Overview

Dataset statistics

Number of variables10
Number of observations806
Missing cells2286
Missing cells (%)28.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.8 KiB
Average record size in memory86.2 B

Variable types

Numeric4
Text2
Categorical3
Unsupported1

Alerts

금지사유 has a high cardinality: 51 distinct valuesHigh cardinality
코드유형 is highly overall correlated with 등록일자 and 3 other fieldsHigh correlation
등록일자 is highly overall correlated with 코드유형 and 4 other fieldsHigh correlation
갱신일자 is highly overall correlated with 등록일자 and 1 other fieldsHigh correlation
계정권한코드 is highly overall correlated with 사용구분 and 1 other fieldsHigh correlation
사용구분 is highly overall correlated with 코드유형 and 4 other fieldsHigh correlation
금지사유 is highly overall correlated with 코드유형 and 4 other fieldsHigh correlation
등록아이디 is highly overall correlated with 코드유형 and 4 other fieldsHigh correlation
금지사유 is highly imbalanced (77.5%)Imbalance
갱신일자 has 690 (85.6%) missing valuesMissing
계정권한코드 has 790 (98.0%) missing valuesMissing
계정권한코드명 has 806 (100.0%) missing valuesMissing
계정권한코드명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
코드유형 has 61 (7.6%) zerosZeros

Reproduction

Analysis started2023-12-10 22:03:42.692499
Analysis finished2023-12-10 22:03:44.674507
Duration1.98 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

코드유형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.46402
Minimum0
Maximum99
Zeros61
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-11T07:03:44.737160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median36
Q355
95-th percentile77.75
Maximum99
Range99
Interquartile range (IQR)46

Descriptive statistics

Standard deviation25.169795
Coefficient of variation (CV)0.73032093
Kurtosis-1.2896516
Mean34.46402
Median Absolute Deviation (MAD)20
Skewness0.11013841
Sum27778
Variance633.51858
MonotonicityNot monotonic
2023-12-11T07:03:44.855261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 230
28.5%
0 61
 
7.6%
9 57
 
7.1%
4 51
 
6.3%
16 33
 
4.1%
77 23
 
2.9%
14 21
 
2.6%
81 21
 
2.6%
24 20
 
2.5%
32 19
 
2.4%
Other values (51) 270
33.5%
ValueCountFrequency (%)
0 61
7.6%
1 5
 
0.6%
2 13
 
1.6%
3 3
 
0.4%
4 51
6.3%
5 7
 
0.9%
6 7
 
0.9%
7 12
 
1.5%
8 16
 
2.0%
9 57
7.1%
ValueCountFrequency (%)
99 2
 
0.2%
81 21
2.6%
80 3
 
0.4%
79 6
 
0.7%
78 9
 
1.1%
77 23
2.9%
64 3
 
0.4%
63 2
 
0.2%
62 6
 
0.7%
61 2
 
0.2%

코드번호
Text

Distinct440
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-12-11T07:03:45.178028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.3573201
Min length1

Characters and Unicode

Total characters1900
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique346 ?
Unique (%)42.9%

Sample

1st row744
2nd row745
3rd row746
4th row747
5th row749
ValueCountFrequency (%)
1 22
 
2.7%
2 20
 
2.5%
01 19
 
2.4%
02 18
 
2.2%
04 15
 
1.9%
03 15
 
1.9%
3 14
 
1.7%
05 12
 
1.5%
06 11
 
1.4%
99 11
 
1.4%
Other values (424) 649
80.5%
2023-12-11T07:03:45.639763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 379
19.9%
1 324
17.1%
2 213
11.2%
3 161
8.5%
6 158
8.3%
5 152
8.0%
4 151
 
7.9%
7 136
 
7.2%
9 94
 
4.9%
8 77
 
4.1%
Other values (26) 55
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1845
97.1%
Uppercase Letter 42
 
2.2%
Lowercase Letter 13
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 7
16.7%
E 4
9.5%
S 4
9.5%
N 4
9.5%
Y 3
 
7.1%
C 3
 
7.1%
D 3
 
7.1%
B 2
 
4.8%
R 2
 
4.8%
M 2
 
4.8%
Other values (7) 8
19.0%
Decimal Number
ValueCountFrequency (%)
0 379
20.5%
1 324
17.6%
2 213
11.5%
3 161
8.7%
6 158
8.6%
5 152
8.2%
4 151
 
