Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory129.0 B

Variable types

Categorical3
Numeric8
Text3

Dataset

Description기준_년분기_코드,상권_구분_코드,상권_구분_코드_명,상권_코드,상권_코드_명,서비스_업종_코드,서비스_업종_코드_명,점포_수,유사_업종_점포_수,개업_율,개업_점포_수,폐업_률,폐업_점포_수,프랜차이즈_점포_수
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-15577/S/1/datasetView.do

Alerts

상권_구분_코드_명 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_구분_코드 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_코드 is highly overall correlated with 상권_구분_코드 and 1 other fieldsHigh correlation
점포_수 is highly overall correlated with 유사_업종_점포_수High correlation
유사_업종_점포_수 is highly overall correlated with 점포_수High correlation
개업_율 is highly overall correlated with 개업_점포_수High correlation
개업_점포_수 is highly overall correlated with 개업_율High correlation
폐업_률 is highly overall correlated with 폐업_점포_수High correlation
폐업_점포_수 is highly overall correlated with 폐업_률High correlation
프랜차이즈_점포_수 is highly skewed (γ1 = 24.97316903)Skewed
점포_수 has 377 (3.8%) zerosZeros
개업_율 has 9173 (91.7%) zerosZeros
개업_점포_수 has 9169 (91.7%) zerosZeros
폐업_률 has 9000 (90.0%) zerosZeros
폐업_점포_수 has 8924 (89.2%) zerosZeros
프랜차이즈_점포_수 has 8535 (85.4%) zerosZeros

Reproduction

Analysis started2024-05-03 23:31:25.364681
Analysis finished2024-05-03 23:31:47.538875
Duration22.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20231
7641 
20232
2359 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20232
2nd row20231
3rd row20231
4th row20231
5th row20231

Common Values

ValueCountFrequency (%)
20231 7641
76.4%
20232 2359
 
23.6%

Length

2024-05-03T23:31:47.720499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:31:48.017104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20231 7641
76.4%
20232 2359
 
23.6%

상권_구분_코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
A
7038 
D
1768 
R
1067 
U
 
127

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowD
5th rowR

Common Values

ValueCountFrequency (%)
A 7038
70.4%
D 1768
 
17.7%
R 1067
 
10.7%
U 127
 
1.3%

Length

2024-05-03T23:31:48.242191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:31:48.465656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 7038
70.4%
d 1768
 
17.7%
r 1067
 
10.7%
u 127
 
1.3%

상권_구분_코드_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
골목상권
7038 
발달상권
1768 
전통시장
1067 
관광특구
 
127

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row골목상권
2nd row골목상권
3rd row골목상권
4th row발달상권
5th row전통시장

Common Values

ValueCountFrequency (%)
골목상권 7038
70.4%
발달상권 1768
 
17.7%
전통시장 1067
 
10.7%
관광특구 127
 
1.3%

Length

2024-05-03T23:31:48.781348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T23:31:48.962563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권 7038
70.4%
발달상권 1768
 
17.7%
전통시장 1067
 
10.7%
관광특구 127
 
1.3%

상권_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct1596
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3112885.8
Minimum3001491
Maximum3130327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:49.198352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3001491
5-th percentile3110055
Q13110273.8
median3110584
Q33120049.2
95-th percentile3130179
Maximum3130327
Range128836
Interquartile range (IQR)9775.5

Descriptive statistics

Standard deviation14247.219
Coefficient of variation (CV)0.0045768525
Kurtosis44.733709
Mean3112885.8
Median Absolute Deviation (MAD)398
Skewness-5.8614647
Sum3.1128858 × 1010
Variance2.0298326 × 108
MonotonicityNot monotonic
2024-05-03T23:31:49.581645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3110297 24
 
0.2%
3110316 24
 
0.2%
3001495 23
 
0.2%
3001491 23
 
0.2%
3001494 23
 
0.2%
3110272 22
 
0.2%
3001493 21
 
0.2%
3110502 21
 
0.2%
3110260 20
 
0.2%
3110399 20
 
0.2%
Other values (1586) 9779
97.8%
ValueCountFrequency (%)
3001491 23
0.2%
3001492 19
0.2%
3001493 21
0.2%
3001494 23
0.2%
3001495 23
0.2%
3001496 18
0.2%
3110001 8
 
