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

Categorical2
Numeric4
Text2

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
Unnamed: 7 is highly overall correlated with NODE_ID and 4 other fieldsHigh 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 imbalanced (99.6%)Imbalance
CRDNT_Y is highly skewed (γ1 = 57.61070526)Skewed
STTN_TY has 5326 (53.3%) zerosZeros

Reproduction

Analysis started2024-05-11 09:37:22.654410
Analysis finished2024-05-11 09:37:32.749971
Duration10.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_DE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20240101
2566 
20240301
2508 
20240201
2475 
20240401
2451 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20240101
2nd row20240301
3rd row20240201
4th row20240301
5th row20240201

Common Values

ValueCountFrequency (%)
20240101 2566
25.7%
20240301 2508
25.1%
20240201 2475
24.8%
20240401 2451
24.5%

Length

2024-05-11T09:37:33.100478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:37:33.573539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20240101 2566
25.7%
20240301 2508
25.1%
20240201 2475
24.8%
20240401 2451
24.5%

NODE_ID
Real number (ℝ)

HIGH CORRELATION 

Distinct7428
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1312349 × 108
Minimum1 × 108
Maximum1.2900006 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:37:34.135964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1 × 108
5-th percentile1.0100016 × 108
Q11.079001 × 108
median1.1300051 × 108
Q31.190003 × 108
95-th percentile1.2300052 × 108
Maximum1.2900006 × 108
Range29000058
Interquartile range (IQR)11100198

Descriptive statistics

Standard deviation6972108.9
Coefficient of variation (CV)0.061632725
Kurtosis-1.0956571
Mean1.1312349 × 108
Median Absolute Deviation (MAD)5999530.5
Skewness-0.1639649
Sum1.1312349 × 1012
Variance4.8610303 × 1013
MonotonicityNot monotonic
2024-05-11T09:37:34.773623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108900249 4
 
< 0.1%
101000270 4
 
< 0.1%
106000043 4
 
< 0.1%
101000053 4
 
< 0.1%
104900009 4
 
< 0.1%
107000263 4
 
< 0.1%
113900091 4
 
< 0.1%
124000010 4
 
< 0.1%
105000067 4
 
< 0.1%
116000066 4
 
< 0.1%
Other values (7418) 9960
99.6%
ValueCountFrequency (%)
100000001 1
< 0.1%
100000002 1
< 0.1%
100000003 1
< 0.1%
100000004 2
< 0.1%
100000006 1
< 0.1%
100000008 1
< 0.1%
100000010 2
< 0.1%
100000014 1
< 0.1%
100000015 1
< 0.1%
100000017 1
< 0.1%
ValueCountFrequency (%)
129000059 2
< 0.1%
124900140 1
< 0.1%
124900139 1
< 0.1%
124900138 1
< 0.1%
124900135 1
< 0.1%
124900133 2
< 0.1%
124900132 2
< 0.1%
124900130 1
< 0.1%
124900129 1
< 0.1%
124900128 1
< 0.1%

STTN_NO
Real number (ℝ)

HIGH CORRELATION 

Distinct7420
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14238.192
Minimum1001
Maximum25999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:37:35.347611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile2251.5
Q18571.5
median14531
Q320497.25
95-th percentile24433.1
Maximum25999
Range24998
Interquartile range (IQR)11925.75

Descriptive statistics

Standard deviation6980.517
Coefficient of variation (CV)0.49026708
Kurtosis-1.099196
Mean14238.192
Median Absolute Deviation (MAD)5964.5
Skewness-0.15939728
Sum1.4238192 × 108
Variance48727618
MonotonicityNot monotonic
2024-05-11T09:37:35.853021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4547 5
 
0.1%
4552 4
 
< 0.1%
15143 4
 
< 0.1%
8759 4
 
< 0.1%
3680 4
 
< 0.1%
23907 4
 
< 0.1%
4506 4
 
< 0.1%
11680 4
 
< 0.1%
7137 4
 
< 0.1%
6580 4
 
< 0.1%
Other values (7410) 9959
99.6%
ValueCountFrequency (%)
1001 1
 
< 0.1%
1002 1
 
< 0.1%
1003 1
 
< 0.1%
1004 2
< 0.1%
1006 1
 
< 0.1%
1008 2
< 0.1%
1011 1
 
< 0.1%
1013 2
< 0.1%
1016 3
< 0.1%
1017 2
< 0.1%
ValueCountFrequency (%)
25999 1
 
< 0.1%
25998 1
 
< 0.1%
25997 1
 
< 0.1%
25994 1
 
< 0.1%
25990 1
 
< 0.1%
25786 3
< 0.1%
25785 1
 
< 0.1%
25784 1
 
< 0.1%
25764 1
 
< 0.1%
25763 1
 
< 0.1%

STTN_NM
Text

Distinct5506
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:37:36.510786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length7.6399
Min length2

