Overview

Dataset statistics

Number of variables37
Number of observations1503
Missing cells17349
Missing cells (%)31.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory434.6 KiB
Average record size in memory296.1 B

Variable types

Text20
Categorical17

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21707/F/1/datasetView.do

Alerts

Unnamed: 10 is highly imbalanced (51.2%)Imbalance
Unnamed: 11 is highly imbalanced (69.1%)Imbalance
Unnamed: 15 is highly imbalanced (56.1%)Imbalance
Unnamed: 16 is highly imbalanced (55.0%)Imbalance
Unnamed: 35 is highly imbalanced (98.8%)Imbalance
Unnamed: 36 is highly imbalanced (67.8%)Imbalance
Unnamed: 3 has 93 (6.2%) missing valuesMissing
Unnamed: 4 has 83 (5.5%) missing valuesMissing
Unnamed: 5 has 128 (8.5%) missing valuesMissing
Unnamed: 6 has 268 (17.8%) missing valuesMissing
Unnamed: 18 has 1501 (99.9%) missing valuesMissing
Unnamed: 21 has 1501 (99.9%) missing valuesMissing
Unnamed: 22 has 1501 (99.9%) missing valuesMissing
Unnamed: 23 has 1501 (99.9%) missing valuesMissing
Unnamed: 24 has 1501 (99.9%) missing valuesMissing
Unnamed: 25 has 1501 (99.9%) missing valuesMissing
Unnamed: 26 has 1501 (99.9%) missing valuesMissing
Unnamed: 27 has 1501 (99.9%) missing valuesMissing
Unnamed: 28 has 1501 (99.9%) missing valuesMissing
Unnamed: 30 has 1501 (99.9%) missing valuesMissing
Unnamed: 31 has 1501 (99.9%) missing valuesMissing
Unnamed: 32 has 88 (5.9%) missing valuesMissing
Unnamed: 33 has 88 (5.9%) missing valuesMissing
Unnamed: 34 has 88 (5.9%) missing valuesMissing

Reproduction

Analysis started2024-04-17 18:41:37.205051
Analysis finished2024-04-17 18:41:38.255057
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1302
Distinct (%)86.7%
Missing1
Missing (%)0.1%
Memory size11.9 KiB
2024-04-18T03:41:38.512228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.2416778
Min length6

Characters and Unicode

Total characters9375
Distinct characters30
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

Unique1102 ?
Unique (%)73.4%

Sample

1st row조사지점코드
2nd rowEXAMIN_SPOT_CD
3rd row01-003
4th row01-004
5th row01-005
ValueCountFrequency (%)
07-1144 2
 
0.1%
23-323 2
 
0.1%
19-116 2
 
0.1%
04-227 2
 
0.1%
21-249 2
 
0.1%
23-278 2
 
0.1%
04-222 2
 
0.1%
23-285 2
 
0.1%
19-076 2
 
0.1%
04-2026 2
 
0.1%
Other values (1292) 1482
98.7%
2024-04-18T03:41:39.021152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1944
20.7%
- 1500
16.0%
1 1450
15.5%
2 1382
14.7%
3 684
 
7.3%
4 573
 
6.1%
5 467
 
5.0%
6 353
 
3.8%
7 353
 
3.8%
8 334
 
3.6%
Other values (20) 335
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7855
83.8%
Dash Punctuation 1500
 
16.0%
Uppercase Letter 12
 
0.1%
Other Letter 6
 
0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1
8.3%
D 1
8.3%
C 1
8.3%
I 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
N 1
8.3%
M 1
8.3%
A 1
8.3%
Other values (2) 2
16.7%
Decimal Number
ValueCountFrequency (%)
0 1944
24.7%
1 1450
18.5%
2 1382
17.6%
3 684
 
8.7%
4 573
 
7.3%
5 467
 
5.9%
6 353
 
4.5%
7 353
 
4.5%
8 334
 
4.3%
9 315
 
4.0%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 1500
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9357
99.8%
Latin 12
 
0.1%
Hangul 6
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1944
20.8%
- 1500
16.0%
1 1450
15.5%
2 1382
14.8%
3 684
 
7.3%
4 573
 
6.1%
5 467
 
5.0%
6 353
 
3.8%
7 353
 
3.8%
8 334
 
3.6%
Other values (2) 317
 
3.4%
Latin
ValueCountFrequency (%)
T 1
8.3%
D 1
8.3%
C 1
8.3%
I 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
N 1
8.3%
M 1
8.3%
A 1
8.3%
Other values (2) 2
16.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9369
99.9%
Hangul 6
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1944
20.7%
- 1500
16.0%
1 1450
15.5%
2 1382
14.8%
3 684
 
7.3%
4 573
 
6.1%
5 467
 
5.0%
6 353
 
3.8%
7 353
 
3.8%
8 334
 
3.6%
Other values (14) 329
 
3.5%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Distinct1278
Distinct (%)85.1%
Missing1
Missing (%)0.1%
Memory size11.9 KiB
2024-04-18T03:41:39.258878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length8.0099867
Min length1

Characters and Unicode

Total characters12031
Distinct characters664
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1072 ?
Unique (%)71.4%

Sample

1st row조사지점명
2nd rowEXAMIN_SPOT_NM
3rd row신흥모피명품전문크리닝.
4th rowGS25
5th row세검정정류장
ValueCountFrequency (%)
105
 
4.0%
입구 19
 
0.7%
종로3가역 16
 
0.6%
아파트 15
 
0.6%
맞은편 15
 
0.6%
교대역 14
 
0.5%
우리은행 14
 
0.5%
건너편 13
 
0.5%
정문 13
 
0.5%
주차장 12
 
0.5%
Other values (1775) 2380
91.0%
2024-04-18T03:41:39.595230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1115
 
9.3%
234
 
1.9%
184
 
1.5%
166
 
1.4%
147
 
1.2%
147
 
1.2%
141
 
1.2%
137
 
1.1%
137
 
1.1%
131
 
1.1%
Other values (654) 9492
78.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9372
77.9%
Space Separator 1115
 
9.3%
Decimal Number 486
 
4.0%
Uppercase Letter 404
 
3.4%
Lowercase Letter 388
 
3.2%
Open Punctuation 85
 
0.7%
Close Punctuation 85
 
0.7%
Other Punctuation 57
 
0.5%
Dash Punctuation 32
 
0.3%
Math Symbol 4
 
< 0.1%
Other values (2) 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
234
 
2.5%
184
 
2.0%
166
 
1.8%
147
 
1.6%
147
 
1.6%
141
 
1.5%
137
 
1.5%
137
 
1.5%
131
 
1.4%
130
 
1.4%
Other values (581) 7818
83.4%
Uppercase Letter
ValueCountFrequency (%)
T 40
 
9.9%
G 35
 
8.7%
S 30
 
7.4%
C 28
 
6.9%
N 24
 
5.9%
K 24
 
5.9%
I 23
 
5.7%
A 23
 
5.7%
M 22
 
5.4%
B 20
 
5.0%
Other values (15) 135
33.4%
Lowercase Letter
ValueCountFrequency (%)
o 45
11.6%
r 38
 
9.8%
e 33
 
8.5%
i 32
 
8.2%
a 31
 
8.0%
l 25
 
6.4%
t 25
 
6.4%
m 22
 
5.7%
n 19
 
4.9%
s 16
 
4.1%
Other values (13) 102
26.3%
Decimal Number
ValueCountFrequency (%)
1 120
24.7%
2 70
14.4%
0 62
12.8%
3 61
12.6%
5 47
 