0.1%
3110002 7
 
0.1%
3110003 11
0.1%
3110004 7
 
0.1%
ValueCountFrequency (%)
3130327 4
 
< 0.1%
3130326 4
 
< 0.1%
3130325 2
 
< 0.1%
3130324 3
 
< 0.1%
3130323 4
 
< 0.1%
3130322 3
 
< 0.1%
3130321 2
 
< 0.1%
3130320 6
0.1%
3130319 6
0.1%
3130318 10
0.1%
Distinct1596
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:31:50.112628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length7.2696
Min length2

Characters and Unicode

Total characters72696
Distinct characters446
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

Unique107 ?
Unique (%)1.1%

Sample

1st row응암1동주민센터
2nd row송파역 2번
3rd row신림우방아파트
4th row논현역
5th row동대문종합시장(동대문종합시장 신관, 동대문종합시장D동상가)
ValueCountFrequency (%)
1번 519
 
3.9%
2번 439
 
3.3%
3번 409
 
3.1%
4번 341
 
2.6%
5번 180
 
1.4%
6번 132
 
1.0%
관광특구 127
 
1.0%
7번 103
 
0.8%
8번 100
 
0.8%
연신내역 57
 
0.4%
Other values (1467) 10868
81.9%
2024-05-03T23:31:50.956017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3787
 
5.2%
3275
 
4.5%
2481
 
3.4%
2249
 
3.1%
1787
 
2.5%
1680
 
2.3%
1479
 
2.0%
1364
 
1.9%
1232
 
1.7%
1206
 
1.7%
Other values (436) 52156
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63184
86.9%
Decimal Number 3511
 
4.8%
Space Separator 3275
 
4.5%
Close Punctuation 1147
 
1.6%
Open Punctuation 1147
 
1.6%
Uppercase Letter 213
 
0.3%
Other Punctuation 178
 
0.2%
Connector Punctuation 22
 
< 0.1%
Lowercase Letter 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3787
 
6.0%
2481
 
3.9%
2249
 
3.6%
1787
 
2.8%
1680
 
2.7%
1479
 
2.3%
1364
 
2.2%
1232
 
1.9%
1206
 
1.9%
1112
 
1.8%
Other values (398) 44807
70.9%
Uppercase Letter
ValueCountFrequency (%)
K 41
19.2%
T 27
12.7%
G 24
11.3%
B 20
9.4%
D 17
8.0%
S 16
 
7.5%
C 14
 
6.6%
H 13
 
6.1%
N 13
 
6.1%
M 11
 
5.2%
Other values (4) 17
8.0%
Decimal Number
ValueCountFrequency (%)
1 1010
28.8%
2 676
19.3%
3 594
16.9%
4 453
12.9%
5 261
 
7.4%
6 165
 
4.7%
8 127
 
3.6%
7 124
 
3.5%
9 71
 
2.0%
0 30
 
0.9%
Other Punctuation
ValueCountFrequency (%)
, 126
70.8%
? 23
 
12.9%
. 15
 
8.4%
& 9
 
5.1%
! 5
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
a 9
47.4%
h 4
21.1%
t 2
 
10.5%
m 2
 
10.5%
e 2
 
10.5%
Space Separator
ValueCountFrequency (%)
3275
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1147
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1147
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63184
86.9%
Common 9280
 
12.8%
Latin 232
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3787
 
6.0%
2481
 
3.9%
2249
 
3.6%
1787
 
2.8%
1680
 
2.7%
1479
 
2.3%
1364
 
2.2%
1232
 
1.9%
1206
 
1.9%
1112
 
1.8%
Other values (398) 44807
70.9%
Common
ValueCountFrequency (%)
3275
35.3%
) 1147
 
12.4%
( 1147
 
12.4%
1 1010
 
10.9%
2 676
 
7.3%
3 594
 
6.4%
4 453
 
4.9%
5 261
 
2.8%
6 165
 
1.8%
8 127
 
1.4%
Other values (9) 425
 
4.6%
Latin
ValueCountFrequency (%)
K 41
17.7%
T 27
11.6%
G 24
10.3%
B 20
8.6%
D 17
7.3%
S 16
 
