Overview

Dataset statistics

Number of variables12
Number of observations606
Missing cells759
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.9 KiB
Average record size in memory101.2 B

Variable types

Numeric4
Text5
Categorical3

Dataset

Description연번,책방 이름,구 코드,구 이름,주소,전화번호,홈페이지 url,책방 구분,책방 구분명,위도,경도,SNS url
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21062/S/1/datasetView.do

Alerts

책방 구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
책방 구분명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 책방 구분 and 1 other fieldsHigh correlation
구 코드 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 구 코드 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 구 이름High correlation
구 이름 is highly overall correlated with 구 코드 and 2 other fieldsHigh correlation
전화번호 has 50 (8.3%) missing valuesMissing
홈페이지 url has 368 (60.7%) missing valuesMissing
SNS url has 341 (56.3%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-05-11 07:07:14.173667
Analysis finished2024-05-11 07:07:16.699701
Duration2.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct606
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2670.9967
Minimum2283
Maximum3402
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-05-11T16:07:16.762056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2283
5-th percentile2317.25
Q12448.25
median2622.5
Q32793.75
95-th percentile3276
Maximum3402
Range1119
Interquartile range (IQR)345.5

Descriptive statistics

Standard deviation281.47196
Coefficient of variation (CV)0.10538087
Kurtosis-0.04619084
Mean2670.9967
Median Absolute Deviation (MAD)173
Skewness0.83396452
Sum1618624
Variance79226.466
MonotonicityNot monotonic
2024-05-11T16:07:16.873519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2283 1
 
0.2%
2578 1
 
0.2%
2580 1
 
0.2%
2581 1
 
0.2%
2582 1
 
0.2%
2586 1
 
0.2%
2587 1
 
0.2%
3347 1
 
0.2%
2588 1
 
0.2%
2589 1
 
0.2%
Other values (596) 596
98.3%
ValueCountFrequency (%)
2283 1
0.2%
2284 1
0.2%
2285 1
0.2%
2286 1
0.2%
2287 1
0.2%
2288 1
0.2%
2289 1
0.2%
2290 1
0.2%
2293 1
0.2%
2294 1
0.2%
ValueCountFrequency (%)
3402 1
0.2%
3401 1
0.2%
3384 1
0.2%
3383 1
0.2%
3382 1
0.2%
3381 1
0.2%
3361 1
0.2%
3350 1
0.2%
3349 1
0.2%
3348 1
0.2%

책방 이름
Text

Distinct591
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2024-05-11T16:07:17.134806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length5.9059406
Min length2

Characters and Unicode

Total characters3579
Distinct characters451
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique580 ?
Unique (%)95.7%

Sample

1st row1984
2nd row21세기문고
3rd rowC&S서점
4th rowItaewon books
5th rowPRNT
ValueCountFrequency (%)
알라딘 17
 
2.3%
책방 11
 
1.5%
서점 8
 
1.1%
바로드림센터 6
 
0.8%
더북스 4
 
0.5%
동아서점 3
 
0.4%
서재 3
 
0.4%
노원문고 3
 
0.4%
그림책방 3
 
0.4%
문화서점 3
 
0.4%
Other values (662) 679
91.8%
2024-05-11T16:07:17.537357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 230
 
6.4%
점 210
 
5.9%
문 140
 
3.9%
고 140
 
3.9%
137
 
3.8%
책 118
 
3.3%
스 82
 
2.3%
) 79
 
2.2%
( 79
 
2.2%
방 77
 
2.2%
Other values (441) 2287
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3192
89.2%
Space Separator 137
 
3.8%
Close Punctuation 79
 
2.2%
Open Punctuation 79
 
2.2%
Lowercase Letter 34
 
0.9%
Uppercase Letter 34
 
0.9%
Decimal Number 22
 
0.6%
Other Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 230
 
7.2%
점 210
 
6.6%
문 140
 
4.4%
고 140
 
4.4%
책 118
 
3.7%
스 82
 
2.6%
방 77
 
2.4%
북 62
 
1.9%
이 44
 
1.4%
적 35
 
1.1%
Other values (401) 2054
64.3%
Uppercase Letter
ValueCountFrequency (%)
P 4
11.8%
S 4
11.8%
C 4
11.8%
T 4
11.8%
I 3
8.8%
N 2
 