9.7%
6 39
 
8.0%
4 30
 
6.2%
7 24
 
4.9%
8 19
 
3.9%
9 14
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 37
64.9%
, 10
 
17.5%
& 5
 
8.8%
# 2
 
3.5%
? 1
 
1.8%
@ 1
 
1.8%
/ 1
 
1.8%
Math Symbol
ValueCountFrequency (%)
| 2
50.0%
+ 2
50.0%
Space Separator
ValueCountFrequency (%)
1115
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9366
77.8%
Common 1866
 
15.5%
Latin 792
 
6.6%
Han 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
234
 
2.5%
184
 
2.0%
166
 
1.8%
147
 
1.6%
147
 
1.6%
141
 
1.5%
137
 
1.5%
137
 
1.5%
131
 
1.4%
130
 
1.4%
Other values (575) 7812
83.4%
Latin
ValueCountFrequency (%)
o 45
 
5.7%
T 40
 
5.1%
r 38
 
4.8%
G 35
 
4.4%
e 33
 
4.2%
i 32
 
4.0%
a 31
 
3.9%
S 30
 
3.8%
C 28
 
3.5%
l 25
 
3.2%
Other values (38) 455
57.4%
Common
ValueCountFrequency (%)
1115
59.8%
1 120
 
6.4%
( 85
 
4.6%
) 85
 
4.6%
2 70
 
3.8%
0 62
 
3.3%
3 61
 
3.3%
5 47
 
2.5%
6 39
 
2.1%
. 37
 
2.0%
Other values (14) 145
 
7.8%
Han
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9365
77.8%
ASCII 2658
 
22.1%
CJK 7
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1115
41.9%
1 120
 
4.5%
( 85
 
3.2%
) 85
 
3.2%
2 70
 
2.6%
0 62
 
2.3%
3 61
 
2.3%
5 47
 
1.8%
o 45
 
1.7%
T 40
 
1.5%
Other values (62) 928
34.9%
Hangul
ValueCountFrequency (%)
234
 
2.5%
184
 
2.0%
166
 
1.8%
147
 
1.6%
147
 
1.6%
141
 
1.5%
137
 
1.5%
137
 
1.5%
131
 
1.4%
130
 
1.4%
Other values (574) 7811
83.4%
None
ValueCountFrequency (%)
1
100.0%
CJK
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Unnamed: 2
Categorical

Distinct28
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
11230
 
94
<NA>
 
93
11220
 
92
11010
 
86
11020
 
86
Other values (23)
1052 

Length

Max length5
Median length5
Mean length4.9367931
Min length3

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row구코드
3rd rowGU_CD
4th row11010
5th row11010

Common Values

ValueCountFrequency (%)
11230 94
 
6.3%
<NA> 93
 
6.2%
11220 92
 
6.1%
11010 86
 
5.7%
11020 86
 
5.7%
11110 80
 
5.3%
11240 72
 
4.8%
11190 72
 
4.8%
11140 61
 
4.1%
11150 60
 
4.0%
Other values (18) 707
47.0%

Length

2024-04-18T03:41:39.717796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11230 94
 
6.3%
na 93
 
6.2%
11220 92
 
6.1%
11010 86
 
5.7%
11020 86
 
5.7%
11110 80
 
5.3%
11240 72
 
4.8%
11190 72
 
4.8%
11140 61
 
4.1%
11150 60
 
4.0%
Other values (18) 707
47.0%

Unnamed: 3
Text

MISSING 

Distinct353
Distinct (%)25.0%
Missing93
Missing (%)6.2%
Memory size11.9 KiB
2024-04-18T03:41:39.992908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9971631
Min length3

Characters and Unicode

Total characters9866
Distinct characters19
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)5.8%

Sample

1st row동코드
2nd rowDONG_CD
3rd row1101055
4th row1101055
5th row1101055
ValueCountFrequency (%)
1101061 35
 
2.5%
1102055 32
 
2.3%
1123064 22
 
1.6%
1119054 21
 
1.5%
1102054 20
 
1.4%
1114066 17
 
1.2%
1111079 16
 
1.1%
1115051 14
 
1.0%
1102052 14
 
1.0%
1115072 13
 
0.9%
Other values (343) 1206
85.5%
2024-04-18T03:41:40.390780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3748
38.0%
0 2078
21.1%
5 817
 
8.3%
2 781
 
7.9%
6 660
 
6.7%
7 576
 
5.8%
4 381
 
3.9%
3 345
 
3.5%
8 256
 
2.6%
9 214
 
2.2%
Other values (9) 10
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9856
99.9%
Uppercase Letter 6
 
0.1%
Other Letter 3
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3748
38.0%
0 2078
21.1%
5 817
 
8.3%
2 781
 
7.9%
6 660
 
6.7%
7 576
 
5.8%
4 381
 
3.9%
3 345
 
3.5%
8 256
 
2.6%
9 214
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
D 2
33.3%
O 1
16.7%
N 1
16.7%
G 1
16.7%
C 1
16.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9857
99.9%
Latin 6
 
0.1%
Hangul 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3748
38.0%
0 2078
21.1%
5 817
 
8.3%
2 781
 
7.9%
6 660
 
6.7%
7 576
 
5.8%
4 381
 
3.9%
3 345
 
3.5%
8 256
 
2.6%
9 214
 
2.2%
Latin
ValueCountFrequency (%)
D 2
33.3%
O 1
16.7%
N 1
16.7%
G 1
16.7%
C 1
16.7%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9863
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3748
38.0%
0 2078
21.1%
5 817
 
8.3%
2 781
 
7.9%
6 660
 
6.7%
7 576
 
5.8%
4 381
 
3.9%
3 345
 
3.5%
8 256
 
2.6%
9 214
 
2.2%
Other values (6) 7
 
0.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 4
Text

MISSING 

Distinct672
Distinct (%)47.3%
Missing83
Missing (%)5.5%
Memory size11.9 KiB
2024-04-18T03:41:40.712659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.7584507
Min length1

Characters and Unicode

Total characters3917
Distinct characters26
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique339 ?
Unique (%)23.9%

Sample

1st row주번지
2nd rowBUNJI
3rd row127
4th row94
5th row92
ValueCountFrequency (%)
50 18
 
1.3%
1 16
 
1.1%
60 12
 
0.8%
0 11
 
0.8%
56 10
 
0.7%
7 10
 
0.7%
33 9
 
0.6%
88 9
 
0.6%
111 9
 
0.6%
20 8
 
0.6%
Other values (661) 1307
92.1%
2024-04-18T03:41:41.126854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 690
17.6%
2 447
11.4%
3 421
10.7%
6 386
9.9%
5 366
9.3%
4 363
9.3%
0 326
8.3%
7 302
7.7%
9 295
7.5%
8 251
 
6.4%
Other values (16) 70
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3847
98.2%
Dash Punctuation 47
 
1.2%
Other Letter 17
 
0.4%
Uppercase Letter 5
 
0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 690
17.9%
2 447
11.6%
3 421
10.9%
6 386
10.0%
5 366
9.5%
4 363
9.4%
0 326
8.5%
7 302
7.9%
9 295
7.7%
8 251
 
6.5%
Other Letter
ValueCountFrequency (%)
5
29.4%
5
29.4%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
U 1
20.0%
I 1
20.0%
J 1
20.0%
N 1
20.0%
B 1
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%
Space Separator
ValueCountFrequency (%)
  1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3895
99.4%
Hangul 17
 
0.4%
Latin 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 690
17.7%
2 447
11.5%
3 421
10.8%
6 386
9.9%
5 366
9.4%
4 363
9.3%
0 326
8.4%
7 302
7.8%
9 295
7.6%
8 251
 