6.9%
C 14
 
6.0%
H 13
 
5.6%
N 13
 
5.6%
M 11
 
4.7%
Other values (9) 36
15.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63184
86.9%
ASCII 9512
 
13.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3787
 
6.0%
2481
 
3.9%
2249
 
3.6%
1787
 
2.8%
1680
 
2.7%
1479
 
2.3%
1364
 
2.2%
1232
 
1.9%
1206
 
1.9%
1112
 
1.8%
Other values (398) 44807
70.9%
ASCII
ValueCountFrequency (%)
3275
34.4%
) 1147
 
12.1%
( 1147
 
12.1%
1 1010
 
10.6%
2 676
 
7.1%
3 594
 
6.2%
4 453
 
4.8%
5 261
 
2.7%
6 165
 
1.7%
8 127
 
1.3%
Other values (28) 657
 
6.9%
Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:31:51.564873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCS300013
2nd rowCS200029
3rd rowCS100001
4th rowCS200039
5th rowCS300011
ValueCountFrequency (%)
cs100001 216
 
2.2%
cs300011 215
 
2.1%
cs100010 206
 
2.1%
cs200033 202
 
2.0%
cs300043 201
 
2.0%
cs300001 200
 
2.0%
cs100008 194
 
1.9%
cs200028 188
 
1.9%
cs300002 185
 
1.8%
cs300010 168
 
1.7%
Other values (90) 8025
80.2%
2024-05-03T23:31:52.528213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34371
43.0%
C 10000
 
12.5%
S 10000
 
12.5%
3 7799
 
9.7%
2 6683
 
8.4%
1 4916
 
6.1%
4 1737
 
2.2%
8 1090
 
1.4%
5 903
 
1.1%
7 892
 
1.1%
Other values (2) 1609
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
75.0%
Uppercase Letter 20000
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34371
57.3%
3 7799
 
13.0%
2 6683
 
11.1%
1 4916
 
8.2%
4 1737
 
2.9%
8 1090
 
1.8%
5 903
 
1.5%
7 892
 
1.5%
6 864
 
1.4%
9 745
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
C 10000
50.0%
S 10000
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
75.0%
Latin 20000
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34371
57.3%
3 7799
 
13.0%
2 6683
 
11.1%
1 4916
 
8.2%
4 1737
 
2.9%
8 1090
 
1.8%
5 903
 
1.5%
7 892
 
1.5%
6 864
 
1.4%
9 745
 
1.2%
Latin
ValueCountFrequency (%)
C 10000
50.0%
S 10000
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34371
43.0%
C 10000
 
12.5%
S 10000
 
12.5%
3 7799
 
9.7%
2 6683
 
8.4%
1 4916
 
6.1%
4 1737
 
2.2%
8 1090
 
1.4%
5 903
 
1.1%
7 892
 
1.1%
Other values (2) 1609
 
2.0%
Distinct100
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-03T23:31:53.120566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length4.3354
Min length2

Characters and Unicode

Total characters43354
Distinct characters163
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

Unique0 ?
Unique (%)0.0%

Sample

1st row유아의류
2nd row네일숍
3rd row한식음식점
4th rowDVD방
5th row일반의류
ValueCountFrequency (%)
한식음식점 216
 
2.1%
일반의류 215
 
2.1%
커피-음료 206
 
2.0%
부동산중개업 202
 
2.0%
전자상거래업 201
 
2.0%
슈퍼마켓 200
 
2.0%
분식전문점 194
 
1.9%
미용실 188
 
1.8%
편의점 185
 
1.8%
반찬가게 168
 
1.6%
Other values (94) 8278
80.7%
2024-05-03T23:31:54.163026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1838
 
4.2%
1518
 
3.5%
1199
 
2.8%
1137
 
2.6%
909
 
2.1%
855
 
2.0%
830
 
1.9%
789
 
1.8%
754
 
1.7%
689
 
1.6%
Other values (153) 32836
75.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42422
97.9%
Dash Punctuation 338
 
0.8%
Space Separator 253
 
0.6%
Uppercase Letter 204
 
0.5%
Other Punctuation 137
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1838
 
