Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells41599
Missing cells (%)34.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory111.0 B

Variable types

Numeric7
Text3
Categorical1
Boolean1

Dataset

Description서울시내 위치한 식당의 품질정보를 적어놓은 데이터 입니다. 식당명, 어워드정보, RTI지수, 온라인화진행여부, 수용태세지수, 인기도 등 식당품질정보를 포함하고 있습니다.
Author서울관광재단
URLhttps://www.data.go.kr/data/15098044/fileData.do

Alerts

(RTI)지수 is highly overall correlated with 수용태세지수 and 1 other fieldsHigh correlation
수용태세지수 is highly overall correlated with (RTI)지수High correlation
인기도 is highly overall correlated with (RTI)지수High correlation
지점명 has 7033 (70.3%) missing valuesMissing
어워드정보설명 has 9542 (95.4%) missing valuesMissing
온라인화진행여부 has 784 (7.8%) missing valuesMissing
인기도 has 2820 (28.2%) missing valuesMissing
트립어드바이저평점 has 9667 (96.7%) missing valuesMissing
씨트립평점 has 9974 (99.7%) missing valuesMissing
네이버평점 has 1712 (17.1%) missing valuesMissing
식당(ID) has unique valuesUnique

Reproduction

Analysis started2023-12-12 00:02:13.576150
Analysis finished2023-12-12 00:02:21.525528
Duration7.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

식당(ID)
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135740.56
Minimum10018
Maximum769766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:21.611177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10018
5-th percentile36442.9
Q160191.5
median89805
Q3121101
95-th percentile650477.5
Maximum769766
Range759748
Interquartile range (IQR)60909.5

Descriptive statistics

Standard deviation173037.83
Coefficient of variation (CV)1.2747688
Kurtosis7.1509686
Mean135740.56
Median Absolute Deviation (MAD)30380
Skewness2.9248316
Sum1.3574056 × 109
Variance2.994209 × 1010
MonotonicityNot monotonic
2023-12-12T09:02:21.791597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95559 1
 
< 0.1%
102868 1
 
< 0.1%
49184 1
 
< 0.1%
61193 1
 
< 0.1%
58590 1
 
< 0.1%
58598 1
 
< 0.1%
62741 1
 
< 0.1%
93640 1
 
< 0.1%
120077 1
 
< 0.1%
75396 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
10018 1
< 0.1%
10045 1
< 0.1%
10083 1
< 0.1%
10087 1
< 0.1%
10758 1
< 0.1%
10991 1
< 0.1%
12266 1
< 0.1%
12271 1
< 0.1%
12273 1
< 0.1%
12294 1
< 0.1%
ValueCountFrequency (%)
769766 1
< 0.1%
769740 1
< 0.1%
769726 1
< 0.1%
769631 1
< 0.1%
769616 1
< 0.1%
769559 1
< 0.1%
769545 1
< 0.1%
769529 1
< 0.1%
769508 1
< 0.1%
769481 1
< 0.1%
Distinct8580
Distinct (%)85.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:02:22.379905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length28
Mean length5.1211
Min length1

Characters and Unicode

Total characters51211
Distinct characters1099
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8012 ?
Unique (%)80.1%

Sample

1st row우리
2nd row그린푸드 생선구이
3rd row카페 컴플렉스
4th row고향마을
5th row정미당
ValueCountFrequency (%)
카페 83
 
0.7%
이디야커피 68
 
0.6%
파리바게뜨 63
 
0.6%
커피 34
 
0.3%
투썸플레이스 32
 
0.3%
본죽 27
 
0.2%
교촌치킨 23
 
0.2%
스타벅스 20
 
0.2%
김밥천국 20
 
0.2%
이삭토스트 19
 
0.2%
Other values (9232) 11039
96.6%
2023-12-12T09:02:22.852778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1428
 
2.8%
1347
 
2.6%
1045
 
2.0%
891
 
1.7%
662
 
1.3%
624
 
1.2%
573
 
1.1%
560
 
1.1%
523
 
1.0%
516
 
1.0%
Other values (1089) 43042
84.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47905
93.5%
Space Separator 1428
 