6.4%
Other values (2) 48
 
1.2%
Hangul
ValueCountFrequency (%)
5
29.4%
5
29.4%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Latin
ValueCountFrequency (%)
U 1
20.0%
I 1
20.0%
J 1
20.0%
N 1
20.0%
B 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3899
99.5%
Hangul 17
 
0.4%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 690
17.7%
2 447
11.5%
3 421
10.8%
6 386
9.9%
5 366
9.4%
4 363
9.3%
0 326
8.4%
7 302
7.7%
9 295
7.6%
8 251
 
6.4%
Other values (6) 52
 
1.3%
Hangul
ValueCountFrequency (%)
5
29.4%
5
29.4%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
None
ValueCountFrequency (%)
  1
100.0%

Unnamed: 5
Text

MISSING 

Distinct146
Distinct (%)10.6%
Missing128
Missing (%)8.5%
Memory size11.9 KiB
2024-04-18T03:41:41.358071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.4530909
Min length1

Characters and Unicode

Total characters1998
Distinct characters21
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)4.0%

Sample

1st row부번지
2nd rowBUBUN
3rd row11
4th row2
5th row0
ValueCountFrequency (%)
0 273
19.9%
1 154
 
11.2%
2 66
 
4.8%
4 65
 
4.7%
3 52
 
3.8%
5 51
 
3.7%
7 49
 
3.6%
8 40
 
2.9%
9 37
 
2.7%
6 36
 
2.6%
Other values (135) 551
40.1%
2024-04-18T03:41:41.717105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 477
23.9%
0 355
17.8%
2 264
13.2%
3 160
 
8.0%
4 148
 
7.4%
5 138
 
6.9%
7 131
 
6.6%
6 115
 
5.8%
8 97
 
4.9%
9 97
 
4.9%
Other values (11) 16
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982
99.2%
Other Punctuation 5
 
0.3%
Uppercase Letter 5
 
0.3%
Other Letter 5
 
0.3%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 477
24.1%
0 355
17.9%
2 264
13.3%
3 160
 
8.1%
4 148
 
7.5%
5 138
 
7.0%
7 131
 
6.6%
6 115
 
5.8%
8 97
 
4.9%
9 97
 
4.9%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
40.0%
U 2
40.0%
N 1
20.0%
Other Punctuation
ValueCountFrequency (%)
? 3
60.0%
, 2
40.0%
Space Separator
ValueCountFrequency (%)
  1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1988
99.5%
Latin 5
 
0.3%
Hangul 5
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 477
24.0%
0 355
17.9%
2 264
13.3%
3 160
 
8.0%
4 148
 
7.4%
5 138
 
6.9%
7 131
 
6.6%
6 115
 
5.8%
8 97
 
4.9%
9 97
 
4.9%
Other values (3) 6
 
0.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Latin
ValueCountFrequency (%)
B 2
40.0%
U 2
40.0%
N 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1992
99.7%
Hangul 5
 
0.3%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 477
23.9%
0 355
17.8%
2 264
13.3%
3 160
 
8.0%
4 148
 
7.4%
5 138
 
6.9%
7 131
 
6.6%
6 115
 
5.8%
8 97
 
4.9%
9 97
 
4.9%
Other values (5) 10
 
0.5%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
None
ValueCountFrequency (%)
  1
100.0%

Unnamed: 6
Text

MISSING 

Distinct834
Distinct (%)67.5%
Missing268
Missing (%)17.8%
Memory size11.9 KiB
2024-04-18T03:41:42.001487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length6.1417004
Min length1

Characters and Unicode

Total characters7585
Distinct characters298
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique560 ?
Unique (%)45.3%

Sample

1st row도로명
2nd rowROAD_NM
3rd row세검정로 230
4th row세검정길
5th row종로 244-1
ValueCountFrequency (%)
없음 81
 
4.0%
도봉로 24
 
1.2%
천호대로 19
 
0.9%
남부순환로 17
 
0.8%
강남대로 15
 
0.7%
종로 14
 
0.7%
한천로 12
 
0.6%
동일로 11
 
0.5%
14 11
 
0.5%
13 10
 
0.5%
Other values (917) 1794
89.3%
2024-04-18T03:41:42.387755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
846
 
11.2%
773
 
10.2%
1 465
 
6.1%
449
 
5.9%
2 315
 
4.2%
3 237
 
3.1%
4 232
 
3.1%
7 178
 
2.3%
6 162
 
2.1%
8 155
 
2.0%
Other values (288) 3773
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4577
60.3%
Decimal Number 2140
28.2%
Space Separator 773
 
10.2%
Dash Punctuation 60
 
0.8%
Close Punctuation 14
 
0.2%
Open Punctuation 14
 
0.2%
Uppercase Letter 6
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
846
 
18.5%
449
 
9.8%
154
 
3.4%
139
 
3.0%
84
 
1.8%
83
 
1.8%
68
 
1.5%
62
 
1.4%
58
 
1.3%
56
 
1.2%
Other values (267) 2578
56.3%
Decimal Number
ValueCountFrequency (%)
1 465
21.7%
2 315
14.7%
3 237
11.1%
4 232
10.8%
7 178
 
8.3%
6 162
 
7.6%
8 155
 
7.2%
0 146
 
6.8%
5 134
 
6.3%
9 116
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
D 1
16.7%
A 1
16.7%
O 1
16.7%
R 1
16.7%
N 1
16.7%
M 1
16.7%
Space Separator
ValueCountFrequency (%)
773
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4576
60.3%
Common 3002
39.6%
Latin 6
 
0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
846
 
18.5%
449
 
9.8%
154
 
3.4%
139
 
3.0%
84
 
1.8%
83
 
1.8%
68
 
1.5%
62
 
1.4%
58
 
1.3%
56
 
1.2%
Other values (266) 2577
56.3%
Common
ValueCountFrequency (%)
773
25.7%
1 465
15.5%
2 315
10.5%
3 237
 
7.9%
4 232
 
7.7%
7 178
 
5.9%
6 162
 
5.4%
8 155
 
5.2%
0 146
 
4.9%
5 134
 
4.5%
Other values (5) 205
 
6.8%
Latin
ValueCountFrequency (%)
D 1
16.7%
A 1
16.7%
O 1
16.7%
R 1
16.7%
N 1
16.7%
M 1
16.7%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4568
60.2%
ASCII 3008
39.7%
Compat Jamo 8
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
846
 
18.5%
449
 
9.8%
154
 
3.4%
139
 
3.0%
84
 
1.8%
83
 
1.8%
68
 
1.5%
62
 
1.4%
58
 
1.3%
56
 
1.2%
Other values (265) 2569
56.2%
ASCII
ValueCountFrequency (%)
773
25.7%
1 465
15.5%
2 315
10.5%
3 237
 
7.9%
4 232
 
7.7%
7 178
 
5.9%
6 162
 
5.4%
8 155
 
5.2%
0 146
 
4.9%
5 134
 
4.5%
Other values (11) 211
 
7.0%
Compat Jamo
ValueCountFrequency (%)
8
100.0%
CJK
ValueCountFrequency (%)
1
100.0%

Unnamed: 7
Categorical

Distinct20
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
3
330 
4
271 
2
250 
5
211 
6
124 
Other values (15)
317 

Length

Max length8
Median length1
Mean length1.1929474
Min length1

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row보도너비
3rd rowFTPTH_BT
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 330
22.0%
4 271
18.0%
2 250
16.6%
5 211
14.0%
6 124
 