4.3%
1518
 
3.6%
1199
 
2.8%
1137
 
2.7%
909
 
2.1%
855
 
2.0%
830
 
2.0%
789
 
1.9%
754
 
1.8%
689
 
1.6%
Other values (146) 31904
75.2%
Uppercase Letter
ValueCountFrequency (%)
C 69
33.8%
P 69
33.8%
D 44
21.6%
V 22
 
10.8%
Dash Punctuation
ValueCountFrequency (%)
- 338
100.0%
Space Separator
ValueCountFrequency (%)
253
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 137
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42422
97.9%
Common 728
 
1.7%
Latin 204
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1838
 
4.3%
1518
 
3.6%
1199
 
2.8%
1137
 
2.7%
909
 
2.1%
855
 
2.0%
830
 
2.0%
789
 
1.9%
754
 
1.8%
689
 
1.6%
Other values (146) 31904
75.2%
Latin
ValueCountFrequency (%)
C 69
33.8%
P 69
33.8%
D 44
21.6%
V 22
 
10.8%
Common
ValueCountFrequency (%)
- 338
46.4%
253
34.8%
/ 137
18.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42422
97.9%
ASCII 932
 
2.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1838
 
4.3%
1518
 
3.6%
1199
 
2.8%
1137
 
2.7%
909
 
2.1%
855
 
2.0%
830
 
2.0%
789
 
1.9%
754
 
1.8%
689
 
1.6%
Other values (146) 31904
75.2%
ASCII
ValueCountFrequency (%)
- 338
36.3%
253
27.1%
/ 137
14.7%
C 69
 
7.4%
P 69
 
7.4%
D 44
 
4.7%
V 22
 
2.4%

점포_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4303
Minimum0
Maximum849
Zeros377
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:54.577221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile17
Maximum849
Range849
Interquartile range (IQR)3

Descriptive statistics

Standard deviation20.114686
Coefficient of variation (CV)3.7041575
Kurtosis517.58214
Mean5.4303
Median Absolute Deviation (MAD)1
Skewness18.708663
Sum54303
Variance404.6006
MonotonicityNot monotonic
2024-05-03T23:31:54.983861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3901
39.0%
2 1744
17.4%
3 1047
 
10.5%
4 622
 
6.2%
5 417
 
4.2%
0 377
 
3.8%
6 277
 
2.8%
7 235
 
2.4%
8 174
 
1.7%
9 149
 
1.5%
Other values (113) 1057
 
10.6%
ValueCountFrequency (%)
0 377
 
3.8%
1 3901
39.0%
2 1744
17.4%
3 1047
 
10.5%
4 622
 
6.2%
5 417
 
4.2%
6 277
 
2.8%
7 235
 
2.4%
8 174
 
1.7%
9 149
 
1.5%
ValueCountFrequency (%)
849 1
< 0.1%
578 1
< 0.1%
452 1
< 0.1%
419 1
< 0.1%
401 1
< 0.1%
385 1
< 0.1%
372 1
< 0.1%
354 1
< 0.1%
336 2
< 0.1%
326 1
< 0.1%

유사_업종_점포_수
Real number (ℝ)

HIGH CORRELATION 

Distinct136
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8648
Minimum0
Maximum849
Zeros76
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:55.377098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q35
95-th percentile18.05
Maximum849
Range849
Interquartile range (IQR)4

Descriptive statistics

Standard deviation20.916469
Coefficient of variation (CV)3.566442
Kurtosis459.72128
Mean5.8648
Median Absolute Deviation (MAD)1
Skewness17.651846
Sum58648
Variance437.49867
MonotonicityNot monotonic
2024-05-03T23:31:55.627687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3843
38.4%
2 1802
18.0%
3 1095
 
10.9%
4 640
 
6.4%
5 455
 
4.5%
6 325
 
3.2%
7 256
 
2.6%
8 186
 
1.9%
9 170
 
1.7%
10 128
 
1.3%
Other values (126) 1100
 
11.0%
ValueCountFrequency (%)
0 76
 
0.8%
1 3843
38.4%
2 1802
18.0%
3 1095
 
10.9%
4 640
 
6.4%
5 455
 
4.5%
6 325
 
3.2%
7 256
 
2.6%
8 186
 
1.9%
9 170
 
1.7%
ValueCountFrequency (%)
849 1
< 0.1%
578 1
< 0.1%
452 1
< 0.1%
437 1
< 0.1%
420 1
< 0.1%
401 1
< 0.1%
373 1
< 0.1%
363 1
< 0.1%
339 1
< 0.1%
336 1
< 0.1%