2.8%
Decimal Number 640
 
1.2%
Lowercase Letter 418
 
0.8%
Uppercase Letter 396
 
0.8%
Other Punctuation 193
 
0.4%
Close Punctuation 109
 
0.2%
Open Punctuation 108
 
0.2%
Dash Punctuation 6
 
< 0.1%
Other Symbol 2
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1347
 
2.8%
1045
 
2.2%
891
 
1.9%
662
 
1.4%
624
 
1.3%
573
 
1.2%
560
 
1.2%
523
 
1.1%
516
 
1.1%
506
 
1.1%
Other values (1008) 40658
84.9%
Uppercase Letter
ValueCountFrequency (%)
E 40
 
10.1%
A 32
 
8.1%
O 29
 
7.3%
F 28
 
7.1%
C 24
 
6.1%
M 23
 
5.8%
B 23
 
5.8%
N 19
 
4.8%
R 18
 
4.5%
T 17
 
4.3%
Other values (16) 143
36.1%
Lowercase Letter
ValueCountFrequency (%)
e 60
14.4%
o 42
 
10.0%
a 41
 
9.8%
s 30
 
7.2%
t 29
 
6.9%
n 27
 
6.5%
u 20
 
4.8%
i 20
 
4.8%
c 19
 
4.5%
r 18
 
4.3%
Other values (14) 112
26.8%
Decimal Number
ValueCountFrequency (%)
1 114
17.8%
0 92
14.4%
2 76
11.9%
9 75
11.7%
5 60
9.4%
7 58
9.1%
4 54
8.4%
3 52
8.1%
6 33
 
5.2%
8 26
 
4.1%
Other Punctuation
ValueCountFrequency (%)
& 96
49.7%
, 35
 
18.1%
. 30
 
15.5%
! 9
 
4.7%
' 6
 
3.1%
# 5
 
2.6%
· 5
 
2.6%
/ 4
 
2.1%
: 3
 
1.6%
Other Symbol
ValueCountFrequency (%)
° 1
50.0%
1
50.0%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Symbol
ValueCountFrequency (%)
+ 1
50.0%
~ 1
50.0%
Space Separator
ValueCountFrequency (%)
1428
100.0%
Close Punctuation
ValueCountFrequency (%)
) 109
100.0%
Open Punctuation
ValueCountFrequency (%)
( 108
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47896
93.5%
Common 2490
 
4.9%
Latin 816
 
1.6%
Han 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1347
 
2.8%
1045
 
2.2%
891
 
1.9%
662
 
1.4%
624
 
1.3%
573
 
1.2%
560
 
1.2%
523
 
1.1%
516
 
1.1%
506
 
1.1%
Other values (1000) 40649
84.9%
Latin
ValueCountFrequency (%)
e 60
 
7.4%
o 42
 
5.1%
a 41
 
5.0%
E 40
 
4.9%
A 32
 
3.9%
s 30
 
3.7%
O 29
 
3.6%
t 29
 
3.6%
F 28
 
3.4%
n 27
 
3.3%
Other values (42) 458
56.1%
Common
ValueCountFrequency (%)
1428
57.3%
1 114
 
4.6%
) 109
 
4.4%
( 108
 
4.3%
& 96
 
3.9%
0 92
 
3.7%
2 76
 
3.1%
9 75
 
3.0%
5 60
 
2.4%
7 58
 
2.3%
Other values (19) 274
 
11.0%
Han
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47896
93.5%
ASCII 3297
 
6.4%
CJK 9
 
< 0.1%
None 6
 
< 0.1%
Number Forms 2
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1428
43.3%
1 114
 
3.5%
) 109
 
3.3%
( 108
 
3.3%
& 96
 
2.9%
0 92
 
2.8%
2 76
 
2.3%
9 75
 
2.3%
e 60
 
1.8%
5 60
 
1.8%
Other values (66) 1079
32.7%
Hangul
ValueCountFrequency (%)
1347
 
2.8%
1045
 
2.2%
891
 
1.9%
662
 
1.4%
624
 
1.3%
573
 
1.2%
560
 
1.2%
523
 
1.1%
516
 
1.1%
506
 
1.1%
Other values (1000) 40649
84.9%
None
ValueCountFrequency (%)
· 5
83.3%
° 1
 
16.7%
CJK
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

지점명
Text

MISSING 

Distinct1408
Distinct (%)47.5%
Missing7033
Missing (%)70.3%
Memory size156.2 KiB
2023-12-12T09:02:23.089651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length4.4101786
Min length2