Characters and Unicode

Total characters76399
Distinct characters637
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

Unique2814 ?
Unique (%)28.1%

Sample

1st row마곡동로사거리
2nd row장한평역
3rd row가오리역
4th row신도림역
5th row구룡중학교앞
ValueCountFrequency (%)
현대아파트 13
 
0.1%
북서울꿈의숲 12
 
0.1%
삼성래미안아파트 12
 
0.1%
성원아파트 11
 
0.1%
한신아파트 10
 
0.1%
합정역 10
 
0.1%
디지털미디어시티역 10
 
0.1%
홍대입구역 10
 
0.1%
종로2가 9
 
0.1%
연신내역 9
 
0.1%
Other values (5497) 9895
98.9%
2024-05-11T09:37:37.783992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2191
 
2.9%
파 2125
 
2.8%
아 2098
 
2.7%
트 2045
 
2.7%
. 2012
 
2.6%
교 1765
 
2.3%
구 1500
 
2.0%
역 1465
 
1.9%
대 1317
 
1.7%
지 1259
 
1.6%
Other values (627) 58622
76.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 71039
93.0%
Decimal Number 2354
 
3.1%
Other Punctuation 2034
 
2.7%
Uppercase Letter 639
 
0.8%
Close Punctuation 151
 
0.2%
Open Punctuation 149
 
0.2%
Lowercase Letter 24
 
< 0.1%
Dash Punctuation 8
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2191
 
3.1%
파 2125
 
3.0%
아 2098
 
3.0%
트 2045
 
2.9%
교 1765
 
2.5%
구 1500
 
2.1%
역 1465
 
2.1%
대 1317
 
1.9%
지 1259
 
1.8%
학 1198
 
1.7%
Other values (582) 54076
76.1%
Uppercase Letter
ValueCountFrequency (%)
K 77
12.1%
T 76
11.9%
S 70
11.0%
C 68
10.6%
P 43
 
6.7%
M 43
 
6.7%
A 43
 
6.7%
G 39
 
6.1%
D 31
 
4.9%
B 28
 
4.4%
Other values (14) 121
18.9%
Decimal Number
ValueCountFrequency (%)
1 644
27.4%
2 514
21.8%
3 347
14.7%
4 216
 
9.2%
5 146
 
6.2%
0 136
 
5.8%
7 102
 
4.3%
6 94
 
4.0%
9 94
 
4.0%
8 61
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 2012
98.9%
· 12
 
0.6%
& 8
 
0.4%
? 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
e 22
91.7%
k 1
 
4.2%
t 1
 
4.2%
Close Punctuation
ValueCountFrequency (%)
) 151
100.0%
Open Punctuation
ValueCountFrequency (%)
( 149
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 71039
93.0%
Common 4697
 
6.1%
Latin 663
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2191
 
3.1%
파 2125
 
3.0%
아 2098
 
3.0%
트 2045
 
2.9%
교 1765
 
2.5%
구 1500
 
2.1%
역 1465
 
2.1%
대 1317
 
1.9%
지 1259
 
1.8%
학 1198
 
1.7%
Other values (582) 54076
76.1%
Latin
ValueCountFrequency (%)
K 77
11.6%
T 76
11.5%
S 70
10.6%
C 68
10.3%
P 43
 
6.5%
M 43
 
6.5%
A 43
 
6.5%
G 39
 
5.9%
D 31
 
4.7%
B 28
 
4.2%
Other values (17) 145
21.9%
Common
ValueCountFrequency (%)
. 2012
42.8%
1 644
 
13.7%
2 514
 
10.9%
3 347
 
7.4%
4 216
 
4.6%
) 151
 
3.2%
( 149
 
3.2%
5 146
 
3.1%
0 136
 
2.9%
7 102
 
2.2%
Other values (8) 280
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 71039
93.0%
ASCII 5348
 
7.0%
None 12
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2191
 
3.1%
파 2125
 
3.0%
아 2098
 
3.0%
트 2045
 
2.9%
교 1765
 
2.5%
구 1500
 
2.1%
역 1465
 
2.1%
대 1317
 
1.9%
지 1259
 
1.8%
학 1198
 
1.7%
Other values (582) 54076
76.1%
ASCII
ValueCountFrequency (%)
. 2012
37.6%
1 644
 
