Overview

Dataset statistics

Number of variables11
Number of observations184
Missing cells175
Missing cells (%)8.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.8 KiB
Average record size in memory93.7 B

Variable types

Categorical3
Text3
Numeric5

Alerts

업체유형명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
폐업일자 is highly overall correlated with 영업상태명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
영업상태명 is highly overall correlated with 폐업일자High correlation
소재지도로명주소 has 2 (1.1%) missing valuesMissing
소재지지번주소 has 2 (1.1%) missing valuesMissing
소재지우편번호 has 4 (2.2%) missing valuesMissing
WGS84위도 has 3 (1.6%) missing valuesMissing
WGS84경도 has 3 (1.6%) missing valuesMissing
폐업일자 has 161 (87.5%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:46:43.087459
Analysis finished2023-12-10 22:46:45.820349
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
고양시
36 
용인시
21 
성남시
17 
수원시
11 
평택시
10 
Other values (21)
89 

Length

Max length4
Median length3
Mean length3.0326087
Min length3

Unique

Unique4 ?
Unique (%)2.2%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 36
19.6%
용인시 21
11.4%
성남시 17
 
9.2%
수원시 11
 
6.0%
평택시 10
 
5.4%
안산시 10
 
5.4%
시흥시 10
 
5.4%
파주시 8
 
4.3%
화성시 8
 
4.3%
안성시 6
 
3.3%
Other values (16) 47
25.5%

Length

2023-12-11T07:46:46.101782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 36
19.6%
용인시 21
11.4%
성남시 17
 
9.2%
수원시 11
 
6.0%
평택시 10
 
5.4%
안산시 10
 
5.4%
시흥시 10
 
5.4%
파주시 8
 
4.3%
화성시 8
 
4.3%
안성시 6
 
3.3%
Other values (16) 47
25.5%

사업장명
Text

Distinct178
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-11T07:46:46.298226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.9130435
Min length4

Characters and Unicode

Total characters1456
Distinct characters169
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)93.5%

Sample

1st row그린렌터카(주)
2nd row(주)이엔에이렌탈
3rd row(주)금마렌터카
4th row(주)나인렌터카
5th row고릴라렌트카(주)
ValueCountFrequency (%)
주식회사 6
 
3.1%
주)대상렌트카 2
 
1.0%
주)세진렌트카 2
 
1.0%
주)조은렌트카 2
 
1.0%
주)에이스렌트카 2
 
1.0%
주)에스에스렌터카 2
 
1.0%
엔터프라이즈코리아 2
 
1.0%
명성렌터카 1
 
0.5%
주)렌트코리아 1
 
0.5%
주)다원렌터카 1
 
0.5%
Other values (172) 172
89.1%
2023-12-11T07:46:46.630456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 153
 
10.5%
렌 151
 
10.4%
카 149
 
10.2%
( 143
 
9.8%
) 143
 
9.8%
트 129
 
8.9%
이 44
 
3.0%
터 29
 
2.0%
스 27
 
1.9%
에 23
 
1.6%
Other values (159) 465
31.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1157
79.5%
Open Punctuation 143
 
9.8%
Close Punctuation 143
 
9.8%
Space Separator 9
 
0.6%
Decimal Number 3
 
0.2%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 153
 
13.2%
렌 151
 
13.1%
카 149
 
12.9%
트 129
 
11.1%
이 44
 
3.8%
터 29
 
2.5%
스 27
 
2.3%
에 23
 
2.0%
아 16
 
1.4%
리 14
 
1.2%
Other values (153) 422
36.5%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
4 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 143
100.0%
Close Punctuation
ValueCountFrequency (%)
) 143
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1158
79.5%
Common 298
 
20.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 153
 
13.2%
렌 151
 
13.0%
카 149
 
12.9%
트 129
 
11.1%
이 44
 
3.8%
터 29
 
2.5%
스 27
 
2.3%
에 23
 
2.0%
아 16
 
1.4%
리 14
 
1.2%
Other values (154) 423
36.5%
Common
ValueCountFrequency (%)
( 143
48.0%
) 143
48.0%
9
 