8.3%
7 96
 
6.4%
<NA> 80
 
5.3%
8 41
 
2.7%
1 39
 
2.6%
9 19
 
1.3%
Other values (10) 42
 
2.8%

Length

2024-04-18T03:41:42.505244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 330
22.0%
4 271
18.0%
2 250
16.6%
5 211
14.0%
6 124
 
8.3%
7 96
 
6.4%
na 80
 
5.3%
8 41
 
2.7%
1 39
 
2.6%
10 19
 
1.3%
Other values (10) 42
 
2.8%

Unnamed: 8
Categorical

Distinct17
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1
378 
4
277 
6
245 
2
162 
8
132 
Other values (12)
309 

Length

Max length6
Median length1
Mean length1.2009315
Min length1

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st row<NA>
2nd row차선수
3rd rowTFCLNE
4th row8
5th row7

Common Values

ValueCountFrequency (%)
1 378
25.1%
4 277
18.4%
6 245
16.3%
2 162
10.8%
8 132
 
8.8%
<NA> 80
 
5.3%
5 62
 
4.1%
7 55
 
3.7%
3 42
 
2.8%
10 39
 
2.6%
Other values (7) 31
 
2.1%

Length

2024-04-18T03:41:42.627965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 378
25.1%
4 277
18.4%
6 245
16.3%
2 162
10.8%
8 132
 
8.8%
na 80
 
5.3%
5 62
 
4.1%
7 55
 
3.7%
3 42
 
2.8%
10 39
 
2.6%
Other values (7) 31
 
2.1%

Unnamed: 9
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
955 
466 
<NA>
 
80
버스차로유무
 
1
BUS_CARTRK_ENNC_SE
 
1

Length

Max length18
Median length1
Mean length1.174318
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
955
63.5%
466
31.0%
<NA> 80
 
5.3%
버스차로유무 1
 
0.1%
BUS_CARTRK_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:42.741937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:42.829584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
955
63.5%
466
31.0%
na 80
 
5.3%
버스차로유무 1
 
0.1%
bus_cartrk_ennc_se 1
 
0.1%

Unnamed: 10
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1014 
407 
<NA>
 
80
중앙선여부
 
1
CTLN_AT_SE
 
1

Length

Max length10
Median length1
Mean length1.16833
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1014
67.5%
407
27.1%
<NA> 80
 
5.3%
중앙선여부 1
 
0.1%
CTLN_AT_SE 1
 
0.1%

Length

2024-04-18T03:41:42.918263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:43.015528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1014
67.5%
407
27.1%
na 80
 
5.3%
중앙선여부 1
 
0.1%
ctln_at_se 1
 
0.1%

Unnamed: 11
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1298 
 
123
<NA>
 
80
장애물유무
 
1
OBSTC_ENNC_SE
 
1

Length

Max length13
Median length1
Mean length1.170326
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1298
86.4%
123
 
8.2%
<NA> 80
 
5.3%
장애물유무 1
 
0.1%
OBSTC_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:43.106096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:43.196662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1298
86.4%
123
 
8.2%
na 80
 
5.3%
장애물유무 1
 
0.1%
obstc_ennc_se 1
 
0.1%

Unnamed: 12
Categorical

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
가로수
459 
기타
242 
기둥(가로등 등)
158 
상가 고정 장애물 등
138 
없음
125 
Other values (19)
381 

Length

Max length12
Median length11
Mean length4.7139055
Min length2

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
가로수 459
30.5%
기타 242
16.1%
기둥(가로등 등) 158
 
10.5%
상가 고정 장애물 등 138
 
9.2%
없음 125
 
8.3%
불법주정차 92
 
6.1%
<NA> 80
 
5.3%
노점상/가판대 71
 
4.7%
연석 21
 
1.4%
기둥(가로등등) 20
 
1.3%
Other values (14) 97
 
6.5%

Length

2024-04-18T03:41:43.307415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가로수 469
22.2%
296
14.0%
기타 242
11.5%
기둥(가로등 158
 
7.5%
상가 138
 
6.5%
고정 138
 
6.5%
장애물 138
 
6.5%
없음 125
 
5.9%
불법주정차 92
 
4.4%
na 80
 
3.8%
Other values (19) 232
11.0%

Unnamed: 13
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
보도전용
1011 
보도차도겸용
298 
자동차겸용
112 
<NA>
 
80
보행도로구분
 
1

Length

Max length12
Median length4
Mean length4.4777112
Min length4

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row보행도로구분
3rd rowWALK_ROAD_SE
4th row보도전용
5th row자동차겸용

Common Values

ValueCountFrequency (%)
보도전용 1011
67.3%
보도차도겸용 298
 
19.8%
자동차겸용 112
 
7.5%
<NA> 80
 
5.3%
보행도로구분 1
 
0.1%
WALK_ROAD_SE 1
 
0.1%

Length

2024-04-18T03:41:43.427917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:43.529888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보도전용 1011
67.3%
보도차도겸용 298
 
19.8%
자동차겸용 112
 
7.5%
na 80
 
5.3%
보행도로구분 1
 
0.1%
walk_road_se 1
 
0.1%

Unnamed: 14
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
885 
536 
<NA>
 
80
점자블록유무
 
1
BRLL_BLCK_ENNC_SE
 
1

Length

Max length17
Median length1
Mean length1.1736527
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
885
58.9%
536
35.7%
<NA> 80
 
5.3%
점자블록유무 1
 
0.1%
BRLL_BLCK_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:43.626876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:43.714716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
885
58.9%
536
35.7%
na 80
 
5.3%
점자블록유무 1
 
0.1%
brll_blck_ennc_se 1
 
0.1%

Unnamed: 15
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1121 
300 
<NA>
 
80
경사로유무
 
1
SLPW__ENNC_SE
 
1

Length

Max length13
Median length1
Mean length1.170326
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1121
74.6%
300
 
20.0%
<NA> 80
 
5.3%
경사로유무 1
 
0.1%
SLPW__ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:43.803505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:43.886223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1121
74.6%
300
 
20.0%
na 80
 
5.3%
경사로유무 1
 
0.1%
slpw__ennc_se 1
 
0.1%

Unnamed: 16
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
1099 
322 
<NA>
 
80
펜스유무
 
1
FENC_ENNC_SE
 
1

Length

Max length12
Median length1
Mean length1.1689953
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1099
73.1%
322
 
21.4%
<NA> 80
 
5.3%
펜스유무 1
 
0.1%
FENC_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:43.978601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:44.060888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1099
73.1%
322
 
21.4%
na 80
 
5.3%
펜스유무 1
 
0.1%
fenc_ennc_se 1
 
0.1%

Unnamed: 17
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
860 
561 
<NA>
 
80
버스정류장유무
 
1
BUS_STOPG_IPLA_ENNC_SE
 
1

Length

Max length22
Median length1
Mean length1.1776447
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
860
57.2%
561
37.3%
<NA> 80
 
5.3%
버스정류장유무 1
 
0.1%
BUS_STOPG_IPLA_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:44.155505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:44.255901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
860
57.2%
561
37.3%
na 80
 
5.3%
버스정류장유무 1
 
0.1%
bus_stopg_ipla_ennc_se 1
 
0.1%

Unnamed: 18
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:44.388263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11.5
Mean length11.5
Min length6

Characters and Unicode

Total characters23
Distinct characters15
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row기타시설유무
2nd rowETC_FCLTY_ENNC_SE
ValueCountFrequency (%)
기타시설유무 1
50.0%
etc_fclty_ennc_se 1
50.0%
2024-04-18T03:41:44.602035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3
13.0%
C 3
13.0%
_ 3
13.0%
T 2
 