Characters and Unicode

Total characters13085
Distinct characters405
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique963 ?
Unique (%)32.5%

Sample

1st row안녕인사동점
2nd row성수점
3rd row건대점
4th row본점
5th row엔씨백화점 강서점
ValueCountFrequency (%)
본점 115
 
3.8%
2호점 24
 
0.8%
송파점 23
 
0.8%
목동점 22
 
0.7%
홍대점 20
 
0.7%
신림점 20
 
0.7%
수유점 20
 
0.7%
강남점 19
 
0.6%
역삼점 18
 
0.6%
노원점 18
 
0.6%
Other values (1418) 2767
90.2%
2023-12-12T09:02:23.448472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3027
 
23.1%
400
 
3.1%
376
 
2.9%
266
 
2.0%
239
 
1.8%
222
 
1.7%
189
 
1.4%
171
 
1.3%
166
 
1.3%
147
 
1.1%
Other values (395) 7882
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12651
96.7%
Decimal Number 219
 
1.7%
Uppercase Letter 108
 
0.8%
Space Separator 98
 
0.7%
Other Punctuation 5
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Control 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3027
23.9%
400
 
3.2%
376
 
3.0%
266
 
2.1%
239
 
1.9%
222
 
1.8%
189
 
1.5%
171
 
1.4%
166
 
1.3%
147
 
1.2%
Other values (354) 7448
58.9%
Uppercase Letter
ValueCountFrequency (%)
C 19
17.6%
T 12
11.1%
N 11
10.2%
K 9
8.3%
S 8
 
7.4%
D 7
 
6.5%
G 6
 
5.6%
I 6
 
5.6%
M 4
 
3.7%
F 4
 
3.7%
Other values (12) 22
20.4%
Decimal Number
ValueCountFrequency (%)
1 82
37.4%
2 75
34.2%
3 18
 
8.2%
0 10
 
4.6%
4 10
 
4.6%
5 10
 
4.6%
7 6
 
2.7%
6 3
 
1.4%
9 3
 
1.4%
8 2
 
0.9%
Other Punctuation
ValueCountFrequency (%)
& 2
40.0%
. 1
20.0%
, 1
20.0%
/ 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
h 1
50.0%
e 1
50.0%
Space Separator
ValueCountFrequency (%)
98
100.0%
Control
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12651
96.7%
Common 324
 
2.5%
Latin 110
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3027
23.9%
400
 
3.2%
376
 
3.0%
266
 
2.1%
239
 
1.9%
222
 
1.8%
189
 
1.5%
171
 
1.4%
166
 
1.3%
147
 
1.2%
Other values (354) 7448
58.9%
Latin
ValueCountFrequency (%)
C 19
17.3%
T 12
10.9%
N 11
10.0%
K 9
 
8.2%
S 8
 
7.3%
D 7
 
6.4%
G 6
 
5.5%
I 6
 
5.5%
M 4
 
3.6%
F 4
 
3.6%
Other values (14) 24
21.8%
Common
ValueCountFrequency (%)
98
30.2%
1 82
25.3%
2 75
23.1%
3 18
 
5.6%
0 10
 
3.1%
4 10
 
3.1%
5 10
 
3.1%
7 6
 
1.9%
6 3
 
0.9%
9 3
 
0.9%
Other values (7) 9
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12651
96.7%
ASCII 434
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3027
23.9%
400
 
3.2%
376
 
3.0%
266
 
2.1%
239
 
1.9%
222
 
1.8%
189
 
1.5%
171
 
1.4%
166
 
1.3%
147
 
1.2%
Other values (354) 7448
58.9%
ASCII
ValueCountFrequency (%)
98
22.6%
1 82
18.9%
2 75
17.3%
C 19
 