3.0%
1 2
 
0.7%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1157
79.5%
ASCII 298
 
20.5%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 153
 
13.2%
렌 151
 
13.1%
카 149
 
12.9%
트 129
 
11.1%
이 44
 
3.8%
터 29
 
2.5%
스 27
 
2.3%
에 23
 
2.0%
아 16
 
1.4%
리 14
 
1.2%
Other values (153) 422
36.5%
ASCII
ValueCountFrequency (%)
( 143
48.0%
) 143
48.0%
9
 
3.0%
1 2
 
0.7%
4 1
 
0.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

소재지도로명주소
Text

MISSING 

Distinct180
Distinct (%)98.9%
Missing2
Missing (%)1.1%
Memory size1.6 KiB
2023-12-11T07:46:46.958269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length41
Mean length30.192308
Min length18

Characters and Unicode

Total characters5495
Distinct characters259
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

Unique179 ?
Unique (%)98.4%

Sample

1st row경기도 고양시 일산동구 고양대로 881, 1층 (풍동)
2nd row경기도 고양시 일산동구고봉로 258(중산동,4층)
3rd row경기도 고양시 일산동구 경의로 25-35, 201동 835호 (백석동)
4th row경기도 고양시 일산동구 백마로 195, 2163호 (장항동, 일산방송컴플렉스 방송관련시설)
5th row경기도 고양시 일산서구 중앙로 1406, 507호 (주엽동, 한솔코아)
ValueCountFrequency (%)
경기도 182
 
15.5%
고양시 36
 
3.1%
용인시 21
 
1.8%
성남시 17
 
1.4%
일산동구 16
 
1.4%
기흥구 13
 
1.1%
분당구 11
 
0.9%
수원시 11
 
0.9%
일산서구 10
 
0.9%
시흥시 10
 
0.9%
Other values (554) 848
72.2%
2023-12-11T07:46:47.942541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
993
 
18.1%
1 208
 
3.8%
기 202
 
3.7%
시 200
 
3.6%
동 199
 
3.6%
경 186
 
3.4%
도 184
 
3.3%
로 175
 
3.2%
) 144
 
2.6%
( 144
 
2.6%
Other values (249) 2860
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3111
56.6%
Space Separator 993
 
18.1%
Decimal Number 910
 
16.6%
Close Punctuation 144
 
2.6%
Open Punctuation 144
 
2.6%
Other Punctuation 137
 
2.5%
Dash Punctuation 44
 
0.8%
Uppercase Letter 12
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 202
 
6.5%
시 200
 
6.4%
동 199
 
6.4%
경 186
 
6.0%
도 184
 
5.9%
로 175
 
5.6%
구 111
 
3.6%
호 85
 
2.7%
양 73
 
2.3%
산 61
 
2.0%
Other values (224) 1635
52.6%
Decimal Number
ValueCountFrequency (%)
1 208
22.9%
2 138
15.2%
4 92
10.1%
0 91
10.0%
3 88
9.7%
5 80
 
8.8%
7 65
 
7.1%
6 62
 
6.8%
9 45
 
4.9%
8 41
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 3
25.0%
A 2
16.7%
R 1
 
8.3%
W 1
 
8.3%
E 1
 
8.3%
O 1
 
8.3%
C 1
 
8.3%
T 1
 
8.3%
U 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 136
99.3%
. 1
 
0.7%
Space Separator
ValueCountFrequency (%)
993
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3111
56.6%
Common 2372
43.2%
Latin 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 202
 
6.5%
시 200
 
6.4%
동 199
 
6.4%
경 186
 
6.0%
도 184
 
5.9%
로 175
 
5.6%
구 111
 
3.6%
호 85
 
2.7%
양 73
 
2.3%
산 61
 
2.0%
Other values (224) 1635
52.6%
Common
ValueCountFrequency (%)
993
41.9%
1 208
 
8.8%
) 144
 
6.1%
( 144
 
6.1%
2 138
 
5.8%
, 136
 
5.7%
4 92
 
3.9%
0 91
 
3.8%
3 88
 
3.7%
5 80
 
3.4%
Other values (6) 258
 
10.9%
Latin
ValueCountFrequency (%)
B 3
25.0%
A 2
16.7%
R 1
 
8.3%
W 1
 
8.3%
E 1
 
8.3%
O 1
 
8.3%
C 1
 
8.3%
T 1
 
8.3%
U 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3111
56.6%
ASCII 2384
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
993
41.7%
1 208
 
