Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells12
Missing cells (%)1.2%
Duplicate rows1
Duplicate rows (%)1.0%
Total size in memory8.3 KiB
Average record size in memory85.3 B

Variable types

Text3
Categorical4
Numeric2
DateTime1

Alerts

base_ymd has constant value ""Constant
Dataset has 1 (1.0%) duplicate rowsDuplicates
area_nm is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
city_gn_gu_cd is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
city_do_cd is highly overall correlated with xpos_lo and 3 other fieldsHigh correlation
xpos_lo is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
ypos_la is highly overall correlated with city_do_cd and 2 other fieldsHigh correlation
city_do_cd is highly imbalanced (69.1%)Imbalance
city_gn_gu_cd is highly imbalanced (72.2%)Imbalance
area_nm is highly imbalanced (69.1%)Imbalance
tel_no has 12 (12.0%) missing valuesMissing

Reproduction

Analysis started2023-12-10 10:15:01.475378
Analysis finished2023-12-10 10:15:04.444286
Duration2.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

entrp_nm
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:15:04.799442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length12.4
Min length7

Characters and Unicode

Total characters1240
Distinct characters120
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)91.0%

Sample

1st rowNH농협은행 어진동지점
2nd row신한은행 태백지점
3rd rowKB국민은행 가평지점
4th rowNH농협은행 가평군청출장소
5th rowKB국민은행 가평지점
ValueCountFrequency (%)
우리은행 22
 
11.5%
kb국민은행 18
 
9.4%
신한은행 17
 
8.9%
keb하나은행 13
 
6.8%
nh농협은행 10
 
5.2%
ibk기업은행 8
 
4.2%
우리은행365 5
 
2.6%
신한은행365 3
 
1.6%
대치역지점 3
 
1.6%
대치동지점 3
 
1.6%
Other values (79) 90
46.9%
2023-12-10T19:15:05.610644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
은 100
 
8.1%
행 100
 
8.1%
92
 
7.4%
점 66
 
5.3%
지 61
 
4.9%
B 44
 
3.5%
K 43
 
3.5%
역 37
 
3.0%
동 31
 
2.5%
리 29
 
2.3%
Other values (110) 637
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 963
77.7%
Uppercase Letter 145
 
11.7%
Space Separator 92
 
7.4%
Decimal Number 36
 
2.9%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
은 100
 
10.4%
행 100
 
10.4%
점 66
 
6.9%
지 61
 
6.3%
역 37
 
3.8%
동 31
 
3.2%
리 29
 
3.0%
우 27
 
2.8%
신 27
 
2.8%
한 21
 
2.2%
Other values (91) 464
48.2%
Uppercase Letter
ValueCountFrequency (%)
B 44
30.3%
K 43
29.7%
E 13
 
9.0%
H 11
 
7.6%
I 10
 
6.9%
N 10
 
6.9%
S 4
 
2.8%
G 3
 
2.1%
T 2
 
1.4%
C 2
 
1.4%
Other values (3) 3
 
2.1%
Decimal Number
ValueCountFrequency (%)
3 12
33.3%
5 12
33.3%
6 12
33.3%
Space Separator
ValueCountFrequency (%)
92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 963
77.7%
Latin 145
 
11.7%
Common 132
 
10.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
은 100
 
10.4%
행 100
 
10.4%
점 66
 
6.9%
지 61
 
6.3%
역 37
 
3.8%
동 31
 
3.2%
리 29
 
3.0%
우 27
 
2.8%
신 27
 
2.8%
한 21
 
2.2%
Other values (91) 464
48.2%
Latin
ValueCountFrequency (%)
B 44
30.3%
K 43
29.7%
E 13
 
9.0%
H 11
 
7.6%
I 10
 
6.9%
N 10
 
6.9%
S 4
 
2.8%
G 3
 
2.1%
T 2
 
1.4%
C 2
 
1.4%
Other values (3) 3
 
2.1%
Common
ValueCountFrequency (%)
92
69.7%
3 12
 
9.1%
5 12
 
9.1%
6 12
 
9.1%
) 2
 
1.5%
( 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 963
77.7%
ASCII 277
 
22.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
은 100
 
10.4%
행 100
 
10.4%
점 66
 
6.9%
지 61
 
6.3%
역 37
 
3.8%
동 31
 
3.2%
리 29
 
3.0%
우 27
 
2.8%
신 27
 
2.8%
한 21
 
2.2%
Other values (91) 464
48.2%
ASCII
ValueCountFrequency (%)
92
33.2%
B 44
15.9%
K 43
15.5%
E 13
 