8.7%
N 2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (5) 5
21.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14
60.9%
Other Letter 6
26.1%
Connector Punctuation 3
 
13.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3
21.4%
C 3
21.4%
T 2
14.3%
N 2
14.3%
F 1
 
7.1%
L 1
 
7.1%
Y 1
 
7.1%
S 1
 
7.1%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
60.9%
Hangul 6
26.1%
Common 3
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3
21.4%
C 3
21.4%
T 2
14.3%
N 2
14.3%
F 1
 
7.1%
L 1
 
7.1%
Y 1
 
7.1%
S 1
 
7.1%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Common
ValueCountFrequency (%)
_ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
73.9%
Hangul 6
 
26.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3
17.6%
C 3
17.6%
_ 3
17.6%
T 2
11.8%
N 2
11.8%
F 1
 
5.9%
L 1
 
5.9%
Y 1
 
5.9%
S 1
 
5.9%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Unnamed: 19
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
879 
542 
<NA>
 
80
지하철유무
 
1
SUBWAY_ENNC_SE
 
1

Length

Max length14
Median length1
Mean length1.1709914
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
879
58.5%
542
36.1%
<NA> 80
 
5.3%
지하철유무 1
 
0.1%
SUBWAY_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:44.702784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:44.795165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
879
58.5%
542
36.1%
na 80
 
5.3%
지하철유무 1
 
0.1%
subway_ennc_se 1
 
0.1%

Unnamed: 20
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
874 
547 
<NA>
 
80
횡단보도유무
 
1
CRSLK_ENNC_SE
 
1

Length

Max length13
Median length1
Mean length1.1709914
Min length1

Unique

Unique2 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
874
58.2%
547
36.4%
<NA> 80
 
5.3%
횡단보도유무 1
 
0.1%
CRSLK_ENNC_SE 1
 
0.1%

Length

2024-04-18T03:41:44.884888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:44.970057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
874
58.2%
547
36.4%
na 80
 
5.3%
횡단보도유무 1
 
0.1%
crslk_ennc_se 1
 
0.1%

Unnamed: 21
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:45.072122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8.5
Mean length8.5
Min length4

Characters and Unicode

Total characters17
Distinct characters14
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row보도형태
2nd rowFTPTH_STLE_CN
ValueCountFrequency (%)
보도형태 1
50.0%
ftpth_stle_cn 1
50.0%
2024-04-18T03:41:45.269411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 3
17.6%
_ 2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
F 1
 
5.9%
P 1
 
5.9%
H 1
 
5.9%
S 1
 
5.9%
Other values (4) 4
23.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11
64.7%
Other Letter 4
 
23.5%
Connector Punctuation 2
 
11.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 3
27.3%
F 1
 
9.1%
P 1
 
9.1%
H 1
 
9.1%
S 1
 
9.1%
L 1
 
9.1%
E 1
 
9.1%
C 1
 
9.1%
N 1
 
9.1%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11
64.7%
Hangul 4
 
23.5%
Common 2
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 3
27.3%
F 1
 
9.1%
P 1
 
9.1%
H 1
 
9.1%
S 1
 
9.1%
L 1
 
9.1%
E 1
 
9.1%
C 1
 
9.1%
N 1
 
9.1%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
76.5%
Hangul 4
 
23.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 3
23.1%
_ 2
15.4%
F 1
 
7.7%
P 1
 
7.7%
H 1
 
7.7%
S 1
 
7.7%
L 1
 
7.7%
E 1
 
7.7%
C 1
 
7.7%
N 1
 
7.7%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Unnamed: 22
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:45.384168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length12
Mean length12
Min length7

Characters and Unicode

Total characters24
Distinct characters18
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row글로벌존지역명
2nd rowGLOBAL_ZN_AREA_NM
ValueCountFrequency (%)
글로벌존지역명 1
50.0%
global_zn_area_nm 1
50.0%
2024-04-18T03:41:45.824998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3
 
12.5%
A 3
 
12.5%
N 2
 
8.3%
L 2
 
8.3%
1
 
4.2%
B 1
 
4.2%
E 1
 
4.2%
R 1
 
4.2%
Z 1
 
4.2%
O 1
 
4.2%
Other values (8) 8
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 14
58.3%
Other Letter 7
29.2%
Connector Punctuation 3
 
12.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3
21.4%
N 2
14.3%
L 2
14.3%
B 1
 
7.1%
E 1
 
7.1%
R 1
 
7.1%
Z 1
 
7.1%
O 1
 
7.1%
G 1
 
7.1%
M 1
 
7.1%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14
58.3%
Hangul 7
29.2%
Common 3
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3
21.4%
N 2
14.3%
L 2
14.3%
B 1
 
7.1%
E 1
 
7.1%
R 1
 
7.1%
Z 1
 
7.1%
O 1
 
7.1%
G 1
 
7.1%
M 1
 
7.1%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Common
ValueCountFrequency (%)
_ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17
70.8%
Hangul 7
29.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 3
17.6%
A 3
17.6%
N 2
11.8%
L 2
11.8%
B 1
 
5.9%
E 1
 
5.9%
R 1
 
5.9%
Z 1
 
5.9%
O 1
 
5.9%
G 1
 
5.9%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Unnamed: 23
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:45.935602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9.5
Mean length9.5
Min length5

Characters and Unicode

Total characters19
Distinct characters14
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row주거지역명
2nd rowRESIDE_AREA_NM
ValueCountFrequency (%)
주거지역명 1
50.0%
reside_area_nm 1
50.0%
2024-04-18T03:41:46.127907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3
15.8%
R 2
10.5%
_ 2
10.5%
A 2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
S 1
 
5.3%
Other values (4) 4
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12
63.2%
Other Letter 5
26.3%
Connector Punctuation 2
 
10.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3
25.0%
R 2
16.7%
A 2
16.7%
S 1
 
8.3%
I 1
 
8.3%
D 1
 
8.3%
N 1
 
8.3%
M 1
 
8.3%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
63.2%
Hangul 5
26.3%
Common 2
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3
25.0%
R 2
16.7%
A 2
16.7%
S 1
 
8.3%
I 1
 
8.3%
D 1
 
8.3%
N 1
 
8.3%
M 1
 
8.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
73.7%
Hangul 5
 
26.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3
21.4%
R 2
14.3%
_ 2
14.3%
A 2
14.3%
S 1
 
7.1%
I 1
 
7.1%
D 1
 
7.1%
N 1
 
7.1%
M 1
 
7.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 24
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:46.278017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9.5
Mean length9.5
Min length5

Characters and Unicode

Total characters19
Distinct characters13
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row지역중심명
2nd rowAREA_CENTER_NM
ValueCountFrequency (%)
지역중심명 1
50.0%
area_center_nm 1
50.0%
2024-04-18T03:41:46.479420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3
15.8%
A 2
10.5%
R 2
10.5%
_ 2
10.5%
N 2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12
63.2%
Other Letter 5
26.3%
Connector Punctuation 2
 
10.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3
25.0%
A 2
16.7%
R 2
16.7%
N 2
16.7%
C 1
 
8.3%
T 1
 
8.3%
M 1
 
8.3%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12
63.2%
Hangul 5
26.3%
Common 2
 
10.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3
25.0%
A 2
16.7%
R 2
16.7%
N 2
16.7%
C 1
 
8.3%
T 1
 
8.3%
M 1
 
8.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14
73.7%
Hangul 5
 
26.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3
21.4%
A 2
14.3%
R 2
14.3%
_ 2
14.3%
N 2
14.3%
C 1
 