8.7%
) 144
 
6.0%
( 144
 
6.0%
2 138
 
5.8%
, 136
 
5.7%
4 92
 
3.9%
0 91
 
3.8%
3 88
 
3.7%
5 80
 
3.4%
Other values (15) 270
 
11.3%
Hangul
ValueCountFrequency (%)
기 202
 
6.5%
시 200
 
6.4%
동 199
 
6.4%
경 186
 
6.0%
도 184
 
5.9%
로 175
 
5.6%
구 111
 
3.6%
호 85
 
2.7%
양 73
 
2.3%
산 61
 
2.0%
Other values (224) 1635
52.6%

소재지지번주소
Text

MISSING 

Distinct174
Distinct (%)95.6%
Missing2
Missing (%)1.1%
Memory size1.6 KiB
2023-12-11T07:46:48.262264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length36
Mean length25.917582
Min length14

Characters and Unicode

Total characters4717
Distinct characters228
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

Unique167 ?
Unique (%)91.8%

Sample

1st row경기도 고양시 일산동구 풍동 660-2번지
2nd row경기도 고양시 일산동구 중산동 1669-1번지 4층
3rd row경기도 고양시 일산동구 백석동 201동 835호
4th row경기도 고양시 일산동구 장항동 869번지
5th row경기도 고양시 일산서구 주엽동 72-2번지 한솔코아507호
ValueCountFrequency (%)
경기도 182
 
18.2%
고양시 36
 
3.6%
용인시 21
 
2.1%
일산동구 17
 
1.7%
성남시 17
 
1.7%
기흥구 13
 
1.3%
일산서구 12
 
1.2%
수원시 11
 
1.1%
분당구 11
 
1.1%
안산시 10
 
1.0%
Other values (437) 668
66.9%
2023-12-11T07:46:48.738745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
816
 
17.3%
시 200
 
4.2%
기 198
 
4.2%
1 198
 
4.2%
동 193
 
4.1%
지 187
 
4.0%
도 184
 
3.9%
경 182
 
3.9%
번 179
 
3.8%
2 134
 
2.8%
Other values (218) 2246
47.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2823
59.8%
Decimal Number 935
 
19.8%
Space Separator 816
 
17.3%
Dash Punctuation 128
 
2.7%
Uppercase Letter 11
 
0.2%
Other Punctuation 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 200
 
7.1%
기 198
 
7.0%
동 193
 
6.8%
지 187
 
6.6%
도 184
 
6.5%
경 182
 
6.4%
번 179
 
6.3%
구 107
 
3.8%
양 67
 
2.4%
호 64
 
2.3%
Other values (194) 1262
44.7%
Decimal Number
ValueCountFrequency (%)
1 198
21.2%
2 134
14.3%
0 104
11.1%
3 87
9.3%
5 83
8.9%
4 74
 
7.9%
6 72
 
7.7%
9 69
 
7.4%
7 66
 
7.1%
8 48
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
U 1
9.1%
T 1
9.1%
R 1
9.1%
E 1
9.1%
W 1
9.1%
O 1
9.1%
C 1
9.1%
Space Separator
ValueCountFrequency (%)
816
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 128
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2823
59.8%
Common 1883
39.9%
Latin 11
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 200
 
7.1%
기 198
 
7.0%
동 193
 
6.8%
지 187
 
6.6%
도 184
 
6.5%
경 182
 
6.4%
번 179
 
6.3%
구 107
 
3.8%
양 67
 
2.4%
호 64
 
2.3%
Other values (194) 1262
44.7%
Common
ValueCountFrequency (%)
816
43.3%
1 198
 
10.5%
2 134
 
7.1%
- 128
 
6.8%
0 104
 
5.5%
3 87
 
4.6%
5 83
 
4.4%
4 74
 
3.9%
6 72
 
3.8%
9 69
 
3.7%
Other values (5) 118
 
6.3%
Latin
ValueCountFrequency (%)
B 2
18.2%
A 2
18.2%
U 1
9.1%
T 1
9.1%
R 1
9.1%
E 1
9.1%
W 1
9.1%
O 1
9.1%
C 1
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2823
59.8%
ASCII 1894
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
816
43.1%
1 198
 