5.9%
B 2
 
5.9%
A 2
 
5.9%
Y 2
 
5.9%
E 2
 
5.9%
Other values (4) 5
14.7%
Lowercase Letter
ValueCountFrequency (%)
o 9
26.5%
a 4
11.8%
e 3
 
8.8%
t 3
 
8.8%
f 3
 
8.8%
n 3
 
8.8%
l 2
 
5.9%
k 2
 
5.9%
w 1
 
2.9%
b 1
 
2.9%
Other values (3) 3
 
8.8%
Decimal Number
ValueCountFrequency (%)
4 5
22.7%
1 5
22.7%
2 4
18.2%
7 3
13.6%
9 2
 
9.1%
8 1
 
4.5%
5 1
 
4.5%
3 1
 
4.5%
Space Separator
ValueCountFrequency (%)
137
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3192
89.2%
Common 319
 
8.9%
Latin 68
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 230
 
7.2%
점 210
 
6.6%
문 140
 
4.4%
고 140
 
4.4%
책 118
 
3.7%
스 82
 
2.6%
방 77
 
2.4%
북 62
 
1.9%
이 44
 
1.4%
적 35
 
1.1%
Other values (401) 2054
64.3%
Latin
ValueCountFrequency (%)
o 9
 
13.2%
a 4
 
5.9%
P 4
 
5.9%
S 4
 
5.9%
C 4
 
5.9%
T 4
 
5.9%
I 3
 
4.4%
e 3
 
4.4%
t 3
 
4.4%
f 3
 
4.4%
Other values (17) 27
39.7%
Common
ValueCountFrequency (%)
137
42.9%
) 79
24.8%
( 79
24.8%
4 5
 
1.6%
1 5
 
1.6%
2 4
 
1.3%
7 3
 
0.9%
9 2
 
0.6%
8 1
 
0.3%
5 1
 
0.3%
Other values (3) 3
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3192
89.2%
ASCII 387
 
10.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 230
 
7.2%
점 210
 
6.6%
문 140
 
4.4%
고 140
 
4.4%
책 118
 
3.7%
스 82
 
2.6%
방 77
 
2.4%
북 62
 
1.9%
이 44
 
1.4%
적 35
 
1.1%
Other values (401) 2054
64.3%
ASCII
ValueCountFrequency (%)
137
35.4%
) 79
20.4%
( 79
20.4%
o 9
 
2.3%
4 5
 
1.3%
1 5
 
1.3%
a 4
 
1.0%
P 4
 
1.0%
S 4
 
1.0%
2 4
 
1.0%
Other values (30) 57
14.7%

구 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.50495
Minimum300
Maximum324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-05-11T16:07:17.648725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum300
5-th percentile300
Q1304
median313
Q3318
95-th percentile323
Maximum324
Range24
Interquartile range (IQR)14

Descriptive statistics

Standard deviation7.5459458
Coefficient of variation (CV)0.02422416
Kurtosis-1.187591
Mean311.50495
Median Absolute Deviation (MAD)6
Skewness-0.09692884
Sum188772
Variance56.941298
MonotonicityNot monotonic
2024-05-11T16:07:17.776713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
313 73
 
12.0%
300 66
 
10.9%
301 35
 
5.8%
320 33
 
5.4%
322 31
 
5.1%
315 30
 
5.0%
312 24
 
4.0%
310 24
 
4.0%
307 23
 
3.8%
323 23
 
3.8%
Other values (15) 244
40.3%
ValueCountFrequency (%)
300 66
10.9%
301 35
5.8%
302 21
 
3.5%
303 17
 
2.8%
304 15
 
2.5%
305 8
 
1.3%
306 13
 
2.1%
307 23
 
3.8%
308 11
 
1.8%
309 17
 
2.8%
ValueCountFrequency (%)
324 16
2.6%
323 23
3.8%
322 31
5.1%
321 19
3.1%
320 33
5.4%
319 22
3.6%
318 21
3.5%
317 10
 
1.7%
316 12
 
2.0%
315 30
5.0%

구 이름
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
마포구
73 
종로구
66 
중구
 
35
관악구
 
33
강남구
 
31
Other values (20)
368 

Length

Max length4
Median length3
Mean length3.029703
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row마포구
2nd row강남구
3rd row강남구
4th row용산구
5th row동작구

