Overview

Dataset statistics

Number of variables10
Number of observations419
Missing cells831
Missing cells (%)19.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory80.3 B

Variable types

Unsupported1
Categorical4
Text5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12967/F/1/datasetView.do

Alerts

Unnamed: 6 is highly overall correlated with Unnamed: 1High correlation
Unnamed: 5 is highly overall correlated with Unnamed: 1High correlation
Unnamed: 1 is highly overall correlated with Unnamed: 5 and 2 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 1High correlation
Unnamed: 5 is highly imbalanced (84.3%)Imbalance
Unnamed: 6 is highly imbalanced (62.3%)Imbalance
Unnamed: 7 is highly imbalanced (65.7%)Imbalance
Unnamed: 8 has 410 (97.9%) missing valuesMissing
Unnamed: 9 has 413 (98.6%) missing valuesMissing
외국어 가능 약국 현황 (서울 열린데이터 광장) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-06 11:14:40.129756
Analysis finished2024-04-06 11:14:42.300390
Duration2.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

외국어 가능 약국 현황 (서울 열린데이터 광장)
Unsupported

REJECTED  UNSUPPORTED 

Missing2
Missing (%)0.5%
Memory size3.4 KiB

Unnamed: 1
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
용산구
43 
송파구
38 
서초구
37 
서대문구
33 
마포구
25 
Other values (22)
243 

Length

Max length4
Median length3
Mean length3.1264916
Min length2

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
용산구 43
 
10.3%
송파구 38
 
9.1%
서초구 37
 
8.8%
서대문구 33
 
7.9%
마포구 25
 
6.0%
동작구 24
 
5.7%
중구 24
 
5.7%
성동구 21
 
5.0%
동대문구 20
 
4.8%
강북구 18
 
4.3%
Other values (17) 136
32.5%

Length

2024-04-06T20:14:42.430627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
용산구 43
 
10.3%
송파구 38
 
9.1%
서초구 37
 
8.8%
서대문구 33
 
7.9%
마포구 25
 
6.0%
동작구 24
 
5.7%
중구 24
 
5.7%
성동구 21
 
5.0%
동대문구 20
 
4.8%
강북구 18
 
4.3%
Other values (16) 136
32.5%

Unnamed: 2
Text

Distinct381
Distinct (%)91.4%
Missing2
Missing (%)0.5%
Memory size3.4 KiB
2024-04-06T20:14:42.935870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length5.2494005
Min length3

Characters and Unicode

Total characters2189
Distinct characters275
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

Unique353 ?
Unique (%)84.7%

Sample

1st row약국이름
2nd row김해약국
3rd row보령약국
4th row서울종로약국
5th row수도약국
ValueCountFrequency (%)
푸른약국 3
 
0.7%
태평양약국 3
 
0.7%
나무약국 3
 
0.7%
코끼리약국 3
 
0.7%
수약국 3
 
0.7%
후문약국 3
 
0.7%
열린약국 3
 
0.7%
정다운약국 3
 
0.7%
혜민약국 2
 
0.5%
대한약국 2
 
0.5%
Other values (371) 389
93.3%
2024-04-06T20:14:43.750957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
약 420
 
19.2%
국 418
 
19.1%
리 59
 
2.7%
온 39
 
1.8%
누 34
 
1.6%
이 33
 
1.5%
화 22
 
1.0%
신 21
 
1.0%
한 21
 
1.0%
사 20
 
0.9%
Other values (265) 1102
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2172
99.2%
Decimal Number 9
 
0.4%
Lowercase Letter 7
 
0.3%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
약 420
 
19.3%
국 418
 
19.2%
리 59
 
2.7%
온 39
 
1.8%
누 34
 
1.6%
이 33
 
1.5%
화 22
 
1.0%
신 21
 
1.0%
한 21
 
1.0%
사 20
 
0.9%
Other values (250) 1085
50.0%
Decimal Number
ValueCountFrequency (%)
3 2
22.2%
2 2
22.2%
1 1
11.1%
4 1
11.1%
7 1
11.1%
5 1
11.1%
6 1
11.1%
Lowercase Letter
ValueCountFrequency (%)
t 1
14.3%
h 1
14.3%
g 1
14.3%
i 1
14.3%
l 1
14.3%
e 1
14.3%
d 1
14.3%
Uppercase Letter
ValueCountFrequency (%)
N 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2172
99.2%
Common 9
 