10.5%
2 134
 
7.1%
- 128
 
6.8%
0 104
 
5.5%
3 87
 
4.6%
5 83
 
4.4%
4 74
 
3.9%
6 72
 
3.8%
9 69
 
3.6%
Other values (14) 129
 
6.8%
Hangul
ValueCountFrequency (%)
시 200
 
7.1%
기 198
 
7.0%
동 193
 
6.8%
지 187
 
6.6%
도 184
 
6.5%
경 182
 
6.4%
번 179
 
6.3%
구 107
 
3.8%
양 67
 
2.4%
호 64
 
2.3%
Other values (194) 1262
44.7%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct145
Distinct (%)80.6%
Missing4
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean13974.872
Minimum10056
Maximum18534
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:46:48.882184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10056
5-th percentile10308.55
Q110872
median13786
Q316842.25
95-th percentile18143
Maximum18534
Range8478
Interquartile range (IQR)5970.25

Descriptive statistics

Standard deviation2849.8599
Coefficient of variation (CV)0.20392744
Kurtosis-1.4884155
Mean13974.872
Median Absolute Deviation (MAD)2961
Skewness0.029102698
Sum2515477
Variance8121701.3
MonotonicityNot monotonic
2023-12-11T07:46:49.035947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17006 6
 
3.3%
10381 5
 
2.7%
10355 4
 
2.2%
10311 3
 
1.6%
10237 3
 
1.6%
12614 3
 
1.6%
15054 2
 
1.1%
18143 2
 
1.1%
16832 2
 
1.1%
10459 2
 
1.1%
Other values (135) 148
80.4%
(Missing) 4
 
2.2%
ValueCountFrequency (%)
10056 2
1.1%
10057 1
 
0.5%
10059 1
 
0.5%
10101 1
 
0.5%
10237 3
1.6%
10262 1
 
0.5%
10311 3
1.6%
10319 1
 
0.5%
10353 1
 
0.5%
10355 4
2.2%
ValueCountFrequency (%)
18534 1
0.5%
18533 1
0.5%
18434 1
0.5%
18390 2
1.1%
18343 1
0.5%
18298 1
0.5%
18292 1
0.5%
18143 2
1.1%
18114 1
0.5%
17949 1
0.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct159
Distinct (%)87.8%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean37.436919
Minimum36.98746
Maximum37.91643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:46:49.185450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.98746
5-th percentile37.007866
Q137.275904
median37.384
Q337.660167
95-th percentile37.825231
Maximum37.91643
Range0.92896996
Interquartile range (IQR)0.38426232

Descriptive statistics

Standard deviation0.23594419
Coefficient of variation (CV)0.0063024466
Kurtosis-0.76779515
Mean37.436919
Median Absolute Deviation (MAD)0.16080188
Skewness0.064374632
Sum6776.0824
Variance0.055669659
MonotonicityNot monotonic
2023-12-11T07:46:49.350691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2739540026 4
 
2.2%
37.6916953311 3
 
1.6%
37.3530437805 3
 
1.6%
37.675644415 2
 
1.1%
37.8467190402 2
 
1.1%
37.3509203615 2
 
1.1%
37.3500382689 2
 
1.1%
37.677160707 2
 
1.1%
37.6818795509 2
 
1.1%
37.6627713248 2
 
1.1%
Other values (149) 157
85.3%
(Missing) 3
 
1.6%
ValueCountFrequency (%)
36.9874602977 1
0.5%
36.9876308405 1
0.5%
36.9957611865 1
0.5%
36.9991157739 1
0.5%
36.9994328927 1
0.5%
37.0016945612 1
0.5%
37.0028016463 1
0.5%
37.0042840679 1
0.5%
37.0045495544 1
0.5%
37.0078657506 1
0.5%
ValueCountFrequency (%)
37.9164302608 1
0.5%
37.9041015763 1
0.5%
37.9029853253 1
0.5%
37.9021988401 1
0.5%
37.8994129925 1
0.5%
37.8591403065 1
0.5%
37.8572819564 1
0.5%
37.8467190402 2
1.1%
37.825231394 1
0.5%
37.8136244849 1
0.5%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct159
Distinct (%)87.8%
Missing3
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean126.98228
Minimum126.62312
Maximum127.71254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:46:49.512134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62312
5-th percentile126.72709
Q1126.79404
median126.98613
Q3127.12313
95-th percentile127.24457
Maximum127.71254
Range1.08942
Interquartile range (IQR)0.32908762