Common Values

ValueCountFrequency (%)
마포구 73
 
12.0%
종로구 66
 
10.9%
중구 35
 
5.8%
관악구 33
 
5.4%
강남구 31
 
5.1%
강서구 30
 
5.0%
서대문구 24
 
4.0%
노원구 24
 
4.0%
성북구 23
 
3.8%
송파구 23
 
3.8%
Other values (15) 244
40.3%

Length

2024-05-11T16:07:17.902957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마포구 73
 
12.0%
종로구 66
 
10.9%
중구 35
 
5.8%
관악구 33
 
5.4%
강남구 31
 
5.1%
강서구 30
 
5.0%
서대문구 24
 
4.0%
노원구 24
 
4.0%
성북구 23
 
3.8%
송파구 23
 
3.8%
Other values (15) 244
40.3%

주소
Text

Distinct600
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2024-05-11T16:07:18.223067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length32
Mean length16.379538
Min length6

Characters and Unicode

Total characters9926
Distinct characters346
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique594 ?
Unique (%)98.0%

Sample

1st row마포구 동교로 194 혜원빌딩
2nd row강남구 남부순환로359길 31
3rd row강남구 남부순환로 2806 군인공제회관 지하1층
4th row용산구 녹사평대로 208
5th row동작구 만양로1길 1 1층
ValueCountFrequency (%)
1층 75
 
3.2%
지하1층 69
 
2.9%
종로구 62
 
2.6%
지하 60
 
2.5%
2층 60
 
2.5%
마포구 57
 
2.4%
중구 34
 
1.4%
관악구 30
 
1.3%
강남구 28
 
1.2%
강서구 27
 
1.1%
Other values (1070) 1868
78.8%
2024-05-11T16:07:18.672835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1783
 
18.0%
로 649
 
6.5%
1 641
 
6.5%
구 575
 
5.8%
2 376
 
3.8%
길 287
 
2.9%
3 275
 
2.8%
층 249
 
2.5%
4 223
 
2.2%
5 195
 
2.0%
Other values (336) 4673
47.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5496
55.4%
Decimal Number 2468
24.9%
Space Separator 1783
 
18.0%
Dash Punctuation 99
 
1.0%
Uppercase Letter 33
 
0.3%
Other Punctuation 22
 
0.2%
Lowercase Letter 15
 
0.2%
Open Punctuation 4
 
< 0.1%
Close Punctuation 4
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 649
 
11.8%
구 575
 
10.5%
길 287
 
5.2%
층 249
 
4.5%
하 150
 
2.7%
지 145
 
2.6%
동 126
 
2.3%
대 106
 
1.9%
강 103
 
1.9%
서 102
 
1.9%
Other values (300) 3004
54.7%
Uppercase Letter
ValueCountFrequency (%)
B 8
24.2%
A 6
18.2%
C 4
12.1%
Y 2
 
6.1%
F 2
 
6.1%
S 2
 
6.1%
M 2
 
6.1%
N 2
 
6.1%
L 1
 
3.0%
I 1
 
3.0%
Other values (3) 3
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 641
26.0%
2 376
15.2%
3 275
11.1%
4 223
 
9.0%
5 195
 
7.9%
6 174
 
7.1%
0 171
 
6.9%
7 157
 
6.4%
9 129
 
5.2%
8 127
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
e 3
20.0%
b 3
20.0%
l 3
20.0%
m 2
13.3%
c 2
13.3%
a 1
 
6.7%
n 1
 
6.7%
Space Separator
ValueCountFrequency (%)
1783
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5496
55.4%
Common 4382
44.1%
Latin 48
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 649
 
11.8%
구 575
 
10.5%
길 287
 
5.2%
층 249
 
4.5%
하 150
 
2.7%
지 145
 
2.6%
동 126
 
2.3%
대 106
 
1.9%
강 103
 
1.9%
서 102
 
1.9%
Other values (300) 3004
54.7%
Latin
ValueCountFrequency (%)
B 8
16.7%
A 6
12.5%
C 4
 
8.3%
e 3
 
6.2%
b 3
 
6.2%
l 3
 
6.2%
Y 2
 
4.2%
m 2
 
4.2%
F 2
 
4.2%
c 2
 
4.2%
Other values (10) 13
27.1%
Common
ValueCountFrequency (%)
1783
40.7%
1 641
 
14.6%
2 376
 
8.6%
3 275
 
6.3%
4 223
 
5.1%
5 195
 
4.5%
6 174
 
4.0%
0 171
 
3.9%
7 157
 
3.6%
9 129
 
2.9%
Other values (6) 258
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5496
55.4%
ASCII 4430
44.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1783
40.2%
1 641
 