0.4%
Latin 8
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
약 420
 
19.3%
국 418
 
19.2%
리 59
 
2.7%
온 39
 
1.8%
누 34
 
1.6%
이 33
 
1.5%
화 22
 
1.0%
신 21
 
1.0%
한 21
 
1.0%
사 20
 
0.9%
Other values (250) 1085
50.0%
Latin
ValueCountFrequency (%)
N 1
12.5%
t 1
12.5%
h 1
12.5%
g 1
12.5%
i 1
12.5%
l 1
12.5%
e 1
12.5%
d 1
12.5%
Common
ValueCountFrequency (%)
3 2
22.2%
2 2
22.2%
1 1
11.1%
4 1
11.1%
7 1
11.1%
5 1
11.1%
6 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2172
99.2%
ASCII 17
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
약 420
 
19.3%
국 418
 
19.2%
리 59
 
2.7%
온 39
 
1.8%
누 34
 
1.6%
이 33
 
1.5%
화 22
 
1.0%
신 21
 
1.0%
한 21
 
1.0%
사 20
 
0.9%
Other values (250) 1085
50.0%
ASCII
ValueCountFrequency (%)
3 2
 
11.8%
2 2
 
11.8%
N 1
 
5.9%
1 1
 
5.9%
4 1
 
5.9%
7 1
 
5.9%
5 1
 
5.9%
6 1
 
5.9%
t 1
 
5.9%
h 1
 
5.9%
Other values (5) 5
29.4%

Unnamed: 3
Text

Distinct416
Distinct (%)99.8%
Missing2
Missing (%)0.5%
Memory size3.4 KiB
2024-04-06T20:14:44.343775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length26.443645
Min length8

Characters and Unicode

Total characters11027
Distinct characters351
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

Unique415 ?
Unique (%)99.5%

Sample

1st row주소 (도로명)
2nd row서울 종로구 종로 206(종로5가)
3rd row서울 종로구 종로 203(종로5가)
4th row서울 종로구 대학로 117 (명륜동4가)
5th row서울 종로구 인사동길 40 (관훈동)
ValueCountFrequency (%)
서울 416
 
17.8%
1층 65
 
2.8%
용산구 43
 
1.8%
송파구 38
 
1.6%
서초구 37
 
1.6%
서대문구 33
 
1.4%
마포구 25
 
1.1%
중구 25
 
1.1%
동작구 24
 
1.0%
성동구 21
 
0.9%
Other values (984) 1609
68.9%
2024-04-06T20:14:45.109644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1947
 
17.7%
서 531
 
4.8%
1 512
 
4.6%
동 495
 
4.5%
로 465
 
4.2%
구 443
 
4.0%
울 420
 
3.8%
) 366
 
3.3%
( 366
 
3.3%
2 299
 
2.7%
Other values (341) 5183
47.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6110
55.4%
Space Separator 1947
 
17.7%
Decimal Number 1860
 
16.9%
Close Punctuation 366
 
3.3%
Open Punctuation 366
 
3.3%
Other Punctuation 267
 
2.4%
Dash Punctuation 55
 
0.5%
Uppercase Letter 48
 
0.4%
Math Symbol 4
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 531
 
8.7%
동 495
 
8.1%
로 465
 
7.6%
구 443
 
7.3%
울 420
 
6.9%
대 162
 
2.7%
호 145
 
2.4%
층 133
 
2.2%
가 119
 
1.9%
길 103
 
1.7%
Other values (305) 3094
50.6%
Uppercase Letter
ValueCountFrequency (%)
B 16
33.3%
A 8
16.7%
I 4
 
8.3%
G 3
 
6.2%
C 3
 
6.2%
N 2
 
4.2%
K 2
 
4.2%
D 2
 
4.2%
L 2
 
4.2%
F 1
 
2.1%
Other values (5) 5
 
10.4%
Decimal Number
ValueCountFrequency (%)
1 512
27.5%
2 299
16.1%
3 194
 
10.4%
0 189
 
10.2%
4 154
 
8.3%
5 135
 
7.3%
7 110
 
5.9%
6 108
 
5.8%
8 81
 
4.4%
9 78
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 263
98.5%
. 3
 
1.1%
/ 1
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 2
66.7%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
1947
100.0%
Close Punctuation
ValueCountFrequency (%)
) 366
100.0%
Open Punctuation
ValueCountFrequency (%)
( 366
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Math Symbol
ValueCountFrequency (%)
~ 4
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6110
55.4%
Common 4866
44.1%
Latin 51
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 531
 
8.7%
동 495
 
8.1%
로 465
 
7.6%
구 443
 
7.3%
울 420
 
6.9%
대 162
 
2.7%
호 145
 
2.4%
층 133
 
2.2%
가 119
 
1.9%
길 103
 
1.7%
Other values (305) 3094
50.6%
Common
ValueCountFrequency (%)
1947
40.0%
1 512
 