4.4%
3 18
 
4.1%
T 12
 
2.8%
N 11
 
2.5%
0 10
 
2.3%
4 10
 
2.3%
5 10
 
2.3%
Other values (31) 89
20.5%

지역명
Categorical

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
강남구
793 
중구
786 
마포구
 
585
송파구
 
564
영등포구
 
494
Other values (20)
6778 

Length

Max length4
Median length3
Mean length3.0385
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서초구
2nd row중구
3rd row강남구
4th row강남구
5th row도봉구

Common Values

ValueCountFrequency (%)
강남구 793
 
7.9%
중구 786
 
7.9%
마포구 585
 
5.9%
송파구 564
 
5.6%
영등포구 494
 
4.9%
강서구 462
 
4.6%
서초구 461
 
4.6%
종로구 428
 
4.3%
광진구 416
 
4.2%
관악구 399
 
4.0%
Other values (15) 4612
46.1%

Length

2023-12-12T09:02:23.600208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 793
 
7.9%
중구 786
 
7.9%
마포구 585
 
5.9%
송파구 564
 
5.6%
영등포구 494
 
4.9%
강서구 462
 
4.6%
서초구 461
 
4.6%
종로구 428
 
4.3%
광진구 416
 
4.2%
관악구 399
 
4.0%
Other values (15) 4612
46.1%

어워드정보설명
Text

MISSING 

Distinct66
Distinct (%)14.4%
Missing9542
Missing (%)95.4%
Memory size156.2 KiB
2023-12-12T09:02:23.864320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length50
Mean length14.056769
Min length10

Characters and Unicode

Total characters6438
Distinct characters41
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

Unique19 ?
Unique (%)4.1%

Sample

1st row안심식당(2020)
2nd row모범음식점(2018)
3rd row안심식당(2021)
4th row안심식당(2020)
5th row안심식당(2021)
ValueCountFrequency (%)
안심식당(2020 79
 
15.6%
블루리본(2021 47
 
9.3%
미쉐린 21
 
4.2%
모범음식점(2021 18
 
3.6%
모범음식점(2007 18
 
3.6%
가이드(2021),미쉐린 16
 
3.2%
모범음식점(2015 14
 
2.8%
모범음식점(2009),안심식당(2020 14
 
2.8%
모범음식점(2017 14
 
2.8%
안심식당(2021 14
 
2.8%
Other values (59) 250
49.5%
2023-12-12T09:02:24.275240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 952
14.8%
0 885
13.7%
( 586
 
9.1%
) 586
 
9.1%
468
 
7.3%
1 299
 
4.6%
288
 
4.5%
288
 
4.5%
288
 
4.5%
288
 
4.5%
Other values (31) 1510
23.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2747
42.7%
Decimal Number 2344
36.4%
Open Punctuation 586
 
9.1%
Close Punctuation 586
 
9.1%
Other Punctuation 128
 
2.0%
Space Separator 47
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
468
17.0%
288
10.5%
288
10.5%
288
10.5%
288
10.5%
180
 
6.6%
180
 
6.6%
180
 
6.6%
71
 
2.6%
71
 
2.6%
Other values (17) 445
16.2%
Decimal Number
ValueCountFrequency (%)
2 952
40.6%
0 885
37.8%
1 299
 
12.8%
7 39
 
1.7%
8 35
 
1.5%
9 34
 
1.5%
6 30
 
1.3%
4 28
 
1.2%
5 25
 
1.1%
3 17
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 586
100.0%
Close Punctuation
ValueCountFrequency (%)
) 586
100.0%
Other Punctuation
ValueCountFrequency (%)
, 128
100.0%
Space Separator
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3691
57.3%
Hangul 2747
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
468
17.0%
288
10.5%
288
10.5%
288
10.5%
288
10.5%
180
 
6.6%
180
 
6.6%
180
 
6.6%
71
 
2.6%
71
 
2.6%
Other values (17) 445
16.2%
Common
ValueCountFrequency (%)
2 952
25.8%
0 885
24.0%
( 586
15.9%
) 586
15.9%
1 299
 