14.5%
2 376
 
8.5%
3 275
 
6.2%
4 223
 
5.0%
5 195
 
4.4%
6 174
 
3.9%
0 171
 
3.9%
7 157
 
3.5%
9 129
 
2.9%
Other values (26) 306
 
6.9%
Hangul
ValueCountFrequency (%)
로 649
 
11.8%
구 575
 
10.5%
길 287
 
5.2%
층 249
 
4.5%
하 150
 
2.7%
지 145
 
2.6%
동 126
 
2.3%
대 106
 
1.9%
강 103
 
1.9%
서 102
 
1.9%
Other values (300) 3004
54.7%

전화번호
Text

MISSING 

Distinct521
Distinct (%)93.7%
Missing50
Missing (%)8.3%
Memory size4.9 KiB
2024-05-11T16:07:18.879703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length11.861511
Min length1

Characters and Unicode

Total characters6595
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique516 ?
Unique (%)92.8%

Sample

1st row02-325-1984
2nd row02-3463-1880
3rd row02-2190-2178
4th row02-793-8249
5th row070-4177-0021
ValueCountFrequency (%)
1544-2514 18
 
3.3%
1544-1900 15
 
2.7%
070-4070-0204 2
 
0.4%
0507-1305-5475 2
 
0.4%
02-944-2651 1
 
0.2%
02-374-8917 1
 
0.2%
0507-1413-4144 1
 
0.2%
02-3411-3215 1
 
0.2%
02-529-5949 1
 
0.2%
0507-1414-6922 1
 
0.2%
Other values (510) 510
92.2%
2024-05-11T16:07:19.228453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1072
16.3%
- 1071
16.2%
2 894
13.6%
1 540
8.2%
4 525
8.0%
7 516
7.8%
5 496
7.5%
3 453
6.9%
8 342
 
5.2%
9 329
 
5.0%
Other values (2) 357
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5491
83.3%
Dash Punctuation 1071
 
16.2%
Space Separator 33
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1072
19.5%
2 894
16.3%
1 540
9.8%
4 525
9.6%
7 516
9.4%
5 496
9.0%
3 453
8.2%
8 342
 
6.2%
9 329
 
6.0%
6 324
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 1071
100.0%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1072
16.3%
- 1071
16.2%
2 894
13.6%
1 540
8.2%
4 525
8.0%
7 516
7.8%
5 496
7.5%
3 453
6.9%
8 342
 
5.2%
9 329
 
5.0%
Other values (2) 357
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1072
16.3%
- 1071
16.2%
2 894
13.6%
1 540
8.2%
4 525
8.0%
7 516
7.8%
5 496
7.5%
3 453
6.9%
8 342
 
5.2%
9 329
 
5.0%
Other values (2) 357
 
5.4%

홈페이지 url
Text

MISSING 

Distinct231
Distinct (%)97.1%
Missing368
Missing (%)60.7%
Memory size4.9 KiB
2024-05-11T16:07:19.465010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length99
Median length46
Mean length31.554622
Min length13

Characters and Unicode

Total characters7510
Distinct characters67
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique226 ?
Unique (%)95.0%

Sample

1st rowhttps://prntseoul.com
2nd rowhttp://77page.com
3rd rowhttps://blog.naver.com/dnfladydgk
4th rowhttps://blog.naver.com/experiencelibrary
5th rowhttp://www.bookgore.com/
ValueCountFrequency (%)
http://www.storagebookandfilm.com 3
 
1.3%
http://www.nowonbook.com 3
 
1.3%
https://www.the-ref.kr 2
 
0.8%
https://smartstore.naver.com/kenektidxbookstore 2
 
0.8%
https://www.bookslibro.com 2
 
0.8%
http://blog.naver.com/now_afterbooks 1
 
0.4%
http://www.eraebook.co.kr 1
 
0.4%
https://www.frederic.co.kr 1
 
0.4%
http://hululuc.com 1
 
0.4%
http://www.2sangbook.com 1
 
0.4%
Other values (222) 222
92.9%
2024-05-11T16:07:19.824076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 810
 