8.2%
7 136
 
7.4%
9 94
 
5.1%
8 77
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
a 3
23.1%
j 2
15.4%
c 2
15.4%
o 1
 
7.7%
e 1
 
7.7%
b 1
 
7.7%
m 1
 
7.7%
z 1
 
7.7%
k 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1845
97.1%
Latin 55
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 7
 
12.7%
E 4
 
7.3%
S 4
 
7.3%
N 4
 
7.3%
Y 3
 
5.5%
C 3
 
5.5%
D 3
 
5.5%
a 3
 
5.5%
B 2
 
3.6%
R 2
 
3.6%
Other values (16) 20
36.4%
Common
ValueCountFrequency (%)
0 379
20.5%
1 324
17.6%
2 213
11.5%
3 161
8.7%
6 158
8.6%
5 152
8.2%
4 151
 
8.2%
7 136
 
7.4%
9 94
 
5.1%
8 77
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 379
19.9%
1 324
17.1%
2 213
11.2%
3 161
8.5%
6 158
8.3%
5 152
8.0%
4 151
 
7.9%
7 136
 
7.2%
9 94
 
4.9%
8 77
 
4.1%
Other values (26) 55
 
2.9%

코드명
Text

Distinct722
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
2023-12-11T07:03:45.886462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length16
Mean length6.0781638
Min length2

Characters and Unicode

Total characters4899
Distinct characters362
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique678 ?
Unique (%)84.1%

Sample

1st row경산시외급행
2nd row경산예비
3rd row영천마을일반
4th row영천마을좌석
5th row고령마을일반
ValueCountFrequency (%)
인천좌석버스 15
 
1.7%
일반 14
 
1.6%
기타 12
 
1.3%
변경 11
 
1.2%
시외버스 8
 
0.9%
좌석 6
 
0.7%
마산/창원/진해 5
 
0.6%
노선 5
 
0.6%
업체 5
 
0.6%
신고 4
 
0.4%
Other values (734) 817
90.6%
2023-12-11T07:03:46.299099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
196
 
4.0%
스 181
 
3.7%
버 173
 
3.5%
시 150
 
3.1%
( 92
 
1.9%
) 92
 
1.9%
기 75
 
1.5%
차 75
 
1.5%
좌 74
 
1.5%
석 73
 
1.5%
Other values (352) 3718
75.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4093
83.5%
Decimal Number 225
 
4.6%
Space Separator 196
 
4.0%
Uppercase Letter 98
 
2.0%
Open Punctuation 92
 
1.9%
Close Punctuation 92
 
1.9%
Other Punctuation 64
 
1.3%
Dash Punctuation 21
 
0.4%
Lowercase Letter 9
 
0.2%
Connector Punctuation 8
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 181
 
4.4%
버 173
 
4.2%
시 150
 
3.7%
기 75
 
1.8%
차 75
 
1.8%
좌 74
 
1.8%
석 73
 
1.8%
일 69
 
1.7%
행 68
 
1.7%
외 66
 
1.6%
Other values (308) 3089
75.5%
Uppercase Letter
ValueCountFrequency (%)
S 11
11.2%
T 10
10.2%
E 9
 
9.2%
D 7
 
7.1%
G 7
 
7.1%
A 7
 
7.1%
C 7
 
7.1%
I 6
 
6.1%
R 5
 
5.1%
B 5
 
5.1%
Other values (8) 24
24.5%
Decimal Number
ValueCountFrequency (%)
0 70
31.1%
1 39
17.3%
2 27
 
12.0%
6 25
 
11.1%
8 15
 
6.7%
5 13
 
5.8%
3 12
 
5.3%
9 10
 
4.4%
4 8
 
3.6%
7 6
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
e 2
22.2%
c 2
22.2%
t 2
22.2%
a 1
11.1%
h 1
11.1%
o 1
11.1%
Other Punctuation
ValueCountFrequency (%)
/ 31
48.4%
, 27
42.2%
. 5
 
7.8%
: 1
 
1.6%
Space Separator
ValueCountFrequency (%)
196
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4093
83.5%
Common 699
 