4.7%
3 12
 
4.3%
5 12
 
4.3%
6 12
 
4.3%
H 11
 
4.0%
I 10
 
3.6%
N 10
 
3.6%
Other values (9) 18
 
6.5%

load_addr
Text

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:15:06.234582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length26
Mean length18.13
Min length10

Characters and Unicode

Total characters1813
Distinct characters146
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)86.0%

Sample

1st row세종특별자치시 가름로 238-1 농협은행 어진동지점
2nd row강원 태백시 황지로 188-1
3rd row경기 가평군 가평읍 석봉로 167
4th row경기 가평군 가평읍 석봉로 181
5th row경기 가평군 가평읍 신용길 3 플러스마트
ValueCountFrequency (%)
서울 90
19.6%
강남구 90
19.6%
강남대로 21
 
4.6%
논현로 17
 
3.7%
남부순환로 11
 
2.4%
도산대로 11
 
2.4%
도곡로 9
 
2.0%
개포로 8
 
1.7%
가평군 5
 
1.1%
경기 5
 
1.1%
Other values (159) 192
41.8%
2023-12-10T19:15:06.945926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
359
19.8%
남 126
 
6.9%
강 116
 
6.4%
구 93
 
5.1%
서 92
 
5.1%
울 90
 
5.0%
로 88
 
4.9%
1 46
 
2.5%
2 41
 
2.3%
8 39
 
2.2%
Other values (136) 723
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1136
62.7%
Space Separator 359
 
19.8%
Decimal Number 297
 
16.4%
Uppercase Letter 18
 
1.0%
Dash Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 126
 
11.1%
강 116
 
10.2%
구 93
 
8.2%
서 92
 
8.1%
울 90
 
7.9%
로 88
 
7.7%
대 38
 
3.3%
빌 27
 
2.4%
딩 27
 
2.4%
도 21
 
1.8%
Other values (113) 418
36.8%
Uppercase Letter
ValueCountFrequency (%)
S 5
27.8%
G 4
22.2%
M 1
 
5.6%
H 1
 
5.6%
I 1
 
5.6%
D 1
 
5.6%
T 1
 
5.6%
O 1
 
5.6%
W 1
 
5.6%
E 1
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 46
15.5%
2 41
13.8%
8 39
13.1%
3 37
12.5%
0 32
10.8%
4 27
9.1%
6 26
8.8%
5 21
7.1%
9 15
 
5.1%
7 13
 
4.4%
Space Separator
ValueCountFrequency (%)
359
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1136
62.7%
Common 659
36.3%
Latin 18
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 126
 
11.1%
강 116
 
10.2%
구 93
 
8.2%
서 92
 
8.1%
울 90
 
7.9%
로 88
 
7.7%
대 38
 
3.3%
빌 27
 
2.4%
딩 27
 
2.4%
도 21
 
1.8%
Other values (113) 418
36.8%
Common
ValueCountFrequency (%)
359
54.5%
1 46
 
7.0%
2 41
 
6.2%
8 39
 
5.9%
3 37
 
5.6%
0 32
 
4.9%
4 27
 
4.1%
6 26
 
3.9%
5 21
 
3.2%
9 15
 
2.3%
Other values (2) 16
 
2.4%
Latin
ValueCountFrequency (%)
S 5
27.8%
G 4
22.2%
M 1
 
5.6%
H 1
 
5.6%
I 1
 
5.6%
D 1
 
5.6%
T 1
 
5.6%
O 1
 
5.6%
W 1
 
5.6%
E 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1136
62.7%
ASCII 677
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
359
53.0%
1 46
 
6.8%
2 41
 
6.1%
8 39
 
5.8%
3 37
 
5.5%
0 32
 
4.7%
4 27
 
4.0%
6 26
 
3.8%
5 21
 
3.1%
9 15
 
2.2%
Other values (13) 34
 
5.0%
Hangul
ValueCountFrequency (%)
남 126
 
11.1%
강 116
 
10.2%
구 93
 
8.2%
서 92
 
8.1%
울 90
 
7.9%
로 88
 
7.7%
대 38
 
3.3%
빌 27
 
2.4%
딩 27
 
2.4%
도 21
 
1.8%
Other values (113) 418
36.8%

city_do_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
90 
41
 
5
42
 
3
<NA>
 
2

Length

Max length4
Median length2
Mean length2.04
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row42
3rd row41
4th row41
5th row41