10.8%
t 660
 
8.8%
/ 637
 
8.5%
. 477
 
6.4%
s 412
 
5.5%
r 350
 
4.7%
e 346
 
4.6%
h 343
 
4.6%
a 336
 
4.5%
p 300
 
4.0%
Other values (57) 2839
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5925
78.9%
Other Punctuation 1370
 
18.2%
Decimal Number 107
 
1.4%
Uppercase Letter 61
 
0.8%
Connector Punctuation 26
 
0.3%
Dash Punctuation 15
 
0.2%
Space Separator 4
 
0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 810
13.7%
t 660
 
11.1%
s 412
 
7.0%
r 350
 
5.9%
e 346
 
5.8%
h 343
 
5.8%
a 336
 
5.7%
p 300
 
5.1%
m 288
 
4.9%
c 283
 
4.8%
Other values (16) 1797
30.3%
Uppercase Letter
ValueCountFrequency (%)
C 8
13.1%
A 7
11.5%
B 6
 
9.8%
E 6
 
9.8%
R 4
 
6.6%
D 4
 
6.6%
X 3
 
4.9%
J 3
 
4.9%
N 3
 
4.9%
W 2
 
3.3%
Other values (11) 15
24.6%
Decimal Number
ValueCountFrequency (%)
1 19
17.8%
2 14
13.1%
0 12
11.2%
9 12
11.2%
4 11
10.3%
5 10
9.3%
8 8
7.5%
3 8
7.5%
7 7
 
6.5%
6 6
 
5.6%
Other Punctuation
ValueCountFrequency (%)
/ 637
46.5%
. 477
34.8%
: 237
 
17.3%
% 15
 
1.1%
@ 2
 
0.1%
? 2
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Math Symbol
ValueCountFrequency (%)
= 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5986
79.7%
Common 1524
 
20.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 810
13.5%
t 660
 
11.0%
s 412
 
6.9%
r 350
 
5.8%
e 346
 
5.8%
h 343
 
5.7%
a 336
 
5.6%
p 300
 
5.0%
m 288
 
4.8%
c 283
 
4.7%
Other values (37) 1858
31.0%
Common
ValueCountFrequency (%)
/ 637
41.8%
. 477
31.3%
: 237
 
15.6%
_ 26
 
1.7%
1 19
 
1.2%
% 15
 
1.0%
- 15
 
1.0%
2 14
 
0.9%
0 12
 
0.8%
9 12
 
0.8%
Other values (10) 60
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7510
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 810
 
10.8%
t 660
 
8.8%
/ 637
 
8.5%
. 477
 
6.4%
s 412
 
5.5%
r 350
 
4.7%
e 346
 
4.6%
h 343
 
4.6%
a 336
 
4.5%
p 300
 
4.0%
Other values (57) 2839
37.8%

책방 구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
2
522 
1
84 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 522
86.1%
1 84
 
13.9%

Length

2024-05-11T16:07:19.939882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:07:20.018835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 522
86.1%
1 84
 
13.9%

책방 구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
새책방
522 
헌책방
84 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row새책방
2nd row새책방
3rd row새책방
4th row헌책방
5th row새책방

Common Values

ValueCountFrequency (%)
새책방 522
86.1%
헌책방 84
 
13.9%

Length

2024-05-11T16:07:20.112772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T16:07:20.202813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
새책방 522
86.1%
헌책방 84
 
13.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct576
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552184
Minimum37.44935
Maximum37.684345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-05-11T16:07:20.305359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.44935
5-th percentile37.477872
Q137.516755
median37.554805
Q337.578931
95-th percentile37.645832
Maximum37.684345
Range0.2349946
Interquartile range (IQR)0.062176238

Descriptive statistics

Standard deviation0.046686331
Coefficient of variation (CV)0.0012432388
Kurtosis-0.14158927
Mean37.552184
Median Absolute Deviation (MAD)0.028219123
Skewness0.22392207
Sum22756.624
Variance0.0021796135
MonotonicityNot monotonic
2024-05-11T16:07:20.417196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5694258617472 15
 