8.1%
, 128
 
3.5%
47
 
1.3%
7 39
 
1.1%
8 35
 
0.9%
9 34
 
0.9%
Other values (4) 100
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3691
57.3%
Hangul 2747
42.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 952
25.8%
0 885
24.0%
( 586
15.9%
) 586
15.9%
1 299
 
8.1%
, 128
 
3.5%
47
 
1.3%
7 39
 
1.1%
8 35
 
0.9%
9 34
 
0.9%
Other values (4) 100
 
2.7%
Hangul
ValueCountFrequency (%)
468
17.0%
288
10.5%
288
10.5%
288
10.5%
288
10.5%
180
 
6.6%
180
 
6.6%
180
 
6.6%
71
 
2.6%
71
 
2.6%
Other values (17) 445
16.2%

(RTI)지수
Real number (ℝ)

HIGH CORRELATION 

Distinct3248
Distinct (%)32.5%
Missing17
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.3305058
Minimum0.0076653
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:24.431647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0076653
5-th percentile0.0186212
Q11.2676818
median4.2189393
Q34.3592577
95-th percentile4.4959934
Maximum5
Range4.9923347
Interquartile range (IQR)3.0915759

Descriptive statistics

Standard deviation1.5908998
Coefficient of variation (CV)0.47767513
Kurtosis-0.48860771
Mean3.3305058
Median Absolute Deviation (MAD)0.197843
Skewness-1.1546597
Sum33248.439
Variance2.5309622
MonotonicityNot monotonic
2023-12-12T09:02:24.573101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.910544 1643
 
16.4%
0.0186212 773
 
7.7%
3.8151542 437
 
4.4%
4.0717341 254
 
2.5%
4.0210963 182
 
1.8%
4.2453604 129
 
1.3%
4.2834283 120
 
1.2%
4.1340264 110
 
1.1%
4.3592577 75
 
0.8%
4.2803609 69
 
0.7%
Other values (3238) 6191
61.9%
ValueCountFrequency (%)
0.0076653 1
 
< 0.1%
0.0186212 773
7.7%
0.1577147 20
 
0.2%
0.2150434 1
 
< 0.1%
0.2353111 1
 
< 0.1%
0.2646091 1
 
< 0.1%
0.3152318 1
 
< 0.1%
0.3429411 1
 
< 0.1%
0.347733 1
 
< 0.1%
0.4287739 1
 
< 0.1%
ValueCountFrequency (%)
5.0 3
< 0.1%
4.8968712 1
 
< 0.1%
4.8403388 1
 
< 0.1%
4.8255549 1
 
< 0.1%
4.7841364 1
 
< 0.1%
4.753893 2
< 0.1%
4.7536271 1
 
< 0.1%
4.7423832 1
 
< 0.1%
4.7282373 1
 
< 0.1%
4.7263887 1
 
< 0.1%

온라인화진행여부
Boolean

MISSING 

Distinct2
Distinct (%)< 0.1%
Missing784
Missing (%)7.8%
Memory size97.7 KiB
False
7397 
True
1819 
(Missing)
784 
ValueCountFrequency (%)
False 7397
74.0%
True 1819
 
18.2%
(Missing) 784
 
7.8%
2023-12-12T09:02:24.692574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

수용태세지수
Real number (ℝ)

HIGH CORRELATION 

Distinct286
Distinct (%)2.9%
Missing50
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.21252884
Minimum0.029
Maximum0.644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:24.819414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.029
5-th percentile0.029
Q10.201
median0.216
Q30.247
95-th percentile0.293
Maximum0.644
Range0.615
Interquartile range (IQR)0.046

Descriptive statistics

Standard deviation0.068499406
Coefficient of variation (CV)0.3223064
Kurtosis2.7388134
Mean0.21252884
Median Absolute Deviation (MAD)0.019
Skewness-0.98338109
Sum2114.662
Variance0.0046921686
MonotonicityNot monotonic
2023-12-12T09:02:25.008631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.216 1171
 
11.7%
0.215 936
 
9.4%
0.029 742
 
7.4%
0.233 426
 
4.3%
0.17 396
 
4.0%
0.201 364
 
3.6%
0.2 311
 
3.1%
0.217 307
 
3.1%
0.232 202
 
2.0%
0.214 168
 
1.7%
Other values (276) 4927
49.3%
ValueCountFrequency (%)
0.029 742
7.4%
0.038 1
 