10.5%
) 366
 
7.5%
( 366
 
7.5%
2 299
 
6.1%
, 263
 
5.4%
3 194
 
4.0%
0 189
 
3.9%
4 154
 
3.2%
5 135
 
2.8%
Other values (9) 441
 
9.1%
Latin
ValueCountFrequency (%)
B 16
31.4%
A 8
15.7%
I 4
 
7.8%
G 3
 
5.9%
C 3
 
5.9%
N 2
 
3.9%
e 2
 
3.9%
K 2
 
3.9%
D 2
 
3.9%
L 2
 
3.9%
Other values (7) 7
13.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6110
55.4%
ASCII 4917
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1947
39.6%
1 512
 
10.4%
) 366
 
7.4%
( 366
 
7.4%
2 299
 
6.1%
, 263
 
5.3%
3 194
 
3.9%
0 189
 
3.8%
4 154
 
3.1%
5 135
 
2.7%
Other values (26) 492
 
10.0%
Hangul
ValueCountFrequency (%)
서 531
 
8.7%
동 495
 
8.1%
로 465
 
7.6%
구 443
 
7.3%
울 420
 
6.9%
대 162
 
2.7%
호 145
 
2.4%
층 133
 
2.2%
가 119
 
1.9%
길 103
 
1.7%
Other values (305) 3094
50.6%

Unnamed: 4
Text

Distinct417
Distinct (%)100.0%
Missing2
Missing (%)0.5%
Memory size3.4 KiB
2024-04-06T20:14:45.537817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.290168
Min length4

Characters and Unicode

Total characters4708
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)100.0%

Sample

1st row전화번호
2nd row02-2267-1551
3rd row02-763-8181
4th row02-3676-4000
5th row02-732-3336
ValueCountFrequency (%)
02-2200-1577 1
 
0.2%
02-363-0888 1
 
0.2%
02-855-3843 1
 
0.2%
02-838-1158 1
 
0.2%
02-839-4551 1
 
0.2%
02-830-3995 1
 
0.2%
070-4222-1177 1
 
0.2%
02-2625-6040 1
 
0.2%
02-6956-2627 1
 
0.2%
02-851-2811 1
 
0.2%
Other values (407) 407
97.6%
2024-04-06T20:14:46.394846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 831
17.7%
2 784
16.7%
0 694
14.7%
3 355
7.5%
7 336
7.1%
5 315
 
6.7%
8 301
 
6.4%
4 287
 
6.1%
1 284
 
6.0%
9 281
 
6.0%
Other values (6) 240
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3871
82.2%
Dash Punctuation 831
 
17.7%
Other Letter 4
 
0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 784
20.3%
0 694
17.9%
3 355
9.2%
7 336
8.7%
5 315
8.1%
8 301
 
7.8%
4 287
 
7.4%
1 284
 
7.3%
9 281
 
7.3%
6 234
 
6.0%
Other Letter
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 831
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4704
99.9%
Hangul 4
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 831
17.7%
2 784
16.7%
0 694
14.8%
3 355
7.5%
7 336
7.1%
5 315
 
6.7%
8 301
 
6.4%
4 287
 
6.1%
1 284
 
6.0%
9 281
 
6.0%
Other values (2) 236
 
5.0%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4704
99.9%
Hangul 4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 831
17.7%
2 784
16.7%
0 694
14.8%
3 355
7.5%
7 336
7.1%
5 315
 
6.7%
8 301
 
6.4%
4 287
 
6.1%
1 284
 
6.0%
9 281
 
6.0%
Other values (2) 236
 
5.0%
Hangul
ValueCountFrequency (%)
전 1
25.0%
화 1
25.0%
번 1
25.0%
호 1
25.0%

Unnamed: 5
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
○
398 
<NA>
 
19
가능 외국어
 
1
영어
 
1

Length

Max length6
Median length1
Mean length1.150358
Min length1

Unique

Unique2 ?
Unique (%)0.5%

Sample

1st row<NA>
2nd row가능 외국어
3rd row영어
4th row○
5th row○

Common Values

ValueCountFrequency (%)
○ 398
95.0%
<NA> 19
 
4.5%
가능 외국어 1
 
0.2%
영어 1
 
0.2%

Length

2024-04-06T20:14:46.617917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:14:46.787444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
○ 398
94.8%
na 19
 