Descriptive statistics

Standard deviation0.20659224
Coefficient of variation (CV)0.0016269375
Kurtosis1.3083588
Mean126.98228
Median Absolute Deviation (MAD)0.15507961
Skewness0.79311067
Sum22983.793
Variance0.042680352
MonotonicityNot monotonic
2023-12-11T07:46:49.683738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1501984866 4
 
2.2%
126.757794391 3
 
1.6%
127.7125378506 3
 
1.6%
126.7466711562 2
 
1.1%
126.8773825908 2
 
1.1%
127.1115538623 2
 
1.1%
126.7403285317 2
 
1.1%
126.746996027 2
 
1.1%
126.781004791 2
 
1.1%
126.6346925628 2
 
1.1%
Other values (149) 157
85.3%
(Missing) 3
 
1.6%
ValueCountFrequency (%)
126.6231178426 1
0.5%
126.6237028334 1
0.5%
126.6346925628 2
1.1%
126.7037706706 1
0.5%
126.7044273786 1
0.5%
126.7203592701 1
0.5%
126.7219539127 1
0.5%
126.7254604009 1
0.5%
126.7270875055 1
0.5%
126.73153564 1
0.5%
ValueCountFrequency (%)
127.7125378506 3
1.6%
127.6319323596 1
 
0.5%
127.4592678088 1
 
0.5%
127.3799294385 1
 
0.5%
127.2783993027 1
 
0.5%
127.2707604817 1
 
0.5%
127.2507841939 1
 
0.5%
127.2445701661 1
 
0.5%
127.2406900007 1
 
0.5%
127.227357122 1
 
0.5%

인허가일자
Real number (ℝ)

Distinct166
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20111717
Minimum19990920
Maximum20180302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:46:49.817329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19990920
5-th percentile20020555
Q120080331
median20120116
Q320150314
95-th percentile20171011
Maximum20180302
Range189382
Interquartile range (IQR)69982.75

Descriptive statistics

Standard deviation46103.959
Coefficient of variation (CV)0.002292393
Kurtosis-0.26454853
Mean20111717
Median Absolute Deviation (MAD)30699.5
Skewness-0.66821624
Sum3.7005559 × 109
Variance2.125575 × 109
MonotonicityNot monotonic
2023-12-11T07:46:49.981711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20080331 3
 
1.6%
19990920 2
 
1.1%
20160913 2
 
1.1%
20051019 2
 
1.1%
20010525 2
 
1.1%
20100324 2
 
1.1%
20100519 2
 
1.1%
20101020 2
 
1.1%
20110222 2
 
1.1%
20110926 2
 
1.1%
Other values (156) 163
88.6%
ValueCountFrequency (%)
19990920 2
1.1%
20000201 1
0.5%
20010323 1
0.5%
20010525 2
1.1%
20010724 1
0.5%
20010821 1
0.5%
20020125 1
0.5%
20020528 1
0.5%
20020709 1
0.5%
20021109 1
0.5%
ValueCountFrequency (%)
20180302 1
0.5%
20180117 1
0.5%
20180109 1
0.5%
20171218 1
0.5%
20171215 1
0.5%
20171212 1
0.5%
20171211 1
0.5%
20171206 1
0.5%
20171101 1
0.5%
20171027 1
0.5%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
운영중
161 
폐업 등
23 

Length

Max length4
Median length3
Mean length3.125
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row운영중
3rd row운영중
4th row운영중
5th row운영중

Common Values

ValueCountFrequency (%)
운영중 161
87.5%
폐업 등 23
 
12.5%

Length

2023-12-11T07:46:50.131864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:46:50.243442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 161
77.8%
폐업 23
 
11.1%
등 23
 
11.1%

폐업일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)95.7%
Missing161
Missing (%)87.5%
Infinite0
Infinite (%)0.0%
Mean20117980
Minimum19990920
Maximum20180122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-12-11T07:46:50.358290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19990920
5-th percentile20016484
Q120100422
median20130701
Q320155706
95-th percentile20161182
Maximum20180122
Range189202
Interquartile range (IQR)55284.5