Common Values

ValueCountFrequency (%)
11 90
90.0%
41 5
 
5.0%
42 3
 
3.0%
<NA> 2
 
2.0%

Length

2023-12-10T19:15:07.161306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:07.334280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 90
90.0%
41 5
 
5.0%
42 3
 
3.0%
na 2
 
2.0%

city_gn_gu_cd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11680
90 
41820
 
5
<NA>
 
2
42760
 
2
42190
 
1

Length

Max length5
Median length5
Mean length4.98
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row<NA>
2nd row42190
3rd row41820
4th row41820
5th row41820

Common Values

ValueCountFrequency (%)
11680 90
90.0%
41820 5
 
5.0%
<NA> 2
 
2.0%
42760 2
 
2.0%
42190 1
 
1.0%

Length

2023-12-10T19:15:07.612246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:07.978610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11680 90
90.0%
41820 5
 
5.0%
na 2
 
2.0%
42760 2
 
2.0%
42190 1
 
1.0%

xpos_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11851
Minimum127.01982
Maximum128.9898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:08.184307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01982
5-th percentile127.02152
Q1127.03118
median127.03905
Q3127.06272
95-th percentile127.51587
Maximum128.9898
Range1.969979
Interquartile range (IQR)0.0315445

Descriptive statistics

Standard deviation0.29021033
Coefficient of variation (CV)0.0022829904
Kurtosis24.683669
Mean127.11851
Median Absolute Deviation (MAD)0.0111965
Skewness4.7946973
Sum12711.851
Variance0.084222036
MonotonicityNot monotonic
2023-12-10T19:15:08.405696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.037619 5
 
5.0%
127.06334 4
 
4.0%
127.058535 2
 
2.0%
127.030905 2
 
2.0%
127.028417 2
 
2.0%
127.031561 2
 
2.0%
127.020169 2
 
2.0%
127.031713 2
 
2.0%
127.053029 2
 
2.0%
127.038088 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
127.019817 1
1.0%
127.020169 2
2.0%
127.020873 1
1.0%
127.021046 1
1.0%
127.021549 1
1.0%
127.021897 1
1.0%
127.02207 1
1.0%
127.023666 1
1.0%
127.024714 1
1.0%
127.025084 1
1.0%
ValueCountFrequency (%)
128.989796 1
1.0%
128.470943 1
1.0%
128.390253 1
1.0%
127.546498 1
1.0%
127.517527 1
1.0%
127.515785 1
1.0%
127.510479 1
1.0%
127.509801 1
1.0%
127.265994 1
1.0%
127.264761 1
1.0%

ypos_la
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.492045
Minimum36.495189
Maximum37.839816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:15:08.708392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.495189
5-th percentile37.478608
Q137.491333
median37.497549
Q337.513859
95-th percentile37.613187
Maximum37.839816
Range1.344627
Interquartile range (IQR)0.02252625

Descriptive statistics

Standard deviation0.16323026
Coefficient of variation (CV)0.0043537304
Kurtosis27.519432
Mean37.492045
Median Absolute Deviation (MAD)0.0087395
Skewness-4.3449985
Sum3749.2045
Variance0.026644116
MonotonicityNot monotonic
2023-12-10T19:15:09.001981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.502141 5
 
5.0%
37.499194 4
 
4.0%
37.492758 2
 
2.0%
37.521017 2
 
2.0%
37.497452 2
 
2.0%
37.491333 2
 
2.0%
37.515874 2
 
2.0%
37.51368 2
 
2.0%
37.489083 2
 
2.0%
37.497324 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
36.495189 1
1.0%
36.499291 1
1.0%
37.174978 1
1.0%
37.370938 1
1.0%
37.478576 1
1.0%
37.47861 1
1.0%
37.478716 1
1.0%
37.480284 1
1.0%
37.480919 1
1.0%
37.48399 1
1.0%
ValueCountFrequency (%)
37.839816 1
1.0%
37.831287 1
1.0%
37.830055 1
1.0%
37.8265 1
1.0%
37.708028 1
1.0%
37.608195 1
1.0%
37.527101 1
1.0%
37.526101 1
1.0%
37.524054 1
1.0%
37.523601 1
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울
90 
경기
 
5
강원
 
3
세종
 
2

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row세종
2nd row강원
3rd row경기
4th row경기
5th row경기

Common Values

ValueCountFrequency (%)
서울 90
90.0%
경기 5
 
5.0%
강원 3
 
3.0%
세종 2
 
2.0%

Length

2023-12-10T19:15:09.239750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:09.425230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 90
90.0%
경기 5
 