14.3%
Latin 107
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 181
 
4.4%
버 173
 
4.2%
시 150
 
3.7%
기 75
 
1.8%
차 75
 
1.8%
좌 74
 
1.8%
석 73
 
1.8%
일 69
 
1.7%
행 68
 
1.7%
외 66
 
1.6%
Other values (308) 3089
75.5%
Latin
ValueCountFrequency (%)
S 11
 
10.3%
T 10
 
9.3%
E 9
 
8.4%
D 7
 
6.5%
G 7
 
6.5%
A 7
 
6.5%
C 7
 
6.5%
I 6
 
5.6%
R 5
 
4.7%
B 5
 
4.7%
Other values (14) 33
30.8%
Common
ValueCountFrequency (%)
196
28.0%
( 92
13.2%
) 92
13.2%
0 70
 
10.0%
1 39
 
5.6%
/ 31
 
4.4%
, 27
 
3.9%
2 27
 
3.9%
6 25
 
3.6%
- 21
 
3.0%
Other values (10) 79
11.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4093
83.5%
ASCII 806
 
16.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
196
24.3%
( 92
11.4%
) 92
11.4%
0 70
 
8.7%
1 39
 
4.8%
/ 31
 
3.8%
, 27
 
3.3%
2 27
 
3.3%
6 25
 
3.1%
- 21
 
2.6%
Other values (34) 186
23.1%
Hangul
ValueCountFrequency (%)
스 181
 
4.4%
버 173
 
4.2%
시 150
 
3.7%
기 75
 
1.8%
차 75
 
1.8%
좌 74
 
1.8%
석 73
 
1.8%
일 69
 
1.7%
행 68
 
1.7%
외 66
 
1.6%
Other values (308) 3089
75.5%

사용구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
1
563 
0
243 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 563
69.9%
0 243
30.1%

Length

2023-12-11T07:03:46.410530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:46.496722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 563
69.9%
0 243
30.1%

금지사유
Categorical

HIGH CARDINALITY  HIGH CORRELATION  IMBALANCE 

Distinct51
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
<NA>
695 
서울버스
 
16
지하철
 
10
노선체계개편
 
8
2019년도 요금 조정
 
4
Other values (46)
73 

Length

Max length18
Median length4
Mean length4.471464
Min length2

Unique

Unique30 ?
Unique (%)3.7%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 695
86.2%
서울버스 16
 
2.0%
지하철 10
 
1.2%
노선체계개편 8
 
1.0%
2019년도 요금 조정 4
 
0.5%
서울/경인택시 4
 
0.5%
운영체계신규코드 3
 
0.4%
목포버스(마이비영역) 3
 
0.4%
사천버스(마이비영역) 3
 
0.4%
KG버스연계추가 3
 
0.4%
Other values (41) 57
 
7.1%

Length

2023-12-11T07:03:46.599750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 695
82.8%
서울버스 16
 
1.9%
지하철 10
 
1.2%
노선체계개편 8
 
1.0%
요금 5
 
0.6%
2019년도 4
 
0.5%
조정 4
 
0.5%
서울/경인택시 4
 
0.5%
광양버스(마이비영역 3
 
0.4%
업체 3
 
0.4%
Other values (57) 87
 
10.4%

등록일자
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)23.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0098086 × 1013
Minimum2.0070613 × 1013
Maximum2.0220608 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-11T07:03:46.717070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0070613 × 1013
5-th percentile2.0070613 × 1013
Q12.0070613 × 1013
median2.0091218 × 1013
Q32.0091218 × 1013
95-th percentile2.0190923 × 1013
Maximum2.0220608 × 1013
Range1.4999501 × 1011
Interquartile range (IQR)2.0604966 × 1010

Descriptive statistics

Standard deviation3.8844188 × 1010
Coefficient of variation (CV)0.0019327307
Kurtosis1.469241
Mean2.0098086 × 1013
Median Absolute Deviation (MAD)2.0397945 × 1010
Skewness1.6571945
Sum1.6199058 × 1016
Variance1.5088709 × 1021
MonotonicityNot monotonic
2023-12-11T07:03:47.176997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20091218120101 269
33.4%
20070613154423 171
21.2%
20070613154428 33
 