< 0.1%
0.044 44
 
0.4%
0.053 5
 
0.1%
0.055 1
 
< 0.1%
0.057 2
 
< 0.1%
0.058 3
 
< 0.1%
0.06 4
 
< 0.1%
0.062 2
 
< 0.1%
0.063 10
 
0.1%
ValueCountFrequency (%)
0.644 1
< 0.1%
0.52 1
< 0.1%
0.516 1
< 0.1%
0.514 1
< 0.1%
0.511 1
< 0.1%
0.506 1
< 0.1%
0.464 1
< 0.1%
0.462 1
< 0.1%
0.459 1
< 0.1%
0.457 1
< 0.1%

인기도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)0.6%
Missing2820
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean0.18097075
Minimum0.01
Maximum0.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:25.154245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.06
median0.16
Q30.33
95-th percentile0.33
Maximum0.67
Range0.66
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.13086261
Coefficient of variation (CV)0.72311468
Kurtosis-1.6259816
Mean0.18097075
Median Absolute Deviation (MAD)0.14
Skewness0.04618662
Sum1299.37
Variance0.017125022
MonotonicityNot monotonic
2023-12-12T09:02:25.309043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.33 2494
24.9%
0.01 667
 
6.7%
0.02 444
 
4.4%
0.03 387
 
3.9%
0.04 294
 
2.9%
0.06 286
 
2.9%
0.07 262
 
2.6%
0.08 224
 
2.2%
0.09 193
 
1.9%
0.1 174
 
1.7%
Other values (33) 1755
17.5%
(Missing) 2820
28.2%
ValueCountFrequency (%)
0.01 667
6.7%
0.02 444
4.4%
0.03 387
3.9%
0.04 294
2.9%
0.06 286
2.9%
0.07 262
 
2.6%
0.08 224
 
2.2%
0.09 193
 
1.9%
0.1 174
 
1.7%
0.11 142
 
1.4%
ValueCountFrequency (%)
0.67 2
< 0.1%
0.61 1
< 0.1%
0.59 1
< 0.1%
0.56 1
< 0.1%
0.52 1
< 0.1%
0.49 2
< 0.1%
0.43 2
< 0.1%
0.4 2
< 0.1%
0.39 2
< 0.1%
0.38 2
< 0.1%

트립어드바이저평점
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)2.1%
Missing9667
Missing (%)96.7%
Infinite0
Infinite (%)0.0%
Mean4.1651652
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:25.448874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q34.5
95-th percentile5
Maximum5
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5793014
Coefficient of variation (CV)0.13908246
Kurtosis0.26370713
Mean4.1651652
Median Absolute Deviation (MAD)0.5
Skewness-0.47083013
Sum1387
Variance0.33559011
MonotonicityNot monotonic
2023-12-12T09:02:25.564034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4.0 143
 
1.4%
4.5 75
 
0.8%
5.0 61
 
0.6%
3.0 26
 
0.3%
3.5 25
 
0.2%
2.5 2
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 9667
96.7%
ValueCountFrequency (%)
2.0 1
 
< 0.1%
2.5 2
 
< 0.1%
3.0 26
 
0.3%
3.5 25
 
0.2%
4.0 143
1.4%
4.5 75
0.8%
5.0 61
0.6%
ValueCountFrequency (%)
5.0 61
0.6%
4.5 75
0.8%
4.0 143
1.4%
3.5 25
 
0.2%
3.0 26
 
0.3%
2.5 2
 
< 0.1%
2.0 1
 
< 0.1%

씨트립평점
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)34.6%
Missing9974
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean4.1461538
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:25.680369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.125
Q14
median4.15
Q34.65
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.80011538
Coefficient of variation (CV)0.19297773
Kurtosis9.3027244
Mean4.1461538
Median Absolute Deviation (MAD)0.2
Skewness-2.5261695
Sum107.8
Variance0.64018462
MonotonicityNot monotonic
2023-12-12T09:02:25.801641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4.0 10
 