2.5%
37.5706568981549 7
 
1.2%
37.5679798569884 2
 
0.3%
37.573776066701 2
 
0.3%
37.5395404996998 2
 
0.3%
37.5409328113161 2
 
0.3%
37.5364764239853 2
 
0.3%
37.5693683779231 2
 
0.3%
37.5717572313412 2
 
0.3%
37.5855682577741 2
 
0.3%
Other values (566) 568
93.7%
ValueCountFrequency (%)
37.4493499425115 1
0.2%
37.4505063344668 1
0.2%
37.4506617287092 1
0.2%
37.455143719434 1
0.2%
37.4566169360635 1
0.2%
37.4595006193821 1
0.2%
37.4609911113644 1
0.2%
37.4645381767935 1
0.2%
37.4686620625239 1
0.2%
37.4689381362592 1
0.2%
ValueCountFrequency (%)
37.6843445449287 1
0.2%
37.6834512512412 1
0.2%
37.6781841932825 1
0.2%
37.6674429516351 1
0.2%
37.666911617005 1
0.2%
37.6645718853517 1
0.2%
37.6621909006666 1
0.2%
37.6587118738827 1
0.2%
37.6579748616037 1
0.2%
37.6576975981725 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct576
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.97949
Minimum126.80296
Maximum127.17115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.5 KiB
2024-05-11T16:07:20.537647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80296
5-th percentile126.8546
Q1126.92027
median126.98038
Q3127.03337
95-th percentile127.10459
Maximum127.17115
Range0.36818479
Interquartile range (IQR)0.11309982

Descriptive statistics

Standard deviation0.075694348
Coefficient of variation (CV)0.00059611475
Kurtosis-0.56756748
Mean126.97949
Median Absolute Deviation (MAD)0.057865394
Skewness0.11385238
Sum76949.571
Variance0.0057296343
MonotonicityNot monotonic
2024-05-11T16:07:20.656832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.00804756339 15
 
2.5%
127.006479473351 7
 
1.2%
126.995365114169 2
 
0.3%
127.004180855855 2
 
0.3%
126.949490374862 2
 
0.3%
126.993591556374 2
 
0.3%
126.867389257907 2
 
0.3%
127.004721112823 2
 
0.3%
127.018765856837 2
 
0.3%
127.000601419034 2
 
0.3%
Other values (566) 568
93.7%
ValueCountFrequency (%)
126.80296035927 1
0.2%
126.81297893299 1
0.2%
126.813650452864 1
0.2%
126.822484282875 1
0.2%
126.823249100217 1
0.2%
126.831580783627 1
0.2%
126.831692301904 1
0.2%
126.832970268909 1
0.2%
126.833972867298 1
0.2%
126.834185904309 1
0.2%
ValueCountFrequency (%)
127.17114514801 1
0.2%
127.155472908009 1
0.2%
127.15511458665 1
0.2%
127.155000732652 1
0.2%
127.153830303625 1
0.2%
127.153137021369 1
0.2%
127.147108511955 1
0.2%
127.146146799638 1
0.2%
127.145304402527 1
0.2%
127.144013803921 1
0.2%

SNS url
Text

MISSING 

Distinct263
Distinct (%)99.2%
Missing341
Missing (%)56.3%
Memory size4.9 KiB
2024-05-11T16:07:20.918554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length47
Mean length39.060377
Min length14

Characters and Unicode

Total characters10351
Distinct characters53
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)98.5%

Sample

1st rowhttps://www.instagram.com/1984store
2nd rowhttps://www.instagram.com/itaewon_foreign_bookstore
3rd rowhttps://www.instagram.com/prntseoul
4th rowhttps://www.instagram.com/gaga77page
5th rowhttps://www.instagram.com/gamseongingan
ValueCountFrequency (%)
https://www.instagram.com/the_reference_seoul 2
 
0.8%
https://www.instagram.com/graphic.fan 2
 
0.8%
https://www.instagram.com/kenektidxbookstore 2
 
0.8%
https://www.instagram.com/pumpkin_vege_book 1
 
0.4%
https://www.instagram.com/the_present_world 1
 
0.4%
https://www.instagram.com/kenektid_flagship 1
 
0.4%
https://www.instagram.com/ongodangbook 1
 
0.4%
https://www.instagram.com/bulon0802 1
 
0.4%
https://www.instagram.com/allornothing_deardark 1
 
0.4%
http://www.instagram.com/worldmag.co.kr 1
 
0.4%
Other values (253) 253
95.1%
2024-05-11T16:07:21.302217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 906
 
8.8%
o 858
 
8.3%
/ 843
 
8.1%
w 816
 
7.9%
s 756
 
7.3%
a 740
 
7.1%
m 600
 
5.8%
. 573
 
5.5%
n 454
 
4.4%
i 413
 
4.0%
Other values (43) 3392
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8439
81.5%
Other Punctuation 1681
 