개업_율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0833
Minimum0
Maximum100
Zeros9173
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:56.085524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.57598
Coefficient of variation (CV)5.0765515
Kurtosis58.561447
Mean2.0833
Median Absolute Deviation (MAD)0
Skewness7.2314642
Sum20833
Variance111.85135
MonotonicityNot monotonic
2024-05-03T23:31:56.328157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 9173
91.7%
100 78
 
0.8%
50 75
 
0.8%
33 61
 
0.6%
20 60
 
0.6%
25 58
 
0.6%
17 50
 
0.5%
4 40
 
0.4%
7 39
 
0.4%
13 39
 
0.4%
Other values (23) 327
 
3.3%
ValueCountFrequency (%)
0 9173
91.7%
1 10
 
0.1%
2 31
 
0.3%
3 38
 
0.4%
4 40
 
0.4%
5 38
 
0.4%
6 37
 
0.4%
7 39
 
0.4%
8 33
 
0.3%
9 32
 
0.3%
ValueCountFrequency (%)
100 78
0.8%
67 2
 
< 0.1%
50 75
0.8%
44 1
 
< 0.1%
40 3
 
< 0.1%
36 1
 
< 0.1%
33 61
0.6%
29 3
 
< 0.1%
25 58
0.6%
23 1
 
< 0.1%

개업_점포_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1205
Minimum0
Maximum15
Zeros9169
Zeros (%)91.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:56.623382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56339284
Coefficient of variation (CV)4.6754592
Kurtosis199.84309
Mean0.1205
Median Absolute Deviation (MAD)0
Skewness11.222106
Sum1205
Variance0.31741149
MonotonicityNot monotonic
2024-05-03T23:31:56.979748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 9169
91.7%
1 665
 
6.7%
2 90
 
0.9%
3 35
 
0.4%
4 20
 
0.2%
11 4
 
< 0.1%
6 4
 
< 0.1%
5 4
 
< 0.1%
7 4
 
< 0.1%
9 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 9169
91.7%
1 665
 
6.7%
2 90
 
0.9%
3 35
 
0.4%
4 20
 
0.2%
5 4
 
< 0.1%
6 4
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
11 4
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 4
 
< 0.1%
6 4
 
< 0.1%
5 4
 
< 0.1%
4 20
0.2%

폐업_률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4899
Minimum0
Maximum200
Zeros9000
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:57.310703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum200
Range200
Interquartile range (IQR)0

Descriptive statistics

Standard deviation11.870762
Coefficient of variation (CV)4.7675657
Kurtosis57.846399
Mean2.4899
Median Absolute Deviation (MAD)0
Skewness6.96695
Sum24899
Variance140.91499
MonotonicityNot monotonic
2024-05-03T23:31:57.558340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 9000
90.0%
50 99
 
1.0%
100 92
 
0.9%
33 64
 
0.6%
20 59
 
0.6%
3 54
 
0.5%
4 51
 
0.5%
7 50
 
0.5%
8 50
 
0.5%
6 49
 
0.5%
Other values (23) 432
 
4.3%
ValueCountFrequency (%)
0 9000
90.0%
1 24
 
0.2%
2 45
 
0.4%
3 54
 
0.5%
4 51
 
0.5%
5 48
 
0.5%
6 49
 
0.5%
7 50
 
0.5%
8 50
 
0.5%
9 33
 
0.3%
ValueCountFrequency (%)
200 2
 
< 0.1%
100 92
0.9%
67 6
 
0.1%
57 1
 
< 0.1%
50 99
1.0%
40 7
 
0.1%
38 1
 
< 0.1%
33 64
0.6%
29 5
 
0.1%
25 43
0.4%

폐업_점포_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1619
Minimum0
Maximum23
Zeros8924
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:57.801861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69680481
Coefficient of variation (CV)4.303921
Kurtosis284.18077
Mean0.1619
Median Absolute Deviation (MAD)0
Skewness12.750921
Sum1619
Variance0.48553694
MonotonicityNot monotonic
2024-05-03T23:31:58.145257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8924
89.2%
1 832
 