0.1%
5.0 4
 
< 0.1%
4.3 3
 
< 0.1%
4.7 3
 
< 0.1%
4.4 2
 
< 0.1%
3.0 1
 
< 0.1%
1.0 1
 
< 0.1%
3.5 1
 
< 0.1%
4.5 1
 
< 0.1%
(Missing) 9974
99.7%
ValueCountFrequency (%)
1.0 1
 
< 0.1%
3.0 1
 
< 0.1%
3.5 1
 
< 0.1%
4.0 10
0.1%
4.3 3
 
< 0.1%
4.4 2
 
< 0.1%
4.5 1
 
< 0.1%
4.7 3
 
< 0.1%
5.0 4
 
< 0.1%
ValueCountFrequency (%)
5.0 4
 
< 0.1%
4.7 3
 
< 0.1%
4.5 1
 
< 0.1%
4.4 2
 
< 0.1%
4.3 3
 
< 0.1%
4.0 10
0.1%
3.5 1
 
< 0.1%
3.0 1
 
< 0.1%
1.0 1
 
< 0.1%

네이버평점
Real number (ℝ)

MISSING 

Distinct163
Distinct (%)2.0%
Missing1712
Missing (%)17.1%
Infinite0
Infinite (%)0.0%
Mean4.38309
Minimum0.5
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:02:25.930594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile4.01
Q14.27
median4.38
Q34.5
95-th percentile4.78
Maximum5
Range4.5
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.26051331
Coefficient of variation (CV)0.059435994
Kurtosis26.720207
Mean4.38309
Median Absolute Deviation (MAD)0.12
Skewness-2.5080551
Sum36327.05
Variance0.067867185
MonotonicityNot monotonic
2023-12-12T09:02:26.054763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 244
 