Descriptive statistics

Standard deviation47098.475
Coefficient of variation (CV)0.0023411135
Kurtosis1.8278003
Mean20117980
Median Absolute Deviation (MAD)29681
Skewness-1.3319608
Sum4.6271354 × 108
Variance2.2182663 × 109
MonotonicityNot monotonic
2023-12-11T07:46:50.467701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
20140212 2
 
1.1%
20130521 1
 
0.5%
19990920 1
 
0.5%
20160628 1
 
0.5%
20130701 1
 
0.5%
20160204 1
 
0.5%
20151208 1
 
0.5%
20160323 1
 
0.5%
20160825 1
 
0.5%
20161222 1
 
0.5%
Other values (12) 12
 
6.5%
(Missing) 161
87.5%
ValueCountFrequency (%)
19990920 1
0.5%
20010525 1
0.5%
20070112 1
0.5%
20080331 1
0.5%
20091124 1
0.5%
20100324 1
0.5%
20100519 1
0.5%
20101020 1
0.5%
20110222 1
0.5%
20120326 1
0.5%
ValueCountFrequency (%)
20180122 1
0.5%
20161222 1
0.5%
20160825 1
0.5%
20160628 1
0.5%
20160323 1
0.5%
20160204 1
0.5%
20151208 1
0.5%
20140212 2
1.1%
20131015 1
0.5%
20130925 1
0.5%

업체유형명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
자동차대여업
184 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자동차대여업
2nd row자동차대여업
3rd row자동차대여업
4th row자동차대여업
5th row자동차대여업

Common Values

ValueCountFrequency (%)
자동차대여업 184
100.0%

Length

2023-12-11T07:46:50.577351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:46:50.679803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차대여업 184
100.0%

Interactions

2023-12-11T07:46:44.987354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.634247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.976813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.310706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.636666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:45.057752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.697028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.045869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.372950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.700695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:45.126203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.763175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.110096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.434403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.766871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:45.212540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.834147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.173232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.496563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.839039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:45.330771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:43.907009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.242123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.565882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:46:44.912284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:46:50.743247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명소재지우편번호WGS84위도WGS84경도인허가일자영업상태명폐업일자
시군명1.0000.9910.9680.9590.3780.4940.384
소재지우편번호0.9911.0000.9370.7210.3990.4780.657
WGS84위도0.9680.9371.0000.5720.4190.4170.515
WGS84경도0.9590.7210.5721.0000.3090.3420.000
인허가일자0.3780.3990.4190.3091.0000.2110.863
영업상태명0.4940.4780.4170.3420.2111.000NaN
폐업일자0.3840.6570.5150.0000.863NaN1.000
2023-12-11T07:46:50.839932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명
영업상태명1.0000.366
시군명0.3661.000
2023-12-11T07:46:50.914576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도인허가일자폐업일자시군명영업상태명
소재지우편번호1.000-0.9180.4730.1250.3610.8970.345
WGS84위도-0.9181.000-0.399-0.127-0.3450.7620.312
WGS84경도0.473-0.3991.0000.1620.1680.7620.335
인허가일자0.125-0.1270.1621.0000.3730.1350.168
폐업일자0.361-0.3450.1680.3731.0000.0001.000
시군명0.8970.7620.7620.1350.0001.0000.366
영업상태명0.3450.3120.3350.1681.0000.3661.000

Missing values

2023-12-11T07:46:45.486564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:46:45.621170image/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.
2023-12-11T07:46:45.742233image/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