4.1%
20070613154422 30
 
3.7%
20190923131421 30
 
3.7%
20070821110619 29
 
3.6%
20100127165212 11
 
1.4%
20160405000000 10
 
1.2%
20100127165213 8
 
1.0%
20160417000000 7
 
0.9%
Other values (179) 208
25.8%
ValueCountFrequency (%)
20070613154422 30
 
3.7%
20070613154423 171
21.2%
20070613154427 6
 
0.7%
20070613154428 33
 
4.1%
20070613154635 1
 
0.1%
20070615131549 1
 
0.1%
20070712000000 1
 
0.1%
20070713000000 3
 
0.4%
20070721140536 1
 
0.1%
20070721140615 1
 
0.1%
ValueCountFrequency (%)
20220608164337 1
0.1%
20220608164324 1
0.1%
20220608164315 1
0.1%
20220608164220 1
0.1%
20220608164201 1
0.1%
20220608163933 1
0.1%
20220608163906 1
0.1%
20220225111406 1
0.1%
20220225111322 1
0.1%
20220117135125 1
0.1%

등록아이디
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size6.4 KiB
000000
273 
thsckdejr
258 
00000000
173 
ADMIN
33 
ckdejr12
32 
Other values (2)
37 

Length

Max length10
Median length9
Mean length7.3883375
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row000000
2nd row000000
3rd row000000
4th row000000
5th row000000

Common Values

ValueCountFrequency (%)
000000 273
33.9%
thsckdejr 258
32.0%
00000000 173
21.5%
ADMIN 33
 
4.1%
ckdejr12 32
 
4.0%
CORE 30
 
3.7%
0000000000 7
 
0.9%

Length

2023-12-11T07:03:47.358987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:03:47.502168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
000000 273
33.9%
thsckdejr 258
32.0%
00000000 173
21.5%
admin 33
 
4.1%
ckdejr12 32
 
4.0%
core 30
 
3.7%
0000000000 7
 
0.9%

갱신일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)99.1%
Missing690
Missing (%)85.6%
Infinite0
Infinite (%)0.0%
Mean2.0137619 × 1013
Minimum2.0070619 × 1013
Maximum2.0220608 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-11T07:03:47.674680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0070619 × 1013
5-th percentile2.0070795 × 1013
Q12.0080415 × 1013
median2.0160508 × 1013
Q32.0191014 × 1013
95-th percentile2.0220608 × 1013
Maximum2.0220608 × 1013
Range1.4998907 × 1011
Interquartile range (IQR)1.1059904 × 1011

Descriptive statistics

Standard deviation5.6861681 × 1010
Coefficient of variation (CV)0.0028236546
Kurtosis-1.6163237
Mean2.0137619 × 1013
Median Absolute Deviation (MAD)6.0100058 × 1010
Skewness0.065458297
Sum2.3359638 × 1015
Variance3.2332508 × 1021
MonotonicityNot monotonic
2023-12-11T07:03:47.832322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211215140704 2
 
0.2%
20070821121317 1
 
0.1%
20081217164515 1
 
0.1%
20081215195909 1
 
0.1%
20081215195827 1
 
0.1%
20081215195807 1
 
0.1%
20081215195744 1
 
0.1%
20081215195423 1
 
0.1%
20081215195407 1
 
0.1%
20200225174521 1
 
0.1%
Other values (105) 105
 
13.0%
(Missing) 690
85.6%
ValueCountFrequency (%)
20070619100927 1
0.1%
20070621102746 1
0.1%
20070621102754 1
0.1%
20070621102759 1
0.1%
20070723152000 1
0.1%
20070723152012 1
0.1%
20070819150335 1
0.1%
20070819150350 1
0.1%
20070819150404 1
0.1%
20070821121307 1
0.1%
ValueCountFrequency (%)
20220608173904 1
0.1%
20220608173846 1
0.1%
20220608173553 1
0.1%
20220608173537 1
0.1%
20220608173454 1
0.1%
20220608173431 1
0.1%
20220608173358 1
0.1%
20220608173321 1
0.1%
20220608173239 1
0.1%
20220608173153 1
0.1%

계정권한코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)93.8%
Missing790
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean7.4375
Minimum0
Maximum14
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2023-12-11T07:03:47.962536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q13.75
median7.5
Q311.25
95-th percentile14
Maximum14
Range14
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.6614554
Coefficient of variation (CV)0.62675031
Kurtosis-1.2759977
Mean7.4375
Median Absolute Deviation (MAD)4
Skewness-0.064789512
Sum119
Variance21.729167
MonotonicityNot monotonic
2023-12-11T07:03:48.065477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14 2
 