4.5%
가능 1
 
0.2%
외국어 1
 
0.2%
영어 1
 
0.2%

Unnamed: 6
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
361 
○
57 
중국어
 
1

Length

Max length4
Median length4
Mean length3.5894988
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 361
86.2%
○ 57
 
13.6%
중국어 1
 
0.2%

Length

2024-04-06T20:14:46.998834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:14:47.191592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 361
86.2%
○ 57
 
13.6%
중국어 1
 
0.2%

Unnamed: 7
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
<NA>
369 
○
49 
일본어
 
1

Length

Max length4
Median length4
Mean length3.646778
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 369
88.1%
○ 49
 
11.7%
일본어 1
 
0.2%

Length

2024-04-06T20:14:47.488650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T20:14:47.701213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 369
88.1%
○ 49
 
11.7%
일본어 1
 
0.2%

Unnamed: 8
Text

MISSING 

Distinct5
Distinct (%)55.6%
Missing410
Missing (%)97.9%
Memory size3.4 KiB
2024-04-06T20:14:47.937146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.5555556
Min length2

Characters and Unicode

Total characters32
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)22.2%

Sample

1st row기타
2nd row스페인어
3rd row프랑스어
4th row스페인어
5th row스페인어
ValueCountFrequency (%)
스페인어 3
33.3%
프랑스어 2
22.2%
독일어 2
22.2%
기타 1
 
11.1%
러시아어 1
 
11.1%
2024-04-06T20:14:48.413468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
어 8
25.0%
스 5
15.6%
페 3
 
9.4%
인 3
 
9.4%
프 2
 
6.2%
랑 2
 
6.2%
독 2
 
6.2%
일 2
 
6.2%
기 1
 
3.1%
타 1
 
3.1%
Other values (3) 3
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
어 8
25.0%
스 5
15.6%
페 3
 
9.4%
인 3
 
9.4%
프 2
 
6.2%
랑 2
 
6.2%
독 2
 
6.2%
일 2
 
6.2%
기 1
 
3.1%
타 1
 
3.1%
Other values (3) 3
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
어 8
25.0%
스 5
15.6%
페 3
 
9.4%
인 3
 
9.4%
프 2
 
6.2%
랑 2
 
6.2%
독 2
 
6.2%
일 2
 
6.2%
기 1
 
3.1%
타 1
 
3.1%
Other values (3) 3
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
어 8
25.0%
스 5
15.6%
페 3
 
9.4%
인 3
 
9.4%
프 2
 
6.2%
랑 2
 
6.2%
독 2
 
6.2%
일 2
 
6.2%
기 1
 
3.1%
타 1
 
3.1%
Other values (3) 3
 
9.4%

Unnamed: 9
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing413
Missing (%)98.6%
Memory size3.4 KiB
2024-04-06T20:14:48.731800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11.5
Mean length10.833333
Min length2

Characters and Unicode

Total characters65
Distinct characters39
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st row(2023. 1. 1. 기준)
2nd row비고
3rd row전산원 상담만 가능
4th row오전시간 한정
5th row일본어는 간단회화만 가능
ValueCountFrequency (%)
가능 3
17.6%
1 2
11.8%
일본어는 2
11.8%
2023 1
 
5.9%
기준 1
 
5.9%
비고 1
 
5.9%
전산원 1
 
5.9%
상담만 1
 
5.9%
오전시간 1
 
5.9%
한정 1
 
5.9%
Other values (3) 3
17.6%
2024-04-06T20:14:49.290008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11
 
16.9%
능 3
 
4.6%
. 3
 
4.6%
가 3
 
4.6%
만 3
 
4.6%
전 2
 
3.1%
일 2
 
3.1%
본 2
 
3.1%
2 2
 
3.1%
어 2
 
3.1%
Other values (29) 32
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43
66.2%
Space Separator 11
 
16.9%
Decimal Number 6
 
9.2%
Other Punctuation 3
 
4.6%
Open Punctuation 1
 
1.5%
Close Punctuation 1
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
능 3
 
7.0%
가 3
 
7.0%
만 3
 
7.0%
전 2
 
4.7%
일 2
 
4.7%
본 2
 
4.7%
어 2
 
4.7%
는 2
 
4.7%
간 2
 
4.7%
정 1
 
2.3%
Other values (21) 21
48.8%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
3 1
16.7%
0 1
16.7%
Space Separator
ValueCountFrequency (%)
11
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43
66.2%
Common 22
33.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
능 3
 