5.0%
강원 3
 
3.0%
세종 2
 
2.0%

tel_no
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing12
Missing (%)12.0%
Memory size932.0 B
2023-12-10T19:15:09.902828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length11.511364
Min length10

Characters and Unicode

Total characters1013
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique88 ?
Unique (%)100.0%

Sample

1st row044-862-2504
2nd row033-552-3101
3rd row031-582-4687
4th row031-582-9086
5th row031-582-4686
ValueCountFrequency (%)
02-501-1639 1
 
1.1%
02-538-1111 1
 
1.1%
02-547-0413 1
 
1.1%
02-540-6400 1
 
1.1%
02-511-7151 1
 
1.1%
02-3443-3600 1
 
1.1%
02-546 1
 
1.1%
02-546-3501 1
 
1.1%
02-511-7596 1
 
1.1%
02-554-8002 1
 
1.1%
Other values (80) 80
88.9%
2023-12-10T19:15:10.674399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 179
17.7%
0 158
15.6%
2 122
12.0%
5 116
11.5%
1 95
9.4%
4 92
9.1%
3 79
7.8%
6 57
 
5.6%
8 41
 
4.0%
9 37
 
3.7%
Other values (3) 37
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 830
81.9%
Dash Punctuation 179
 
17.7%
Other Punctuation 2
 
0.2%
Space Separator 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
19.0%
2 122
14.7%
5 116
14.0%
1 95
11.4%
4 92
11.1%
3 79
9.5%
6 57
 
6.9%
8 41
 
4.9%
9 37
 
4.5%
7 33
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1013
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 179
17.7%
0 158
15.6%
2 122
12.0%
5 116
11.5%
1 95
9.4%
4 92
9.1%
3 79
7.8%
6 57
 
5.6%
8 41
 
4.0%
9 37
 
3.7%
Other values (3) 37
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 179
17.7%
0 158
15.6%
2 122
12.0%
5 116
11.5%
1 95
9.4%
4 92
9.1%
3 79
7.8%
6 57
 
5.6%
8 41
 
4.0%
9 37
 
3.7%
Other values (3) 37
 
3.7%

homepage_url
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
www.wooribank.com/
27 
www.shinhan.com/
20 
obank.kbstar.com/
20 
https://www.kebhana.com/
13 
banking.nonghyup.com/
10 

Length

Max length24
Median length21
Mean length18.88
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbanking.nonghyup.com/
2nd rowwww.shinhan.com/
3rd rowobank.kbstar.com/
4th rowbanking.nonghyup.com/
5th rowobank.kbstar.com/

Common Values

ValueCountFrequency (%)
www.wooribank.com/ 27
27.0%
www.shinhan.com/ 20
20.0%
obank.kbstar.com/ 20
20.0%
https://www.kebhana.com/ 13
13.0%
banking.nonghyup.com/ 10
 
10.0%
https://www.ibk.co.kr/ 10
 
10.0%

Length

2023-12-10T19:15:10.992355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:15:11.242850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
www.wooribank.com 27
27.0%
www.shinhan.com 20
20.0%
obank.kbstar.com 20
20.0%
https://www.kebhana.com 13
13.0%
banking.nonghyup.com 10
 
10.0%
https://www.ibk.co.kr 10
 
10.0%

base_ymd
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-31 00:00:00
Maximum2020-12-31 00:00:00
2023-12-10T19:15:11.453292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:11.626898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-10T19:15:03.574242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:03.192783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:03.757322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:15:03.382211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:15:11.767814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmtel_nohomepage_url
entrp_nm1.0000.9950.6270.8860.9670.9670.8811.0001.000
load_addr0.9951.0001.0001.0001.0001.0001.0001.0000.949
city_do_cd0.6271.0001.0001.0001.0001.0001.0001.0000.369
city_gn_gu_cd0.8861.0001.0001.0001.0001.0001.0001.0000.242
xpos_lo0.9671.0001.0001.0001.0001.0001.0001.0000.554
ypos_la0.9671.0001.0001.0001.0001.0001.0001.0000.554
area_nm0.8811.0001.0001.0001.0001.0001.0001.0000.377
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.000
homepage_url1.0000.9490.3690.2420.5540.5540.3771.0001.000
2023-12-10T19:15:12.338848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
area_nmcity_gn_gu_cdcity_do_cdhomepage_url
area_nm1.0000.9951.0000.248
city_gn_gu_cd0.9951.0000.9950.155
city_do_cd1.0000.9951.0000.160
homepage_url0.2480.1550.1601.000
2023-12-10T19:15:12.493571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
xpos_loypos_lacity_do_cdcity_gn_gu_cdarea_nmhomepage_url
xpos_lo1.000-0.3360.9890.9950.9900.227
ypos_la-0.3361.0000.9890.9950.9900.227
city_do_cd0.9890.9891.0000.9951.0000.160
city_gn_gu_cd0.9950.9950.9951.0000.9950.155
area_nm0.9900.9901.0000.9951.0000.248
homepage_url0.2270.2270.1600.1550.2481.000