12.0%
2 514
 
9.6%
3 347
 
6.5%
4 216
 
4.0%
) 151
 
2.8%
( 149
 
2.8%
5 146
 
2.7%
0 136
 
2.5%
7 102
 
1.9%
Other values (34) 931
17.4%
None
ValueCountFrequency (%)
· 12
100.0%

CRDNT_X
Text

Distinct7430
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T09:37:38.769405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8323
Min length4

Characters and Unicode

Total characters108323
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

Unique5188 ?
Unique (%)51.9%

Sample

1st row126.8339922
2nd row127.0667269
3rd row127.016555
4th row126.8892229
5th row127.057243
ValueCountFrequency (%)
126.8849527 4
 
< 0.1%
127.0184028 4
 
< 0.1%
126.9973679 4
 
< 0.1%
127.088351 4
 
< 0.1%
127.0064961 4
 
< 0.1%
127.0455245 4
 
< 0.1%
126.9948588 4
 
< 0.1%
127.0331283 4
 
< 0.1%
127.0985869 4
 
< 0.1%
126.9325189 4
 
< 0.1%
Other values (7420) 9960
99.6%
2024-05-11T09:37:40.909438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16940
15.6%
2 16230
15.0%
6 11111
10.3%
7 10498
9.7%
. 9997
9.2%
9 9155
8.5%
0 8681
8.0%
8 7353
6.8%
3 6235
 
5.8%
5 6094
 
5.6%
Other values (3) 6029
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98320
90.8%
Other Punctuation 9997
 
9.2%
Other Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16940
17.2%
2 16230
16.5%
6 11111
11.3%
7 10498
10.7%
9 9155
9.3%
0 8681
8.8%
8 7353
7.5%
3 6235
 
6.3%
5 6094
 
6.2%
4 6023
 
6.1%
Other Letter
ValueCountFrequency (%)
단 3
50.0%
지 3
50.0%
Other Punctuation
ValueCountFrequency (%)
. 9997
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 108317
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16940
15.6%
2 16230
15.0%
6 11111
10.3%
7 10498
9.7%
. 9997
9.2%
9 9155
8.5%
0 8681
8.0%
8 7353
6.8%
3 6235
 
5.8%
5 6094
 
5.6%
Hangul
ValueCountFrequency (%)
단 3
50.0%
지 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 108317
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16940
15.6%
2 16230
15.0%
6 11111
10.3%
7 10498
9.7%
. 9997
9.2%
9 9155
8.5%
0 8681
8.0%
8 7353
6.8%
3 6235
 
5.8%
5 6094
 
5.6%
Hangul
ValueCountFrequency (%)
단 3
50.0%
지 3
50.0%

CRDNT_Y
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7440
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.577828
Minimum37.43052
Maximum126.81715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:37:41.460627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.43052
5-th percentile37.472954
Q137.503952
median37.550265
Q337.591187
95-th percentile37.647266
Maximum126.81715
Range89.386633
Interquartile range (IQR)0.08723522

Descriptive statistics

Standard deviation1.5469264
Coefficient of variation (CV)0.041165934
Kurtosis3321.7675
Mean37.577828
Median Absolute Deviation (MAD)0.044147975
Skewness57.610705
Sum375778.28
Variance2.3929812
MonotonicityNot monotonic
2024-05-11T09:37:42.085728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.55546657 4
 
< 0.1%
37.6002114 4
 
< 0.1%
37.54758473 4
 
< 0.1%
37.61986393 4
 
< 0.1%
37.5763719 4
 
< 0.1%
37.55177868 4
 
< 0.1%
37.55821 4
 
< 0.1%
37.56615891 4
 
< 0.1%
37.59253079 4
 
< 0.1%
37.65530988 4
 
< 0.1%
Other values (7430) 9960
99.6%
ValueCountFrequency (%)
37.43051994 2
< 0.1%
37.43094691 1
< 0.1%
37.43371743 1
< 0.1%
37.43451289 2
< 0.1%
37.43497355 1
< 0.1%
37.43500421 2
< 0.1%
37.43552416 1
< 0.1%
37.43732107 2
< 0.1%
37.43898774 2
< 0.1%
37.43948788 1
< 0.1%
ValueCountFrequency (%)
126.8171528 2
< 0.1%
126.8155483 1
< 0.1%
37.690177 1
< 0.1%
37.68987622 1
< 0.1%
37.68933105 1
< 0.1%
37.68901186 1
< 0.1%
37.688568 1
< 0.1%
37.68798832 2
< 0.1%
37.68722247 1
< 0.1%
37.68721096 1
< 0.1%