7.1%
T 1
 
7.1%
M 1
 
7.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 25
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:46.599555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length14
Min length7

Characters and Unicode

Total characters28
Distinct characters18
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row지역중심상세명
2nd rowAREA_CENTER_DETAIL_NM
ValueCountFrequency (%)
지역중심상세명 1
50.0%
area_center_detail_nm 1
50.0%
2024-04-18T03:41:46.821682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 4
14.3%
_ 3
 
10.7%
A 3
 
10.7%
T 2
 
7.1%
N 2
 
7.1%
R 2
 
7.1%
L 1
 
3.6%
I 1
 
3.6%
D 1
 
3.6%
C 1
 
3.6%
Other values (8) 8
28.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 18
64.3%
Other Letter 7
 
25.0%
Connector Punctuation 3
 
10.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 4
22.2%
A 3
16.7%
T 2
11.1%
N 2
11.1%
R 2
11.1%
L 1
 
5.6%
I 1
 
5.6%
D 1
 
5.6%
C 1
 
5.6%
M 1
 
5.6%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 18
64.3%
Hangul 7
 
25.0%
Common 3
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 4
22.2%
A 3
16.7%
T 2
11.1%
N 2
11.1%
R 2
11.1%
L 1
 
5.6%
I 1
 
5.6%
D 1
 
5.6%
C 1
 
5.6%
M 1
 
5.6%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Common
ValueCountFrequency (%)
_ 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
75.0%
Hangul 7
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 4
19.0%
_ 3
14.3%
A 3
14.3%
T 2
9.5%
N 2
9.5%
R 2
9.5%
L 1
 
4.8%
I 1
 
4.8%
D 1
 
4.8%
C 1
 
4.8%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Unnamed: 26
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:46.934496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10
Mean length10
Min length5

Characters and Unicode

Total characters20
Distinct characters14
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row지구중심명
2nd rowDSTRC_CENTER_NM
ValueCountFrequency (%)
지구중심명 1
50.0%
dstrc_center_nm 1
50.0%
2024-04-18T03:41:47.130224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2
10.0%
R 2
10.0%
C 2
10.0%
_ 2
10.0%
E 2
10.0%
N 2
10.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (4) 4
20.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13
65.0%
Other Letter 5
 
25.0%
Connector Punctuation 2
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 2
15.4%
R 2
15.4%
C 2
15.4%
E 2
15.4%
N 2
15.4%
D 1
7.7%
S 1
7.7%
M 1
7.7%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13
65.0%
Hangul 5
 
25.0%
Common 2
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2
15.4%
R 2
15.4%
C 2
15.4%
E 2
15.4%
N 2
15.4%
D 1
7.7%
S 1
7.7%
M 1
7.7%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
75.0%
Hangul 5
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2
13.3%
R 2
13.3%
C 2
13.3%
_ 2
13.3%
E 2
13.3%
N 2
13.3%
D 1
6.7%
S 1
6.7%
M 1
6.7%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 27
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:47.246817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13.5
Mean length13.5
Min length8

Characters and Unicode

Total characters27
Distinct characters19
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row지구중심상세내용
2nd rowDSTRC_CENTER_DETAIL
ValueCountFrequency (%)
지구중심상세내용 1
50.0%
dstrc_center_detail 1
50.0%
2024-04-18T03:41:47.455853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 3
 
11.1%
E 3
 
11.1%
_ 2
 
7.4%
C 2
 
7.4%
D 2
 
7.4%
R 2
 
7.4%
1
 
3.7%
I 1
 
3.7%
A 1
 
3.7%
N 1
 
3.7%
Other values (9) 9
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17
63.0%
Other Letter 8
29.6%
Connector Punctuation 2
 
7.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 3
17.6%
E 3
17.6%
C 2
11.8%
D 2
11.8%
R 2
11.8%
I 1
 
5.9%
A 1
 
5.9%
N 1
 
5.9%
S 1
 
5.9%
L 1
 
5.9%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17
63.0%
Hangul 8
29.6%
Common 2
 
7.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 3
17.6%
E 3
17.6%
C 2
11.8%
D 2
11.8%
R 2
11.8%
I 1
 
5.9%
A 1
 
5.9%
N 1
 
5.9%
S 1
 
5.9%
L 1
 
5.9%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19
70.4%
Hangul 8
29.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 3
15.8%
E 3
15.8%
_ 2
10.5%
C 2
10.5%
D 2
10.5%
R 2
10.5%
I 1
 
5.3%
A 1
 
5.3%
N 1
 
5.3%
S 1
 
5.3%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Unnamed: 28
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:47.572413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14.5
Mean length14.5
Min length8

Characters and Unicode

Total characters29
Distinct characters18
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row도심부도심지역명
2nd rowCDCT_SUB_CDCT_AREA_NM
ValueCountFrequency (%)
도심부도심지역명 1
50.0%
cdct_sub_cdct_area_nm 1
50.0%
2024-04-18T03:41:47.783393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 4
13.8%
C 4
13.8%
A 2
 
6.9%
2
 
6.9%
T 2
 
6.9%
D 2
 
6.9%
2
 
6.9%
1
 
3.4%
1
 
3.4%
1
 
3.4%
Other values (8) 8
27.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17
58.6%
Other Letter 8
27.6%
Connector Punctuation 4
 
13.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4
23.5%
A 2
11.8%
T 2
11.8%
D 2
11.8%
S 1
 
5.9%
U 1
 
5.9%
B 1
 
5.9%
R 1
 
5.9%
E 1
 
5.9%
N 1
 
5.9%
Other Letter
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17
58.6%
Hangul 8
27.6%
Common 4
 
13.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4
23.5%
A 2
11.8%
T 2
11.8%
D 2
11.8%
S 1
 
5.9%
U 1
 
5.9%
B 1
 
5.9%
R 1
 
5.9%
E 1
 
5.9%
N 1
 
5.9%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Common
ValueCountFrequency (%)
_ 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21
72.4%
Hangul 8
 
27.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 4
19.0%
C 4
19.0%
A 2
9.5%
T 2
9.5%
D 2
9.5%
S 1
 
4.8%
U 1
 
4.8%
B 1
 
4.8%
R 1
 
4.8%
E 1
 
4.8%
Other values (2) 2
9.5%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Unnamed: 29
Categorical

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
3종주거
430 
2종주거
361 
<NA>
228 
일반상업
225 
1종주거
77 
Other values (8)
182 

Length

Max length8
Median length4
Mean length3.8689288
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row용도구분
3rd rowPRPOS_NM
4th row2종주거
5th row2종주거

Common Values

ValueCountFrequency (%)
3종주거 430
28.6%
2종주거 361
24.0%
<NA> 228
15.2%
일반상업 225
15.0%
1종주거 77
 
5.1%
준주거 64
 
4.3%
준공업 63
 
4.2%
녹지 37
 
2.5%
중심상업 10
 
0.7%
근린상업 5
 
0.3%
Other values (3) 3
 
0.2%

Length

2024-04-18T03:41:47.884793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3종주거 430
28.6%
2종주거 361
24.0%
na 228
15.2%
일반상업 225
15.0%
1종주거 77
 
5.1%
준주거 64
 
4.3%
준공업 63
 
4.2%
녹지 37
 
2.5%
중심상업 10
 
0.7%
근린상업 5
 
0.3%
Other values (3) 3
 
0.2%

Unnamed: 30
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:47.992450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length9
Min length6