Missing values

2023-12-10T19:15:04.024880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:15:04.341394image/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

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmtel_nohomepage_urlbase_ymd
0NH농협은행 어진동지점세종특별자치시 가름로 238-1 농협은행 어진동지점<NA><NA>127.26599436.495189세종044-862-2504banking.nonghyup.com/2020-12-31
1신한은행 태백지점강원 태백시 황지로 188-14242190128.98979637.174978강원033-552-3101www.shinhan.com/2020-12-31
2KB국민은행 가평지점경기 가평군 가평읍 석봉로 1674141820127.51047937.830055경기031-582-4687obank.kbstar.com/2020-12-31
3NH농협은행 가평군청출장소경기 가평군 가평읍 석봉로 1814141820127.50980137.831287경기031-582-9086banking.nonghyup.com/2020-12-31
4KB국민은행 가평지점경기 가평군 가평읍 신용길 3 플러스마트4141820127.51578537.8265경기031-582-4686obank.kbstar.com/2020-12-31
5KB국민은행365경기 가평군 가평읍 읍내리4141820127.51752737.839816경기<NA>obank.kbstar.com/2020-12-31
6가평휴게소(춘천방향)IBK기업은행365경기 가평군 설악면 미사리4141820127.54649837.708028경기<NA>https://www.ibk.co.kr/2020-12-31
7평창휴게소(강릉방향)IBK기업은행365강원 평창군 용평면 이목정리4242760128.47094337.608195강원<NA>https://www.ibk.co.kr/2020-12-31
8NH농협은행 정부세종청사교육문체부출장소세종특별자치시 갈매로 408 정부세종청사 14-1동 교육부 1층<NA><NA>127.26476136.499291세종044-864-4160banking.nonghyup.com/2020-12-31
9신한은행 양재역금융센터서울 강남구 강남대로 2401111680127.0343337.485255서울02-3462-0101www.shinhan.com/2020-12-31
entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmtel_nohomepage_urlbase_ymd
90NH농협은행 신사동금융센터서울 강남구 도산대로 125 농협신사동지점1111680127.0220737.517583서울02-543-5101banking.nonghyup.com/2020-12-31
91IBK기업은행 신사동지점서울 강남구 도산대로 139 제이타워1111680127.02366637.518335서울02-511-5804https://www.ibk.co.kr/2020-12-31
92KB국민은행 신사중앙지점서울 강남구 도산대로 149 진우빌딩1111680127.02471437.51865서울02-543-7862obank.kbstar.com/2020-12-31
93신한은행 학동기업금융센터서울 강남구 도산대로 2211111680127.03090537.521017서울02-544-8200www.shinhan.com/2020-12-31
94신한은행 학동지점서울 강남구 도산대로 221 동남빌딩1111680127.03090537.521017서울02-545-0506www.shinhan.com/2020-12-31
95우리은행 도산대로금융센터서울 강남구 도산대로 235 평화빌딩 1층1111680127.0326437.52159서울02-546-4077www.wooribank.com/2020-12-31
96IBK기업은행 언주로지점서울 강남구 도산대로 307 백영빌딩1111680127.03489437.522293서울02-544-9157https://www.ibk.co.kr/2020-12-31
97신한은행 도산대로지점서울 강남구 도산대로 3181111680127.03632437.521953서울02-540-0361www.shinhan.com/2020-12-31
98우리은행 학동지점서울 강남구 도산대로 322 파라다이스빌딩1111680127.03716937.522213서울02-512-514www.wooribank.com/2020-12-31
99IBK기업은행 청담동지점서울 강남구 도산대로 446 경원오피스텔1111680127.04508137.523601서울02-515-1853https://www.ibk.co.kr/2020-12-31

Duplicate rows

Most frequently occurring

entrp_nmload_addrcity_do_cdcity_gn_gu_cdxpos_loypos_laarea_nmtel_nohomepage_urlbase_ymd# duplicates
0신한은행365서울 강남구 대치동1111680127.0633437.499194서울<NA>www.shinhan.com/2020-12-312