16.2%
Connector Punctuation 129
 
1.2%
Decimal Number 83
 
0.8%
Uppercase Letter 11
 
0.1%
Space Separator 5
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 906
10.7%
o 858
10.2%
w 816
 
9.7%
s 756
 
9.0%
a 740
 
8.8%
m 600
 
7.1%
n 454
 
5.4%
i 413
 
4.9%
r 402
 
4.8%
h 374
 
4.4%
Other values (16) 2120
25.1%
Decimal Number
ValueCountFrequency (%)
2 16
19.3%
1 14
16.9%
0 13
15.7%
4 9
10.8%
3 8
9.6%
7 8
9.6%
8 5
 
6.0%
5 4
 
4.8%
9 4
 
4.8%
6 2
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
G 2
18.2%
E 2
18.2%
U 1
9.1%
C 1
9.1%
H 1
9.1%
A 1
9.1%
J 1
9.1%
B 1
9.1%
D 1
9.1%
Other Punctuation
ValueCountFrequency (%)
/ 843
50.1%
. 573
34.1%
: 264
 
15.7%
? 1
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 129
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8450
81.6%
Common 1901
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 906
10.7%
o 858
10.2%
w 816
 
9.7%
s 756
 
8.9%
a 740
 
8.8%
m 600
 
7.1%
n 454
 
5.4%
i 413
 
4.9%
r 402
 
4.8%
h 374
 
4.4%
Other values (25) 2131
25.2%
Common
ValueCountFrequency (%)
/ 843
44.3%
. 573
30.1%
: 264
 
13.9%
_ 129
 
6.8%
2 16
 
0.8%
1 14
 
0.7%
0 13
 
0.7%
4 9
 
0.5%
3 8
 
0.4%
7 8
 
0.4%
Other values (8) 24
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 906
 
8.8%
o 858
 
8.3%
/ 843
 
8.1%
w 816
 
7.9%
s 756
 
7.3%
a 740
 
7.1%
m 600
 
5.8%
. 573
 
5.5%
n 454
 
4.4%
i 413
 
4.0%
Other values (43) 3392
32.8%

Interactions

2024-05-11T16:07:15.818833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:14.847829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.167562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.527370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.905414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:14.921942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.256481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.606565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.988118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.000784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.357906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.674404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:16.073452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.082191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.444806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:07:15.741293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:07:21.404042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구 코드구 이름책방 구분책방 구분명위도경도
연번1.0000.1360.2550.9660.9660.1300.085
구 코드0.1361.0001.0000.2180.2180.8960.901
구 이름0.2551.0001.0000.3010.3010.9310.939
책방 구분0.9660.2180.3011.0001.0000.1940.222
책방 구분명0.9660.2180.3011.0001.0000.1940.222
위도0.1300.8960.9310.1940.1941.0000.659
경도0.0850.9010.9390.2220.2220.6591.000
2024-05-11T16:07:21.494821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
책방 구분책방 구분명구 이름
책방 구분1.0000.9930.255
책방 구분명0.9931.0000.255
구 이름0.2550.2551.000
2024-05-11T16:07:21.581012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구 코드위도경도구 이름책방 구분책방 구분명
연번1.000-0.0710.0300.0570.0900.8360.836
구 코드-0.0711.000-0.710-0.1250.9870.0920.092
위도0.030-0.7101.0000.2050.6640.1470.147
경도0.057-0.1250.2051.0000.6850.1690.169
구 이름0.0900.9870.6640.6851.0000.2550.255
책방 구분0.8360.0920.1470.1690.2551.0000.993
책방 구분명0.8360.0920.1470.1690.2550.9931.000

Missing values

2024-05-11T16:07:16.386490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:07:16.533366image/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.
2024-05-11T16:07:16.632303image/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

연번책방 이름구 코드구 이름주소전화번호홈페이지 url책방 구분책방 구분명위도경도SNS url
022831984313마포구마포구 동교로 194 혜원빌딩02-325-1984<NA>2새책방37.557385126.922886https://www.instagram.com/1984store
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6012749황룡서점323송파구송파구 마천로 27102-400-4501<NA>2새책방37.497805127.147109<NA>
6023350회전문서재320관악구조원로 2길 55 1층 4호 노란색 벽0507-1455-7025https://booking.naver.com/afterwork-library2새책방37.48128126.90138<NA>
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