0.2%
0 1
 
0.1%
6 1
 
0.1%
8 1
 
0.1%
5 1
 
0.1%
11 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
7 1
 
0.1%
10 1
 
0.1%
Other values (5) 5
 
0.6%
(Missing) 790
98.0%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
14 2
0.2%
13 1
0.1%
12 1
0.1%
11 1
0.1%
10 1
0.1%
9 1
0.1%
8 1
0.1%
7 1
0.1%
6 1
0.1%
5 1
0.1%

계정권한코드명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing806
Missing (%)100.0%
Memory size7.2 KiB

Interactions

2023-12-11T07:03:44.036088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.135330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.423626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.737401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:44.139460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.203315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.501294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.807775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:44.259385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.279758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.581648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.886544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:44.333709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.352441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.658668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:03:43.964688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:03:48.165772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드유형사용구분금지사유등록일자등록아이디갱신일자계정권한코드
코드유형1.0000.8880.9960.7960.8620.838NaN
사용구분0.8881.0000.9510.6570.6750.0000.967
금지사유0.9960.9511.0000.9770.9991.0000.970
등록일자0.7960.6570.9771.0000.8180.9380.000
등록아이디0.8620.6750.9990.8181.0000.8291.000
갱신일자0.8380.0001.0000.9380.8291.0000.290
계정권한코드NaN0.9670.9700.0001.0000.2901.000
2023-12-11T07:03:48.301416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용구분금지사유등록아이디
사용구분1.0000.6320.727
금지사유0.6321.0000.727
등록아이디0.7270.7271.000
2023-12-11T07:03:48.402129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드유형등록일자갱신일자계정권한코드사용구분금지사유등록아이디
코드유형1.0000.6890.327NaN0.7200.7330.673
등록일자0.6891.0000.6780.4110.6560.6510.621
갱신일자0.3270.6781.0000.4970.0000.6860.492
계정권한코드NaN0.4110.4971.0000.5410.3780.655
사용구분0.7200.6560.0000.5411.0000.6320.727
금지사유0.7330.6510.6860.3780.6321.0000.727
등록아이디0.6730.6210.4920.6550.7270.7271.000

Missing values

2023-12-11T07:03:44.432524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:03:44.546704image/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.
2023-12-11T07:03:44.632935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

코드유형코드번호코드명사용구분금지사유등록일자등록아이디갱신일자계정권한코드계정권한코드명
055744경산시외급행0<NA>20091218120101000000<NA><NA><NA>
155745경산예비0<NA>20091218120101000000<NA><NA><NA>
255746영천마을일반0<NA>20091218120101000000<NA><NA><NA>
355747영천마을좌석0<NA>20091218120101000000<NA><NA><NA>
455749고령마을일반0<NA>20091218120101000000<NA><NA><NA>
555750고령마을좌석0<NA>20091218120101000000<NA><NA><NA>
655752상주시내일반0<NA>20091218120101000000<NA><NA><NA>
755753상주시내좌석0<NA>20091218120101000000<NA><NA><NA>
855754상주시외좌석0<NA>20091218120101000000<NA><NA><NA>
955756청도시내일반0<NA>20091218120101000000<NA><NA><NA>
코드유형코드번호코드명사용구분금지사유등록일자등록아이디갱신일자계정권한코드계정권한코드명
79655530경기좌석(일반좌석)1<NA>20091218120101000000<NA><NA><NA>
79755531경기좌석(사용안함)0<NA>20091218120101000000<NA><NA><NA>
79855532경기좌석(사용안함)0<NA>20091218120101000000<NA><NA><NA>
79955533경기좌석(직행좌석)1<NA>20091218120101000000<NA><NA><NA>
80055580경기마을버스(700)1<NA>20091218120101000000<NA><NA><NA>
80155581경기마을버스(800)1<NA>20091218120101000000<NA><NA><NA>
80255582경기마을버스(900)1<NA>20091218120101000000<NA><NA><NA>
80355599경기시외버스1<NA>20091218120101000000<NA><NA><NA>
80455601부산일반버스0<NA>20091218120101000000<NA><NA><NA>
80555602부산좌석버스0<NA>20091218120101000000<NA><NA><NA>