STTN_TY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1754682
Minimum0
Maximum37.560801
Zeros5326
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T09:37:42.568348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile5
Maximum37.560801
Range37.560801
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.4994011
Coefficient of variation (CV)1.1489026
Kurtosis10.054761
Mean2.1754682
Median Absolute Deviation (MAD)0
Skewness1.0972432
Sum21754.682
Variance6.2470059
MonotonicityNot monotonic
2024-05-11T09:37:43.087584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 5326
53.3%
5.0 3840
38.4%
1.0 310
 
3.1%
4.0 281
 
2.8%
3.0 144
 
1.4%
6.0 96
 
1.0%
37.56073797 2
 
< 0.1%
37.5608008 1
 
< 0.1%
ValueCountFrequency (%)
0.0 5326
53.3%
1.0 310
 
3.1%
3.0 144
 
1.4%
4.0 281
 
2.8%
5.0 3840
38.4%
6.0 96
 
1.0%
37.56073797 2
 
< 0.1%
37.5608008 1
 
< 0.1%
ValueCountFrequency (%)
37.5608008 1
 
< 0.1%
37.56073797 2
 
< 0.1%
6.0 96
 
1.0%
5.0 3840
38.4%
4.0 281
 
2.8%
3.0 144
 
1.4%
1.0 310
 
3.1%
0.0 5326
53.3%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9997 
1
 
3

Length

Max length4
Median length4
Mean length3.9991
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> 9997
> 99.9%
1 3
 
< 0.1%

Length

2024-05-11T09:37:43.714599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T09:37:44.023319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9997
> 99.9%
1 3
 
< 0.1%

Interactions

2024-05-11T09:37:30.203059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:25.448828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:27.285350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:28.785761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:30.619138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:25.940651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:27.708332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:29.151006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:31.019052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:26.306673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:28.105757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:29.462386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:31.332221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:26.601724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:28.483828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T09:37:29.826092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T09:37:44.256814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DENODE_IDSTTN_NOCRDNT_YSTTN_TY
STDR_DE1.0000.0000.0060.0000.000
NODE_ID0.0001.0000.9860.0460.160
STTN_NO0.0060.9861.0000.0500.324
CRDNT_Y0.0000.0460.0501.0001.000
STTN_TY0.0000.1600.3241.0001.000
2024-05-11T09:37:44.615524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
STDR_DEUnnamed: 7
STDR_DE1.0001.000
Unnamed: 71.0001.000
2024-05-11T09:37:45.004974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NODE_IDSTTN_NOCRDNT_YSTTN_TYSTDR_DEUnnamed: 7
NODE_ID1.0000.997-0.6730.0290.0001.000
STTN_NO0.9971.000-0.6730.0260.0031.000
CRDNT_Y-0.673-0.6731.000-0.0130.0001.000
STTN_TY0.0290.026-0.0131.0000.0001.000
STDR_DE0.0000.0030.0000.0001.0001.000
Unnamed: 71.0001.0001.0001.0001.0001.000

Missing values

2024-05-11T09:37:31.852051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T09:37:32.492147image/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
69802024010111500092316021마곡동로사거리126.833992237.5590731.0<NA>
27493202403011050003216631장한평역127.066726937.5613850.0<NA>
15978202402011080000349122가오리역127.01655537.641880.0<NA>
328942024030111600000117001신도림역126.889222937.5096761.0<NA>
238562024020112200023023334구룡중학교앞127.05724337.4858030.0<NA>
2860202401011070001778272대농빌라앞127.043213637.6016150.0<NA>
248582024020112400006125161천호역2번출구.현대백화점127.123922437.5403480.0<NA>
463392024040111790006218579가산디지털단지역126.883952837.4806535.0<NA>
121222024010112300049224715잠실역127.096996437.5126673.0<NA>
28912202403011089000659799보람미용실127.015432537.625345.0<NA>
STDR_DENODE_IDSTTN_NOSTTN_NMCRDNT_XCRDNT_YSTTN_TYUnnamed: 7
320772024030111490008115517우성아파트.월촌초등학교126.876953837.5411735.0<NA>
465262024040111800000419004남부지방법원등기국.구로세무서(에이스하이테크시티)126.899745837.514041.0<NA>
28963202403011089002379852백운교회127.010946737.6442855.0<NA>
446682024040111400029415413신목동역-서울지방식품의약품안전청126.883530437.5436430.0<NA>
95652024010111990018520745극동아파트앞126.951363737.5110735.0<NA>
446322024040111400043215358양서중학교126.830243137.5327960.0<NA>
195542024020111400043715780신정역3번출구126.857984337.5251540.0<NA>
99742024010112000021021312동작마트126.938356237.4742820.0<NA>
114172024010112200070323529도곡한신아파트127.041433637.4865460.0<NA>
352402024030112000041321346재넘이고개126.935367337.4928280.0<NA>