8.3%
2 148
 
1.5%
3 37
 
0.4%
4 22
 
0.2%
5 15
 
0.1%
6 6
 
0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
11 2
 
< 0.1%
Other values (7) 7
 
0.1%
ValueCountFrequency (%)
0 8924
89.2%
1 832
 
8.3%
2 148
 
1.5%
3 37
 
0.4%
4 22
 
0.2%
5 15
 
0.1%
6 6
 
0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
23 1
 
< 0.1%
21 1
 
< 0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 1
 
< 0.1%
11 2
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
< 0.1%
7 3
< 0.1%

프랜차이즈_점포_수
Real number (ℝ)

SKEWED  ZEROS 

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4345
Minimum0
Maximum127
Zeros8535
Zeros (%)85.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-03T23:31:58.492724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum127
Range127
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.5961093
Coefficient of variation (CV)5.9749352
Kurtosis944.69795
Mean0.4345
Median Absolute Deviation (MAD)0
Skewness24.973169
Sum4345
Variance6.7397837
MonotonicityNot monotonic
2024-05-03T23:31:58.907364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 8535
85.4%
1 763
 
7.6%
2 283
 
2.8%
3 126
 
1.3%
4 92
 
0.9%
5 53
 
0.5%
6 36
 
0.4%
7 22
 
0.2%
8 20
 
0.2%
9 13
 
0.1%
Other values (27) 57
 
0.6%
ValueCountFrequency (%)
0 8535
85.4%
1 763
 
7.6%
2 283
 
2.8%
3 126
 
1.3%
4 92
 
0.9%
5 53
 
0.5%
6 36
 
0.4%
7 22
 
0.2%
8 20
 
0.2%
9 13
 
0.1%
ValueCountFrequency (%)
127 1
< 0.1%
105 1
< 0.1%
63 1
< 0.1%
62 1
< 0.1%
52 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
33 1
< 0.1%
32 1
< 0.1%
31 1
< 0.1%