Characters and Unicode

Total characters18
Distinct characters14
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row거주유형구분
2nd rowRESIDE_TY_SE
ValueCountFrequency (%)
거주유형구분 1
50.0%
reside_ty_se 1
50.0%
2024-04-18T03:41:48.193735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 3
16.7%
S 2
11.1%
_ 2
11.1%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
R 1
 
5.6%
Other values (4) 4
22.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10
55.6%
Other Letter 6
33.3%
Connector Punctuation 2
 
11.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 3
30.0%
S 2
20.0%
R 1
 
10.0%
I 1
 
10.0%
D 1
 
10.0%
T 1
 
10.0%
Y 1
 
10.0%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10
55.6%
Hangul 6
33.3%
Common 2
 
11.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 3
30.0%
S 2
20.0%
R 1
 
10.0%
I 1
 
10.0%
D 1
 
10.0%
T 1
 
10.0%
Y 1
 
10.0%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
66.7%
Hangul 6
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 3
25.0%
S 2
16.7%
_ 2
16.7%
R 1
 
8.3%
I 1
 
8.3%
D 1
 
8.3%
T 1
 
8.3%
Y 1
 
8.3%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Unnamed: 31
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing1501
Missing (%)99.9%
Memory size11.9 KiB
2024-04-18T03:41:48.300108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length7
Mean length7
Min length5

Characters and Unicode

Total characters14
Distinct characters12
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row입지유형명
2nd rowLCT_TY_NM
ValueCountFrequency (%)
입지유형명 1
50.0%
lct_ty_nm 1
50.0%
2024-04-18T03:41:48.509947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2
14.3%
_ 2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
L 1
7.1%
C 1
7.1%
Y 1
7.1%
Other values (2) 2
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7
50.0%
Other Letter 5
35.7%
Connector Punctuation 2
 
14.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 2
28.6%
L 1
14.3%
C 1
14.3%
Y 1
14.3%
N 1
14.3%
M 1
14.3%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7
50.0%
Hangul 5
35.7%
Common 2
 
14.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2
28.6%
L 1
14.3%
C 1
14.3%
Y 1
14.3%
N 1
14.3%
M 1
14.3%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9
64.3%
Hangul 5
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2
22.2%
_ 2
22.2%
L 1
11.1%
C 1
11.1%
Y 1
11.1%
N 1
11.1%
M 1
11.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 32
Text

MISSING 

Distinct1219
Distinct (%)86.1%
Missing88
Missing (%)5.9%
Memory size11.9 KiB
2024-04-18T03:41:48.712388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.862191
Min length3

Characters and Unicode

Total characters16785
Distinct characters19
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

Unique1023 ?
Unique (%)72.3%

Sample

1st rowX좌표
2nd rowXCRD_LC
3rd row196423.97707
4th row196315.80243
5th row196357.17125
ValueCountFrequency (%)
191908.20465 2
 
0.1%
198424.8385 2
 
0.1%
200589.73287 2
 
0.1%
202664.1041 2
 
0.1%
197023.9525 2
 
0.1%
205406.1643 2
 
0.1%
202235.72226 2
 
0.1%
205353.19439 2
 
0.1%
205511.48892 2
 
0.1%
203129.17125 2
 
0.1%
Other values (1209) 1395
98.6%
2024-04-18T03:41:49.014397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 2037
12.1%
1 1982
11.8%
9 1774
10.6%
0 1749
10.4%
. 1413
8.4%
8 1395
8.3%
6 1377
8.2%
3 1347
8.0%
7 1271
7.6%
5 1246
7.4%
Other values (9) 1194
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15362
91.5%
Other Punctuation 1413
 
8.4%
Uppercase Letter 7
 
< 0.1%
Other Letter 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2037
13.3%
1 1982
12.9%
9 1774
11.5%
0 1749
11.4%
8 1395
9.1%
6 1377
9.0%
3 1347
8.8%
7 1271
8.3%
5 1246
8.1%
4 1184
7.7%
Uppercase Letter
ValueCountFrequency (%)
X 2
28.6%
C 2
28.6%
R 1
14.3%
D 1
14.3%
L 1
14.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1413
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16776
99.9%
Latin 7
 
< 0.1%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2037
12.1%
1 1982
11.8%
9 1774
10.6%
0 1749
10.4%
. 1413
8.4%
8 1395
8.3%
6 1377
8.2%
3 1347
8.0%
7 1271
7.6%
5 1246
7.4%
Other values (2) 1185
7.1%
Latin
ValueCountFrequency (%)
X 2
28.6%
C 2
28.6%
R 1
14.3%
D 1
14.3%
L 1
14.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16783
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2037
12.1%
1 1982
11.8%
9 1774
10.6%
0 1749
10.4%
. 1413
8.4%
8 1395
8.3%
6 1377
8.2%
3 1347
8.0%
7 1271
7.6%
5 1246
7.4%
Other values (7) 1192
7.1%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 33
Text

MISSING 

Distinct1219
Distinct (%)86.1%
Missing88
Missing (%)5.9%
Memory size11.9 KiB
2024-04-18T03:41:49.234704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.884806
Min length3

Characters and Unicode

Total characters16817
Distinct characters19
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

Unique1023 ?
Unique (%)72.3%

Sample

1st rowY좌표
2nd rowYCRD_LC
3rd row455511.52968
4th row455621.38262
5th row455680.8258
ValueCountFrequency (%)
444568.06339 2
 
0.1%
451587.64469 2
 
0.1%
448411.50983 2
 
0.1%
454120.89819 2
 
0.1%
449502.64361 2
 
0.1%
461452.55966 2
 
0.1%
444657.79841 2
 
0.1%
461637.00677 2
 
0.1%
461891.71093 2
 
0.1%
444526.28356 2
 
0.1%
Other values (1209) 1395
98.6%
2024-04-18T03:41:49.578744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 3457
20.6%
5 1834
10.9%
. 1413
8.4%
6 1347
 
8.0%
8 1337
 
8.0%
3 1334
 
7.9%
2 1310
 
7.8%
7 1255
 
7.5%
1 1236
 
7.3%
9 1219
 
7.2%
Other values (9) 1075
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15394
91.5%
Other Punctuation 1413
 
8.4%
Uppercase Letter 7
 
< 0.1%
Other Letter 2
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3457
22.5%
5 1834
11.9%
6 1347
 
8.8%
8 1337
 
8.7%
3 1334
 
8.7%
2 1310
 
8.5%
7 1255
 
8.2%
1 1236
 
8.0%
9 1219
 
7.9%
0 1065
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
Y 2
28.6%
C 2
28.6%
R 1
14.3%
D 1
14.3%
L 1
14.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1413
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16808
99.9%
Latin 7
 
< 0.1%
Hangul 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3457
20.6%
5 1834
10.9%
. 1413
8.4%
6 1347
 
8.0%
8 1337
 
8.0%
3 1334
 
7.9%
2 1310
 
7.8%
7 1255
 
7.5%
1 1236
 
7.4%
9 1219
 
7.3%
Other values (2) 1066
 
6.3%
Latin
ValueCountFrequency (%)
Y 2
28.6%
C 2
28.6%
R 1
14.3%
D 1
14.3%
L 1
14.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16815
> 99.9%
Hangul 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3457
20.6%
5 1834
10.9%
. 1413
8.4%
6 1347
 
8.0%
8 1337
 
8.0%
3 1334
 
7.9%
2 1310
 
7.8%
7 1255
 
7.5%
1 1236
 
7.4%
9 1219
 
7.2%
Other values (7) 1073
 
6.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 34
Text

MISSING 

Distinct979
Distinct (%)69.2%
Missing88
Missing (%)5.9%
Memory size11.9 KiB
2024-04-18T03:41:49.770397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length12.990813
Min length5