7.0%
가 3
 
7.0%
만 3
 
7.0%
전 2
 
4.7%
일 2
 
4.7%
본 2
 
4.7%
어 2
 
4.7%
는 2
 
4.7%
간 2
 
4.7%
정 1
 
2.3%
Other values (21) 21
48.8%
Common
ValueCountFrequency (%)
11
50.0%
. 3
 
13.6%
2 2
 
9.1%
1 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%
3 1
 
4.5%
0 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43
66.2%
ASCII 22
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
50.0%
. 3
 
13.6%
2 2
 
9.1%
1 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%
3 1
 
4.5%
0 1
 
4.5%
Hangul
ValueCountFrequency (%)
능 3
 
7.0%
가 3
 
7.0%
만 3
 
7.0%
전 2
 
4.7%
일 2
 
4.7%
본 2
 
4.7%
어 2
 
4.7%
는 2
 
4.7%
간 2
 
4.7%
정 1
 
2.3%
Other values (21) 21
48.8%

Correlations

2024-04-06T20:14:49.488135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
Unnamed: 11.0001.000NaNNaN0.7081.000
Unnamed: 51.0001.0000.6730.6721.0001.000
Unnamed: 6NaN0.6731.0000.6050.000NaN
Unnamed: 7NaN0.6720.6051.000NaNNaN
Unnamed: 80.7081.0000.000NaN1.000NaN
Unnamed: 91.0001.000NaNNaNNaN1.000
2024-04-06T20:14:49.694315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 6Unnamed: 5Unnamed: 1Unnamed: 7
Unnamed: 61.0000.4691.0000.410
Unnamed: 50.4691.0000.9690.469
Unnamed: 11.0000.9691.0001.000
Unnamed: 70.4100.4691.0001.000
2024-04-06T20:14:49.891837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 1Unnamed: 5Unnamed: 6Unnamed: 7
Unnamed: 11.0000.9691.0001.000
Unnamed: 50.9691.0000.4690.469
Unnamed: 61.0000.4691.0000.410
Unnamed: 71.0000.4690.4101.000

Missing values

2024-04-06T20:14:41.488112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T20:14:41.789084image/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.
2024-04-06T20:14:42.082229image/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

외국어 가능 약국 현황 (서울 열린데이터 광장)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
0NaN<NA><NA><NA><NA><NA><NA><NA><NA>(2023. 1. 1. 기준)
1연번자치구약국이름주소 (도로명)전화번호가능 외국어<NA><NA><NA>비고
2NaN<NA><NA><NA><NA>영어중국어일본어기타<NA>
31종로구김해약국서울 종로구 종로 206(종로5가)02-2267-1551○○<NA><NA><NA>
42종로구보령약국서울 종로구 종로 203(종로5가)02-763-8181○○○<NA><NA>
53종로구서울종로약국서울 종로구 대학로 117 (명륜동4가)02-3676-4000○○<NA><NA><NA>
64종로구수도약국서울 종로구 인사동길 40 (관훈동)02-732-3336○<NA>○<NA><NA>
75종로구수약국서울 종로구 종로 33 그랑서울 지하1층02-778-5985○○○<NA><NA>
86종로구신서대문대학약국서울 종로구 경교장1길 102-730-0737○<NA><NA><NA><NA>
97종로구신아산약국서울 종로구 종로 255-2 (종로5가)02-763-4600○<NA>○<NA><NA>
외국어 가능 약국 현황 (서울 열린데이터 광장)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9
409407강동구베스트약국서울 강동구 양재대로 1325(성내동)02-474-2447○<NA><NA><NA><NA>
410408강동구강남약국서울 강동구 양재대로 1355(성내동)02-486-7823○○<NA><NA><NA>
411409강동구명일마트종로약국서울 강동구 고덕로 276, 지하(명일동, 이마트)02-428-7845○<NA><NA><NA><NA>
412410강동구파란약국서울 강동구 양재대로 1325(성내동)02-484-2479○<NA><NA><NA><NA>
413411강동구대한약국서울 강동구 천호대로 1027(천호동)02-483-9165○<NA><NA><NA><NA>
414412강동구한림약국서울 강동구 성안로 149(천호동)02-486-9744○<NA>○<NA>일본어는 파트타임 약사님만 가능
415413강동구시민당약국서울 강동구 양재대로 1478(길동)02-476-8100○<NA>○<NA><NA>
416414강동구위드팜천사약국서울 강동구 천호대로 1107, 101호(길동)02-484-5152○<NA>○<NA><NA>
417415강동구두레약국서울 강동구 고덕로83길 6(고덕동)02-429-1662○<NA>○<NA><NA>
418416강동구강동태평양약국서울 강동구 양재대로 1343 강동태평양약국02-473-3377○<NA><NA><NA><NA>