2.4%
4.33 231
 
2.3%
4.4 212
 
2.1%
4.38 212
 
2.1%
4.36 210
 
2.1%
4.39 202
 
2.0%
4.35 197
 
2.0%
4.42 194
 
1.9%
4.37 192
 
1.9%
4.41 184
 
1.8%
Other values (153) 6210
62.1%
(Missing) 1712
 
17.1%
ValueCountFrequency (%)
0.5 2
< 0.1%
1.0 1
 
< 0.1%
1.25 1
 
< 0.1%
1.5 1
 
< 0.1%
1.83 1
 
< 0.1%
2.0 3
< 0.1%
2.25 1
 
< 0.1%
2.5 1
 
< 0.1%
2.75 2
< 0.1%
2.8 1
 
< 0.1%
ValueCountFrequency (%)
5.0 155
1.6%
4.98 3
 
< 0.1%
4.97 7
 
0.1%
4.96 3
 
< 0.1%
4.95 4
 
< 0.1%
4.94 6
 
0.1%
4.93 3
 
< 0.1%
4.92 9
 
0.1%
4.91 9
 
0.1%
4.9 24
 
0.2%

Interactions

2023-12-12T09:02:20.182612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.364449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.362540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.986354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.741882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.591672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.357242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.312110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.474675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.473278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.145649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.888486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.733549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.487991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.434692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.581671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.562664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.241045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.019179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.813118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.582849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.537478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.694637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.652845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.344329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.146408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.911802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.704000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.668697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.806063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.737647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.444644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.266307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.029896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.829649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.791075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:15.912091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.815295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.542540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.363782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.138032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.976449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.895888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.266178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:16.897653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:17.652202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:18.463065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:19.248780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:02:20.089422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:02:26.134009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식당(ID)지역명어워드정보설명(RTI)지수온라인화진행여부수용태세지수인기도트립어드바이저평점씨트립평점네이버평점
식당(ID)1.0000.7710.7620.3490.1300.3710.0500.0000.0000.008
지역명0.7711.0000.8300.2520.2620.3060.2200.0000.8060.081
어워드정보설명0.7620.8301.0000.3140.3140.7250.7390.0000.7210.598
(RTI)지수0.3490.2520.3141.0000.2930.7990.4360.1980.0000.208
온라인화진행여부0.1300.2620.3140.2931.0000.4080.1390.0000.5310.110
수용태세지수0.3710.3060.7250.7990.4081.0000.2410.0000.1770.157
인기도0.0500.2200.7390.4360.1390.2411.0000.2030.0000.127
트립어드바이저평점0.0000.0000.0000.1980.0000.0000.2031.0000.0000.037
씨트립평점0.0000.8060.7210.0000.5310.1770.0000.0001.0000.841
네이버평점0.0080.0810.5980.2080.1100.1570.1270.0370.8411.000
2023-12-12T09:02:26.243815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온라인화진행여부지역명
온라인화진행여부1.0000.227
지역명0.2271.000
2023-12-12T09:02:26.328940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식당(ID)(RTI)지수수용태세지수인기도트립어드바이저평점씨트립평점네이버평점지역명온라인화진행여부
식당(ID)1.000-0.097-0.2090.0190.021-0.1140.0910.4640.139
(RTI)지수-0.0971.0000.6220.556-0.2220.2160.1690.0990.292
수용태세지수-0.2090.6221.0000.3530.1060.2440.1670.1200.409
인기도0.0190.5560.3531.0000.0820.1210.1070.0790.106
트립어드바이저평점0.021-0.2220.1060.0821.000-0.2950.1110.0000.000
씨트립평점-0.1140.2160.2440.121-0.2951.0000.0760.3540.341
네이버평점0.0910.1690.1670.1070.1110.0761.0000.0280.084
지역명0.4640.0990.1200.0790.0000.3540.0281.0000.227
온라인화진행여부0.1390.2920.4090.1060.0000.3410.0840.2271.000

Missing values

2023-12-12T09:02:21.043690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:02:21.249592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T09:02:21.415611image/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

식당(ID)식당명지점명지역명어워드정보설명(RTI)지수온라인화진행여부수용태세지수인기도트립어드바이저평점씨트립평점네이버평점
5426295559우리<NA>서초구<NA>0.018621<NA>0.029<NA><NA><NA><NA>
905141212그린푸드 생선구이<NA>중구<NA>0.910544N0.201<NA><NA><NA>4.57
69823114364카페 컴플렉스<NA>강남구<NA>4.403066N0.2630.33<NA><NA>4.64
3069366805고향마을<NA>강남구<NA>4.314401Y0.360.16<NA><NA>4.26
3996577831정미당<NA>도봉구<NA>3.872674N0.280.01<NA><NA>4.32
5632398150돼지네포차<NA>강남구<NA>4.134026N0.2150.07<NA><NA>4.32
92029164036한남북엇국안녕인사동점종로구<NA>4.159919N0.2160.3<NA><NA>4.25
93750648590혜윰<NA>중구<NA>0.018621<NA>0.029<NA><NA><NA><NA>
67473111513최우수<NA>성북구<NA>4.518484N0.2340.33<NA><NA>4.49
60658103349카페브룩스<NA>성북구<NA>3.815154N0.2040.01<NA><NA><NA>
식당(ID)식당명지점명지역명어워드정보설명(RTI)지수온라인화진행여부수용태세지수인기도트립어드바이저평점씨트립평점네이버평점
3831075820도참치쌍문본점도봉구<NA>4.242312N0.2520.28<NA><NA>4.43
3337369990오늘은여기<NA>강동구<NA>0.018621<NA>0.029<NA><NA><NA><NA>
59043101450키다리아저씨<NA>마포구<NA>4.283255N0.2490.33<NA><NA>4.49
72901118248시시<NA>용산구<NA>0.018621<NA>0.029<NA><NA><NA><NA>
87991142035명인만두상봉역점중랑구<NA>4.389945N0.2290.33<NA><NA>4.31
966641965일치프리아니신세계백화점 본점중구<NA>4.021096N0.20.03<NA><NA>4.25
5174592511secret base(시크릿 베이스)<NA>강서구<NA>0.018621<NA>0.029<NA><NA><NA><NA>
99526768836한마루가든<NA>동작구<NA>3.970008N0.230.03<NA><NA>4.1
4856588744작은마을<NA>서초구<NA>3.588957Y0.266<NA><NA><NA>5.0
71210116127용산회집<NA>용산구<NA>4.251087N0.2150.1<NA><NA>4.24