Characters and Unicode

Total characters18382
Distinct characters22
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique739 ?
Unique (%)52.2%

Sample

1st row집계구코드
2nd rowSM_GU_CD
3rd row1101055010007
4th row1101055020005
5th row1101055010002
ValueCountFrequency (%)
1102055060001 25
 
1.8%
1101061040001 16
 
1.1%
1102054070001 14
 
1.0%
1102052020001 12
 
0.8%
1101061030002 10
 
0.7%
1101061020001 7
 
0.5%
1111066040001 7
 
0.5%
1123051030002 7
 
0.5%
1122054040003 7
 
0.5%
1121064010001 6
 
0.4%
Other values (969) 1304
92.2%
2024-04-18T03:41:50.067265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7474
40.7%
1 4882
26.6%
2 1479
 
8.0%
5 999
 
5.4%
6 822
 
4.5%
3 763
 
4.2%
7 679
 
3.7%
4 654
 
3.6%
8 348
 
1.9%
9 269
 
1.5%
Other values (12) 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18369
99.9%
Uppercase Letter 6
 
< 0.1%
Other Letter 5
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7474
40.7%
1 4882
26.6%
2 1479
 
8.1%
5 999
 
5.4%
6 822
 
4.5%
3 763
 
4.2%
7 679
 
3.7%
4 654
 
3.6%
8 348
 
1.9%
9 269
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
M 1
16.7%
C 1
16.7%
U 1
16.7%
G 1
16.7%
S 1
16.7%
D 1
16.7%
Other Letter
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18371
99.9%
Latin 6
 
< 0.1%
Hangul 5
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7474
40.7%
1 4882
26.6%
2 1479
 
8.1%
5 999
 
5.4%
6 822
 
4.5%
3 763
 
4.2%
7 679
 
3.7%
4 654
 
3.6%
8 348
 
1.9%
9 269
 
1.5%
Latin
ValueCountFrequency (%)
M 1
16.7%
C 1
16.7%
U 1
16.7%
G 1
16.7%
S 1
16.7%
D 1
16.7%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18377
> 99.9%
Hangul 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7474
40.7%
1 4882
26.6%
2 1479
 
8.0%
5 999
 
5.4%
6 822
 
4.5%
3 763
 
4.2%
7 679
 
3.7%
4 654
 
3.6%
8 348
 
1.9%
9 269
 
1.5%
Other values (7) 8
 
< 0.1%
Hangul
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Unnamed: 35
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
2015
1500 
<NA>
 
1
년도
 
1
YEAR
 
1

Length

Max length4
Median length4
Mean length3.9986693
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st row<NA>
2nd row년도
3rd rowYEAR
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2015 1500
99.8%
<NA> 1
 
0.1%
년도 1
 
0.1%
YEAR 1
 
0.1%

Length

2024-04-18T03:41:50.189994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:50.288725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015 1500
99.8%
na 1
 
0.1%
년도 1
 
0.1%
year 1
 
0.1%

Unnamed: 36
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
본조사
1227 
계절요인조사
200 
지하철
 
49
북촌
 
24
<NA>
 
1
Other values (2)
 
2

Length

Max length10
Median length3
Mean length3.3892216
Min length2

Unique

Unique3 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
본조사 1227
81.6%
계절요인조사 200
 
13.3%
지하철 49
 
3.3%
북촌 24
 
1.6%
<NA> 1
 
0.1%
조사구분 1
 
0.1%
EXAMIN_CLS 1
 
0.1%

Length

2024-04-18T03:41:50.384154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:41:50.475217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본조사 1227
81.6%
계절요인조사 200
 
13.3%
지하철 49
 
3.3%
북촌 24
 
1.6%
na 1
 
0.1%
조사구분 1
 
0.1%
examin_cls 1
 
0.1%

Sample

유동인구_조사지점정보_2015Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27Unnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1조사지점코드조사지점명구코드동코드주번지부번지도로명보도너비차선수버스차로유무중앙선여부장애물유무장애물종류보행도로구분점자블록유무경사로유무펜스유무버스정류장유무기타시설유무지하철유무횡단보도유무보도형태글로벌존지역명주거지역명지역중심명지역중심상세명지구중심명지구중심상세내용도심부도심지역명용도구분거주유형구분입지유형명X좌표Y좌표집계구코드년도조사구분
2EXAMIN_SPOT_CDEXAMIN_SPOT_NMGU_CDDONG_CDBUNJIBUBUNROAD_NMFTPTH_BTTFCLNEBUS_CARTRK_ENNC_SECTLN_AT_SEOBSTC_ENNC_SEOBSTC_KND_CLWALK_ROAD_SEBRLL_BLCK_ENNC_SESLPW__ENNC_SEFENC_ENNC_SEBUS_STOPG_IPLA_ENNC_SEETC_FCLTY_ENNC_SESUBWAY_ENNC_SECRSLK_ENNC_SEFTPTH_STLE_CNGLOBAL_ZN_AREA_NMRESIDE_AREA_NMAREA_CENTER_NMAREA_CENTER_DETAIL_NMDSTRC_CENTER_NMDSTRC_CENTER_DETAILCDCT_SUB_CDCT_AREA_NMPRPOS_NMRESIDE_TY_SELCT_TY_NMXCRD_LCYCRD_LCSM_GU_CDYEAREXAMIN_CLS
301-003신흥모피명품전문크리닝.11010110105512711<NA>38가로수보도전용<NA><NA><NA><NA><NA><NA><NA><NA><NA>2종주거<NA><NA>196423.97707455511.5296811010550100072015본조사
401-004GS25110101101055942세검정로 23037가로수자동차겸용<NA><NA><NA><NA><NA><NA><NA><NA><NA>2종주거<NA><NA>196315.80243455621.3826211010550200052015본조사
501-005세검정정류장110101101055920세검정길45가로수보도전용<NA><NA><NA><NA><NA><NA><NA><NA><NA>1종주거<NA><NA>196357.17125455680.825811010550100022015본조사
601-008안성타워內 굿모닝파워공인중개사.1101011010567272<NA>44기둥(가로등 등)보도차도겸용<NA><NA><NA><NA><NA><NA><NA><NA><NA>2종주거<NA><NA>197904.19277456718.3499611010560300022015본조사
701-009복실 손뜨기.1101011010568846<NA>27가로수보도전용<NA><NA><NA><NA><NA><NA><NA><NA><NA>2종주거<NA><NA>196360.44943456405.8929611010560100052015본조사
801-01624시 동대문 설렁탕.1101011010685510<NA>36기타보도전용<NA><NA><NA><NA><NA><NA><NA><NA><NA>일반상업<NA><NA>200966.43423452483.7364211010680100032015본조사
901-019서울전문학교.1101011010711650<NA>57가로수보도전용<NA><NA><NA><NA><NA><NA><NA><NA><NA>일반상업<NA><NA>201686.66781452747.4159411010710100082015본조사
유동인구_조사지점정보_2015Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24Unnamed: 25Unnamed: 26Unnamed: 27Unnamed: 28Unnamed: 29Unnamed: 30Unnamed: 31Unnamed: 32Unnamed: 33Unnamed: 34Unnamed: 35Unnamed: 36
149340-015경남빌라옆<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149440-016MOON<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149540-017가회민화 박물관<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149640-018지형공방홍벽헌<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149740-019비원비 버리하우스<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149840-020원서공원 입구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
149940-021커피 Biwon<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
150040-022용수산 비원점<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
150140-023한정식 장원<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌
150240-024믿음 미용실<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2015북촌