Interactions

2024-05-03T23:31:44.716388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:31.312519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:33.005746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:35.127547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:37.205411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:38.795208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.005693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:42.800843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:44.917018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:31.513942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:33.279066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:35.393456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:37.463217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:38.979409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.202230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:43.066764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:45.116666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:31.698691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:33.532155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:35.641265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:37.617274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:39.230661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.370758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:43.318639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:45.370049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:31.863829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:33.780999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:35.886953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:37.772608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:39.481243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.538201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:43.565066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:45.630986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:32.049210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:34.049807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:36.150954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:37.947102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:39.745514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.716324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:43.752336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:45.911386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:32.233240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:34.325405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:36.423013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:38.195269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:40.223700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:41.980341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:43.936223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:46.105963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:32.464856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:34.617990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:36.694663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:38.426772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:40.600922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:42.267264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:44.210141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:46.386619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:32.728343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:34.863447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:36.941974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:38.623214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:40.824538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:42.526045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T23:31:44.454826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T23:31:59.280226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_코드서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
기준_년분기_코드1.0000.5160.5160.2140.0780.0780.0140.0140.0440.0230.0000.0000.030
상권_구분_코드0.5161.0001.0001.0000.2440.2440.2380.2620.0270.1950.0000.1730.163
상권_구분_코드_명0.5161.0001.0001.0000.2440.2440.2380.2620.0270.1950.0000.1730.163
상권_코드0.2141.0001.0001.0000.2500.2500.2110.2280.0300.2340.0000.1390.204
서비스_업종_코드0.0780.2440.2440.2501.0001.0000.0000.0000.2560.1880.0980.1410.077
서비스_업종_코드_명0.0780.2440.2440.2501.0001.0000.0000.0000.2560.1880.0980.1410.077
점포_수0.0140.2380.2380.2110.0000.0001.0000.9970.0000.4830.0000.9140.365
유사_업종_점포_수0.0140.2620.2620.2280.0000.0000.9971.0000.0000.5800.0000.8960.490
개업_율0.0440.0270.0270.0300.2560.2560.0000.0001.0000.2430.2710.0000.000
개업_점포_수0.0230.1950.1950.2340.1880.1880.4830.5800.2431.0000.0000.6350.716
폐업_률0.0000.0000.0000.0000.0980.0980.0000.0000.2710.0001.0000.0000.000
폐업_점포_수0.0000.1730.1730.1390.1410.1410.9140.8960.0000.6350.0001.0000.431
프랜차이즈_점포_수0.0300.1630.1630.2040.0770.0770.3650.4900.0000.7160.0000.4311.000
2024-05-03T23:31:59.741673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_구분_코드_명상권_구분_코드기준_년분기_코드
상권_구분_코드_명1.0001.0000.351
상권_구분_코드1.0001.0000.351
기준_년분기_코드0.3510.3511.000
2024-05-03T23:32:00.109560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_코드점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수기준_년분기_코드상권_구분_코드상권_구분_코드_명
상권_코드1.0000.1150.1210.0110.0140.0650.0620.0670.3511.0001.000
점포_수0.1151.0000.9320.2670.2830.2930.2620.0970.0100.1090.109
유사_업종_점포_수0.1210.9321.0000.2800.2960.3210.2870.3420.0100.1200.120
개업_율0.0110.2670.2801.0000.9950.2620.2610.2100.0460.0140.014
개업_점포_수0.0140.2830.2960.9951.0000.2790.2800.2230.0280.1170.117
폐업_률0.0650.2930.3210.2620.2791.0000.9580.2050.0000.0000.000
폐업_점포_수0.0620.2620.2870.2610.2800.9581.0000.2020.0000.0780.078
프랜차이즈_점포_수0.0670.0970.3420.2100.2230.2050.2021.0000.0320.1130.113
기준_년분기_코드0.3510.0100.0100.0460.0280.0000.0000.0321.0000.3510.351
상권_구분_코드1.0000.1090.1200.0140.1170.0000.0780.1130.3511.0001.000
상권_구분_코드_명1.0000.1090.1200.0140.1170.0000.0780.1130.3511.0001.000

Missing values

2024-05-03T23:31:46.778709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T23:31:47.279665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_코드상권_코드_명서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
9724520232A골목상권3110478응암1동주민센터CS300013유아의류1100000
4380520231A골목상권3111016송파역 2번CS200029네일숍1100000
3775320231A골목상권3110864신림우방아파트CS100001한식음식점56171001
6033620231D발달상권3120185논현역CS200039DVD방225015010
6561320231R전통시장3130012동대문종합시장(동대문종합시장 신관, 동대문종합시장D동상가)CS300011일반의류525221210
7967020232A골목상권3110079후암동주민센터CS300043전자상거래업454500000
4730520231A골목상권3111089상일여고(상일초등학교)CS300016안경1100000
1484020231A골목상권3110341북한산우이역 2번CS200016당구장1100000
3863220231A골목상권3110883은천초등학교(은천교앞)CS200022복권방1100000
6856720231R전통시장3130111동원전통종합시장(동원시장, 동원전통시장 상점가)CS200028미용실79111002
기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_코드상권_코드_명서비스_업종_코드서비스_업종_코드_명점포_수유사_업종_점포_수개업_율개업_점포_수폐업_률폐업_점포_수프랜차이즈_점포_수
1810720231A골목상권3110417월계역 3번CS100005제과점1100000
2318320231A골목상권3110535영천시장입구CS300021문구1100000
2065920231A골목상권3110469응암정보도서관CS300041예술품1100000
9939720232A골목상권3110537충정로현대아파트CS300018의약품1100000
7470920231R전통시장3130289봉일시장CS100005제과점1100000
8743620232A골목상권3110258면동초등학교CS100002중식음식점0000010
1664520231A골목상권3110387쌍문초등학교CS200003예술학원22005010
2413620231A골목상권3110555홍익지구대CS200029네일숍1100000
4322220231A골목상권3111006석촌고분역 1번CS200026자동차미용3300000
8933820232A골목상권3110300삼선동주민센터CS300005주류도매1100000