.980835 1
 
< 0.1%
126.9681135 1
 
< 0.1%
126.9679393 1
 
< 0.1%
126.9620346 1
 
< 0.1%
126.8806238 1
 
< 0.1%
126.8805398 1
 
< 0.1%
37.6904888 1
 
< 0.1%

STTN_TY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1173715
Minimum0
Maximum37.581273
Zeros5509
Zeros (%)55.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:39:10.091785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile5
Maximum37.581273
Range37.581273
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.7023752
Coefficient of variation (CV)1.2762877
Kurtosis33.25198
Mean2.1173715
Median Absolute Deviation (MAD)0
Skewness2.9201668
Sum21173.715
Variance7.3028319
MonotonicityNot monotonic
2024-05-11T09:39:10.533334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 5509
55.1%
5.0 3708
37.1%
1.0 300
 
3.0%
4.0 272
 
2.7%
3.0 133
 
1.3%
6.0 66
 
0.7%
37.5585545 3
 
< 0.1%
37.55792331 2
 
< 0.1%
37.58127348 1
 
< 0.1%
37.53920805 1
 
< 0.1%
Other values (5) 5
 
0.1%
ValueCountFrequency (%)
0.0 5509
55.1%
1.0 300
 
3.0%
3.0 133
 
1.3%
4.0 272
 
2.7%
5.0 3708
37.1%
6.0 66
 
0.7%
37.53920805 1
 
< 0.1%
37.55204425 1
 
< 0.1%
37.552282 1
 
< 0.1%
37.5565519 1
 
< 0.1%
ValueCountFrequency (%)
37.58127348 1
 
< 0.1%
37.58105871 1
 
< 0.1%
37.56062347 1
 
< 0.1%
37.5585545 3
 
< 0.1%
37.55792331 2
 
< 0.1%
37.5565519 1
 
< 0.1%
37.552282 1
 
< 0.1%
37.55204425 1
 
< 0.1%
37.53920805 1
 
< 0.1%
6.0 66
0.7%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9988 
0
 
10
5
 
2

Length

Max length4
Median length4
Mean length3.9964
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> 9988
99.9%
0 10
 
0.1%
5 2
 
< 0.1%

Length

2024-05-11T09:39:11.219551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:39:11.534352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9988
99.9%
0 10
 
0.1%
5 2
 
< 0.1%

Interactions

2024-05-11T09:38:57.170917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:50.060798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:52.136476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:53.772561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:55.451933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:57.610503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:50.473397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:52.465113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:54.180230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:55.830339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:58.014394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:50.953840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:52.768289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:54.487069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:56.200105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:58.314496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:51.391384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:53.054320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:54.789159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:56.491086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:58.654553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:51.804832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:53.335576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:55.120953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:38:56.813697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:39:11.742920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TYUnnamed: 7
STDR_DE1.0000.1390.1460.0000.0000.000
NODE_ID0.1391.0000.9860.1160.2030.866
STTN_NO0.1460.9861.0000.1210.3440.866
CRDNT_Y0.0000.1160.1211.0001.000NaN
STTN_TY0.0000.2030.3441.0001.000NaN
Unnamed: 70.0000.8660.866NaNNaN1.000
2024-05-11T09:39:12.088097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TYUnnamed: 7
STDR_DE1.000-0.016-0.0150.011-0.0060.000
NODE_ID-0.0161.0000.997-0.6710.0100.663
STTN_NO-0.0150.9971.000-0.6730.0090.663
CRDNT_Y0.011-0.671-0.6731.000-0.0191.000
STTN_TY-0.0060.0100.009-0.0191.0001.000
Unnamed: 70.0000.6630.6631.0001.0001.000

Missing values

2024-05-11T09:38:59.115387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:38:59.840222image/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
40848202104011089001469514모아빌라127.007492137.6381875.0<NA>
475052021040112090013521855남강중고등학교입구126.921452237.4740845.0<NA>
2134202101011050002276314장평중학교앞127.070344437.5648760.0<NA>
593202101011010001312236버티고개127.006024237.5478040.0<NA>
109242021010112100097222999양재역신한은행앞127.035237837.4829090.0<NA>
807752021070111300049414217홍대입구126.92057637.5546530.0<NA>
210872021020111800002919114영등포역126.909150937.5170870.0<NA>
442802021040111500002216118공항시장126.808927237.5622520.0<NA>
430672021040111200036813769아현역126.957277137.5575030.0<NA>
38360202104011020003063576반포대교(가상)126.993748137.519276.0<NA>
STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
448362021040111500061216895나이아가라.리버파크.골든서울호텔126.878220337.5487070.0<NA>
427612021040111200003813121이대부고126.94647637.5663180.0<NA>
78412202107011089000469828청수탕127.012654337.6368325.0<NA>
190222021020111400010515208양천노인종합복지관126.85902637.5101610.0<NA>
539362021050110990013510736창동이마트127.046170937.6513335.0<NA>
64793202106011060002597356송곡여고양원역127.105637.6054730.0<NA>
58902021010111300041214012합정역126.913997237.5496821.0<NA>
451002021040111600016217256구로역·NC신구로점126.88255737.5018270.0<NA>
930982021080111290025813976빙그레슈퍼앞126.921513137.5820275.0<NA>
960742021080111800024619337성애병원126.923091637.5115130.0<NA>