시군명사업장명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도인허가일자영업상태명폐업일자업체유형명
0고양시그린렌터카(주)경기도 고양시 일산동구 고양대로 881, 1층 (풍동)경기도 고양시 일산동구 풍동 660-2번지1031937.678889126.79321320171206운영중<NA>자동차대여업
1고양시(주)이엔에이렌탈경기도 고양시 일산동구고봉로 258(중산동,4층)경기도 고양시 일산동구 중산동 1669-1번지 4층1035537.682215126.77831420170920운영중<NA>자동차대여업
2고양시(주)금마렌터카경기도 고양시 일산동구 경의로 25-35, 201동 835호 (백석동)경기도 고양시 일산동구 백석동 201동 835호<NA><NA><NA>20151106운영중<NA>자동차대여업
3고양시(주)나인렌터카경기도 고양시 일산동구 백마로 195, 2163호 (장항동, 일산방송컴플렉스 방송관련시설)경기도 고양시 일산동구 장항동 869번지1040337.654822126.77076820141010운영중<NA>자동차대여업
4고양시고릴라렌트카(주)경기도 고양시 일산서구 중앙로 1406, 507호 (주엽동, 한솔코아)경기도 고양시 일산서구 주엽동 72-2번지 한솔코아507호1038637.669381126.76387920140729운영중<NA>자동차대여업
5고양시(주)아시아렌트카경기도 고양시 덕양구 용현로 3, 508호 (행신동, 행신프라자)경기도 고양시 덕양구 행신동 766-2번지 행신프라자 508호1052637.613077126.83562320140313운영중<NA>자동차대여업
6고양시(주)씨앤피렌터카경기도 고양시 일산동구 호수로 662, 914호 (장항동, 삼성라끄빌)경기도 고양시 일산동구 장항동 751번지 삼성라끄빌 914호1040137.660759126.7658820140122운영중<NA>자동차대여업
7고양시고질라렌트카경기도 고양시 일산서구 중앙로 1406, 5층 507호 (주엽동)경기도 고양시 일산서구 주엽동 72-2번지 한솔코아(주) 5층 507호1038637.669381126.76387920131015폐업 등20131015자동차대여업
8고양시엔터프라이즈코리아경기도 고양시 일산동구 일산로 429경기도 고양시 일산동구 정발산동 1205번지1035937.672689126.77857620130925폐업 등20130925자동차대여업
9고양시(주)세연렌트카경기도 고양시 일산서구 일중로 17, 503호 (일산동, 포오스빌딩)경기도 고양시 일산서구 일산동 524-16번지 포오스빌딩 503호1035337.681342126.77419420130715운영중<NA>자동차대여업
시군명사업장명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도인허가일자영업상태명폐업일자업체유형명
174하남시자보렌트카주식회사경기도 하남시 하남대로739번길 38 (신장동)경기도 하남시 신장동 454-55번지1296137.53715127.21129520080301운영중<NA>자동차대여업
175하남시주식회사 알씨렌트카경기도 하남시 대청로 33, 618호 (신장동, 베스코아빌딩)경기도 하남시 신장동 523-1번지1294737.5411127.21601220030301운영중<NA>자동차대여업
176화성시서현렌트카경기도 화성시 융건로 47-12 (기안동)경기도 화성시 기안동 457-555번지1834337.217121126.98495720150721운영중<NA>자동차대여업
177화성시주식회사 금호엔터프라이즈경기도 화성시 떡전골로 128-16 (진안동)경기도 화성시 진안동 543-7번지1839037.209875127.03294920140313운영중<NA>자동차대여업
178화성시(주)에스케이렌트카경기도 화성시 팔탄면 삼천병마로 355-91, 201호경기도 화성시 팔탄면 가재리 514-7번지1853337.147126126.92030120130706운영중<NA>자동차대여업
179화성시(주)청명렌트카경기도 화성시 동탄반송길 15-1경기도 화성시 반송동 35-4번지1843437.209749127.06376220130213운영중<NA>자동차대여업
180화성시(주)허스키렌트카경기도 화성시 팔탄면 3.1만세로 877-9경기도 화성시 팔탄면 매곡리 100번지1853437.118701126.88905720120203운영중<NA>자동차대여업
181화성시(주)카웨딩경기도 화성시 떡전골로 128-16, 3층 (진안동)경기도 화성시 진안동 543-7번지 3층1839037.209875127.03294920120113운영중<NA>자동차대여업
182화성시(주)화성렌트카경기도 화성시 비봉면 화성로 2056경기도 화성시 비봉면 쌍학리 246-3번지1829237.247416126.89159320080331운영중<NA>자동차대여업
183화성시(주)경도렌트카경기도 화성시 봉담읍 와우안길 94경기도 화성시 봉담읍 와우리 156-90번지1829837.219498126.97400820040408운영중<NA>자동차대여업